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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 (August 2026)
Seamless Mobile Indoor Navigation With VLP-PDR
Author: Alcazar Fernandez Aitor, De-La-Llana-Calvo Alvaro, Gardel Alfredo, Gil Vera Ruben, Lazaro-Galilea Jose Luis, Perez-Navarro Antoni
Published in: IEEE Sensors Journal (Volume: 24, Issue: 7, April 2024)
An indoor positioning system (IPS) is for seamless, effortless indoor exploration where GPS fails. This smartphone application combines Visible Light Positioning (VLP) and Pedestrian Dead Reckoning (PDR) to provide accurate indoor guidance. Using the phone's camera and sensors, the system corrects inertial drift through information embedded in lighting. This hybrid approach drastically reduces localization error, offering a reliable, low-cost solution for enhancing the indoor navigation experience without requiring internet connectivity.
Broadband Electrical Matching Network for PZN-PT Single Crystal Underwater Acoustic Transducer
Author: Mao Zhineng, Zhang Shuangjie, Jia Yudong, Shi Huaduo, Zang Liufei, Zhang Jin
Published in: IEEE Sensors Journal (Volume: 26, Issue: 5, March 2026)
Summary Contributed by: Zhineng Mao (Author)
Broadband signals are highly desirable in underwater applications because they enhance information transmission efficiency, improve resolution, and provide better anti-reverberation capabilities. However, conventional acoustic and electrical matching methods struggle to achieve effective broadband impedance matching and power factor optimization simultaneously. To overcome this limitation, this paper introduces a compact transformer-LC electrical matching network for single-crystal transducers, which effectively expands the bandwidth, enhances transmission performance, and improves the power factor.
Bio-Immobilization of Plasma-Polymerized Heptylamine and Glutaraldehyde for K-SPR Glucose Biosensing
Author: Menon P. Susthitha, Abdullah Noraidatulakma, Gafor Abdul Halim Abdul, Hamzah Azrul Azlan, Jamil Nur Akmar, Kamaruzaman Lydia, Karim Ilmi Munirah, Kassim Syara, Mohamed Mohd Ambri, Mustaffa Siti Nasuha, Rahman Azaham Abdul, Siow Kim Shyong, Yasin Muhammad Feidhul Hakim Fatah, Yee Lee Pey
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: P. Susthitha Menon (Author)
Biosensors have revolutionized biomedical diagnostics by facilitating rapid, real-time, and point-of-care (POC) testing. This paper presents a label-free optical biosensor for glucose detection based on a Kretschmann-configured surface plasmon resonance (K-SPR) system. The sensor enables stable enzyme immobilization and reliable glucose detection using plasma-polymerized heptylamine and glutaraldehyde crosslinking. It demonstrates high sensitivity and can detect glucose levels within both normal and diabetic ranges, highlighting its potential for improved biomedical diagnostics.
Design and Simulation of an MEMS Capacitive Pressure Sensor With Multiple Annular Cavities
Author: Li Xiaohao, Bao Mingzheng, Liu Kun, Xie Yuanhua, Zhou Yang
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: Xiaohao Li (Author)
Advancements in micromachining technology have intensified research on microelectromechanical system (MEMS)-based pressure sensors. This research proposes a novel MEMS capacitive pressure sensor with multiple annular cavities to improve the tradeoff between sensitivity and linearity. The segmented cavity structure enables uniform diaphragm deformation and consistent capacitance variation. Simulation shows it achieves low nonlinearity with high sensitivity, along with superior overload capability, making it suitable for reliable pressure measurement in demanding environments.
An EMAT Based on a Novel Extrusion Static Magnetizer for Thickness Measurement of Steel Plates
Author: Xu Jiang, Hu Yulin, Zheng Zheng
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: Jiang Xu (Author)
Existing symmetrical repulsive magnetizers face challenges due to a divergent upper magnetic field and low utilization efficiency. This paper introduces a novel extrusion static magnetizer that enhances magnetic field efficiency via a central magnet-silicon steel composite structure. This innovation significantly improves the signal amplitude and signal-to-noise ratio (SNR) of electromagnetic acoustic transducer (EMAT) thickness measurements, providing a more stable and reliable solution for detecting the thickness of steel plates.
Compound Fault Diagnosis of Rolling Bearings Based on the PSD-Guided Cyclostationarity Feature Mode Decomposition
Author: Wang Feng, Huang Yufeng, Sheng Zilin, Shi Yifei
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: Feng Wang (Author)
Identifying compound faults in rolling bearings can be challenging due to overlapping noise and irrelevant interference. This study introduces a novel diagnostic method called Power Spectral Density (PSD)-Guided Cyclostationarity Feature Mode Decomposition (PCFMD). This approach uses power spectral density to guide adaptive filtering design, offering a more accurate and efficient method for identifying overlapping faults, supporting predictive maintenance, monitoring critical machinery, and helping minimize unexpected failures in industrial systems.
A Lightweight Perception Enhancement Network for Real-Time and Accurate Internal Surface Defect Detection of Cold-Drawn Steel Pipes
Author: Song Kechen, Yan Yunhui, Chen Hongshu, Tan You, Zhang Yu
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: You Tan (Author)
Detecting internal surface defects in cold-drawn steel pipes can be challenging due to limited access and complex lighting conditions. This study introduces a Pipe Internal Surface Detection (PISD) robot equipped with vision sensors and a lightweight perception enhancement network (LPENet) for accurate detection in industrial settings. This approach enables faster, more reliable quality inspection while keeping computational costs low, making it practical for industrial inspections and deployment in demanding manufacturing environments.
Hierarchical Labeling and Layered-Training-Based CNN: An Application in Bearing Fault Diagnosis
Author: Liu Jiangchao, Ruan Diwang, Cui Yi, Li Zhaorong, Qian Yiliang, Yan Jianping
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: Jiangchao Liu (Author)
Industrial systems largely depend on rolling bearings for smooth operation. However, diagnosing fault reliably can be challenging due to the limited interpretability of deep learning models. This paper proposes a hierarchical labeling and progressive freezing mechanism, combined with a layered training strategy, to address this challenge. This method improves diagnostic interpretability, accuracy, and reliability, making it suitable for diagnosing complex fault types in industrial applications.
Author: Javier Gonzalez, Moreno Juan R., Peale Robert
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: F. J. González (Author)
Terahertz (THz) waves are a bridge between microwaves and infrared light. This study presents a new terahertz detector fabricated from a miniature bowtie antenna connected to a silicon-platinum thermoelectric junction. This detector operates at room temperature without requiring an external power supply and achieves state-of-the-art sensitivity across the 3.8–8 THz band. The innovation paves the way for affordable, compact THz sensing applications in security screening, biomedical imaging, and wireless communications.
Published in: IEEE Sensors Journal (Volume: 26, Issue: 1, January 2026)
Summary Contributed by: Rachel F. Bellisle (Author)
Wearable strain sensors embedded into clothing can effectively track human movement. This paper proposes a novel knot-based strain sensor integrated into the ankle of an astronaut's full-body spacesuit aboard the International Space Station. By employing machine learning techniques alongside the ankle sensor system, the researchers successfully classified non-local body movements while minimizing hysteresis. This innovation shows potential for various applications, including space missions, sports tracking, and home-based rehabilitation.
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.
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.
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