Signals

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

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 Q_u, insertion loss, selectivity, and structural simplicity. Through targeted intra-cavity field manipulation, the filter attains a Q_u 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. Full article

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. Full article

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. Full article

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 ($S_{11}$) and the transmission coefficient ($S_{21}$). 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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article