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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 (January 2025)
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.
Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things
Author: Maede Zolanvari, Marcio A. Teixeira, Lav Gupta, Khaled M. Khan, Raj Jain
Published in: IEEE Internet of Things Journal (Volume: 6, Issue: 4, August 2019)
Summary Contributed by: Anupama
A cyberattack on the Industrial Internet of Things (IIoT) could have devastating consequences. The researchers have conducted a detailed assessment of existing IIoT protocols for cyber vulnerability. The case study demonstrates the effectiveness of the proposed machine learning (ML)-based intrusion detection system (IDS) against cyberattacks. An in-house developed testbed simulated real-world IIoT scenarios and potential cyberattacks to evaluate the performance of the proposed ML-based system.
Single-Channel DoA Estimation Based on Nonuniform Time-Modulated Array With Asynchronous Sampling
Author: Li Long, Han Jiaqi, Liu Gong-Xu, Mu Yajie, Shi Yan, Wang Xin, Xia Dexiao
Published in: IEEE Sensors Journal (Volume: 24, Issue: 14, July 2024)
Summary Contributed by: Long Li (Author)
Direction-of-arrival (DoA) is essential for accurate target positioning and is important in wireless communication, radar detection, satellite navigation, etc. This paper introduces a single-channel direction-of-arrival (DoA) estimation method using a nonuniform time-modulated array (NTMA) with asynchronous sampling. The technique reduces hardware complexity and improves estimation accuracy using a single-channel receiver and an optimized modulation scheme. The results demonstrate the system's effectiveness, especially in applications with limited resources and where synchronous sampling is challenging.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 14, July 2022)
Summary Contributed by: Payal Savani
Rice, a Global staple, is often plagued by various diseases that impact crop production. The challenge of identifying these diseases exacerbates the issue. While deep learning is a powerful tool in image processing and computer vision, its application in plant disease recognition has been restricted. This paper introduces MobInc-Net, a lightweight Inception network that recognizes and detects rice plant diseases. It offers a practical solution that achieves high accuracy even in challenging conditions.
Self-Driven Photodetectors Based on Flexible Silicon Nanowires Array Surface-Passivated With Tin-Based Perovskites
Author: Yang Shengyi, Ge Zhenhua, Jiang Yurong, Wang Ying, Xin Haiyuan, Zhang Zhenheng, Zou Bingsuo
Published in: IEEE Sensors Journal (Volume: 24, Issue: 14, July 2024)
Summary Contributed by: Shengyi Yang (Author)
Silicon nanowire (Si-NW) photodetectors show great potential as efficient, self-driven, and compact devices in optoelectronic applications. However, their intrinsic surface defects reduce their responsivity and specific detectivity, limiting their performance. This paper introduces a novel self-driven photodetector based on flexible silicon nanowires array surface passivated with tin-based perovskites (FASnBr₃). The innovative design significantly enhances device performance and flexibility, making it a promising candidate for next-generation photodetectors.
Published in: IEEE Sensors Journal (Volume: 23, Issue: 3, February 2023)
Summary Contributed by: Saurabh Dubey
Anomalies between trains and platform doors threaten intercity railway safety. The paper proposes a method for anomaly detection using train predeparture key frame extraction and an Image-inpainting Anomaly Detection Network (IADN) based on image-inpainting autoencoder (AE) and local abnormal information enhancement and global-attentive reconstruction error (GARE). The tested results show effective and accurate anomaly detection, even outperforming state-of-the-art methods, ensuring safety with potential applications in security and locomotive industries.
Published in: IEEE Sensors Journal (Volume: 24, Issue: 15, August 2024)
Summary Contributed by: Nhien-An Le-Khac (Author)
Human activity recognition (HAR) using multiple sensors offers higher accuracy but raises privacy and convenience issues, while single sensors often lack detail and accuracy. The paper proposes Virtual Fusion with Contrastive Learning (VFCL), a novel framework for single-sensor-based activity recognition. Virtual fusion uses data from multiple sensors across different modalities for training but requires only one for predictions, while contrastive learning improves the accuracy and performance of each sensor independently.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 13, July 2022)
Summary Contributed by: Saurabh Dubey
Early detection of breast cancer saves lives. The research presents a novel and adaptable breast cancer detection system integrating dual-polarized Ultra-Wideband (UWB) antennas on flexible Kapton polyimide, ensuring high precision. Eight UWB units surround the breast phantom and reconstruct 3D images using a delay-and-sum (DAS) algorithm to locate tumors with minimal clutter. Wearable and versatile, it can detect tumors with a 15 mm edge-to-edge distance, offering convenient health monitoring and self-diagnosis.
Design, Fabrication, and Validation of a Flexible Tactile Sensor for a Hand Prosthesis
Author: Kuo Chung-hsien, Nguyen Dai-Dong, Su Shun-Feng, Xie Wu-Qi
Published in: IEEE Sensors Journal (Volume: 24, Issue: 16, March 2024)
Summary Contributed by: Chung-Hsien Kuo (Author)
The design of a flexible tactile sensor using liquid metal (LM) and elastic fibers provides sensing capability and enhances the performance of a hand prosthesis. This study details the measurement principles of the LM-based force sensor, the design and fabrication process, and the sensor signal processing circuit. The proposed flexible tactile sensor offers reliable performance with high sensitivity in three axes, low error, and improved functionality in hand prostheses.
The researchers present a novel target classification technique incorporating mmWave radar and deep learning models to classify moving objects. The system provides a wide field of view by orienting the antenna in elevation and rotating it in the horizontal field. With 97 – 99 % accuracy, the proposed classification technique is a cost-effective and dependable system for a wide range of autonomous applications.
The two-faced sensor system, JANUS, is designed to help beekeepers track bee activities like ‘Swarming’ and ‘Robbing’. The outward-looking Doppler radar monitors the bee flights while the inward-looking piezoelectric transducer senses the vibrations made by bees inside the hive. Researchers were able to use the level, duration and correlation between the two sensor signals to provide sufficient indication about different types of bee activity.
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