A systematic review of compressive sensing: Concepts, implementations and applications
Compressive Sensing (CS) is a new sensing modality, which compresses the signal being
acquired at the time of sensing. Signals can have sparse or compressible representation …
acquired at the time of sensing. Signals can have sparse or compressible representation …
Deep-sparse-representation-based features for speech recognition
Features derived using sparse representation (SR)-based approaches have been shown to
yield promising results for speech recognition tasks. In most of the approaches, the SR …
yield promising results for speech recognition tasks. In most of the approaches, the SR …
Exploiting sparsity of hyperspectral image: A novel approach for compressive hyperspectral image reconstruction using deep learning
X Liu, C Wang, Q Zhang, Z Yu, Z Zheng - Optics Communications, 2024 - Elsevier
Compressive hyperspectral imaging is an emerging technology that captures compressed
two-dimensional (2D) measurements and subsequently reconstructs three-dimensional (3D) …
two-dimensional (2D) measurements and subsequently reconstructs three-dimensional (3D) …
Compressive sensing: Methods, techniques, and applications
According to the latest research, it is very much clear that in future we require a huge amount
of data as modern advancement in communication and signal processing generates a large …
of data as modern advancement in communication and signal processing generates a large …
Signal-processing framework for ultrasound compressed sensing data: envelope detection and spectral analysis
Acquisition times and storage requirements have become increasingly important in signal-
processing applications, as the sizes of datasets have increased. Hence, compressed …
processing applications, as the sizes of datasets have increased. Hence, compressed …
Greedy dictionary learning for kernel sparse representation based classifier
We present a novel dictionary learning (DL) approach for sparse representation based
classification in kernel feature space. These sparse representations are obtained using …
classification in kernel feature space. These sparse representations are obtained using …
Comparison of cepstral analysis based on voiced-segment extraction and voice tasks for discriminating dysphonic and normophonic Korean speakers
Objectives This study investigated whether there are differences in the discriminatory power
of cepstral analysis according to the voiced-segment extraction method and voice tasks …
of cepstral analysis according to the voiced-segment extraction method and voice tasks …
Sparse coding based features for speech units classification
In this work, we propose sparse representation based features for speech units classification
tasks. In order to effectively capture the variations in a speech unit, the proposed method …
tasks. In order to effectively capture the variations in a speech unit, the proposed method …
Greedy double sparse dictionary learning for sparse representation of speech signals
This paper proposes a greedy double sparse (DS) dictionary learning algorithm for speech
signals, where the dictionary is the product of a predefined base dictionary, and a sparse …
signals, where the dictionary is the product of a predefined base dictionary, and a sparse …
Sparse reconstruction based on the ADMM and Lasso-LSQR for bearings vibration signals
W Song, MN Nazarova, Y Zhang, T Zhang, M Li - IEEE Access, 2017 - ieeexplore.ieee.org
In this paper, we introduce a novel method for reconstructing roller bearings vibration
signals. As well as the sparse reconstruction algorithm, our approach is based on the Lasso …
signals. As well as the sparse reconstruction algorithm, our approach is based on the Lasso …