Results 281 to 290 of about 48,878 (307)

Sparse Representation of the Transients in Mechanical Signals

2017
This chapter focuses on the sparse representation of the transients in mechanical signals. Sparse representation means that the signal can be represented by an optimal linear combination of atoms by a specialized over-complete dictionary, leading to the sparsity of representation coefficients.
Zhongkui Zhu, Wei Fan, Gaigai Cai, Weiguo Huang, Juanjuan Shi   +4 more
openaire   +1 more source

SABR: sparse, anchor-based representation of the speech signal

Interspeech 2015, 2015
We present SABR (Sparse, Anchor-Based Representation), an analysis technique to decompose the speech signal into speaker-dependent and speaker-independent components. Given a collection of utterances for a particular speaker, SABR uses the centroid for each phoneme as an acoustic “anchor,” then applies Lasso regularization to represent each speech ...
Christopher Liberatore, Sandesh Aryal, Zelun Wang, Seth Polsley, Ricardo Gutierrez-Osuna   +4 more
openaire   +1 more source

Signal reconstruction in sensor arrays using sparse representations

Signal Processing, 2006
We propose a technique of multisensor signal reconstruction based on the assumption, that source signals are spatially sparse, as well as have sparse representation in a chosen dictionary in time domain. This leads to a large scale convex optimization problem, which involves combined l1-l2 norm minimization. The optimization is carried by the truncated
Dmitri Model, Michael Zibulevsky
openaire   +1 more source

Sparse Signal Representation with Dispersion Dictionary

2011
Sparse decomposition of a signal can be obtained by decomposing signal in an overcomplete dictionary. The overcomplete dictionary function is generally all well-localized and well adapted to the signal’s local structures. Although choosing an appropriate atom dictionary for analyses improves the performance of the analyses, modeling of the dispersion ...
Zhang Yanhong, Guo Jinku, Wu Jinying
openaire   +1 more source

Sparse Representation of Signals in Hardy Space

2013
Mathematically, signals can be seen as functions in certain spaces. And processing is more efficient in a sparse representation where few coefficients reveal the information. Such representations are constructed by decomposing signals into elementary waveforms. A set of all elementary waveforms is called a dictionary.
Shuang Li, Tao Qian
openaire   +1 more source

Sparse Representation for Signal Classification

2007
Ke Huang 0005, Selin Aviyente
openaire   +2 more sources

Remote Heart Rate Estimation by Pulse Signal Reconstruction Based on Structural Sparse Representation

Electronics (Switzerland), 2022
Weihua Ou, Jiahao Xiong, Ou Weihua
exaly  

Sparse Representation Based Anomalies Detection in Electrocardiography Signals

2017
In this article, we present the use of sparse representation of signal and dictionary learning method for solving the problem of anomaly detection. The analyzed signal was presented as a set of correct ECG structures and outliers (characterizing different types of disorders).
openaire   +1 more source

A Hierarchical Discriminative Sparse Representation Classifier for EEG Signal Detection

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021
Xiaoqing Gu, Cong Zhang, Tongguang Ni
exaly  

Sparse signal representation and its applications in ultrasonic NDE

Ultrasonics, 2012
Guang-Ming Zhang
exaly