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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 (March 2023)
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.
Chipless RFID Sensors for Wearable Applications: A Review
Author: Santanu Kumar Behera
Published in: IEEE Sensors Journal (Volume: 22, Issue: 2, December 2022)
Summary Contributed by: Anupama
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
Author: Juan P. Espineira, Jonathan Robinson, Jakobus Groenewald, Pak Hung Chan, Valentina Donzella
Published in: IEEE Sensors Journal (Volume: 21, Issue: 8, April 2021)
Summary Contributed by: Juan P. Espineira (Author)
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
Author: Chia-Hsing Lin, Hao-Sheng Zhang, Chia-Ping Lin, Guo-Dung J. Su
Published in: IEEE Sensors Journal (Volume: 22, Issue: 1, January2022)
Summary Contributed by: Dayarnab Baidya
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
Author: Runjie He, Chuanxin Teng, Santosh Kumar, Carlos Marques, Rui Min
Published in: IEEE Sensors Journal (Volume: 22, Issue: 2, January 2022)
Summary Contributed by: Rui Min (Author)
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
Author: 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)
Summary Contributed by: E.V.V. Hari Charan
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
Author: Yuan Liu, Haiyan Wang, Xiaohong Shen, Ruiqin Zhao, Lin Cai
Published in: IEEE Sensors Journal (Volume: 21, Issue: 19, October 2021)
Summary Contributed by: Yuan Liu (Author)
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.
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.
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.
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