IEEE open journal of signal processing最新文献_第7页

Quaternion-Domain Super MDS for Robust 3D Localization 鲁棒三维定位的四元数域超级MDS

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-03-14 DOI: 10.1109/OJSP.2026.3693590

Alessio Lukaj;Keigo Masuoka;Takumi Takahashi;Giuseppe Thadeu Freitas de Abreu;Hideki Ochiai

{"title":"Quaternion-Domain Super MDS for Robust 3D Localization","authors":"Alessio Lukaj;Keigo Masuoka;Takumi Takahashi;Giuseppe Thadeu Freitas de Abreu;Hideki Ochiai","doi":"10.1109/OJSP.2026.3693590","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3693590","url":null,"abstract":"This paper proposes a novel low-complexity three-dimensional (3D) localization algorithm for wireless sensor networks, termed quaternion-domain super multi-dimensional scaling (QD-SMDS). The algorithm is based on a reformulation of the SMDS, originally developed in the real domain and for two-dimensional (2D) problems, using quaternion algebra. By representing 3D coordinates as quaternions, the method constructs a rank-1 Gram edge kernel (GEK) matrix that integrates both relative distance and angular information between nodes, which enhances the noise reduction effect achieved through low-rank truncation employing singular value decomposition (SVD), thereby improving robustness against information loss. To further reduce computational complexity, we also propose a variant of QD-SMDS that eliminates the need for the computationally expensive SVD by leveraging the inherent structure of the quaternion-domain GEK matrix. This alternative directly estimates node coordinates using only matrix multiplications within the quaternion domain. Simulation results demonstrate that the proposed method significantly improves localization accuracy compared to the original SMDS algorithm, especially in scenarios with substantial measurement errors. The proposed method also achieves comparable localization accuracy without requiring SVD.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"621-642"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11520270","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-Time Non-Uniform Fast Fourier Transform for Multibeam Digital Beamforming 多波束数字波束形成的实时非均匀快速傅立叶变换

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-08-04 DOI: 10.1109/OJSP.2026.3719966

Piero Angeletti;Adem Coskun;Izzet Kale

引用次数: 0

SAS: Semantic-Aware Sampling for Generative Dataset Distillation SAS：生成数据集蒸馏的语义感知采样

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-06-12 DOI: 10.1109/OJSP.2026.3703204

Mingzhuo Li;Guang Li;Linfeng Ye;Jiafeng Mao;Takahiro Ogawa;Konstantinos N. Plataniotis;Miki Haseyama

{"title":"SAS: Semantic-Aware Sampling for Generative Dataset Distillation","authors":"Mingzhuo Li;Guang Li;Linfeng Ye;Jiafeng Mao;Takahiro Ogawa;Konstantinos N. Plataniotis;Miki Haseyama","doi":"10.1109/OJSP.2026.3703204","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3703204","url":null,"abstract":"Deep neural networks have achieved impressive performance across a wide range of tasks, but this success often comes with substantial computational and storage costs due to large-scale training data. Dataset distillation addresses this challenge by constructing compact yet informative datasets that enable efficient model training while maintaining downstream performance. However, most existing approaches primarily emphasize matching data distributions or downstream training statistics, with limited attention to preserving high-level semantic information in the distilled data. In this work, we introduce a semantic-aware perspective for dataset distillation by leveraging Contrastive Language–Image Pretraining (CLIP) as a semantic prior for post-sampling. Our goal is to obtain distilled datasets that are not only compact but also semantically class-discriminative and diverse. To this end, we design three semantic scoring functions that quantify class relevance, inter-class separability, and intra-set diversity in a pretrained semantic space. Based on image pools generated by existing distillation methods, we further develop a two-stage strategy for effective sampling: the first stage filters semantically discriminative samples to form a reliable candidate set, and the second stage performs a dynamic diversity-aware selection to reduce redundancy while preserving semantic coverage. Extensive experiments across multiple datasets, image pools, and downstream models demonstrate consistent performance gains, highlighting the effectiveness of incorporating semantic information into dataset distillation.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"668-677"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11561066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Source Attribution of AI-Generated Images in Data Scarcity Conditions via Training-Free Resynthesis 数据稀缺条件下人工智能生成图像的来源归属

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-07-03 DOI: 10.1109/OJSP.2026.3710125

Pietro Bongini;Meiling Li;Andrea Costanzo;Benedetta Tondi;Mauro Barni

{"title":"Source Attribution of AI-Generated Images in Data Scarcity Conditions via Training-Free Resynthesis","authors":"Pietro Bongini;Meiling Li;Andrea Costanzo;Benedetta Tondi;Mauro Barni","doi":"10.1109/OJSP.2026.3710125","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3710125","url":null,"abstract":"Attributing synthetic images to the source that generated them is a difficult problem, particularly in data-scarcity conditions requiring the adoption of few-shot or zero-shot learning strategies. In this paper, we tackle this problem by introducing a training-free attribution method based on image resynthesis. Our method works by first automatically generating a textual prompt which describes the target image, and then using it to resynthesize the image with each candidate generator. Attribution is obtained by matching the image to the model whose resynthesis is most similar to the original one within an appropriate feature space - chosen based on a preliminary experimental analysis - among several possibilities offered by modern deep learning image analysis tools. Experiments using state-of-the-art few-shot models and other baselines show that our resynthesis-based method outperforms existing techniques when only a limited number of images are available for training or fine-tuning. Particular attention is given to assessing the robustness of the proposed method against common image processing operators and its adversarial robustness. In particular, the experimental results show that our method is robust to post-processing and that its adversarial robustness is significantly higher than that of baselines.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"730-745"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11594904","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Improved Primal-Dual Distributed Stochastic Gradient Driven Distributed Active Noise Control System 自适应改进原对偶分布随机梯度驱动分布主动噪声控制系统

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-06-08 DOI: 10.1109/OJSP.2026.3701314

Asutosh Kar;Pradeep K. Shill;Abhishek S. Shamkuwar;Samridhi Saini;Vasundhara;S. Shoba;Mads Græsbøll Christensen

{"title":"Adaptive Improved Primal-Dual Distributed Stochastic Gradient Driven Distributed Active Noise Control System","authors":"Asutosh Kar;Pradeep K. Shill;Abhishek S. Shamkuwar;Samridhi Saini;Vasundhara;S. Shoba;Mads Græsbøll Christensen","doi":"10.1109/OJSP.2026.3701314","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3701314","url":null,"abstract":"Distributed active noise control (DANC) systems, typically employing the filtered-x least mean squares algorithm, are widely used for spatial noise reduction in acoustic networks. However, due to acoustic coupling among the nodes, vulnerability to impulsive interference, and degradation in non-Gaussian noise environments, the performance of these systems degrades. Additionally, most of the existing DANC frameworks neglect practical limitations in communication, such as transmission delays and interruptions, which limits their scalability in real-world deployments. To address these challenges, an adaptive improved distributed stochastic gradient (AIDSG) algorithm grounded within the primal-dual optimization framework is introduced in this paper. The proposed approach initiates an informed primal-dual update across nodes with instantaneous dual feedback, which results in resilience against colored and impulsive noise, accelerates convergence, and increases adaptability to dynamically changing acoustic environments. Four types of algorithm variants are presented, which address the trade-off between convergence speed and steady-state error. Simulation results on a five-node network validate the superiority of the AIDSG algorithm over conventional DANC and diffusion-based schemes while demonstrating better residual reduction performance, faster mean square convergence, and a better mean noise ratio under additive white Gaussian noise, colored noise, and impulsive noise conditions.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"643-658"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11553366","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Speaker-Disentangled Chunk-Wise Regression for Syllabic Tokenization 音节标记化的说话人解纠缠块智能回归

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-07-08 DOI: 10.1109/OJSP.2026.3711530

Ryota Komatsu;Kota Kawakita;Takuma Okamoto;Takahiro Shinozaki

引用次数: 0

SAND: The Challenge on Speech Analysis for Neurodegenerative Disease Assessment 语音分析对神经退行性疾病评估的挑战

IF 2.6

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-07-13 DOI: 10.1109/OJSP.2026.3713044

Giovanna Sannino;Ivanoe De Falco;Nadia Brancati;Laura Verde;Maria Frucci;Daniel Riccio;Vincenzo Bevilacqua;Antonio Di Marino;Lucia Aruta;Valentina Virginia Iuzzolino;Gianmaria Senerchia;Myriam Spisto;Raffaele Dubbioso

{"title":"SAND: The Challenge on Speech Analysis for Neurodegenerative Disease Assessment","authors":"Giovanna Sannino;Ivanoe De Falco;Nadia Brancati;Laura Verde;Maria Frucci;Daniel Riccio;Vincenzo Bevilacqua;Antonio Di Marino;Lucia Aruta;Valentina Virginia Iuzzolino;Gianmaria Senerchia;Myriam Spisto;Raffaele Dubbioso","doi":"10.1109/OJSP.2026.3713044","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3713044","url":null,"abstract":"Recent advances in Artificial Intelligence (AI) and the exploration of noninvasive, objective biomarkers, such as speech signals, have encouraged the development of algorithms to support the early diagnosis of neurodegenerative diseases, including Amyotrophic Lateral Sclerosis (ALS). Voice changes in subjects suffering from ALS typically manifest as progressive dysarthria, which is a prominent neurodegenerative symptom because it affects patients as the disease progresses. Since voice signals are complex data, the development and use of advanced AI techniques are fundamental to extracting distinctive patterns from them. Validating AI algorithms for ALS diagnosis and monitoring using voice signals is challenging, particularly due to the lack of annotated datasets. In this work, we present the outcome of a collaboration between a multidisciplinary team of clinicians and computer science experts to create both a clinically annotated dataset and the “Speech Analysis for Neurodegenerative Diseases” (SAND) challenge based on it. By analyzing voice disorders, the SAND challenge provides an opportunity to develop, test, and evaluate AI models for the automatic early identification and prediction of ALS disease progression.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"766-775"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11606418","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temporal Dynamics Based Adaptive Control Mechanism for Improved Narrowband Noise Cancellation 基于时间动态的改进窄带消噪自适应控制机制

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-03-11 DOI: 10.1109/OJSP.2026.3692409

Pradeep K. Shill;Asutosh Kar;Mads Græsbøll Christensen

{"title":"Temporal Dynamics Based Adaptive Control Mechanism for Improved Narrowband Noise Cancellation","authors":"Pradeep K. Shill;Asutosh Kar;Mads Græsbøll Christensen","doi":"10.1109/OJSP.2026.3692409","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3692409","url":null,"abstract":"Traditional hybrid narrowband active noise control (HNANC) systems typically rely on fixed or energy-based variable step-size adaptation. These adaptation methods sometimes result in suboptimal convergence and poor trade-offs between convergence speed and steady-state performance in the presence of nonstationary tonal disturbances and secondary-path modelling errors. These methods often necessitate persistent external references and face challenges in managing feedforward-feedback loop interactions, which impairs robustness in practical narrowband noise environments. This paper proposes a temporal coherence–driven adaptive control mechanism for HNANC systems, in which the instantaneous coherence between a predefined reference signal and the residual error is used as a tracking-confidence metric to regulate adaptive step-sizes. The temporal coherence emphasizes structured narrowband components while suppressing updates driven by incoherent residuals, thereby improving adaptation behavior under nonstationary conditions. The proposed approach integrates a supporting control filter with coherence-based step-size scheduling to coordinate feedforward and feedback adaptation using normalized least mean square algorithms. Simulation and experimental results under multiple narrowband noise scenarios demonstrate faster retracking and improved stability compared to state-of-the-art HNANC methods, with a modest increase in computational load.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"551-568"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11515074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Spatio-Temporal and Frequency Guided Fusion Network for Event-to-Video Reconstruction 用于事件到视频重构的深度时空和频率引导融合网络

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-03-14 DOI: 10.1109/OJSP.2026.3693230

Ramna Maqsood;Paulo Nunes;Caroline Conti;Luís Ducla Soares

{"title":"Deep Spatio-Temporal and Frequency Guided Fusion Network for Event-to-Video Reconstruction","authors":"Ramna Maqsood;Paulo Nunes;Caroline Conti;Luís Ducla Soares","doi":"10.1109/OJSP.2026.3693230","DOIUrl":"https://doi.org/10.1109/OJSP.2026.3693230","url":null,"abstract":"Event-to-video (E2V) reconstruction has gained significant attention recently for its advantages in enabling high dynamic range and fast motion capture capabilities. However, event data encodes only relative brightness changes, lacking the absolute intensity information necessary for accurate reconstruction. Recent methods incorporate previously reconstructed images to provide intensity references but process them in the spatial domain where low- and high-frequency components are highly coupled. This spatial processing typically leads to the degradation of fine details and introduces artifacts such as over-smoothing, blurring and low contrast reconstruction. To address this, we propose a deep spatio-temporal and frequency guided fusion network for E2V reconstruction (DSTFN-E2V), featuring a dual-path architecture with two key components: i) a prior frequency decomposition module (PFDM), and ii) a spatio-temporal event-driven feature extraction module (STEM). The PFDM decouples low- and high-frequency information from previously reconstructed images and current event voxel grid via a 2D discrete wavelet transform, processing the low-frequency subband through residual blocks to preserve structural coherence and intensity references, while an edge-detail refinement module (ERM) enhances edge and texture details from high-frequency subbands. The frequency-specific features from PFDM and the spatio-temporal features from STEM are then integrated through the proposed event-image fusion blocks (EIFBs) that apply cross-attention across three encoder stages, enabling simultaneous structural preservation and detail recovery. Experiments on four real-world datasets demonstrate that DSTFN-E2V achieves state-of-the-art results with 12% SSIM improvements while being 50% faster than recent attention-based methods, with superior edge fidelity and reduced artifacts.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"7 ","pages":"541-550"},"PeriodicalIF":2.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11520274","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148171749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Failing to Reflect Reality: Gaussian Splatting’s Collapse on Non-Lambertian Surfaces 不能反映现实：高斯飞溅在非朗伯曲面上的坍缩

IF 2.7

IEEE open journal of signal processing Pub Date : 2026-01-01 Epub Date: 2026-04-21 DOI: 10.1109/OJSP.2026.3685574

Eva Dubar;Sarah Dury;Gauthier Lafruit;Daniele Bonatto

引用次数: 0