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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 (July 2024)
Balanced Adaptation Regularization Based Transfer Learning for Unsupervised Cross-Domain Fault Diagnosis
Author: Qin Hu, Xiaosheng Si, Aisong Qin, Yunrong Lv, Mei Liu
Published in: IEEE Sensors Journal (Volume: 22, Issue: 12, June 2022)
Summary Contributed by: Qin Hu (Author)
Fault diagnosis technology for rolling bearings is crucial for preventing mechanical accidents. In the field of fault diagnosis, inconsistent data distribution due to variable working conditions hampers diagnostic accuracy. This study proposes a novel method, Balanced Adaptation Regularization-based Transfer Learning (BARTL), leveraging enhanced multi-scale sample entropies. BARTL improves feature discriminability and similarity across conditions, achieving accurate diagnosis and surpassing existing transfer learning methods, as validated by two public datasets.
Deep Transfer Learning With Self-Attention for Industry Sensor Fusion Tasks
Author: Ze Zhang, Michael Farnsworth, Boyang Song, Divya Tiwari, Ashutosh Tiwari
Published in: IEEE Sensors Journal (Volume: 22, Issue: 15, August 2022)
Summary Contributed by: Anupama
The paper introduces a promising approach using deep transfer learning techniques to address the challenges in processing multisource, heterogeneous data in Industry 4.0. By repurposing a Transformer model pre-trained from data-rich natural language domain, the proposed method allows industrial applications to leverage deep learning capabilities with minimal training data requirements. It represents a significant step toward making Industry 4.0 more efficient, faster, and cost effective.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 11, June 2022)
Summary Contributed by: Weiguan Zhang (Author)
In smart control of robots, proximity and pressure information complement each other in detecting objects from approach to contact. Using a simple and cost-effective fabrication method, the researchers developed a textile-based sensor combining magneto-straining (proximity) and piezoresistive modes (pressure). This sensor exhibits high sensitivity for proximity and pressure perception. The unique design offers a seamless transition between modes, making it suitable for applications in human-machine interaction and intelligent prosthetics.
Usage of IR Sensors in the HVAC Systems, Vehicle and Manufacturing Industries: A Review
Author: Muhammad Adeel Altaf, Jongsik Ahn, Danish Khan, Min Young Kim
Published in: IEEE Sensors Journal (Volume: 22, Issue: 10, May 2022)
Summary Contributed by: Kamalesh Tripathy
Thermal sensors are used in various industries to measure temperature and convert it into a readable output. Its selection depends on cost, resolution, and accuracy, which are crucial factors to consider when designing the sensor system. This paper explores the significance of infrared sensors as thermal sensors in detecting temperature, movement, and occupancy. It reviews the use of thermal sensors in HVAC (Heating, ventilation, and air conditioning) systems, vehicles, and manufacturing industries.
Self-Supervised Monocular Depth Estimation Using Hybrid Transformer Encoder
Author: Seung-Jun Hwang, Sung-Jun Park, Joong-Hwan Baek, Byungkyu Kim
Published in: IEEE Sensors Journal (Volume: 22, Issue: 19, October 2022)
Summary Contributed by: Seung-Jun Hwang (Author)
Depth estimation using camera sensors is vital in autonomous driving, robotics, 3D scene reconstruction, and augmented reality. This paper presents a novel method for monocular-camera depth estimation using a hybrid transformer encoder-decoder. The self-supervised view synthesis method used eliminates the need for depth supervision. It uses a cost-volume structure, combining neural network and transformer architectures for accurate depth prediction. The system enhances global feature representation with an attention decoder, improving depth estimation accuracy.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 20, October 2022)
Summary Contributed by: Saurabh Dubey
The CMOS Algae Growth Period Monitor, consisting of an algae sensor and a CMOS converter, is a cutting-edge solution for monitoring algae growth in algaculture applications. It utilizes a proton exchange membrane to translate algae growth data into a duty cycle, facilitating rapid assessment. With increased sensitivity, it functions in suboptimal conditions and has a maximum linear error of only 0.49%. Integration with IoT technology holds potential applications in the advanced monitoring of aquatic life.
Joint Hybrid 3D Beamforming Relying on Sensor-Based Training for Reconfigurable Intelligent Surface Aided TeraHertz-Based Multiuser Massive MIMO Systems
Author: Xufang Wang, Zihuai Lin, Feng Lin, Lajos Hanzo
Published in: IEEE Sensors Journal (Volume: 22, Issue: 14, July 2022)
Summary Contributed by: Zihuai Lin (Author)
The Terahertz (THz) era promises lightning-fast internet but faces challenges like signal degradation over distances. The paper introduces a joint 3D-beamformer for THz Multi-user Massive multiple-input multiple-output (MIMO) systems, leveraging Reconfigurable Intelligent Surfaces to improve signal strength and coverage. The system uses smart sensing to beam signals directly to devices, promising unparalleled speeds and connection stability, even in crowded spaces. Scalable and adaptable, the proposed architecture is the future of connectivity.
OFET and OECT, Two Types of Organic Thin-Film Transistor Used in Glucose and DNA Biosensors: A Review
Author: Xin Ma, Hongquan Chen, Peiwen Zhang, Martin C. Hartel, Xiaona Cao, Sibel Emir Diltemiz, Qinglei Zhang, Javed Iqbal, Natan Roberto de Barros, Liyan Liu, Hao Liu
Published in: IEEE Sensors Journal (Volume: 22, Issue: 12, June 2022)
Summary Contributed by: Anupama
Diabetes leads to serious health challenges and is a leading cause of numerous chronic diseases. This study explores electronics-based biosensors, particularly Organic Field Effect Transistors (OFET) and Organic Electrochemical Transistors (OECT), as adept glucose and DNA biosensors in diabetes management. The biosensors, fabricated from biodegradable natural materials, offer a flexible, cost-effective, and easily accessible solution, showcasing exceptional sensitivity and selectivity in their performance.
Concentrated Coverage Path Planning Algorithm of UAV Formation for Aerial Photography
Author: Yi Cao, Xianghong Cheng, Jinzhen Mu
Published in: IEEE Sensors Journal (Volume: 22, Issue: 11, June 2022)
Summary Contributed by: Yi Cao (Author)
Ensuring successful aerial photography requires effective coverage path planning for UAVs. However, complexities in outdoor spaces pose challenges, necessitating innovative solutions. This paper introduces a novel concentrated coverage path planning algorithm, revolutionizing traditional probabilistic roadmap methods. Integrating round-trip mode and path constraints optimizes coverage while minimizing computational complexity and repetitions. The approach enhances efficiency and feasibility in real-world environments, promising to redefine aerial photography landscapes.
Real-Time Deep Anomaly Detection Framework for Multivariate Time-Series Data in Industrial IoT
Author: Hussain Nizam, Samra Zafar, Zefeng Lv, Fan Wang, Xiaopeng Hu
Published in: IEEE Sensors Journal (Volume: 22, Issue: 23, December 2022)
Summary Contributed by: Payal Savani
In the realm of smart machines and interconnected devices, the Industrial Internet of Things (IIoT) is ushering in a revolution across industries. Due to a constant stream of diverse and time-stamped data, real-time anomaly detection becomes paramount for industrial process improvement. The article explores a hybrid deep anomaly detection (DAD) model that could accurately identify real-time anomalies. Experimental results showcase its superior performance in terms of accuracy and precision over existing methods.
Deep Learning Approach for Detecting Work-Related Stress Using Multimodal Signals
Author: Wonju Seo, Namho Kim, Cheolsoo Park, Sung-Min Park
Published in: IEEE Sensors Journal (Volume: 22, Issue: 12, June 2022)
Summary Contributed by: Cheolsoo Park (Author)
Work-related stress should be detected and managed to avoid adverse impacts on individuals and society. This study proposes a deep learning approach to detect work-related stress automatically by analyzing multimodal signals. Deep neural networks, facial expressions, and physiological signals were fused at different levels to achieve promising accuracy. The novel approach of studying the level of work-related stress with just a 10-second-long electrocardiogram, respiration, and facial images shows potential for effective stress detection.
Research on Self-Powered Coded Angle Sensor for Rock Climbing Training
Author: Jun Zhang, Chuan Wu
Published in: IEEE Sensors Journal (Volume: 22, Issue: 18, September 2022)
Summary Contributed by: Payal Savani
Rock climbing is an adventure or competitive sport in which monitoring speed is vital. It is measured using an angle sensor entangled with climbers, causing safety concerns. The self-powered angle sensor offers a practical alternative to conventional rock-climbing sensors. The proposed self-powered coded angle sensors based on a single-electrode triboelectric nanogenerator can accurately measure the rotation angle, direction, and speed in indoor and outdoor climbing, even without a power supply.
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.
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.
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.
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