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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 2025)
Deep Neural Network-Assisted Terahertz Metasurface Sensors for the Detection of Lung Cancer Biomarkers
Author: Hu Fangrong, Su An, Yang Mo, Chen Jie, Lin Shangjun, Ma Xiaoya
Published in: IEEE Sensors Journal (Volume: 24, Issue: 10, May 2024)
Summary Contributed by: Saurabh Dubey
Terahertz (THz) metasurface sensing technology, combined with deep neural networks (DNNs), offers an innovative method for detecting lung cancer biomarkers, including miRNA-21, miRNA-92a, and miRNA-339-3p. This integrated system achieved a classification accuracy of 97.22% for miRNA-21, showcasing its effectiveness as a low-energy, label-free solution for early cancer detection. It demonstrates significant advantages over traditional methods for real-time clinical applications, facilitating rapid diagnosis and monitoring of lung cancer.
Self-Powered and Cost-Effective Wireless Sensor Node for Air Quality Monitoring With an Optically Transparent Smart Antenna System
Author: Maria Bermudez Arboleda, Atif Shamim
Published in: IEEE Sensors Journal (Volume: 24, Issue: 18, September 2024)
Summary Contributed by: Maria Bermudez Arboleda (Author)
Air quality monitoring is crucial for the environment and public health. However, traditional air quality monitoring systems are bulky, costly, and challenging to install. To overcome these shortcomings, this study introduces a compact, self-powered sensor node with optically transparent, reconfigurable antennas and modular solar panels. Its innovative design enables 80% size reduction. Equipped with comprehensive pollutant detectors, easy deployment of these cost-effective sensors enables high-resolution monitoring, previously unattainable with traditional systems.
Design and Output Voltage Model of Folding Magnetized Electronic Skin for Intelligent Manipulator
Author: Guoheng Lin, Ling Weng; Hui Zhang, Yang Liu, Yuxin Chen, Zhuolin Li
Published in: EEE Sensors Journal (Volume: 24, Issue: 18, September 2024)
Summary Contributed by: Payal Savani
Flexible electronic skin (e-skin) broadens the application of tactile sensing and flexible sensor technology. The paper presents the design and modeling of a folding magnetized electronic skin with advanced multilayer designs and mathematical modeling for intelligent manipulators. It also combines flexible electronics and magnetic sensing for enhanced flexibility and precision. This e-skin boosts robotics performance in complex tasks requiring precise object handling and interaction by providing tactile and spatial awareness.
A Self-Calibration Method for Engineering Using 3-D Laser Scanning System Based on Cube Vertices
Author: Hongqiang Chen, Ruiheng Xia, Yi Zhang, Hua Deng, Kejun Li
Published in: IEEE Sensors Journal (Volume: 24, Issue: 3, February 2024)
Summary Contributed by: Yi Zhang (Author)
To meet the rapid calibration needs of engineering sites, a novel self-calibration technique is proposed for 3D scanners using 2D light detection and ranging (LiDAR) and a rotating platform. The calibration process uses a cube of known size as the calibration object, ensuring a straightforward process with no additional equipment. This approach addresses the limitations of shape-based calibration, enabling efficient on-site calibration and accurate measurements for most engineering applications.
A Multi-Sensor Tactile System Based on Fiber Bragg Grating Sensors for Soft Tissue Palpation
Author: M. Pulcinelli, L. Zoboli, F. De Tommasi, C. Massaroni, V. Altomare, A. Grasso, A. Gizzi, E. Schena, D. Lo Presti
Published in: IEEE Sensors Journal (Volume: 24, Issue: 16, August 2024)
Summary Contributed by: Saurabh Dubey
Diagnostic tissue palpitation is a common clinical practice to detect soft tissue tumors. This paper presents an innovative multi-sensor tactile system based on Fiber Bragg Grating sensors for non-invasive soft tissue palpation. It improves diagnostic accuracy with enhanced spatial resolution, reduced probe size, and minimized crosstalk errors. The simple and non-invasive tool is effective for soft tissue cancer and tumor detection, with the future potential for automated detection.
Two-port MEMS (Micro-Electro-Mechanical Systems) microphones offer better directional sensitivity than one-port designs but may be more affected by vibrations. The paper presents a simple universal expression for the vibration sensitivity of two-port MEMS microphones. It analytically and experimentally demonstrates that the vibration sensitivity of two-port MEMS mics is independent of their natural frequency (i.e., their stiffness) and is inversely proportional to frequency. Experimental results confirm these findings across various microphone designs.
Published in: IEEE Sensors Journal (Volume: 24, Issue: 13, July 2024)
Summary Contributed by: Anupama
An integrated sensing and communication (ISAC) system combines communication and radar sensing functions to optimize resource utilization. This paper has developed a hybrid analog-digital beamforming method for multi-user, multi-beam ISAC scenarios. The approach prioritizes communication performance through iterative optimization of digital and analog precoders while ensuring effective radar sensing. The proposed algorithm enhances cost, power and spectrum efficiency, and performance, making it ideal for next-generation communication systems.
The increasing threat of harmful algal blooms necessitates affordable and accessible water quality monitoring. This research presents a low-cost, portable, Internet of Thing (IoT)-enabled fluorometer-nephelometer for measuring key water quality parameters. This open-source, customizable system can be adapted to various applications, from single-point measurements to distributed networks. By adjusting sensitivity and adding components, it can monitor diverse aquatic environments, aiding in the research and management of marine ecosystems.
YOLOX-SAR: High-Precision Object Detection System Based on Visible and Infrared Sensors for SAR Remote Sensing
Author: Qiang Guo, Jianing Liu, Mykola Kaliuzhnyi
Published in: IEEE Sensors Journal (Volume: 22, Issue: 17, September 2022)
Summary Contributed by: Saurabh Dubey
Object detection using Synthetic Aperture Radar (SAR) sensors is significant in artificial intelligence, signal processing, radar imaging, and image processing. However, complex electromagnetic scattering backgrounds create challenges in accurate detection. The paper proposes the state-of-the-art YOLOX-SAR system, built upon the YOLOX architecture with advanced features and techniques for precise SAR image object detection. Incorporating technological advancements such as Meta-ACON and CBAM promises improved accuracy and robustness of SAR image object detection.
Published in: IEEE Sensors Journal (Volume: 24, Issue: 17, September 2024)
Summary Contributed by: Weileun Fang (Author)
The pandemic created the demand for a suitable alternative to lab tests to quickly detect antibodies with high precision. This paper presents a fast, sensitive, and accurate new system to detect COVID-19 neutralizing antibodies using optical spectroscopy and hybrid machine learning. The method evaluates immunity by detecting antibodies that block virus-receptor interactions, thus effectively monitoring vaccine efficacy and immune responses. Its high accuracy and scalability make it suitable for diagnostic and research applications.
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.
Human Activity Recognition (HAR) is a field that recognizes human activities from raw time-series signals acquired through embedded sensors of smartphones and wearable devices among others. Deep learning networks modeled after neural network of human brain are widely used in HAR system to retrieve and classify distinct activities. AT present they can accurately recognize simple human activities which make them very useful in Smartphone HAR systems.
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