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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>2026-07-06T18:37:57.32+00:00</pubDate>
        <ttl>60</ttl>
        <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>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1844194</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1844194</link>
        <title><![CDATA[Classification of infrasonic signals based on time-frequency representation]]></title>
        <pubdate>2026-06-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hao Yin</author><author>Yu Lu</author><author>Yunfen Chang</author><author>Kai Zhang</author><author>Yunhui Wu</author><author>Fan Yang</author><author>Xuexu Li</author><author>Xinliang Pang</author><author>Peng Li</author>
        <description><![CDATA[Accurate classification of infrasound signals is an important research topic in the fields of nuclear explosion monitoring and natural disaster early warning. Current studies often rely on empirical selection of time-frequency analysis methods to characterize denoised signals, lacking a systematic evaluation of different methods under noisy conditions within a unified framework. To address this issue, this study takes six types of non-denoised infrasound signals—including nuclear tests, chemical explosions, and lightning—as research objects, and systematically compares the comprehensive performance of Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT), and Hilbert–Huang Transform (HHT) in a convolutional neural network (CNN) classification model. Experimental results show that STFT achieves the best overall performance under the configuration of a Hanning window with a length of 1 s and NFFT = 1,024, with a classification accuracy of 98.35%, significantly outperforming CWT and HHT. Meanwhile, STFT requires the shortest computation time (0.85 s) and exhibits the fastest model convergence. Further analysis reveals that the inherent smoothing and consistency provided by the fixed window function of STFT contribute to stronger robustness and feature stability in noisy environments. This study simplifies the classification pipeline, avoids information loss and computational overhead caused by denoising preprocessing, emphasizes the importance of selecting time-frequency methods under noisy conditions, and provides a more efficient and reliable engineering solution for real-time infrasound monitoring systems, demonstrating strong practical application value.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1821900</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1821900</link>
        <title><![CDATA[SwinPyramidNet: a deep learning network for pear leaf disease classification using hybrid convolutional feature pyramids and swin transformer]]></title>
        <pubdate>2026-06-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tejas Sharma</author><author>Nikhil Shinde</author><author>J. Mukundh</author><author>R. Karthik</author><author>K. Suganthi</author>
        <description><![CDATA[IntroductionPear is rich in dietary fiber, vitamins, and minerals, but its cultivation has been affected by the adverse effects of climate change. Leaf health serves as an early indicator of potential fruit damage, making timely disease detection essential to prevent substantial economic losses. Conventional diagnosis methods, relying on manual visual inspection, are often labor-intensive, costly, and prone to errors. Recent advances in deep learning have enabled accurate image-based disease classification, providing an effective alternative for automated disease detection in precision agriculture.MethodsThis study introduces SwinPyramidNet, a novel dual-track deep learning architecture for pear leaf disease classification. The network integrates the strengths of Convolutional Neural Networks (CNNs) and Transformers to enhance classification performance. The Swin Transformer branch captures global contextual information by modeling long-range dependencies and complex patterns, while the Hybrid Convolutional Feature Pyramid Network (HCFPNet) branch focuses on extracting fine-grained local features through multi-scale convolutional processing. The outputs from both branches are fused and refined using a coordinate attention mechanism, which encodes positional information to emphasize spatially relevant features across channels. This complementary feature extraction strategy enables the network to effectively learn both global and local disease characteristics from pear leaf images.ResultsThe proposed network achieved a classification accuracy of 92.69% on the Diamos dataset. The experimental findings demonstrate the effectiveness of combining Transformer-based global feature learning with CNN-based local feature extraction for pear leaf disease classification. The coordinate attention module further contributed to improved feature representation by highlighting disease-relevant regions.DiscussionThe obtained results indicate that SwinPyramidNet can serve as a reliable and efficient approach for automated pear leaf disease diagnosis. The integration of global contextual information and fine-grained local features enhances the network’s ability to distinguish between disease classes with high accuracy. These findings highlight the applicability of hybrid CNN–Transformer architectures in smart agriculture and plant disease management systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1831207</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1831207</link>
        <title><![CDATA[Detection of bacterial and viral pneumonia in pediatric chest radiographs using fusion of mediastinum and lung imaging biomarkers]]></title>
        <pubdate>2026-05-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sukanta Kumar Tulo</author><author>Shilpi Ruchi Kerketta</author><author>O. Rahul Manohar</author><author>Pramod Martha</author>
        <description><![CDATA[IntroductionDiagnosis of pneumonia in pediatric patients remains challenging due to the similarity of manifestations across different pneumonia types. Evaluation of variations in two clinically significant regions, the lung and the mediastinum, on chest radiographs could assist in accurate disease identification. In this work, the morphological characteristics of the mediastinum and lungs are analyzed, and multiple wrapper-based biomarker fusion techniques are employed to enhance the differentiation of bacterial and viral pneumonia.MethodsThe pediatric radiographic images are acquired from a publicly accessible dataset. A hybrid segmentation model combining edge and region-based level set techniques is employed to segment the lungs and mediastinum. Furthermore, morphological imaging biomarkers such as geometric and Hu moments are extracted from the segmented masks and statistically analyzed. Multiple wrapper-based biomarker fusion methods are implemented using Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) algorithms. Further, the optimal fused imaging biomarkers are fed to LDA and SVM classifiers to differentiate the conditions.Results and DiscussionResults indicate that the employed hybrid model could segment both lungs and mediastinum regions from raw radiographic images. The extracted imaging biomarkers effectively characterize the morphological variations. In bacterial pneumonia, the mean lung area is reduced, whereas the mean mediastinum area is increased compared to viral pneumonia. The SVM classifier provided better F-measures of 75.5%, 81.3%, and 82.9% to differentiate bacterial and viral pneumonia using individual mediastinum, lung, and fused biomarkers, respectively, compared to the LDA classifier. Further, enhanced F-measures of 76.5%, 82.0%, and 87.3% are obtained using the LDA-based wrapper selected mediastinum, lung, and fused biomarkers, respectively. The findings indicate that fusion of imaging biomarkers from the lung and mediastinum regions achieves better performance than individual biomarkers.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1787092</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1787092</link>
        <title><![CDATA[Multi-scale pyramid spatial atrous channel attention enhanced ResNet50V2 for explainable pulmonary fibrosis diagnosis]]></title>
        <pubdate>2026-05-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>K. Mahapackialakshmi</author><author>G. Jaffino</author>
        <description><![CDATA[IntroductionPulmonary fibrosis is a progressive interstitial lung disease where delayed or erroneous diagnosis may result in severe clinical consequences. While chest CT imaging serves as the principal method for evaluation, manual interpretation is time-consuming and susceptible to observer variability. Moreover, most modern technologies operate as “black boxes,” resulting in diminished transparency in clinical decision-making. To overcome these limitations, this study proposes a novel Pyramid Spatial Atrous Channel Attention (PSACA)-based feature enhancement module integrated into the ResNet50V2 deep transfer learning framework for automated pulmonary fibrosis classification from chest CT images.MethodsThe proposed model integrates with PSACA into the ResNet50V2 backbone, prioritizing spatial attention before channel attention, with Atrous Spatial Pyramid Pooling added between them to enhance discriminative feature representation in disease-relevant regions. A multi-level pyramid model, which is designed on parallel dilated convolutions, provides hierarchical contextual detail by incrementally increasing receptive fields, providing the ability to model robustly local-to-global pulmonary patterns. By combining spatial attention, atrous spatial pyramid pooling, and channel attention, the fibrosis-relevant regions and informative feature channels are selectively enhanced and lead to a better detection of normal and fibrotic lung tissues. This combined block is embedded in Stage 3 and Stage 4 of ResNet50V2 to maximize high-level fibrosis representation while keeping computational complexity low.Results and discussionExperimental evaluation on a balanced CT dataset demonstrates that the proposed method achieves a classification accuracy of 99.83%, sensitivity 99.93%, specificity 99.72%, F1-score 99.83%, and precision 99.72%. Grad-CAM++ explainability yields clinically meaningful heatmaps that highlight fibrotic abnormalities, enhancing radiologist confidence and interpretability. Overall, the proposed architecture presents a novel multi-scale attention mechanism designed for pulmonary fibrosis, providing enhanced feature discrimination, greater localization, and enhanced clinical explainability.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1812987</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1812987</link>
        <title><![CDATA[InvZW: invariant feature learning via noise-adversarial training for robust image zero-watermarking]]></title>
        <pubdate>2026-05-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Abdullah All Tanvir</author><author>Frank Y. Shih</author><author>Xin Zhong</author>
        <description><![CDATA[This paper introduces a novel deep learning framework for robust image zero-watermarking based on distortion-invariant feature learning. As a zero-watermarking scheme, our method leaves the original image unaltered and learns a reference signature through optimization in the feature space. The proposed framework consists of two key modules. In the first module, a feature extractor is trained via noise-adversarial learning to generate representations that are both invariant to distortions and semantically expressive. This is achieved by combining adversarial supervision against a distortion discriminator and a reconstruction constraint to retain image content. In the second module, we design a learning-based multibit zero-watermarking scheme where the trained invariant features are projected onto a set of trainable reference codes optimized to match a target binary message. Extensive experiments on diverse image datasets and a wide range of distortions show that our method achieves state-of-the-art robustness in both feature stability and watermark recovery. Comparative evaluations against existing self-supervised and deep watermarking techniques further highlight the superiority of our framework in generalization and robustness.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1795809</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1795809</link>
        <title><![CDATA[Explainable artificial intelligence for the analysis of histopathological images of breast cancer: methods, interpretability and emerging directions]]></title>
        <pubdate>2026-05-12T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Raimondo Fanale</author><author>Barbara Martini</author><author>Filippo Sciarrone</author><author>Roberto Caldelli</author>
        <description><![CDATA[Recent advances in image processing and artificial intelligence have significantly improved the analysis of histopathological images for breast cancer. Deep learning models applied to high-resolution histopathological images (Whole Slide Images, WSIs) are capable of capturing complex, multiscale morphological patterns, achieving high performance in classification, segmentation and risk stratification tasks. However, the increasing architectural complexity of these models has introduced major interpretability issues, limiting their reliability and adoption in sensitive medical image processing applications. Explainable Artificial Intelligence (XAI) has emerged as a key research area within signal and image processing, aiming to make the internal representations and decision-making mechanisms of complex models more transparent and understandable. In the histopathological context, signal- and feature-level interpretability is essential to verify that model predictions are driven by meaningful morphological and textural patterns rather than by spurious correlations or acquisition artefacts. This review work analyses and synthesizes the main XAI techniques applied to the analysis of breast cancer histopathological images, including saliency-based methods, feature attribution approaches, concept-based techniques and intrinsically interpretable architectures. The strengths and limitations of each approach are discussed from an image processing perspective, with particular emphasis on conceptual aspects of spatial localization, multiscale coherence and signal fidelity. Finally, an emerging trend towards multidimensional and composite approaches to explainability is highlighted, which may support the development of standardized evaluation strategies and the design of explainable-by-design image analysis systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1797749</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1797749</link>
        <title><![CDATA[Explainable artificial intelligence approaches in cardiovascular imaging: methodological advances and clinical implications]]></title>
        <pubdate>2026-05-04T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Wentao Yan</author><author>Rui Sun</author><author>Li Shen</author>
        <description><![CDATA[Cardiovascular diseases remain the leading cause of mortality worldwide, making accurate and efficient imaging-based diagnosis indispensable. Modern modalities such as Coronary Computed Tomography Angiography, Cardiac Magnetic Resonance, Echocardiography, and Chest X-Ray enable rich structural and functional assessment; however, the rapid growth of imaging data strains traditional analysis. Deep learning has markedly improved performance across cardiovascular imaging tasks, yet its “black box” nature limits interpretability, clinician trust, and clinical adoption. eXplainable Artificial Intelligence (XAI) addresses this gap by exposing the decision logic of models in human-understandable forms. This review provides a structured synthesis of recent progress in XAI for cardiovascular imaging. We outline the core principles of perturbation-based and backpropagation-based methods, and survey their applications across major modalities for disease characterization, lesion discrimination, and risk stratification. We further analyze current evaluation challenges and methodological limitations, and propose future directions toward robust, trustworthy, and clinically deployable XAI systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1816476</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1816476</link>
        <title><![CDATA[Stochastic signal representation via harmonic parameter factorization]]></title>
        <pubdate>2026-04-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>O. Angelsky</author><author>M. Strynadko</author><author>C. Zenkova</author><author>R. Zaiats</author><author>Xinzheng Zhang</author><author>Jun Zheng</author><author>Jingxian Cai</author>
        <description><![CDATA[IntroductionStochastic waveforms are intrinsic to many physical and telecommunication processes, yet reproducible interfaces for converting them into compact stochastic representations suitable for bitstream-domain processing remain limited.MethodsWe represent each finite analysis window by a small set of dominant harmonic components and encode the interpretable parameters of each component—amplitude, frequency, and phase represented by cos⁡ϕk,⁡sin⁡ϕk, with polarization as an optional extension—into calibrated Bernoulli bitstreams. Validation is performed using a NOT–NOT identity protocol that separates finite-K representational loss from finite-N stochastic encoding error.ResultsThe method provides a compact and reproducible stochastic representation of noisy waveforms and enables transparent fidelity assessment through reconstruction error and process-level statistics, including power spectral density, autocorrelation, and amplitude distributions. The framework also supports direct comparison between truncation-limited and encoding-limited error sources.DiscussionHarmonic parameter factorization offers an interpretable bridge between waveform-domain stochastic signals and probability/bitstream-domain processing, supporting controlled validation and reproducible downstream stochastic signal processing.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1777346</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1777346</link>
        <title><![CDATA[Application of VMD–CNN–LSTM in mechanical fault diagnosis of pump station units]]></title>
        <pubdate>2026-04-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fengshou Zhang</author><author>Mengmeng Cui</author><author>Hongyu Yang</author><author>Jing Feng</author><author>Qingqing Tian</author><author>Yu Tian</author><author>Lei Guo</author>
        <description><![CDATA[Pump station units under prolonged high-load operation are prone to mechanical faults that threaten the safe and stable operation of water diversion projects. Existing diagnostic methods often face challenges in adaptive parameter optimization of variational mode decomposition (VMD), modal aliasing, and insufficient spatiotemporal feature representation. To address these issues, this study proposes an intelligent fault diagnosis framework based on an improved VMD–convolutional neural network (CNN)–long short-term memory (LSTM)-coupled model. The main contributions are as follows. 1) A dedicated parameter optimization strategy is proposed by enhancing the sparrow search algorithm (SSA) with an Osprey-inspired exploration mechanism and a Cauchy mutation operator (resulting in OCSSA). This approach adaptively optimizes VMD parameters, thus overcoming the limitations of manual tuning and local optima. 2) The optimal intrinsic mode function (IMF) is selected based on envelope entropy to effectively mitigate modal aliasing and noise interference. 3) A CNN–LSTM hybrid architecture is constructed to achieve joint spatiotemporal modeling—CNN extracts local spatial features, while LSTM captures temporal dependencies—addressing the shortcomings of single models in comprehensive feature representation. Fault classification is completed via a fully connected layer and a softmax function. Experimental results show that under 5 dB low signal-to-noise ratio (SNR) conditions, the proposed model achieves 80.95% diagnostic accuracy for typical faults such as rotor misalignment and rubbing—a 12.72 percentage point improvement over the baseline CNN–LSTM model—while maintaining competitive training efficiency. Under 20 dB SNR, the accuracy further reaches 97.50%. The model significantly reduces misdiagnosis rates for complex coupled faults, demonstrating superior robustness and engineering applicability. This integrated framework offers a reliable and deployable solution for the intelligent maintenance of pump station units.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1761302</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1761302</link>
        <title><![CDATA[A patch-wise deep residual network (PwDRU-Net102) for multimodal MRI brain tumor segmentation]]></title>
        <pubdate>2026-04-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Manu Singh</author><author>Tanu Singh</author><author>Vinod Patidar</author>
        <description><![CDATA[Gliomas are among the most severe types of brain tumors and can be life-threatening without early detection. Accurate and timely segmentation of brain tumors from MRI scans is crucial for effective treatment planning; however, it remains challenging due to significant variation in tumor shape, size, and location. This paper proposes a 2D Patch-wise Deep Residual U-Net with 102 convolutional layers for automatic tumor segmentation. The approach divides MRI scans into uniform, non-overlapping patches to achieve precise localization and better preserve local features. Residual blocks with identity mapping help mitigate vanishing gradient issues, while dropout layers reduce overfitting during training. T1, T2, and FLAIR modalities from the BraTS 2019 and 2020 datasets were used to evaluate the model. Experimental results show high segmentation accuracy on BraTS 2020 and the Dice Similarity Coefficients (DSC) achieved were 0.9136 (WT), 0.7143 (TC), and 0.7028 (ET). The paper demonstrates that patch-wise deep residual architectures, even with limited training data, can deliver reliable and robust brain tumor segmentation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1728615</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1728615</link>
        <title><![CDATA[Temporal convolutional network architectures: a novel simultaneous spatio-temporal model for comparative analysis]]></title>
        <pubdate>2026-04-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Milad Jabbari</author><author>Eisa Aghchehli</author><author>Chenfei Ma</author><author>Kianoush Nazarpour</author>
        <description><![CDATA[IntroductionConventional temporal-based deep learning models often fail to extract inter- channel information from electromyographic (EMG) signals. Existing spatio-temporal approaches typically sequentially combine spatial and temporal networks, but this strategy increases model complexity and parameter count.MethodWe introduce a simultaneous spatio-temporal convolutional deep network, which integrates spatial and temporal feature extraction connections within a single, explainable deep network.ResultsTo evaluate the new architecture through a comprehensive comparative analysis, we compared its performance and model size with three other established decoding methods. We used two internal and two publicly available EMG databases. We report that the application of convolutional filters in both spatial and temporal directions simultaneously enhances myoelectric decoding accuracy. Finally, we explain the proposed model using the saliency maps method.DiscussionThe findings indicate that the proposed simultaneous spatio-temporal configuration offers reliable classification performance and is well-suited for real-time on-board deployment. The proposed model explains how simultaneous spatio-temporal convolution enhances the contribution of both temporal and spatial components of EMG activity, resulting in improved classification performance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1794236</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1794236</link>
        <title><![CDATA[Dual-prediction and adaptive complexity for reversible watermarking of color images]]></title>
        <pubdate>2026-04-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chen Cui</author><author>Li Li</author><author>Hao Du</author><author>Wen Wang</author>
        <description><![CDATA[To expand the application scope of digital watermarking for color images widely used in social communication, this paper proposes a reversible watermarking scheme for color images. This scheme integrates bidirectional prediction correction embedding, dual dimensional complexity evaluation, and multi-threshold joint control to achieve adaptive optimization prediction of embedding parameters. First, by defining reference and associated channels, bidirectional correction is achieved through forward utilization of the reference image to predict and embed data into associated images, while simultaneously utilizing associated images backward to optimize and correct points with large disturbances during the embedding of the reference image. Second, a dual-dimensional complexity evaluation model is constructed by fusing local variance and visual saliency features, which accurately characterizes both macroscopic edge structures and microscopic texture details of images to precisely localize optimal embedding regions. Finally, multi-threshold joint constraints facilitate adaptive selection of optimal embedding sizes, mitigating distortion induced by capacity expansion and invalid pixel shifts. Experimental results demonstrate that the proposed scheme outperforms state-of-the-art methods, adapting better to the content characteristics of color images and achieving a superior balance between image quality and embedding performance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1778118</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1778118</link>
        <title><![CDATA[Automatic monitoring herbage prehensions in grazing cows using audio signals and deep learning techniques]]></title>
        <pubdate>2026-04-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Roberta Avanzato</author><author>Ludovica Beritelli</author><author>Salvatore Bognanno</author><author>Francesco Beritelli</author><author>Marcella Avondo</author>
        <description><![CDATA[BackgroundAccurate monitoring of feeding behavior in grazing ruminants, particularly the detection of prehension events, is a central challenge for Precision Livestock Farming (PLF). Traditional methods, such as accelerometers, show limitations in the reliable identification of individual events. Acoustic analysis based on deep learning is emerging as a non-invasive and promising alternative.MethodsThis study presents two main contributions: (i) a web-based software platform (built on React.js and TensorFlow.js) for the annotation, visualization, and in-browser inference of audio signals; (ii) a comparative analysis of several 2D-CNN architectures (DenseNet-121, ResNet-101, EfficientNet-B7, and YOLO11s-cls) for the classification of prehension events. Models were trained and tested on a dataset of logarithmic spectrograms (500 ms) derived from audio recordings acquired via collars on cattle.ResultsAnalysis revealed high performance across all architectures. Although DenseNet-121 achieved the highest weighted metrics (Accuracy 83.7%, AUC 0.90), the YOLO11s-cls model demonstrated remarkable competitiveness, achieving nearly identical accuracy (83.1%) but with significantly superior computational efficiency (4.5 ms inference time). Crucially for field applications, YOLO exhibited excellent rejection of non-relevant sounds, with a 91% Specificity on the “no-prehension” class.ConclusionsThe study validates the efficacy of spectrogram-based 2D-CNNs for ingestion monitoring and identifies YOLO as a promising candidate for efficiency-oriented deployment scenarios, offering a favorable trade-off between predictive reliability and low-latency requirements. The developed platform further supports this transition from research to in-field application.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1779355</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1779355</link>
        <title><![CDATA[Optical character recognition based document image quality assessment]]></title>
        <pubdate>2026-04-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>R. Krithika</author><author>J. Joshan Athanesious</author><author>S. Kiruthika</author>
        <description><![CDATA[Optical Character Recognition (OCR) systems play a crucial role in digitizing documents. However, their performance significantly deteriorates when handling low-quality images. Even advanced OCR systems struggle if the input is visually or structurally poor. Therefore, achieving high OCR accuracy requires assessing document image quality in terms of how well characters can be recognized, not just visual clarity. In this work, we propose a Document Image Quality Assessment (DIQA) model that predicts OCR accuracy without requiring the actual execution of an OCR engine. To assess document image quality for OCR performance, twelve distinct features are extracted that capture various aspects of sharpness, focus, edge clarity, and structural distortion. Instead of relying on subjective human opinion scores, we generate labels by measuring the actual OCR accuracy using modern engines like PaddleOCR and Keras OCR. These accuracy scores, calculated using the Levenshtein distance, serve as ground truth labels for training. Using the extracted features and corresponding OCR-based labels, we train the machine learning models to learn the relationship between image characteristics and OCR performance. The proposed models are evaluated using statistical metrics such as RMSE, PLCC, and SROCC to determine the most effective predictor. Our experiments demonstrate the importance of using OCR scores as labels, and the results show that our approach yields improved performance compared to existing baseline methodologies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1827692</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1827692</link>
        <title><![CDATA[Editorial: Emerging optimization, learning and signal processing for next-generation wireless communications and networking]]></title>
        <pubdate>2026-04-09T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Dionysis Kalogerias</author><author>Le Liang</author><author>Mark Eisen</author><author>Athina Petropulu</author><author>Leandros Tassiulas</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1843671</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1843671</link>
        <title><![CDATA[Retraction: Bayesian nonparametric learning and knowledge transfer for object tracking under unknown time-varying conditions]]></title>
        <pubdate>2026-04-08T00:00:00Z</pubdate>
        <category>Retraction</category>
        
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsip.2026.1776807</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsip.2026.1776807</link>
        <title><![CDATA[Self-face viewing attenuates cardiac modulation of corticospinal excitability]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Milana Makarova</author><author>Nikita Fedosov</author><author>Irina Mikhailova</author><author>Maria Nikolaeva</author><author>Alexei Ossadtchi</author><author>Alexey Tumyalis</author><author>Maria Volodina</author>
        <description><![CDATA[IntroductionWhile self-referential attention is thought to enhance interoceptive sensitivity, its effect on cardiac modulation of corticospinal excitability remains unexplored. This pilot study investigated how viewing one’s own face (self-face processing) modulates the cardiac-phase coupling of motor output and whether this heart-brain coupling depends on interoceptive accuracy (heartbeat perception).MethodsIn 15 healthy adults, motor-evoked potentials (MEPs) were elicited via transcranial magnetic stimulation (TMS) at three fixed time points following the R-peak (0, 250, and 500 m) during presentation of either self-face or other-face pictures. A Modulation Index was derived from log-transformed MEPs to quantify cardiac-phase modulation strength. Interoceptive accuracy was assessed via a heartbeat-counting task.ResultsContrary to the hypothesis that self- face viewing would enhance cardiac–motor coupling through inward attentional focus, self-face processing significantly reduced the overall magnitude of cardiac-phase modulation. This attenuation was most pronounced at 0 m and 250 m post-R-peak, corresponding to systolic phase. Across conditions, higher interoceptive accuracy predicted stronger modulation, though this relationship showed a tendency toward attenuation during self-face viewing (interaction p = 0.059).DiscussionThe results of this pilot TMS study suggest that, in a task requiring explicit evaluation of facial stimuli, self-face viewing acts as a potent exteroceptive stimulus that diverts attention away from interoceptive signals, thereby weakening the cardiac-cycle influence on motor excitability. These findings highlight the context-dependency of self-processing effects and suggest a possible link between HCT-based interoceptive accuracy and heart-brain- body coupling.]]></description>
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