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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 (June 2026)
Deep Learning-Integrated Agarose-Coated Micro-Loop Fiber Resonator for Relative Humidity Measurement
Author: Zain Huda, Qureshi Khurram Karim
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: Huda Adnan Zain (Author)
Fiber-optic humidity sensors, especially those with resonators, produce rich spectral data that leads to expensive, slow processing. This work integrates deep learning with an agarose-coated resonator sensor to address this challenge. The approach achieved a sensitivity of 0.2037 dB/%RH and a convolutional neural network (CNN) accuracy of ~98%. It highlights the potential of combining optical sensing with AI, offering a promising solution for environmental monitoring, healthcare, and real-time humidity tracking.
Unified Neural Network-Based Shape Sensing for Continuum Robots Using Fiber Bragg Gratings
Author: Dong Xin, Axinte Dragos, Elsayed Belal, Jones Simon D., Mohammad Abdelkhalick, Shi Hongshen, Wild Samuel, Zeng Tianyi
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: Samuel Wild (Author)
Continuum robots (CRs) or miniaturised robotic snakes need to know their own shape to navigate accurately in confined spaces. Fibre Bragg grating (FBG) sensors offer a path, but deployable straight-core versions often suffer from twist-induced inaccuracies. This paper presents a shape-sensing method using FBG technology combined with a neural network to correct these errors. Validated across two robots of different sizes, it enables real-time, camera-free shape sensing.
Analysing past patterns in machine data within industrial settings can enhance predictive maintenance and improve overall equipment effectiveness (OEE). This study presents a lightweight late-fusion soft sensor model that benchmarks a deep learning forecasting approach using real manufacturing data. It compares various popular techniques and combines multiple models to increase accuracy. This method provides more reliable predictions, enhances performance, minimizes downtime, and boosts efficiency in complex, real-world factory environments.
Foreground Attention Loss and Attention-Guided Convolution for Remote Sensing Object Detection
Author: Tian Hongxian, Li Yunyue, Liu Jueting, Wang Zehua, Wang Zilong, Xu Tingting, Xu Zishan, Yang Wei, chen wei
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: Zilong Wang (Author)
Object interpretation in satellite or aerial images is challenging due to small size, complex backgrounds, indistinguishable features, and varying orientations. Enhancing remote-sensing images can significantly improve their applications in fields like agriculture, urban planning, military surveillance, and disaster response. This paper introduces a lightweight discover→focus approach: Foreground Attention Loss learns an object-density attention map, and Attention-Guided Convolution shifts sampling toward foreground without extra offset branches, substantially improving accuracy with minimal overhead.
Anomaly Detection in Industrial Networks: Current State, Classification, and Key Challenges
Author: Kuchar Karel, Fujdiak Radek
Published in: IEEE Sensors Journal (Volume: 25, Issue: 3, February 2025)
Summary Contributed by: Fynn Foerger (Author)
Magnetic field measurements play a crucial role in medical diagnostics, industrial monitoring, electronic testing, and scientific research. However, traditional volumetric magnetic field measurements can be challenging and time-consuming. This research introduces a 3D magnetic field camera concept featuring surface-mounted sensors that captures detailed magnetic field patterns in under one second. This device can reconstruct magnetic fields at 10 Hz using physics-based methods, opening new opportunities in field validation and generation.
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: Fynn Foerger (Author)
Magnetic field measurements play a crucial role in medical diagnostics, industrial monitoring, electronic testing, and scientific research. However, traditional volumetric magnetic field measurements can be challenging and time-consuming. This research introduces a 3D magnetic field camera concept featuring surface-mounted sensors that captures detailed magnetic field patterns in under one second. This device can reconstruct magnetic fields at 10 Hz using physics-based methods, opening new opportunities in field validation and generation.
Semiquantitative Lateral Flow Assay Using a Handheld Device With a Transmissive Readout
Author: Nguyen Daniel, Aikio Sanna, Hiltunen Jussi, Jaaskelainen Iida, Le Duc, Liedert Christina, Mursula Anu
Published in: IEEE Sensors Journal (Volume: 26, Issue: 2, January 2026)
Summary Contributed by: Daniel Nguyen (Author)
Lateral flow assay (LFA), commonly used in COVID or pregnancy test kits, is usually qualitative, giving either a positive or negative result. This research introduces a handheld optoelectronic reader that shines light through the test strip instead of reflecting it off the surface. It boosts the detectable signal by 82% and enables concentration estimates with less than 3% variability, providing fast, reliable point-of-care testing suitable for various health monitoring applications.
A High-Sensitivity Microwave Sensor Based on Unequal-Width Three-Coupled Lines for Characterizing the Permittivity of Liquids
Author: Su Guo-dong, Le Yi, Li Yu-He, Liu Jun, Sun Lingling
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: Yu-He Li (Author)
Microwave sensors are essential in various areas, including chemical analysis, quality control, biosensing, and industrial applications. However, in sensors, achieving both a wide detection range of permittivity and high sensitivity is challenging. This study introduces a sensor featuring an unequal-width three-coupled-line configuration. The enhanced fringe field, resulting from the width disparity between the central and side lines, significantly improves sensitivity and provides exceptional resolution, making it an efficient solution for liquid analysis.
A Stretchable, Skin-Adhesive FBG-Based Wearable Sensor for Human Health Monitoring: From Vital Signs to Biomechanical Activity
Author: Schena Emiliano, Condo Ilaria, Giannitelli Sara Maria, Lo Presti Daniela, Trombetta Marcella
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: Schena Emiliano (Author)
Measurement accuracy and user comfort are key requirements in wearable systems. This research introduces a device that integrates a fiber Bragg grating (FBG) sensor within a flexible silicone bilayer matrix designed to adhere directly to the human skin and conform to its shape. The effective deformation transfer between the skin and the sensor makes the proposed system a promising solution for healthcare monitoring while ensuring user comfort.
High-Performance THz Nanometamaterial Absorber With Negative Permittivity (0.1–10 THz) for Early Cancer Detection via Circulating Exosomes
Author: Mohammad Alibakhshikenari, Hamza Musa, Islam Md. Shabiul, Islam Mohammad Tariqul, Farmani Ali, Iffat Naqvi Syeda, Koziel Slawomir, Lavadiya Sunil, Miah MD. Sipon, Panda Abinash, Parand Peiman, Sanches Bruno, Virdee Bal S., Ud Din Iftikhar
Published in: IEEE Sensors Journal (Volume: 25, Issue: 18, September 2025)
Summary Contributed by: Anupama
Circulating exosomes are nanoscale vesicles secreted by cells that carry early cancer biomarkers in the blood. This paper introduces a high-performance terahertz (THz) nanometamaterial absorber with negative permittivity across 0.1-10 THz for ultrasensitive detection of cancerous circulating exosomes, distinguishing them from normal ones. The label-free approach demonstrates noninvasive, fast cancer detection with high sensitivity and precision, setting a new benchmark in biomedical sensing that could transform global oncology outcomes.
The recent COVID outbreaks highlighted the need for breathing rate monitoring and increased the demand for hospitalized patients. Monitoring breathing rate is vital for diagnosing diseases and observing patients with pulmonary conditions. The pros and cons of different techniques are studied and categorized under contact and remote modes of respiratory monitoring systems. Various Radar-based methods found to be more suitable for respiration monitoring are discussed.
Radar detection of smaller targets requires lowering the radar cross-section and velocity thresholds. With it, an abundance of target signatures gets generated, making it necessary to classify only relevant targets. Micro-motions of targets are significant characteristics. Micro-Doppler signatures have emerged as an effective method of classifying such targets. The study presents a systematic review of various micro-Doppler-based radar target signature analysis and classification techniques.
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