Monitoring physiological activity and sleep states in real time is challenging, particularly for continuous assessment in daily life settings using wearable IoMT devices. We developed a 24 h wearable system that integrates electrocardiogram (ECG) electrodes for heart rate measurement and a glove-mounted flex sensor for motor responses, connected through an Internet of Medical Things (IoMT) platform. Flex signals were combined using principal component analysis (PCA) to generate a single kinematic channel, then standardized with heart rate. Time-series windows were embedded using TS2Vec and clustered with DBSCAN, while t-SNE was applied only for visualization. The framework identified four physiologically coherent states: (i) nocturnal sleep with the lowest heart rate and minimal motion, (ii) evening pre-sleep with low movement and moderately higher heart rate, (iii) daytime activity with variable motion and mid-range heart rate, and (iv) late-day high-intensity activity with the highest heart rate and increased motor responses. A few outliers were observed during transient body movements or sensor readjustments, which were identified and excluded during preprocessing to ensure stable clustering results. Across 24 h, heart rate ranged from 52 to 96 bpm (mean 77.4), while flexion spanned 0 to 165° (mean 52.5°), showing alignment between movement intensity and cardiac response. This integrated sensing and analytics pipeline provides an interpretable, subject-specific state map that enables continuous remote monitoring of physiological activity and sleep patterns.

This paper presents a novel methodology for the design and calibration of ultra-compact digital-to-analog converters (DACs), integrating architectural redundancy and a digital calibration algorithm. The proposed calibration approach generates pre-distortion codes that correct both positive and negative nonlinearity errors, even in designs with severe mismatch or relaxed layout constraints. This enables the use of aggressively scaled devices while maintaining high linearity and spectral fidelity. The algorithm is architecture-agnostic and compatible with resistor-string, current-steering, and hybrid DAC structures. It operates with minimal memory, low latency, and supports both foreground and background calibration modes. The method is validated through simulation and silicon measurement of three 14-bit DAC architectures fabricated in TSMC 180 nm CMOS. Post-calibration results demonstrate linearity within ±0.5–1.2 LSB, ENOB up to 13.8 bits, and significant improvements in SNR, SFDR, and THD. The compact layouts—occupying as little as 0.0169 mm2—highlight the scalability of the proposed method for applications such as analog AI accelerators and high-density mixed-signal SoCs.

An electrocardiogram (ECG) is a vital diagnostic tool that provides crucial insights into the heart rate, cardiac positioning, origin of electrical potentials, propagation of depolarization waves, and the identification of rhythm and conduction irregularities. Analysis of ECG is essential, especially during pregnancy, where monitoring fetal health is critical. Fetal electrocardiography (fECG) has emerged as a significant modality for evaluating the developmental status and well-being of the fetal heart throughout gestation, facilitating early detection of congenital heart diseases (CHDs) and other cardiac abnormalities. Typically, fECG signals are acquired non-invasively through electrodes placed on the maternal abdomen, which reduces risk and enhances user convenience. However, these signals are often contaminated via various sources, including maternal electrocardiogram (mECG), electromagnetic interference from power lines, baseline drift, motion artifacts, uterine contractions, and high-frequency noise. Such disturbances impair signal fidelity and threaten diagnostic accuracy. This scoping review adhering to PRISMA-ScR guidelines aims to highlight the methods for signal acquisition, existing databases for validation, and a range of algorithms proposed by researchers for improving the quality of fECG. A comprehensive examination of 157,000 uniquely identified publications from Google Scholar, PubMed, and Web of Science have resulted in the selection of 6210 records through a systematic screening of titles, abstracts, and keywords. Subsequently, 141 full-text articles were considered eligible for inclusion in this study (from 1950 to 2026). By critically evaluating established techniques in the current literature, a strategy is proposed for analyzing fECG and calculating heart rate variability (HRV) for identifying fetal heart-related abnormalities. Advances in these methodologies could significantly aid in the diagnosis of fetal heart diseases, assisting timely clinical interventions and prevention.

Myocardial infarction (MI) remains one of the most critical causes of death worldwide, demanding predictive models that balance accuracy with clinical interpretability. This study introduces an explainable artificial intelligence (XAI) framework that integrates least absolute shrinkage and selection operator (LASSO) regression for feature selection, logistic regression for prediction, and Shapley additive explanations (SHAP) for interpretability. Using a dataset of 918 patients and 12 signal-derived clinical variables, the model achieved an accuracy of 87.7%, a recall of 0.87, and an F1 score of 0.89, confirming its robust performance. The key risk factors identified were age, fasting blood sugar, ST depression, flat ST slope, and exercise-induced angina, while the maximum heart rate and upward ST slope served as protective factors. Comparative analyses showed that the SHAP and p-value methods largely aligned, consistently highlighting ST_Slope_Flat and ExerciseAngina_Y, though discrepancies emerged for ST_Slope_Up, which showed limited statistical significance but high SHAP contribution. By combining predictive strength with transparent interpretation, this study addresses the black-box limitations of conventional models and offers actionable insights for clinicians. The findings highlight the potential of signal-driven XAI approaches to improve early detection and patient-centered prevention of MI. Future work should validate these models on larger and more diverse datasets to enhance generalizability and clinical adoption.

The early detection of smoke signals due to wildfires is vital in containing the extent of loss and reducing response time, particularly in inaccessible or forested areas. For lightweight object detection, this study contrasts the YOLOv9-tiny, YOLOv10-nano, YOLOv11-nano, YOLOv12-nano, and YOLOv13-nano algorithms in determining wildfire smoke at extended ranges. We present a robustness- and generalization-checking five-fold cross-validation. This study is also the first of its kind to train and publicly benchmark YOLOv10-nano up to YOLOv13-nano on the given dataset. We investigate and compare the detection performance against the standard performance metrics of precision, recall, F1-score, and mAP50, as well as the performance metrics regarding computational efficiency, including the training and testing time. Our results offer practical implications regarding the trade-off between pre-processing methods and model architectures for smoke detection when applied in real time on ground-based cameras installed on mountains and other high-risk fire locations. The investigation presented in this work provides a model in which implementations of lightweight deep learning models for wildfire early-warning systems can be achieved.

To address the demand for lightweight, high-precision, real-time, and low-computation detection of targets with limited samples—such as laboratory instruments in portable AR devices—this paper proposes a small dataset object detection algorithm based on a hierarchically deployed attention mechanism. The algorithm adopts Rep-YOLOv8 as its backbone. First, an ECA channel attention mechanism is incorporated into the backbone network to extract image features and adaptively adjust channel weights, improving performance with only a minor increase in parameters. Second, a CBAM-spatial module is integrated to enhance region-specific features for small dataset objects, highlighting target characteristics and suppressing irrelevant background noise. Then, in the neck network, the SE attention module is replaced with an eSE attention module to prevent channel information loss caused by dimensional changes. Experiments conducted on both open-source and self-constructed small datasets show that the proposed hierarchical Rep-YOLOv8 model effectively meets the requirements of lightweight design, real-time processing, high accuracy, and low computational cost. On the self-built small dataset, the model achieves a mAP@0.5 of 0.971 across 17 categories, outperforming the baseline Rep-YOLOv8 (0.871) by 11.5%, demonstrating effective recognition and segmentation capability for small dataset objects.

Ham radio has long been a foundational area of practice in electrical engineering. Advances in signal processing, particularly the advent of software-defined radio (SDR), have revolutionized the field, offering new possibilities and modes of operation. This paper introduces a system designed for long-term collection of shortwave propagation data, leveraging SDR technology. It also presents the analysis of the collected data, demonstrating the system’s potential for advancing research in radio wave propagation.

Human action recognition (HAR) based on WiFi channel state information (CSI) has attracted growing attention due to its contactless, privacy-preserving, and cost-effective nature. Recent studies have reported promising results by leveraging deep learning and image-based representations of CSI. However, methodological flaws in experimental protocols, particularly improper dataset partitioning, can lead to data leakage and significantly overestimate model performance. In this paper, we critically analyze a recently published WiFi-CSI-based HAR approach that converts CSI measurements into images and applies deep learning for classification. We show that the original evaluation relied on random data splitting without subject separation, causing substantial data leakage and inflated results. To address this, we reimplemented the method using subject-independent partitioning, which provides a realistic assessment of generalization ability. Furthermore, we conduct a quantitative study of post-training quantization under both correct and flawed partitioning strategies, revealing that methodological errors can conceal the true performance degradation of compressed models. Our findings demonstrate that evaluation protocols strongly influence reported outcomes, not only for baseline models but also for engineering decisions regarding model optimization and deployment. Based on these insights, we provide guidelines for designing robust experimental protocols in WiFi-CSI-based HAR to ensure methodological integrity and reproducibility.

Acoustic scattering is a highly effective tool for non-destructive control and structural analysis. In many real-world applications, understanding acoustic scattering is essential for accurately detecting and characterizing defects, assessing material properties, and evaluating structural integrity without causing damage. One of the most critical aspects of characterizing targets—such as plates, cylinders, and tubes immersed in water—is the analysis of the phase and group velocities of antisymmetric circumferential waves (A1). Phase velocity helps identify and characterize wave modes, while group velocity allows for tracking energy, detecting, and locating anomalies. Together, they are essential for monitoring and diagnosing cylindrical shells. This research employs a Sugeno fuzzy inference system (SFIS) combined with a Fuzzy Subtractive Clustering (FSC) identification technique to predict the velocities of antisymmetric (A1) circumferential signals propagating around an infinitely long cylindrical shell of different b/a radius ratios, where a is the outer radius, and b is the inner radius. These circumferential waves are generated when the shell is excited perpendicularly to its axis by a plane wave. Phase and group velocities are determined by using resonance eigenmode theory, and these results are used as training and testing data for the fuzzy model. The proposed approach demonstrates high accuracy in modeling and predicting the behavior of these circumferential waves. The fuzzy model’s predictions show excellent agreement with the theoretical results, as confirmed by multiple error metrics, including the Mean Absolute Error (MAE), Standard Error (SE), and Mean Relative Error (MRE).

This study presents a method for deriving closed-form solutions for Lagrange multipliers in worst-case performance optimization (WCPO) beamforming. By approximating the array-received signal autocorrelation matrix as a rank-1 Hermitian matrix using the low-rank approximation theory, analytical expressions for the Lagrange multipliers are derived. The method was first developed for a single plane wave scenario and then generalized to multiplane wave cases with an autocorrelation matrix rank of N. Simulations demonstrate that the proposed Lagrange multiplier formula exhibits a performance comparable to that of the second-order cone programming (SOCP) method in terms of signal-to-interference-plus-noise ratio (SINR) and direction-of-arrival (DOA) estimation accuracy, while offering a significant reduction in computational complexity. The proposed method requires three orders of magnitude less computation time than the SOCP and has a computational efficiency similar to that of the diagonal loading (DL) technique, outperforming DL in SINR and DOA estimations. Fourier amplitude spectrum analysis revealed that the beamforming filters obtained using the proposed method and the SOCP shared frequency distribution structures similar to the ideal optimal beamformer (MVDR), whereas the DL method exhibited distinct characteristics. The proposed analytical expressions for the Lagrange multipliers provide a valuable tool for implementing robust and real-time adaptive beamforming for practical applications.