Get exclusive breakthroughs on sensors in IoT, energy, healthcare, and more, delivered straight to your inbox.
"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)
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.
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.
Sensors have become a part of everyday life, seamlessly connecting the physical and electronic worlds. The paper focuses on the current-output sensing technique, providing information and analytical study of various sensors and design guidance of current readout circuits. Additionally, state-of-the-art current-sensing frontends are analyzed concerning gain, bandwidth, stability, and noise. The paper presents insights into general design architectures and their performance tradeoffs.
Vision-based tactile sensors (VBTS) are attracting attention for their application in robotics. As an innovative optical sensor, VBTS leverages tactile sensing to enhance the interpretation and utilization tactile information. The paper presents an overview of the hardware aspects of VBTS, including their technology, capabilities, challenges, and potential solutions. It provides insightful guidelines for optimizing the design and fabrication processes of VBTS to improve their performance.
A non-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
Copyright 2023 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions
This site is also available on your smartphone.
