Found. Trends Signal Process.最新文献

Generalizing Graph Signal Processing: High Dimensional Spaces, Models and Structures 广义图信号处理:高维空间、模型和结构

Found. Trends Signal Process. Pub Date : 2023-01-01 DOI: 10.1561/2000000119

Xingchao Jian, Feng Ji, Wee Peng Tay

引用次数: 6

An Introduction to Quantum Machine Learning for Engineers 工程师量子机器学习导论

Found. Trends Signal Process. Pub Date : 2022-05-11 DOI: 10.48550/arXiv.2205.09510

O. Simeone

引用次数: 27

Signal Decomposition Using Masked Proximal Operators 基于掩模近端算子的信号分解

Found. Trends Signal Process. Pub Date : 2022-02-18 DOI: 10.1561/9781638281030

Bennet E. Meyers, Stephen P. Boyd

{"title":"Signal Decomposition Using Masked Proximal Operators","authors":"Bennet E. Meyers, Stephen P. Boyd","doi":"10.1561/9781638281030","DOIUrl":"https://doi.org/10.1561/9781638281030","url":null,"abstract":"We consider the well-studied problem of decomposing a vector time series signal into components with different characteristics, such as smooth, periodic, nonnegative, or sparse. We describe a simple and general framework in which the components are defined by loss functions (which include constraints), and the signal decomposition is carried out by minimizing the sum of losses of the components (subject to the constraints). When each loss function is the negative log-likelihood of a density for the signal component, this framework coincides with maximum a posteriori probability (MAP) estimation; but it also includes many other interesting cases. Summarizing and clarifying prior results, we give two distributed optimization methods for computing the decomposition, which find the optimal decomposition when the component class loss functions are convex, and are good heuristics when they are not. Both methods require only the masked proximal operator of each of the component loss functions, a generalization of the well-known proximal operator that handles missing entries in its argument. Both methods are distributed, i.e., handle each component separately. We derive tractable methods for evaluating the masked proximal operators of some loss functions that, to our knowledge, have not appeared in the literature.","PeriodicalId":12340,"journal":{"name":"Found. Trends Signal Process.","volume":"11 1","pages":"1-78"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75350048","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: 7

Online Component Analysis, Architectures and Applications 在线组件分析，体系结构和应用

Found. Trends Signal Process. Pub Date : 2022-01-01 DOI: 10.1561/2000000112

João B. O. Souza Filho, Lan-Da Van, T. Jung, Paulo S. R. Diniz

引用次数: 1

Wireless for Machine Learning: A Survey 无线机器学习:调查

Found. Trends Signal Process. Pub Date : 2022-01-01 DOI: 10.1561/2000000114

Henrik Hellström, J. M. B. D. Silva, M. Amiri, Mingzhe Chen, Viktoria Fodor, H. Poor, C. Fischione

引用次数: 18

Bilevel Methods for Image Reconstruction 图像重建的双层方法

Found. Trends Signal Process. Pub Date : 2021-09-20 DOI: 10.1561/2000000111

Caroline Crockett, J. Fessler

{"title":"Bilevel Methods for Image Reconstruction","authors":"Caroline Crockett, J. Fessler","doi":"10.1561/2000000111","DOIUrl":"https://doi.org/10.1561/2000000111","url":null,"abstract":"This review discusses methods for learning parameters for image reconstruction problems using bilevel formulations. Image reconstruction typically involves optimizing a cost function to recover a vector of unknown variables that agrees with collected measurements and prior assumptions. State-of-the-art image reconstruction methods learn these prior assumptions from training data using various machine learning techniques, such as bilevel methods. One can view the bilevel problem as formalizing hyperparameter optimization, as bridging machine learning and cost function based optimization methods, or as a method to learn variables best suited to a specific task. More formally, bilevel problems attempt to minimize an upper-level loss function, where variables in the upper-level loss function are themselves minimizers of a lower-level cost function. This review contains a running example problem of learning tuning parameters and the coefficients for sparsifying filters used in a regularizer. Such filters generalize the popular total variation regularization method, and learned filters are closely related to convolutional neural networks approaches that are rapidly gaining in popularity. Here, the lower-level problem is to reconstruct an image using a regularizer with learned sparsifying filters; the corresponding upper-level optimization problem involves a measure of reconstructed image quality based on training data. This review discusses multiple perspectives to motivate the use of bilevel methods and to make them more easily accessible to different audiences. We then turn to ways to optimize the bilevel problem, providing pros and cons of the variety of proposed approaches. Finally we overview bilevel applications in image reconstruction. 1 ar X iv :2 10 9. 09 61 0v 1 [ m at h. O C ] 2 0 Se p 20 21","PeriodicalId":12340,"journal":{"name":"Found. Trends Signal Process.","volume":"270 1","pages":"121-289"},"PeriodicalIF":0.0,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87081518","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: 17

Operating Characteristics for Classical and Quantum Binary Hypothesis Testing 经典和量子二元假设检验的工作特性

Found. Trends Signal Process. Pub Date : 2021-01-01 DOI: 10.1561/2000000106

Catherine Medlock, A. Oppenheim

{"title":"Operating Characteristics for Classical and Quantum Binary Hypothesis Testing","authors":"Catherine Medlock, A. Oppenheim","doi":"10.1561/2000000106","DOIUrl":"https://doi.org/10.1561/2000000106","url":null,"abstract":"This monograph addresses operating characteristics for binary hypothesis testing in both classical and quantum settings and overcomplete quantum measurements for quantum binary state discrimination. We specifically explore decision and measurement operating characteristics defined as the tradeoff between probability of detection and probability of false alarm as parameters of the pre-decision operator and the binary decision rule are varied. In the classical case we consider in detail the Neyman-Pearson optimality of the operating characteristics when they are generated using threshold tests on a scalar score variable rather than threshold tests on the likelihood ratio. In the quantum setting, informationally overcomplete POVMs are explored to provide robust quantum binary state discrimination. We focus on equal trace rank one POVMs which can be specified by arrangements of points on a sphere that we refer to as an Etro sphere. Catherine A. Medlock and Alan V. Oppenheim (2021), “Operating Characteristics for Classical and Quantum Binary Hypothesis Testing”, Foundations and Trends® in Signal Processing: Vol. 15, No. 1, pp 1–120. DOI: 10.1561/2000000106. Full text available at: http://dx.doi.org/10.1561/2000000106","PeriodicalId":12340,"journal":{"name":"Found. Trends Signal Process.","volume":"150 1","pages":"1-120"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83499864","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: 1

Data-Driven Multi-Microphone Speaker Localization on Manifolds 流形上数据驱动的多麦克风扬声器定位

Found. Trends Signal Process. Pub Date : 2020-10-05 DOI: 10.1561/2000000098

Bracha Laufer-Goldshtein, R. Talmon, S. Gannot

引用次数: 10

Recent Advances in Clock Synchronization for Packet-Switched Networks 分组交换网络时钟同步研究进展

Found. Trends Signal Process. Pub Date : 2020-09-15 DOI: 10.1561/2000000108

Anantha K. Karthik, Rick S. Blum

{"title":"Recent Advances in Clock Synchronization for Packet-Switched Networks","authors":"Anantha K. Karthik, Rick S. Blum","doi":"10.1561/2000000108","DOIUrl":"https://doi.org/10.1561/2000000108","url":null,"abstract":"Speech enhancement is a core problem in audio signal processing with commercial applications in devices as diverse as mobile phones, conference call systems, smart assistants, and hearing aids. An essential component in the design of speech enhancement algorithms is acoustic source localization. Speaker localization is also directly applicable to many other audio related tasks, e.g., automated camera steering, teleconferencing systems, and robot audition. From a signal processing perspective, speaker localization is the task of mapping multichannel speech signals to 3-D source coordinates. To obtain viable solutions for this mapping, an accurate description of the source wave propagation captured by the respective acoustic channel is required. In fact, the acoustic channels can be considered as the spatial fingerprints characterizing the positions of each of the sources in a reverberant enclosure. These fingerprints represent complex reflection patterns stemming from the surfaces and objects characterizing the enclosure. Hence, they are Bracha Laufer-Goldshtein, Ronen Talmon and Sharon Gannot (2020), “Data-Driven Multi-Microphone Speaker Localization on Manifolds”, Foundations and Trends © in Signal Processing: Vol. 14, No. 1–2, pp 1–161. DOI: 10.1561/2000000098. Full text available at: http://dx.doi.org/10.1561/2000000098","PeriodicalId":12340,"journal":{"name":"Found. Trends Signal Process.","volume":"5 1","pages":"360-443"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87485941","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: 9

Compressed Sensing with Applications in Wireless Networks 压缩感知在无线网络中的应用

Found. Trends Signal Process. Pub Date : 2019-11-28 DOI: 10.1561/2000000107

Markus Leinonen, M. Codreanu, G. Giannakis

{"title":"Compressed Sensing with Applications in Wireless Networks","authors":"Markus Leinonen, M. Codreanu, G. Giannakis","doi":"10.1561/2000000107","DOIUrl":"https://doi.org/10.1561/2000000107","url":null,"abstract":"Many natural signals possess only a few degrees of freedom. For instance, the occupied radio spectrum may be intermittently concentrated to only a few frequency bands of the system bandwidth. This special structural feature – signal sparsity – is conducive in designing efficient signal processing techniques for wireless networks. In particular, the signal sparsity can be leveraged by the recently emerged joint sampling and compression paradigm, compressed sensing (CS). This monograph reviews several recent CS advancements in wireless networks with an aim to improve the quality of signal reconstruction or detection while reducing the use of energy, radio, and computation resources. The monograph covers a diversity of compressive data reconstruction, gathering, and detection frameworks in cellular, cognitive, and wireless sensor networking systems. The monograph first gives an overview of the principles of CS for the readers unfamiliar with the topic. For the researchers knowledgeable in CS, the monograph provides in-depth reviews of several interesting CS advancements in designing tailored CS reconstruction techniques for wireless applications. The monograph can serve as a basis for the researchers intended to start working in the field, and altogether, lays a foundation for further research in the covered areas.","PeriodicalId":12340,"journal":{"name":"Found. Trends Signal Process.","volume":"68 1","pages":"1-282"},"PeriodicalIF":0.0,"publicationDate":"2019-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81236996","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: 14