Pub Date : 2022-02-27DOI: 10.1007/s43670-022-00033-7
A. Bandeira, Nikolaus Doppelbauer, Dmitriy Kunisky
{"title":"Dual bounds for the positive definite functions approach to mutually unbiased bases","authors":"A. Bandeira, Nikolaus Doppelbauer, Dmitriy Kunisky","doi":"10.1007/s43670-022-00033-7","DOIUrl":"https://doi.org/10.1007/s43670-022-00033-7","url":null,"abstract":"","PeriodicalId":74751,"journal":{"name":"Sampling theory, signal processing, and data analysis","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88176637","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}
Pub Date : 2022-02-06DOI: 10.1007/s43670-023-00063-9
Esha Saha, Hayden Schaeffer, Giang Tran
{"title":"HARFE: hard-ridge random feature expansion","authors":"Esha Saha, Hayden Schaeffer, Giang Tran","doi":"10.1007/s43670-023-00063-9","DOIUrl":"https://doi.org/10.1007/s43670-023-00063-9","url":null,"abstract":"","PeriodicalId":74751,"journal":{"name":"Sampling theory, signal processing, and data analysis","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89148893","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}
Pub Date : 2022-01-25DOI: 10.1007/s43670-022-00038-2
Varun Khurana, Harish Kannan, A. Cloninger, Caroline Moosmüller
{"title":"Supervised learning of sheared distributions using linearized optimal transport","authors":"Varun Khurana, Harish Kannan, A. Cloninger, Caroline Moosmüller","doi":"10.1007/s43670-022-00038-2","DOIUrl":"https://doi.org/10.1007/s43670-022-00038-2","url":null,"abstract":"","PeriodicalId":74751,"journal":{"name":"Sampling theory, signal processing, and data analysis","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75362826","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}
Pub Date : 2021-12-20DOI: 10.1007/s43670-022-00045-3
M. Iwen, Michael Perlmutter, M. Roach
{"title":"Toward fast and provably accurate near-field ptychographic phase retrieval","authors":"M. Iwen, Michael Perlmutter, M. Roach","doi":"10.1007/s43670-022-00045-3","DOIUrl":"https://doi.org/10.1007/s43670-022-00045-3","url":null,"abstract":"","PeriodicalId":74751,"journal":{"name":"Sampling theory, signal processing, and data analysis","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89194909","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}
{"title":"Iranian Vehicle License Plate Detection based on Cascade Classifier","authors":"Fahimeh Ramazankhani, M. Yazdian-Dehkordi","doi":"10.52547/jsdp.18.3.77","DOIUrl":"https://doi.org/10.52547/jsdp.18.3.77","url":null,"abstract":"","PeriodicalId":74751,"journal":{"name":"Sampling theory, signal processing, and data analysis","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75302408","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}
Reza Kayvan shokooh, M. Okhovvat, Meisam Raees Danaee
{"title":"Extending the Radar Dynamic Range using Adaptive Pulse Compression","authors":"Reza Kayvan shokooh, M. Okhovvat, Meisam Raees Danaee","doi":"10.52547/jsdp.18.3.91","DOIUrl":"https://doi.org/10.52547/jsdp.18.3.91","url":null,"abstract":"","PeriodicalId":74751,"journal":{"name":"Sampling theory, signal processing, and data analysis","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77697141","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}
Recently, the problem of compressive sensing (CS) has attracted lots of attention in the area of signal processing. So, much of the research in this field is being carried out in this issue. One of the applications where CS could be used is wireless sensor networks (WSNs). The structure of WSNs consists of many low power wireless sensors. This requires that any improved algorithm for this application must be optimized in terms of energy consumption. In other words, the computational complexity of algorithms must be as low as possible and should require minimal interaction between the sensors. For such networks, CS has been used in data gathering and data persistence scenario, in order to minimize the total number of transmissions and consequently minimize the network energy consumption and to save the storage by distributing the traffic load and storage throughout the network. In these applications, the compression stage of CS is performed in sensor nodes, whereas the recovering duty is done in the fusion center (FC) unit in a centralized manner. In some applications, there is no FC unit and the recovering duty must be performed in sensor nodes in a cooperative and distributed manner which we have focused on in this paper. Indeed, the notable algorithm for this [ D ow nl oa de d fr om js dp .r ci sp .a c. ir o n 20 22 -0 222 ]
近年来，压缩感知问题在信号处理领域受到了广泛的关注。因此，这个领域的很多研究都是在这个问题上进行的。无线传感器网络(wsn)是CS的应用之一。无线传感器网络的结构由许多低功耗的无线传感器组成。这就要求针对该应用程序的任何改进算法都必须在能耗方面进行优化。换句话说，算法的计算复杂度必须尽可能低，并且应该要求传感器之间的交互最小。在这种网络中，CS被用于数据采集和数据持久化场景，通过在整个网络中分配流量负载和存储，使传输总量最小化，从而使网络能耗最小化，从而节省存储。在这些应用中，CS的压缩阶段在传感器节点中执行，而恢复任务则在融合中心(FC)单元中以集中的方式完成。在一些没有FC单元的应用中，恢复任务必须以协作和分布式的方式在传感器节点上完成，这是本文研究的重点。事实上,著名的算法[D噢问oa de D fr om js dp r ci sp。c .红外o n 20 22 0 222]
{"title":"Distributed and Cooperative Compressive Sensing Recovery Algorithm for Wireless Sensor Networks with Bi-directional Incremental Topology","authors":"G. Azarnia, M. Tinati, Tohid Yousefi Rezaii","doi":"10.52547/jsdp.18.3.65","DOIUrl":"https://doi.org/10.52547/jsdp.18.3.65","url":null,"abstract":"Recently, the problem of compressive sensing (CS) has attracted lots of attention in the area of signal processing. So, much of the research in this field is being carried out in this issue. One of the applications where CS could be used is wireless sensor networks (WSNs). The structure of WSNs consists of many low power wireless sensors. This requires that any improved algorithm for this application must be optimized in terms of energy consumption. In other words, the computational complexity of algorithms must be as low as possible and should require minimal interaction between the sensors. For such networks, CS has been used in data gathering and data persistence scenario, in order to minimize the total number of transmissions and consequently minimize the network energy consumption and to save the storage by distributing the traffic load and storage throughout the network. In these applications, the compression stage of CS is performed in sensor nodes, whereas the recovering duty is done in the fusion center (FC) unit in a centralized manner. In some applications, there is no FC unit and the recovering duty must be performed in sensor nodes in a cooperative and distributed manner which we have focused on in this paper. Indeed, the notable algorithm for this [ D ow nl oa de d fr om js dp .r ci sp .a c. ir o n 20 22 -0 222 ]","PeriodicalId":74751,"journal":{"name":"Sampling theory, signal processing, and data analysis","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86855078","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}
{"title":"An Analytical Model for Predicting the Convergence Behavior of the Least Mean Mixed-Norm (LMMN) Algorithm","authors":"Mesyam Kazemi Eghbal, G. Alipoor","doi":"10.52547/jsdp.18.3.19","DOIUrl":"https://doi.org/10.52547/jsdp.18.3.19","url":null,"abstract":"","PeriodicalId":74751,"journal":{"name":"Sampling theory, signal processing, and data analysis","volume":"84 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75959904","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}
S. Lotfi, M. Mirzarezaee, M. Hosseinzadeh, V. Seydi
{"title":"Analysis of Structural Features in Rumor Conversations Detection in Twitter","authors":"S. Lotfi, M. Mirzarezaee, M. Hosseinzadeh, V. Seydi","doi":"10.52547/jsdp.18.3.45","DOIUrl":"https://doi.org/10.52547/jsdp.18.3.45","url":null,"abstract":"","PeriodicalId":74751,"journal":{"name":"Sampling theory, signal processing, and data analysis","volume":"99 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77575568","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}
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