"IEEE Sensors Alert" is a pilot-project of IEEE Sensors Council as one of its new initiatives. It is a monthly digest to publish teasers and condensed versions of our journal papers in a layman’s language.
Articles Posted in the Month (May 2025)
Acoustofluidic Particle Trapping in a Structured Microchannel Using Lateral Transducer Modes
Fuchsluger Andreas, Andrianov Nikolai, Cselyuszka Norbert, De Pastina Annalisa, Ecker Rafael, Jakoby Bernhard, Mitteramskogler Tina, Voglhuber-Brunnmaier Thomas
Published in: IEEE Sensors Journal (Volume: 24, Issue: 24, December 2024)
Acoustofluidics uses sound waves for contactless manipulation of particles and fluids within microfluidic systems, which is relevant for various medical applications. This study presents a novel lateral-mode acoustofluidic trapping device using a disc-shaped resonator to create a two-dimensional standing wave for efficient particle trapping. The device operates at lower frequencies and efficiently traps large sizes and volumes of particles, demonstrating high predictability, reproducibility, and stability, making it suitable for advanced particle manipulation.
Ultrahigh Sensitivity Surface Plasmon Resonance Magnetic Field Sensor Based on D-Shape Four-Hole Fiber
Chen Zhenshi, Chen Cheng, Chu Paul K., Fu Haihao
Published in: IEEE Sensors Journal (Volume: 24, Issue: 24, December 2024)
Magnetic field sensors are essential in modern technology, with applications ranging from smart gadgets, biomedicine, industrial automation, and environmental monitoring to aerospace technology. This paper presents a highly sensitive sensor using surface plasmon resonance (SPR) and a D-shaped four-hole fiber designed for accurate magnetic field detection. Integrating SPR with fiber optics enhances the devices' sensitivity and performance, advancing its potential applications in areas where detecting weak magnetic signals is crucial.
Antibody-Free SERS Detection of Severe Fever With Thrombocytopenia Syndrome Virus Using Micron Bowl Array PDMS Substrates
Hsu Wei-li, Wang Gou-jen, Lin Ying-Ting, Lin Ze-Cheng, Tseng Ching-Yu
Published in: IEEE Sensors Journal (Volume: 25, Issue: 4, February 2025)
Severe fever with thrombocytopenia syndrome (SFTS) is a newly identified zoonotic infectious disease discovered in several East Asian countries. It is caused by the SFTS virus (SFTSV), also known as Dabie bandavirus or Huaiyangshan virus. The paper presents a novel surface-enhanced Raman scattering (SERS) chip with silver nanoparticles (AgNPs) uniformly deposited on a micron bowl array polydimethylsiloxane (PDMS) substrate to detect the virus responsible for SFTS effectively.
Scorpion-Inspired, Hydrophobic, Highly Sensitive, and Paper-Based Magnetoelastic Biosensor for C-Reactive Protein Detection
Sang Shengbo, Ge Yang, Guo Xing, Yuan Zhongyun, Zhao Dong, Luo Man
Published in: IEEE Sensors Journal (Volume: 24, Issue: 8, April 2024)
C-reactive protein (CRP) in human blood is a vital biomarker for detecting inflammation or acute infection. This study presents a hydrophobic, paper-based magnetoelastic biosensor inspired by scorpion anatomy and made using eco-friendly materials and V-shaped grooves to enhance sensitivity for detecting CRP. The sensor overcomes the limitations of traditional detection methods and offers rapid, cost-effective, and highly sensitive diagnostics for acute inflammation and tissue damage.
Improving the Spatial Resolution of Small Satellites by Implementing a Super-Resolution Algorithm Based on the Optical Imaging Sensor’s Rotation Approach
Published in: IEEE Sensors Journal (Volume: 24, Issue: 24, December 2024)
Image processing can improve the image quality of data transmitted by small satellites. This paper presents a novel method of enhancing small satellite image resolution using a super-resolution algorithm combined with a rotating sensor. The algorithm reconstructs a high-resolution image by boosting spatial resolution. This method increases image clarity by gathering more data in flight and vertical directions and efficiently processing images in approximately 0.713 seconds, making it suitable for microsatellite applications.
Rule the Joule: An Energy Management Design Guide for Self-Powered Sensors
Monagle Daniel, Ponce Eric Andrew, Leeb Steven
Published in: IEEE Sensors Journal (Volume: 24, Issue: 01, January 2024)
Rule the Joule Energy Management System for self-powered sensors features dynamic control, cold-start capability, and robust maximum power point tracking (MPPT) to optimize energy harvesting and storage. Its real-time energy flow regulation reduces reliance on predictive models, enhancing efficiency in noisy environments. Experimental validation confirms its reliability for powering wireless sensor nodes, providing overvoltage protection and improved component longevity, paving the way for advancement in IoT, smart technologies, and wireless sensor networks.
Microfluidic Electrochemical Sensor for Online Detection of Chemical Oxygen Demand Based on AuNPs/Au Electrodes
Yang Xiaozhan, Wu Haotian, Xie Song
Published in: IEEE Sensors Journal (Volume: 24, Issue: 23, December 2024)
Chemical oxygen demand (COD) is a key parameter for water quality assessment. The paper presents a compact, microfluidic electrochemical sensor for real-time monitoring of COD in water. The sensor uses gold nanoparticles (AuNPs) on modified gold electrodes to enhance sensitivity and accuracy. It does real-time COD monitoring with high efficiency and quick response, i.e., 3 minutes. Successful tests on water samples highlight its potential for water quality monitoring and environmental management.
Triaxial 3-D-Channeled Soft Optical Sensor for Tactile Robots
Matteo Lo Preti, Federico Bernabei, Anderson B. Nardin, Lucia Beccai
Published in: IEEE Sensors Journal (Volume: 24, Issue: 17, September 2024)
Robotic systems integrated with sensors show enhanced performance in challenging environments and tasks. The proposed novel triaxial 3D-channeled soft fingertip-shaped optical sensor designed for tactile robots uses transparent channels to detect forces in three dimensions. The compact and flexible sensors made from soft materials with optical waveguides enhance the robot's sensitivity to touch. It offers flexibility and accuracy for real-time tactile feedback, showing potential for real-time force detection in robotic hands.
Published in: IEEE Sensors Journal (Volume: 24, Issue: 24, December 2024)
Accurate plant monitoring is vital for smart agriculture. Traditional pH sensors struggle with light interference and require dark conditions for accurate pH measurement. This study introduces a novel pH image sensor mitigating light interference. Employing dual-pixel technology, it distinguishes between pH and light signals, enabling precise measurements under illumination. Results show errors within 10%, a significant improvement over existing methods. This advancement facilitates real-time multi-ion sensing for optimized crop management.
Noninvasive Blood Glucose Measurement Using RF Spectroscopy and a LightGBM AI Model
Dominic Klyve, Steve Lowe, Kaptain Currie, James H. Anderson, Carl Ward, Barry Shelton
Published in: IEEE Sensors Journal (Volume: 24, Issue: 17, September 2024)
Blood glucose monitoring is crucial in diabetes patients. This paper combines radiofrequency (RF) spectroscopy with a LightGBM AI model to explore a non-invasive method for measuring blood glucose levels. RF signals detect the change in blood glucose concentration in the skin, which is processed through the AI model to estimate blood glucose levels accurately. The approach aims to provide a non-invasive, safe, and convenient way for regular glucose monitoring in diabetic care.
Published in: IEEE Sensors Journal (Volume: 24, Issue: 23, December 2024)
The limitations of conventional temperature sensors in extremely cold environments pose significant challenges across various industries. This paper introduces a microwave sensor utilizing a stripline transmission for low-temperature sensing. The sensor exploits the temperature-sensitive properties of dielectric materials, enabling precise detection of frequency shifts correlated with temperature variations. The innovatively designed sensor is feasible for low and sub-zero temperature measurement owing to its linear sensitivity and stable, repeatable measurements.
N. Bhavana, Mallikarjun M. Kodabagi, B. Muthu Kumar, P. Ajay; N. Muthukumaran, A. Ahilan
Published in: IEEE Sensors Journal (Volume: 24, Issue: 15, August 2024)
Road potholes pose significant risks to road safety and infrastructure. POT-YOLO is a novel real-time detection system combining edge segmentation with the You Look Only Once (YOLOv8) architecture. This innovative approach improves pothole localization by accurately identifying their contours. POT-YOLO demonstrates exceptional performance, ensuring accurate and efficient detection even in challenging conditions. Its real-time capabilities allow for deployment on edge devices, facilitating proactive maintenance and improving road safety.
Analysis and Design of Biplanar Coils Within Magnetic Shielding Room Considering Actual Ferromagnetic Boundaries
Xu Xueping, Liu Yi, Sun Xin, Zhou Weiyong
Published in: IEEE Sensors Journal (Volume: 24, Issue: 24, December 2024)
Studying the coupling relationship between the magnetic shield and coil is crucial for designing a magnetic field coil with high uniformity. This paper presents a novel method for designing high-uniformity biplanar coils inside a closed magnetic shielding room with ferromagnetic boundaries. The approach accounts for the permeability and thickness of the magnetic shielding materials to effectively improve the uniformity of the coil, which is important for achieving a near-zero magnetic field environment.
High-Sensitivity of Self-Powered Gas Sensors Based on Piezoelectric Nanogenerators With Y-Doped 1-D ZnO Nanostructures
Ji Liang-wen, Chu Tung-Te, Chu Yen-Lin, Xie Jun-Hong
Published in: IEEE Sensors Journal (Volume: 24, Issue: 12, June 2024)
Self-powering sensors will make the sensing devices portable. This study explores a self-powered gas sensor using Yttrium-doped zinc oxide (Y-doped ZnO) nanorod arrays and piezoelectric nanogenerators (PENGs). The sensor demonstrated self-powering capability, offering portability and energy efficiency. It exhibits superior sensitivity to carbon monoxide (CO) compared to conventional sensors. The advantages include portability, improved gas adsorption and functionality, facilitating integration into the IoT systems, and wearable sensors for real-time environmental monitoring.
Published in: IEEE Sensors Journal (Volume: 24, Issue: 24, December 2024)
Vertical Hall Devices (VHD) offer a way for accurate low-cost angular position sensing. In-plane stresses on the silicon die caused by packaging or external forces challenge the increasing accuracy requirements in the automotive and automation industry over a wide temperature range. The study presents a novel active stress compensation method to increase the accuracy of angle measurements in CMOS-integrated VHD’s. The approach is particularly beneficial for applications requiring high-accuracy magnetic field sensing.
Published in: IEEE Sensors Journal (Volume: 24, Issue: 18, September 2024)
Contact scanning probes may have issues of reduced accuracy due to high speed and high frequency. This paper presents the innovative contact scanning probe, utilizing dual-parameter feedback to optimize pole configuration. It enhances accuracy by correcting high-speed and high-frequency errors with real-time accuracy and dynamic compensation. The approach aims to improve measurement accuracy and stability in high-speed scanning systems. It has promising potential in precision manufacturing, nanotechnology, and medical fields.
From Simulation to Surgery: Comprehensive Validation of an Optical Sensor for Monitoring Focal Laser Ablation of Solid Organ Tumors
Geoghegan Rory, Hughes Griffith, Marks Leonard, Natarajan Shyam, Priester Alan, Sisk Anthony, Sun Songping, Tirado Richard
Published in: IEEE Sensors Journal (Volume: 24, Issue: 24, December 2024)
Focal laser ablation (FLA) is a minimally invasive procedure using thermal coagulation to treat tumors with fewer side effects than surgery. However, it lacks an accurate and affordable monitoring method. This paper presents an optical sensor for real-time monitoring of the ablation process. The sensor has been validated through simulations, ex vivo, and clinical studies and can detect the coagulation boundary precisely without tissue-specific calibration.
Exploring and Identifying Bias-Instability Noise Sources in Mode-Split MEMS Gyroscopes Based on Electrostatic Frequency Tuning
Jie Lin, Yang Zhao, Guoming Xia, Qin Shi, Anping Qiu
Published in: IEEE Sensors Journal (Volume: 24, Issue: 18, September 2024)
MEMS gyroscope has advanced with the advancements in processing and manufacturing technology. However, their structure and system-based noise level continue to pose challenges in proper implementation and restrict their performance with high accuracy and precision. This paper proposes a system-level noise model that analyzes the relationship between frequency split and noise performance. It identifies noise sources and contributes to bias instability, establishing a frequency-tuning criterion for different noise requirements.
Application of Stone-Derived Substrates in Thin-Film Temperature Sensing
Saeedzadeh Khaanghah Niloofar, Cantarella Giuseppe, Carrasco-Pena Alejandro, Haller Michael, Krik Soufiane, Munzenrieder Niko, Nippa Michael, Rapagnani Nicholas, Van Bezooijen Aart, de Souza Oliveira Hugo
Published in: IEEE Sensors Journal (Volume: 24, Issue: 24, December 2024)
Conventional electronic substrates offer stability and reliability. However, they significantly contribute to electronic waste (e-waste). This study explores stone-based substrates, ranging from natural raw stone to stone paper, as sustainable alternatives for high-performance thin-film temperature sensors. Integrating biodegradable zinc (Zn)-based resistance temperature detectors (RTDs) and amorphous InGaZnO (IGZO)-based thermistors pave the way for dissolvable sensors and reusable substrates, thus leading to less harmful e-waste and advancing eco-friendly electronics.
EV-Fusion: A Novel Infrared and Low-Light Color Visible Image Fusion Network Integrating Unsupervised Visible Image Enhancement
Wang Xia, Sun Qiyang, Yan Changda, Zhang Xin
Published in: IEEE Sensors Journal (Volume: 24, Issue: 4, February 2024)
EV-Fusion is an innovative technology for low-light visibility applications. It enhances infrared and color images, surpassing nine leading methods in visual fidelity, brightness, and spatial frequency. Its real-time, high-contrast fusion capabilities show promise for military surveillance and traffic security, ensuring visibility of critical details in low-light conditions. Built on a Swin Local-Global Block framework, it utilizes color visible image enhancement and intensity image fusion modules to enhance texture and overall image quality.
Classification Strategies for Radar-Based Continuous Human Activity Recognition With Multiple Inputs and Multilabel Output
Ingrid Ullmann, Ronny G. Guendel, Nicolas Christian Kruse, Francesco Fioranelli, Alexander Yarovoy
Published in: IEEE Sensors Journal (Volume: 24, Issue: 24, December 2024)
Fall detection systems can support independent living for people prone to falls. This paper introduces a novel approach using a radar-based continuous human activity recognition(HAR) system to study different activities by processing radar data in segments and using multilabel classification to recognize activities. The study explores deep learning methods, sensor networks, radar data types, and classification settings to enhance the effectiveness of accurate real-time activity recognition and detecting falls.
Enhancing the Detection Sensitivity of PbSe Thin Film TTE Photodetector by Adding Light-Trapping Coatings
Mingjing Chen, Yangyang Zhen, Xingkun Ning, Lide Fang, Shufang Wang
Published in: IEEE Sensors Journal (Volume: 24, Issue: 18, September 2024)
A breakthrough in PbSe thin-film transverse thermoelectric (TTE) photodetectors enhances sensitivity by adding 15-µm graphite coatings with light-trapping structures. This innovation improves photothermal conversion and sensitive light absorption, achieving a detection sensitivity of 415 µV cm/W, five times higher than traditional uncoated films. The increased efficiency positions this sensor as an advanced self-powered, ultrabroadband photodetector with future thermal sensor applications across various fields, ranging from ultraviolet to far-infrared wavelengths.
A Novel Plasmonic Nanoantenna-Based Sensor for Illicit Materials and Drugs Detection
Marco Scalici, Patrizia Livreri
Published in: IEEE Sensors Journal (Volume: 24, Issue: 17, September 2024)
The advancements in terahertz(THz) radiation have opened avenues for potential applications in the security and defense sectors. This paper introduces novel plasmonic nanoantennas-based sensors for detecting illicit materials and drugs through terahertz (THz) spectroscopy. Two innovative designs, the butterfly and shamrock nanoantennas, offer high sensitivity across specific THz frequencies. Their combined performance enables accurate, non-destructive molecular identification, providing an advanced tool for security, biomedical, and environmental applications.
Displacement Sensitivity and Range Enhancement Through Buckled Beam-Assisted OE-HCCR
Qi Zhang, Yizheng Chen, Zhuo Li, Yan Tang, Yun Liang, Yi Huang, Xiaobei Zhang
Published in: IEEE Sensors Journal (Volume: 24, Issue: 18, September 2024)
Displacement measurement sensitivity has broad industrial, scientific and construction applications. The paper proposes an ultrasensitive and wide-range displacement sensor using an open-ended hollow coaxial cable resonator (OE-HCCR) assisted by a buckled beam. The integration of buckled beams enhances sensitivity and extends the measurement range. The sensor demonstrates significant improvements in signal-to-noise ratio and accuracy. The developed system will contribute to advancing micro-scale and nano-scale displacement technology.
Monitoring of Lactococcus Lactis Growth Based on Reduced-Graphene Oxide TFT for Dairy Industry Applications
L. Franchin, S. Casalini, A. Cester; A. Paccagnella, S. Bonaldo
Published in: IEEE Sensors Journal (Volume: 24, Issue: 17, September 2024)
The biological activity of Lactococcus lactis (L. lactis) affects milk fermentation. This paper explores reduced-graphene oxide thin-film (rGO-TFT) to develop an effective biosensor, based on the field-effect transistor, for monitoring bacteria proliferation in dairy plants. This biosensor offers a real-time, highly sensitive, reliable, and non-invasive method to assess fermentation processes by detecting changes in electrical properties as bacterial populations grow, thus enabling precise bacterial growth monitoring in the dairy industry.
Deep Neural Network-Assisted Terahertz Metasurface Sensors for the Detection of Lung Cancer Biomarkers
Hu Fangrong, Su An, Yang Mo, Chen Jie, Lin Shangjun, Ma Xiaoya
Published in: IEEE Sensors Journal (Volume: 24, Issue: 10, May 2024)
Terahertz (THz) metasurface sensing technology, combined with deep neural networks (DNNs), offers an innovative method for detecting lung cancer biomarkers, including miRNA-21, miRNA-92a, and miRNA-339-3p. This integrated system achieved a classification accuracy of 97.22% for miRNA-21, showcasing its effectiveness as a low-energy, label-free solution for early cancer detection. It demonstrates significant advantages over traditional methods for real-time clinical applications, facilitating rapid diagnosis and monitoring of lung cancer.
Self-Powered and Cost-Effective Wireless Sensor Node for Air Quality Monitoring With an Optically Transparent Smart Antenna System
Maria Bermudez Arboleda, Atif Shamim
Published in: IEEE Sensors Journal (Volume: 24, Issue: 18, September 2024)
Air quality monitoring is crucial for the environment and public health. However, traditional air quality monitoring systems are bulky, costly, and challenging to install. To overcome these shortcomings, this study introduces a compact, self-powered sensor node with optically transparent, reconfigurable antennas and modular solar panels. Its innovative design enables 80% size reduction. Equipped with comprehensive pollutant detectors, easy deployment of these cost-effective sensors enables high-resolution monitoring, previously unattainable with traditional systems.
Design and Output Voltage Model of Folding Magnetized Electronic Skin for Intelligent Manipulator
Guoheng Lin, Ling Weng; Hui Zhang, Yang Liu, Yuxin Chen, Zhuolin Li
Published in: EEE Sensors Journal (Volume: 24, Issue: 18, September 2024)
Flexible electronic skin (e-skin) broadens the application of tactile sensing and flexible sensor technology. The paper presents the design and modeling of a folding magnetized electronic skin with advanced multilayer designs and mathematical modeling for intelligent manipulators. It also combines flexible electronics and magnetic sensing for enhanced flexibility and precision. This e-skin boosts robotics performance in complex tasks requiring precise object handling and interaction by providing tactile and spatial awareness.
A Self-Calibration Method for Engineering Using 3-D Laser Scanning System Based on Cube Vertices
Hongqiang Chen, Ruiheng Xia, Yi Zhang, Hua Deng, Kejun Li
Published in: IEEE Sensors Journal (Volume: 24, Issue: 3, February 2024)
To meet the rapid calibration needs of engineering sites, a novel self-calibration technique is proposed for 3D scanners using 2D light detection and ranging (LiDAR) and a rotating platform. The calibration process uses a cube of known size as the calibration object, ensuring a straightforward process with no additional equipment. This approach addresses the limitations of shape-based calibration, enabling efficient on-site calibration and accurate measurements for most engineering applications.
A Multi-Sensor Tactile System Based on Fiber Bragg Grating Sensors for Soft Tissue Palpation
M. Pulcinelli, L. Zoboli, F. De Tommasi, C. Massaroni, V. Altomare, A. Grasso, A. Gizzi, E. Schena, D. Lo Presti
Published in: IEEE Sensors Journal (Volume: 24, Issue: 16, August 2024)
Diagnostic tissue palpitation is a common clinical practice to detect soft tissue tumors. This paper presents an innovative multi-sensor tactile system based on Fiber Bragg Grating sensors for non-invasive soft tissue palpation. It improves diagnostic accuracy with enhanced spatial resolution, reduced probe size, and minimized crosstalk errors. The simple and non-invasive tool is effective for soft tissue cancer and tumor detection, with the future potential for automated detection.
Vibration Sensitivity of One-Port and Two-Port MEMS Microphones
Francis Doyon-D’amour, Carly Stalder, Timothy Hodges, Michel Stephan, Lixue Wu, Triantafillos Koukoulas, Stephane Leahy, Raphael St-Gelais
Published in: IEEE Sensors Journal (Volume: 24, Issue: 18, September 2024)
Two-port MEMS (Micro-Electro-Mechanical Systems) microphones offer better directional sensitivity than one-port designs but may be more affected by vibrations. The paper presents a simple universal expression for the vibration sensitivity of two-port MEMS microphones. It analytically and experimentally demonstrates that the vibration sensitivity of two-port MEMS mics is independent of their natural frequency (i.e., their stiffness) and is inversely proportional to frequency. Experimental results confirm these findings across various microphone designs.
Published in: IEEE Sensors Journal (Volume: 24, Issue: 13, July 2024)
An integrated sensing and communication (ISAC) system combines communication and radar sensing functions to optimize resource utilization. This paper has developed a hybrid analog-digital beamforming method for multi-user, multi-beam ISAC scenarios. The approach prioritizes communication performance through iterative optimization of digital and analog precoders while ensuring effective radar sensing. The proposed algorithm enhances cost, power and spectrum efficiency, and performance, making it ideal for next-generation communication systems.
A Low-Cost LoRa Optical Fluorometer–Nephelometer for Wireless Monitoring of Water Quality Parameters in Real Time
Soheyl Faghir Hagh, Parmida Amngostar, Wenzhe Chen, Jessica Sheridan, Clayton J. Williams, Ana M. Morales-Williams, Dryver Huston, Tian Xia
Published in: IEEE Sensors Journal (Volume: 24, Issue: 13, July 2024)
The increasing threat of harmful algal blooms necessitates affordable and accessible water quality monitoring. This research presents a low-cost, portable, Internet of Thing (IoT)-enabled fluorometer-nephelometer for measuring key water quality parameters. This open-source, customizable system can be adapted to various applications, from single-point measurements to distributed networks. By adjusting sensitivity and adding components, it can monitor diverse aquatic environments, aiding in the research and management of marine ecosystems.
YOLOX-SAR: High-Precision Object Detection System Based on Visible and Infrared Sensors for SAR Remote Sensing
Qiang Guo, Jianing Liu, Mykola Kaliuzhnyi
Published in: IEEE Sensors Journal (Volume: 22, Issue: 17, September 2022)
Object detection using Synthetic Aperture Radar (SAR) sensors is significant in artificial intelligence, signal processing, radar imaging, and image processing. However, complex electromagnetic scattering backgrounds create challenges in accurate detection. The paper proposes the state-of-the-art YOLOX-SAR system, built upon the YOLOX architecture with advanced features and techniques for precise SAR image object detection. Incorporating technological advancements such as Meta-ACON and CBAM promises improved accuracy and robustness of SAR image object detection.
Published in: IEEE Sensors Journal (Volume: 24, Issue: 17, September 2024)
The pandemic created the demand for a suitable alternative to lab tests to quickly detect antibodies with high precision. This paper presents a fast, sensitive, and accurate new system to detect COVID-19 neutralizing antibodies using optical spectroscopy and hybrid machine learning. The method evaluates immunity by detecting antibodies that block virus-receptor interactions, thus effectively monitoring vaccine efficacy and immune responses. Its high accuracy and scalability make it suitable for diagnostic and research applications.
Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things
Maede Zolanvari, Marcio A. Teixeira, Lav Gupta, Khaled M. Khan, Raj Jain
Published in: IEEE Internet of Things Journal (Volume: 6, Issue: 4, August 2019)
A cyberattack on the Industrial Internet of Things (IIoT) could have devastating consequences. The researchers have conducted a detailed assessment of existing IIoT protocols for cyber vulnerability. The case study demonstrates the effectiveness of the proposed machine learning (ML)-based intrusion detection system (IDS) against cyberattacks. An in-house developed testbed simulated real-world IIoT scenarios and potential cyberattacks to evaluate the performance of the proposed ML-based system.
Single-Channel DoA Estimation Based on Nonuniform Time-Modulated Array With Asynchronous Sampling
Li Long, Han Jiaqi, Liu Gong-Xu, Mu Yajie, Shi Yan, Wang Xin, Xia Dexiao
Published in: IEEE Sensors Journal (Volume: 24, Issue: 14, July 2024)
Direction-of-arrival (DoA) is essential for accurate target positioning and is important in wireless communication, radar detection, satellite navigation, etc. This paper introduces a single-channel direction-of-arrival (DoA) estimation method using a nonuniform time-modulated array (NTMA) with asynchronous sampling. The technique reduces hardware complexity and improves estimation accuracy using a single-channel receiver and an optimized modulation scheme. The results demonstrate the system's effectiveness, especially in applications with limited resources and where synchronous sampling is challenging.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 14, July 2022)
Rice, a Global staple, is often plagued by various diseases that impact crop production. The challenge of identifying these diseases exacerbates the issue. While deep learning is a powerful tool in image processing and computer vision, its application in plant disease recognition has been restricted. This paper introduces MobInc-Net, a lightweight Inception network that recognizes and detects rice plant diseases. It offers a practical solution that achieves high accuracy even in challenging conditions.
Self-Driven Photodetectors Based on Flexible Silicon Nanowires Array Surface-Passivated With Tin-Based Perovskites
Yang Shengyi, Ge Zhenhua, Jiang Yurong, Wang Ying, Xin Haiyuan, Zhang Zhenheng, Zou Bingsuo
Published in: IEEE Sensors Journal (Volume: 24, Issue: 14, July 2024)
Silicon nanowire (Si-NW) photodetectors show great potential as efficient, self-driven, and compact devices in optoelectronic applications. However, their intrinsic surface defects reduce their responsivity and specific detectivity, limiting their performance. This paper introduces a novel self-driven photodetector based on flexible silicon nanowires array surface passivated with tin-based perovskites (FASnBr₃). The innovative design significantly enhances device performance and flexibility, making it a promising candidate for next-generation photodetectors.
Published in: IEEE Sensors Journal (Volume: 23, Issue: 3, February 2023)
Anomalies between trains and platform doors threaten intercity railway safety. The paper proposes a method for anomaly detection using train predeparture key frame extraction and an Image-inpainting Anomaly Detection Network (IADN) based on image-inpainting autoencoder (AE) and local abnormal information enhancement and global-attentive reconstruction error (GARE). The tested results show effective and accurate anomaly detection, even outperforming state-of-the-art methods, ensuring safety with potential applications in security and locomotive industries.
Virtual Fusion With Contrastive Learning for Single-Sensor-Based Activity Recognition
Duc-Anh Nguyen, Cuong Pham, Nhien-An Le-Khac
Published in: IEEE Sensors Journal (Volume: 24, Issue: 15, August 2024)
Human activity recognition (HAR) using multiple sensors offers higher accuracy but raises privacy and convenience issues, while single sensors often lack detail and accuracy. The paper proposes Virtual Fusion with Contrastive Learning (VFCL), a novel framework for single-sensor-based activity recognition. Virtual fusion uses data from multiple sensors across different modalities for training but requires only one for predictions, while contrastive learning improves the accuracy and performance of each sensor independently.
Flexible Dual-Polarized UWB Antenna Sensors for Breast Tumor Detection
Hui Li, Haoran Zhang, Yonghua Kong, Changfei Zhou
Published in: IEEE Sensors Journal (Volume: 22, Issue: 13, July 2022)
Early detection of breast cancer saves lives. The research presents a novel and adaptable breast cancer detection system integrating dual-polarized Ultra-Wideband (UWB) antennas on flexible Kapton polyimide, ensuring high precision. Eight UWB units surround the breast phantom and reconstruct 3D images using a delay-and-sum (DAS) algorithm to locate tumors with minimal clutter. Wearable and versatile, it can detect tumors with a 15 mm edge-to-edge distance, offering convenient health monitoring and self-diagnosis.
Design, Fabrication, and Validation of a Flexible Tactile Sensor for a Hand Prosthesis
Kuo Chung-hsien, Nguyen Dai-Dong, Su Shun-Feng, Xie Wu-Qi
Published in: IEEE Sensors Journal (Volume: 24, Issue: 16, March 2024)
The design of a flexible tactile sensor using liquid metal (LM) and elastic fibers provides sensing capability and enhances the performance of a hand prosthesis. This study details the measurement principles of the LM-based force sensor, the design and fabrication process, and the sensor signal processing circuit. The proposed flexible tactile sensor offers reliable performance with high sensitivity in three axes, low error, and improved functionality in hand prostheses.
Characterizing Deep Neural Networks on Edge Computing Systems for Object Classification in 3D Point Clouds
Cristian Wisultschew, Alejandro Pérez, Andrés Otero, Gabriel Mujica, Jorge Portilla
Published in: IEEE Sensors Journal (Volume: 22, Issue: 17, September 2022)
In our technology-driven world, devices require quick and efficient data processing. Edge computing enables rapid local decision-making, preserving bandwidth and privacy. Understanding platform intricacies is crucial for informed decision-making while navigating through vast data. The paper explores innovative approaches and experimental findings, providing insights into Deep Neural Networks (DNNs) architecture performance across diverse edge technologies. This aids in selecting optimal architectures based on performance metrics for specific applications.
Published in: IEEE Sensors Journal (Volume: 24, Issue: 13, July 2024)
Hydrophones are devices that convert underwater acoustic pressure into electrical signals. The paper proposes a hydrophone designed using Aluminum Scandium Nitride (AlScN) piezoelectric micromachined ultrasonic transducers (PMUTs) integrated monolithically on CMOS (Complementary Metal-Oxide-Semiconductor). This single-chip AIScN PMUTs with COMS (PMUTs-on-CMOS) hydrophone offers compactness, high sensitivity, and energy efficiency for underwater acoustic sensing. It supports high-performance underwater detection and has promising applications in underwater communications, sonar, and environmental monitoring systems.
A Retail Object Classification Method Using Multiple Cameras for Vision-Based Unmanned Kiosks
Ji-Ye Jeon, Shin-Woo Kang, Hyuk-Jae Lee, Jin-Sung Kim
Published in: IEEE Sensors Journal (Volume: 22, Issue: 22, November 2022)
Sensor technology, wireless communications, and Internet of Things (IoT) use in unmanned self-checkout systems has elevated the retail experience. This paper presents a vision-based RGB kiosk using a sophisticated combination of multiple cameras and a cutting-edge Convolutional Neural Network (CNN) framework, yielding a 33.67% improvement over conventional methods. It solves inter-classification challenges and intra-class variations in products with 142k+ real-world dataset points, with promising applications in retail and lifestyle sectors.
TSSTDet: Transformation-Based 3-D Object Detection via a Spatial Shape Transformer
Yoo Myungsik, Bui Cuong Duy, Hoang Hiep Anh
Published in: IEEE Sensors Journal (Volume: 24, Issue: 5, March 2024)
Accurate 3D object detection is essential for the safe navigation of autonomous vehicles. The novel transformation-based 3-D object detection via a spatial shape transformer (TSSTDet) overcomes the challenges of incomplete and varying orientations of the obstructions. Its key features include a rotational transformation convolutional backbone (RTConv) for orientation-invariant detection and a voxel-point shape transformer for reconstructing missing parts, thus improving obstacle detection and avoidance and enhancing safe navigation in autonomous driving.
Camera-Augmented Non-Contact Vital Sign Monitoring in Real Time
Arash Shokouhmand, Samuel Eckstrom, Behnood Gholami, Negar Tavassolian
Published in: EEE Sensors Journal (Volume: 22, Issue: 12, June 2022)
Vital signs, such as heart rate (HR) and respiratory rates (RR), are crucial for patients' health assessment. Technological advancements have led to non-contact monitoring using cameras and radars. This paper introduces a camera-guided frequency-modulated continuous-wave (FMCW) radar system for real-time non-contact vital sign monitoring. The novel singular value-based point detection (SVPD) method is designed to optimize respiratory and heart rate monitoring. Experiments show high accuracy and effective vital signs monitoring.
Occupancy Grid Mapping for Automotive Driving Exploiting Clustered Sparsity
Pandharipande Ashish, Joseph Geethu, Myers Nitin, Onen Cagan
Published in: IEEE Sensors Journal (Volume: 24, Issue: 7, April 2024)
Environment mapping is a key component in assisted and autonomous driving. This paper introduces a method to generate 2D occupancy maps using light detection and ranging (LiDAR) and radar data, leveraging their clustered, sparse nature. It presents a linear sensor measurement model and pattern-coupled sparse Bayesian learning approach for occupancy map estimation. Tested with real-world data highlighting the methods, it shows superior performance in detecting obstacles.
A Fully Flexible Hydrogel Electrode for Daily EEG Monitoring
Gencai Shen, Kunpeng Gao, Nan Zhao, Zhuangzhuang Wang, Chunpeng Jiang, Jingquan Liu
Published in: IEEE Sensors Journal (Volume: 22, Issue: 13, July 2022)
The study explores fully flexible hydrogel electrodes for daily monitoring of electroencephalogram (EEG) Signals. Synthesized through a NAGA (n-acryloyl glycinamide) hydrogel enriched with glycerol, these electrodes exhibit enhanced skin conductivity compared to dry and semi-dry electrodes. With stable signal monitoring, 70% deformation tolerance, and 90-day durability, these electrodes prove ideal for daily EEG monitoring, aiding brain activity monitoring applications in Brain Computer Interface studies based on data analysis algorithms.
Glyphosate Detection Through Piezoelectric and Fiber Optic Sensors Based on Molecular Imprinted Polymers
Sequeira Filipa, Bilro Lucia, Gomes Maria Teresa S. R., Oliveira Ricardo, Reis Silvia, Rudnitskaya Alisa, Verissimo Marta
Published in: IEEE Sensors Journal (Volume: 24, Issue: 13, July 2024)
Glyphosate is a harmful herbicide often detected in soil and groundwater. The study demonstrates the proof of concept of a portable “sensing pen” for glyphosate detection using sensors with molecularly imprinted polymers (MIPs). MIPs are engineered to bind glyphosate to enhance detection accuracy. The sensors combine piezoelectric and fiber optic technologies to achieve high sensitivity and specificity. These sensors can rapidly detect glyphosate, offering a cost-effective and reliable solution for environmental monitoring.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 24, December 2022)
Ammonia (NH3), a vital environmental gas, is hazardous to health. Traditional methods to detect ammonia are slow and expensive. The study investigates the sensitivity, selectivity, and optimal operating conditions of Tungsten disulfide (WS2)-based gas sensors. Using materials like WS2 offers fast and effective detection methods, highlighting their strong response to ammonia, selectivity against other gasses, and improved efficiency under light illumination, offering valuable insights for developing more efficient gas detection technologies.
DN-SLAM: A Visual SLAM With ORB Features and NeRF Mapping in Dynamic Environments
Zhang Kehua, Huang Kai, Ruan Chenyu, Zang Qiuyu
Published in: IEEE Sensors Journal (Volume: 24, Issue: 4, February 2024)
Simultaneous Localization and Mapping (SLAM) is essential for robots navigating new environments. DN-SLAM is a novel SLAM system designed to navigate dynamic environments. It integrates ORB (Oriented FAST and Rotated BRIEF) features for robust extraction and neural radiance fields (NeRF) for high-quality 3D representation to accurately estimate trajectory in dynamic environments. Its enhanced real-time tracking and dense mapping in dynamic scenes make it a promising solution for various robotics applications.
Detection of Soil Moisture, Humidity, and Liquid Level Using CPW-Based Interdigital Capacitive Sensor
Shaheen Ahmad, Nabil Khalid,Rashid Mirzavand
Published in: IEEE Sensors Journal (Volume: 22, Issue: 11, June 2022)
The Interdigital Capacitive sensor integrated with CPW (Coplanar Waveguide) feeding is economical and effective for measuring soil moisture, air humidity, and liquid levels. Coated with a Polyvinyl Alcohol layer, it maintains linearity for soil moisture and is reliable for liquid-level detection. Tested at 915 MHz, it exhibits high sensitivity. With low power consumption and reliability, it finds application across diverse fields like agriculture, oil, and medical equipment.
Low Cost, Flexible, Room Temperature Gas Sensor: Polypyrrole-Modified Laser-Induced Graphene for Ammonia Detection
Salehnia Foad, Vilanova X., Llobet Eduard, Romero Nevado Alfonso Jose, Santos Ceballos Jose Carlos
Published in: IEEE Sensors Journal (Volume: 24, Issue: 7, April 2024)
The hazardous nature of ammonia (NH3) makes its monitoring crucial. The paper presents a novel, low-cost, and flexible ammonia gas sensor using polypyrrole-modified laser-induced graphene (PPy@LIG) developed for real-time monitoring and detection of ammonia in atmosphere. It demonstrates enhanced sensitivity, excellent repeatability, and a low detection limit of 1 ppm at room temperature. This breakthrough opens opportunities for advanced air quality monitoring systems and potential applications in agriculture and industrial settings.
Fabrication and Characterization of P3HT/MoS₂ Thin-Film Based Ammonia Sensor Operated at Room Temperature
Ankit Verma, Praveen Kumar Sahu, Vivek Chaudhary, Arun Kumar Singh, V. N. Mishra, Rajiv Prakash
Published in: IEEE Sensors Journal (Volume: 22, Issue: 11, June 2022)
The study presents a high-performance ammonia gas sensor using poly(3-hexylthiophene)/molybdenum disulfide (P3HT/MoS2) nanocomposite in a top-contact organic field-effect transistor (OFET) assembly. The P3HT/MoS2 surface, with superior crystallinity and extended nanofiber morphology, improves charge interaction and transport with ammonia, yielding a gas sensor response of 63.45% at 100 ppm ammonia concentration. The fabricated OFET showcases high efficiency, sensitivity, and non-invasiveness, demonstrating significant potential for environmental protection as an ammonia gas sensor.
Published in: IEEE Sensors Journal (Volume: 24, Issue: 7, April 2024)
Advanced computational models like artificial neural networks (ANN) and particle swarm optimization with artificial neural networks (PSO-ANN) are revolutionizing the prediction of microfluidic biosensor performance. They predict detection times based on critical input variables and identify optimal conditions for enhanced biosensor performance by systematically varying key parameters. Machine learning (ML) algorithms analyze the data to predict outcomes and improve detection accuracy. The findings promise advancements in biosensor technology across diverse applications.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 20, October 2022)
The advancements in human-computer interaction (HCI) have proved effective in training machines and fostered research in automatic emotion recognition. Convolutional neural networks (CNNs) have shown promising results in electroencephalogram (EEG)-based emotion recognition. The study investigates electroencephalogram (EEG) based signals for precise emotional recognition trained over novel and effective Convolutional Neural Networks (CNN) and Contrastive Learning methods. This technology holds promise for future applications in emotional understanding and mental health monitoring.
Development of Flexible Electronic Biosensors for Healthcare Engineering
Yan Jian, Yan Jiasheng, Cheng Jie, Fu Yusheng, Guo Jinhong, Zhao Ying, Zhou Jun
Published in: IEEE Sensors Journal (Volume: 24, Issue: 8, April 2024)
With the potential for real-time health monitoring and personalized diagnosis, wearable biosensors are the future of the healthcare system. The portability and stretchability of flexible electronics allow them to substitute bulky diagnostic devices with wearable devices, thus creating possibilities for non-invasive continuous health monitoring. These biosensors convert physiological data into interpretable information by integrating innovative sensing mechanisms and designs, enabling healthcare professionals to detect warning signs, diagnose diseases, and assess health accurately.
Impact of the Earth Rotation Compensation on MEMS-IMU Preintegration of Factor Graph Optimization
Hailiang Tang, Tisheng Zhang, Xiaoji Niu, Jing Fan, Jingnan Liu
Published in: IEEE Sensors Journal (Volume: 22, Issue: 17, September 2022)
The paper explores the impact of Earth rotation compensation on the microelectromechanical systems-based inertial measurement units (MEMS-IMU) preintegration in navigation system accuracy. Experimental evaluations reveal substantial accuracy degradation without Earth rotation compensation and highlight the transformative potential of refined IMU preintegration in achieving high accuracy. The proposed advanced navigation system integrates global navigation satellite system positioning (GNSS) with IMU preintegration through Factor Graph Optimization, offering a promising avenue for enhanced accuracy and robustness in navigation systems.
Published in: IEEE Sensors Journal (Volume: 24, Issue: 9, May 2024)
Accidental falls across age groups and occupations could affect the quality of life. This work presents an innovative deep-learning approach specifically designed for edge devices for fall detection. The wearable sensor combines a three-axis accelerometer, gyroscope, and pressure sensor. It operates in real-time, recognizing the actions performed and categorizing them as everyday activities or falls. The power-efficient, low-cost, simple model with 99.38% accuracy and 25ms inference time is practical for real-world applications.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 12, June 2022)
Indoor localization based on WiFi Fingerprint techniques, augmented with the proposed FASR (Fingerprint Augment Based on Super-Resolution) framework, demonstrates increased localization accuracy and cost-effectiveness. The study explores the processing of the FASR framework and its super-resolution attributes in multiple modules. Assisted by deep neural network learning models, the framework shows consistent performance across various spatial samples with high position accuracy and promising application in the field of image processing.
Published in: IEEE Sensors Journal (Volume: 23, Issue: 4, February 2023)
Maintaining indoor air quality (IAQ) is crucial for health and wellness. Accurate data analysis and contextual anomaly detection are essential for IAQ monitoring. The paper introduces a hybrid deep-learning model, combining long short-term memory (LSTM) with autoencoder (AE). LSTM learns typical carbon dioxide (CO2) time sequence patterns, while AE computes optimal reconstruction errors and detects anomalies. Achieving 99.50% accuracy in real-world testing, the model shows promise for enhancing IAQ monitoring.
3-D-Printing and Reliability Evaluation of an Easy-to-Fabricate Position Sensing System for Printed Functional Wearable Assistive Devices
Michalec Paweł, Faller Lisa-Marie
Published in: IEEE Sensors Journal (Volume: 24, Issue: 4, February 2024)
The demand for advanced medical devices has created challenges in integrating sensors into cost-effective wearable medical assistive devices. The paper presents a novel, fully 3D-printed linear encoder developed using commercially available electrically conductive and nonconductive materials. This easy-to-fabricate technology was tested in a sensorized hand orthosis (sHO). It aims to enhance the functionality of wearable assistive devices by accurately detecting and monitoring movements, thus offering promising solutions for rehabilitation devices.
CAMs-SLAM: Cloud-Based Multisubmap VSLAM for Multisource Asynchronous Sensing of Biped Climbing Robots
Zhang Hong, Chen Weinan, Gu Shichao, Zhu Lei
Published in: IEEE Sensors Journal (Volume: 24, Issue: 14, July 2024)
Understanding and navigating the surroundings is a key feature of biomimetic Biped climbing robots (BiCRs). This is achieved through visual simultaneous localization and mapping (VSLAM). The paper presents a cloud-based asynchronous multisubmap VSLAM (CAMs-SLAM) system, which assists these robots with necessary computing, mapping, environment perception, and autonomous navigation. Even with weak internet connections and low data, this system demonstrates its feasibility and superiority in autonomous climbing applications, offering practical benefits for real-world use.
TSF: Two-Stage Sequential Fusion for 3D Object Detection
Heng Qi, Peicheng Shi, Zhiqiang Liu, Aixi Yang
Published in: IEEE Sensors Journal (Volume: 22, Issue: 12, June 2022)
This paper introduces a two-stage sequential fusion (TSF) method for 3D object detection in autonomous driving. TSF fuses the raw LiDAR point cloud with corresponding image data to produce an improved point cloud. The study also analyzes the impact of the fusion module on detection capabilities and explores the balance between accuracy and speed. The results show that TSF substantially boosts LiDAR detection accuracy, especially in small-object detection.
GPS Spoofing Detection and Mitigation for Drones Using Distributed Radar Tracking and Fusion
Bethi Pardhasaradhi, Linga Reddy Cenkeramaddi
Published in: IEEE Sensors Journal (Volume: 22, Issue: 11, June 2022)
GPS-based navigation, widely used worldwide, is cost-effective for providing position, velocity, and time data. However, it is susceptible to spoofing, especially in UAVs (unmanned aerial vehicles). The paper proposes a GPS spoofing detection and mitigation method using distributed radar tracking and data fusion techniques. The approach combines primary and secondary data through extended Kalman filters and track-to-track association. It ensures accuracy even during spoofing attacks, making it ideal for drone swarms.
Performance Study of MEMS Piezoresistive Pressure Sensors at Elevated Temperatures
Vinod Belwanshi, Sebin Philip, Anita Topkar
Published in: IEEE Sensors Journal ( Volume: 22, Issue: 10, May 2022)
Micro-Electro-Mechanical-Systems (MEMS) based piezoresistive pressure sensors are widely used across various industries. However, these sensors are prone to failure due to junction leakage at high-temperature applications. The paper presents three piezoresistive pressure sensors designed using different technologies: standard diffused piezoresistors, oxide-isolated polysilicon, and single crystal silicon piezoresistors. Tested up to 200°C and 140 bar, all sensors showed reduced sensitivity with temperature, with oxide-isolated polysilicon sensors exhibiting the best performance.
Fetal Movement Detection by Wearable Accelerometer Duo Based on Machine Learning
Jingyi Xu, Chao Zhao, Bo Ding, Xiaoxia Gu, Wenru Zeng, Liang Qiu, Hong Yu, Yang Shen, Hong Liu
Published in: IEEE Sensors Journal (Volume: 22, Issue: 12, June 2022)
Fetal movement monitoring is vital for fetal health. The accuracy of the maternal perception in monitoring fetal movement varies. In this work, a wearable device with two accelerometers and machine learning algorithms was developed for accurate and continuous fetal movement monitoring. It aims for accuracy comparable to ultrasound. The device showed promising results in fetal movement monitoring, potentially providing valuable insights into the fetal biological profile during the perinatal period.
Single Aerosol Particle Detection by Acoustic Impaction
Nadine Karlen, Tobias Rüggeberg, Bradley Visser, Jana Hoffmann, Daniel A. Weiss, Ernest Weingartner
Published in: IEEE Sensors Journal (Volume: 22, Issue: 12, June 2022)
Accurate detection of aerosol particles is vital for health and addressing climate risks. The paper discusses DustEar, a state-of-the-art measurement method, which employs an acoustic Piezo transducer to accelerate particles in a nozzle, enabling precise detection of individual aerosol particles up to 15 μm. The design reduces airflow interference and noise levels, ensuring accurate single-particle measurement. It has diverse applications in drug quality control, pollution source analysis, and environmental management.
Estimating Relative Angles Using Two Inertial Measurement Units Without Magnetometers
Seung Yun Song, Yinan Pei, Elizabeth T. Hsiao-Wecksler
Published in: IEEE Sensors Journal (Volume: 22, Issue: 20, October 2022)
Wearable technology heavily relies on miniature sensors called inertial measurement units (IMU). IMUs are vital for computing body segment angular kinematics in biomechanics and clinical settings. This study presents a low-cost two 6-axis IMU system without magnetometers to estimate relative angles. It validates existing algorithms for 3D orientation computation. The system's user-friendly design and accurate orientation calculation hold promise for advancements in virtual reality, health monitoring, and wearable technologies.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 14, July 2022)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires rapid and accurate detection to control its spread. When COVID-19, a disease caused by the SARS-CoV-2 virus, became a Global pandemic, a novel method for fast identification of the new coronavirus was developed using Surface Plasmon Resonance (SPR) techniques. This paper reviews the potential of SPR-based biosensing chips and sensors for portable devices to rapidly and accurately detect the SARS-CoV-2 virus.
Balanced Adaptation Regularization Based Transfer Learning for Unsupervised Cross-Domain Fault Diagnosis
Qin Hu, Xiaosheng Si, Aisong Qin, Yunrong Lv, Mei Liu
Published in: IEEE Sensors Journal (Volume: 22, Issue: 12, June 2022)
Fault diagnosis technology for rolling bearings is crucial for preventing mechanical accidents. In the field of fault diagnosis, inconsistent data distribution due to variable working conditions hampers diagnostic accuracy. This study proposes a novel method, Balanced Adaptation Regularization-based Transfer Learning (BARTL), leveraging enhanced multi-scale sample entropies. BARTL improves feature discriminability and similarity across conditions, achieving accurate diagnosis and surpassing existing transfer learning methods, as validated by two public datasets.
Deep Transfer Learning With Self-Attention for Industry Sensor Fusion Tasks
Ze Zhang, Michael Farnsworth, Boyang Song, Divya Tiwari, Ashutosh Tiwari
Published in: IEEE Sensors Journal (Volume: 22, Issue: 15, August 2022)
The paper introduces a promising approach using deep transfer learning techniques to address the challenges in processing multisource, heterogeneous data in Industry 4.0. By repurposing a Transformer model pre-trained from data-rich natural language domain, the proposed method allows industrial applications to leverage deep learning capabilities with minimal training data requirements. It represents a significant step toward making Industry 4.0 more efficient, faster, and cost effective.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 11, June 2022)
In smart control of robots, proximity and pressure information complement each other in detecting objects from approach to contact. Using a simple and cost-effective fabrication method, the researchers developed a textile-based sensor combining magneto-straining (proximity) and piezoresistive modes (pressure). This sensor exhibits high sensitivity for proximity and pressure perception. The unique design offers a seamless transition between modes, making it suitable for applications in human-machine interaction and intelligent prosthetics.
Usage of IR Sensors in the HVAC Systems, Vehicle and Manufacturing Industries: A Review
Muhammad Adeel Altaf, Jongsik Ahn, Danish Khan, Min Young Kim
Published in: IEEE Sensors Journal (Volume: 22, Issue: 10, May 2022)
Thermal sensors are used in various industries to measure temperature and convert it into a readable output. Its selection depends on cost, resolution, and accuracy, which are crucial factors to consider when designing the sensor system. This paper explores the significance of infrared sensors as thermal sensors in detecting temperature, movement, and occupancy. It reviews the use of thermal sensors in HVAC (Heating, ventilation, and air conditioning) systems, vehicles, and manufacturing industries.
Self-Supervised Monocular Depth Estimation Using Hybrid Transformer Encoder
Seung-Jun Hwang, Sung-Jun Park, Joong-Hwan Baek, Byungkyu Kim
Published in: IEEE Sensors Journal (Volume: 22, Issue: 19, October 2022)
Depth estimation using camera sensors is vital in autonomous driving, robotics, 3D scene reconstruction, and augmented reality. This paper presents a novel method for monocular-camera depth estimation using a hybrid transformer encoder-decoder. The self-supervised view synthesis method used eliminates the need for depth supervision. It uses a cost-volume structure, combining neural network and transformer architectures for accurate depth prediction. The system enhances global feature representation with an attention decoder, improving depth estimation accuracy.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 20, October 2022)
The CMOS Algae Growth Period Monitor, consisting of an algae sensor and a CMOS converter, is a cutting-edge solution for monitoring algae growth in algaculture applications. It utilizes a proton exchange membrane to translate algae growth data into a duty cycle, facilitating rapid assessment. With increased sensitivity, it functions in suboptimal conditions and has a maximum linear error of only 0.49%. Integration with IoT technology holds potential applications in the advanced monitoring of aquatic life.
Joint Hybrid 3D Beamforming Relying on Sensor-Based Training for Reconfigurable Intelligent Surface Aided TeraHertz-Based Multiuser Massive MIMO Systems
Xufang Wang, Zihuai Lin, Feng Lin, Lajos Hanzo
Published in: IEEE Sensors Journal (Volume: 22, Issue: 14, July 2022)
The Terahertz (THz) era promises lightning-fast internet but faces challenges like signal degradation over distances. The paper introduces a joint 3D-beamformer for THz Multi-user Massive multiple-input multiple-output (MIMO) systems, leveraging Reconfigurable Intelligent Surfaces to improve signal strength and coverage. The system uses smart sensing to beam signals directly to devices, promising unparalleled speeds and connection stability, even in crowded spaces. Scalable and adaptable, the proposed architecture is the future of connectivity.
OFET and OECT, Two Types of Organic Thin-Film Transistor Used in Glucose and DNA Biosensors: A Review
Xin Ma, Hongquan Chen, Peiwen Zhang, Martin C. Hartel, Xiaona Cao, Sibel Emir Diltemiz, Qinglei Zhang, Javed Iqbal, Natan Roberto de Barros, Liyan Liu, Hao Liu
Published in: IEEE Sensors Journal (Volume: 22, Issue: 12, June 2022)
Diabetes leads to serious health challenges and is a leading cause of numerous chronic diseases. This study explores electronics-based biosensors, particularly Organic Field Effect Transistors (OFET) and Organic Electrochemical Transistors (OECT), as adept glucose and DNA biosensors in diabetes management. The biosensors, fabricated from biodegradable natural materials, offer a flexible, cost-effective, and easily accessible solution, showcasing exceptional sensitivity and selectivity in their performance.
Concentrated Coverage Path Planning Algorithm of UAV Formation for Aerial Photography
Yi Cao, Xianghong Cheng, Jinzhen Mu
Published in: IEEE Sensors Journal (Volume: 22, Issue: 11, June 2022)
Ensuring successful aerial photography requires effective coverage path planning for UAVs. However, complexities in outdoor spaces pose challenges, necessitating innovative solutions. This paper introduces a novel concentrated coverage path planning algorithm, revolutionizing traditional probabilistic roadmap methods. Integrating round-trip mode and path constraints optimizes coverage while minimizing computational complexity and repetitions. The approach enhances efficiency and feasibility in real-world environments, promising to redefine aerial photography landscapes.
Real-Time Deep Anomaly Detection Framework for Multivariate Time-Series Data in Industrial IoT
Hussain Nizam, Samra Zafar, Zefeng Lv, Fan Wang, Xiaopeng Hu
Published in: IEEE Sensors Journal (Volume: 22, Issue: 23, December 2022)
In the realm of smart machines and interconnected devices, the Industrial Internet of Things (IIoT) is ushering in a revolution across industries. Due to a constant stream of diverse and time-stamped data, real-time anomaly detection becomes paramount for industrial process improvement. The article explores a hybrid deep anomaly detection (DAD) model that could accurately identify real-time anomalies. Experimental results showcase its superior performance in terms of accuracy and precision over existing methods.
Deep Learning Approach for Detecting Work-Related Stress Using Multimodal Signals
Wonju Seo, Namho Kim, Cheolsoo Park, Sung-Min Park
Published in: IEEE Sensors Journal (Volume: 22, Issue: 12, June 2022)
Work-related stress should be detected and managed to avoid adverse impacts on individuals and society. This study proposes a deep learning approach to detect work-related stress automatically by analyzing multimodal signals. Deep neural networks, facial expressions, and physiological signals were fused at different levels to achieve promising accuracy. The novel approach of studying the level of work-related stress with just a 10-second-long electrocardiogram, respiration, and facial images shows potential for effective stress detection.
Research on Self-Powered Coded Angle Sensor for Rock Climbing Training
Jun Zhang, Chuan Wu
Published in: IEEE Sensors Journal (Volume: 22, Issue: 18, September 2022)
Rock climbing is an adventure or competitive sport in which monitoring speed is vital. It is measured using an angle sensor entangled with climbers, causing safety concerns. The self-powered angle sensor offers a practical alternative to conventional rock-climbing sensors. The proposed self-powered coded angle sensors based on a single-electrode triboelectric nanogenerator can accurately measure the rotation angle, direction, and speed in indoor and outdoor climbing, even without a power supply.
Hallway Gait Monitoring Using Novel Radar Signal Processing and Unsupervised Learning
Hajar Abedi, Jennifer Boger, Plinio P. Morita, Alexander Wong, George Shaker
Published in: IEEE Sensors Journal (Volume: 22, Issue: 15, August 2022)
The novel hallway gait monitoring system developed leveraging radar signal processing and unsupervised machine learning introduces the future of personalized gait monitoring of individuals without wearable devices. It aims to create a system capable of monitoring human gait indoors and in natural settings using radar technology. This breakthrough architecture offers non-invasive and precise monitoring, paving the way for enhanced patient care and personal health insights.
Value of Information in Wireless Sensor Network Applications and the IoT: A Review
Faiga Alawad, Frank Alexander Kraemer
Published in: IEEE Sensors Journal (Volume: 22, Issue: 10, May 2022)
The Value of Information (VoI) is crucial in handling large data volumes in the Industrial Internet of Things (IIoT), filtering redundant information, and optimizing data processes. It is a key metric influencing path-planning algorithms, source switching, and trajectory optimization in mobile sensors. The paper systematically reviews VoI definitions, categorizing valuation methods and performance across applications, and provides guidelines for parameterized and adaptable VoI techniques to optimize diverse systems.
Video Hand Gestures Recognition Using Depth Camera and Lightweight CNN
David González León, Jade Gröli, Sreenivasa Reddy Yeduri, Daniel Rossier, Romuald Mosqueron, Om Jee Pandey, Linga Reddy Cenkeramaddi,
Published in: IEEE Sensors Journal (Volume: 22, Issue: 14, July 2022)
Hand gesture recognition has become an integral part of Human-Computer Interactions. The paper introduces a methodology using a video-based dataset and convolutional neural network (CNN) model. It utilizes an RGB-Depth camera to create a dataset of six distinct hand gestures. A lightweight CNN model is then developed to detect and classify hand movements. The experimental results highlight its accuracy and efficiency, facilitating its practical use in scenarios demanding precise gesture recognition.
Non-Enzymatic Glucose Detection Based on GO/Ag Nanocomposite in SiO2 Trench Embedded Field Effect Transistor
Monica Naorem, Roy P. Paily
Published in: IEEE Sensors Journal (Volume: 22, Issue: 16, August 2022)
SiO2 trench-embedded Field Effect Transistors (FET) with a graphene oxide-silver ( GO/Ag) nanocomposite for non-enzymatic glucose detection are crucial for effective diabetes management by accurately monitoring glucose concentration range of 1 μM to 10 mM. Validated through structural analysis, the fabricated device structure exhibits excellent glucose storage and sensing abilities, ensuring stability, reproducibility, and selectivity. Compact and easy to produce, it has promising applications in portable glucose sensors for point-of-care diagnostics and healthcare.
An EEG Data Processing Approach for Emotion Recognition
Guofa Li, Delin Ouyang, Yufei Yuan, Wenbo Li, Zizheng Guo, Xingda Qu, Paul Green
Published in: IEEE Sensors Journal (Volume: 22, Issue: 11, June 2022)
Emotion recognition has garnered interest from researchers because of its importance in affective computing. Facial expressions can mask human emotions. However, studies show that electroencephalogram (EEG) signals can recognize and identify human emotions. Hence, EEG has emerged as an alternate method for emotion recognition. The paper proposes a novel approach through a reduced number of EEG electrode channels and a normalization method, demonstrating its promising applications in real-time emotion recognition.
Technologies Driving the Shift to Smart Farming: A Review
Nabila ElBeheiry, Robert S. Balog
Published in: IEEE Sensors Journal (Volume: 23, Issue: 3, February 2023)
Agriculture requires sustainable solutions, especially when facing challenges like climate change, unqualified farmers, and urbanization. Smart farming (SF) helps enhance crop quality and quantity with minimal labor, ensuring sustainable agriculture and consistent food supply to meet the global food demand. This survey includes various themes like sensors, communication, big data, actuators, and data analysis. The article emphasizes integrating multiple technologies, highlighting popular SF systems: remote monitoring, autonomous, and intelligent decision-making.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 15, August 2022)
Material selection is crucial when designing and fabricating metasurfaces. The metasurface optical element produces a tightly focused beam from a visible laser, thus maximizing light utilization and minimizing energy loss. This study focuses on enhancing metasurface microfabrication precision and achieving remarkable light transmittance through traditionally impermeable silicon, providing opportunities for developing miniature wearable technology, cameras, and augmented reality (AR) devices and has significant implications for micro-scanning within confined spatial domains.
E-Nose System Based on Fourier Series for Gases Identification and Concentration Estimation From Food Spoilage
Jie Luo, Zehao Zhu, Wen Lv, Jian Wu, Jianhua Yang, Min Zeng, Nantao Hu, Yanjie Su, Ruili Liu, Zhi Yang
Published in: IEEE Sensors Journal (Volume: 23, Issue: 4, February 2023)
Improper and prolonged storage of perishable food items like meat and fruits may lead to microbial contamination and spoilage. The paper presents an electronic nose (EN) system, a portable electronic gas-sensing device comprising a sensor array-based gas identification, concentration estimation system, and data acquisition circuit boards. A machine learning algorithm assists it. It accurately detects gases like ammonia (NH3), hydrogen sulphide (H2S), and ethanol (C2H5OH) emitted by spoiled food.
Monolithic Sensor Integration in CMOS Technologies
Daniel Fernández, Piotr Michalik, Juan Valle, Saoni Banerji, Josep Maria Sánchez-Chiva, Jordi Madrenas
Published in: IEEE Sensors Journal (Volume: 23, Issue: 2, January 2023)
In the wearable market, where the area is a limiting factor, monolithic sensor integration with mainstream gadget electronics can provide an efficient means to archive a small device footprint while maintaining good performance. For a minimum cost, CMOS-MEMS devices built using backend-of-line (BEOL) interconnections as structural material offer an interesting, cost-effective approach with the potential to become a market game-changer.
An Improved YOLOv5 Crack Detection Method Combined With Transformer
Xuezhi Xiang, Zhiyuan Wang, Yulong Qiao
Published in: IEEE Sensors Journal (Volume: 22, Issue: 14, July 2022)
A robust pavement crack detection network is imperative to mitigate traffic accidents and minimize maintenance costs. The paper proposed an efficient hybrid model by merging YOLOv5 and Transformer, utilizing one-stage architecture and long-range dependency capture for reliable crack detection. The network's performance is further improved using test time augmentation (TTA) for crack detection. An efficient solution for urban pavement damage detection, it paves the way for expanding datasets to tackle diverse pavement issues.
Angle-Insensitive Human Motion and Posture Recognition Based on 4D Imaging Radar and Deep Learning Classifiers
Yubin Zhao, Alexander Yarovoy, Francesco Fioranelli
Published in: IEEE Sensors Journal (Volume: 22, Issue: 12, June 2022)
Human Activity Recognition (HAR) is crucial to support the needs of an aging population. The paper proposes a new method for HAR using 4D imaging radars. The technique combines point cloud and spectrogram data to capture the spatial and temporal features of the activities. The method is tested on an experimental dataset and performs better than existing alternatives.
Gait-Based Person Identification and Intruder Detection Using mm-Wave Sensing in Multi-Person Scenario
Zhongfei Ni, Binke Huang
Published in: IEEE Sensors Journal (Volume: 22, Issue: 10, May 2022)
MGait is a multi-person identification and intruder detection system that utilizes mm-wave sensing to identify users based on gait micro-Doppler (m-D) signatures. Individuals are continuously tracked in indoor scenarios using low-cost mm-wave radars in range-Doppler (R-D) space frame by frame to extract their distinct gait signatures. Trained in a deep learning-based Gaussian mixture loss model, MGait is a promising solution for biometrics and has wide applications in health and security.
Design Methodology for Industrial Internet-of-Things Wireless Systems
Carlos Mendes da Costa, Peter Baltus
Published in: IEEE Sensors Journal (Volume: 21, Issue: 4, February 2021)
The rise in Internet of Things (IoT) usage in industrial applications requires robust wireless systems. The researchers proposed an innovative approach for designing low-latency, power-efficient, and reliable wireless systems. This paper comprehensively studies system requirements, hardware, and architecture for optimal design choices. The prototype design was validated through practical experiments. It demonstrates superior performance compared to existing wireless standards.
Online Learning for Active Odor Sensing Based on a QCM Gas Sensor Array and an Odor Blender
Manuel Aleixandre, Takamichi Nakamoto
Published in: IEEE Sensors Journal (Volume: 22, Issue: 23, December 2022)
This work presents an active odor sensing system that blends odorous ingredients, iteratively adjusting their mix ratios to match the sensor response of a target scent. Online learning adapts the sensor model parameters in real-time, optimizing the control loop to compensate for drift and humidity variations. It improves the accuracy and robustness despite the inherent limitations of gas sensors.
Research of Low-Power MEMS-Based Micro Hotplates Gas Sensor: A Review
Zhenyu Yuan, Fan Yang, Fanli Meng, Kaiyuan Zuo, Jin Li
Published in: IEEE Sensors Journal (Volume: 21, Issue: 17, September 2021)
The MEMS-based micro hotplate gas sensors are small and mass-producible with excellent performance compared to traditional ceramic tube sensors. Energy efficiency is a crucial parameter of portable, reliable sensors. Heat loss significantly increases the power consumption of hotplates. To optimize energy consumption and efficiency, an analytical study of heat loss in different parts of sensor parts and their remedies through fabrication methods is presented.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 15, August 2022)
Scientific and technological advancement has increased the risk of environmental pollutants like chemicals, heavy metals, and toxic gases, thus affecting human health. The need is to detect the contaminants at the source for corrective measures. This paper discusses the sensitivity, selectivity, response time, recovery time, and repeatability of nanocomposite thin-film-based optical fiber sensors coated with metal oxide semiconductors, polymers, metals, carbon nanotubes, graphene, etc., in relation to monitoring environmental health.
Wireless Communication and Power Harvesting in Wearable Contact Lens Sensors
Mengyao Yuan, Rupam Das, Eve McGlynn, Rami Ghannam, Qammer H. Abbasi, Hadi Heidari
Published in: IEEE Sensors Journal (Volume: 21, Issue: 11, June 2021)
Our corneal surface and tears make an excellent alternative to blood as a source of biomarkers. Incorporating sensors into contact lenses could provide a convenient, non-invasive platform for continuously detecting and monitoring diseases like glaucoma, diabetes, and heart disease. Researchers explored current technologies for sensing materials, energy, and data transmission techniques in electronic contact lens sensors. The study systematically explores the challenges and future trends.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 15, August 2022)
Technology to detect signs of life under rubble could save lives. The study proposes a novel radar system with a two-step computation model for detecting live victim's vital signs behind or under obstructions. The first step is to establish a region of interest and identify obstacles. The second step detects respiratory vital signs patterns through time-varying phase data. Experimental demonstration of the proposed system shows significant accuracy in detecting live victims.
Published in: IEEE Sensors Journal (Volume: 21, Issue: 9, May 2021)
Sensors have been pivotal in making human life comfortable. Sensor technology offers many research opportunities for innovations, contributing to smart living. Here the authors have streamlined the latest literature in the field, providing an overview of sensors and their role in our lives. The article summarizes research into four main sensor-based applications: fitness tracking, emotions analysis, sleep tracking, and food intake monitoring.
MS-YOLO: Object Detection Based on YOLOv5 Optimized Fusion Millimeter-Wave Radar and Machine Vision
Yunyun Song, Zhengyu Xie, Xinwei Wang, Yingquan Zou
Published in: IEEE Sensors Journal (Volume: 22, Issue: 15, August 2022)
Multi-sensor fusion is becoming an increasingly crucial part of the environmental perception system in autonomous driving. Experience the cutting-edge fusion of millimeter-wave radar and machine vision in the proposed MS-YOLO, powered by You Only Look Once (YOLOv5) algorithm. The proposed fusion model offers exceptional accuracy in detecting objects and provides immediate real-time insights regardless of light and weather conditions.
BabyPose: Real-Time Decoding of Baby’s Non-Verbal Communication Using 2D Video-Based Pose Estimation
M. Mücahit Enes Yurtsever, Süleyman Eken
Published in: IEEE Sensors Journal (Volume: 22, Issue: 14, July 2022)
Decoding or understanding non-verbal communication forms in babies is paramount to parents. These forms are expressed in various poses and body-language signals, which can be interpreted using a Human Pose Estimation method. Babies are tracked in real-time using 2D videos. Pose estimators that model these poses into keypoints are then employed to recognize and monitor these activities. This study delves into these estimation models that interpret baby poses with 99% accuracy.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 22, November 2022)
Vision-based tactile sensors (VBTS) are attracting attention for their application in robotics. As an innovative optical sensor, VBTS leverages tactile sensing to enhance the interpretation and utilization tactile information. The paper presents an overview of the hardware aspects of VBTS, including their technology, capabilities, challenges, and potential solutions. It provides insightful guidelines for optimizing the design and fabrication processes of VBTS to improve their performance.
Smart Healthcare: RL-Based Task Offloading Scheme for Edge-Enable Sensor Networks
Rahul Yadav, Weizhe Zhang, Ibrahim A. Elgendy, Guozhong Dong, Muhammad Shafiq, Asif Ali Laghari, Shiv Prakash
Published in: IEEE Sensors Journal (Volume: 21, Issue: 22, November 2021)
Smart healthcare systems produce massive data, which is challenging to manage. The Internet of Medical Things (IoMT) and Artificial intelligence (AI) based smart healthcare systems and applications have shown potential in intelligent and accurate data management and support healthcare. While the edge-enabled network provides necessary computational resources to deal with enormous data, the proposed Computation Offloading using Reinforcement Learning (CORL) algorithm minimizes total latency and energy consumption.
A Radar-Based Human Activity Recognition Using a Novel 3-D Point Cloud Classifier
Zheqi Yu, Ahmad Taha, William Taylor, Adnan Zahid, Khalid Rajab, Hadi Heidari, Muhammad Ali Imran, Qammer H. Abbasi
Published in: IEEE Sensors Journal (Volume: 22, Issue: 19, October 2022)
Wearable sensors for human activity recognition (HAR) have many uses, especially in health, surveillances and man-machine conversation. Technological advancements have enabled non-invasive, contactless sensing methods to detect human activities. However, insufficient training data severely affects the performance of HAR applications. The paper discusses the dataset collection using 3-D cloud point technology and deep learning algorithms to classify the dataset, which will enable a transition from wearable to contactless sensing.
Published in: IEEE Sensors Journal (Volume: 21, Issue: 8, April 2021)
Origami is the art of paper folding to create a two-dimensional and three-dimensional sculpture. Inspired by this art, the researchers developed a flexible pressure sensor, which finds various applications in wearable devices and healthcare products. The origami structure inherently offers higher sensitivity and measurement range. These pressure sensors can easily be fabricated with 3-D printing technology, making them low-cost and enabling mass production.
Data Fusion Based on Temperature Monitoring of Aquaculture Ponds With Wireless Sensor Networks
Haohui Chen, Xinyuan Nan, Sibo Xia
Published in: IEEE Sensors Journal (Volume: 23, Issue: 1, January 2023)
Aquaculture is the farming of aquatic animals and plants in a controlled environment. The water temperature is a vital environmental factor that affects the water quality and life of the aquatic species. The paper proposes an effective real-time aquaculture temperature monitoring method through a layered and clustered wireless sensor networks (WSNs) framework. It is more effective in temperature monitoring with improved accuracy in comparison to the traditional monitoring methods.
Image-Based Force Estimation in Medical Applications: A Review
Ali A. Nazari, Farrokh Janabi-Sharifi, Kourosh Zareinia
Published in: IEEE Sensors Journal (Volume: 21, Issue: 7, April 2021)
Precise, real-time force estimation is still an ongoing challenge in minimally invasive robotic surgical (MIRS) interventions. The applied force depends on the size and deformity of the tissue. Advanced imaging techniques and deep-learning algorithms provide efficient object recognition and force estimation in MIRS. The researchers present a comprehensive review of image-based force estimation techniques for MIRS haptic force feedback.
Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron
Umer Saeed, Syed Yaseen Shah, Adnan Zahid, Jawad Ahmad, Muhammad Ali Imran, Qammer H. Abbasi, Syed Aziz Shah
Published in: IEEE Sensors Journal (Volume: 21, Issue: 18, September 2021)
The COVID-19 pandemic highlighted the need for contactless respiration monitoring systems. A software-defined radio frequency sensing technique integrated with a deep learning algorithm was proposed for the non-invasive monitoring of various breathing patterns. The system used variations in channel state information produced by human motions to identify six distinct respiratory patterns. The prototype could classify these respiratory patterns with up to 99% accuracy.
On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review
Muhammad Asif Khan, Hamid Menouar, Aisha Eldeeb, Adnan Abu-Dayya, Flora D. Salim
Published in: IEEE Sensors Journal (Volume: 22, Issue: 12, June 2022)
The increasing number of commercial drones poses severe threats to the security of critical infrastructure and people’s privacy. A drone detection system thus becomes inevitable to detect unauthorized drones in the low altitude airspace. This paper delves into the various aspects of an efficient, reliable, robust, and scalable drone detection system by investigating the four fundamental technologies and the associated challenges and limitations.
Inkjet-Printed, Nanofiber-Based Soft Capacitive Pressure Sensors for Tactile Sensing
Riikka Mikkonen, Anastasia Koivikko, Tiina Vuorinen, Veikko Sariola, Matti Mäntysalo
Published in: IEEE Sensors Journal (Volume: 21, Issue: 23, December 2021)
Soft electronics enable lighter and more flexible human-machine interfaces. Learn about an inexpensive, additive approach to fabricating flexible, soft electronics using inkjet printing. Inkjet-printed micro-structured dielectric layers were sandwiched between conductive mesh electrodes to form the capacitive tactile pressure sensors. The sensor exhibits high sensitivity, long-term repeatability, and low hysteresis. The proposed approach can fabricate inexpensive, customizable soft electronics human-machine interfaces.
Precise Detection and Quantitative Prediction of Blood Glucose Level With an Electronic Nose System
Zhenyi Ye, Jie Wang, Hao Hua, Xiangdong Zhou, Qiliang Li
Published in: IEEE Sensors Journal (Volume: 22, Issue: 13, July 2022)
Monitoring blood glucose levels after exercise, diet, and medication is vital, especially in people with diabetes. A low-cost, no-pain glucose measurement method outside clinical settings for diabetes patients is essential. The work presents a non-invasive glucose measurement method using a novel electronic nose (E-Nose) device enabled by the machine learning algorithm. The proposed method is capable of precise qualitative glucose identification and quantitative analysis.
Towards Precision Agriculture: IoT-Enabled Intelligent Irrigation Systems Using Deep Learning Neural Network
Pankaj Kumar Kashyap, Sushil Kumar, Ankita Jaiswal, Mukesh Prasad, Amir H. Gandomi
Published in: IEEE Sensors Journal (Volume: 21, Issue: 16, August 2021)
Smart and precise irrigation planning plays a crucial role in preventing excess water usage and waste. Various machine learning-based irrigation models have been proposed. However, the proposed models should consider unpredictable climate changes. The researchers propose an intelligent neural network model considering the historical temporal dynamics of soil and climate. The prototype efficiently predicts volumetric water demand one day in advance.
Toward a Bio-Inspired Acoustic Sensor: Achroia grisella’s Ear
Lara Díaz-García, Andrew Reid, Joseph C. Jackson-Camargo, James F. C. Windmill
Published in: IEEE Sensors Journal (Volume: 22, Issue: 18, September 2022)
Taking inspiration from nature can be advantageous when facing challenges in engineering and technology. The researchers overcame one such challenge of manufacturing miniature directional acoustic sensors by studying Achroia grisella, a small moth capable of directional hearing using one ear. Inspired by the shape of the moth eardrum, equations, simulations, and passive directional 3D printed samples were developed and examined with Laser Doppler Vibrometry.
An Implantable Antenna Sensor for Medical Applications
Wei Wang, Xiu-Wei Xuan, Wan-Yi Zhao, Hong-Kuai Nie
Published in: IEEE Sensors Journal (Volume: 21, Issue: 13, July 2021)
Emerging technologies have led to the development of implantable medical devices, providing new methods for diagnosing and treating diseases. The researchers present a sensor prototype with an S-shaped monopole antenna with a closed-loop design. The prototype outperforms concurrent implantable devices concerning size, radiation gain, and sensitivity. The proposed sensor offers a minimally invasive way to monitor and diagnose cancer tumors and can save countless lives.
Smart Bandage With Wireless Strain and Temperature Sensors and Batteryless NFC Tag
Pablo Escobedo, Mitradip Bhattacharjee, Fatemeh Nikbakhtnasrabadi, Ravinder Dahiya
Published in: IEEE Internet of Things Journal (Volume: 8, Issue: 6, March 2021)
Smart bandages can accelerate healing, avoiding infections of severe injuries or surgical wounds by real-time wound assessments. The wound’s healing state can be predicted by tracking parameters like temperature, pressure, pH, and acidity. A smart bandage prototype embedded with wireless temperature and pressure sensors based on a conductive polymer, PEDOT: PSS (poly(3,4-ethylenedioxythiophene) polystyrene sulfonate), and an NFC (Near-field communication) tag is proposed. This battery-less system provides a cost-effective alternative for medical applications.
Metal-Organic Framework Materials Coupled to Optical Fibers for Chemical Sensing: A Review
Chen Zhu, Rex E. Gerald, Jie Huang
Published in: IEEE Sensors Journal (Volume: 21, Issue: 18, September 2021)
Metal-Organic Frameworks (MOFs) are crystalline nano-porous materials composed of inorganic metal nodes incorporated with organic ligands. Their remarkable structural and physicochemical tunability makes them superior to conventional chemo-sensory materials. The researcher presented a review of MOF-Optical fiber (OF) sensors based on a change in refractive index induced by adsorbed guest molecules. It demonstrated the promising potential of MOFs as dielectric coatings on OF for highly sensitive and selective chemical sensing.
Wireless Characterization and Assessment of an UWB-Based System in Industrial Environments
Imanol Picallo Guembe, Peio Lopez-Iturri, Hicham Klaina, Guillermo Glaria Ezker, Félix Sáez De Jauregui Urdanoz, José Luis Zabalza Cestau, Leyre Azpilicueta, Francisco Falcone
Published in: IEEE Access ( Volume: 9)
Novel Ultra-Wideband (UWB)-based wireless communication system offers precision location and tracking in industrial settings. The electromechanical interference and heavy machinery can cause severe degradation of signal. The researchers present a hybrid deterministic 3D-RL approximation algorithm for wireless channel characterization of UWB systems in industrial indoor application. The proposed methodology enables optimal system planning and implementation of UWB-based indoor tracking systems in industrial environments.
Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements, and Challenges
Praveen Kumar Reddy Maddikunta, Saqib Hakak , Mamoun Alazab, Sweta Bhattacharya, Thippa Reddy Gadekallu, Wazir Zada Khan, Quoc-Viet Pham
Published in: IEEE Sensors Journal (Volume: 21, Issue: 16, August 2021)
Smart agriculture is the future to meet the growing food demand. Implementing information and communication technology (ICT) with unmanned aerial vehicles (UAVs) gives a better way to monitor farming under challenging conditions. Smart and precision agriculture demand knowledge of IoT (Internet of Things) applications, design architecture, protocols, etc. This paper explores different aspects of UAV implementations, Bluetooth-based wireless communication, agricultural sensors, design architecture, etc., and their future trends.
A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges
Patrick McEnroe, Shen Wang, Madhusanka Liyanage
Published in: IEEE Sensors Journal (Volume: 9, Issue: 17, September 2022)
Unmanned aerial vehicles (UAV) applications are often heavily dependent on artificial intelligence (AI) methods. Traditional cloud-based AI can find it hard to meet various UAV requirements, such as low latency and energy consumption. Edge AI, where AI is run on-device or at edge servers, is a viable solution. The researchers present an in-depth review of the convergence of edge AI and UAVs.
CNN-Based Classification for Point Cloud Object With Bearing Angle Image
Chien-Chou Lin, Chih-Hung Kuo, Hsin-Te Chiang
Published in: IEEE Sensors Journal (Volume: 22, Issue: 1, January 2022)
Currently available 3D object classifiers combine point clouds and color images. They use complex models requiring an enormous memory to store their parameters. An efficient alternative method is proposed that utilizes information solely from point clouds. The point cloud objects are converted into bearing angle (BA) images and then classified by convolutional neural networks. This method achieves high accuracy while using significantly less time and memory.
Published in: IEEE Sensors Journal (Volume: 21, Issue: 18, September 2021)
The researchers present a novel target classification technique incorporating mmWave radar and deep learning models to classify moving objects. The system provides a wide field of view by orienting the antenna in elevation and rotating it in the horizontal field. With 97 – 99 % accuracy, the proposed classification technique is a cost-effective and dependable system for a wide range of autonomous applications.
The Machine Learnings Leading the Cuffless PPG Blood Pressure Sensors Into the Next Stage
Paul C.-P. Chao, Chih-Cheng Wu, Duc Huy Nguyen, Ba-Sy Nguyen, Pin-Chia Huang, Van-Hung Le
Published in: IEEE Sensors Journal (Volume: 21, Issue: 11, June 2021)
Regular blood pressure (BP) monitoring helps in managing Cardiovascular diseases. Recent works on machine learning based on measured photoplethysmogram (PPG) waveforms have shown a strong possibility of estimating blood pressure by cuffless devices, such as recursive neural networks (RNN), long short-term memory (LSTM), etc. The challenge lies in the successful commercialization of cuffless BP sensors.
A Nanometer Resolution Wearable Wireless Medical Device for Non Invasive Intracranial Pressure Monitoring
Rodrigo de A. P. Andrade, Helder Eiki Oshiro, Caio Kioshi Miyazaki, Cintya Yukie Hayashi, Marcos Antonio de Morais, Rodrigo Brunelli, João Paulo Carmo
Published in: IEEE Sensors Journal (Volume: 21, Issue: 20, October 2021)
Non-invasive intracranial pressure monitoring (NIICP) by measuring skull deformation has been studied extensively for assessing intracranial pressure and compliance. The researchers used this principle to design a novel wireless sensor. The proposed sensor is small, portable, cost-effective, and highly sensitive. It offers a more accurate clinical evaluation of intracranial dynamics and has the potential for a wide range of applications.
Published in: IEEE Sensors Journal (Volume: 21, Issue: 18, September 2021)
3D printing has emerged as a novel fabrication process for producing customized sensors at low cost. A uniaxial Ti6Al4V alloy accelerometer prototype was designed and fabricated using the Laser Powder Bed Fusion (L-PBF) technique. With micro dimensions and comparable differential sensitivity, the proposed prototype showcases a new genre of 3D-printed metal sensors which are low-cost, customizable, efficient, and durable.
Published in: IEEE Sensors Journal (Volume: 21, Issue: 11, June 2021)
The tactile force sensor is the key enabling device for machines to interact with humans and objects. Due to the growing demand for smart-machine, metaverse, gaming, etc., miniaturized tactile force sensing chips have attracted attention and have also been developed through different detection and process technologies. The paper summarizes various tactile force sensors designed and fabricated based on the semiconductor CMOS processes.
Deep Learning for Patient-Independent Epileptic Seizure Prediction Using Scalp EEG Signals
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Published in: IEEE Sensors Journal (Volume: 21, Issue: 7, April 2021)
Epilepsy is one of the most prevalent neurological diseases among humans. It can lead to severe brain injuries, strokes, and brain tumors. Early detection of seizures is critical to improving the day-to-day lives of patients. The researchers propose two deep learning approaches, using EEG data as input, which can detect epileptic seizures one hour before they occur.
Colon Cancer Detection by Designing and Analytical Evaluation of a Water-Based THz Metamaterial Perfect Absorber
Zohreh Vafapour, William Troy, Ali Rashidi
Published in: IEEE Sensors Journal (Volume: 21, Issue: 17, September 2021)
Early detection of colon cancer is vital for patient survival. The researchers propose a novel device for the detection of colon cancer using terahertz electromagnetic waves. The device detects differences in water concentration within healthy and cancerous colon tissues by using surface plasmon polaritons (SPP). It shows potential as a less invasive, safer, faster, and early detection procedure for colon cancer.
IoT Enabled, Leaf Wetness Sensor on the Flexible Substrates for In-Situ Plant Disease Management
Kamlesh S. Patle, Riya Saini, Ahlad Kumar, Sandeep G. Surya, Vinay S. Palaparthy, Khaled N. Salama
Published in: IEEE Sensors Journal (Volume: 21, Issue: 17, September 2021)
Early detection of plant diseases can prevent crop failure. The Leaf wetness duration (LWD) that leads to fungal infections is a major concern among farmers. Using LWD as a parameter, researchers developed disease detection models with an Internet of Things (IoT)-enabled leaf wetness sensor (LWS) prototype fabricated on flexible substrates. Researchers tested the prototypes on medicinal plants. The prototype made comparatively accurate, precise, and early disease predictions.
Fabrication Strategies and Measurement Techniques for Performance Improvement of Graphene/Graphene Derivative Based FET Gas Sensor Devices: A Review
Partha Bhattacharyya
Published in: IEEE Sensors Journal (Volume: 21, Issue: 9, May 2021)
Graphene and graphene derivative-based field effect transistor (FET) gas sensors are the most recent inclusion in the gas sensor family, having enormous potential to detect a wide variety of oxidizing and reducing target species with high sensitivity. The researchers adopted various strategies to improve the different performance index of the sensor either at the fabrication/device stage or at the operational/measurement technique level.
Determination of Salinity and Sugar Concentration by Means of a Circular-Ring Monopole Textile Antenna-Based Sensor
Mariam El Gharbi, Marc Martinez-Estrada, Raúl Fernández-García, and Ignacio Gil
Published in: IEEE Sensors Journal (Volume: 21, Issue: 21, November 2021)
Excessive sugar and salt in the diet are harmful to health. A wearable antenna sensor with real-time sugar and salt monitoring could help monitor salt and sugar intake. The researchers here propose an e-textile-based monopole antenna sensor prototype for it. The simple fabrication process, compact size, and high sensitivity make the prototype ideal for a blood sugar or sodium sensor.
Published in: IEEE Sensors Journal (Volume: 21, Issue: 13, July 2021)
The portable Point-of-care (POC) devices that can detect diseases will revolutionize healthcare. The researchers propose a miniature wireless cell fiber-spectrophotometer prototype for real-time bio-markers detection and diagnostic support outside the laboratories. The low-power and lightweight spectrometer can identify biomarkers, such as tagged proteins or genetic materials, even in small samples. It promises effective point-of-care solutions for rapid testing of infectious diseases like SARS-CoV-2 or cancer.
False-Alarm-Controllable Radar Detection for Marine Target Based on Multi Features Fusion via CNNs
Xiaolong Chen, Ningyuan Su, Yong Huang, Jian Guan
Published in: IEEE Sensors Journal (Volume: 21, Issue: 7, April 2021)
Sea surface target detection is vital for maritime security, surveillance, and rescue operations. The complexity of the marine environment makes it difficult to achieve robust, reliable, and adaptive target detection. Deep learning methods show better feature extraction ability and classification accuracy. The study proposes a new approach to marine target detection in complex background conditions using marine dual-channel convolutional neural networks (MDCCNN) with a false-alarm controllable classifier (FACC).
Multiplexed Silicon Nanowire Tunnel FET-Based Biosensors With Optimized Multi-Sensing Currents
Sihyun Kim, Ryoongbin Lee, Daewoong Kwon, Tae-Hyeon Kim, Tae Jung Park, Sung-Jin Choi, Hyun-Sun Mo, Dae Hwan Kim, Byung-Gook Park
Published in: IEEE Sensors Journal (Volume: 21, Issue: 7, April 2021)
Multiplexed sensing is an efficient approach for diagnosing and monitoring progressive diseases like cancer. This paper implements the independent detection of two different biomolecules in a novel single tunnel field-effect transistor (TFET) sensor. Since this new sensor can be perfectly co-integrated with complementary metal–oxide–semiconductor (CMOS) read-out circuits by state-of-art industry CMOS process, the mass production of CMOS-based biochips for multiplexed sensing is possible.
Micro-Doppler Based Target Recognition With Radars: A Review
Ali Hanif, Muhammad Muaz, Azhar Hasan, Muhammad Adeel
Published in: IEEE Sensors Journal (Volume: 22, Issue: 4, February 2022)
Radar detection of smaller targets requires lowering the radar cross-section and velocity thresholds. With it, an abundance of target signatures gets generated, making it necessary to classify only relevant targets. Micro-motions of targets are significant characteristics. Micro-Doppler signatures have emerged as an effective method of classifying such targets. The study presents a systematic review of various micro-Doppler-based radar target signature analysis and classification techniques.
A Hybrid Posture Detection Framework: Integrating Machine Learning and Deep Neural Networks
Sidrah Liaqat, Kia Dashtipour, Kamran Arshad, Khaled Assaleh, Naeem Ramzan
Published in: IEEE Sensors Journal (Volume: 21, Issue: 7, April 2021)
Posture detection is important for monitoring health status. Incorrect postures may cause muscle weaknesses and body pain. The novel hybrid remote posture detection method developed by integrating machine learning (ML) and deep learning (DL) has produced promising results with 98% accuracy. Remote posture monitoring has the edge over camera-based, and wearable devices due to its privacy preserved feature.
Application of Physiological Sensors for Personalization in Semi-Autonomous Driving: A Review
Edric John Cruz Nacpil, Zheng Wang, Kimihiko Nakano
Published in: IEEE Sensors Journal (Volume: 21, Issue: 18, September 2021)
With the increasing popularity and demand of autonomous vehicles, the safety, privacy, and comfort of the occupants have become prime concerns. The vehicle system can use various sensors to monitor the occupants' physical and mental health and collect behavioral data for further research and improvisation. The researchers present a detailed study on physiological sensors incorporated into the autonomous vehicle for emergencies or non-emergency circumstances.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 1, January 2022)
Butanone is an organic compound found in nature in traces and produced industrially on a large scale. It is extensively used in household products, industries, and labs. However, prolonged exposure to butanone is harmful, making its sensing and detection important. The researchers present an investigation on the butanone sensing properties of ZnO sensors and the effect of particle size on the detection of butanone by ZnO nanocrystals.
Online Wear Particle Detection Sensors for Wear Monitoring of Mechanical Equipment?A Review
Ran Jia, Liyong Wang, Changsong Zheng, Tao Chen
Published in: IEEE Sensors Journal (Volume: 22, Issue: 4, February 2022)
Mechanical equipment with moving parts is prone to wear that may lead to mechanical failures, damage, or even accidents. Monitoring the machinery to check its health and alert of any probable wear status is essential. The researchers here review the pros and cons of online wear particle detection sensors for real-time wear monitoring of the wear state of mechanical equipment.
Current Sensing Front-Ends: A Review and Design Guidance
Da Ying, Drew A. Hall
Published in: IEEE Sensors Journal (Volume: 21, Issue: 20, October 2021)
Sensors have become a part of everyday life, seamlessly connecting the physical and electronic worlds. The paper focuses on the current-output sensing technique, providing information and analytical study of various sensors and design guidance of current readout circuits. Additionally, state-of-the-art current-sensing frontends are analyzed concerning gain, bandwidth, stability, and noise. The paper presents insights into general design architectures and their performance tradeoffs.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 2, January 2022)
Wearable gait recognition systems incorporating MEMS (micro-electromechanical systems) sensors are in demand because of their pivotal use in disease prevention, robotics, and identity recognition. Data pre-processing, filtering, and segmenting can successfully assist in detecting human gait. The patterns of the gaits are then analyzed to derive meaningful results. This exciting new domain has proven to be a lifesaver time and again.
Experimental Demonstration of Accurate Noncontact Measurement of Arterial Pulse Wave Displacements Using 79-GHz Array Radar
Yuji Oyamada, Takehito Koshisaka, Takuya Sakamoto
Published in: IEEE Sensors Journal (Volume: 21, Issue: 7, April 2021)
Early detection and diagnosis of Cardiovascular diseases save lives. The arterial pulse wave velocity (PWV) is one of the essential parameters to diagnose and monitor cardiovascular risk and condition. In the emerging trends of noncontact monitoring, the researchers experimentally demonstrated the accuracy of contactless technology for measuring arterial pulse wave propagation using an array radar system and laser displacement sensors that could replace contact monitoring.
Soft Biomimetic Optical Tactile Sensing With the TacTip: A Review
Nathan F. Lepora
Published in: IEEE Sensors Journal (Volume: 21, Issue: 19, October 2021)
The sense of touch has a different significance in the human body than other senses, like hearing, sight, smell, and taste. The dexterous use of our hands for touch depends on the intelligent use of tactile perception. However, robotic hands lack the same level of dexterity as human hands. The researchers are working to develop methods to simulate the capabilities of the human sense of touch in machines.
Wireless Power and Data Transmission for Implanted Devices via Inductive Links: A Systematic Review
Mohammad Javad Karimi, Alexandre Schmid, Catherine Dehollain
Published in: IEEE Sensors Journal (Volume: 21, Issue: 6, March2021)
Implantable medical devices (IMD) are developed to control and report acquired biological data from an implanted device in the body or brain to an external stage for biomedical purposes. They receive power from batteries or wireless power transmissions (WPT). Due to their simplicity and safety, magnetic waves are extensively studied and developed for powering in biomedical applications.
Multi-Sensor Complex Network Data Fusion Under the Condition of Uncertainty of Coupling Occurrence Probability
Xianfeng Li, Sen Xu
Published in: IEEE Sensors Journal (Volume: 21, Issue: 22, November 2021)
Complex multi-sensor networks face challenges in storage management, data processing and resource optimization. Data fusion methods analyze and integrate diverse sensor information to produce coherent and accurate information. Researchers propose an adaptive weighted fusion algorithm on grouped sensor data that can efficiently reduce data redundancy, optimize resources, and lower network congestion. It showed higher accuracy and energy efficiency than other fusion algorithms.
Swin-Depth: Using Transformers and Multi-Scale Fusion for Monocular-Based Depth Estimation
Zeyu Cheng, Yi Zhang, Chengkai Tang
Published in: IEEE Sensors Journal (Volume: 21, Issue: 23, December 2021)
Depth estimation using monocular sensors is an important and challenging task in computer vision. The paper proposes a monocular depth estimation network Swin-Depth, which estimates the depth of a scene from only a single image. The proposed method achieved state-of-the-art results on challenging datasets based on hierarchical representation learning in Transformer-based monocular depth estimation networks and multi-scale fusion attention. It provides an accurate and efficient solution to the depth estimation problem.
Chipless RFID Sensors for Wearable Applications: A Review
Santanu Kumar Behera
Published in: IEEE Sensors Journal (Volume: 22, Issue: 2, December 2022)
RFID-a radio-frequency identification technology, is gaining popularity as a wireless sensor for track and trace applications. The high cost of chipped-RFID tags makes them unsuitable for mass production. The Chipless-RFID tag is lightweight, durable, reliable, and energy-sufficient and can be mass-produced using inexpensive conductive inks or yarns. Improving range and data capacity in chipless-RFID tags could make them indispensable as wearables.
Realistic LiDAR With Noise Model for Real-Time Testing of Automated Vehicles in a Virtual Environment
Juan P. Espineira, Jonathan Robinson, Jakobus Groenewald, Pak Hung Chan, Valentina Donzella
Published in: IEEE Sensors Journal (Volume: 21, Issue: 8, April 2021)
The advancement in the automotive industry has made connected automotive solutions a reality. However, real-time testing and evaluating the solutions in the virtual environment is crucial to validate their safety and reliability. The paper presents a simulated LiDAR model with a rain model that runs in real-time in a high-fidelity simulated environment. It enables real-time testing using LiDAR data to be completed in a virtual environment.
Design and Realization of Wide Field-of-View 3D MEMS LiDAR
Chia-Hsing Lin, Hao-Sheng Zhang, Chia-Ping Lin, Guo-Dung J. Su
Published in: IEEE Sensors Journal (Volume: 22, Issue: 1, January2022)
The usual MEMS mirror-based Light detection and ranging (LiDAR) systems are suitably lightweight and accurate but have a narrow field of view (FOV). The proposed LiDAR prototype with a customized wide-angle lens in front of the MEMS mirrors could successfully scan a large FOV to produce a 3D image with negligible distortion. Successful integration may increase its potential use in autonomous vehicles, drones, mobile robotic devices, disaster prediction etc.
Polymer Optical Fiber Liquid Level Sensor: A Review
Runjie He, Chuanxin Teng, Santosh Kumar, Carlos Marques, Rui Min
Published in: IEEE Sensors Journal (Volume: 22, Issue: 2, January 2022)
Polymer optical fibers (POFs) are compact, flexible, and resistant to chemical corrosion and electromagnetic interference. POFs are an excellent choice for high accuracy liquid level sensing. The materials like polymethyl methacrylate (PMMA) and perfluorinated polymer (CYTOP) are selected based on their bandwidth, chemical, and absorption characteristics. POFs with intensity modulation and wavelength modulation show better performance in liquid-level sensing.
Recent Applications of Different Microstructure Designs in High Performance Tactile Sensors: A Review
Xuguang Sun, Tiezhu Liu, Jun Zhou, Lei Yao, Shuli Liang, Ming Zhao, Chunxiu Liu, Ning Xue
Published in: IEEE Sensors Journal (Volume: 21, Issue: 9, May2021)
The Internet of Things, robot hands, and smart living has increased the demand to develop flexible tactile sensors. The tactile sensors have perspective applications in healthcare monitoring, electronic skin, and artificial intelligence. The microstructure of the sensing unit is an essential factor in developing and improving the tactile sensor's sensitivity, response time, resolution, and robustness.
Fundamentals and Advancements of Topology Discovery in Underwater Acoustic Sensor Networks: A Review
Yuan Liu, Haiyan Wang, Xiaohong Shen, Ruiqin Zhao, Lin Cai
Published in: IEEE Sensors Journal (Volume: 21, Issue: 19, October 2021)
Underwater acoustic sensor networks (UANs) are an enabling technology to explore and uncover the mysterious oceans, a vast unknown territory on Earth. The first and often neglected challenge to building a UAN is to discover network topology. The study presented here provides a comprehensive review of existing approaches for UAN topology discovery, the challenges, and the opportunities beckoning further research.
Susceptibility of Stimuli-Responsive Hydrogels With Embedded Magnetic Microparticles for Inductively Wireless Chemical Sensing
J. H. Park, S. H. Song, M. Ochoa, H. Jiang
Published in: IEEE Sensors Journal (Volume: 22, Issue: 2, January 2022)
Understanding the role of pH value in clinical diagnostic and drug delivery has recently gained research interest. Measuring pH value helps diagnose and assess different medical conditions, like skin structure and wound status, during a healing process. pH sensing is also used to control the release of the drug at the site of its measurement. The paper presents the susceptibility characterization of magnetic microparticles for sensing pH values for biomedical applications.
Flexible Strain and Temperature Sensing NFC Tag for Smart Food Packaging Applications
Pablo Escobedo, Mitradip Bhattacharjee, Fatemeh Nikbakhtnasrabadi, Ravinder Dahiya
Published in: IEEE Sensors Journal (Volume: 21, Issue: 23, December 2021)
Neglecting quality monitoring can lead to contamination and degradation of packaged food. Temperature variation during storage encourages the growth of microorganisms and bacteria, making supervision essential for quality control. Smart packaging with inbuilt temperature and strain sensors can detect these anomalies caused by microbial contamination. The sensor also incorporates an NFC (near field communication) tag and an LED (light emitting diode) indicator for user-friendly notification.
Published in: IEEE Sensors Journal (Volume: 21, Issue: 22, November 2021)
IoT applications, with their unique functionality and applications, are improving human lives. Analysis of a large amount of sensor data collected from these applications is made possible with the help of AI. The convergence of AI and IoT has proven to be a successful idea and has found its applications in health care, agriculture, the environment, and transportation.
Published in: IEEE Sensors Journal (Volume: 21, Issue: 8, April 2021)
In autonomous systems, mmWave radar sensors are highly reliable for target localization and tracking. However, due to the limited number of transceivers, they cannot accurately estimate the angle of arrival (AoA) of the targets. The researchers developed a novel machine learning-based AoA estimation and field of view (FoV) enhancement techniques for mmWave FMCW radars operating in the frequency range of 77-81 GHz. It has an improved field of view in both azimuth and elevation.
Textile-Based Pressure Sensors for Monitoring Prosthetic-Socket Interfaces
Jordan Tabor, Talha Agcayazi, Aaron Fleming, Brendan Thompson, Ashish Kapoor, Ming Liu, Michael Y. Lee, He Huang, Alper Bozkurt, Tushar K. Ghosh
Published in: IEEE Sensors Journal (Volume: 21, Issue: 7, April 2021)
Transtibial amputees face many challenges while wearing prosthetic devices, including chronic discomfort. The commercially available rigid sensors are often used to understand the inner prosthetic environment better. It causes amputees additional discomfort during use. Here, the researchers propose a flexible, textile-based sensing method for prosthetic monitoring and a systematic approach to testing and integrating the sensors within prosthetics.
Published in: IEEE Sensors Journal (Volume: 21, Issue: 7, April 2021)
Early-stage lung cancer is difficult to detect. High-accuracy lung cancer diagnostic methods have previously been reported by measuring the concentration of nonanal gas in exhaled breath. Here, the researchers used alkaline catalysts in nonanal detection reactions inside a suitable glass with nanoscale pores by developing an alkali-resistant porous glass, thus fabricating a simple and highly sensitive nonanal gas sensor.
Contact and Remote Breathing Rate Monitoring Techniques: A Review
Mohamed Ali, Ali Elsayed, Arnaldo Mendez, Yvon Savaria, Mohamad Sawan
Published in: IEEE Sensors Journal (Volume: 21, Issue: 13, November 2021)
The recent COVID outbreaks highlighted the need for breathing rate monitoring and increased the demand for hospitalized patients. Monitoring breathing rate is vital for diagnosing diseases and observing patients with pulmonary conditions. The pros and cons of different techniques are studied and categorized under contact and remote modes of respiratory monitoring systems. Various Radar-based methods found to be more suitable for respiration monitoring are discussed.
A Low-Noise Instrumentation Amplifier With Built-in Anti-Aliasing for Hall Sensors
Robbe Riem, Johan Raman, Jonas Borgmans, Pieter Rombouts
Published in: IEEE Sensors Journal (Volume: 21, Issue: 17, September 2021)
A silicon validation of an In-the-Loop Sampling Amplifier (ILSA) was proposed as a Hall sensor's core pre-conditioning analog interface circuit. It has the advantages of high one-step gain, low noise, low offset, and inherent anti-aliasing. It can be connected directly to any analog-to-digital converter. The resultant Hall system is a compact, low-noise readout architecture with a digital output.
Dielectrics for Non-Contact ECG Bioelectrodes: A Review
Alhassan Haruna Umar, Mohd Afzan Othman, Fauzan Khairi Che Harun, Yusmeeraz Yusof
Published in: IEEE Sensors Journal (Volume: 21, Issue: 17, September 2021)
After years of existence and research efforts, dielectric materials in non-contact bioelectrodes guarantee the hope and survival of patients with heart abnormalities. Without painful skin abrasion, cardiac monitoring devices could reliably ensure constant care and well-being of patients. The researchers provide invaluable insights into the influence of dielectric materials that could change the future of ECG monitoring systems.
Portable Tools for COVID-19 Point-of-Care Detection: A Review
Elga F. Saki, Samuel A. Setiawan, Dedy H. B. Wicaksono
Published in: IEEE Sensors Journal (Volume: 21, Issue: 21, November 2021)
The Global pandemic, COVID-19, surged the demand for easy-to-use, low-cost, portable, sensitive, and quick diagnostic devices with accurate detection probability for SARS-CoV-2 diagnosis. The researchers responded fast by developing various detection methods based on the target biomarkers. The focus is on the new approach using sensing arrays combined with artificial intelligence (AI) analysis to develop portable tools for reliable, inexpensive, and sensitive COVID-19 point-of-care detection.
A Hybrid Camera System for High-Resolutionization of Target Objects in Omnidirectional Images
Chinthaka Premachandra, Masaya Tamaki
Published in: IEEE Sensors Journal (Volume: 21, Issue: 9, May 2021)
The growing use of cameras in crucial applications such as surveillance demands intensive research in image capturing and processing. Capturing a high-quality comprehensive view of a site is required. A hybrid camera platform consisting of an omnidirectional camera for a wide angle of images and a pan-tilt camera for handling the resolution of images is proposed. Multiple experiments demonstrated its ability to capture high-resolution images with a 360-degree panorama.
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging
Rajesh Kumar, Abdullah Aman Khan, Jay Kumar, Zakria, Noorbakhsh Amiri Golilarz, Simin Zhang, Yang Ting, Chengyu Zheng, Wenyong Wang
Published in: IEEE Sensors Journal (Volume: 21, Issue: 14, October 2021)
COVID-19, the global pandemic, highlighted the need for global collaboration. Massive real-life COVID-19 patients' data were required for identifying positive cases and understanding the nature and spread of the rapidly evolving Coronavirus. A collaborative capsule-based deep-learning model was built to segment and classify COVID-19 infections using Computed Tomography (CT) imaging. The privacy concerns of the organizations, data authentication and normalization were addressed using a blockchain-based federated learning process. It resulted in rapid and accurate detection of COVID-19 symptoms without compromising privacy concerns.
Multifunctional Electronic Skin With a Stack of Temperature and Pressure Sensor Arrays
Yogeenth Kumaresan, Oliver Ozioko, Ravinder Dahiya
Published in: IEEE Sensors Journal (Volume: 21, Issue: 23, December 2021)
The rapid advancements in flexible electronics, nanotechnology and material science have enabled engineers and scientists to realise a flexible electronic skin (e-skin) with human-like sensing capabilities. The multifunctionality of such e-skin is proposed to enable robots with human-like dexterity, cognitive skills and abilities. This is anticipated to significantly advance interesting areas such as healthcare, robotics, and human–machine interfaces.
NO2 Gas Sensor Using Iodine Doped Graphene at Room Temperature with Electric Field Enhanced Recovery
Monica Jaiswal, Robin Kumar, Jagjiwan Mittal, and Pika Jha
Published in: IEEE Sensors Journal (Volume: 22, Issue: 7, April 2022)
Sensing NO2 gas in the air is an upcoming field of study due to its increased presence as an environmental pollutant and adverse effect on human health. Researchers developed a novel NO2 gas sensor using a synthesized material called the Iodine-doped Multilayer Graphene (I-MLG) to detect NO2 at its minimum concentration in air. The small doping of iodine in multi-layered graphene and the field effect transistor (FET) structure of the sensor makes the fabricated sensor rapid, reversible, compact and a good NO2 gas sensor.
Using Adaptive Wireless Transmission of Wearable Sensor Device for Target Heart Rate Monitoring of Sports Information
Zhenyong Han
Published in: IEEE Sensors Journal (Volume: 21, Issue: 22, November 2021)
The growing consciousness for health and fitness supported by technological advancements has made smart wearable sensor devices for health monitoring popular. A target heart rate monitoring system, extracting clean heart signals from a polluted source, is proposed for effective micro-monitoring using an adaptive optimization algorithm. The adaptive wireless transmission of sports information and detection system for smart wear is then integrated with a smartphone to display the analysis to the user in a readable format.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 7, April 2022)
The humidity sensors are in massive demand for widespread applications in modern industries and agriculture. The novel differential humidity sensor designed by integrating air-filled substrate integrated waveguide (SIW), metal grid holes (METGH) and loaded with humidity sensitive (HS) materials boosted humidity sensing response. The longitudinally stacking of sensing and referencing resulted in a compact design, anti-temperature-interference ability, and enhanced sensitivity and resolution that could detect minor variations in environmental humidity.
Published in: IEEE Sensors Journal (Volume: 21, Issue: 18, October 2021)
In vascular interventional surgery, experienced physicians rely on proximal force sensing to detect collisions and reduce vascular injury caused by surgical tools. However, in robot-assisted tele-interventional surgery (RATIS), providing high-precision force feedback to the physician is still the main challenge. The researchers developed a haptic robot-assisted catheter operating system with a novel spring-based haptic force interface. With a closed loop force adjustment system, the haptic force interface can provide accurate force feedback. Moreover, a collision protection function with a proximal-force-based collision detection algorithm was proposed to improve surgical safety. In case of no collision, transparency of the teleoperated system is realized; in case of collision, the provided haptic force will be amplified. The results demonstrated the usability of the developed haptic robot-assisted catheter operating system with collision protection function.
Self-Powered Cardiac Monitoring: Maintaining Vigilance With Multi-Modal Harvesting and E-Textiles
Luis Javier Lopez Ruiz, Matthew Ridder, Dawei Fan, Jiaqi Gong , Braden Max Li, Amanda C. Mills, Elizabeth Cobarrubias, Jason Strohmaier, Jesse S. Jur , and John Lach
Published in: IEEE Sensors Journal (Volume: 21, Issue: 2, January 2021)
The advancements in sensors and circuits have led to the development of self-powered wearable sensing systems. Among many it could be used for uninterrupted active and vigilant cardiac monitoring by means of sensing and streaming electrocardiogram (ECG) and motion data to a smartphone. It enables effective care in a non-intrusive manner. Multi-modal energy harvesting from natural sources and integration with e-textiles make these sensing systems a considerable success.
Respiratory Monitoring During Physical Activities with a Multi-Sensor Smart Garment and Related Algorithms
Carlo Massaroni , Joshua Di Tocco, Marco Bravi, Arianna Carnevale, Daniela Lo Presti, Riccardo Sabbadini, Sandra Miccinilli, Silvia Sterzi, Domenico Formica, Emiliano Schena
Published in: IEEE Sensors Journal (Volume: 20, Issue: 4, February 2020)
Wearable devices for continuous monitoring of physiological parameters have acquired significance for their usage in healthcare and sports science. Among other vital parameters, the measurement of respiration rate is crucial since it could be used to detect physiological abnormalities and health status changes and even help predict cardiac arrest. The multi-sensor smart garments made of conductive yarns show great potential in developing efficient, noninvasive, and unobtrusive respiration rate monitors.
Optimization of Sports Training Systems Based on Wireless Sensor Networks Algorithms
Jun Yang and Wu Lv
Published in: IEEE Sensors Journal (Volume: 21, Issue: 22, November 2021)
Sports trainers traditionally use vision-based motion recognition technology and qualitative analysis to develop personalized training strategies for each athlete. Wireless Sensor Network-based smart monitoring system can track an athlete’s performance and biometrics in real-time and analyze it on cloud-based platforms. The results can help the trainers develop an informed training strategy for the athletes.
A Linear Slope Analyzing Strategy of GMR Sensor Transfer Curve for Static Detection of Magnetic Nanoparticles
Shuai Yuan , Yimeng Du, and Philip W. T. Pong
Published in: IEEE Sensors Journal (Volume: 21, Issue: 21, November 2021)
Magnetic Nanoparticles (MNPs) have found promising applications in various upcoming technologies. The quantity of MNPs helps detect the biomolecules of the magnetic bio-detection platform marked by magnetic labels. Compared to the traditional subtraction method, a new linear slope analyzing strategy based on giant magnetoresistance (GMR) platform shows impressive performance.
Electronic Waste Reduction through Devices and Printed Circuit Boards designed for Circularity
Moupali Chakraborty; Jeff Kettle; Ravinder Dahiya
Published in: IEEE Journal on Flexible Electronics (Volume: 1, Issue: 1, Jan. 2022)
The extensive use of electronic goods has accelerated the threat of the rise in electronics waste (e-Waste). Its unregulated disposal causes environmental and health issues. Despite international policies and associated legalization, the exponential growth in production of waste Printed Circuit boards (WPCBs), use of poor raw material and energy-hungry manufacturing processes have spiked this problem as unsustainable. The use of emerging eco-friendly materials, resource-efficient manufacturing processes, and new technologies are needed to improve the industry's sustainability.
Electronic textiles (E-textiles) have recently emerged as a promising technology and will soon transform the wearable industry. Due to its flexibility and ease of embedding in garments, wearable sensing has become a favourite choice for continuous health monitoring of athletes and medical patients. Advancements in textile-based sensors significantly impact the quality of life and will play an important role in the field of Internet of Things (IoT).
Though still not widespread in clinical applications, the detection of circulating tumor cells (CTC) attracts researchers' interest as a technique to diagnose and monitor cancer patients. They have been working on developing a miniaturized, portable, low cost, mass-producible device with the potential for automated and non-invasive diagnostics of CTCs in blood samples at point-of-care locations.
Portable Sensing Devices for Detection of COVID-19: A Review
Deniz Sadighbayan, Ebrahim Ghafar-Zadeh
Published in: IEEE Sensors Journal (Volume: 21, Issue: 9, May 2021)
Portable biosensing systems are crucial in deterrence, timely detection, and intensive care of pandemic-causing diseases like the disastrous COVID-19. The Global emergency led researchers to accelerate the development of portable diagnostic devices for recognizing SARS-CoV-2 and advancing the existing biosensor technology such as electrochemical, optical, and electrical for detecting other viruses and low viral loads.
A PCA-based method to select the number and the body location of piezoresistive sensors in a wearable system for respiratory monitoring
Luigi Raiano, Joshua Di Tocco, Carlo Massaroni, Giovanni Di Pino, Emiliano Schena, Domenico Formica
Published in: IEEE Sensors Journal (Volume: 21, Issue: 5, March 2021)
The optimal number and body location of piezoresistive sensors to design wearables for monitoring respiratory rate are still debated. A Principal Component Analysis (PCA) based method developed to address this challenge considered different references (i.e., at rest and during walking/running). Trials demonstrated that real-time situations strongly influence the number of sensors and their location to optimize wearable performances.
A Wearable, Multimodal Sensing System to Monitor Knee Joint Health
Caitlin N. Teague, J. Alex Heller, Brandi N. Nevius, Andrew M. Carek , Samer Mabrouk , Florencia Garcia-Vicente, Omer T. Inan , Mozziyar Etemadi
Published in: IEEE Sensors Journal ( Volume: 20, Issue: 18, September 2020)
Knee injuries and other minor or chronic knee conditions are prevalent. Monitoring rehabilitation or medication progress in knee treatment is time-consuming, expensive, and requires regular imaging, follow-ups, and several tests. However, knee health can be monitored and “joint health score” calculated remotely with wearable sensors that pick up sound, swelling, temperature and motion. Packaging these sensors into a wearable brace is vital for monitoring the knee.
Janus: A Combined Radar and Vibration Sensor for Beehive Monitoring
Herbert M. Aumann; Margery K. Aumann; Nuri W. Emanetoglu
Published in: IEEE Sensors Letters (Volume: 5, Issue: 3, March 2021)
The two-faced sensor system, JANUS, is designed to help beekeepers track bee activities like ‘Swarming’ and ‘Robbing’. The outward-looking Doppler radar monitors the bee flights while the inward-looking piezoelectric transducer senses the vibrations made by bees inside the hive. Researchers were able to use the level, duration and correlation between the two sensor signals to provide sufficient indication about different types of bee activity.
Ernie W. Hill, Aravind Vijayaragahvan, Kostya Novoselov
Published in: IEEE Sensors Journal (Volume: 11, Issue: 12, December 2011)
Graphene is often called a ‘miracle material’ due to its exceptional mechanical, electrical and chemical properties. It is a highly conductive, thinnest yet strongest, transparent and non-porous layer of pure carbon atoms in a honeycomb structure. Graphene has immense potential for fabricating various types of flexible sensors like mechanical, magnetic, electrochemical, biosensors, optical sensors etc.
Published in: IEEE Sensors Journal (Volume: 13, Issue: 4, April 2013)
Humans are paying a heavy price for economic growth and overall development, whether infrastructure or industrial growth. The pollution and greenhouse gas emissions have led to environmental concerns and climate change, affecting health and life’s quality. However, a rise in environmental awareness created a demand for Environment monitoring systems (EMS) to detect the source and quantify the pollution level by providing a real-time data monitoring and alarm system.
Published in: IEEE Sensors Journal (Volume: 11, No: 9, September 2011)
Time of Flight (ToF) camera sensor has emerged as a promising technology. Depth intensity pixel associated higher frame rate images, lightweight, compact design, and reduced power consumption and errors in the output have built great potential for ToF imaging in various domains. Despite its limitations like low resolutions and high noise, the ToF cameras are extensively used in computer graphics, machine vision, and robotics.
Published in: IEEE Sensors Journal (Volume: 1, No: 4, December 2001)
From mechanical to automatic to self-driven cars, the emerging sensors are revolutionizing the automobile industry. Sensors have emerged as essential components of the automotive electronic control system. The three major areas of automotive systems application–powertrain, chassis, and body are all controlled by arrays of sensors. Advancing automotive sensor technologies have a significant impact on the present with immense scope for the future development of automotive systems.
Published in: IEEE Sensors Journal (Volume: 1, Issue: 4, December 2001)
The human desire for accuracy in exploration and guided navigation has brought inventive changes in inertial sensors technology. Integrating inertial sensors with external aids like Doppler, star tracker, or Global Positioning Systems (GPS) improves their accuracy, enhances reliability, and helps overcome inertial drift. Its vast applications in autonomous vehicles, military and space technology, etc., demand the need for extremely low-cost, small size, efficient and batch-producible sensors.
A Wireless, Passive Carbon Nanotube-Based Gas Sensor
Keat Ghee Ong, Kefeng Zeng, and Craig A. Grimes
Published in: IEEE Sensors Journal (Volume: 2, Issue: 2, April 2002)
Multiwall carbon nanotube-silicon dioxide (MWNT-SiO2) coated composite surface has been developed as a linear, responsive, sensitive gas sensors for O2, CO2 and NH3 gases. The presence of gas concentration is measured by measuring corresponding changes in permittivity and conductivity of MWNT which consequently changes its resonant frequency. The advent of MWNT-SiO2 offers an enormous potential to build low cost, highly sensitive, linear, passive, portable, low power wireless gas sensors.
Multi-Sensor Chip for Monitoring Key Parameters in Bioprocesses
Nurul IzniRusli, Irene Pia Vincentini, Frederik Ceyssens, Michael Kraft
Published in: IEEE Sensors Journal (Volume: 21, Issue: 18, September 2021)
Wearable electronics, mobile applications, the Internet of Things (IoT) technology, and remote monitoring have revolutionized the health care system. The technological advancement in microfabrication techniques has enabled miniaturization and multi-sensing biosensors devices to monitor vital parameters in bioprocesses. Along with its multiple benefits like compact size, low cost, low power consumption, it can also monitor Cell’s density, oxygen, lactate, glucose, temperature, and pH in real-time.
Experimental Environments for the Internet of Things: A Review
Luis Eduardo Lima, Bruno Yuji Lino Kimura, Valério Rosset
Published in: IEEE Sensors Journal (Volume: 19, Issue: 9, MAY 2019)
The Internet of Things (IoT) connects different objects embedded with sensors, software and state-of-the-art technologies through the internet, enabling them to communicate in real-time through the wired or wireless communication system. Its application requires validation before actual implementation to reduce the risk involved, especially in security and privacy. The development of experimental environment (testbeds) provides an efficient platform for conducting practical IoT experiments under real conditions and precise testing techniques for wireless sensor networks (WSN) and IoT applications before implementation.
Adam T. Hayes, Alcherio Martinoli, Rodney M. Goodman
Published in: IEEE SENSORS JOURNAL (VOL. 2, NO. 3, JUNE 2002)
Humans have expertise in using animal’s evolved olfactory senses to their benefit. With the emergence of chemical sensors, efforts are made to make mobile odor-source sensing robots. However, odors cannot be sensed or measured by a single parameter such as wavelength or frequency. Studies show that a group of autonomous mobile robots using suitable algorithms performs superior to a single complex robot in odor localization tasks.
Yazan Qiblawey, Muhammad E. H. Chowdhury, Farayi Musharavati, Erfan Zalnezhad, Amith Khandakar, and Mohammad Tariqul Islam
Published in: IEEE Sensors Journal (Volume: 21, Issue: 6, March 2021)
With fast growing older population, the need for knee or hip implants has grown tremendously. These implants have short lifetime due to implant wear, loosening, and misalignment. Therefore it becomes imperative to monitor the implants to avoid unexpected failure and unnoticed deterioration. Smart, instrumented implants can provide accurate monitoring of the implant, delaying the revision surgeries and its consequences.
Silicon piezoresistive stress sensors and their application in electronic packaging
J.C. Suhling, R.C. Jaeger
Published in: IEEE Sensors Journal (Volume: 1, Issue: 1, June 2001)
The integrated circuits are known for high circuit densities that raise concerns for thermal, mechanical and low-cost packaging material induced stresses. All these put together either cause the chip to fail or perform against the design. Study of such stresses is mandatory before rolling out the chips from a Fab lab. Silicon Piezoresistive Stress Sensors have shown great potential for understanding and detecting stress distributions in electronic packages. It eventually helps in calibrating the IC parameters, selecting wafer planes and packaging materials, etc.
Human Activity Recognition With Smartphone and Wearable Sensors Using Deep Learning Techniques: A Review
E. Ramanujam, Thinagaran Perumal, S. Padmavathi
Published in: IEEE Sensors Journal (Volume: 21, Issue: 12, June 2021)
Human Activity Recognition (HAR) is a field that recognizes human activities from raw time-series signals acquired through embedded sensors of smartphones and wearable devices among others. Deep learning networks modeled after neural network of human brain are widely used in HAR system to retrieve and classify distinct activities. AT present they can accurately recognize simple human activities which make them very useful in Smartphone HAR systems.
Anindya Nag, Subhas Chandra Mukhopadhyay, Jürgen Kosel.
Published in: IEEE Sensors Journal (Volume: 17, Issue: 13, July 2017)
The use of sensors in the application world has drastically improved the human life. Sensors have reduced the time required to study events from hours, to a few seconds or minutes. Nowadays sensing systems are being used in gas sensing, environmental monitoring, as well as the food industry. Monitoring of physiological parameters is being done through Wearable Flexible Sensors (WFS). Tremendous scientific research is going on to develop sensors with better sustainability and sensitivity and to overcome challenges regarding handling of the generated data, comfort of the person concerned, and the power consumed by the devices. Better manufacturing techniques will help develop newer sensors encompassing all income categories in the near future.
Technologies for Printing Sensors and Electronics Over Large Flexible Substrates: A Review
Saleem Khan, LeandroLorenzelli, Ravinder S. Dahiya
Published in: IEEE Sensors Journal (Volume: 15, Issue: 6, June 2015)
Printed sensors and electronics have attracted greater interest as printing facilitates low cost fabrication. With the advent of increased research and demonstration of printed sensors and electronics, we are not far from enabling large area electronics on flexible substrates through cost effective printing technologies on a wide scale. The cost- effectiveness of printing technologies and employing them for flexible electronics will open up new classes of applications, and dramatically change the electronics industry landscape.
Published in: IEEE Sensors Journal (Volume: 13, Issue: 10, October 2013)
Research in the field of flexible sensor skins has progressed significantly in last few decades. With patch antenna technology, these sensors can be wirelessly interrogated with very simple and low-power sensor circuitries for strain sensing, crack detection, shear measurement, bio-chemical sensing etc. Since the information is encoded as frequency, frequency division multiplexing can be exploited to form a large sensor array, hence making the antenna sensor an excellent candidate for flexible sensor skin implementation.
Vladimir J. Lumelsky, Michael S. Shur, and Sigurd Wagner
Published in: IEEE Sensors Journal (Volume: 1, Issue: 1, June 2001)
Sensitive skin is a large array of sensors embedded in a flexible, stretchable, and/or foldable substrate that might cover the surface of a moving machine. It will make possible the use of machines in unsupervised environments by making them ‘cautious’ and find impressive applications in service industry, health industry, bioengineering, space exploration, and many more.
Published in: IEEE Sensors Journal (Volume: 2, Issue: 3, June 2002)
Pattern Analysis constitutes a critical building block in the development of gas sensor array instruments, which are potential substitutes to the human olfactory system. The process of analysis can be split into signal pre-processing, dimensionality reduction, prediction and validation. The design for successful pattern recognition for human olfactory system requires careful consideration and critical evaluation of various methods.
MalithMaheepala, Abbas Z. Kouzani, Matthew A. Joordens
Published in: IEEE Sensors Journal (Volume: 20, Issue: 8, April 2020)
It’s impossible to imagine the modern lifestyle without positioning systems and navigation technologies. The widely used Global positioning system (GPS) and navigation technologiesare ineffective in indoor environments. The emergence of indoor positioning technologies came as a boon for the indoor. Indoor positioning using light signals have immensepotential to provide a reliable and more accurate solution to the indoor positioning systems.
Utilizing Blockchain to Overcome Cyber Security Concerns in the Internet of Things
Bandar Alotaibi
Published in: IEEE Sensors Journal (Volume: 19, Issue: 23, December 2019)
The Internet of Things (IoT), a wide network of internet connected objects to exchange data, has found impressive solutions in the last two decades which improved people’s lives. To maximise the benefit of IoT applications, blockchain technology has been explored and utilized to enhance the security limitations of the IoT technology.
Published in: IEEE Sensors Journal (Volume: 20, Issue: 8, April 2020)
The emerging application of consumer grade EEG measuring sensors and advancement in technology, which had made it portable and affordable, has made it accessible in the general market. EEG devices now have a broader use other than medicine. The researchers, educationists, game developers, engineers, and psychiatrists are enthusiastic about the consumer grade EEG device, which has made the study of the brain accessible and its application quite convenient.
Published in: IEEE Sensors Journal (Volume: 20, Issue: 8, April 2020)
The embedded sensors in camera-enabled devices like the drones and smartphones have opened immense opportunities of bringing imaging capabilities to sensor networks. The networks of connected cameras using 5G, Zigbee, WiFi, Bluetooth protocols result in an advanced technology of visual sensors platforms. Very soon we expect on-board Artificial Intelligence and Machine Learning enabled System-on-Chip wireless visual sensor network (WVSN) platforms for IoT applications.
Sensory Systems in Micro-Processor Controlled Prosthetic Leg: A Review
Nur Azah Hamzaid, Nur Hidayah Mohd Yusof, Farahiyah Jasni.
Published in: IEEE Sensors Journal (Volume: 20, Issue: 9, May 2020)
In recent years, Micro-processor controlled prosthetic legs (MPCPL) are being preferred over conventional prosthetics because they use actuators to replace missing joint function and hence are more functional. Due to this the user’s walking gait and metabolic energy consumption can be imitated very well. The state-of-the-art MPCPL takes commands from the brain through muscles motion, converts that into the user’s gait intention and performs the locomotive motion based on the kinetics sensory system’s input. Very soon the comfort of the motion control will be complimented by taking inputs of eyes and ears to ensure gait further safer.
Jacob T. Robinson, Eric Pohlmeyer, Malte C. Gather, Caleb Kemere, John E. Kitching, George G. Malliaras, Adam Marblestone, Kenneth L. Shepard.
Published in: IEEE Sensors Journal (Volume: 19, Issue: 22, November 2020)
Brain-sensing technologies have immense opportunities and challenges for researchers to explore and identify the best strategies to translate them into products and therapies to improve patients’ lives with neurological and other disorders. It raises the possibility of creating neural interfaces that can more effectively restore or repair neural function and reveal fundamental properties of neural information processing. The transition of these technologies into commercial products and therapies has enormous scope in the future.
Sensors and Systems for Wearable Environmental Monitoring Toward IoT-Enabled Applications: A Review
Md Abdulla Al Mamun and Mehmet Rasit Yuce.
Published in: IEEE Sensors Journal (Volume: 19, Issue: 18, September 2019)
Environmental pollution has a significant impact on the health of the people and the atmosphere around them. The advancement in microelectronics, communication technologies, and miniature environmental sensing devices has boosted the wearable environmental monitoring systems (WEMS) to monitor environmental pollution. Since there is a strong interrelation between environmental pollution with the economic consequences and escalations in healthcare costs, wearable environmental devices are boon for society.
Authors: Anshul Gaur, Abhishek Singh, Ashok Kumar, Kishor S. Kulkarni, Sayantani Lala, Kamal Kapoor, Vishal Srivastava, Anuj Kumar, and Subhas Chandra Mukhopadhyay
Published in: IEEE Sensors Journal (Volume: 19, Issue: 9, May 2019)
The progress on fire sensing technologies has been quite substantial due to advancements in sensing, information, and communications technologies. The sensing system’s hardware and algorithm ensure its excellent ability to detect early fire with less false positives. The information and communication technology focuses on issuing an early warning to notify the occupants and the fire department. Developing a robust fire system demands establishing the benchmark parameters for heat, flame, smoke, and gas levels detection in every fire scenario.
Sensing as a Service: Challenges, Solutions and Future Directions
Xiang Sheng, Jian Tang, Xuejie Xiao and Guoliang Xue.
Published in: IEEE Sensors Journal (Volume: 13, Issue: 10, October 2013)
Mobile phones do have various sensors which are used for exciting sensing applications by creating a cloud computing platform. This could be used as crowd-sourced platform to create innumerable novel sensing applications which are energy-efficient too.
Wearable Flexible Sensors:Wearable devices made of flexible materials are the future. These are used to monitor the physiological parameters of a person to minimize any malfunctioning happening in the body. read more
Sensing as a Service: Challenges, Solutions and Future Directions: Mobile phones do have various sensors which are used for exciting sensing applications by creating a cloud computing platform. This could be used as crowd-sourced platform to create innumerable novel sensing applications which are energy-efficient too. read more
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