Results 11 to 20 of about 322,181 (285)
Discrete uncertainty principles and sparse signal processing [PDF]
Journal of Fourier Analysis and Applications, 2017 We develop new discrete uncertainty principles in terms of numerical sparsity, which is a continuous proxy for the 0-norm. Unlike traditional sparsity, the continuity of numerical sparsity naturally accommodates functions which are nearly sparse.
Bandeira, Afonso S. +2 more
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A unified approach to sparse signal processing [PDF]
EURASIP Journal on Advances in Signal Processing, 2012 A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which
Akram Aldroubi +7 more
core +5 more sources
Sparse Recovery from Combined Fusion Frame Measurements [PDF]
, 2011 Sparse representations have emerged as a powerful tool in signal and information processing, culminated by the success of new acquisition and processing techniques such as Compressed Sensing (CS).
Boufounos, Petros T. +2 more
core +1 more source
A DCA‐based sparse coding for video summarization with MCP
IET Image Processing, 2023 Video summarization offers a summary version that conveys the primary information of a longer video. The main challenges of video summarization are related to keyframe extraction and saliency mapping.
Yujie Li +3 more
doaj +1 more source
Asynchronous processing of sparse signals
IET Signal Processing, 2014 Unlike synchronous processing, asynchronous processing is more efficient in biomedical and sensing networks applications as it is free from aliasing constraints and quantization error in the amplitude, it allows continuous–time processing and more importantly data is only acquired in significant parts of the signal. We consider signal decomposers based
Azime Can‐Cimino +2 more
openaire +1 more source
Deep Learning Meets Sparse Regularization: A signal processing perspective
IEEE Signal Processing Magazine, 2023 Deep learning has been wildly successful in practice and most state-of-the-art machine learning methods are based on neural networks. Lacking, however, is a rigorous mathematical theory that adequately explains the amazing performance of deep neural networks.
Rahul Parhi, Robert D. Nowak
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A note on orthogonal matching pursuit under restricted isometry property
IET Signal Processing, 2022 The orthogonal matching pursuit (OMP) algorithm is a classical greedy algorithm widely used in compressed sensing. The number of iterations required for the OMP algorithm to perform exact the recovery of sparse signals is a fundamental problem in signal ...
Xueping Chen +3 more
doaj +1 more source
Kinetically Consistent Data Assimilation for Plant PET Sparse Time Activity Curve Signals
Frontiers in Plant Science, 2022 Time activity curve (TAC) signal processing in plant positron emission tomography (PET) is a frontier nuclear science technique to bring out the quantitative fluid dynamic (FD) flow parameters of the plant vascular system and generate knowledge on crops ...
Nicola D'Ascenzo +9 more
doaj +1 more source
Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault Features
Shock and Vibration, 2016 Sparse signal representations attract much attention in the community of signal processing because only a few coefficients are required to represent a signal and these coefficients make the signal understandable.
Wei Peng +3 more
doaj +1 more source
Perspectives on Theories and Methods of Structural Signal Processing
Leida xuebao, 2015 Over the past decade structural signal processing is an emerging field, which gained researchers' intensive attentions in various areas including the applied mathematics, physics, information theory, signal processing, and so on.
Li Lian-lin, Zhou Xiao-yang, Cui Tie-jun
doaj +1 more source