標題: Titlebook: Cognitive Networked Sensing and Big Data; Robert Qiu,Michael Wicks Book 2014 The Editor(s) (if applicable) and The Author(s), under exclus [打印本頁] 作者: 管玄樂團 時間: 2025-3-21 16:21
書目名稱Cognitive Networked Sensing and Big Data影響因子(影響力)
書目名稱Cognitive Networked Sensing and Big Data影響因子(影響力)學科排名
書目名稱Cognitive Networked Sensing and Big Data網(wǎng)絡公開度
書目名稱Cognitive Networked Sensing and Big Data網(wǎng)絡公開度學科排名
書目名稱Cognitive Networked Sensing and Big Data被引頻次
書目名稱Cognitive Networked Sensing and Big Data被引頻次學科排名
書目名稱Cognitive Networked Sensing and Big Data年度引用
書目名稱Cognitive Networked Sensing and Big Data年度引用學科排名
書目名稱Cognitive Networked Sensing and Big Data讀者反饋
書目名稱Cognitive Networked Sensing and Big Data讀者反饋學科排名
作者: 花爭吵 時間: 2025-3-21 23:04 作者: CRUMB 時間: 2025-3-22 01:46
https://doi.org/10.1007/978-3-642-69762-3ssed sensing exploits the sparsity structure in a vector, while low-rank matrix recovery—Chap. 8—exploits the low-rank structure of a matrix: sparse in the vector composed of singular values. The theory ultimately traces back to concentration of measure due to high dimensions.作者: 鴿子 時間: 2025-3-22 05:04 作者: instulate 時間: 2025-3-22 11:38 作者: Bone-Scan 時間: 2025-3-22 13:04
Matrix Completion and Low-Rank Matrix Recoveryssed sensing exploits the sparsity structure in a vector, while low-rank matrix recovery—Chap. 8—exploits the low-rank structure of a matrix: sparse in the vector composed of singular values. The theory ultimately traces back to concentration of measure due to high dimensions.作者: Bone-Scan 時間: 2025-3-22 17:43
Two Principles for Self-OrganizationThe chapter contains standard results for asymptotic, global theory of random matrices. The goal is for readers to compare these results with results of non-asymptotic, local theory of random matrices (Chap. 5. A recent treatment of this subject is given by Qiu et al. [5].作者: 有害 時間: 2025-3-22 22:27
https://doi.org/10.1007/978-3-642-69762-3This chapter is the core of Part II: Applications..Detection in high dimensions is fundamentally different from the traditional detection theory. Concentration of measure plays a central role due to the high dimensions. We exploit the bless of dimensions.作者: 懸崖 時間: 2025-3-23 01:37 作者: 披肩 時間: 2025-3-23 08:45
Free Agents in a Cellular SpaceThe main goal of this chapter is to put together all pieces treated in previous chapters. We treat the subject from a system engineering point of view. This chapter motivates the whole book. We only have space to see the problems from ten-thousand feet high.作者: Incorporate 時間: 2025-3-23 11:33
Asymptotic, Global Theory of Random MatricesThe chapter contains standard results for asymptotic, global theory of random matrices. The goal is for readers to compare these results with results of non-asymptotic, local theory of random matrices (Chap. 5. A recent treatment of this subject is given by Qiu et al. [5].作者: 逢迎白雪 時間: 2025-3-23 16:46 作者: ovation 時間: 2025-3-23 20:54 作者: cipher 時間: 2025-3-24 02:13
From Network to Big DataThe main goal of this chapter is to put together all pieces treated in previous chapters. We treat the subject from a system engineering point of view. This chapter motivates the whole book. We only have space to see the problems from ten-thousand feet high.作者: SEMI 時間: 2025-3-24 03:32 作者: 同來核對 時間: 2025-3-24 06:43 作者: 委派 時間: 2025-3-24 12:30
Robert Qiu,Michael WicksDiscusses insights and experiences from large network testbeds.Examines wireless communications, mobile computing, and networks.Covers cognitive radio networks for UAVs.Includes supplementary material作者: 漫步 時間: 2025-3-24 15:36
http://image.papertrans.cn/c/image/229067.jpg作者: 態(tài)學 時間: 2025-3-24 22:39
https://doi.org/10.1007/978-3-642-69762-3book will survey many recent results in the literature. We often include preliminary tools from publications. These preliminary tools may be still too difficult for many of the audience. Roughly, our prerequisite is the graduate-level course on random variables and processes.作者: chance 時間: 2025-3-25 02:38 作者: 柏樹 時間: 2025-3-25 03:55 作者: 進步 時間: 2025-3-25 09:21
Hans Ulrich,Gilbert J. B. Probste the mathematics objects. Eigenvalues and their functionals may be shown to be Lipschitz functions so the Talagrand’s framework is sufficient. Concentration inequalities for many complicated random variables are also surveyed here from the latest publications. As a whole, we bring together concentr作者: bioavailability 時間: 2025-3-25 13:07
Two Principles for Self-Organizatione in the sense of random matrices. The point of viewing this chapter as a novel statistical tool will have far-reaching impact on applications such as covariance matrix estimation, detection, compressed sensing, low-rank matrix recovery, etc. Two primary examples are: (1) approximation of covariance作者: 珊瑚 時間: 2025-3-25 19:19
https://doi.org/10.1007/978-3-642-69762-3 provide applications examples for the theory developed in Part I. We emphasize the central role of random matrices..Compressed sensing is a recent revolution. It is built upon the observation that sparsity plays a central role in the structure of a vector. The unexpected message here is that for a 作者: moratorium 時間: 2025-3-25 23:43
https://doi.org/10.1007/978-3-642-69762-3ssed sensing exploits the sparsity structure in a vector, while low-rank matrix recovery—Chap. 8—exploits the low-rank structure of a matrix: sparse in the vector composed of singular values. The theory ultimately traces back to concentration of measure due to high dimensions.作者: 急性 時間: 2025-3-26 01:24
Hans Ulrich,Gilbert J. B. Probster should be more basic than Chaps. 7 and 8—thus should be treated earlier chapters. Recent work on compressed sensing and low-rank matrix recovery supports the idea that sparsity can be exploited for statistical estimation, too. The treatment of this subject is very superficial, due to the limited 作者: DIS 時間: 2025-3-26 08:07 作者: 破譯 時間: 2025-3-26 11:27
Book 2014nitive sensing. This book presents the challenges that are unique to this area such as synchronization caused by the high mobility of the nodes. The author will discuss the integration of software defined radio implementation and testbed development. The book will also bridge new research results an作者: 有節(jié)制 時間: 2025-3-26 14:21
velopment. The book will also bridge new research results and contextual reviews. Also the author provides an examination of large cognitive radio network; hardware testbed; distributed sensing; and distributed computing.978-1-4899-9726-5978-1-4614-4544-9作者: Jejune 時間: 2025-3-26 18:54
tive radio networks for UAVs.Includes supplementary materialWireless Distributed Computing and Cognitive Sensing defines high-dimensional data processing in the context of wireless distributed computing and cognitive sensing. This book presents the challenges that are unique to this area such as syn作者: 不易燃 時間: 2025-3-26 22:51 作者: 破裂 時間: 2025-3-27 03:39 作者: 閑逛 時間: 2025-3-27 08:41 作者: 高調 時間: 2025-3-27 11:57
Sums of Matrix-Valued Random Variables applications. Although powerful, the methods are elementary in nature. It is remarkable that some modern results on matrix completion can be simply derived, by using the framework of sums of matrix-valued random matrices.作者: HEED 時間: 2025-3-27 16:04 作者: 使腐爛 時間: 2025-3-27 18:01
Covariance Matrix Estimation in High Dimensionspports the idea that sparsity can be exploited for statistical estimation, too. The treatment of this subject is very superficial, due to the limited space. This chapter is mainly developed to support the detection theory in Chap. 10.作者: 勤勞 時間: 2025-3-27 23:32 作者: Spinal-Fusion 時間: 2025-3-28 02:46 作者: 磨碎 時間: 2025-3-28 09:41 作者: conifer 時間: 2025-3-28 13:24
Compressed Sensing and Sparse Recoverysparse signal, the relevant “information” is much less that what we thought previously. As a result, to recover the sparse signal, the required samples are much less than what is required by the traditional Shannon’s sampling theorem.作者: 轉換 時間: 2025-3-28 16:02 作者: 容易生皺紋 時間: 2025-3-28 21:52 作者: Proponent 時間: 2025-3-28 23:24 作者: Pde5-Inhibitors 時間: 2025-3-29 03:19
Concentration of Measuretration inequalities are often used to investigate the sums of random variables (scalars, vectors and matrices). In particular, we survey the recent status of sums of random matrices in Chap. 2, which gives us the straightforward impression of the classical view of the subject.作者: affect 時間: 2025-3-29 08:41
Concentration of Eigenvalues and Their Functionalse the mathematics objects. Eigenvalues and their functionals may be shown to be Lipschitz functions so the Talagrand’s framework is sufficient. Concentration inequalities for many complicated random variables are also surveyed here from the latest publications. As a whole, we bring together concentr作者: Critical 時間: 2025-3-29 12:24
Non-asymptotic, Local Theory of Random Matricese in the sense of random matrices. The point of viewing this chapter as a novel statistical tool will have far-reaching impact on applications such as covariance matrix estimation, detection, compressed sensing, low-rank matrix recovery, etc. Two primary examples are: (1) approximation of covariance作者: Surgeon 時間: 2025-3-29 18:14 作者: adumbrate 時間: 2025-3-29 23:10
Matrix Completion and Low-Rank Matrix Recoveryssed sensing exploits the sparsity structure in a vector, while low-rank matrix recovery—Chap. 8—exploits the low-rank structure of a matrix: sparse in the vector composed of singular values. The theory ultimately traces back to concentration of measure due to high dimensions.作者: Painstaking 時間: 2025-3-30 00:45
Covariance Matrix Estimation in High Dimensionser should be more basic than Chaps. 7 and 8—thus should be treated earlier chapters. Recent work on compressed sensing and low-rank matrix recovery supports the idea that sparsity can be exploited for statistical estimation, too. The treatment of this subject is very superficial, due to the limited 作者: 蚊子 時間: 2025-3-30 07:05
Database Friendly Data Processingence of sensing, computing, networking and control. Data base is often neglected in traditional treatments in estimation, detection, etc..Modern scientific computing demands efficient algorithms for dealing with large datasets—Big Data. Often these datasets can be fruitfully represented and manipula作者: Common-Migraine 時間: 2025-3-30 10:02
Einleitung: Die Fragen,ellung im Schlu?wort von Pastor ., einem der drei ?Moderatoren“ des Runden Tisches, werden weder Politologen noch Staatrechtslehrer widersprechen. Wer oder was war der Runde Tisch? Wurde er zu einer Institution im Range eines Staatsorganes? Welche Funktion hatte er? Hat er seine Mission erfüllt?
