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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 (May 2024)
Hallway Gait Monitoring Using Novel Radar Signal Processing and Unsupervised Learning
Author: Hajar Abedi, Jennifer Boger, Plinio P. Morita, Alexander Wong, George Shaker
Published in: IEEE Sensors Journal (Volume: 22, Issue: 15, August 2022)
Summary Contributed by: Hajar Abedi (Author)
The novel hallway gait monitoring system developed leveraging radar signal processing and unsupervised machine learning introduces the future of personalized gait monitoring of individuals without wearable devices. It aims to create a system capable of monitoring human gait indoors and in natural settings using radar technology. This breakthrough architecture offers non-invasive and precise monitoring, paving the way for enhanced patient care and personal health insights.
Value of Information in Wireless Sensor Network Applications and the IoT: A Review
Author: Faiga Alawad, Frank Alexander Kraemer
Published in: IEEE Sensors Journal (Volume: 22, Issue: 10, May 2022)
Summary Contributed by: Anupama
The Value of Information (VoI) is crucial in handling large data volumes in the Industrial Internet of Things (IIoT), filtering redundant information, and optimizing data processes. It is a key metric influencing path-planning algorithms, source switching, and trajectory optimization in mobile sensors. The paper systematically reviews VoI definitions, categorizing valuation methods and performance across applications, and provides guidelines for parameterized and adaptable VoI techniques to optimize diverse systems.
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.
Non-Enzymatic Glucose Detection Based on GO/Ag Nanocomposite in SiO2 Trench Embedded Field Effect Transistor
Author: Monica Naorem, Roy P. Paily
Published in: IEEE Sensors Journal (Volume: 22, Issue: 16, August 2022)
Summary Contributed by: Saurabh Dubey
SiO2 trench-embedded Field Effect Transistors (FET) with a graphene oxide-silver ( GO/Ag) nanocomposite for non-enzymatic glucose detection are crucial for effective diabetes management by accurately monitoring glucose concentration range of 1 μM to 10 mM. Validated through structural analysis, the fabricated device structure exhibits excellent glucose storage and sensing abilities, ensuring stability, reproducibility, and selectivity. Compact and easy to produce, it has promising applications in portable glucose sensors for point-of-care diagnostics and healthcare.
An EEG Data Processing Approach for Emotion Recognition
Author: Guofa Li, Delin Ouyang, Yufei Yuan, Wenbo Li, Zizheng Guo, Xingda Qu, Paul Green
Published in: IEEE Sensors Journal (Volume: 22, Issue: 11, June 2022)
Summary Contributed by: Yufei Yuan (Author)
Emotion recognition has garnered interest from researchers because of its importance in affective computing. Facial expressions can mask human emotions. However, studies show that electroencephalogram (EEG) signals can recognize and identify human emotions. Hence, EEG has emerged as an alternate method for emotion recognition. The paper proposes a novel approach through a reduced number of EEG electrode channels and a normalization method, demonstrating its promising applications in real-time emotion recognition.
Technologies Driving the Shift to Smart Farming: A Review
Author: Nabila ElBeheiry, Robert S. Balog
Published in: IEEE Sensors Journal (Volume: 23, Issue: 3, February 2023)
Summary Contributed by: Vinay S Palaparthy
Agriculture requires sustainable solutions, especially when facing challenges like climate change, unqualified farmers, and urbanization. Smart farming (SF) helps enhance crop quality and quantity with minimal labor, ensuring sustainable agriculture and consistent food supply to meet the global food demand. This survey includes various themes like sensors, communication, big data, actuators, and data analysis. The article emphasizes integrating multiple technologies, highlighting popular SF systems: remote monitoring, autonomous, and intelligent decision-making.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 15, August 2022)
Summary Contributed by: Satoshi Ikezawa (Author)
Material selection is crucial when designing and fabricating metasurfaces. The metasurface optical element produces a tightly focused beam from a visible laser, thus maximizing light utilization and minimizing energy loss. This study focuses on enhancing metasurface microfabrication precision and achieving remarkable light transmittance through traditionally impermeable silicon, providing opportunities for developing miniature wearable technology, cameras, and augmented reality (AR) devices and has significant implications for micro-scanning within confined spatial domains.
E-Nose System Based on Fourier Series for Gases Identification and Concentration Estimation From Food Spoilage
Author: Jie Luo, Zehao Zhu, Wen Lv, Jian Wu, Jianhua Yang, Min Zeng, Nantao Hu, Yanjie Su, Ruili Liu, Zhi Yang
Published in: IEEE Sensors Journal (Volume: 23, Issue: 4, February 2023)
Summary Contributed by: Leena Jha
Improper and prolonged storage of perishable food items like meat and fruits may lead to microbial contamination and spoilage. The paper presents an electronic nose (EN) system, a portable electronic gas-sensing device comprising a sensor array-based gas identification, concentration estimation system, and data acquisition circuit boards. A machine learning algorithm assists it. It accurately detects gases like ammonia (NH3), hydrogen sulphide (H2S), and ethanol (C2H5OH) emitted by spoiled food.
Monolithic Sensor Integration in CMOS Technologies
Author: Daniel Fernández, Piotr Michalik, Juan Valle, Saoni Banerji, Josep Maria Sánchez-Chiva, Jordi Madrenas
Published in: IEEE Sensors Journal (Volume: 23, Issue: 2, January 2023)
Summary Contributed by: Daniel Fernández (Author)
In the wearable market, where the area is a limiting factor, monolithic sensor integration with mainstream gadget electronics can provide an efficient means to archive a small device footprint while maintaining good performance. For a minimum cost, CMOS-MEMS devices built using backend-of-line (BEOL) interconnections as structural material offer an interesting, cost-effective approach with the potential to become a market game-changer.
An Improved YOLOv5 Crack Detection Method Combined With Transformer
Author: Xuezhi Xiang, Zhiyuan Wang, Yulong Qiao
Published in: IEEE Sensors Journal (Volume: 22, Issue: 14, July 2022)
Summary Contributed by: Kamalesh Tripathy
A robust pavement crack detection network is imperative to mitigate traffic accidents and minimize maintenance costs. The paper proposed an efficient hybrid model by merging YOLOv5 and Transformer, utilizing one-stage architecture and long-range dependency capture for reliable crack detection. The network's performance is further improved using test time augmentation (TTA) for crack detection. An efficient solution for urban pavement damage detection, it paves the way for expanding datasets to tackle diverse pavement issues.
Angle-Insensitive Human Motion and Posture Recognition Based on 4D Imaging Radar and Deep Learning Classifiers
Author: Yubin Zhao, Alexander Yarovoy, Francesco Fioranelli
Published in: IEEE Sensors Journal (Volume: 22, Issue: 12, June 2022)
Summary Contributed by: Francesco Fioranelli (Author)
Human Activity Recognition (HAR) is crucial to support the needs of an aging population. The paper proposes a new method for HAR using 4D imaging radars. The technique combines point cloud and spectrogram data to capture the spatial and temporal features of the activities. The method is tested on an experimental dataset and performs better than existing alternatives.
Gait-Based Person Identification and Intruder Detection Using mm-Wave Sensing in Multi-Person Scenario
Author: Zhongfei Ni, Binke Huang
Published in: IEEE Sensors Journal (Volume: 22, Issue: 10, May 2022)
Summary Contributed by: Saurabh Dubey
MGait is a multi-person identification and intruder detection system that utilizes mm-wave sensing to identify users based on gait micro-Doppler (m-D) signatures. Individuals are continuously tracked in indoor scenarios using low-cost mm-wave radars in range-Doppler (R-D) space frame by frame to extract their distinct gait signatures. Trained in a deep learning-based Gaussian mixture loss model, MGait is a promising solution for biometrics and has wide applications in health and security.
Design Methodology for Industrial Internet-of-Things Wireless Systems
Author: Carlos Mendes da Costa, Peter Baltus
Published in: IEEE Sensors Journal (Volume: 21, Issue: 4, February 2021)
Summary Contributed by: Payal Savani
The rise in Internet of Things (IoT) usage in industrial applications requires robust wireless systems. The researchers proposed an innovative approach for designing low-latency, power-efficient, and reliable wireless systems. This paper comprehensively studies system requirements, hardware, and architecture for optimal design choices. The prototype design was validated through practical experiments. It demonstrates superior performance compared to existing wireless standards.
Online Learning for Active Odor Sensing Based on a QCM Gas Sensor Array and an Odor Blender
Author: Manuel Aleixandre, Takamichi Nakamoto
Published in: IEEE Sensors Journal (Volume: 22, Issue: 23, December 2022)
Summary Contributed by: Manuel Aleixandre (Author)
This work presents an active odor sensing system that blends odorous ingredients, iteratively adjusting their mix ratios to match the sensor response of a target scent. Online learning adapts the sensor model parameters in real-time, optimizing the control loop to compensate for drift and humidity variations. It improves the accuracy and robustness despite the inherent limitations of gas sensors.
Research of Low-Power MEMS-Based Micro Hotplates Gas Sensor: A Review
Author: Zhenyu Yuan, Fan Yang, Fanli Meng, Kaiyuan Zuo, Jin Li
Published in: IEEE Sensors Journal (Volume: 21, Issue: 17, September 2021)
Summary Contributed by: Anupama
The MEMS-based micro hotplate gas sensors are small and mass-producible with excellent performance compared to traditional ceramic tube sensors. Energy efficiency is a crucial parameter of portable, reliable sensors. Heat loss significantly increases the power consumption of hotplates. To optimize energy consumption and efficiency, an analytical study of heat loss in different parts of sensor parts and their remedies through fabrication methods is presented.
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 fusion is becoming an increasingly crucial part of the environmental perception system in autonomous driving. Experience the cutting-edge fusion of millimeter-wave radar and machine vision in the proposed MS-YOLO, powered by You Only Look Once (YOLOv5) algorithm. The proposed fusion model offers exceptional accuracy in detecting objects and provides immediate real-time insights regardless of light and weather conditions.
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