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A sparsity-aware QR decomposition algorithm for efficient cooperative localization

Ke X. Zhou, Stergios I. Roumeliotis
2012 2012 IEEE International Conference on Robotics and Automation  
state-of-the-art QR decomposition algorithm for sparse matrices. 1 In the remainder of the paper, computational complexity refers exclusively to time (or processing) complexity.  ...  In contrast to the standard EKF update, whose complexity is up to O(N 4 ) (N is the number of robots in a team), we introduce a Modified Householder QR algorithm which fully exploits the sparse structure  ...  CONCLUSION In this paper, we have developed an efficient algorithm for QR decomposition of sparse matrices, namely the Modified Householder QR.  ... 
doi:10.1109/icra.2012.6225324 dblp:conf/icra/ZhouR12 fatcat:c4kokyrzabbgnbju3grsppha54

Application of Compressive Sensing in Cognitive Radio Communications: A Survey

Shree Krishna Sharma, Eva Lagunas, Symeon Chatzinotas, Bjorn Ottersten
2016 IEEE Communications Surveys and Tutorials  
Furthermore, we present a generalized framework for constructing the REM in compressive settings.  ...  In this regard, several researchers have already applied the CS theory in various settings considering the sparsity in different domains.  ...  QR decomposition approach appears to be the fastest one [38] .  ... 
doi:10.1109/comst.2016.2524443 fatcat:qvil3rvugzfnlpvpuf5nuklk6m

Evaluation Model of College Physical Education Based on Random Matrix Simulation Algorithm

Jie Liu, Wei Li, Ning Cao
2022 Mathematical Problems in Engineering  
For the improvement of the singular value algorithm, two random algorithms are proposed, namely, the standard random k-SVD algorithm and the fast random k-SVD algorithm.  ...  In this study, improved algorithms based on kernel norm minimization are proposed, namely, standard CUR decomposition and fast CUR decomposition.  ...  method for finding a representative set of columns. ere are some tricks to make the local marker selection algorithm more efficient. (1) e k-centroid clustering can be solved approximately rather than  ... 
doi:10.1155/2022/4399848 fatcat:azw6wdzt7jcrdjryl2jjue3nbq

Linear algebra software for large-scale accelerated multicore computing

A. Abdelfattah, H. Anzt, J. Dongarra, M. Gates, A. Haidar, J. Kurzak, P. Luszczek, S. Tomov, I. Yamazaki, A. YarKhan
2016 Acta Numerica  
To generate the extreme level of parallelism needed for the efficient use of these systems, algorithms of interest are redesigned and then split into well-chosen computational tasks.  ...  Examples are given with fundamental dense linear algebra algorithms – from the LU, QR, Cholesky, and LDLT factorizations needed for solving linear systems of equations, to eigenvalue and singular value  ...  They form the tiled QR algorithm shown in Algorithm 2. The operation count for Algorithm 2 is 25% higher than that of the LAPACK algorithm for QR factorization.  ... 
doi:10.1017/s0962492916000015 fatcat:cwsstweghjaj7ff6fu62lmn6ce

A survey of direct methods for sparse linear systems

Timothy A. Davis, Sivasankaran Rajamanickam, Wissam M. Sid-Lakhdar
2016 Acta Numerica  
They exploit the sparsity of a matrix to solve problems economically: much faster and using far less memory than if all the entries of a matrix were stored and took part in explicit computations.  ...  Wilkinson defined a sparse matrix as one with enough zeros that it pays to take advantage of them. 1 This informal yet practical definition captures the essence of the goal of direct methods for solving  ...  Acknowledgments We would like to thank Iain Duff for his comments on a draft of this paper.  ... 
doi:10.1017/s0962492916000076 fatcat:u4dqyjkjqnelll5e3ywm7lqkca

Compressive Sensing-Based IoT Applications: A Review

Hamza Djelouat, Abbes Amira, Faycal Bensaali
2018 Journal of Sensor and Actuator Networks  
CS is a novel signal acquisition and compression theory that exploits the sparsity behavior of most natural signals and IoT architectures to achieve power-efficient, real-time platforms that can grant  ...  For the latter, compressive sensing (CS) is a very attractive paradigm to be incorporated in the design of IoT platforms.  ...  Acknowledgments: We would like to thank Hamza Baali from Hamad Bin Khalifa University (HBKU), Qatar, for his valuable discussion related to this paper.  ... 
doi:10.3390/jsan7040045 fatcat:elkvfq4dvnbdffvxtehz3lgpla

Design of large polyphase filters in the Quadratic Residue Number System

Gian Carlo Cardarilli, Alberto Nannarelli, Yann Oster, Massimo Petricca, Marco Re
2010 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers  
In this paper, we give a theoretical framework for a simple, sparsity based thresholding algorithm.  ...  , University of Toronto / Sharif University; Glenn Gulak, University of Toronto This paper presents a prototype of a highthroughput 4x4 64-QAM MIMO receiver consisting of a channel matrix QR decomposition  ... 
doi:10.1109/acssc.2010.5757589 fatcat:ccxnu5owr5fyrcjcqukumerueq

Implementation of Fog computing for reliable E-health applications

Razvan Craciunescu, Albena Mihovska, Mihail Mihaylov, Sofoklis Kyriazakos, Ramjee Prasad, Simona Halunga
2015 2015 49th Asilomar Conference on Signals, Systems and Computers  
Finally, we will provide 'structured' CS algorithms for the joint estimation scheme and evaluate its performance.  ...  Specifically, we introduce a concept for sparse joint activity, channel and data detection in the context of the Coded ALOHA (FDMA) protocol.  ...  This paper proposes distributed sparsity-aware adaptive algorithms based on the conjugate gradient (CG) method and the diffusion strategy for parameter estimation over sensor networks.  ... 
doi:10.1109/acssc.2015.7421170 dblp:conf/acssc/CraciunescuMMKP15 fatcat:qm6mki5z6bcvrfimkmqjyrxaxm

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TIP 2021 6335-6348 Towards Efficient Scene Understanding via Squeeze Reasoning. Li, X., +, A Non-Local Superpatch-Based Algorithm Exploiting Low Rank Prior for tified Experimental Protocol.  ...  ., +, TIP 2021 7446-7457 Gaussian noise A Non-Local Superpatch-Based Algorithm Exploiting Low Rank Prior for Restoration of Hyperspectral Images.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Table of Contents

2020 IEEE Signal Processing Letters  
Du, A. Aubry, A. De Maio, and G. Cui 885 An Exploratory Method for Smooth/Transient Decomposition . C.  ...  Cho 860 Active Speaker Detection and Localization in Videos Using Low-Rank and Kernelized Sparsity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Liu 1824 A Universal Technique for Analysing Discrete Super-Resolution Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H.  ... 
doi:10.1109/lsp.2020.3040840 fatcat:ezrfzwo6tjbkfhohq2tgec4m6y

Intelligent, smart and scalable cyber-physical systems

V. Vijayakumar, V. Subramaniyaswamy, Jemal Abawajy, Longzhi Yang, V. Vijayakumar, V. Subramaniyaswamy, Jemal Abawajy, Longzhi Yang
2019 Journal of Intelligent & Fuzzy Systems  
Components of an iCPS must have a high degree of autonomy while cooperating with each other in a robust, scalable and decentralized way.  ...  Intelligent, self-aware, self-managing and self-configuring pervasive systems can be built to improve quality of process across a variety of application domains, helping to address a number of contemporary  ...  Reza Langari for his supportive guidance during the entire process.  ... 
doi:10.3233/jifs-179108 fatcat:4hghoxr4prccxjpfg5juwzoie4

2021 Index IEEE Transactions on Industrial Informatics Vol. 17

2021 IEEE Transactions on Industrial Informatics  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TII June 2021 4096-4106 Decomposition ADTT: A Highly Efficient Distributed Tensor-Train Decomposition Method for IIoT Big Data.  ...  ., +, TII July QR-3S: A High Payload QR Code Secret Sharing System for Industrial Inter-Deteriorating Products.  ... 
doi:10.1109/tii.2021.3138206 fatcat:ulsazxgmpfdmlivigjqgyl7zre

Ping-pong beam training for reciprocal channels with delay spread

Elisabeth de Carvalho, Jorgen Bach Andersen
2015 2015 49th Asilomar Conference on Signals, Systems and Computers  
A distributed consensus algorithm for estimating the number of nodes in a wireless sensor network in the presence of communication noise is proposed.  ...  The model only Spectrum efficiency (SE) and energy efficiency (EE) have become two key performance measures for the future wireless communication system.  ...  This paper proposes distributed sparsity-aware adaptive algorithms based on the conjugate gradient (CG) method and the diffusion strategy for parameter estimation over sensor networks.  ... 
doi:10.1109/acssc.2015.7421451 dblp:conf/acssc/CarvalhoA15 fatcat:mqokuvnh3zg45licnfbgxyvxfu

Compressive Spectrum Sensing for Cognitive Radio Networks [article]

Fatima Salahdine
2018 arXiv   pre-print
A cognitive radio system has the ability to observe and learn from the environment, adapt to the environmental conditions, and use the radio spectrum more efficiently.  ...  It allows reducing the number of samples required for high dimensional signal acquisition while keeping the essential information.  ...  Under the non-cooperative sensing category, also called local sensing, each SU seeks for its own objectives and does not take into account other users.  ... 
arXiv:1802.03674v1 fatcat:6ddexxzymjfqzpcw6cfoia42xy

Discovery of Latent Factors in High-dimensional Data Using Tensor Methods [article]

Furong Huang
2016 arXiv   pre-print
Dimensionality reduction techniques such as random projection are incorporated for a highly parallel and scalable tensor decomposition algorithm.  ...  This is the first work that gives global convergence guarantees for the stochastic gradient descent on non-convex functions with exponentially many local minima and saddle points.  ...  By employing this algorithm, we obtain an efficient unsupervised learning algorithm for a wide class of latent variable models.  ... 
arXiv:1606.03212v1 fatcat:j2nlv4fpyvbfjidutiofteuu4u
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