arXiv - EE - Signal Processing最新文献_第2页

Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep Unrolling 利用低函数张量分解和深度解卷进行网络流量自适应异常检测

arXiv - EE - Signal Processing Pub Date : 2024-09-17 DOI: arxiv-2409.11529

Lukas Schynol, Marius Pesavento

{"title":"Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep Unrolling","authors":"Lukas Schynol, Marius Pesavento","doi":"arxiv-2409.11529","DOIUrl":"https://doi.org/arxiv-2409.11529","url":null,"abstract":"Anomaly detection (AD) is increasingly recognized as a key component for\u0000ensuring the resilience of future communication systems. While deep learning\u0000has shown state-of-the-art AD performance, its application in critical systems\u0000is hindered by concerns regarding training data efficiency, domain adaptation\u0000and interpretability. This work considers AD in network flows using incomplete\u0000measurements, leveraging a robust tensor decomposition approach and deep\u0000unrolling techniques to address these challenges. We first propose a novel\u0000block-successive convex approximation algorithm based on a regularized\u0000model-fitting objective where the normal flows are modeled as low-rank tensors\u0000and anomalies as sparse. An augmentation of the objective is introduced to\u0000decrease the computational cost. We apply deep unrolling to derive a novel deep\u0000network architecture based on our proposed algorithm, treating the\u0000regularization parameters as learnable weights. Inspired by Bayesian\u0000approaches, we extend the model architecture to perform online adaptation to\u0000per-flow and per-time-step statistics, improving AD performance while\u0000maintaining a low parameter count and preserving the problem's permutation\u0000equivariances. To optimize the deep network weights for detection performance,\u0000we employ a homotopy optimization approach based on an efficient approximation\u0000of the area under the receiver operating characteristic curve. Extensive\u0000experiments on synthetic and real-world data demonstrate that our proposed deep\u0000network architecture exhibits a high training data efficiency, outperforms\u0000reference methods, and adapts seamlessly to varying network topologies.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Single-stage TTS with Masked Audio Token Modeling and Semantic Knowledge Distillation 采用屏蔽音频令牌建模和语义知识提炼技术的单级 TTS

arXiv - EE - Signal Processing Pub Date : 2024-09-17 DOI: arxiv-2409.11003

Gerard I. Gállego, Roy Fejgin, Chunghsin Yeh, Xiaoyu Liu, Gautam Bhattacharya

引用次数: 0

Linear Jamming Bandits: Learning to Jam 5G-based Coded Communications Systems 线性干扰匪帮：学习干扰基于 5G 的编码通信系统

arXiv - EE - Signal Processing Pub Date : 2024-09-17 DOI: arxiv-2409.11191

Zachary Schutz, Daniel J. Jakubisin, Charles E. Thornton, R. Michael Buehrer

引用次数: 0

On the normalized signal to noise ratio in covariance estimation 关于协方差估计中的归一化信噪比

arXiv - EE - Signal Processing Pub Date : 2024-09-17 DOI: arxiv-2409.10896

Tzvi Diskin, Ami Wiesel

引用次数: 0

NirvaWave: An Accurate and Efficient Near Field Wave Propagation Simulator for 6G and Beyond NirvaWave：适用于 6G 及更高频率的精确高效近场波传播模拟器

arXiv - EE - Signal Processing Pub Date : 2024-09-17 DOI: arxiv-2409.11293

Vahid Yazdnian, Yasaman Ghasempour

{"title":"NirvaWave: An Accurate and Efficient Near Field Wave Propagation Simulator for 6G and Beyond","authors":"Vahid Yazdnian, Yasaman Ghasempour","doi":"arxiv-2409.11293","DOIUrl":"https://doi.org/arxiv-2409.11293","url":null,"abstract":"The extended near-field range in future mm-Wave and sub-THz wireless networks\u0000demands a precise and efficient near-field channel simulator for understanding\u0000and optimizing wireless communications in this less-explored regime. This paper\u0000presents NirvaWave, a novel near-field channel simulator, built on scalar\u0000diffraction theory and Fourier principles, to provide precise wave propagation\u0000response in complex wireless mediums under custom user-defined transmitted EM\u0000signals. NirvaWave offers an interface for investigating novel near-field\u0000wavefronts, e.g., Airy beams, Bessel beams, and the interaction of mmWave and\u0000sub-THz signals with obstructions, reflectors, and scatterers. The simulation\u0000run-time in NirvaWave is orders of magnitude lower than its EM software\u0000counterparts that directly solve Maxwell Equations. Hence, NirvaWave enables a\u0000user-friendly interface for large-scale channel simulations required for\u0000developing new model-driven and data-driven techniques. We evaluated the\u0000performance of NirvaWave through direct comparison with EM simulation software.\u0000Finally, we have open-sourced the core codebase of NirvaWave in our GitHub\u0000repository (https://github.com/vahidyazdnian1378/NirvaWave).","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural Fields for Adaptive Photoacoustic Computed Tomography 用于自适应光声计算机断层扫描的神经场

arXiv - EE - Signal Processing Pub Date : 2024-09-17 DOI: arxiv-2409.10876

Tianao Li, Manxiu Cui, Cheng Ma, Emma Alexander

{"title":"Neural Fields for Adaptive Photoacoustic Computed Tomography","authors":"Tianao Li, Manxiu Cui, Cheng Ma, Emma Alexander","doi":"arxiv-2409.10876","DOIUrl":"https://doi.org/arxiv-2409.10876","url":null,"abstract":"Photoacoustic computed tomography (PACT) is a non-invasive imaging modality\u0000with wide medical applications. Conventional PACT image reconstruction\u0000algorithms suffer from wavefront distortion caused by the heterogeneous speed\u0000of sound (SOS) in tissue, which leads to image degradation. Accounting for\u0000these effects improves image quality, but measuring the SOS distribution is\u0000experimentally expensive. An alternative approach is to perform joint\u0000reconstruction of the initial pressure image and SOS using only the PA signals.\u0000Existing joint reconstruction methods come with limitations: high computational\u0000cost, inability to directly recover SOS, and reliance on inaccurate simplifying\u0000assumptions. Implicit neural representation, or neural fields, is an emerging\u0000technique in computer vision to learn an efficient and continuous\u0000representation of physical fields with a coordinate-based neural network. In\u0000this work, we introduce NF-APACT, an efficient self-supervised framework\u0000utilizing neural fields to estimate the SOS in service of an accurate and\u0000robust multi-channel deconvolution. Our method removes SOS aberrations an order\u0000of magnitude faster and more accurately than existing methods. We demonstrate\u0000the success of our method on a novel numerical phantom as well as an\u0000experimentally collected phantom and in vivo data. Our code and numerical\u0000phantom are available at https://github.com/Lukeli0425/NF-APACT.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Golden Ratio Search: A Low-Power Adversarial Attack for Deep Learning based Modulation Classification 黄金比率搜索：基于深度学习的调制分类的低功耗对抗攻击

arXiv - EE - Signal Processing Pub Date : 2024-09-17 DOI: arxiv-2409.11454

Deepsayan Sadhukhan, Nitin Priyadarshini Shankar, Sheetal Kalyani

引用次数: 0

Time-Varying Graph Signal Estimation among Multiple Sub-Networks 多个子网络间的时变图信号估计

arXiv - EE - Signal Processing Pub Date : 2024-09-17 DOI: arxiv-2409.10915

Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi, Yuichi Tanaka

{"title":"Time-Varying Graph Signal Estimation among Multiple Sub-Networks","authors":"Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi, Yuichi Tanaka","doi":"arxiv-2409.10915","DOIUrl":"https://doi.org/arxiv-2409.10915","url":null,"abstract":"This paper presents an estimation method for time-varying graph signals among\u0000multiple sub-networks. In many sensor networks, signals observed are associated\u0000with nodes (i.e., sensors), and edges of the network represent the inter-node\u0000connectivity. For a large sensor network, measuring signal values at all nodes\u0000over time requires huge resources, particularly in terms of energy consumption.\u0000To alleviate the issue, we consider a scenario that a sub-network, i.e.,\u0000cluster, from the whole network is extracted and an intra-cluster analysis is\u0000performed based on the statistics in the cluster. The statistics are then\u0000utilized to estimate signal values in another cluster. This leads to the\u0000requirement for transferring a set of parameters of the sub-network to the\u0000others, while the numbers of nodes in the clusters are typically different. In\u0000this paper, we propose a cooperative Kalman filter between two sub-networks.\u0000The proposed method alternately estimates signals in time between two\u0000sub-networks. We formulate a state-space model in the source cluster and\u0000transfer it to the target cluster on the basis of optimal transport. In the\u0000signal estimation experiments of synthetic and real-world signals, we validate\u0000the effectiveness of the proposed method.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Geometry Aware Meta-Learning Neural Network for Joint Phase and Precoder Optimization in RIS 用于 RIS 中联合相位和前导器优化的几何感知元学习神经网络

arXiv - EE - Signal Processing Pub Date : 2024-09-17 DOI: arxiv-2409.11270

Dahlia Devapriya, Sheetal Kalyani

引用次数: 0

Uncertainty Decomposition and Error Margin Detection of Homodyned-K Distribution in Quantitative Ultrasound 定量超声中同源性 K 分布的不确定性分解与误差边际检测

arXiv - EE - Signal Processing Pub Date : 2024-09-17 DOI: arxiv-2409.11583

Dorsa Ameri, Ali K. Z. Tehrani, Ivan M. Rosado-Mendez, Hassan Rivaz

{"title":"Uncertainty Decomposition and Error Margin Detection of Homodyned-K Distribution in Quantitative Ultrasound","authors":"Dorsa Ameri, Ali K. Z. Tehrani, Ivan M. Rosado-Mendez, Hassan Rivaz","doi":"arxiv-2409.11583","DOIUrl":"https://doi.org/arxiv-2409.11583","url":null,"abstract":"Homodyned K-distribution (HK-distribution) parameter estimation in\u0000quantitative ultrasound (QUS) has been recently addressed using Bayesian Neural\u0000Networks (BNNs). BNNs have been shown to significantly reduce computational\u0000time in speckle statistics-based QUS without compromising accuracy and\u0000precision. Additionally, they provide estimates of feature uncertainty, which\u0000can guide the clinician's trust in the reported feature value. The total\u0000predictive uncertainty in Bayesian modeling can be decomposed into epistemic\u0000(uncertainty over the model parameters) and aleatoric (uncertainty inherent in\u0000the data) components. By decomposing the predictive uncertainty, we can gain\u0000insights into the factors contributing to the total uncertainty. In this study,\u0000we propose a method to compute epistemic and aleatoric uncertainties for\u0000HK-distribution parameters ($alpha$ and $k$) estimated by a BNN, in both\u0000simulation and experimental data. In addition, we investigate the relationship\u0000between the prediction error and both uncertainties, shedding light on the\u0000interplay between these uncertainties and HK parameters errors.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0