Signals

Accurate R-peak detection in electrocardiogram (ECG) signals is fundamental for cardiovascular analysis. However, most existing methods address differences in sampling frequency (f_s) through signal resampling or transfer learning, which may alter the temporal definition of annotated events. In this study, we propose a f_s 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 f_s. The trained model is directly applied to higher-f_s 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 f_s-consistent ECG peak detection. Full article

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%. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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 $(\alpha ,\beta )$ 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 $(\alpha ,\beta )=(2,2)$, 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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article