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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 (June 2023)
Fabrication Strategies and Measurement Techniques for Performance Improvement of Graphene/Graphene Derivative Based FET Gas Sensor Devices: A Review
Author: 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
Author: Mariam El Gharbi, Marc Martinez-Estrada, Raúl Fernández-García, and Ignacio Gil
Published in: IEEE Sensors Journal (Volume: 21, Issue: 21, November 2021)
Summary Contributed by: Kamalesh Tripathy
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)
Summary Contributed by: Vahid Khojasteh Lazarjan (Author)
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
Author: Xiaolong Chen, Ningyuan Su, Yong Huang, Jian Guan
Published in: IEEE Sensors Journal (Volume: 21, Issue: 7, April 2021)
Summary Contributed by: Shradha Makhija
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
Author: 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)
Summary Contributed by: Sihyun Kim (author)
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
Author: Ali Hanif, Muhammad Muaz, Azhar Hasan, Muhammad Adeel
Published in: IEEE Sensors Journal (Volume: 22, Issue: 4, February 2022)
Summary Contributed by: Anupama
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.
Published in: IEEE Sensors Journal (Volume: 21, Issue: 7, April 2021)
Summary Contributed by: Sidrah Liaqat (Author)
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
Author: Edric John Cruz Nacpil, Zheng Wang, Kimihiko Nakano
Published in: IEEE Sensors Journal (Volume: 21, Issue: 18, September 2021)
Summary Contributed by: Kamalesh Tripathy
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)
Summary Contributed by: Hongmin Zhu (author)
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
Author: Ran Jia, Liyong Wang, Changsong Zheng, Tao Chen
Published in: IEEE Sensors Journal (Volume: 22, Issue: 4, February 2022)
Summary Contributed by: Laxmeesha Somappa
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
Author: Da Ying, Drew A. Hall
Published in: IEEE Sensors Journal (Volume: 21, Issue: 20, October 2021)
Summary Contributed by: Drew A. Hall (Author)
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)
Summary Contributed by: Pranjali Maru
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.
Published in: IEEE Sensors Journal (Volume: 21, Issue: 7, April 2021)
Summary Contributed by: Takuya Sakamoto (Author)
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
Author: Nathan F. Lepora
Published in: IEEE Sensors Journal (Volume: 21, Issue: 19, October 2021)
Summary Contributed by: Dayarnab Baidya
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
Author: Mohammad Javad Karimi, Alexandre Schmid, Catherine Dehollain
Published in: IEEE Sensors Journal (Volume: 21, Issue: 6, March2021)
Summary Contributed by: Mohammad Javad Karimi (Author)
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.
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.
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.
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