Get exclusive breakthroughs on sensors in IoT, energy, healthcare, and more, delivered straight to your inbox.
"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 (September 2023)
An Implantable Antenna Sensor for Medical Applications
Author: Wei Wang, Xiu-Wei Xuan, Wan-Yi Zhao, Hong-Kuai Nie
Published in: IEEE Sensors Journal (Volume: 21, Issue: 13, July 2021)
Summary Contributed by: Payal Savani
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 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
Author: Chen Zhu, Rex E. Gerald, Jie Huang
Published in: IEEE Sensors Journal (Volume: 21, Issue: 18, September 2021)
Summary Contributed by: Dayarnab Baidya
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
Author: 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)
Summary Contributed by: Francisco Falcone (Author)
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
Author: 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)
Summary Contributed by: Kamalesh Tripathy
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
Author: Patrick McEnroe, Shen Wang, Madhusanka Liyanage
Published in: IEEE Sensors Journal (Volume: 9, Issue: 17, September 2022)
Summary Contributed by: Patrick McEnroe (Author)
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.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 1, January 2022)
Summary Contributed by: Anupama
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.
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
Author: 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)
Summary Contributed by: E.V.V. Hari Charan
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
Author: 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)
Summary Contributed by: Anupama
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)
Summary Contributed by: Sheng-Kai Yeh (Author)
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.
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
Author: Zohreh Vafapour, William Troy, Ali Rashidi
Published in: IEEE Sensors Journal (Volume: 21, Issue: 17, September 2021)
Summary Contributed by: Anupama
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
Author: 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)
Summary Contributed by: Margi Hingrajia
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.
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.
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.
A non-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
Copyright 2023 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions
This site is also available on your smartphone.
