scholarly journals Efficient data compression in perception and perceptual memory.

2020 ◽  
Vol 127 (5) ◽  
pp. 891-917 ◽  
Author(s):  
Christopher J. Bates ◽  
Robert A. Jacobs
Keyword(s):  
Data Compression ◽  
Perceptual Memory ◽  
Efficient Data

Energy Efficient Data Compression in Cloud based IoT

2021 ◽  
pp. 1-1
Author(s):  
Halah Mohammed Al-Kadhim ◽  
Hamed S. Al-Raweshidy
Keyword(s):  
Data Compression ◽  
Energy Efficient ◽  
Efficient Data

An efficient data compression technique based on BPDN for scattered fields from complex targets

2017 ◽  
Vol 60 (10) ◽  
Author(s):  
Xiangyin Quan ◽  
Bingchen Zhang ◽  
Zhengdao Wang ◽  
Chao Gao ◽  
Yirong Wu
Keyword(s):  
Data Compression ◽  
Compression Technique ◽  
Efficient Data ◽  
Scattered Fields

scholarly journals Measuring Non-Gaussianity by Phi-Transformed and Fuzzy Histograms

2012 ◽  
Vol 2012 ◽  
pp. 1-13
Author(s):  
Claudia Plant ◽  
Son Mai Thai ◽  
Junming Shao ◽  
Fabian J. Theis ◽  
Anke Meyer-Baese ◽  
...  
Keyword(s):  
Data Analysis ◽  
Data Compression ◽  
Theoretic Model ◽  
General Technique ◽  
Information Theoretic ◽  
Efficient Data ◽  
Information Theoretic Measures ◽  
Fully Automatic ◽  
Non Gaussian ◽  
Ica Algorithm

Independent component analysis (ICA) is an essential building block for data analysis in many applications. Selecting the truly meaningful components from the result of an ICA algorithm, or comparing the results of different algorithms, however, is nontrivial problems. We introduce a very general technique for evaluating ICA results rooted in information-theoretic model selection. The basic idea is to exploit the natural link between non-Gaussianity and data compression: the better the data transformation represented by one or several ICs improves the effectiveness of data compression, the higher is the relevance of the ICs. We propose two different methods which allow an efficient data compression of non-Gaussian signals: Phi-transformed histograms and fuzzy histograms. In an extensive experimental evaluation, we demonstrate that our novel information-theoretic measures robustly select non-Gaussian components from data in a fully automatic way, that is, without requiring any restrictive assumptions or thresholds.

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