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"IEEE Sensors Alert" is a new service of the IEEE Sensors Council. Started as one of its new initiatives, this weekly digest publishes teasers and condensed versions of our journal papers in layperson's language.
Articles Posted in the Month (December 2024)
Design, Fabrication, and Validation of a Flexible Tactile Sensor for a Hand Prosthesis
Author: Kuo Chung-hsien, Nguyen Dai-Dong, Su Shun-Feng, Xie Wu-Qi
Published in: IEEE Sensors Journal (Volume: 24, Issue: 16, March 2024)
Summary Contributed by: Chung-Hsien Kuo (Author)
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.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 17, September 2022)
Summary Contributed by: Payal Savani
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)
Summary Contributed by: Arantxa Uranga (Author)
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
Author: Ji-Ye Jeon, Shin-Woo Kang, Hyuk-Jae Lee, Jin-Sung Kim
Published in: IEEE Sensors Journal (Volume: 22, Issue: 22, November 2022)
Summary Contributed by: Saurabh Dubey
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
Author: Yoo Myungsik, Bui Cuong Duy, Hoang Hiep Anh
Published in: IEEE Sensors Journal (Volume: 24, Issue: 5, March 2024)
Summary Contributed by: Myungsik Yoo (Author)
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
Author: Arash Shokouhmand, Samuel Eckstrom, Behnood Gholami, Negar Tavassolian
Published in: EEE Sensors Journal (Volume: 22, Issue: 12, June 2022)
Summary Contributed by: Payal Savani
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.
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
Author: Gencai Shen, Kunpeng Gao, Nan Zhao, Zhuangzhuang Wang, Chunpeng Jiang, Jingquan Liu
Published in: IEEE Sensors Journal (Volume: 22, Issue: 13, July 2022)
Summary Contributed by: Saurabh Dubey
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
Author: 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)
Summary Contributed by: Sequeira Filipa (Author)
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)
Summary Contributed by: Payal Savani
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.
Published in: IEEE Sensors Journal (Volume: 24, Issue: 4, February 2024)
Summary Contributed by: Zhang Kehua (Author)
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.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 11, June 2022)
Summary Contributed by: Saurabh Dubey
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
Author: 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)
Summary Contributed by: Salehnia Foad (Author)
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
Author: 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)
Summary Contributed by: Anupama
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)
Summary Contributed by: Kaziz Sameh (Author)
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.
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.
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.
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