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IEEE OPEN JOURNAL OF signal processing 2025年 6卷 247-255页

作者： Rey, Samuel Das, Bishwadeep Isufi, Elvin King Juan Carlos Univ Dept Signal Theory & Commun Madrid 28933 Spain Delft Univ Technol Intelligent Syst Dept NL-2628 CD Delft Netherlands

This paper addresses the problem of online network topology inference for expanding graphs from a stream of spatiotemporal signals. Online algorithms for dynamic graph learning are crucial in delay-sensitive applications or when changes in topology occur rapidly. While existing works focus on inferring the connectivity within a fixed set of nodes, in practice, the graph can grow as new nodes join the network. This poses additional challenges like modeling temporal dynamics involving signals and graphs of different sizes. This growth also increases the computational complexity of the learning process, which may become prohibitive. To the best of our knowledge, this is the first work to tackle this setting. We propose a general online algorithm based on projected proximal gradient descent that accounts for the increasing graph size at each iteration. Recursively updating the sample covariance matrix is a key aspect of our approach. We introduce a strategy that enables different types of updates for nodes that just joined the network and for previously existing nodes. To provide further insights into the proposed method, we specialize it in Gaussian Markov random field settings, where we analyze the computational complexity and characterize the dynamic cumulative regret. Finally, we demonstrate the effectiveness of the proposed approach using both controlled experiments and real-world datasets from epidemic and financial networks.

关键词： Topology Network topology Heuristic algorithms signal processing algorithms signal processing Covariance matrices Mathematical models Inference algorithms Computational complexity Vectors Topology inference dynamic graph learning online graph learning expanding graphs graph signal processing

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Charbonnier Quasi Hyperbolic Momentum Spline Based Incremental Strategy for Nonlinear Distributed Active Noise Control

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2025年 6卷 1-15页

作者： Kranthi, Rajapantula Vasundhara Kar, Asutosh Christensen, Mads Grasboll Natl Inst Technol Dept Elect & Commun Engn Warangal 506004 India Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Elect & Commun Engn Jalandhar 144008 India Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark

Noise mitigation proves to be a challenging task for active noise control in the existence of nonlinearities. In such environments, functional link neural network (FLN) and adaptive exponential FLN techniques improve the performance of distributed active noise control systems. Nonlinear spline approaches are well known for their low computational complexity and ability to effectively alleviate noise in nonlinear systems. This paper proposes a new cost function for distributed active noise control (DANC) system which is based on the Charbonnier quasi hyperbolic momentum spline (CQHMS) involving incremental approach. This incremental based CQHMS DANC method employs Charbonnier loss and quasi hyperbolic momentum approach which minimizes gradient variance and local crossover points in order to enhance the convergence and steady-state performance. The technique being proposed demonstrates enhanced performance and achieves accelerated convergence when compared to existing techniques in a range of nonlinear DANC scenarios in lieu of varied nonlinear primary path and nonlinear secondary path conditions.

关键词： Noise Splines (mathematics) Microphones Convergence Adaptive systems Adaptation models Prevention and mitigation Loudspeakers Adaptive filters signal processing algorithms Charbonnier function nonlinear distributed ANC quasi hyperbolic momentum spline

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Snake Optimizer Improved Variational Mode Decomposition for Short-Term Prediction of Vehicle Charging Loads

IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY

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IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY 2025年 12卷 76-87页

作者： Guan, Quanxue Liu, Qinhe Tao, Shaocong Xu, Yunjian Zhou, Di Chen, Haoyong Tan, Xiaojun Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Campus Shenzhen 518107 Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Peoples R China Shenzhen Acad Metrol & Qual Inspect Shenzhen 100062 Peoples R China South China Univ Technol Sch Elect Power Guangzhou 510640 Peoples R China

The rapid proliferation of electric vehicles (EVs) significantly impacts the power grid, necessitating effective forecasting of charging loads. For ultra short-term load prediction, this paper proposes a Snake Optimization (SO)-Variational Mode Decomposition (VMD)-Long Short-Term Memory (LSTM) algorithm trained by only the historical charging data. Before the prediction starts, the VMD method is utilized to minimize the data complexity, yielding several multiple Intrinsic Mode Functions (IMFs) that correspond to the charging load features at different time scales. The VMD parameters are automatically adjusted using the SO method, instead of the trial-and-error method, to trade off the prediction accuracy against computational overhead. Once the parameters of the VMD are determined, the same number of LSTM networks are employed to forecast the corresponding charging loads from these IMFs, with one LSTM for each IMF. Due to the VMD, IMFs with spanned center frequencies containing few irregularities make the prediction simple. These LSTM outcomes are then summed to obtain the overall load prediction. Experiments are carried out to show that the proposed parallel structure of multiple LSTM networks can achieve high prediction accuracy without requiring complex model structures. Our proposed algorithm outperforms the traditional prediction methods including Gate Recurrent Unit, Extreme Learning Machine, LSTM, and their combination with VMD, significantly reducing the Root Mean Square Error and the Mean Absolute Error by 30.1% and 32.9% in comparison with the optimal VMD-LSTM approach, and by 59.3% and 62.6% with respect to the baseline LSTM method.

关键词： Long short term memory Load modeling Predictive models Prediction algorithms Feature extraction Accuracy Optimization Data models signal processing algorithms Convolutional neural networks Electric vehicle charging station load forecasting long short-term memory snake optimization variational mode decomposition

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Robust Sequential Phase Estimation Using Multi-Temporal SAR Image Series

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IEEE signal processing LETTERS 2025年 32卷 811-815页

作者： El Hajjar, Dana Ginolhac, Guillaume Yan, Yajing El Korso, Mohammed Nabil Univ Savoie Mont Blanc LISTIC F-74000 Annecy France Univ Paris Saclay Cent Supelec L2S F-91190 Gif Sur Yvette France

Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) exploits Synthetic Aperture Radar images time series (SAR-TS) for surface deformation monitoring via phase difference (with respect to a reference image) estimation. Most of the actual state-of-the-art MT-InSAR rely on temporal covariance matrix of the SAR-TS, assuming Gaussian distribution. However, these approaches become computationally expensive when the time series lengthens and new images are added to the data vector. This paper proposes a novel approach to sequentially integrate each newly acquired image using Phase Linking (PL) and Maximum Likelihood Estimation (MLE). The methodology divides the data into blocks, using previous images and estimations as a prior to sequentially estimate the phase of the new image. Actually, this framework allows to consider non Gaussian distributions, such as a mixture of scaled Gaussian distribution, which is particularly important to consider when dealing with urban areas.

关键词： Covariance matrices Coherence Synthetic aperture radar signal processing algorithms Radar polarimetry Maximum likelihood estimation Vectors Time series analysis Radar imaging Gaussian distribution Multi-temporal interferometric synthetic aperture radar maximum likelihood estimation covariance matrix estimation mixture of scaled Gaussian distribution

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An Accelerated Successive Convex Approximation Scheme With Exact Step Sizes for L1-Regression

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2025年 6卷 184-193页

作者： Schynol, Lukas Hemsing, Moritz Pesavento, Marius Tech Univ Darmstadt D-64283 Darmstadt Germany

We consider the minimization of $\ell _{1}$-regularized least-squares problems. A recent optimization approach uses successive convex approximations with an exact line search, which is highly competitive, especially i... 详细信息

We consider the minimization of $\ell _algorithms$-regularized least-squares problems. A recent optimization approach uses successive convex approximations with an exact line search, which is highly competitive, especially in sparse problem instances. This work proposes an acceleration scheme for the successive convex approximation technique with a negligible additional computational cost. We demonstrate this scheme by devising three related accelerated algorithms with provable convergence. The first introduces an additional descent step along the past optimization trajectory in the variable update that is inspired by Nesterov's accelerated gradient method and uses a closed-form step size. The second performs a simultaneous descent step along both the best response and the past trajectory, thereby finding a two-dimensional step size, also in closed-form. The third algorithm combines the previous two approaches. All algorithms are hyperparameter-free. Empirical results confirm that the acceleration approaches improve the convergence rate compared to benchmark algorithms, and that they retain the benefits of successive convex approximation also in non-sparse instances.

关键词： signal processing algorithms Trajectory Vectors Optimization Approximation algorithms Gradient methods Convergence Computational efficiency signal processing Sparse matrices Compressed sensing FISTA LASSO L1-regularized least squares Nesterov's accelerated gradient method optimization successive convex approximation

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Exploring the Impact of Bistatic Target Reflectivity in ISAC-Enabled V2V Setup Across Diverse Geometrical Road Layouts

IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY

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IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY 2025年 6卷 948-968页

作者： Khan, Aamir Ullah Mint, Saw James Shah, Syed Najaf Haider Schneider, Christian Robert, Joerg Tech Univ Ilmenau Elect Measurements & Signal Proc Grp D-98693 Ilmenau Germany

Integrated Sensing and Communication (ISAC) is an intriguing emerging research area that combines radar sensing and communication functionalities in a unified platform, capitalizing on shared aspects of signal processing, spectrum utilization, and system design. For sensing applications, the reflectivity of objects between Transmitter (TX) and Receiver (RX) is crucial. It is normally modeled as a uniform scatterer or a group of uniform scatterers in wireless channels. These models do not take into account the dependence of reflectivity on the aspect angles of incident and scattering waves, the composed material, and the geometry of the objects. Therefore, we model the reflectivity of target vehicles using their bistatic Radar Cross Section (RCS), as in radar sensing, within a Vehicle to Vehicle (V2V) setup under the Integrated Sensing and Communication (ISAC) framework. Moreover, we consider constant and variable bistatic Target Reflectivity (TR) integrated setups with two diverse traffic scenarios. These traffic scenarios are modeled to be realistic, with diverse geometrical road layouts, variable vehicle velocities, distinct vehicle positions, and the presence of Diffuse (DI) scattering components. Then, we inspect the impact of the bistatic TR on the behavior of the wireless channel and target detection capability. The variable TR integrated setup leads to a more accurate realization of the scenario, leading to outcomes that closely resemble real-world conditions. The results show the substantial impact of the geometrical setup on the distribution of TR, which emphasizes the need to integrate TR into ISAC-enabled V2V channel models.

关键词： Roads Channel models Radar Integrated sensing and communication Scattering Layout Wireless communication Reflectivity Object detection Radar cross-sections Autonomous driving channel models radar cross-section radar sensing radar clutter signal processing algorithms vehicle-to-vehicle communication

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Adaptive Anomaly Detection in Network Flows With Low-Rank Tensor Decompositions and Deep Unrolling

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2025年 6卷 417-433页

作者： Schynol, Lukas Pesavento, Marius Tech Univ Darmstadt Darmstadt Germany

Anomaly detection (AD) is increasingly recognized as a key component for ensuring the resilience of future communication systems. While deep learning has shown state-of-the-art AD performance, its application in critical systems is hindered by concerns regarding training data efficiency, domain adaptation and interpretability. This work considers AD in network flows using incomplete measurements, leveraging a robust tensor decomposition approach and deep unrolling techniques to address these challenges. We first propose a novel block-successive convex approximation algorithm based on a regularized model-fitting objective where the normal flows are modeled as low-rank tensors and anomalies as sparse. An augmentation of the objective is introduced to decrease the computational cost. We apply deep unrolling to derive a novel deep network architecture based on our proposed algorithm, treating the regularization parameters as learnable weights. Inspired by Bayesian approaches, we extend the model architecture to perform online adaptation to per-flow and per-time-step statistics, improving AD performance while maintaining a low parameter count and preserving the problem's permutation equivariances. To optimize the deep network weights for detection performance, we employ a homotopy optimization approach based on an efficient approximation of the area under the receiver operating characteristic curve. Extensive experiments on synthetic and real-world data demonstrate that our proposed deep network architecture exhibits a high training data efficiency, outperforms reference methods, and adapts seamlessly to varying network topologies.

关键词： Tensors Computer architecture Computational modeling Adaptation models signal processing algorithms Artificial neural networks Data models Computational efficiency Anomaly detection Training data area under the curve deep unrolling low-rank network flows robust principal component analysis tensor decomposition

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Enabling Model-Based Design for Real-Time Spike Detection

IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY

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IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2025年 6卷 312-319页

作者： Di Florio, Mattia Bornat, Yannick Care, Marta Cota, Vinicius Rosa Buccelli, Stefano Chiappalone, Michela Univ Genoa Dept Informat Bioengn Robot Syst Engn DIBRIS I-16145 Genoa Italy Univ Bordeaux Lab Integrat Mat Syst IMS Bordeaux INP CNRS UMR 5218 F-33405 Talence France IRCCS Osped Policlin San Martino I-16132 Genoa Italy Ist Italiano Tecnol Rehab Technol Lab I-16163 Genoa Italy

Goal: This study addresses the inherent difficulties in the creation of neuroengineering devices for real-time neural signal processing, a task typically characterized by intricate and technically demanding processes. Beneath the substantial hardware advancements in neurotechnology, there is often rather complex low-level code that poses challenges in terms of development, documentation, and long-term maintenance. Methods: We adopted an alternative strategy centered on Model-Based Design (MBD) to simplify the creation of neuroengineering systems and reduce the entry barriers. MBD offers distinct advantages by streamlining the design workflow, from modelling to implementation, thus facilitating the development of intricate systems. A spike detection algorithm has been implemented on a commercially available system based on a Field-Programmable Gate Array (FPGA) that combines neural probe electronics with configurable integrated circuit. The entire process of data handling and data processing was performed within the Simulink environment, with subsequent generation of hardware description language (HDL) code tailored to the FPGA hardware. Results: The validation was conducted through in vivo experiments involving six animals and demonstrated the capability of our MBD-based real time processing (latency <= 100.37 s) to achieve the same performances of offline spike detection. Conclusions: This methodology can have a significant impact in the development of neuroengineering systems by speeding up the prototyping of various system architectures. We have made all project code files open source, thereby providing free access to fellow scientists interested in the development of neuroengineering systems.

关键词： Hardware design languages Software packages Codes Field programmable gate arrays Computer architecture Hardware signal processing algorithms Neural engineering signal processing Computational modeling in vivo experiments HDL coder Field-Programmable Gate Array (FPGA) neuroengineering

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Field Evaluation of a Softwarized Modem From the Perspective of 5G Cell Search

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第6期24卷 4987-5002页

作者： Lim, Dawoon Jeong, Subin Kim, Bitna Lee, Yelan Shin, Hye Jin Kim, Juyeop Hyundai Mobis Uiwang 16891 South Korea Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea Sookmyung Womens Univ Dept Elect Engn Seoul 04310 South Korea

Modem softwarization, where baseband signals are fully processed using software on a general-purpose CPU, is a promising technology in mobile communications due to its simplicity and flexibility in realizing various features. On the other hand, many still question the effectiveness of a softwarized modem in commercial environments concerning performance and complexity. Motivated by this perspective, this paper presents the design and implementation of a softwarized modem with the specific feature of 5G cell search for field evaluation. Based on the baseline algorithms of 5G cell search in Open Air Interface (OAI), we propose a new software architecture which can efficiently manage a 5G cell search procedure and decompose the overall 5G cell search into sub-algorithms. We also design and implement novel sub-algorithms that enhance the detection of Synchronization signal Blocks (SSBs). Our softwarized modem utilizes dual-rate sampling to significantly reduce computation complexity during timing offset estimation. It also adaptively detects synchronization signals or cell identities based on the presence of inter-cell interference or multi-path fading. The performance evaluation through field experiments concludes that our softwarized modem outperforms the baseline, and the proposed sub-algorithms are effective in enhancing cell search performance. The detection probability and time consumption results for our softwarized modem confirm that it is feasible for commercial uses.

关键词： Modems Computer architecture Microprocessors 5G mobile communication Synchronization signal processing algorithms Symbols Software algorithms OFDM Complexity theory 5G cell search softwarized modem field evaluation synchronization

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Sectoring Approach for Performance Enhancement of MTT and Its Application on JPDA Algorithm

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IEEE ACCESS 2025年 13卷 67758-67768页

作者： Cobankaya, Mustafa Latif Elrouby, Alaa B. Secretariat Def Ind TR-06420 Ankara Turkiye Ankara Yildirim Beyazit Univ Elect & Elect Engn Dept TR-06010 Ankara Turkiye

Recent advancements in multi-target tracking (MTT) technologies and algorithms have led to improved accuracy and efficiency in tracking multiple objects in dynamic environments. However, as the number of targets increases, MTT methods often struggle with computational demands and tracking capacity. This paper presents a novel sectoring approach on the Joint Probabilistic Data Association (JPDA) algorithm to enhance its tracking performance in MTT scenarios. The sectoring method divides the tracking area into distinct sectors and assigns a separate JPDA tracker for each sector, yielding two primary benefits in environments with large numbers of targets. First, it increases the capacity of the JPDA tracker to handle a larger number of targets. Second, it reduces computational complexity when tracking a high number of targets. On the other hand, the approach introduces a trade-off by slightly increasing computational complexity in scenarios with fewer targets. Experimental results were obtained through simulations of 10 different scenarios, each with 37 varying numbers of targets and three different sector configurations, amounting to a total of 1110 simulations. The findings demonstrate that the sectoring approach achieves up to a 95% reduction in computational complexity and increases the number of tracked targets from 24 to 40 in randomly generated scenarios. Results also revealed that the effects of the sectoring approach become more pronounced as the number of sectors increases.

关键词： Target tracking signal processing algorithms Radar tracking Kalman filters Computational complexity Heuristic algorithms Computational modeling Approximation algorithms Surveillance Data models Multi-target tracking joint probabilistic data association computational complexity high number of maneuvering targets sectoring approach

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