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"IEEE Sensors Alert" is a pilot project 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 (April 2023)
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.
Multi-Sensor Complex Network Data Fusion Under the Condition of Uncertainty of Coupling Occurrence Probability
Author: Xianfeng Li, Sen Xu
Published in: IEEE Sensors Journal (Volume: 21, Issue: 22, November 2021)
Summary Contributed by: Payal Savani
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
Author: Zeyu Cheng, Yi Zhang, Chengkai Tang
Published in: IEEE Sensors Journal (Volume: 21, Issue: 23, December 2021)
Summary Contributed by: Zeyu Cheng (Author)
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.
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.
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.
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