This work presents a compact constant-current (CC)-based relaxation converter where the patch sensor produces a linear sensing voltage, enabling controlled and linear frequency variation through direct resistance-to-voltage mapping. Implemented in a 65 nm CMOS process, the design occupies 0.00897 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> and consumes optimum power of 3.24 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> W at a 0.9 V supply, while operating reliably across 0.8-1.0 V. The sensitivity is 2.84 MHz/V at 0.9 V, decreasing by about 10% at 0.8 V and increasing by roughly 26 % at 1.0 V. Overall, the proposed converter delivers 5.5× higher sensitivity and improved power efficiency compared to conventional relaxation-oscillator interfaces. The measured oscillation range of 1.4-4.5 MHz with non-linearity error <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula>0.36 % which supports high-resolution, tunable operation suitable for biomedical and wearable sensing applications.

We present a comprehensive experimental validation of a latent heat based phase transition sensor employing Fiber Bragg Gratings (FBGs) for sub-millimeter ice detection. While the sensing principle itself leverages the well-known exothermic signature of water-to-ice freezing, our work demonstrates its performance across a range of practical conditions. Using a commercial refrigerator (Panasonic NR-A195L/2014, –18°C setpoint, ±0.5°C stability) and a Micron Optics SM-130 interrogator (1 pm resolution, 1 kHz sampling), we evaluated 3D printed water column holders with depths from 5 mm down to 500 μm. Through targeted signal processing, we successfully isolated both the icing and deicing spectral signatures. The sensor reliably produced the characteristic icing spectral signature for columns ≥ 500 μm, establishing this as the system's minimum detectable thickness. Five consecutive freeze and thaw cycles confirmed negligible drift and stable onset amplitudes. Crucially, we introduce a fog-immunity protocol using an ultrasonic mist maker to show that sub threshold frost does not trigger false positives. These novel experimental structures and validation procedures substantiate the FBG phase-transition sensor's robustness and specificity, paving the way for reliable real-time icing surveillance in aerospace, renewable-energy, and power-transmission applications.

Parkinson's disease (PD), a prevalent neurodegenerative disorder that is frequently misdiagnosed with similar symptoms. This study investigates the feasibility of a wrist-worn wearable antenna for identifying actual PD from false PD (essential and physiological tremors). Antenna sensitivity was measured with the biological tis sues. Reflection properties were measured while patients performed four motor activities. To overcome class imbalance, a GAN was used for data augmentation. Signals were normalized and converted into 2-D representations using the Gaussian-windowed Gabor wavelet transform (Gw-GWT) and the short-time Fourier synchrosqueezed transform (ST-FSST). A deep-learning model (DLM) shallow hybrid CNN-transformer network (SHCT-Net) is utilized for obtaining local and global features, with benchmark DLMs serving as comparisons. The original and enhanced datasets were trained and evaluated using 5-fold subject-wise cross-validation (5-FSWCV). The suggested model, with only 0.7 million parameters, obtained 93.22% precision, 91.12% accuracy and 91.12% F1-score.

Partial Discharge (PD) activity is a pre-cursor for insulation degradation which may eventually lead to catastrophic failure of power apparatus with severe social and economic consequences. Electromagnetic emissions gathered over ultra-high frequency (UHF) range (0.5 to 3 GHz) during PD events is widely investigated for health monitoring of high voltage (HV) apparatus. However, large bandwidth of PD induced UHF signals requires high speed digitizer and large storage space which have restricted field deployment of UHF technique for online PD detection. In this work, we present UHF based PD pulse detection framework for remote monitoring and logging of faults in dielectric insulation using ThingSpeak<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> cloud. PD events captured by a calibrated UHF sensor from HV PD test cell using the proposed online PD detection framework indicated throughput as high as 83.8% compared with reference UHF sensor measurements gathered by a digital storage oscilloscope. PD detection throughput was observed to be limited by the envelope detection threshold.<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in</sub> (10 mV) and cumulative propagation delay <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pd</sub> (∼1 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μs</i>) in the architecture implementation. The proposed UHF sensing technique is low cost compared to conventional approaches, detects a wide range of insulation defects, and scalable for smart monitoring of HV assets using Internet of Things (IoT).

This letter reports the first characterization of InAlN/GaN-on-Si solid-state detectors under ultra-high dose-per-pulse (UH-DPP) electron irradiation for FLASH radiotherapy applications. Unpassivated Ni/Au metal-semiconductor-metal (MSM) and two-dimensional electron gas (2DEG) interdigital transducer (IDT) detectors exhibited the best transient performance, with the 2DEG IDT resolving individual pulses as short as 3 μs rise and 325 μs fall times and achieving a signal-to-noise ratio (SNR) exceeding 220 under 2 Gy pulses. Across a dose-per-pulse (DPP) range of 0.05-0.5 Gy, the 2DEG IDT detector demonstrated consistent, dose-dependent voltage responses with sensitivities of 0.5-0.9 V/Gy and a normalized sensitivity of 48.75 nC/Gy/mm2, one to two orders of magnitude higher than reported SiC and diamond detectors. These results establish InAlN/GaN detectors as promising candidates for compact, high-speed, and radiation-tolerant dosimetry systems capable of real-time single-pulse monitoring in clinical FLASH radiotherapy.

[trailer] Sensors play a fundamental role in sensing the environment for autonomous vehicle (AV) perception, providing accurate and reliable data essential for understanding and navigating the surroundings. LiDAR sensors are widely used for their ability to generate detailed 3D point cloud data of the surroundings. Bird's-Eye View (BEV) detection utilizes this point cloud data to identify objects such as cars and cyclists from a top-down perspective. This LiDAR sensor-based perception approach is essential for understanding complex environments and ensuring safe navigation in real-time driving scenarios. This letter presents DSFNet, a compact LiDAR-only network for BEV perception. The model integrates an efficient pillar-based encoder with a proposed dual-scale fusion (DSF) backbone, designed to mitigate performance and complexity issues associated with LiDAR sensors. The backbone reduces parameter count by approximately 50 percent compared to standard architectures while maintaining competitive detection accuracy. By capturing both local detail and global context, DSFNet enhances feature representation for sparse and irregular LiDAR data. Evaluations on the official KITTI BEV benchmark demonstrate strong performance in car and cyclist detection, highlighting suitability for real-time sensor-driven applications.

In this work, we introduce a new broadband magnetic probe characterized by the dual-component measurement. A new detection structure, which is composed of a pair of horizontal grounded loops and a pair of vertical differential loops, is introduced into the proposed magnetic probe to achieve two orthogonal magnetic field components. Among them, the horizontal grounded loops are utilized to measure a vertical magnetic field component (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H_x</i>), while the vertical differential loops are used to test a horizontal magnetic field component (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H_y</i>). Moreover, in order to reduce the probe's profile, the ground planes and horizontal ground planes are together printed on the outer layers. A near-field test system with a standard 50 Ω microstrip line is applied to characterize the manufactured magnetic probe. The measured results demonstrate that the transmission coefficients of the probe exceed -46.7 dB in the x-direction across the 3–12 GHz band and are greater than -50 dB in the y-direction from 3.1 to 12 GHz. Therefore, the proposed magnetic probe not only has a wide working bandwidth but also enables dual-component magnetic-field measurement.

Hand gesture recognition systems often struggles when a gesture is occluded by the hand itself or by the other hand. To address this problem, multiview gestures may be used while training a gesture recognition system. Unfortunately, there are very few datasets available that contains different views of every single gesture. In this paper, we propose a method to handle occlusions in single-view sign gestures by generating multiple views from a single-view gesture using conditional multiview gesture synthesis. The generated views help to solve the occlusion problem, thereby enhancing the recognition performance. Additionally, we introduce a vision-based Multiview Hand Gesture Recognition (MHGR) framework that utilizes the generated multiview gestures for gesture recognition. Experiments conducted on the HGM-4 dataset demonstrate that the generated images are of high quality, photo-realistic, and significantly improve the recognition accuracy compared to some other existing methods.