Signals doi: 10.3390/signals7040070

Authors: Theodoros Saganas Spyridon Votis Fotios Kantas Foivos S. Kanellos Georgios Rigas Andreas P. Katsenos Yannis V. Simos Lampros Lakkas Georgios S. Markopoulos Vasiliki Kostadima Spyridon Konitsiotis Dimitrios Peschos Konstantinos I. Tsamis

Spasticity is a common and disabling consequence of upper motor neuron lesions, and its follow-up relies largely on clinical scales that may not fully reflect daily-life mobility. This prospective single-arm exploratory pilot study examined wearable-derived daily-life mobility metrics around a scheduled botulinum neurotoxin type A (BoNT-A) injection cycle and assessed their associations with established clinical measures. Thirteen patients with focal spasticity secondary to stroke (n = 11) or multiple sclerosis (n = 2) underwent baseline and 6-week follow-up assessment using the Modified Ashworth Scale (MAS), Motricity Index (MI), the Timed Up and Go test (TUG) and a multi-sensor wearable monitoring system. Device-derived outcomes included gait impairment, gait speed, stride length, angular velocity, and lack of movement. Clinical scales generally changed in the expected post-treatment direction, with reduced MAS scores and descriptive or directional improvements in MI scores and TUG performance. Wearable metrics captured descriptive changes in daily mobility, notably a reduction in lack of movement. Furthermore, in exploratory pooled analyses, selected gait-related metrics tended to be associated with TUG performance and lower-extremity MAS. These findings highlight the potential of wearable telemonitoring to complement conventional clinical scales by providing objective, real-world data, supporting its further validation as a tool for longitudinal spasticity management and neurorehabilitation. These device-derived metrics should be interpreted as mobility-related digital biomarkers rather than direct surrogate measures of spasticity.

Signals doi: 10.3390/signals7040069

Authors: Marcin Kołodziej Andrzej Majkowski Tomasz Rywik

This study compares the effectiveness of two approaches to speech emotion recognition for three affective states in Polish: sad, neutral, and happy. Both a set of acoustic features—capturing prosodic, phonatory, temporal, spectral, and cepstral properties—and representations learned by self-supervised models (wav2vec 2.0 and WavLM) were analyzed. Experiments were conducted on the nEMO corpus, comprising 2327 recordings from nine speakers, using a rigorous leave-one-subject-out protocol to evaluate cross-speaker generalization. In the feature-based approach, 107 acoustic features were used, and classification was performed with logistic regression and, additionally, SVM variants. In the deep learning approach, the wav2vec2-base and WavLM-base models were fine-tuned for the three-class task. The best results were achieved by the self-supervised models: WavLM reached a global balanced accuracy of 0.727 and a macro-F1 score of 0.710, while wav2vec 2.0 achieved 0.722 and 0.695, respectively. Both outperformed the feature-based approach (BAcc = 0.627, macro-F1 = 0.584). Confusion matrix analysis showed that the greatest difficulty lies in distinguishing the neutral class from the sad and happy classes, whereas sad and happy classes are more clearly separable. Feature utility analysis (SFS under the LOSO protocol) indicated the significant role of cepstral features (MFCCs and their derivatives), complemented by selected prosodic and temporal features. An additional comparison of SVM classifiers suggested that the main limitation of this approach lies in the signal representation itself rather than solely in the choice of classifier. Explainability analyses of the deep models, using layer-wise probing and integrated gradients, showed that affective information is best represented in intermediate layers, and that model decisions rely on locally salient segments of the signal. Furthermore, a speaker adaptation experiment demonstrated that personalization significantly improves classification performance, highlighting the potential of such methods for long-term monitoring of affective expression changes in the same individual.

Signals doi: 10.3390/signals7040068

Authors: Apsorn Sattayakhom Kosin Kalarat Waluka Amaek Pavarud Puangsri Matina Ngodngamthaweesuk Phanit Koomhin

Arousal is essential for cognitive awareness, and it typically declines during prolonged tasks. Currently, mobile devices are frequently used during breaks to relax and counteract this decline. However, their impact on neurophysiological recovery remains poorly understood. Therefore, this study compared the effects of traditional quiet rest versus a mobile task break on cortical arousal using spectral analysis of electroencephalography (EEG) signals in twenty healthy young females (20–25 years). Raw EEG data were transformed using Fast Fourier Transform (FFT) to determine power spectral densities, with a state of physiological underarousal first induced via a prolonged eyes-closed condition. Results revealed that this state was characterized by reduced alpha/beta power and delta/theta synchronization starting at the 4th minute. Although traditional quiet rest suppressed delta/theta synchronization, it failed to sustain cortical arousal, with alpha and beta powers declining by the 8th minute. In contrast, passive social media browsing acted as a potent neurocognitive stimulant, not only sustaining arousal but markedly increasing high-frequency beta power by the 16th minute. Furthermore, preliminary network-level connectivity analysis using Phase Locking Value (PLV) revealed that mobile tasks induced widespread beta-band synchronization across frontal-midline regions, suggesting enhanced functional coupling within the executive control network. In conclusion, in healthy young females, while mobile tasks strategically counteract low arousal, they fail to facilitate the neurophysiological disengagement necessary for true recovery. These findings underscore the importance of digital hygiene, highlighting a distinction between alertness-boosting activities and recovery-focused rest. The results suggest that mobile tasks may create a subjective perception of rest despite objective signs of sustained cortical activation, implying that such activities may not facilitate genuine neurophysiological recovery within the context of short-term neurophysiological modulation.

Signals doi: 10.3390/signals7040067

Authors: Myoungbeom Chung

Playground safety for preschool and early elementary school children has become an increasingly important issue for families and local communities. In semi-open playground environments, direct supervision by caregivers is often difficult, while conventional positioning approaches may be costly, infrastructure-dependent, or unreliable due to frequent signal obstructions. This study presents an integrated low-cost monitoring system that combines commercial BLE beacons, a single smart device installed in the playground, a push-notification server, and a caregiver smartphone application to detect children’s entrance and exit events and visualize their playground usage logs. Rather than proposing a new localization algorithm, the main contribution of this work is the practical system integration and field validation of BLE-based entrance/exit detection in semi-open playground settings. The smart device continuously scans beacon RSSI values, applies Kalman-filter-based smoothing and threshold-based state transitions, and transmits detected events to the server, where daily, weekly, and monthly statistics are generated and visualized for caregivers. Field experiments conducted at five real playgrounds showed that the proposed system achieved over 99% entrance/exit detection accuracy with an average response time of less than 7 s. These results demonstrate that reliable playground entry/exit monitoring can be implemented at low cost, with simple infrastructure and practical deployment in residential environments.

Signals doi: 10.3390/signals7040066

Authors: Devi Sowjanya Padala Lin Li Hetali Tank Ming Zhang Jeffrey B. Driban Timothy McAlindon Juan Shan

Segmentation of bone marrow lesions (BML) is vital for quantifying lesion volume, a biomarker associated with cartilage damage and pain in knee osteoarthritis (KOA). Manual segmentation is challenging due to low contrast and variable lesion locations. We propose an automated deep learning method featuring a dual-stream encoder that integrates global image-level and local patch-level features. The study included 300 participants from the Osteoarthritis Initiative (OAI) database, each with approximately 36 intermediate-weighted fat-suppressed (IWFS) magnetic resonance (MR) images. The ground truth masks were manually annotated by trained research staff. With physical batch size 32, the model achieved a 2D Dice similarity coefficient (DSC) of 0.68, 3D DSC of 0.62, Intersection over Union (IoU) of 0.51, precision of 0.76, sensitivity of 0.62, and Pearson’s correlation coefficient (r) of 0.85 between manually labelled and automatically generated volumes. Using an effective batch size of 64 via gradient accumulation, the model achieved 2D DSC of 0.63, 3D DSC of 0.65, IoU of 0.48, precision of 0.75, sensitivity of 0.6, and r of 0.98 for volume correlation. The model outperformed baselines at batch size 32 across almost all evaluated metrics and remained robust at batch size 64, with strong volumetric correlation and improved 3D DSC, IoU, and sensitivity.

Signals doi: 10.3390/signals7040065

Authors: Ali Sarafnia M. Omair Ahmad M. N. S. Swamy

The conventional method of optimizing the parameter of a first-order differential microphone array (DMA) in the presence of noise is to maximize the array gain, i.e., to maximize the noise reduction. Such optimization can be accomplished only in the case of channel noise scenario, where the maximum array gain exists. However, in the case of diffuse or point source noise, the array gain is not upper bounded when the microphones are very closely spaced. Hence, the classical method fails in these two cases. In this paper, we present a method of designing a first-order DMA that ensures effective noise reduction in all the three cases, namely, channel, diffuse, and point-source noise, by utilizing the concept of spectral entropy measure (SEM). Thus, the SEM-based method is superior to the existing maximum array gain method, since it provides the optimal design parameter in all the three noise cases. The performance of the first-order DMA designed using the spectral entropy-based measure is evaluated for an input speech signal in the presence of the three noise scenarios mentioned above. This research underscores the effectiveness of the spectral entropy-based measure in overcoming the limitations posed by the classical method of designing a first-order DMA.

Signals doi: 10.3390/signals7040064

Authors: Reka Sandaruwan Gallena Watthage Anil Fernando

Adaptive bitrate streaming over HTTP relies on carefully constructed bitrate ladders and ordered sets of bitrate–resolution pairs to deliver optimal perceptual quality under fluctuating network conditions. While content-aware methods based on convex hull optimisation have substantially improved ladder efficiency for Video-on-Demand, they require exhaustive multi-resolution pre-encoding that is computationally prohibitive under the real-time constraints of live streaming. This challenge is compounded by the H.266/Versatile Video Coding (VVC) standard, which offers approximately 50% compression gains over HEVC at 8–10× the encoding complexity. This paper presents LiveCH-VVC, a latency-aware dynamic bitrate ladder prediction framework for VVC-encoded live streaming over Low-Latency DASH (LL-DASH) with CMAF packaging. The framework introduces four integrated modules: (i) a Lightweight Dual-Path CNN (LDP-CNN), obtained via teacher–student knowledge distillation (∼5 M parameters, 148 ms GPU inference), that jointly extracts spatial–temporal features from raw frames and compression-domain statistics from a fast VVC probe encode; (ii) an adaptive scene change detector with exponential moving average thresholding (F1 = 0.925) that triggers ladder updates only upon significant complexity shifts; (iii) a temporally augmented XGBoost multi-label classifier that predicts latency-constrained Pareto-optimal bitrate–resolution pairs; and (iv) an online adaptation engine that integrates Common Media Client Data (CMCD) feedback from CDN edge servers for continuous closed-loop refinement. Comprehensive evaluation on 81 UHD sequences (∼4050 CMAF segments) from three benchmark datasets demonstrates an average BD-Rate of +0.68% relative to the per-segment oracle convex hull 5.4× better than the state-of-the-art ARTEMIS framework (+3.67%) while achieving 73.3% encoding time savings, 2.37 s end-to-end latency, and a QoE score of 81.6 in live simulation with 100 concurrent clients. Ablation analysis confirms that the dual-path compression-domain branch (+0.44 pp) and temporal context augmentation (+0.35 pp) are the primary performance drivers, while the online adaptation mechanism provides 42% relative improvement over extended streaming sessions.

Signals doi: 10.3390/signals7040063

Authors: Mingjie Qiu Jianming Wang Guangxin Wu

Aiming at core bottlenecks in weak and small target detection in radar range-Doppler (RD) spectra under low signal-to-noise ratio (SNR)—including severe performance degradation of traditional constant false alarm rate (CFAR) detectors and the inherent trade-off difficulty faced by existing deep learning methods in balancing detection accuracy, localization precision, and real-time performance—this paper proposes a progressive sub-pixel-level intelligent detection algorithm named F2DN-CCWL. The algorithm constructs a three-stage detection pipeline: global candidate screening, local fine discrimination, and weighted localization, and implements a full-stack customized design covering network architecture, soft-label training strategy, and post-processing modules. Simulation and field-measured results demonstrate that at −20 dB SNR, the proposed algorithm achieves a detection probability of 95.3%, a false alarm rate of 3.1%, an average localization error of 0.76 pixels, and a single-frame inference latency of 47.21 ms. This method offers a high-performance engineering solution for radar-based detection of low observable targets.

Signals doi: 10.3390/signals7040062

Authors: Yutaka Yoshida Kiyoko Yokoyama

Accurate R-peak detection in electrocardiogram (ECG) signals is fundamental for cardiovascular analysis. However, most existing methods address differences in sampling frequency (fs) through signal resampling or transfer learning, which may alter the temporal definition of annotated events. In this study, we propose a fs consistent framework for ECG R-peak detection that avoids both resampling and retraining. The proposed method is based on low-sampling morphological learning combined with physiological temporal constraints (PTC). A lightweight classifier based on Extreme Gradient Boosting (XGB) was trained on 128-Hz ECG data from the MIT-BIH Normal Sinus Rhythm Database to learn local morphological structures, and feature extraction is defined in milliseconds with time-normalized derivatives to ensure consistency across fs. The trained model is directly applied to higher-fs datasets (360 Hz, 500 Hz, and 1000 Hz) without modification. Final peak locations are determined through deterministic processing, including PTC and local snap processing. Experimental results demonstrated that the proposed method achieved stable detection performance across multiple sampling frequencies. When evaluated in a sample-wise manner, the proposed method achieved mean F1-scores of 0.885 on MIT-BIH Arrhythmia Database (360 Hz), 0.848 on Lobachevsky University Electrocardiography Database (LUDB, 500 Hz, sinus rhythm), 0.837 on LUDB (500 Hz, arrhythmia), and 0.953 on PTB Diagnostic ECG Database (1000 Hz), without any resampling or retraining. The integration of probabilistic candidate detection and deterministic temporal alignment enables consistent peak localization under cross-frequency conditions. These findings demonstrate that augmenting machine learning with deterministic decision mechanisms provides a principled framework for fs-consistent ECG peak detection.

Signals doi: 10.3390/signals7040061

Authors: Wejdan Altawallbeh Huthaifa Obeidat Issam Trrad Hazem Al-Otum

Photovoltaic systems have recently attracted significant attention for the free, clean, and sustainable energy they generate. In this work, thermal image processing techniques were developed and utilized to classify hot spots on solar photovoltaic panels. Thermal images were classified into three categories: (a) ideal images, where images do not contain hot spots; (b) images affected by shadow; and (c) images affected by bird drops. The proposed classification was developed using image processing techniques, including histogram analysis, contrast enhancement, and filtering tools. The attained classes are then matched to the decrease in electrical power output. The proposed method was applied to thermal images to detect and classify the target hot spot. Experimental results showed that the estimated error was approximately 6.3% of the total number of images used in the research, with error rates of 6.57% for the shadow hot spot type and 6.67% for the bird drops (mud-like class). Moreover, the accuracy of the proposed method was around 93.7%.

Signals doi: 10.3390/signals7040060

Authors: Hao Liu Jaehyeon Nam Jaecheon Lee Shunming Li Haibo Zhang Jiantao Lu Changpeng Cai

Intelligent fault diagnosis of rotating machinery typically assumes that training and test data share the same operating speed, an assumption that rarely holds in industrial end-of-line testing, where a rotor must be certified across a range of shaft speeds. In this paper, we expose this assumption through a systematic benchmark of four deep learning architectures (TCN, 1D-CNN, BiLSTM, and CNN-BiLSTM) on a laboratory rotor testbench with three operating speeds (1000, 2000, and 3000 rpm) and four unbalance fault classes. Under within-speed 5-fold cross-validation, all four models achieve a perfect macro-F1 of 1.000, offering no basis for architecture selection. Under Leave-One-Speed-Out (LOSO) evaluation (train on two speeds, test on the held-out speed), performance drops substantially and diverges across models: BiLSTM 0.180, TCN 0.270, 1D-CNN 0.271, and CNN-BiLSTM 0.401. We trace the LOSO gap to the unbalance centrifugal force law F = meω2, which makes speed-confounded features unreliable under cross-speed testing. CNN-BiLSTM improves the mean LOSO macro-F1 by 48% relative to the stronger single-module baseline, 1D-CNN. Although CNN-BiLSTM achieves the highest LOSO performance among the evaluated architectures, it still does not surpass the physics-informed LightGBM baseline of 0.487. Therefore, the primary contribution of this work is not to solve cross-speed diagnosis, but to demonstrate that conventional same-speed evaluation substantially overestimates model capability and that LOSO provides a more deployment-relevant benchmark for future algorithm development.

Signals doi: 10.3390/signals7040059

Authors: Marko Jurišić Igor Tomičić Andrija Bernik

User Behaviour Analytics (UBA) relies heavily on sequential data to detect anomalies such as insider threats. Traditional approaches often model user behaviour as discrete sequences of events using Recurrent Neural Networks (RNNs) like Long Short-Term Memory (LSTM) networks. These methods implicitly treat time steps as uniform, ignoring the irregular time intervals inherent in user logs. In this paper, we present the first application of Neural Controlled Differential Equations (NCDEs) to user behaviour analytics, a class of continuous-time models that naturally handle irregularly-timed event data. We compare a simple LSTM predictor against an NCDE predictor on the CERT 4.2 and 6.2 insider threat dataset. We demonstrate that standard discrete-time models (LSTMs) produce noisy loss signals on sparse data, forcing downstream classifiers to rely on fragile error spikes. In contrast, Neural CDEs generate stable, continuous error signals. NCDE roughly tripled the F1 of the discrete baseline (0.364 vs. 0.133) on the challenging CERT 6.2 dataset.

Signals doi: 10.3390/signals7030058

Authors: Nelson Chambi Celso Sanga Alejandra Sanga Piero Sanga

Predictive maintenance in underground mining faces challenges due to severe conditions such as confined environments, high humidity, presence of silica dust, and restricted access. This study develops a predictive framework based on oil analysis and machine learning for multiple compartments of mining equipment (engine, hydraulic system, transmission, differential). Samples were processed under ASTM standards, integrating wear metal concentrations (Fe, Cu, Cr, Pb, Al), physicochemical properties (viscosity, TBN, soot), and contaminants (Si, Na). Based on tribology, interpretable ratios were constructed. Three algorithms (Random Forest, Gradient Boosting, and XGBoost) were evaluated using cross-validation. XGBoost achieved the best balance (F1 = 0.852, AUC = 0.975), with a recall of 94.5% for the critical class and only 3 false negatives out of 199 test samples, while Random Forest presented the highest global discrimination power (AUC = 0.978). SHAP revealed that viscosity at 100 °C is the most important predictor (SHAP ~0.9), surpassing iron. No temporal wear trend was found (R2 = 0.000). Threshold optimization to 0.25 reduced false negatives by 67% (from 9 to 3). The framework provides interpretable predictions with uncertainty quantification for underground environments.

Signals doi: 10.3390/signals7030057

Authors: Salihu Dausu Ibrahim Emmanuel Majiyebo Eronu Aliyu Ozovehe Sanni Muhammad Uthman Sunday Oladayo Oladejo

Unauthorised FM broadcasting poses significant challenges to spectrum regulators globally, contributing to interference, degraded service quality, and national security threats. While traditional spectrum monitoring relies primarily on carrier frequency and power measurements, this study demonstrates that FM baseband features—specifically the multiplex (MPX) signal structure, pilot tone, and Radio Data System (RDS) subcarrier—provide robust discriminative markers for detecting non-compliant transmissions. Using a real-world dataset of 3710 pre-processed records collected across Nigeria’s capital region between 2021 and 2024, we extracted and analysed six transmission parameters: assigned frequency, band occupancy (±100 kHz), MPX overshoot percentage, pilot tone presence, and RDS indicators. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel was trained to distinguish compliant licensed stations from regulatory non-compliant transmissions—encompassing both unlicensed transmitters and technically non-compliant licensed operators—achieving 99.96% accuracy, 99.38% precision, and 99.63% recall with a false alarm rate of 0.026%. A Comparative analysis against baseline feature sets confirmed that integrating MPX, pilot, and RDS significantly improved detection robustness compared with carrier-only approaches. Results demonstrate that feature-rich baseband analysis enables scalable, cost-effective regulatory enforcement, reducing manual monitoring burden while enhancing detection reliability. This framework offers practical applicability for spectrum management agencies in resource-constrained environments where unauthorised broadcasting remains prevalent.

Signals doi: 10.3390/signals7030056

Authors: Barlian Henryranu Prasetio Edita Rosana Widasari

Speech-based stress analysis is relevant to forensic-oriented speech processing, security screening, and behavioral monitoring, yet its reliability is often limited by speaker variability, recording conditions, and acoustic mismatch. This study proposes an interpretable multi-domain signal processing framework that models stress-related speech variation through excitation dynamics, vocal tract characteristics, and temporal modulation patterns. The framework integrates source–filter decomposition, residual-domain analysis, harmonic structure analysis, modulation spectrum characterization, and prosodic variability into a unified representation. The SUSAS corpus is used as the primary dataset for supervised stress evaluation. RAVDESS and SAVEE are employed only as controlled arousal-related proxy datasets to examine the consistency of stress-related acoustic patterns, rather than as physiological stress ground truth. VoxCeleb is used exclusively for robustness and domain-variability analysis because it lacks stress labels. For probabilistic evidence assessment, Gaussian mixture models are adopted as the more interpretable density estimator, while normalizing flow is included as a flexible performance-oriented comparator for modeling non-Gaussian feature distributions. Evaluation incorporates likelihood ratio analysis, DET curves, EER, ablation studies, and robustness testing. The proposed framework achieves an EER of 5.8% in the primary supervised evaluation, showing competitive performance while preserving physically meaningful interpretation.

Signals doi: 10.3390/signals7030055

Authors: Artem Bykov Nurkamilya Daurenbayeva Syrym Zhakypbekov Aigul Bissarinova Almas Nurlanuly Duriya Daniyarova

Distributed seismic sensor networks integrated into the Internet of Things (IoT) infrastructure enable continuous condition monitoring of large-scale engineering structures. During long-term operation, however, measurement channels are subject to sensitivity drift, increased noise, and pulse artifacts that statistically mimic real vibration events. Related deep-learning techniques for noisy and ill-posed inverse problems have demonstrated the value of combining principled physical priors with deep models. Although the application domain differs, the underlying methodological insight—that constrained, physics-aware feature mappings can stabilize learning under noisy and partially observed conditions—directly motivates the use of a parameterized quantum circuit as a nonlinear feature transformer in the present work, where Hilbert space mapping serves as an analogous structural prior for the latent representation. Three principal fault modes are considered in this work, corresponding to the dominant degradation mechanisms observed in long-term seismic instrumentation: sensor drift, increased noise, and sensor failure. Each fault mode produces a distinct signature in the windowed feature space; the proposed model is trained to discriminate between them based on the latent CNN-LSTM-VQC representation. We propose a hybrid quantum-inspired deep-learning model (QC-DL) for the detection and diagnosis of channel-degradation anomalies. The architecture combines a 1D-CNN+LSTM feature extractor with a parameterized variational quantum circuit (VQC) used as a nonlinear feature transformer. All quantum experiments were performed on the QPanda3 CPUQVM simulator. The data were split chronologically prior to windowing to avoid information leakage. On real-world labeled accelerometric data with four operating modes (normal/drift/high-noise/failure), the QC-DL model achieved a macro-averaged F1 score of approximately 0.69 and per-class AUC values in the range 0.88–0.99. The mean early-detection latency was 1.6 s versus 2.1 s for the CNN-LSTM baseline (~24% reduction). An ablation study against a parameter-matched classical MLP showed that the gain is modest and not solely attributable to additional nonlinearity. The reported p-values (p = 0.70, p = 0.29) do not establish statistical significance. The results support the feasibility of hybrid quantum-inspired deep learning for sensor-channel verification, while highlighting the need for evaluation on real NISQ hardware. This paper proposes a hybrid quantum-inspired approach for detecting and diagnosing such anomalies in the time series of distributed seismic networks. The architecture combines a classical temporal feature extraction module based on one-dimensional convolutional layers and a recurrent long short-term memory (LSTM) network, which generates a latent window representation of the signal, with a parameterized variational quantum circuit used as a nonlinear feature processor in a hybrid computational circuit. Experimental validation was performed on real-world labeled data with multiple sensor degradation modes. The evaluation was organized in a scoring framework aligned with autonomous operation through window ranking and threshold alarm generation. In the experiments, the proposed model provided a macro-averaged F1 score of approximately 0.69 and area under the receiver operating characteristic (AUC) curve values in the range of 0.88–0.99 across classes, outperforming baseline deep models. The average early detection latency was 1.6 s versus 2.1 s for the baseline recurrent model (a 24% reduction). An ablative comparison with a control model based on a classical multilayer perceptron of comparable dimension confirmed that the improvement is not limited to the addition of additional nonlinearity. The obtained results indicate the potential of quantum-supported deep learning for improving the reliability of long-term vibration monitoring and verifying the correctness of sensor channels in distributed seismic networks.

Signals doi: 10.3390/signals7030054

Authors: Luis Viloria Edmanuel Cruz Cesar Pinzon-Acosta

Manual arc welding is highly dependent on operator skill, leading to variability in weld quality and an increased risk of defects; therefore, reliable monitoring methods for Shielded Metal Arc Welding (SMAW) are required, particularly in manual environments where process variability and environmental noise are inherent. This study proposes a monitoring approach for classifying SMAW process parameters using airborne acoustic signals generated by the welding arc. Welding experiments were conducted on carbon steel plates of different thicknesses (3, 6, and 12 mm) using E6010, E6011, E6013, and E7018 electrodes under Alternating Current (AC) and Direct Current (DC) configurations; acoustic signals were recorded in real time and processed using Mel-Frequency Cepstral Coefficients (MFCCs) and deep audio embeddings from pre-trained VGGish and YAMNet models as inputs to artificial neural network classifiers for multi-output classification of welding process parameters. Model performance was evaluated using per-target metrics (accuracy and macro F1-score) and joint multi-output metrics (Exact Match and Hamming Accuracy). MFCC-based models significantly outperformed embedding-based approaches, achieving up to 94.51% Exact Match and 97.88% Hamming Accuracy, while reducing computational costs. These results demonstrate the feasibility of SMAW monitoring using arc sound, suggesting that spectral features are an effective solution for welding-process monitoring and a promising foundation for future weld-quality monitoring systems.

Signals doi: 10.3390/signals7030053

Authors: Weiquan Wang K.M Shahriar Alam Adib Foisal Ahmed Riaz-ul-haque Mian

Wafer and FPGA testing remains costly in semiconductor manufacturing. This paper studies random sampling, stratified sampling, and k-means sampling under a partial-measurement setting with Gaussian Process Regression (GPR), and introduces Short Distance Elimination (SDE), a spatial screening rule that spreads selected training points over the layout. Combining value-based sampling with SDE yields two hybrid methods: S-SDE, which applies SDE within stratified subsets, and K-SDE, which applies SDE within k-means clusters. A calibration-based protocol fixes the value-group labels and SDE thresholds before target-file prediction. The SDE thresholds are selected from (α,β) configurations in {0,1,2,3,4}, excluding (0,0), using local window statistics and RMSD-based calibration sweeps. Both wafer and FPGA calibration files support (α,β)=(2,2), which is then fixed for all remaining experiments. Experiments on industrial wafer data and actual FPGA silicon data, repeated across four random seeds, show that K-SDE reduces RMSD by 15.84% for wafer data and 13.03% for FPGA data relative to k-means sampling, while S-SDE reduces RMSD by 16.26% and 8.63% relative to stratified sampling.

Signals doi: 10.3390/signals7030052

Authors: AnuraagChandra Singh Thakur Masudul Imtiaz

Automatic modulation classification (AMC) is a core capability for spectrum monitoring, adaptive receivers, and electronic support. Most radio-frequency machine learning (RFML) studies train multi-class classifiers on benchmark datasets that contain a single modulation per recording at baseband. In operational settings, however, the objective is often to detect only a small set of signals of interest, making large multi-class models unnecessarily expensive to train and deploy. In addition, multi-class formulations can increase false-alarm risk due to confusion among non-essential classes and may allocate model capacity inefficiently to distinctions that are irrelevant for the operational objective. This paper investigates an alternative workflow based on targeted binary transformer detectors and evaluates their robustness under practical RF complications. Using the RadioML 2018.01A dataset, we construct binary detection tasks with BPSK as the signal of interest and introduce three increasingly realistic conditions: (i) center-frequency shifts away from baseband, (ii) sampling-rate mismatches via decimation and interpolation, and (iii) multi-signal mixtures where modulations co-occur either in frequency (simultaneous transmissions) or in time (temporal concatenation). The results show that baseband-trained detectors do not generalize to center-frequency-shifted signals, and multi-signal interference can cause complete detection failure unless explicitly modeled during training. We investigate early-exit transformer inference to reduce computation on high-confidence examples, showing it maintains (and occasionally improves) detection performance. We also evaluate inter-modulation transfer learning and intra-modulation adaptation from baseband to mixed- and multi-signal scenarios.

Signals doi: 10.3390/signals7030051

Authors: Yang Shen Xubin Liang Xiaolin Hu Shuping Wang

Similar to a web search engine, we have developed a computer-based acoustic search engine tailored for the critical scenario of high-speed aircraft ground explosion monitoring, addressing the long-standing challenge of real-time localization for such high-impact events. Unlike conventional acoustic source localization techniques, our method uniquely resolves the separation and localization of multiple aliasing events, which are prevalent in high-speed aircraft explosion scenarios due to complex shock wave propagation and overlapping signatures. We first calculate the waveforms of all possible acoustic sources over 2D grids. Then, a dimensionality reduction method and fast search technology are applied to the database. Once a high-speed aircraft ground explosion occurs, the real-time system returns detection feedback by matching real-time data with the pre-established search database. Different from other artificial intelligence (AI)-based approaches, the acoustic search engine can handle multiple aliased acoustic events in real time and does not require any prior information or input parameters—a key advantage for emergency response to high-speed aircraft explosions where predefined parameters are often unavailable. Both synthetic tests and field data applications (using actual acoustic records from high-speed aircraft ground explosion experiments) demonstrate the method’s credibility in detecting and localizing multiple acoustic sources.

Signals doi: 10.3390/signals7030050

Authors: Rylen Garlitz Allen Shamsi Ratree Wayland

Speech emotion recognition systems are typically trained on audio sampled at conventional bandwidths that exclude frequencies above approximately 8 kHz, yet the contribution of extended high-frequency information to vocal emotion recognition remains unclear. This study examines how spectral bandwidth influences automatic emotion classification using the RAVDESS corpus of acted speech and song. Recordings were low-pass filtered to simulate multiple bandwidth conditions (8, 12, and 16 kHz, along with the original full-bandwidth signal), and classification was performed using a Random Forest model trained on mel-spectral features. In addition to classification accuracy, we analyzed permutation-based spectral feature importance and the geometry of the classifier’s posterior-probability space. Bandwidth restriction had relatively modest effects on classification accuracy overall, with mean accuracy ranging from approximately 55% to 77% across conditions, although its impact was greater for speech than for song. Feature-importance analyses indicated that the model depends primarily on low- and mid-frequency spectral information, whereas higher-frequency and EHF regions show increased importance when available. Geometry analyses showed no reliable evidence that bandwidth altered the global structure of the stimulus-level emotion space, although spectral truncation reduced separability for certain emotion contrasts, particularly in speech at normal emotional intensity. These results indicate that most acoustic information supporting categorical emotion recognition resides in lower spectral regions, while EHF information provides supplementary acoustic information that may refine some emotional distinctions under specific conditions.

Signals doi: 10.3390/signals7030049

Authors: Ian G. Bearden Valentin Buchakchiev Daniel Ivanov Mira Gencheva Venelin Kozhuharov Yury A. Melikyan

Silicon photomultipliers (SiPMs) have become widely used as photodetectors in high-energy physics, nuclear physics, medical imaging, and space applications. In many of these fields, SiPMs are required to operate in high-radiation environments, which are notoriously problematic for silicon sensors. For this reason, it is essential to study the changes in their performance characteristics after exposure to radiation. In this study, a number of SiPM samples were exposed to non-uniform radiation at the CHARM facility at CERN. Half of the samples were operated above breakdown during the test, while others remained off. Intermittent measurements allowed for tracking the changes in I-V curves and signal shapes during the irradiation itself. The focus was on detecting differences in irradiation damage between the operational and non-operational SiPM samples. The I-V curves and signal shapes in both cases for three different types of SiPM are presented, and a comparison is made.

Signals doi: 10.3390/signals7030048

Authors: Huijie Zhu Wei Wang Cui Yang Youjun Xiang Jiawei Li Yuheng Xu

Frequency-hopping (FH) signals appear as small rectangular pulses in time-frequency spectrograms. At low signal-to-noise ratios (SNRs), noise along the frequency axis, caused by short-time Fourier transform (STFT) spectral leakage, blurs pulse boundaries, while the varying scales of hop rectangles exceed the capacity of a single receptive field. This paper presents YOLO11-FH, a modified YOLO11 detector that introduces two signal-processing-motivated modules. A FreqSmoothBlock (FSB) uses a (3,1) depthwise convolution to smooth exclusively along the frequency axis, while adding only 5C parameters. A TFMultiResBlock (TFMRB) fuses three parallel dilated convolution branches (dilation rates of 1, 2, and 3) to cover different hop scales, replacing a heavier C3k2 module. The detection head is further simplified by halving the Bottleneck repeat count and disabling the deep submodule at the P5 scale. On a simulated FH dataset (SNRs ranging from −15 dB to −10 dB, five jamming types), YOLO11-FH achieves 96.04% mean average precision (mAP)@0.5 and 76.18% mAP@0.5:0.95, outperforming the YOLO11n baseline by 0.95 and 2.91 percentage points (pp) with 2.9% fewer parameters.

Signals doi: 10.3390/signals7030047

Authors: Peiming Zhang Jiangang Lu Jiajia Fu Xinyue Di Kai Fang Jie Tang Cui Yang

Accurately capturing spatiotemporal dependencies and enabling effective decision support are core challenges in Intelligent Transportation Systems (ITS). Existing research often treats traffic prediction and path planning as isolated tasks. Moreover, mainstream prediction models struggle with long-term periodic patterns, while Reinforcement Learning (RL)-based planning often suffers from inefficient exploration in sparse topologies. To address these issues, this paper proposes a unified framework combining a spectral-temporal Graph Neural Network (GNN) and bi-directional decoding RL. Specifically, a time-frequency dual-stream adaptive learning module is introduced for prediction. Fast Fourier Transform (FFT) and Gated Recurrent Unit (GRU) are employed to capture global frequency periodicities and local temporal dynamics, respectively. Their adaptive fusion effectively mitigates the long-sequence information forgetting problem. For path planning, the task is formulated as sequence generation. A graph-aware attention encoder with adjacency masking is designed, and heuristic feature embeddings are incorporated to guide efficient exploration. Furthermore, a bi-directional autoregressive decoding strategy enhances robustness against topological bottlenecks. On PEMSD4 and PEMSD8, the proposed predictor achieves MAE/RMSE/MAPE values of 18.211/30.433/12.006 and 13.587/23.566/8.955, respectively. Path-planning simulations on the PEMSD4-derived sparse topology further demonstrate stable bi-directional RL optimization, faster convergence with heuristic guidance, and a sparsity-aware encoder that reduces redundant attention interactions in sparse road networks. These results validate the effectiveness of the proposed “predict-then-plan” paradigm.

Signals doi: 10.3390/signals7030046

Authors: Chia-Yen Yang Fan-Ning Kuo Hsin-Yung Chen

Despite being distinct clinical entities, major depressive disorder (MDD) and Parkinson’s disease (PD) have some shared physiological pathways, including mitochondrial dysfunction and inflammation. Our interest was whether these common physiological mechanisms are reflected in brain activity variations as well. Therefore, this study aimed to identify common characteristics in resting-state electroencephalography (EEG) between the conditions by comparing features among patients with MDD, PD, and healthy controls. The methodology comprised two stages: analyzing differences between patients and healthy individuals and exploring consistent trends between PD and MDD, based on EEG data from PRED + CT database. Age-corrected regression analysis of five EEG features revealed PD and MDD had the following overlapping features: shared abnormalities in theta, alpha and beta relative power, as well as sample entropy in the delta (centroparietal, temporal, and parietal areas), theta (parieto-occipital), and gamma (central) bands. Furthermore, interhemispheric asymmetry was evident across all bands, especially in the frontal and centroparietal regions. When combining these findings with their directional trends (positive or negative), common EEG features included increased theta and decreased alpha-beta power, along with increased parieto-occipital and reduced gamma entropy at FCz. These findings suggest shared EEG markers between PD and MDD, supporting the potential for efficient neurological disorder diagnosis.

Signals doi: 10.3390/signals7030045

Authors: Cristiano Fidani Alessandro Piscini Massimo Calcara Gianfranco Cianchini Maurizio Soldani Angelo De Santis Dario Sabbagh Martina Orlando Loredana Perrone

A thermal infrared (TIR) camera was installed at Mefite Lake in Valle d’Ansanto, Irpinia (Italy), to assess whether small variations in cold CO2 flux can be resolved thermally. To our knowledge, this is the first systematic attempt to extract short-period degassing dynamics from TIR data at Mefite. Infrared thermal images taken over a three-hour nighttime interval revealed the spatial distribution and extent of natural CO2 emissions. The high sampling frequency of one minute detected unexpected thermal variability from the source. The extent of temperature variations across the entire site reached almost 3 °C, with durations typically ranging from a few minutes to tens of minutes. Spectral analysis of the temperature time series reported a 1/f-type noise pattern, with significant periods of 2–3 min, 5 min, 26 min, and 61 min observed at different locations. Further intermediate periods were observed at individual points. Differences and delays in temperature variations appeared to be related to distance from the structure’s centre and the presence of water. These temperature fluctuations were interpreted as changes in the gaseous emission flow caused by a few kPa of CO2 escaping due to pressure variations. The gas thermally interacts with the underlying soil, adding or removing heat at the surface. These results demonstrate that high-frequency infrared thermography provides a sensitive and practical tool for quantifying short-term flux variability at natural CO2 vents and for improving the characterisation of their degassing dynamics.

Signals doi: 10.3390/signals7030044

Authors: Prajat Paul Mohamed Mehfoud Bouh Manan Vinod Shah Forhad Hossain Ashir Ahmed

Automatic speech recognition has advanced rapidly for high-resource languages, yet performance remains limited for low-resource languages such as Bangla, particularly in telehealth settings. Most systems rely on a standardized 16 kHz sampling rate, a design choice despite evidence that Bangla contains sibilant fricatives and other phonetic cues with substantial high-frequency energy that may be suppressed under bandwidth and latency constraints. This study evaluates audio sampling rate as a controllable signal-level parameter for Bangla telehealth ASR to identify an empirically grounded operating range balancing transcription accuracy, execution time, and network bandwidth. Twenty real-world Bangla doctor–patient consultations were deterministically resampled to 55 configurations between 8 kHz and 32 kHz and transcribed using a fixed cloud-based ASR system. Session-level Word Error Rate, execution latency, payload bandwidth, and high-frequency phonetic content were analyzed using a composite sibilant-likelihood score. WER decreased from 0.338 at 8 kHz to a local minimum of 0.232 at 18.75 kHz, with gains plateauing beyond this range despite substantial bandwidth increases. Elbow-point, Pareto frontier, weighted scoring, and Minimum Acceptable Trade-off analyses converged on an optimal region between 17.25 and 18.75 kHz, demonstrating that sampling rate optimization improves ASR accuracy without proportional resource costs in telehealth settings.

Signals doi: 10.3390/signals7030043

Authors: Sirine Aouine Chaieb Mahdi Abdelkarim Majdi Bahrouni Ali Gharsallah

This paper presents a compact, single-layer substrate-integrated waveguide (SIW) bandpass filter for 5.8 GHz WiMAX applications. The filter achieves an improved performance trade-off through a novel hybrid design strategy that combines central vertical perturbation vias with symmetrically etched complementary split-ring resonators (CSRRs). This configuration implements a hybrid magnetic–electric perturbation within a single cavity, enabling simultaneous control of electric and magnetic field confinement. The proposed topology achieves an optimized balance among unloaded quality factor Qu, insertion loss, selectivity, and structural simplicity. Through targeted intra-cavity field manipulation, the filter attains a Qu of 239.7, a narrow fractional bandwidth of 3.08% (5.75–5.93 GHz), and a low insertion loss of 1.12 dB. It also delivers enhanced selectivity compared to conventional single-cavity designs and performs competitively with multi-resonator architectures. An equivalent circuit model accurately captures the via–CSRR interaction and agrees closely with full-wave electromagnetic simulations. Experimental results confirm excellent return loss and robust performance across the entire WiMAX band (5.725–5.850 GHz). Thus, the proposed filter offers a practical, high-performance, and manufacturable solution for selective RF front-end applications.

Signals doi: 10.3390/signals7030042

Authors: Mostafa Moallim Taqwa A. Alhaj Fatin A. Elhaj Inshirah Idris Tasneem Darwish

Audio forgery has emerged as a significant security and forensic challenge, driven by rapid advances in generative artificial intelligence and the widespread availability of audio editing tools, which enable the creation of highly realistic manipulated speech with minimal technical expertise. Existing approaches predominantly operate at the file level, providing only coarse binary decisions without identifying when or where manipulation occurs. This study addresses fine-grained temporal localization through a unified frame-level localization framework. We introduce a controlled forgery generation framework derived from the TIMIT speech corpus, applying atomic, localized manipulations under strict temporal constraints and producing precise frame-level annotations across diverse manipulation types. Building on this dataset, we then propose a transform-agnostic localization-driven detection approach using temporal inconsistency modeling, enabling unified analysis across heterogeneous manipulations at frame-level resolution. To analyze forensic evidence, we present an evidence-stratified modeling paradigm comparing three complementary strategies: a handcrafted anomaly-based method, a deep localization model leveraging pretrained wav2vec 2.0 representations, and a hybrid approach combining both through confidence-aware fusion and temporal consistency reinforcement. A systematic experimental analysis evaluates the effects of representation adaptation, hybrid fusion, and manipulation type on detection and localization performance. Results show that handcrafted features are insufficient for reliable frame-level localization, while task-adapted wav2vec 2.0 achieves strong and consistent performance. The hybrid approach does not consistently improve frame-level accuracy but yields substantial gains in segment-level localization by enforcing temporal coherence. Per-transform analysis confirms robust performance across most manipulations, with deletion-based operations remaining the most challenging.

Signals doi: 10.3390/signals7030041

Authors: Joshua Turnipseed Benedito J. B. Fonseca

This paper investigates how well eGeMAPS features can be used to classify depression from a patient’s speech audio samples through the use of statistical resampling methods. We use permutation tests to evaluate, with high confidence, whether eGeMAPS features and the speaker’s depression status are dependent. We use bootstrap confidence intervals to test, with high confidence, whether eGeMAPS features are able to better discriminate depression in male speakers than in female speakers. Lastly, we compare the detection power of different subsets of the eGeMAPS features. We use an open-source dataset of depressed and non-depressed speakers (E-DAIC), an open-source audio feature extractor (eGeMAPS), and open-source machine learning classifiers (WEKA) to enable replication of results and establish a baseline for future studies.

Signals doi: 10.3390/signals7030040

Authors: Maged A. Aldhaeebi Abdulbaset Ali Thamer S. Almoneef

This paper presents a non-imaging landmine detection system that integrates a highly sensitive multiple-input multiple-output (MIMO) microwave sensor with a machine learning (ML) classifier for automated classification. The proposed sensor consists of two circular patch elements fed with two ports designed in a unique configuration, comprising a dual loop with a cross dipole, for enhancing sensitivity to changes in the environmental electrical properties (dielectric constant and electrical conductivity) induced by buried metallic objects. It operates in dual bands of 1.58 GHz and 1.75 GHz, within the operating frequency range of 1.3 to 2 GHz. The system’s performance was assessed using full-wave simulations and experimental measurements, involving a sand-filled foam container with a metal surrogate landmine placed at different depths. The sensor’s performance was evaluated by monitoring changes in the magnitude and phase of the reflection coefficient (S11) and the transmission coefficient (S21). The acquired scattering parameters data were processed using a Support Vector Machine (SVM) algorithm for automated classification. Results demonstrate the sensor’s high capability in detecting metallic targets at various depths and standoff distances. Compared to conventional imaging technologies, this system offers significant advantages in cost, simplicity, and ease of data processing. The SVM models trained on measurement data with proper feature selection showed a high level of agreement with their counterparts trained on simulation data. Stratified k-fold cross-validation was used to improve the reliability of accuracy metrics, with results showing 85% or higher mean accuracy in all classification scenarios.

Signals doi: 10.3390/signals7030039

Authors: Mladen Tomic Marko Gulic

Denoising one-dimensional signals by wavelet shrinkage critically depends on the choice of wavelet basis, yet basis selection is often guided by heuristics rather than explicit statistical criteria. This paper investigates the relationship between wavelet-basis properties and the shape of the probability density function (PDF) of the detail coefficients in the coarsest retained detail subband. On this basis, it proposes the shape of this PDF as a criterion for wavelet-basis selection. We hypothesize that, for a fixed decomposition depth, noise model, and shrinkage rule, a basis better matched to the signal’s local regularity produces a narrower and more sharply peaked coefficient PDF in this subband than a mismatched basis and can therefore serve as a data-driven indicator for basis selection. To evaluate the consistency of this proposal, we perform controlled hard-thresholding experiments on six canonical test signals, five wavelet bases, and additive white Gaussian noise. Although the test signals differ significantly in local regularity and features, the relationship between basis suitability and PDF shape is confirmed for each of them. Therefore, the results suggest that the proposed PDF-shape criterion is a valuable indicator for wavelet-basis selection.

Signals doi: 10.3390/signals7020038

Authors: Mahmoud Elomda Ahmed A. Ibrahim Mahmoud Abdelaziz

This paper presents a hardware-accelerated signal processing architecture for OFDM-based vehicular networks that integrates crypto-agile adaptive encryption on a Xilinx Kintex-7 FPGA. The encryption layer is tightly coupled to the OFDM modulation/demodulation pipeline, enabling secure real-time signal processing for V2X communications without disrupting the baseband chain. A context-aware pre-selection unit dynamically selects among hardware cipher primitives based on latency constraints, security requirements, and channel conditions. The current prototype implements and synthesizes AES-128 as the primary block cipher, while ASCON (NIST lightweight AEAD) and Keccak (SHA-3 foundation) are validated through RTL simulation and architectural integration, demonstrating crypto-agility across block, AEAD, and sponge-based primitives. DES is retained solely as a legacy reference for backward-compatibility evaluation and is not recommended for secure V2X deployment. The design adopts a modular decoupling strategy in which cryptographic engines interface with a unified buffering and interleaving subsystem, enabling hardware-based single-cycle cipher switching without partial reconfiguration. FPGA results demonstrate sub-microsecond cryptographic processing latencies with moderate resource utilization, preserving the timing budget of latency-sensitive vehicular services. AES-128 provides standard-strength encryption, while ASCON and Keccak offer lightweight and sponge-based alternatives suited to constrained IoV platforms. Specifically, the implemented AES-128 core achieves a throughput of 1.02 Gbps with a switching latency of 86 ns, verified across 10 randomized transitions with a 99.99% success rate and zero data corruption. The ASCON and Keccak cores attain throughput-to-area efficiencies of 2.01 and 1.47 Mbps/LUT, respectively, at a unified clock frequency of 50 MHz. All acronyms are defined at first use and a complete list of abbreviations is provided prior to the reference section.

Signals doi: 10.3390/signals7020037

Authors: Udara Jayasinghe Anil Fernando

Quantum image transmission over noisy communication channels remains a challenge due to the fragility of quantum states and their susceptibility to channel impairments. Existing quantum encoding schemes often exhibit limited noise resilience, while advanced approaches introduce computational and implementation complexity. To address these limitations, this paper proposes a circular polarization-based quantum encoding framework for image transmission over error-prone channels. In the proposed approach, source images are compressed and source-encoded using standard image coding formats, including the joint photographic experts group (JPEG) standard and the high-efficiency image file format (HEIF), and converted into classical bitstreams. The resulting bitstreams are protected using channel coding and mapped onto quantum states via circular polarization representations, where left- and right-hand circularly polarized states encode binary information. The encoded quantum states are transmitted over noisy quantum channels to model channel impairments. At the receiver, appropriate quantum decoding and channel decoding operations are applied to recover the classical bitstream, followed by source decoding to reconstruct the image. The performance of the proposed framework is evaluated using image quality metrics, including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and universal quality index (UQI). Simulation results demonstrate that the proposed circular polarization-based encoding scheme outperforms existing quantum image encoding techniques, achieving channel SNR gains of 4 dB over state-of-the-art Hadamard-based encoding and 3 dB over frequency-domain quantum encoding methods under severe noise conditions. These results indicate that circular polarization-based quantum encoding provides improved noise robustness and reconstruction fidelity for practical quantum image transmission systems.

Signals doi: 10.3390/signals7020036

Authors: Domingos Sávio Tavares Mendes Junior Rafael Suzuki Bayma Alexandre Luiz Amarante Mesquita

This study investigated the influence of hyperparameter selection within an EEMD–AR–SVM framework for bearing fault diagnosis under constant- and variable-speed operating conditions. Two preprocessing configurations, namely, Method 1, in which EEMD was applied after segmentation, and Method 2, in which EEMD preceded segmentation, were evaluated under three rotational regimes—constant speed, acceleration (Test A), and deceleration (Test B)—while number of Intrinsic Mode Functions (N), autoregressive model order (L), and segment length were systematically varied towards identifying combinations that maximized classification accuracy. The results showed the methods achieved 100% accuracy under constant-speed operation. However, Method 2 consistently outperformed Method 1 under nonstationary regimes, reaching 94.12% accuracy during acceleration and 95.00% during deceleration. The outer race remained the most challenging fault type, although its separability substantially improved when EEMD was performed prior to segmentation. The findings demonstrated, in a clear and interpretable manner, that the empirical choice of N and L directly affects classifier accuracy in stationary and nonstationary scenarios and the order of preprocessing steps plays a decisive role in diagnostic reliability. Such contributions provide a reproducible methodological basis for advancing vibration-based fault diagnosis and support the development of interpretable, high-performance predictive maintenance strategies for industrial environments.

Signals doi: 10.3390/signals7020035

Authors: Yun Zhou Fulai Wang Nan Jiang Zhanling Wang Chen Pang Lei Zhang Yongzhen Li Ping Wang

Jamming pattern identification is a crucial prerequisite for countering jamming. Combined jamming exhibits complex structures and diverse forms, making it difficult for traditional identification methods to extract suitable and stable features for effective discrimination. To address this challenge, this paper proposes a combined jamming identification method based on joint modulation of linear frequency modulation, phase coding and phase coding frequency modulation (LFM-PC-PCFM) waveforms. Building upon the time–frequency entropy features of combined interference, this method enhances the separability of jamming features in the radar-transmitted waveform dimension. The experiment employed the SVM classification algorithm based on particle swarm optimization for validation. Experiments demonstrate that the combined jamming recognition method under LFM-PC-PCFM waveform modulation achieves higher and more stable recognition accuracy than traditional LFM single-waveform modulation under jamming-to-noise ratios ranging from −10 dB to 30 dB.

Signals doi: 10.3390/signals7020034

Authors: Tamoor Aziz Chalie Charoenlarpnopparut Srijidtra Mahapakulchai Babatunde Oluwaseun Ajayi Mayowa Emmanuel Bamisaye

Diabetic retinopathy is a prevalent condition and a major public health concern due to its detrimental impact on eyesight. Diabetes is a root cause of its development and damages small blood vessels caused by prolonged high blood sugar levels. The degenerative consequences of diabetic retinopathy are irrevocable if not diagnosed in the early stages of its progression. This ailment triggers the development of retinal lesions, which can be identified for diagnosis and prognosis. However, lesion detection is challenging due to their similarity in intensity profiles to other retinal features, inconsistent sizes, and random locations. This research evaluates a custom deep learning network for classifying retinal images and compares it with the state-of-the-art classifiers. The novel preprocessing method is introduced to reduce the complexity of the diagnostic process and to enhance classification performance by adaptively enhancing images. Despite being a shallow network, the proposed model yields competitive results with an accuracy of 87.66% and an F1-score of 0.78. The evaluation metrics indicate that class imbalance affects the performance of the proposed model despite using the weighted cross-entropy loss. The future contribution will be the inclusion of generative adversarial networks for generating synthetic images to balance the dataset. This research aims to develop a robust computer-aided diagnostic system as a second interpreter for ophthalmologists during the diagnosis and prognosis stages.

Signals doi: 10.3390/signals7020033

Authors: Jan-Ray Liao Wen Lin Li-Wen Chang

In recent years, image denoising has seen a shift from traditional non-local self-similarity methods like BM3D to deep-learning based approaches that use learnable convolutions and attention mechanisms. While pixel-level attention is effective at capturing long-range relationships similar to non-local self-similarity based methods, it incurs extremely high computational costs that scale quadratically with image resolution. As an alternative, channel-wise attention is resolution-independent and computationally efficient but may miss crucial spatial details. In this paper, an adjustable attention mechanism is introduced that bridges the gap between pixel and channel attentions. In the proposed model, average pooling and variable-size convolutions are added before attention calculation to adjust spatial resolution and, thus, allow dynamical adjustment of computational complexity. This adjustable attention is applied in a transformer-based U-Net architecture and achieves performance comparable to state-of-the-art methods in both real and Gaussian blind denoising tasks. To be more concrete, the proposed method achieves a Peak Signal-to-Noise Ratio of 39.65 dB and a Structural Similarity Index Measure of 0.913 on the Smartphone Image Denoising Dataset. Therefore, the proposed method demonstrates a balance between efficiency and denoising quality.

Signals doi: 10.3390/signals7020032

Authors: Xin-Qi Liao Yih-Min Chen

Compared to DVB-S2, DVB-S2X features a more intricate signaling structure. These signaling fields are employed not only in standard frames but are also frequently utilized within superframe structures. While rapid synchronization and decoding of these fields are critical, utilizing brute-force search methods incurs prohibitive computational costs. Therefore, this paper proposes a Joint Maximum Likelihood (JML) detection model tailored for the Fast Walsh–Hadamard Transform (FWHT). This approach allows for simultaneous synchronization and decoding while reducing number of real addition operations per codeword by approximately 15 times compared to brute-force methods. Consequently, the proposed architecture provides a highly efficient solution applicable to DVB-S2X and backward compatible with DVB-S2.

Signals doi: 10.3390/signals7020031

Authors: Lifang Fan Shaomin Liang Yanpeng Liu Guangbo Xiang Wei Zhang Xuexi Min

Airborne electromagnetic (AEM) surveys map subsurface electrical structures by deploying transmitter and receiver coils on an airborne platform. However, platform-induced vibrations are transmitted to the sensors, generating strong motion-induced noise that severely degrades signal quality. To mitigate such noise, this study proposed the use of granular materials as a cushioning medium. An impact model based on the Discrete Element Method (DEM) was developed and validated against drop-weight experiments. Both granular material properties and impactor characteristics were investigated. The study examined the cushioning effects on both the base plate and the impactor under impact loading, and the sensitivity of key parameters was evaluated. The results showed that granular properties had minimal influence on the impactor peak force. Increasing particle Young’s modulus, density, or friction coefficient led to higher peak forces on the base plate, with Young’s modulus and density having significantly stronger effects than friction coefficient. Additionally, both the impactor size and velocity correlate positively with the peak forces transmitted to the base plate and experienced by the impactor. Under thin layer conditions, the impactor force was more sensitive to impact parameters, while in thick layers it was mainly determined by particle rearrangement and energy dissipation mechanisms. These findings reveal the mechanisms governing granular cushioning and provide a theoretical basis for vibration isolation design in AEM systems to preserve high-fidelity signals.

Signals doi: 10.3390/signals7020030

Authors: Ziqiang Zhu Haigen Zhou Ao Wei Junfeng Yuan Handong Tan Manping Yang Zuoxi Jiang Marco Alfano

Airborne electromagnetic detection systems are highly susceptible to low-frequency motion-induced noise, which significantly degrades the extraction of weak geological signals. Conventional signal processing methods alone are often insufficient to suppress mechanically induced vibration noise, resulting in signal distortion and reduced detection reliability. To address this limitation, this study proposes an active noise suppression strategy that integrates mechanical vibration isolation with advanced signal processing. A pneumatic vibration isolation platform based on a cable-driven parallel robot (CDPR) architecture is developed to achieve precise orientation correction and effective vibration isolation. The system employs kinematic modeling and a servo-controlled pneumatic cylinder driven by a proportional directional valve to enable accurate dynamic regulation. Numerical simulations conducted in the Advanced Modeling and Simulation Environment (AMESim), combined with proportional–integral–derivative (PID) control, demonstrate that piston displacement overshoot is constrained within 0.2 mm. Furthermore, targeted filtering techniques are applied to enhance signal quality. Experimental results show that the response time for continuous step input is 0.18–0.2 s, with a steady-state error below 0.3 mm, confirming robust control performance. The proposed framework provides an effective low-noise solution for airborne electromagnetic detection and can improve survey reliability in deep resource exploration.

Signals doi: 10.3390/signals7020029

Authors: Dino Otero Ariel Amadio Leandro Robles Dávila Marcos Maillot Cristian Bonini Walter Legnani

The Virial theorem has been applied with considerable success in various fields of natural sciences. This work proposes an extension of the theorem applied to discrete data series. This application will be called the Virial theorem extension and can be applied to the numerical solution of nonlinear dynamic systems represented by difference equations, such as logistic, discubic and random number generators, the numerical solution of differential equations like the nonlinear double pendulum and a series of pseudorandom numbers and its reciprocals. For this purpose, a coefficient was derived from the discrete Virial formalism. This coefficient can be used to detect when a time series is obtained as the solution of a differential equation, in which case the coefficient is close to 1, and when the data come from other sources, in which case it takes different values. With reference to chaotic dynamic systems, the discrete Virial coefficient shows the feasibility in the detection of a change in behavior, as an alternative to the traditional calculation of Lyapunov exponents, and it is a thousand times faster. The convergence speed of the final value of the discrete Virial coefficient of a dynamic system in a non-chaotic regime is between one and five orders of magnitude greater than in the chaotic regime, thus extending results in non-Hamiltonian systems, previously found by another author in Hamiltonian systems. The results obtained show that the proposal characterizes and distinguishes different types of behavior from the series under study. It also shows great sensitivity to the evolution of the series, even anticipating critical points. The proposed method to construct the discrete Virial extension does not require the existence of a Hamiltonian, which allows its application to a series obtained experimentally or from any differential equation. From a general point of view, this research shows a series of properties that can be reinterpreted in light of the discrete Virial coefficient, providing a novel and versatile tool, given its minimal applicability requirements. For pseudorandom number series, the extension reveals a consistent, quasi-mirror behavior between its kinetic and potential factors, suggesting an underlying structural property.

Signals doi: 10.3390/signals7020028

Authors: Yutaka Yoshida Kiyoko Yokoyama

Sample-wise detection of P-, R-, and T-peaks in electrocardiograms (ECGs) is challenging because each peak type is sparsely represented (≈1:500 samples in a typical 10-s, 500-Hz ECG at 60 bpm), such that even a small number of false-positives (FPs) can markedly degrade positive predictive value (PPV) and limit the practicality of classifier-only approaches. This study proposes a lightweight ECG peak detection framework that combines binary classifiers with physiological temporal constraints (PTC) to address extreme sample-level class imbalance. Local morphological features are first evaluated using lightweight machine-learning models, among which XGBoost (XGB) exhibited the most stable score-ranking performance. Rather than directly thresholding classifier outputs, prediction scores are interpreted within the framework, which encodes physiological timing relationships. R-peaks are detected using score ranking combined with a refractory-period constraint, and the detected R-peaks serve as temporal landmarks for subsequent P- and T-peak detection within physiologically plausible time windows reflecting the P–QRS–T sequence. Quantitative evaluation was conducted using the Lobachevsky University Electrocardiography Database, hereafter referred to as LUDB. With a temporal tolerance of ±20 ms, the XGB-based system achieved an F1-score of 0.87 for R-peak detection (sensitivity 0.96, PPV 0.79), corresponding to approximately 9–10 true R-peaks with only 2–3 FP samples per 10-s segment. For P- and T-peaks, F1-scores of 0.70 and 0.69 were obtained, respectively. Additional evaluation on arrhythmic LUDB records demonstrated robust R-peak detection across rhythm types. In AF-related rhythms, where organized P waves are physiologically absent, the framework appropriately suppressed P-peak detections, with false-positive rates remaining below 0.31%. Qualitative application to ECG recordings from the PTB-XL database further demonstrated physiologically consistent behavior. These results indicate that reliable and interpretable ECG peak detection under extreme class imbalance can be achieved by integrating lightweight classifiers within the proposed framework, without reliance on complex deep learning architectures.

Signals doi: 10.3390/signals7020027

Authors: Ali Sarafnia M. Omair Ahmad M.N.S. Swamy

Distortion of speech in real-life communication is inevitable, affecting its quality. Conventionally, the effectiveness of a speech system in terms of the perceptual quality of the speech it produces has been assessed using a time-consuming subjective metric, the mean opinion score. There are a number of objective metrics that can be used instead of the mean opinion score to assess the perceptual quality of the speech signal. The objective of this paper is to propose and validate a new objective metric, the spectral entropy-based metric (SEM), designed to evaluate the perceptual quality of speech and perceptual naturalness by quantifying spectral coherence. While other metrics focus on intelligibility, this study aims to fill a gap in naturalness assessment. The core novelty of this work lies in offering a diagnostic perspective on spectral coherence, an indicator of speech naturalness that is often not explicitly addressed by other metrics. To demonstrate the effectiveness of the proposed metric in evaluating the perceptual quality, we consider fixed-beam and steerable-beam first-order differential microphone arrays. Compared with other objective metrics, it is shown that the proposed SEM is more sensitive to spectral coherence, a predominant indicator of the naturalness of the output speech signal of a speech system.

Signals doi: 10.3390/signals7020026

Authors: Md. Selim Al Mamun Fatema Akhter

The accurate positioning of location in indoor environment has become crucial in many location-based services, mainly where global positioning systems (GPSs) are unavailable or fail to navigate correctly. Conventional fingerprint-based approaches face challenges with instability, low accuracy, and being sensitive to changes in the environment. This study proposes a robust fingerprint-based machine learning (ML) model for dynamic environment indoor navigation in real time. The proposed model uses link quality indicator (LQI) values from IEEE 802.15.4 as fingerprints and supervised learning algorithms, showing high accuracy and a strong ability to adapt to changes in the environment. A room within a building floor has been regarded as the unit of location identification instead of the user’s exact coordinates to make the suggested model more relevant under practical conditions. The model was trained and tested using a real LQI dataset collected from varied indoor conditions to ensure the system can adapt effectively and operate consistently in dynamic environments and signal conditions. The results show that the proposed model surpasses fingerprinting indoor navigation in room detection accuracy and flexibility to environmental changes. An implemented prototype proved the real-time capability of the proposal in smart buildings, hospitals, and industrial IoT settings.

Signals doi: 10.3390/signals7020025

Authors: Peter Swartz Saiyu Ren Shuxia Sun James Martin

The Nyquist Folding Receiver is an architecture that uses Compressed Sensing to convert analog radio frequency signals into digital signals. Analog-to-Digital Converter architectures that implement Compressed Sensing are collectively known as Analog-to-Information. Sparse bandlimited analog signals with frequency bands above the Nyquist frequency of a traditional Analog-to-Digital Converter can be recovered by Analog-to-Information receivers. Recovery of these signals is affected by the selection of a Compressed Sensing recovery algorithm. Typical recovery algorithms selected for recovery of Nyquist Folding Receiver-compressed outputs use iterative methods to find the solution. This work presents a machine learning approach to signal reconstruction. The proposed method uses a neural network to learn the mapping from compressed samples to the original signal. The neural network is trained on a set of synthetic signals generated by a new open-source Analog-to-Information simulator called SpectraMelt. The results show that the neural network can effectively reconstruct the original signal from the compressed samples, achieving better performance than traditional iterative methods.

Signals doi: 10.3390/signals7020024

Authors: Huu-Son Nguyen Masaki Kurosawa Koichiro Ishibashi Ryou Tanaka Cong-Kha Pham Guanghao Sun

This study presents a harmonic-based method for non-contact heart rate (HR) estimation from continuous-wave (CW) Doppler radar signals, validated across multiple species including humans and small animals (cat). Traditional frequency-domain methods struggle when the HR fundamental frequency is weak or overlaps with respiratory components. The proposed approach addresses this by identifying three higher-order HR harmonics (2nd, 3rd, and 4th) then reconstructing the HR fundamental frequency from their integer ratios (3/2, 4/3, 2/1). The algorithm processes 20-s sliding windows (1-s overlap) using bandpass filtering to remove respiratory components and HR fundamental while preserving higher harmonics, followed by Power Spectral Density (PSD) analysis. When a complete harmonic set cannot be found, the proposed algorithm switches to harmonic pair detection, enhancing robustness when one harmonic is absent or attenuated. Besides, an adaptive tolerance mechanism enables detection under non-ideal conditions. The method was validated using a public human dataset and an experimental cat dataset with varied positions (supine/prone) and anesthesia levels (1–3% isoflurane). For humans, the algorithm achieved HR Accuracy consistently above 98% with an average RMSE of 1.33 bpm (MAPE: 1.29%, MAE: 0.86 bpm) and Bland-Altman bias below 0.9 bpm. For the cat dataset, performance was even better with HR Accuracy remaining above 99%, an average RMSE of 0.39 bpm (MAPE: 0.22%, MAE: 0.30 bpm), and bias below 0.14 bpm.

Signals doi: 10.3390/signals7020023

Authors: Longqing Li Wenjun Xie Deming Hu Jingke Nie Fei Xie Zhiping Huang Yongjie Zhao

Signal-to-noise ratio (SNR) estimation plays a crucial role in communication systems, directly impacting the quality and reliability of signal transmission. This paper proposes a novel deep learning framework aimed at enhancing the accuracy and robustness of SNR estimation. The framework converts received signals into time–frequency matrices as feature inputs, effectively capturing both temporal and spectral characteristics through time–frequency analysis. Extensive experimental results across an SNR range of −5 dB to 15 dB demonstrate that our method achieves a mean squared error (MSE) that closely approaches the theoretical Cramér–Rao bound (CRB), comparable to data-aided (DA) maximum likelihood methods. A quantitative analysis reveals that, even under challenging conditions, such as a low SNR of −5 dB, the model maintains superior accuracy with a mean absolute error (MAE) as low as 0.352, significantly outperforming traditional M2M4 and NDA estimators. The model’s performance was systematically evaluated in a wide range of scenarios, encompassing various signal modulation formats, upsampling factors, multipath fading channels, frequency offsets, phase shifts, and roll-off factors. The evaluation highlights its exceptional generalization capability and robustness, with high performance and stability maintained even in challenging and dynamic environments.

Signals doi: 10.3390/signals7020021

Authors: Yuan Xing Xuedong Ding Haowen Pan

Modern manufacturing quality control demands intelligent, adaptive inspection systems capable of real-time contamination detection with minimal computational overhead. We present a five-agent vision framework for material-aware contamination detection in manufacturing environments. The system comprises: a Material Classification Agent that identifies contamination type (fiber, sand, or mixed), three Material-Specific Detection Agents, each employing patch-based CNNs optimized for their respective material with dynamic patch size selection (128 px, 256 px, 384 px), and an Adaptation Agent that monitors performance and eliminates consistently failing patch size configurations. This hierarchical architecture enables intelligent routing to specialized detectors and continuous refinement through performance-driven adaptation. The Material Classification Agent achieves 98% accuracy in contamination type identification. Material-specific agents demonstrate F1-scores of 0.968 (fiber), 0.977 (sand), and 0.977 (mixed) with real-time inference (2.40–11.11 ms per 512 × 512 image). The Adaptation Agent implements selective patch size elimination: configurations failing quality thresholds (F1 < 0.5) across multiple evaluation cycles are removed from the detection pipeline. On the synthetic test split used in this study, comparative evaluation against PatchCore, WinCLIP, and PaDiM shows 3–45× higher F1-scores with superior accuracy–latency trade-offs, validating the efficacy of specialized material-aware architectures for manufacturing contamination detection.

Signals doi: 10.3390/signals7020022

Authors: Mawardi Bahri Marni Rezki Samsul Ariffin Abdul Karim Nasrullah Bachtiar Muhammad Zakir Bannu Addul Samad

The offset fractional Fourier transform (OFrFT) has emerged as a mathematical tool for time-frequency analysis of non-stationary signals. However, there has been relatively little research on investigating properties including the convolution theorem and the sampling formulas. This paper investigates the new form of convolution theorem and its product theorem associated with the OFrFT. We also establish a sampling formula related to the transformation. Finally, a simple example is displayed to verify the results related to the sampling formula for the OFrFT.

Signals doi: 10.3390/signals7020020

Authors: Yutaka Yoshida Kiyoko Yokoyama

Electrocardiography (ECG) is a key biosensing technique for assessing cardiac function and autonomic activity. Accurate detection of R-peaks and precise calculation of R-R intervals (RRIs) are essential for heart rate variability (HRV) analysis; however, automated detection algorithms remain vulnerable to local misdetections, such as false positives or missed beats (false negatives), caused by noise, baseline fluctuations, or waveform variability. Conventional correction approaches based on filter or threshold adjustment may introduce new errors outside the target region, highlighting the need for an intuitive and localized manual correction capability. To address this issue, we developed a prototype graphical user interface (GUI)-based ECG viewer implemented in Fortran for high computational efficiency. The system enables interactive insertion and deletion of detected R-peaks, with recalculation of the RRI time series and automatic updating of related analyses, including power spectral density, histograms, Lorenz plots, and polar plots. Validation using synthetic ECG signals at four sampling frequencies (125–1000 Hz) and three display time scales (2, 5, and 10 s) demonstrated correction errors below 0.7% and stable update times within 20–30 ms. When applied to real ECG recordings from the MIT-BIH Arrhythmia Database (records 115, 122, and 209; MLII lead), the GUI-derived RRIs achieved accuracies exceeding 0.985 at a strict ±10 ms tolerance and reached 1.000 at ±20 ms or higher, including recordings with frequent atrial premature contractions. These results indicate that the proposed system provides reliable feedback for localized correction of R-peak misdetections without altering the underlying ECG signal. The proposed algorithm may support future research and experimental applications in biosignal processing.

Signals doi: 10.3390/signals7020019

Authors: Alexey García Padilla Ivonne Bazán Trujillo Carlos A. Negreira Casares

Displacement estimation methods in strain elastography use ultrasound signals to estimate displacements and strain in soft tissue. Although several methods exist, systematic comparisons under controlled simulation conditions for lesions smaller than 5 mm are limited. Evaluating axial accuracy for superficial and intermediate depths in small breast cancer lesions is clinically important, as early-stage detection with existing techniques remains challenging. In this study, speckle tracking, the Doppler method, and the combined autocorrelation method (CAM) were used to estimate axial displacements from simulated elastography signals. The performance of these methods was assessed using the root mean square error (RMSE) for displacement field, strain, and tumor size estimation in a three-layer model comprising healthy tissue–tumor–healthy tissue. An extended analysis considering anatomically realistic tissue and motion artifacts conditions for the case of smallest lesion is presented. Finally, the CAM method, which obtained the best results, was assessed varying SNR/strain values. Simulation results show that CAM outperforms the other methods in displacement estimation across early-stage tumor sizes at both superficial and intermediate depths in all performed tests.

Signals doi: 10.3390/signals7010018

Authors: Malaz Alfa Fars Samann Thomas Schanze

Background: Electrocardiography (ECG), particularly the 12-lead configuration, is a crucial method for identifying heart rhythm abnormalities. However, its effectiveness can be reduced by noise contamination. State-of-the-art denoising methods based on neural networks have demonstrated promising performance in denoising complex biosignals like ECG. However, most of these methods have focused on denoising single-lead ECG recordings. Methods: This research aims to leverage the inherent correlation among multi-lead ECG signals. Therefore, a multi-lead convolutional denoising autoencoder (ML-CDAE) model is proposed, to learn more effective representations, leading simultaneously to improved denoising performance and enhanced quality of 12-lead ECG recordings. Results: The findings indicate that ML-CDAE consistently outperforms a single-lead convolutional denoising autoencoder (SL-CDAE) and fully convolutional denoising autoencoder (FCN-DAE) model in denoising ECG signals corrupted by a mixture of physical noises. In particular, the mean squared error (MSE) and signal-to-noise ratio improvement (SNRimp) are used as evaluation metrics to assess the performance. Conclusions: The strong correlation among multi-lead ECG signals can be leveraged not only to enhance the denoising performance of the ML-CDAE model but also to simultaneously denoise 12-lead ECG signals more successfully compared to both the SL-CDAE and FCN-DAE models.

Signals doi: 10.3390/signals7010017

Authors: Christos Kalogeropoulos Konstantinos Theofilatos Seferina Mavroudi

Electroencephalography (EEG) has transitioned from a subjective observational method into a data-intensive analytical field that utilises sophisticated algorithms and mathematical models. This review provides a holistic foundation by detailing the neurophysiological basis, recording techniques, and applications of EEG before providing a rigorous examination of traditional and modern analytical pillars. Statistical and Time-Series Analysis, Spectral and Time-Frequency Analysis, Spatial Analysis and Source Modelling, Connectivity and Network Analysis, and Nonlinear and Chaotic Analysis are explored. Afterwards, while acknowledging the historical role of Machine Learning (ML) and Deep Learning (DL) architectures, such as Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), this review shifts the primary focus toward current state-of-the-art Artificial Intelligence (AI) trends. We place emphasis on the emergence of Foundation Models, including Large Language Models (LLMs) and Large Vision Models (LVMs), adapted for high-dimensional neural sequences. Finally, we explore the integration of Generative AI for data augmentation and review Explainable AI (XAI) frameworks designed to bridge the gap between “black-box” decoding and clinical interpretability. We conclude that the next generation of EEG analysis will likely converge into Neuro-Symbolic architectures, synergising the massive generative power of foundation models with the rigorous, rule-based interpretability of classical signal theory.

Signals doi: 10.3390/signals7010016

Authors: Manal Hilali Abdellah Ezzati Said Ben Alla Ahmed El Badaoui

Automatic emotion recognition based on EEG has been a key research frontier in recent years, involving the direct extraction of emotional states from brain dynamics. However, existing deep learning approaches often treat EEG either as a sequence or as a static spatial map, thereby failing to jointly capture the temporal evolution and spatial dependencies underlying emotional responses. To address this limitation, we propose an Interpretable Residual Spatio-Temporal Graph Attention Network (IRSTGANet) that integrates temporal convolutional encoding with residual graph-attention blocks. The temporal module enhances short-term EEG dynamics, while the graph-attention layers learn adaptive node connectivity relationships and preserve contextual information through residual links. Evaluated on the DEAP and SEED datasets, the proposed model achieved exceptional performance on valence and arousal, as well as four-class and nine-class classification on the DEAP dataset and on the three-class SEED dataset, exceeding state-of-the-art methods. These results demonstrate that combining temporal enhancement with residual graph attention yields both improved recognition performance and interpretable insights into emotion-related neural connectivity.

Signals doi: 10.3390/signals7010015

Authors: Seyed Reza Razavi Ashish H. Shah Zahra Moussavi

Despite successful revascularization, patients with non-ST elevation myocardial infarction (NSTEMI) remain at higher risk of mortality and morbidity. Accurately predicting mortality risk in this cohort can improve outcomes through timely interventions. This study for the first time predicts 1-year all-cause mortality in an NSTEMI cohort using features extracted primarily from the aortic pressure (AP) signal recorded during cardiac catheterization. We analyzed data from 497 NSTEMI patients (66.3 ± 12.9 years, 187 (37.6%) females) retrospectively. We developed three survival models, the multivariate Cox proportional hazards, DeepSurv, and random survival forest, to predict mortality. Then, used Shapley additive explanations (SHAP) to interpret the decision-making process of the best survival model. Using 5-fold stratified cross-validation, DeepSurv achieved an average C-index of 0.935, an IBS of 0.028, and a mean time-dependent AUC of 0.939, outperforming the other models. Ejection systolic time, ejection systolic period, the difference between systolic blood pressure and dicrotic notch pressure (DesP), skewness, the age-modified shock index, and myocardial oxygen supply/demand ratio were identified by SHAP as the most characteristic AP features. In conclusion, AP signal features offer valuable prognostic insight for predicting 1-year all-cause mortality in the NSTEMI population, leading to enhanced risk stratification and clinical decision-making.

Signals doi: 10.3390/signals7010014

Authors: Oussama Boufares Wajdi Saadaoui Mohamed Boussif

Foreground segmentation and background subtraction are critical components in many computer vision applications, such as intelligent video surveillance, urban security systems, and obstacle detection for autonomous vehicles. Although extensively studied over the past decades, these tasks remain challenging, particularly due to rapid illumination changes, dynamic backgrounds, cast shadows, and camera movements. The emergence of supervised deep learning-based methods has significantly enhanced performance, surpassing traditional approaches on the benchmark dataset CDnet2014. In this context, this paper provides a comprehensive review of recent supervised deep learning techniques applied to background subtraction, along with an in-depth comparative analysis of state-of-the-art approaches available on the official CDnet2014 results platform. Specifically, we examine several key architecture families, including convolutional neural networks (CNN and FCN), encoder–decoder models such as FgSegNet and Motion U-Net, adversarial frameworks (GAN), Transformer-based architectures, and hybrid methods combining intermittent semantic segmentation with rapid detection algorithms such as RT-SBS-v2. Beyond summarizing existing works, this review contributes a structured cross-family comparison under a unified benchmark, a focused analysis of performance behavior across challenging CDnet2014 scenarios, and a critical discussion of the trade-offs between segmentation accuracy, robustness, and computational efficiency for practical deployment.

Signals doi: 10.3390/signals7010013

Authors: Hussam Rostum József Vásárhelyi

Feature extraction and description are fundamental components of visual perception systems used in applications such as visual odometry, Simultaneous Localization and Mapping (SLAM), and autonomous navigation. In resource-constrained platforms, such as Unmanned Aerial Vehicles (UAVs), achieving real-time hardware acceleration on Field-Programmable Gate Arrays (FPGAs) is challenging. This work demonstrates an FPGA-based implementation of an adaptive ORB (Oriented FAST and Rotated BRIEF) feature extraction pipeline designed for high-throughput and energy-efficient embedded vision. The proposed architecture is a completely new design for the main algorithmic blocks of ORB, including the FAST (Features from Accelerated Segment Test) feature detector, Gaussian image filtering, moment computation, and descriptor generation. Adaptive mechanisms are introduced to dynamically adjust thresholds and filtering behavior, improving robustness under varying illumination conditions. The design is developed using a High-Level Synthesis (HLS) approach, where all processing modules are implemented as reusable hardware IP cores and integrated at the system level. The architecture is deployed and evaluated on two FPGA platforms, PYNQ-Z2 and KRIA KR260, and its performance is compared against CPU and GPU implementations using a dedicated C++ testbench based on OpenCV. Experimental results demonstrate significant improvements in throughput and energy efficiency while maintaining stable and scalable performance, making the proposed solution suitable for real-time embedded vision applications on UAVs and similar platforms. Notably, the FPGA implementation increases DSP utilization from 11% to 29% compared to the previous designs implemented by other researchers, effectively offloading computational tasks from general purpose logic (LUTs and FFs), reducing LUT usage by 6% and FF usage by 13%, while maintaining overall design stability, scalability, and acceptable thermal margins at 2.387 W. This work establishes a robust foundation for integrating the optimized ORB pipeline into larger drone systems and opens the door for future system-level enhancements.

Signals doi: 10.3390/signals7010012

Authors: Tomoki Matsunaga Hiroaki Saito

Multitrack music transcription is the task of converting music recordings into symbolic music representations that are assigned to individual instruments. This task requires simultaneous transcription of note onset and offset events for individual instruments. In addition, the limited resources of many transcription datasets make multitrack music transcription challenging. Thus, even state-of-the-art transcription systems are inadequate for applications requiring high accuracy. In this paper, we propose a framework to jointly transcribe onsets and frames for multiple instruments by integrating a deep learning architecture based on U-Net with an architecture based on Perceiver, which is a variant of the Transformer architecture. The proposed framework effectively detects the pitches of different instruments by employing the multi-layer combined frequency and periodicity (ML-CFP) with multilayered frequency-domain and quefrency-domain features as the input data representation. Our experiments demonstrate that the proposed multitrack music transcription system outperforms existing systems on five transcription datasets, including low-resource datasets. Furthermore, we evaluate the proposed system in terms of instrument type and show that the system provides high-quality transcription results for the predominant instruments.

Signals doi: 10.3390/signals7010011

Authors: Nefeli Chatzisavvidou Stefanos Papasotiriou Ioanna Vrachni Konstantinos Kantelis Petros Nicopolitidis Georgios Papadimitriou

With rapid advances in nanotechnology and synthetic biology, biological nanonetworks are emerging for biomedical and environmental applications within the Internet of Bio-NanoThings. While they rely on molecular communication, experimental validation remains limited, especially for non-ideal effects such as molecular accumulation. In this work, we present a novel table-top experimental system that emulates the core functionalities of a biological nanonetwork and is straightforward to reproduce in standard laboratory environments, also making it suitable for educational demonstrations. To the best of our knowledge, this is the first experimental platform that incorporates two end nodes capable of acting interchangeably as transmitter and receiver, thereby enabling true bidirectional molecular communication. Information transfer is realized through controlled release, advection and diffusion of molecules, using molecular concentration coding analogous to concentration shift keying, while the receiver decodes messages by comparing measured concentrations against predefined thresholds. Based on the measurements reported herein, the drop-based algorithm substantially outperforms the threshold-based scheme. Specifically, it reduces first-message latency by more than 2.5× across the tested volumes and reduces latest-message latency by up to 71%, providing approximately 3.7× better message delivery. A key experimental outcome is the observation of channel saturation: beyond a certain operating period, residual molecules accumulate and effectively saturate the medium, inhibiting reliable further message exchange until sufficient clearance occurs. This saturation-induced “channel memory” emerges as a fundamental practical constraint on sustained communication and achievable data rates. Overall, the proposed platform provides a scalable, controllable, and experimentally accessible testbed for systematically studying signal degradation, saturation, clearance dynamics, and throughput limits, thereby bridging the gap between theoretical models and practical implementations in the Internet of Bio-NanoThings era.

Signals doi: 10.3390/signals7010010

Authors: Yifan Liu Yonggang Xu Yanping Zhu Xue Zou Huaming Zhang

Bearing fault diagnosis is critical in rotating machinery, and collecting and analyzing vibration signals from faulty bearings is a widely employed method in fault diagnosis. To efficiently extract the information of periodic pulse from complex signals and accurately identify fault characteristic frequencies, this paper proposes a BSNA (Bispectral Slice Negentropy Analysis) method. This method leverages the nonlinear characteristics of bispectral analysis and the sensitivity of negentropy measures to transform one-dimensional signals into two-dimensional spectra. By utilizing the demodulation capability of the time-frequency modulation bispectrum, it highlights the relationship between resonance bands and modulation frequency, while maximizing the preservation of critical fault information and minimizing the impact of interference signals. The fault information contained in the slices is subsequently quantified using the CSNE (correlation spectral negentropy), which effectively captures the magnitude of periodic pulse energy. By calculating the CSNE of each modulation frequency slice and visualizing it, the energy distribution of periodic pulses within each slice can be effectively observed. The feasibility of this method in rolling bearing fault diagnosis has been validated through simulation analysis and experimental comparison. This approach enables the accurate identification of fault characteristic frequency and its harmonics, thereby significantly enhancing the accuracy and robustness of fault diagnosis, particularly in complex and noisy background environments.

Signals doi: 10.3390/signals7010009

Authors: Shaoxun Lu Huabo Liu

This paper focuses on the design of robust fusion estimators for multi-sensor systems experiencing constrained communications, model uncertainties, and random packet dropouts. To mitigate the impact of modeling errors, a sensitivity-penalized robust state estimator is employed at each local estimator. At the local fusion estimators, a centralized robust fusion estimation algorithm is derived by improving the cost function of the sensitivity-penalized estimator. The implementation of an event-triggered strategy effectively alleviates the burden on the communication channels linking the sensors and the fusion center. Moreover, the fusion estimator is capable of handling packet drops caused by unreliable communication channels, and the pseudo cross-covariance matrix is accordingly formulated. Sufficient conditions are derived to ensure the uniform boundedness of the estimation error for the proposed robust fusion estimator. Finally, simulation experiments using a tractor-car system validate the performance and advantages of the presented algorithm.

Signals doi: 10.3390/signals7010008

Authors: Faroq Awin Yasser Alginahi Esam Abdel-Raheem

This survey delves into the Firebug Swarm Optimization (FSO) algorithm, an advanced global optimization algorithm that plays a pivotal role in modern swarm intelligence optimization techniques. It explores the core principles of the FSO algorithm and examines the various hybrid variants developed to address complex optimization challenges. This survey also traces the evolution of swarm optimization methods, shedding light onto the natural phenomena and biological processes that have inspired these algorithms. Furthermore, it highlights the diverse real-world applications of the FSO algorithm, showcasing its effectiveness in fields such as engineering, data science, and artificial intelligence. To provide a comprehensive comparison, the survey includes a case study that evaluates the FSO algorithm’s performance against other existing algorithms. Lastly, the survey identifies key open research questions and suggests potential future directions for advancing the FSO algorithm and other nature-inspired optimization techniques, aiming to overcome current limitations and unlock new possibilities.

Signals doi: 10.3390/signals7010007

Authors: Ajan Ahmed Masudul H. Imtiaz

Self-supervised learning (SSL) models have achieved remarkable success in speaker verification tasks, yet their robustness to real-world audio degradation remains insufficiently characterized. This study presents a comprehensive analysis of how audio quality degradation affects three prominent SSL-based speaker verification systems (WavLM, Wav2Vec2, and HuBERT) across three diverse datasets: TIMIT, CHiME-6, and Common Voice. We systematically applied 21 degradation conditions spanning noise contamination (SNR levels from 0 to 20 dB), reverberation (RT60 from 0.3 to 1.0 s), and codec compression (various bit rates), then measured both objective audio quality metrics (PESQ, STOI, SNR, SegSNR, fwSNRseg, jitter, shimmer, HNR) and speaker verification performance metrics (EER, AUC-ROC, d-prime, minDCF). At the condition level, multiple regression with all eight quality metrics explained up to 80% of the variance in minDCF for HuBERT and 78% for WavLM, but only 35% for Wav2Vec2; EER predictability was lower (69%, 67%, and 28%, respectively). PESQ was the strongest single predictor for WavLM and HuBERT, while Shimmer showed the highest single-metric correlation for Wav2Vec2; fwSNRseg yielded the top single-metric R2 for WavLM, and PESQ for HuBERT and Wav2Vec2 (with much smaller gains for Wav2Vec2). WavLM and HuBERT exhibited more predictable quality-performance relationships compared to Wav2Vec2. These findings establish quantitative relationships between measurable audio quality and speaker verification accuracy at the condition level, though substantial within-condition variability limits utterance-level prediction accuracy.

Signals doi: 10.3390/signals7010006

Authors: Roman N. Ipanov Aleksey A. Komarov

To obtain radar images of a group of small space objects or to resolve individual elements of complex space objects in near-Earth orbit, a radar system must have high spatial resolution. High range resolution is achieved by using complex probing signals with a wide spectrum bandwidth. Achieving high angular resolution for small or complex space objects is based on the inverse synthetic aperture antenna effect. Among the various classes of complex signals, only two have found practical application in Inverse Synthetic Aperture Radar (ISAR) systems so far: the Linear Frequency-Modulated signal (chirp) and the Stepped-Frequency signal. Over the coherent integration interval of the echo signals, which corresponds to the ISAR aperture synthesis time, the combined correlation characteristics of the signal ensemble are analyzed. A high level of integral correlation noise in the ensemble of probing signals degrades the quality of the radar image. Therefore, a probing signal with a Zero Autocorrelation Zone (ZACZ) is highly relevant for ISAR applications. In this work, through simulation, radar images of a complex space object were obtained using both chirp and ZACZ probing signals. A comparative analysis of the correlation characteristics of the echo signals and the resulting radar images of the complex space object was performed.

Signals doi: 10.3390/signals7010005

Authors: Arunee Promsri

Leg dominance has been linked to an increased risk of lower-limb injuries in sports. This study examined bilateral asymmetry in muscle synergy patterns during one-leg stance on stable and multiaxial unstable surfaces. Twenty-five active young adults (25.6 ± 3.9 years) performed unipedal stance tasks on their dominant and non-dominant legs while surface electromyography (EMG) was recorded from seven lower-limb muscles per leg. Muscle synergies were extracted using non-negative matrix factorization (NMF), and structural similarity was assessed via cosine similarity with the Hungarian matching algorithm. Four consistent synergies were identified under both surface conditions, accounting for 88% of the total variance. On the stable surface, significant asymmetry in muscle weightings was observed in the rectus femoris (p = 0.030) for Synergy 1 and in the rectus femoris (p = 0.042), tibialis anterior (p = 0.024), peroneus longus (p = 0.023), and soleus (p = 0.006) for Synergy 2. On the unstable surface, asymmetry was evident in the biceps femoris (p = 0.048) for Synergy 2 and the rectus femoris (p = 0.045) for Synergy 3. Overall, dominance-related asymmetry was more pronounced under stable conditions and became more subtle as postural demand increased, revealing bilateral asymmetry in neuromuscular coordination during unipedal stance.

Signals doi: 10.3390/signals7010004

Authors: Innocent Tujyinama Bessam Abdulrazak Rachid Hedjam

Background and Objective: The accurate detection of sleep stages and disorders in older adults is essential for the effective diagnosis and treatment of sleep disorders affecting millions worldwide. Although Polysomnography (PSG) remains the primary method for monitoring sleep in medical settings, it is costly and time-consuming. Recent automated models have not fully explored and effectively fused the sleep features that are essential to identify sleep stages and disorders. This study proposes a novel automated model for detecting sleep stages and disorders in older adults by analyzing PSG recordings. PSG data include multiple channels, and the use of our proposed advanced methods reveals the potential correlations and complementary features across EEG, EOG, and EMG signals. Methods: In this study, we employed three novel advanced architectures, (1) CNNs, (2) CNNs with Bi-LSTM, and (3) CNNs with a transformer encoder, for the automatic classification of sleep stages and disorders using multichannel PSG data. The CNN extracts local features from RGB spectrogram images of EEG, EOG, and EMG signals individually, followed by an appropriate column-wise feature fusion block. The Bi-LSTM and transformer encoder are then used to learn and capture intra-epoch feature transition rules and dependencies. A residual connection is also applied to preserve the characteristics of the original joint feature maps and prevent gradient vanishing. Results: The experimental results in the CAP sleep database demonstrated that our proposed CNN with transformer encoder method outperformed standalone CNN, CNN with Bi-LSTM, and other advanced state-of-the-art methods in sleep stages and disorders classification. It achieves an accuracy of 95.2%, Cohen’s kappa of 93.6%, MF1 of 91.3%, and MGm of 95% for sleep staging, and an accuracy of 99.3%, Cohen’s kappa of 99.1%, MF1 of 99.2%, and MGm of 99.6% for disorder detection. Our model also achieves superior performance to other state-of-the-art approaches in the classification of N1, a stage known for its classification difficulty. Conclusions: To the best of our knowledge, we are the first group going beyond the standard to investigate and innovate a model architecture which is accurate and robust for classifying sleep stages and disorders in the elderly for both patient and non-patient subjects. Given its high performance, our method has the potential to be integrated and deployed into clinical routine care settings.

Signals doi: 10.3390/signals7010003

Authors: Alessandro Filisetti Pietro Bia Germana Luciani Margherita Losardo Riccardo Ardoino Antonio Manna

The absence of a recognizable P wave in an electrocardiogram (ECG) is a critical indicator for the diagnosis of atrial fibrillation (AF). An algorithm capable of distinguishing between physiological and pathological states in a short period of time could serve as a valuable tool for timely and effective diagnosis, even in a home setting. To achieve this goal, a deterministic algorithm is proposed. The Fantasia Database and the AF Termination Challenge Database were used for training the model. Subsequently, for the test session, a one-minute recording was extracted from the Autonomic Aging Dataset and the Long-Term AF Database. After band-pass filtering, characteristic points such as R-peaks and P waves were extracted. The R-peak detection algorithm was compared with the gold standard Pan-Tompkins, obtaining a p-value > 0.05 on the Fantasia Database, which means that there is no statistical difference between them. Subsequently derived features such as duration, amplitude, subtended area, and P wave slope have been used to discriminate healthy subjects from AF patients. The P-wave slope emerged as the most effective feature, achieving a classification accuracy of 100% and 96% for the training and test sets, respectively. This algorithm thus represents a significant advancement as it achieves a performance comparable to other deterministic methods based on P wave analysis using only one-minute recordings, thereby enabling accurate diagnosis in a shorter time frame.

Signals doi: 10.3390/signals7010002

Authors: Vanel Lazcano Mabel Vega-Rojas Felipe Calderero

Scene depth information is a key component of any robotic mobile application. Range sensors, such as LiDAR, sonar, or radar, capture depth data of a scene. However, the data captured by these sensors frequently presents missing regions or information with a low confidence level. These missing regions in the depth data could be large areas without information, making it difficult to make decisions, for instance, for an autonomous vehicle. Recovering depth data has become a primary activity for computer vision applications. This work proposes and evaluates an interpolation model to infer dense depth maps from a Lab color space reference picture and an incomplete-depth image embedded in a completion pipeline. The complete proposal pipeline comprises convolutional layers and a convex combination of the infinity Laplacian and non-local means model. The proposed model infers dense depth maps by considering depth data and utilizing clues from a color picture of the scene, along with a metric for computing differences between two pixels. The work contributes (i) the convex combination of the two models to interpolate the data, and (ii) the proposal of a class of function suitable for balancing between different models. The obtained results show that the model outperforms similar models in the KITTI dataset and outperforms our previous implementation in the NYU_v2 dataset, dropping the MSE by 34.86%, 3.35%, and 34.42% for 4×, 8×, 16× upsampling tasks, respectively.

Signals doi: 10.3390/signals7010001

Authors: Antonio Santos da Silva Kevin Herman Muraro Gularte Giovanni Almeida Santos Davi Salomão Soares Corrêa Luís Felipe Oliveira de Melo João Paulo Javidi da Costa José Alfredo Ruiz Vargas Daniel Alves da Silva Tai Fei

Autonomous vehicles (AVs) are transforming transportation by improving safety, efficiency, and intelligence through integrated sensing, computing, and communication technologies. However, their growing reliance on Vehicle-to-Everything (V2X) communication exposes them to cybersecurity vulnerabilities, particularly at the physical layer. Among these, jamming attacks represent a critical threat by disrupting wireless channels and compromising message delivery, severely impacting vehicle coordination and safety. This work investigates the robustness of New Radio (NR)-V2X-enabled vehicular systems under jamming conditions through a dual-methodology approach. First, two Cooperative Intelligent Transport System (C-ITS) scenarios standardized by 3GPP—Do Not Pass Warning (DNPW) and Intersection Movement Assist (IMA)—are implemented in the OMNeT++ simulation environment using Simu5G, Veins, and SUMO. The simulations incorporate four types of jamming strategies and evaluate their impact on key metrics such as packet loss, signal quality, inter-vehicle spacing, and collision risk. Second, a complementary laboratory experiment is conducted using AnaPico vector signal generators (a Keysight Technologies brand) and an Anritsu multi-channel spectrum receiver, replicating controlled wireless conditions to validate the degradation effects observed in the simulation. The findings reveal that jamming severely undermines communication reliability in NR-V2X systems, both in simulation and in practice. These findings highlight the urgent need for resilient NR-V2X protocols and countermeasures to ensure the integrity of cooperative autonomous systems in adversarial environments.

Signals doi: 10.3390/signals6040074

Authors: Jiapeng Duan Bo Zhang

The safe and stable operation of power systems and other dynamic systems relies on accurate perception of their dynamic processes. Voltage, current, and other measurement signals carry critical information about the system’s state. However, under conditions such as equipment damage, aging, and non-ideal operational conditions of devices under test, over-range phenomena may occur, leading to biased estimation issues in adaptive filters. To address this problem, this paper proposes a variable-parameter subband adaptive filtering algorithm with signal clipping distortion awareness. The algorithm first uses the Expectation-Maximization (EM) process to achieve high-fidelity restoration of damaged signals. Then, by integrating an intelligent steady-state detector and a dual-mode control mechanism, the adaptive filter can adjust key parameters such as step-size, forgetting factor, and regularization parameter based on state perception results. Finally, theoretical analysis proves the unbiased nature of the proposed method. Validation using real-world data from a high-penetration renewable energy power system shows that the algorithm achieves fast tracking during transient events and provides high-precision estimation during steady-state operation, offering an effective solution for real-time, high-accuracy processing of dynamic measurement data in power systems.

Signals doi: 10.3390/signals6040073

Authors: Jakub Konopiński Piotr Sosiński Mikołaj Wanat Piotr Góral

This paper presents a comparative analysis of the influence of Fused Deposition Modeling (FDM) parameters—specifically material type, infill geometry, and density—on the vibro-acoustic characteristics of loudspeaker enclosures. The enclosures were designed as exponential horns to intensify resonance phenomena for precise evaluation. Twelve unique configurations were fabricated using three materials with distinct damping properties (PLA, ABS, wood-composite) and three internal geometries (linear, honeycomb, Gyroid). Key vibro-acoustic properties were assessed via digital signal processing of recorded audio signals, including relative frequency response and time-frequency (spectrogram) analysis, and correlated with a predictive Finite Element Analysis (FEA) model of mechanical vibrations. The study unequivocally demonstrates that a material with a high internal damping coefficient is a critical factor. The wood-composite enabled a reduction in the main resonance amplitude by approximately 4 dB compared to PLA with the same geometry, corresponding to a predicted 86% reduction in mechanical vibration. Furthermore, the results show that a synergy between a high-damping material and an advanced, energy-dissipating infill (Gyroid) is crucial for achieving high acoustic fidelity. The wood-composite with 10% Gyroid infill was identified as the optimal design, offering the most effective resonance damping and the most neutral tonal characteristic. This work provides a valuable contribution to the field by establishing a clear link between FDM parameters and acoustic outcomes, delivering practical guidelines for performance optimization in personalized audio systems.

Signals doi: 10.3390/signals6040072

Authors: Mohammed Javidh Ruchi Shah Mohan Uma Sethuramalingam Prabhu Rajendran Beaulah Jeyavathana

Remote photoplethysmography (rPPG) enables non-contact heart rate estimation but remains highly sensitive to illumination variation and dataset-dependent factors. This study proposes CHROM-Y, a robust 2D feature representation that combines chrominance (Ω, Φ) with luminance (Y) to improve physiological signal extraction under varying lighting conditions. The proposed features were evaluated using U-Net, ResNet-18, and VGG16 for heart rate estimation and waveform reconstruction on the UBFC-rPPG and BhRPPG datasets. On UBFC-rPPG, U-Net with CHROM-Y achieved the best performance with a Peak MAE of 3.62 bpm and RMSE of 6.67 bpm, while ablation experiments confirmed the importance of the Y-channel, showing degradation of up to 41.14% in MAE when removed. Although waveform reconstruction demonstrated low Pearson correlation, dominant frequency preservation enabled reliable frequency-based HR estimation. Cross-dataset evaluation revealed reduced generalization (MAE up to 13.33 bpm and RMSE up to 22.80 bpm), highlighting sensitivity to domain shifts. However, fine-tuning U-Net on BhRPPG produced consistent improvements across low, medium, and high illumination levels, with performance gains of 11.18–29.47% in MAE and 12.48–27.94% in RMSE, indicating improved adaptability to illumination variations.

Signals doi: 10.3390/signals6040071

Authors: Yadpirun Supharakonsakun

This study evaluates Empirical Bayes (EB) c-charts for monitoring count-type data under precautionary (PLF) and logarithmic (LLF) loss functions. By assuming an exponential prior for the Poisson mean, the EB framework enables the construction of predictive densities for future observations. Simulation studies and a real-world dataset on missing rivets in large aircraft were used to compare the methods’ ability to detect out-of-control conditions. The results show that EB–LLF charts exhibit high sensitivity for small and moderate process shifts, and both EB approaches outperform the classical c-chart by integrating prior information to detect shifts earlier while controlling false alarms. These findings highlight the importance of loss function choice and demonstrate the effectiveness of EB charts for robust process monitoring.

Signals doi: 10.3390/signals6040070

Authors: Igor Vujović Joško Šoda Ivana Golub Medvešek

The wavelet transform (WT) is an integral transform primarily used for processing and analyzing nonstationary signals due to its multiresolution property. Multiresolution analysis is one method that finds applications in many fields because of the characteristics of the transform. Over the years, WT has become standard and is integrated into many coding protocols and applications without special mention. Decades of research in the field of wavelets have revealed several stages of development. In the initial stage, the focus was on wavelet families, with scientists deriving new families for emerging applications. The second stage addressed implementation issues, emphasizing more efficient implementation techniques. The next stage involved artificial neural networks (ANNs) that perform WT. This paper reviews the development of WT with examples from maritime applications. We also provide an overview of cutting-edge trends in wavelets and propose the aforementioned stages as a new taxonomy of WT development.

Signals doi: 10.3390/signals6040069

Authors: Alon Kuperman

Second-order notch filters (NFs) with constant coefficients are often used as part of feedback controllers in grid-connected power conversion systems to prevent unwanted harmonic content polluting the closed control loops. In practice, the value of the mains frequency resides within a certain known range rather than remaining constant. Hence, the correct selection of NF coefficients is crucial for ensuring that the desired performance is maintained within the whole expected mains frequency range. Bilinear transformation (BLT) with notch frequency prewarping is often adopted to convert an NF from a continuous to a digital form. While accurately preserving the notch frequency location, the method reduces the filter bandwidth. As a remedy, BLT with both notch frequency and damping ratio prewarping may be employed. Nevertheless, some inaccuracy remains under low sampling-to-notch frequency ratios. This technical note demonstrates that the issue may be solved by prewarping the boundary values of the expected harmonic frequency range rather than the notch frequency and/or damping factor before applying the BLT. Simulation results accurately support the presented issue and proposed solution.

Signals doi: 10.3390/signals6040068

Authors: Kunkun Zhao Yaowei Jin Yizhou Feng Jianqing Li Yuxuan Zhou

Muscle synergies offer valuable insights into the movement strategies employed by the central nervous system and present a promising avenue for clinical applications. However, the field lacks a complete understanding of how surface electromyography processing parameters affect muscle synergy analysis, which in turn has hindered cross-study comparisons and the translation of experimental results to clinical contexts. To address the gap, this study presents a systematic evaluation of interactive effects of three key parameters on muscle synergy analysis, including nine cut-off frequencies of low-pass filters, five normalization methods, and five synergy extraction criteria, covering 225 unique combinations. Using a comprehensive running dataset of 135 subjects, this study examined variance accounted for (VAF) and correlation coefficient (R2) metrics, the number of synergies, and synergy structure consistency under different parameter settings. Synergy similarity was used as a quantitative measure of synergy stability across different parameter settings. The results demonstrated that cut-off frequencies, normalization methods, and criteria choices interactively influenced the outcomes. Notably, VAF consistently yielded higher values than R2, highlighting differences in how these metrics capture explained variance. Error VAF (EVAF) emerged as the most robust criterion for determining the number of synergies, especially when combined with normalization methods by maximum value (MAX), average value (AVE), or unit variance (UVA) and moderately high cut-off frequencies, which led to more stable synergy structures across conditions. Furthermore, the predefined threshold associated with each criterion markedly affected the estimated number of synergies. These findings provide structured guidelines for muscle synergy analysis, helping to standardize preprocessing and extraction parameters, improve reproducibility across studies, and enhance the clinical applicability of synergy-based assessments.

Signals doi: 10.3390/signals6040067

Authors: Arifin Arifin Harmiati Harbi Andi Silvia Indriani Ida Laila Bualkar Abdullah Alridho Irfan Idris Jalu Ahmad Prakosa

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.

Signals doi: 10.3390/signals6040066

Authors: Eungang Son Seungeon Song Bong-Seok Kim Sangdong Kim Jonghun Lee

Foot gesture recognition using a continuous-wave (CW) radar requires implementation on edge hardware with strict latency and memory budgets. Existing structured and unstructured pruning pipelines rely on iterative training–pruning–retraining cycles, increasing search costs and making them significantly time-consuming. We propose a NAS-guided bisection hybrid pruning framework on foot gesture recognition from a continuous-wave (CW) radar, which employs a weighted shared supernet encompassing both block and channel options. The method consists of three major steps. In the bisection-guided NAS structured pruning stage, the algorithm identifies the minimum number of retained blocks—or equivalently, the maximum achievable sparsity—that satisfies the target accuracy under specified FLOPs and latency constraints. Next, during the hybrid compression phase, a global L1 percentile-based unstructured pruning and channel repacking are applied to further reduce memory usage. Finally, in the low-cost decision protocol stage, each pruning decision is evaluated using short fine-tuning (1–3 epochs) and partial validation (10–30% of dataset) to avoid repeated full retraining. We further provide a unified theory for hybrid pruning—formulating a resource-aware objective, a logit-perturbation invariance bound for unstructured pruning/INT8/repacking, a Hoeffding-based bisection decision margin, and a compression (code-length) generalization bound—explaining when the compressed models match baseline accuracy while meeting edge budgets. Radar return signals are processed with a short-time Fourier transform (STFT) to generate unique time–frequency spectrograms for each gesture (kick, swing, slide, tap). The proposed pruning method achieves 20–57% reductions in floating-point operations (FLOPs) and approximately 86% reductions in parameters, while preserving equivalent recognition accuracy. Experimental results demonstrate that the pruned model maintains high gesture recognition performance with substantially lower computational cost, making it suitable for real-time deployment on edge devices.

Signals doi: 10.3390/signals6040065

Authors: Ekaniyere Oko-Odion Isaac Bruce Emmanuel Nti Darko Matthew Crabb Degang Chen

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.

Signals doi: 10.3390/signals6040064

Authors: Houmem Slimi Ala Balti Mounir Sayadi Mohamed Moncef Ben Khelifa

We propose a novel hybrid deep learning framework that synergistically integrates Convolutional Neural Networks (CNNs), Spiking Neural Networks (SNNs), and Generative Adversarial Networks (GANs) for robust and accurate classification of high-resolution frontal and sagittal human gait video sequences—capturing both lower-limb kinematics and upper-body posture—from subjects with Knee Osteoarthritis (KOA), Parkinson’s Disease (PD), and healthy Normal (NM) controls, classified into three disease-type categories. Our approach first employs a tailored CNN backbone to extract rich spatial features from fixed-length clips (e.g., 16 frames resized to 128 × 128 px), which are then temporally encoded and processed by an SNN layer to capture dynamic gait patterns. To address class imbalance and enhance generalization, a conditional GAN augments rare severity classes with realistic synthetic gait sequences. Evaluated on the controlled, marker-based KOA-PD-NM laboratory public dataset, our model achieves an overall accuracy of 99.47%, a sensitivity of 98.4%, a specificity of 99.0%, and an F1-score of 98.6%, outperforming baseline CNN, SNN, and CNN–SNN configurations by over 2.5% in accuracy and 3.1% in F1-score. Ablation studies confirm that GAN-based augmentation yields a 1.9% accuracy gain, while the SNN layer provides critical temporal robustness. Our findings demonstrate that this CNN–SNN–GAN paradigm offers a powerful, computationally efficient solution for high-precision, gait-based disease classification, achieving a 48.4% reduction in FLOPs (1.82 GFLOPs to 0.94 GFLOPs) and 9.2% lower average power consumption (68.4 W to 62.1 W) on Kaggle P100 GPU compared to CNN-only baselines. The hybrid model demonstrates significant potential for energy savings on neuromorphic hardware, with an estimated 13.2% reduction in energy per inference based on FLOP-based analysis, positioning it favorably for deployment in resource-constrained clinical environments and edge computing scenarios.

Signals doi: 10.3390/signals6040063

Authors: Yonggang Zhao Jiongming Lu Jixia Xu Jiechu Miu Hangbo Hua

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.

Signals doi: 10.3390/signals6040062

Authors: Ji-Yeong Jang Ji-Na Lee Ji-Hye Park Ji-Yeoun Lee

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.

Signals doi: 10.3390/signals6040061

Authors: Sanghamitra Subhadarsini Dash Malaya Kumar Nath

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.

Signals doi: 10.3390/signals6040060

Authors: Iosif Polenakis Christos Sarantidis Ioannis Karydis Markos Avlonitis

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.

Signals doi: 10.3390/signals6040059

Authors: Domonkos Varga An Quynh Cao

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.

Signals doi: 10.3390/signals6040058

Authors: Gergely Vakulya Helga Anna Albert-Huszár

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.

Signals doi: 10.3390/signals6040057

Authors: Marco Krondorf

Burst traffic radio systems use short signal bursts, which are prepended with an a priori known preamble sequence. The burst receivers exploit these preamble sequences for burst start detection. The process of burst start detection is commonly known as Packet Detection (PD), which employs preamble sequence cross-correlation and threshold detection. One major figure of merit for PD performance is the so-called ROC—receiver operating characteristics. ROC describes the trade-off between the probability of missed detection vs. the probability of false alarm. This article describes how to calculate the ROC for specified preamble sequences by deriving the probability density function (PDF) of the cross-correlation metric. We address this long-standing problem in the context of LEO (low Earth orbit) satellite systems, where differentially modulated PN (pseudo-noise) sequences are used for packet detection. For this particular class of preamble signals, the standard Ricean PDF assumption no longer holds and needs to be revised accordingly within this article.

Signals doi: 10.3390/signals6040056

Authors: Youssef Nahraoui El Houcein Aassif Samir Elouaham Boujemaa Nassiri

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).

Signals doi: 10.3390/signals6040055

Authors: Tengda Pei Bingnan Pei

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.

Signals doi: 10.3390/signals6040054

Authors: Anggunmeka Luhur Prasasti Achmad Rizal Bayu Erfianto Said Ziani

This study investigated the transformative potential of Compressive Sensing (CS) for optimizing multimodal biomedical signal fusion in Wireless Body Sensor Networks (WBSN), specifically targeting challenges in data storage, power consumption, and transmission bandwidth. Through a Systematic Mapping Study (SMS) and Systematic Literature Review (SLR) following the PRISMA protocol, significant advancements in adaptive CS algorithms and multimodal fusion have been achieved. However, this research also identified crucial gaps in computational efficiency, hardware scalability (particularly concerning the complex and often costly adaptive sensing hardware required for dynamic CS applications), and noise robustness for one-dimensional biomedical signals (e.g., ECG, EEG, PPG, and SCG). The findings strongly emphasize the potential of integrating CS with deep reinforcement learning and edge computing to develop energy-efficient, real-time healthcare monitoring systems, paving the way for future innovations in Internet of Medical Things (IoMT) applications.

Signals doi: 10.3390/signals6040053

Authors: Md. Reasad Zaman Chowdhury Ashek Seum Mahfuzur Rahman Talukder Rashed Al Amin Fakir Sharif Hossain Roman Obermaisser

Autonomous driving has emerged as a rapidly advancing field in both industry and academia over the past decade. Among the enabling technologies, computer vision (CV) has demonstrated high accuracy across various domains, making it a critical component of autonomous vehicle systems. However, CV tasks are computationally intensive and often require hardware accelerators to achieve real-time performance. Field Programmable Gate Arrays (FPGAs) have gained popularity in this context due to their reconfigurability and high energy efficiency. Numerous researchers have explored FPGA-accelerated CV solutions for autonomous driving, addressing key tasks such as lane detection, pedestrian recognition, traffic sign and signal classification, vehicle detection, object detection, environmental variability sensing, and fault analysis. Despite this growing body of work, the field remains fragmented, with significant variability in implementation approaches, evaluation metrics, and hardware platforms. Crucial performance factors, including latency, throughput, power consumption, energy efficiency, detection accuracy, datasets, and FPGA architectures, are often assessed inconsistently. To address this gap, this paper presents a comprehensive literature review of FPGA-accelerated, vision-based autonomous driving systems. It systematically examines existing solutions across sub-domains, categorizes key performance factors and synthesizes the current state of research. This study aims to provide a consolidated reference for researchers, supporting the development of more efficient and reliable next generation autonomous driving systems by highlighting trends, challenges, and opportunities in the field.

Signals doi: 10.3390/signals6040052

Authors: Cristian Felipe Blanco-Diaz Aura Ximena Gonzalez-Cely Denis Delisle-Rodriguez Teodiano Freire Bastos-Filho

Decoding motor imagery (MI) of lower-limb movements from electroencephalography (EEG) signals remains a challenge due to the involvement of deep cortical regions, limiting the applicability of Brain–Computer Interfaces (BCIs). This study proposes a novel protocol that combines passive pedaling (PP) as sensory priming with MI at different speeds (30, 45, and 60 rpm) to improve EEG-based classification. Ten healthy participants performed PP followed by MI tasks while EEG data were recorded. An increase in spectral relative power around Cz associated with both PP and MI was observed, varying with speed and suggesting that PP may enhance cortical engagement during MI. Furthermore, our classification strategy, based on Convolutional Neural Networks (CNNs), achieved an accuracy of 0.87–0.89 across four classes (three speeds and rest). This performance was also compared with the standard Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA), which achieved an accuracy of 0.67–0.76. These results demonstrate the feasibility of multiclass decoding of imagined pedaling velocities and lay the groundwork for speed-adaptive BCIs, supporting future personalized and user-centered neurorehabilitation interventions.

Signals doi: 10.3390/signals6040051

Authors: Youngboo Kim Seungmin Oh Gayoung Kim

Modern healthcare systems are under growing strain from aging populations, urbanization, and rising chronic disease burdens, creating an urgent need for real-time monitoring and informed decision-making. This survey examines how the convergence of Integrated Sensing and Communication (ISAC) and digital-twin technologies can meet that need by analyzing how ISAC unifies sensing and communication to gather and transmit data with high timeliness and reliability and how digital-twin platforms use these streams to maintain continuously updated virtual replicas of patients, devices, and care environments. Our synthesis compares ISAC frequency options across sub-6 GHz, millimeter-wave, and terahertz bandswith respect to resolution, penetration depth, exposure compliance, maturity, and cost, and it discusses joint waveform design and emerging 6G architectures. It also presents reference architecture patterns that connect heterogeneous clinical sensors to ISAC links, data ingestion, semantic interoperability pipelines using Fast Healthcare Interoperability Resources (FHIR) and IEEE 11073, and digital-twin synchronization, and it catalogs clinical and operational applications, together with validation and integration requirements. We conduct a targeted scoping review of peer-reviewed literature indexed in major scholarly databases between January 2015 and July 2025, with inclusion restricted to English-language, peer-reviewed studies already cited by this survey, and we apply a transparent screening and data extraction procedure to support reproducibility. The survey further reviews clinical opportunities enabled by data-synchronized twins, including personalized therapy planning, proactive early-warning systems, and virtual intervention testing, while outlining the technical, clinical, and organizational hurdles that must be addressed. Finally, we examine workflow adaptation; governance and ethics; provider training; and outcome measurement frameworks such as length of stay, complication rates, and patient satisfaction, and we conclude that by highlighting both the integration challenges and the operational upside, this survey offers a foundation for the development of safe, ethical, and scalable data-driven healthcare models.

Signals doi: 10.3390/signals6040050

Authors: Kudratjon Zohirov Mirjakhon Temirov Sardor Boykobilov Golib Berdiev Feruz Ruziboev Khojiakbar Egamberdiev Mamadiyor Sattorov Gulmira Pardayeva Kuvonch Madatov

This paper presents a method for recognizing sign language gestures corresponding to fruit names using electromyography (EMG) signals. The proposed system focuses on classification using a limited number of EMG channels, aiming to reduce classification process complexity while maintaining high recognition accuracy. The dataset (DS) contains EMG signal data of 46 hearing-impaired people and descriptions of fruit names, including apple, pear, apricot, nut, cherry, and raspberry, in sign language (SL). Based on the presented DS, gesture movements were classified using five different classification algorithms—Random Forest, k-Nearest Neighbors, Logistic Regression, Support Vector Machine, and neural networks—and the algorithm that gives the best result for gesture movements was determined. The best classification result was obtained during recognition of the word cherry based on the RF algorithm, and 97% accuracy was achieved.

Signals doi: 10.3390/signals6030049

Authors: Thoalfeqar G. Jarullah Ahmad Saeed Mohammad Musab T. S. Al-Kaltakchi Jabir Alshehabi Al-Ani

Face recognition technology utilizes unique facial features to analyze and compare individuals for identification and verification purposes. This technology is crucial for several reasons, such as improving security and authentication, effectively verifying identities, providing personalized user experiences, and automating various operations, including attendance monitoring, access management, and law enforcement activities. In this paper, comprehensive evaluations are conducted using different face detection and modality segmentation methods, feature extraction methods, and classifiers to improve system performance. As for face detection, four methods are proposed: OpenCV’s Haar Cascade classifier, Dlib’s HOG + SVM frontal face detector, Dlib’s CNN face detector, and Mediapipe’s face detector. Additionally, two types of feature extraction techniques are proposed: hand-crafted features (traditional methods: global local features) and deep learning features. Three global features were extracted, Scale-Invariant Feature Transform (SIFT), Speeded Robust Features (SURF), and Global Image Structure (GIST). Likewise, the following local feature methods are utilized: Local Binary Pattern (LBP), Weber local descriptor (WLD), and Histogram of Oriented Gradients (HOG). On the other hand, the deep learning-based features fall into two categories: convolutional neural networks (CNNs), including VGG16, VGG19, and VGG-Face, and Siamese neural networks (SNNs), which generate face embeddings. For classification, three methods are employed: Support Vector Machine (SVM), a one-class SVM variant, and Multilayer Perceptron (MLP). The system is evaluated on three datasets: in-house, Labelled Faces in the Wild (LFW), and the Pins dataset (sourced from Pinterest) providing comprehensive benchmark comparisons for facial recognition research. The best performance accuracy for the proposed ten-feature extraction methods applied to the in-house database in the context of the facial recognition task achieved 99.8% accuracy by using the VGG16 model combined with the SVM classifier.

Signals doi: 10.3390/signals6030048

Authors: Alejandro Caña-Pino Paula Holgado-López

Stroke remains one of the leading causes of disability worldwide, often resulting in persistent impairments in gait and balance. Traditional rehabilitation methods—though beneficial—are limited by factors such as therapist dependency, low patient adherence, and restricted access. In recent years, sensor-supported technologies, including virtual reality (VR), robotic-assisted gait training (RAGT), and wearable feedback systems, have emerged as promising adjuncts to conventional therapy. This systematic review evaluates the effectiveness of wearable and immersive technologies for gait and balance rehabilitation in adult stroke survivors. Following PRISMA guidelines, a systematic search of the PubMed and ScienceDirect databases retrieved 697 articles. After screening, eight studies published between 2015 and 2025 were included, encompassing 186 participants. The interventions included VR-based gait training, electromechanical devices (e.g., HAL, RAGT), auditory rhythmic cueing, and smart insoles, compared against conventional rehabilitation or baseline function. Most studies reported significant improvements in motor function, dynamic balance, or gait speed, particularly when interventions were intensive, task-specific, and personalized. Patient engagement, adherence, and feasibility were generally high. However, heterogeneity in study design, small sample sizes, and limited long-term data reduced the strength of the evidence. Technologies were typically implemented as complementary tools rather than standalone treatments. In conclusion, wearable and immersive systems represent promising adjuncts to conventional stroke rehabilitation, with potential to enhance motor outcomes and patient engagement. However, the heterogeneity in protocols, small sample sizes, and methodological limitations underscore the need for more robust, large-scale trials to validate their clinical effectiveness and guide implementation.

Signals doi: 10.3390/signals6030047

Authors: Janghoon Choi

In this paper, we propose a novel visual tracking method with a object part-aware attention-based matching (OPAM) mechanism, which leverages local–global attention to enhance visual tracking performance. Our method introduces three key components: (1) a local part-aware global self-attention mechanism that embeds rich contextual information among candidate regions, enabling the model to capture mutual dependencies and relationships effectively, (2) a local part-aware global cross-attention mechanism that injects target-specific information into candidate region features, improving the alignment and discrimination between the target and background, and (3) a global cross-attention mechanism that extracts object holistic information from the target-search feature context for further discriminability. By integrating these attention modules, our approach achieves robust feature aggregation and precise target localization. Extensive experiments on a large-scale tracking benchmark demonstrate that our method shows competitive performance metrics in both accuracy and robustness, particularly under challenging scenarios such as occlusion and appearance changes, while running at real-time speeds.

Signals doi: 10.3390/signals6030046

Authors: José-Joel González-Barbosa Israel Cruz Rangel Alfonso Ramírez-Pedraza Raymundo Ramírez-Pedraza Isabel Bárcenas-Reyes Erick-Alejandro González-Barbosa Miguel Razo-Razo

Bats are ecologically vital mammals, serving as pollinators, seed dispersers, and bioindicators of ecosystem health. Many species inhabit natural caves, which offer optimal conditions for survival but present challenges for direct ecological monitoring due to their dark, complex, and inaccessible environments. Traditional monitoring methods, such as mist-netting, are invasive and limited in scope, highlighting the need for non-intrusive alternatives. In this work, we present a portable multisensor platform designed to operate in underground habitats. The system captures multimodal data, including near-infrared (NIR) imagery, ultrasonic audio, 3D structural data, and RGB video. Focusing on NIR imagery, we evaluate the effectiveness of the YOLO object detection framework for automated bat detection and counting. Experiments were conducted using a dataset of NIR images collected in natural shelters. Three YOLO variants (v10, v11, and v12) were trained and tested on this dataset. The models achieved high detection accuracy, with YOLO v12m reaching a mean average precision (mAP) of 0.981. These results demonstrate that combining NIR imaging with deep learning enables accurate and non-invasive monitoring of bats in challenging environments. The proposed approach offers a scalable tool for ecological research and conservation, supporting population assessment and behavioral studies without disturbing bat colonies.

Signals doi: 10.3390/signals6030045

Authors: Kudratjon Zohirov Sarvar Makhmudjanov Feruz Ruziboev Golib Berdiev Mirjakhon Temirov Gulrukh Sherboboyeva Firuza Achilova Gulmira Pardayeva Sardor Boykobilov

In this article, the classification of signals arising from the movements of the lower limb of the leg (LLL) based on electromyography (EMG) (walking, sitting, up and down the stairs) was carried out. In the data collection process, 25 athletes aged 15–22 were involved, and two types of data sets (DS-dataset) were formed using FreeEMG and Biosignalsplux devices. Six important time and frequency domain features were extracted from the EMG signals—RMS (Root Mean Square), MAV (Mean Absolute Value), WL (Waveform Length), ZC (Zero Crossing), MDF (Median Frequency), and SSCs (Slope Sign Changes). Several classification algorithms were used to detect and classify movements, including RF (Random Forest), NN (Neural Network), SVM (Support Vector Machine), k-NN (k-Nearest Neighbors), and LR (Logistic Regression) models. Analysis of the experimental results showed that the RF algorithm achieved the highest accuracy of 98.7% when classified with DS collected via the Biosignalsplux device, demonstrating an advantage in terms of performance in motion recognition. The results obtained from the open systems used in signal processing enable real-time monitoring of athletes’ physical condition, which plays a crucial role in accurately and rapidly determining the degree of muscle fatigue and the level of physical stress experienced during training sessions, thereby allowing for more effective control of performance and timely prevention of injuries.