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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 (February 2024)
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.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 15, August 2022)
Summary Contributed by: Saurabh Dubey
Scientific and technological advancement has increased the risk of environmental pollutants like chemicals, heavy metals, and toxic gases, thus affecting human health. The need is to detect the contaminants at the source for corrective measures. This paper discusses the sensitivity, selectivity, response time, recovery time, and repeatability of nanocomposite thin-film-based optical fiber sensors coated with metal oxide semiconductors, polymers, metals, carbon nanotubes, graphene, etc., in relation to monitoring environmental health.
Wireless Communication and Power Harvesting in Wearable Contact Lens Sensors
Author: Mengyao Yuan, Rupam Das, Eve McGlynn, Rami Ghannam, Qammer H. Abbasi, Hadi Heidari
Published in: IEEE Sensors Journal (Volume: 21, Issue: 11, June 2021)
Summary Contributed by: Anupama
Our corneal surface and tears make an excellent alternative to blood as a source of biomarkers. Incorporating sensors into contact lenses could provide a convenient, non-invasive platform for continuously detecting and monitoring diseases like glaucoma, diabetes, and heart disease. Researchers explored current technologies for sensing materials, energy, and data transmission techniques in electronic contact lens sensors. The study systematically explores the challenges and future trends.
Technology to detect signs of life under rubble could save lives. The study proposes a novel radar system with a two-step computation model for detecting live victim's vital signs behind or under obstructions. The first step is to establish a region of interest and identify obstacles. The second step detects respiratory vital signs patterns through time-varying phase data. Experimental demonstration of the proposed system shows significant accuracy in detecting live victims.
Published in: IEEE Sensors Journal (Volume: 21, Issue: 9, May 2021)
Summary Contributed by: Payal Savani
Sensors have been pivotal in making human life comfortable. Sensor technology offers many research opportunities for innovations, contributing to smart living. Here the authors have streamlined the latest literature in the field, providing an overview of sensors and their role in our lives. The article summarizes research into four main sensor-based applications: fitness tracking, emotions analysis, sleep tracking, and food intake monitoring.
MS-YOLO: Object Detection Based on YOLOv5 Optimized Fusion Millimeter-Wave Radar and Machine Vision
Author: Yunyun Song, Zhengyu Xie, Xinwei Wang, Yingquan Zou
Published in: IEEE Sensors Journal (Volume: 22, Issue: 15, August 2022)
Summary Contributed by: Yingquan Zou (Author)
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.
BabyPose: Real-Time Decoding of Baby’s Non-Verbal Communication Using 2D Video-Based Pose Estimation
Author: M. Mücahit Enes Yurtsever, Süleyman Eken
Published in: IEEE Sensors Journal (Volume: 22, Issue: 14, July 2022)
Summary Contributed by: Saurabh Dubey
Decoding or understanding non-verbal communication forms in babies is paramount to parents. These forms are expressed in various poses and body-language signals, which can be interpreted using a Human Pose Estimation method. Babies are tracked in real-time using 2D videos. Pose estimators that model these poses into keypoints are then employed to recognize and monitor these activities. This study delves into these estimation models that interpret baby poses with 99% accuracy.
The human desire for accuracy in exploration and guided navigation has brought inventive changes in inertial sensors technology. Integrating inertial sensors with external aids like Doppler, star tracker, or Global Positioning Systems (GPS) improves their accuracy, enhances reliability, and helps overcome inertial drift. Its vast applications in autonomous vehicles, military and space technology, etc., demand the need for extremely low-cost, small size, efficient and batch-producible sensors.
GPS-based navigation, widely used worldwide, is cost-effective for providing position, velocity, and time data. However, it is susceptible to spoofing, especially in UAVs (unmanned aerial vehicles). The paper proposes a GPS spoofing detection and mitigation method using distributed radar tracking and data fusion techniques. The approach combines primary and secondary data through extended Kalman filters and track-to-track association. It ensures accuracy even during spoofing attacks, making it ideal for drone swarms.
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