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        <title>Frontiers in Signal Processing | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/signal-processing</link>
        <description>RSS Feed for Frontiers in Signal Processing | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-11-07T12:24:14.176+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1945432</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1945432</link>
        <title><![CDATA[FG-DPUNet: a feature-aware gating dual-path U-Net for ECG motion artifact removal with waveform integrity]]></title>
        <pubdate>2026-11-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Baitao Lv</author><author>Shaonan Wang</author><author>Chao Yu</author><author>Yang Xu</author><author>Yining Sun</author><author>Xianjun Yang</author>
        <description><![CDATA[Wearable electrocardiography (ECG) has significant clinical value in ambulatory monitoring. However, motion artifacts during daily activities frequently distort faint P/T waves, compromising diagnostic interpretation. Meanwhile, single-branch or static fusion U-Net-based networks struggle to balance effective noise suppression with waveform integrity. Existing denoising methods primarily optimize global metrics such as signal-to-noise ratio improvement (SNRimp), percentage root-mean-square difference (PRD), and root mean square error (RMSE), overlooking that denoising distorts P-QRS-T waveform and thus affects clinical diagnosis. This paper presents FG-DPUNet (Feature-aware Gating Dual-Path U-Net), a framework tailored to address P/T diagnostic waveform distortion induced by motion artifacts in wearable ambulatory ECG monitoring. The framework employs heterogeneous dual branches with kernel sizes of k=7 and k=21 to extract multi-scale ECG features in parallel, and adaptively adjusts fusion weights via a dynamic gating mechanism that operates on shared shallow features to produce temporal weights. To quantitatively assess waveform recovery quality, we introduce the P-QRS-T weighted correlation degradation index (Dw) and P/T wave peak deviation metrics (ΔPR, ΔRT), providing a waveform-aware quantitative evaluation approach. On the MIT-BIH dataset under standard additive noise testing, FG-DPUNet improves the average SNRimp by 5.94 dB and reduces the average PRD from 63.87% to 10.07% compared with state-of-the-art (SOTA) methods. On the BUT real-world motion artifact dataset, cross-subject experiments demonstrate that our model improves Dw by approximately 16% over static dual-branch structures, demonstrating improved morphological preservation under motion artifacts.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1939123</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1939123</link>
        <title><![CDATA[Complexity of frequency fluctuations and the interpretive style in the case study of a set of musical pieces of bass viola da gamba]]></title>
        <pubdate>2026-11-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Igor Lugo</author><author>Martha G. Alatriste-Contreras</author><author>Rafael Sánchez-Guevara</author>
        <description><![CDATA[Based on a computational musicology framework that bridges the gap between scientists and musicians, this case study investigates the feasibility of capturing the relationship between the presence of complex frequency fluctuations and the interpretive style. Because there is no well-known method for capturing such a relationship, we propose a preliminary formulation for identifying differences in musical interpretation. In particular, we focus on a set of musical pieces performed by the same interpreter on the bass viola da gamba. We analyzed digital audio data to best identify statistical regularities in audio frequencies (Hz) and their complex network properties in musical notation. Based on an interdisciplinary approach, we computed the spectral decomposition and translated its frequency components into a network of sounds. We applied a best-fit analysis for identifying the statistical distributions that most precisely describe the behavior of these frequencies, and we computed the centrality measures and identified cliques for characterizing such a network. Our findings reveal statistical regularities in the type of statistical distribution that best describes frequency fluctuations. The centrality measures confirmed the most influential and stable group of sounds in a piece of music, while the identification of the largest clique indicated functional groups of sounds that interact closely. Therefore, using musical signal processing, complex systems, and music theory, we can effectively translate musical audio signals into frequency components and complex networks. However, we cannot conclusively determine the relationship between the presence of complex frequency fluctuations and the interpretive style.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1956948</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1956948</link>
        <title><![CDATA[Physiology-informed spectral-temporal learning with confidence-aware evaluation for single-channel EEG sleep staging]]></title>
        <pubdate>2026-10-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chunming Shih</author><author>Bohan Lv</author><author>Liangfan Zhai</author><author>Fengquan Xu</author>
        <description><![CDATA[IntroductionAutomated sleep staging from single-channel electroencephalography (EEG) may reduce acquisition burden. Limited spatial information and inter-subject variability, however, make prediction difficult.MethodsWe evaluated a physiology-informed spectral-temporal framework for single-channel EEG sleep-stage classification. Each 30-s Fpz-Cz epoch was transformed into a log-power short-time Fourier transform (STFT) representation and modulated across five sleep-related bands. Depthwise-separable convolutions and efficient channel attention extracted local time-frequency patterns. A lightweight Transformer modeled a centered seven-epoch window for center-epoch prediction. Training combined class-balanced focal loss, neighboring-epoch supervision and a transition prior estimated from training labels.ResultsOn a fixed subject-disjoint Sleep-EDF evaluation partition, the framework achieved 0.8469 accuracy, 0.7905 macro F1 and 0.7906 Cohen’s kappa. It yielded higher discrimination metric point estimates than the protocol-matched light CNN, whereas the TCN produced slightly higher accuracy and kappa. The framework yielded the lowest expected calibration error (ECE) point estimate among the three evaluated models (0.0320). Retraining-based ablations removed spectral modulation, transition-prior loss or neighboring-epoch supervision under the same protocol. Across seeds 42, 123 and 2024, accuracy and macro F1 averaged 0.8530 ± 0.0060 and 0.8006 ± 0.0098, respectively. Removing the 20% lowest-confidence epochs yielded 0.9167 accuracy among the remaining automated epochs at 80% coverage. Repeated random referral at the same coverage left accuracy near the full-test-set estimate, whereas confidence-guided referral retained higher accuracy among the automated epochs. Test-time spectral, context and noise stress analyses examined sensitivity of the trained model without retraining.DiscussionThese results support further evaluation of the framework as an offline or delayed pre-scoring aid. The small fixed test cohort and absence of external validation preclude broader reliability or clinical generalization claims. The findings should be interpreted as methodological evidence rather than clinical validation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.2004707</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.2004707</link>
        <title><![CDATA[Editorial: From description to reasoning: multimodal VLMs in smart healthcare]]></title>
        <pubdate>2026-10-21T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Rui Sun</author><author>Tao Lei</author><author>Asoke K. Nandi</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1901600</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1901600</link>
        <title><![CDATA[Deep learning–based pavement crack classification and road condition assessment for intelligent transportation infrastructure]]></title>
        <pubdate>2026-10-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chaymae Chouiekh</author><author>Wafae Hammouch</author><author>Ali Yahyaouy</author><author>My Abdelouahed Sabri</author>
        <description><![CDATA[IntroductionEfficient maintenance of road infrastructure is essential for ensuring traffic safety, preserving pavement serviceability, and supporting the long-term sustainability of transportation networks. However, conventional pavement inspection mainly relies on manual visual assessment, which is time-consuming, costly, and subjective. This study proposes a deep learning–based framework for automated pavement crack classification and road condition assessment.MethodsThe proposed framework uses real-world pavement images collected through the Multifunctional Pavement Assessment System (SMAC). Transfer learning was applied using ResNet50 and EfficientNet-B0 architectures to classify six pavement surface categories. Data augmentation and weighted cross-entropy loss were employed to improve robustness and address class imbalance. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. A Road Condition Index (RCI) was then formulated from the classification results to support maintenance prioritization.ResultsEfficientNet-B0 achieved an overall classification accuracy of 89.8%, outperforming ResNet50 (73.3%). It obtained F1-scores of 0.92 for alligator and longitudinal cracks, 0.93 for transverse cracks, and 1.00 for non-cracked pavement surfaces. The proposed RCI successfully differentiated pavement degradation levels and supported maintenance prioritization.DiscussionThe proposed framework provides an intelligent, interpretable, and practical solution for pavement crack classification, road condition assessment, and maintenance-oriented decision support. These results demonstrate the potential of deep learning for scalable pavement monitoring and more efficient infrastructure management within intelligent transportation systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1786202</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1786202</link>
        <title><![CDATA[Time-causal and time-recursive wavelets]]></title>
        <pubdate>2026-10-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tony Lindeberg</author>
        <description><![CDATA[This study presents a framework for time-causal wavelet analysis. It targets real-time processing of temporal signals, where data from the future are not available. The study builds upon temporal scale-space theory, originating from a complete classification of temporal smoothing kernels that guarantee non-creation of new structures from finer to coarser temporal scale levels. We construct temporal wavelets from the temporal derivatives of a special time-causal smoothing kernel, referred to as the “time-causal limit kernel”—arising from the classification of variation-diminishing smoothing transformations with the complementary requirement of temporal scale covariance—to guarantee self-similar handling of structures in the input signal at different temporal scales. This enables the decomposition of the signal into different components at different scales while adhering to temporal causality. The study establishes theoretical foundations for these time-causal wavelet representations and maps structural relationships to the non-causal Ricker/Mexican hat wavelets. We also describe how efficient discrete approximations of the theory presented can be performed in terms of first-order recursive filters coupled in cascade, enabling numerically well-conditioned real-time processing with low resource usage. We characterize and quantify how the continuous scaling properties transfer to the discrete implementation, demonstrating how the proposed time-causal wavelet representation can reflect the duration of locally dominant temporal structures in the input signal. We propose this notion of time-causal wavelet analysis as a generic multi-purpose tool for signal processing tasks, where streams of signals are to be processed in real time—specifically for signals that may contain local variations over a rich span of temporal scales or more generally for analyzing physical or biophysical temporal phenomena, where a fully time-causal analysis needs to be physically realistic, by not accessing data from the future, as is often done for prerecorded data.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1884243</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1884243</link>
        <title><![CDATA[IndoorCART: a spatially-aware transformer framework for Wi-Fi fingerprinting indoor localization]]></title>
        <pubdate>2026-09-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Balaram Singh</author><author>Rajdeep Chatterjee</author><author>Mahendra Kumar Gourisaria</author><author>Manoj Sahni</author><author>Ernesto León-Castro</author>
        <description><![CDATA[Accurate indoor localization remains difficult in GPS-denied environments because Wi-Fi received signal strength indicator (RSSI) fingerprints are sparse, noisy, device-dependent, and strongly affected by multipath propagation, non-line-of-sight conditions, and environmental dynamics. To address these challenges, we present IndoorCART (Indoor Context-Aware RSSI Transformer), an encoder-only transformer framework designed specifically for coordinate-regression-based Wi-Fi fingerprinting. IndoorCART represents access-point measurements as RSSI tokens, incorporates learnable access-point identity information, applies missing-signal-aware Adaptive Attention to model contextual dependencies among observed and unobserved signals, and combines CLS-token and global-average representations for robust coordinate prediction. The model is evaluated on three public indoor-localization benchmarks, namely, UJIIndoorLoc, TUJI1, and SODIndoorLoc, using a controlled comparison with nine reproducible regression baselines. IndoorCART achieves mean absolute errors of 4.79 m, 0.18 m, and 1.33 m on UJIIndoorLoc, TUJI1, and SODIndoorLoc, respectively, with corresponding R2 scores of 0.99, 0.98, and 0.99. These results show that the proposed context-aware transformer consistently provides the lowest localization error among the implemented baselines and is particularly beneficial in sparse or structurally complex fingerprinting scenarios. We further position the proposed method against recent transformer-based indoor-localization studies through a literature-level comparison, while explicitly distinguishing reported external results from controlled reimplementations. Overall, IndoorCART offers an accurate and reliable framework for Wi-Fi-based indoor positioning; however, practical deployment should further consider model size, inference latency, energy consumption, device heterogeneity, and the modest performance gain observed on densely sampled datasets such as UJIIndoorLoc.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1904505</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1904505</link>
        <title><![CDATA[PDSA-DBUNet: dual-branch U-Net for abdominal medical image segmentation driven by polynomial dynamic sparse attention]]></title>
        <pubdate>2026-09-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jiatong Shang</author><author>Guolin He</author><author>Xuyang Li</author>
        <description><![CDATA[Abdominal medical image segmentation is essential for computer-aided diagnosis, lesion localization, quantitative organ analysis, and treatment planning. However, abdominal computed tomography (CT) images often contain large anatomical variations, complex tissue structures, low contrast, blurred boundaries, and small tumor regions, which make accurate and efficient segmentation challenging. Convolutional neural networks (CNNs) are effective in extracting local textures, edges, and spatial details, but their limited receptive fields restrict long-range dependency modeling. Transformers can capture global contextual relationships, yet dense token modeling introduces redundant background information and high computational cost, while fine-grained local details may be insufficiently preserved. To address these limitations, we propose a Polynomial Dynamic Sparse Attention-driven Dual-Branch U-Net, termed PDSA-DBUNet, for abdominal medical image segmentation. The network adopts a parallel CNN–Transformer architecture, where the CNN branch preserves local boundary and texture information, and the Transformer branch models global semantic dependencies. A dynamic sparse visual token generation module adaptively aggregates dense image tokens into a compact set of discriminative sparse tokens, emphasizing organ regions and blurred boundaries while suppressing irrelevant background responses. A PoM-based polynomial mixing mechanism is further incorporated into the dynamically generated sparse token set to replace conventional multi-head self-attention, enabling efficient global contextual interaction with reduced computational and memory costs. In addition, a Dual-Scale Parallel Attention Fusion (DSPAF) module integrates CNN-derived local details with Transformer-derived global semantics to enhance boundary localization and small-target representation. Experiments on the LiTS and KiTS19 datasets show that PDSA-DBUNet achieves Dice scores of 96.79% and 86.13% on LiTS-Liver and LiTS-Tumors, and 94.03% and 89.06% on Kidney and Kidney-Tumor segmentation tasks, respectively. The model requires 72.87 GFLOPs, contains 41.62M learnable parameters, and has a checkpoint storage size of 129.93 MB. These results demonstrate that PDSA-DBUNet achieves a favorable balance between segmentation accuracy and computational efficiency.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1864603</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1864603</link>
        <title><![CDATA[Computationally efficient hybrid multi-domain transform fusion for secure image steganography using rate–distortion optimization and Toom–Cook acceleration]]></title>
        <pubdate>2026-09-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Abhrendu Bhattacharya</author><author>Raktim Kumar Dey</author><author>Amit R. Welekar</author><author>Rajesh Bose</author><author>Sandip Roy</author><author>Arfat Ahmad Khan</author><author>Muhammad Shoaib Ayub</author><author>Insoo Koo</author>
        <description><![CDATA[In the era of extensive developmental growth, multimedia communication systems demand protective and indiscernible information-hiding techniques that can maintain a balance between robustness, capacity, and computational efficacy. The research gap analysis highlights the necessity for a mathematically defined steganographic strategy that integrates rate–distortion optimization, knowledge-based adaptive embedding, hybrid domain modeling, and statistical invisibility constraints in a non-heuristic approach toward scalable high-capacity transmission systems. The novelty of the proposed strategy lies in the combination of rate–distortion optimization, adaptive CNN-based control for embedding, and histogram preservation of frequency channel embedding within a consolidated information-hiding architecture, within illuminated components of color areas. This ensures observable fidelity and configurational stability. To generate an adaptive embedding-based probability map, a lightweight convolutional neural network that enables a content-aware embedding dependent on local picture complexity is incorporated. Moreover, incorporating a histogram-preservation restriction reduces sensitivity to classical and learning-guided steganalysis and increases analytical security. Furthermore, Toom–Cook accelerated convolution is utilized to achieve improved computational scalability through accelerated transform-domain convolution, ensuring scalability for high-resolution images. Experimental analysis illustrates excellent performance with superior imperceptibility in average values obtained for a peak signal-to-noise ratio (PSNR) maintained above 50 dB (≈51.40 dB) and average structural similarity index measure (SSIM) ≈0.9816, low bit error rates, and near-random detectability (area under curve ≈0.5). Comparative and ablation analyses confirm that the proposed framework outperforms existing spatial, transform-domain, and deep learning-based methods. The proposed approach provides a unified, theoretically constrained, and scalable solution for secure high-capacity image steganography.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1842851</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1842851</link>
        <title><![CDATA[AdaTent++: reliability-guided hybrid test-time adaptation for traffic sign detection under distribution shift]]></title>
        <pubdate>2026-09-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaomei Gai</author><author>Meichun Wang</author><author>Yantong Guo</author><author>Shiyu Wang</author><author>Ye Li</author><author>Rui Fu</author>
        <description><![CDATA[Test-time adaptation (TTA) can improve detector robustness under distribution shift, but indiscriminate online updating may amplify errors from unreliable frames. We present AdaTent++, a reliability-guided hybrid TTA framework for traffic sign detection. For each unlabeled frame, the framework combines predictive entropy and cross-view agreement to select among a smoothed batched normalization (BN)-statistics update, a guarded BN-affine gradient update, and no adaptation. Exponential moving average (EMA) threshold tracking, prototype regularization, gradient control, and state restoration are used as supporting safeguards rather than claimed as independent novelties. On one public traffic sign dataset evaluated under 12 corrupted domains, AdaTent++ achieves a mean average precision (mAP50) of 68.87%, compared with 68.03% for the strongest baseline, T3A, corresponding to an improvement of 0.84 percentage points. It ranks first in three of the twelve domains and is less effective under several structure-destructive corruptions. The results indicate a favorable average trade-off in the studied setting, while cross-dataset, cross-architecture, embedded-device, and long-horizon validation remain open.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1911510</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1911510</link>
        <title><![CDATA[TF-Mamba-DPHNet: a time-frequency state-space and dual-path interaction framework with hybrid cross-scale feature calibration for speech enhancement]]></title>
        <pubdate>2026-09-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shaik AreefaBegam</author><author>Sunnydayal Vanambathina</author>
        <description><![CDATA[IntroductionSpeech enhancement plays a fundamental role in improving the perceptual quality and intelligibility of speech signals degraded by noise. Conventional U-Net architectures effectively capture local spectral information but struggle to model long-range dependencies and may allow residual noise to propagate through skip connections. Transformer-based networks can model global contextual dependencies and generate high-quality enhanced speech; however, their limited capacity to preserve fine-grained high-frequency spectral details can limit their suitability for real-time applications.MethodsTo address these limitations, this paper proposes TF-Mamba-DPHNet, a novel encoder-decoder speech enhancement framework integrating Multi-Scale Feature Extraction (MSFE), Time-Frequency Mamba (TF-Mamba), Dual-Path Higher-Order Information Interaction with Time-Frequency Attention (DPH-TFA), and Hybrid Cross-Scale Feature Calibration (H-CS-FC) modules. The MSFE blocks in the encoder and decoder extract local patterns across multiple receptive fields, capturing fine-grained and global time-frequency cues. The TF-Mamba module models global time-frequency dependencies using selective state-space modeling for effective long-term sequence understanding. At the bottleneck, four stacked DPH-TFA blocks capture long-range dependencies in both the temporal and spectral domains. These global features are fused with hierarchical encoder outputs through H-CS-FC modules, which perform cross-scale-guided feature recalibration to suppress noise leakage in the skip pathways and improve decoder reliability.ResultsEvaluations conducted on the Common Voice and LibriSpeech datasets demonstrate that TF-Mamba-DPHNet effectively improves speech quality and robustness compared with earlier models.DiscussionThe results indicate that combining multi-scale local feature extraction, selective state-space modeling, dual-path time-frequency interaction, and cross-scale feature calibration enables effective modeling of complementary local and global information for robust speech enhancement.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1909928</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1909928</link>
        <title><![CDATA[Modeling the post-extrasystolic potentiation to determine heart failure]]></title>
        <pubdate>2026-08-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Roel J. H. Montree</author><author>Elisabetta Peri</author><author>Lukas R. C. Dekker</author><author>Xi Long</author><author>Reinder Haakma</author><author>Rik Vullings</author>
        <description><![CDATA[IntroductionPremature ventricular contractions (PVC) occur naturally in the human heart and lead to a well-studied increase in cardiac contractility, referred to as post-extrasystolic potentiation (PESP), which behaves independently of many otherwise confounding factors. It is well-known that the degree of potentiation differs between healthy and failing hearts. In this paper, we present a method for non-invasive and non-obtrusive assessment of the PESP, leveraging photoplethysmography (PPG) measurements, revealing information on the degree of potentiation and thereby cardiac functioning.MethodsWe modeled the PESP as a second-order system and we compared the parameters of this system between patients with differing ejection fractions. The method was developed on an internal dataset acquired during percutaneous, electrophysiological procedures. The parameters of the proposed second-order system are determined for each of these patients and the results show a correlation between cardiac ejection fraction and the damping factor, one of the parameters of the system.ResultsThis model-based approach was demonstrated on the earlier mentioned non-obtrusive PPG measurements, as well as invasive blood pressure measurements. Modeling the PESP as a second-order model using information obtained from the non-invasive PPG signal reveals information showing a promising step towards enabling continuous monitoring of cardiac contractility using non-invasive means.DiscussionThe presented method could be applied in non-invasive, non-obtrusive PPG recording devices such as a smartwatch. This provides a continuously monitored estimate of cardiac contractility as an additional clinical parameter, as well as decreasing the time between reduction of contractility and potential hospital admission.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1884114</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1884114</link>
        <title><![CDATA[Seismic phase picking of coal mine microseismic data based on lightweight CNN]]></title>
        <pubdate>2026-08-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Haichen Yin</author><author>Xiufeng Zhang</author><author>Atao Li</author><author>Wei Zhu</author><author>Yuhua Hou</author><author>Yang Chen</author><author>Caiping Lu</author>
        <description><![CDATA[IntroductionIn coal mining, the high-noise underground environment poses significant challenges to the accuracy and stability of microseismic (MS) signal first-arrival picking.MethodsThis study proposes a high-robustness automatic phase picking method based on a lightweight Convolutional Neural Network (CNN). The method uses a multi-layer convolution and adaptive pooling structure to efficiently extract waveform features across temporal scales, and introduces a regression prediction strategy to directly output the first-arrival time, eliminating the quantization error induced by classification-based methods. The model was validated on 1,791 manually labelled single-component records of 597 microseismic events from the 6305 working face of Xinjulong Coal Mine, Shandong Province.ResultsOn the test set, the proportion of first-arrival errors not exceeding 100 ms reached 80.27%. Compared with the traditional STA/LTA method, the overall relative error was significantly reduced, with the average relative error decreasing by approximately 50 ms. The model maintained high picking accuracy and stability under high-noise and complex geological conditions, and under high-SNR conditions it effectively reduced the subjective bias of manual picking.DiscussionThe findings provide reliable technical support for high-precision localization of microseismic events and the early warning of dynamic hazards in coal mines.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1876242</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1876242</link>
        <title><![CDATA[Physiological and subjective evaluation of music processing strategies for hearing-impaired listeners: a pilot study]]></title>
        <pubdate>2026-08-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Duncan A. H. Williams</author><author>Rory Waterhouse-Stocks</author><author>Rebecca Vos</author><author>Trevor Cox</author>
        <description><![CDATA[IntroductionHearing loss affects over 2.5 billion people worldwide and significantly degrades music enjoyment. While advances in digital signal processing (DSP) aim to improve listening experiences, their combined effects on perceived audio quality and physiological engagement remain underexplored.MethodsThis pilot study evaluated hearing-impairment-optimised DSP models from the CADENZA challenge. Eleven participants (eight normal-hearing, three hearing-aid users) listened to musical excerpts processed via four DSP algorithms using circumaural open-back headphones in a semi-anechoic chamber. Signal delivery was tailored using personalized audiogram baselines and volume attenuation. Subjective metrics, Basic Audio Quality (BAQ) and likeability, were collected alongside continuous physiological recordings (electrodermal activity [EDA] and photoplethysmography [PPG]) captured with a medical-grade wrist wearable.ResultsDSP model E021 consistently achieved the highest BAQ and likeability scores across both participant groups, with BAQ and likeability demonstrating a strong positive correlation. Physiological monitoring revealed exploratory associations between autonomic arousal and variability metrics: specifically, phasic EDA variability and inter-beat interval (IBI) variability positively correlated with both BAQ and likeability.DiscussionVariability-based physiological markers present viable pathways for evaluating subjective listener responses in small cohorts. Integrating wearable-derived biometric signals with subjective appraisal provides a framework for developing adaptive, listener-aware DSP strategies that enhance music accessibility for individuals with hearing impairment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1769553</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1769553</link>
        <title><![CDATA[Identification of eye diseases with small fundus image datasets using a new unsupervised deep learning approach]]></title>
        <pubdate>2026-08-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Al Rafi Aurnob</author><author>Sharia Arfin Tanim</author><author>Farook Sattar</author>
        <description><![CDATA[Ocular diseases are a severe global problem, particularly in countries that lack the technology or economy to treat them. Automated analysis of retinal fundus images could enable early screening, but supervised deep learning methods require large, annotated datasets that are expensive and time-consuming to obtain. This study introduces an unsupervised deep learning framework for learning and clustering of retinal image representation that achieves strong performance on small-scale fundus image datasets using only image-level disease labels to guide contrastive pair construction and classification loss, without requiring pixel-level annotations for clustering. The proposed method, NF-HAE-UDL (normalizing flow-guided hybrid autoencoder-based unsupervised deep learning), combines a hybrid autoencoder architecture (ResNet-50 and Swin Transformer) with normalizing flow and contrastive learning to learn discriminative latent representations. The autoencoder generates meaningful feature representations, upon which the normalizing flow maps the encoded features to a more expressive and structured latent space. Meanwhile, contrastive learning encourages the separation of patterns targeted to specific diseases through data augmentation. Evaluation on the STARE dataset (81 images, multi-label) and the IDRiD subset (211 images, binary) demonstrates superior unsupervised clustering performance compared to traditional methods, achieving ARI scores of 0.58±0.10 on STARE and 0.58±0.06 on IDRiD. Auxiliary classification validation on frozen encoder representations confirms that learned embeddings preserve disease discriminative information (88% in-sample accuracy on STARE, 98% on IDRiD), though these results represent quality assessment rather than diagnostic performance. Furthermore, Grad-CAM visualizations validated against expert annotations (IoU =0.52±0.11) highlight clinically meaningful attention to pathological features. The framework demonstrates a promising direction for effective, interpretable, unsupervised retinal image analysis with limited data.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1893420</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1893420</link>
        <title><![CDATA[An explainable AI framework integrating deep learning and large language model for student’s mental health]]></title>
        <pubdate>2026-08-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rupali D. Kasar</author><author>Garima Shukla</author>
        <description><![CDATA[Mental health disorders such as anxiety, depression, and Mild Cognitive Impairment (MCI) are increasingly prevalent among young adults aged 18–30, significantly affecting academic performance, cognitive functioning, and overall wellbeing. Traditional diagnostic approaches depend on subjective assessments and limited clinical observations, making early and accurate detection challenging. To address these limitations, this research proposes an interpretable deep learning-based multimodal method for comprehensive mental health prediction and personalized intervention. The framework integrates heterogeneous data sources, including demographic, cognitive, behavioral, physiological, and neurocognitive indicators collected from clinical settings. Data preprocessing includes imputation, normalization, encoding, and text transformation. A Cross-Directional Feature Learning Network (CDFLN) is employed for robust multimodal feature extraction, followed by a Multi-model Progressive Dense Self-Attention for Cross Domain (MPDSA-CD) architecture for classification of anxiety, depression, and MCI, along with cognitive risk and severity assessment. Model performance is further enhanced by the Starfish Optimization Algorithm for hyperparameter tuning and parameter refinement. To ensure clinical transparency, SHapley Additive exPlanations (SHAP) are utilized to interpret model predictions and identify key risk factors influencing mental health outcomes. The proposed method achieves an accuracy of 99.8%, precision of 99.7%, recall of 99.9%, and F1-score of 99.8%, demonstrating strong robustness, generalization ability, and clinical applicability for early detection and effective psychological intervention in young adults.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1862897</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1862897</link>
        <title><![CDATA[Hybrid CUDA acceleration of wavelet-based MRI image denoising]]></title>
        <pubdate>2026-07-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lesia Hentosh</author><author>Yana Vyklyuk</author><author>Oleh Berezsky</author>
        <description><![CDATA[BackgroundMagnetic resonance imaging is a key modality in medical diagnostics, but its quality is often degraded by noise, which reduces image clarity and may affect subsequent analysis. Wavelet-based denoising is effective for preserving anatomical structures, but its practical use is limited by computational cost. This study aimed to develop a hybrid graphics processing unit execution method for wavelet-based denoising of two-dimensional magnetic resonance images with high throughput and stable reconstruction quality.MethodsA hybrid CUDA-based implementation of wavelet denoising was developed using a standard discrete wavelet transform with adaptive soft-thresholding as the denoising core. The proposed execution method combines data-level and task-level parallelism within a unified graphics processing unit pipeline and includes dual-stream vertical-pass scheduling, fused reconstruction kernels, constant-memory filter taps, symmetric boundary handling, and selective loop unrolling. Experiments were performed on 3,064 grayscale brain magnetic resonance images of size ×512 512 using synthetic Gaussian noise and simulated Rician noise and motion artifacts. An additional sensitivity analysis was conducted to assess the influence of wavelet basis, filter length, and threshold scaling on PSNR, SSIM, mean squared error, and runtime. Reconstruction quality was evaluated using peak signal-to-noise ratio, structural similarity index, and mean squared error, while computational performance was assessed using runtime and throughput. Results were summarized over repeated runs using mean values, standard deviations, and 95% confidence intervals.ResultsUnder Gaussian noise, the proposed method achieved peak signal-to-noise ratio values of approximately 29–30 dB and structural similarity values around 0.90 while maintaining a throughput of 215–220 images per second. Compared with a reproduced data-parallel graphics processing unit wavelet baseline, the proposed implementation reduced runtime by 20%–30%. For Rician noise and motion-related artifacts, the method maintained stable slice-level throughput and consistent reconstruction behavior across the evaluated degradation types.ConclusionThe proposed hybrid CUDA execution method provides an effective high-throughput solution for wavelet-based denoising of two-dimensional magnetic resonance images. By combining dual-stream scheduling, fused reconstruction, and memory-aware execution, the method improves runtime performance while preserving reconstruction quality.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1829279</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1829279</link>
        <title><![CDATA[Extended Kalman filtering on Stiefel manifolds]]></title>
        <pubdate>2026-06-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jordi-Lluís Figueras</author><author>Aron Persson</author><author>Lauri Viitasaari</author>
        <description><![CDATA[A generalization of the extended Kalman filter for Stiefel manifold-valued measurements is presented. Our approach is based on translating between manifold-valued measurements and the ambient space through an injective mapping between scalar true variance on the manifold and the variance of the noise. This mapping inherently takes into account the curvature of the manifold corresponding to the higher order derivatives. To illustrate our approach, we provide simulations on the 2-sphere and the space of orthogonal 4-by-2 matrices which show significant improvement of the Extended Kalman Filter compared to only relying on raw measurements. We also compare our approach to classical Extended Kalman Filter and second order Extended Kalman Filter, and complement our results by physically informed application related to spacecraft measurements. Our results indicate that our algorithm has similar performance as the second order Extended Kalman Filter without requiring the costly computations of the second order derivatives.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1853106</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1853106</link>
        <title><![CDATA[An explainable AI-driven hybrid SE-transformer architecture for robust knee osteoporosis classification from X-ray data]]></title>
        <pubdate>2026-06-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sudhir Kumar Sangula</author><author>Siddique Ibrahim S. P.</author>
        <description><![CDATA[BackgroundEarly and accurate classification of knee osteoporosis from radiographic images is crucial for timely diagnosis and treatment, yet existing deep learning models often lack both contextual understanding and interpretability.ObjectivesTo develop an explainable hybrid deep learning framework that combines Squeeze-and-Excitation (SE) networks, Transformer architecture, BiLSTM, and BiGRU for improved knee osteoporosis classification.MethodsThe proposed model integrates SE blocks for channel-wise feature enhancement, a Transformer for capturing long-range spatial dependencies, and BiLSTM/BiGRU layers for sequential feature learning. Grad-CAM is employed to provide visual explanations of model predictions. Performance is evaluated using a stratified 70:20:10 training, validation, and testing split.ResultsThe framework achieved an accuracy of 91.0%, precision of 91.0%, recall of 90.2%, and an F1-score of 90.4% on the test set. The reported results are based on a single-dataset evaluation and comparisons under identical experimental conditions.ConclusionThe proposed SE-Transformer–BiLSTM–BiGRU framework delivers accurate and interpretable knee osteoporosis classification, demonstrating its potential for computer-aided diagnosis while warranting further validation on external datasets.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1802483</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1802483</link>
        <title><![CDATA[Energy-aware bitrate ladder for VVC streaming with client-side super-resolution]]></title>
        <pubdate>2026-06-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Amritha Premkumar</author><author>Vignesh V. Menon</author><author>Christian Herglotz</author>
        <description><![CDATA[Modern adaptive video streaming must jointly optimize perceptual quality, bitrate efficiency, latency, and energy consumption across heterogeneous client devices. However, existing bitrate-ladder design approaches, whether fixed, per-title, or machine-learned, typically assume that decoded video is directly consumed, overlooking the increasing use of client-side Video Super-Resolution (VSR) and its associated computational and energy costs. Moreover, while the Versatile Video Coding (VVC) standard achieves substantial compression gains, its increased decoding complexity further complicates energy-efficient deployments. To address these challenges, we introduce E2-Stream, a fully measurement-driven framework that integrates VVC encoding, multi-model VSR enhancement, and client-side decoding and enhancement energy profiling for enhancement-aware bitrate-ladder construction. Instead of relying on predictions or heuristics, E2-Stream exhaustively evaluates each resolution–bitrate–VSR configuration on real hardware to capture the true rate–quality–energy behavior, and selects representations that balance perceptual quality and client-side energy consumption. A Just Noticeable Difference (JND)-aware pruning strategy further removes perceptually redundant representations, yielding compact ladders with reduced storage overhead. Unlike prior work focusing solely on spatial super-resolution, we evaluate both lightweight frame-wise SISR models (FSRCNN, ESPCN) and a temporally-aware VSR model (EDVR), enabling direct comparison of quality gains, temporal consistency, and inference cost. We further evaluate E2-Stream on two heterogeneous client devices: (i) a CPU-only system with software VVC decoding and CPU-based inference, and (ii) a GPU-enabled system with hardware-accelerated decoding and GPU-accelerated VSR. Experiments on the Inter-4K dataset show that enhancement-aware ladders preserve sub-JND perceptual quality while achieving 12%–28% bitrate savings. On the CPU-only device, decoding and VSR inference increase energy consumption by up to 85%, highlighting the cost of software-based enhancement. In contrast, on the GPU-enabled device, the same configurations reduce total client-side energy by 25%–60% due to efficient hardware acceleration, while maintaining strong bitrate reductions (up to 40%). Additionally, JND-based pruning reduces storage requirements by up to 58.98% without compromising perceptual quality. These results demonstrate that VSR fundamentally reshapes the rate–quality–energy trade-off and that optimal bitrate ladders are inherently device-dependent. E2-Stream provides a principled and reproducible framework for energy- and enhancement-aware streaming in the VVC era.]]></description>
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