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Sensors

Sensors is an international, peer-reviewed, open access journal on the science and technology of sensors.
Indexed in PubMed | Quartile Ranking JCR - Q2 (Instruments and Instrumentation | Chemistry, Analytical | Engineering, Electrical and Electronic)

All Articles (73,816)

Multi-object tracking faces persistent challenges from occlusions and truncations in monocular vision systems. While stereo vision provides depth information, existing approaches require computationally expensive dense matching or 3D reconstruction. This paper presents a real-time 2.5D stereo multi-object tracking framework combining lightweight stereo matching with resilient tracker management. The stereo matching module employs Direct Linear Transform-based triangulation using only bounding box coordinates, eliminating costly feature extraction while maintaining robust correspondence through geometric constraints. A dual-tracker architecture maintains independent trackers in both views, enabling re-identification when objects become occluded in one view but remain visible in the other. Experimental validation on a refrigerator monitoring dataset demonstrates that StereoSORT achieves a multiple object tracking accuracy (MOTA) of 0.932 and an identification F1 score (IDF1) of 0.823, substantially outperforming monocular trackers, including OC-SORT (IDF1: 0.765) and ByteTrack (IDF1: 0.609). The system achieves a 50.1 mm median depth error, comparable to commercial sensors, while maintaining 70 FPS on standard hardware. These results validate that geometric constraints alone enable robust stereo tracking without appearance features, offering a practical solution for resource-constrained environments where computational efficiency and tracking reliability are equally critical.

5 November 2025

Overall architecture of the proposed 2.5D multi-object tracking framework. The system consists of three sequential stages: (1) Object Detection from Stereo Image Pairs, where independent detection is performed on synchronized left (L) and right (R) images; (2) Stereo Matching for Optimal Object Pairing, which establishes correspondences between detected objects using Direct Linear Transform (DLT)-based triangulation and IoU validation (highlighted in yellow); and (3) Stereo Tracking with Temporal Data Association, which maintains object IDs across frames through tracker management (highlighted in yellow), motion estimation, and stereo track association map updates. The feedback loop from tracking predictions enhances detection and matching in subsequent frames.

As the number of natural and man-made catastrophes has increased in recent years, there has been an increasing need for quicker and more efficient disaster response. Information from traditional sources, such as radio, television, and websites, is sometimes incomplete or delayed. While mobile applications provide a means of enhancing real-time crisis communication, a secure mobile app-based solution has not been fully explored yet. In this paper, we propose a secure and scalable cross-layer disaster management system architecture. To validate the system performance, we developed a user-centred, scalable mobile application known as the disaster emergency events application (DEAPP) for real-time disaster reporting and visualization including disaster notifications and observing the affected areas on an interactive map. The solution connects a web-based backend, cloud database, and native Android mobile app via a cross-layer architecture. Role-based access control, HTTPS connection, and verified event publication all contribute to security. Moreover, Redis caching is employed to expedite data access in emergency situations. The need to verify publicly filed reports to prevent false alarms, safeguard real-time data transfer without slowing down the system, and create an intuitive user interface for individuals in high-stress circumstances are some of the issues that the project attempts to solve. The results obtained show that a mobile system that is secure, scalable, and easy to use can enhance catastrophe awareness and facilitate quicker emergency responses. For developers, researchers, and emergency organisations looking to leverage mobile technology for disaster preparedness, the findings provide helpful insights.

5 November 2025

Proposed disaster emergency management system architecture.

Dysphagia is commonly assessed with qualitative and image-based diagnostic tools, which are often costly, technically demanding, and limited in their ability to support individualized rehabilitation. Electroencephalography (EEG) has recently emerged as a quantitative, cost-effective, and accessible alternative to characterize sensorimotor activity during swallowing, though its potential in dysphagic populations has not been systematically explored. This study investigated neural dynamics in 50 post-stroke dysphagic patients, 32 post-stroke non-dysphagic controls, and 21 healthy adults performing a swallowing task. EEG recordings from primary motor regions (C3, Cz, C4) were analyzed using event-related spectral perturbation (ERSP) to quantify alpha (8–13 Hz) and beta (15–30 Hz) event-related desynchronization, alongside hemispheric lateralization indices. Group comparisons revealed significantly reduced beta desynchronization in both post-stroke groups compared to healthy participants, with additional alpha and beta deficits at C3 and Cz distinguishing dysphagic patients from non-dysphagic controls. Dysphagic patients further exhibited abnormal lateralization not observed in other groups. These findings identify distinct alterations in motor cortical dynamics and hemispheric balance in dysphagia, supporting EEG-derived biomarkers as promising tools for diagnosis and clinical follow-up. The accessibility of EEG reinforces its potential integration into routine workflows to enable objective and personalized management of post-stroke dysphagia.

5 November 2025

Photo of the recording setup and electrode placement.

This paper presents the design and analysis of a compact microstrip fixed-frequency double-inductive-coupled filter with selected band suppression. The filter can be used as an input filter in wireless IoT sensors. The proposed structure has reduced dimensions and improved out-of-band attenuation, achieved through the use of radial stub lines as elements of the resonators. These lines act as capacitors within the passband, while in a selected sub-band as series resonant circuits, effectively enhancing attenuation. The frequency response of the filter is shaped using two transmission zeros: the first one improves the steepness of the frequency response at the upper transition band, while the second increases attenuation in a chosen sub-band of the stopband. An analysis of the filter is presented, and key equations describing its properties are derived. An example filter for the frequency band 2.391–2.525 GHz, with additional suppression introduced in the U-NII 5 GHz band was designed, manufactured and examined. The insertion loss achieved by the proposed filter is lower than 1.6 dB, its attenuation across the whole stopband exceeds 30 dB and reaches over 40 dB in the 4.7–5.9 GHz frequency band.

5 November 2025

The layout of the compact microstrip fixed-frequency double-coupled filter with selected band suppression.

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Advanced Energy Harvesting Technology

Volume II
Editors: Mengying Xie, Kean C. Aw, Junlei Wang, Hailing Fu, Wee Chee Gan
Advanced Energy Harvesting Technology
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Advanced Energy Harvesting Technology

Volume I
Editors: Mengying Xie, Kean C. Aw, Junlei Wang, Hailing Fu, Wee Chee Gan

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Sensors - ISSN 1424-8220Creative Common CC BY license