Results 261 to 270 of about 9,948,343 (299)

Sub-sampled IFFT based compressive sampling

TENCON 2015 - 2015 IEEE Region 10 Conference, 2015
In this paper, a new approach based on Sub-sampled Inverse Fast Fourier Transform (SSIFFT) for efficiently acquiring compressive measurements is proposed, which is motivated by random filter based method and sub-sampled FFT. In our approach, to start with, we multiply the FFT of input signal and that of random-tap FIR filter in frequency domain and ...
null Liang Zhongyin, null Huang Jianjun, null Huang Jingxiong   +2 more
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Compressive quantization versus compressive sampling in image digitization

2012 IEEE Aerospace Conference, 2012
Digital image compression reduces the bandwidth, time, and energy needed for transmission of images and signals, as well as memory needed for their storage. However, it cannot solve the digitization problems. Recently proposed compressive sampling (or sensing) solves these problems by reducing the average number of projections required for representing
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Compressive Sampling with Coefficients Random Permutations for Image Compression

2011 International Conference on Multimedia and Signal Processing, 2011
The different image block has different sparsity or compressibility in transform domain; in general, the blocks in smooth region have stronger sparsity while those in texture or edge region have weaker sparsity. Based on this observation, a novel block DCT based sampling scheme with coefficients random permutations for image compressive sensing has ...
Zhirong Gao, Chengyi Xiong, Cheng Zhou, Hanxin Wang   +3 more
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Measurement Compression in Compressive Sampling Based Distributed Video Coding

2010 2nd International Conference on Information Engineering and Computer Science, 2010
Compressive sampling (CS) theory and distributed video coding (DVC) are two techniques suitable to scenarios where a video codec with simple encoder and complex decoder is desired. The combination of CS theory and DVC is a new research trend in this field and several integrated schemes have now appeared.
Xiaoran Hao, Bojin Zhuang, Anni Cai
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Recurrent networks for compressive sampling

Neurocomputing, 2014
This paper develops two neural network models, based on Lagrange programming neural networks (LPNNs), for recovering sparse signals in compressive sampling. The first model is for the standard recovery of sparse signals. The second one is for the recovery of sparse signals from noisy observations.
Chi-Sing Leung, John Sum, A.G. Constantinides   +2 more
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Photonics-assisted compressive sampling systems

SPIE Proceedings, 2016
In this paper, a systematic review is made on our research related to photonics-assisted compressive sampling (CS) systems including principle, structure and applications. We demonstrate their utility in wideband spectrum sensing and high throughput flow cytometry.
Qiang Guo, Hongwei Chen, Minghua Chen, Sigang Yang, Shizhong Xie   +4 more
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Compressive Sampling With Generalized Polygons

IEEE Transactions on Signal Processing, 2011
We consider the problem of compressed sensing and propose new deterministic low-storage constructions of compressive sampling matrices based on classical finite-geometry generalized polygons. For the noiseless measurements case, we develop a novel exact-recovery algorithm for strictly sparse signals that utilizes the geometry properties of generalized ...
Kanke Gao, Stella N. Batalama, Dimitris A. Pados, Bruce W. Suter   +3 more
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Watermarking for Compressive Sampling Applications

2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2012
Compressive sampling is a newly developed topic in the field of data compression. For current researches, compressive sampling techniques focus on compression performances. There are very few papers aiming at the integration of watermarking into compressive sampling systems.
Hsiang-Cheh Huang, Feng-Cheng Chang, Chun-Hsien Wu, Wei-Hao Lai   +3 more
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Seismology meets compressive sampling

2008
Presented at Cyber-Enabled Discovery and Innovation: Knowledge Extraction as a success story lecture. See for more detail https://www.ipam.ucla.edu/programs/cdi2007/
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Compressive Sampling Code

2021
Feng Wu, Chong Luo, Hancheng Lu
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