A High-performance Information Hiding Algorithm Based on CL Multi-Wavelet Transform and DCT

2012 ◽  
Vol 4 (8) ◽  
pp. 137-144
Author(s):  
Shuai Ren ◽  
Tao Zhang
Keyword(s):  
Wavelet Transform ◽  
Information Hiding ◽  
High Performance ◽  
Performance Information

scholarly journals Soft Error Susceptibility Analysis of SRAM-Based FPGAs in High-Performance Information Systems

2007 ◽  
Vol 54 (6) ◽  
pp. 2714-2726 ◽  
Author(s):  
Hossein Asadi ◽  
Mehdi B. Tahoori ◽  
Brian Mullins ◽  
David Kaeli ◽  
Kevin Granlund
Keyword(s):  
Information Systems ◽  
High Performance ◽  
Soft Error ◽  
Performance Information ◽  
Susceptibility Analysis

Wavelet Transform Based Feature Extraction for EEG Signal Classification

2021 ◽  
Vol 20 ◽  
pp. 199-206
Author(s):  
Seda Postalcioglu
Keyword(s):  
Wavelet Transform ◽  
High Performance ◽  
Feature Vector ◽  
Fast Response ◽  
Eeg Signal ◽  
Real Time Control ◽  
Time Control ◽  
Band Power ◽  
Performance Rate

This study focused on the classification of EEG signal. The study aims to make a classification with fast response and high-performance rate. Thus, it could be possible for real-time control applications as Brain-Computer Interface (BCI) systems. The feature vector is created by Wavelet transform and statistical calculations. It is trained and tested with a neural network. The db4 wavelet is used in the study. Pwelch, skewness, kurtosis, band power, median, standard deviation, min, max, energy, entropy are used to make the wavelet coefficients meaningful. The performance is achieved as 99.414% with the running time of 0.0209 seconds

Enhanced Three-Dimensional Color Conversion Based on Discrete Wavelet Transform

2012 ◽  
Vol 155-156 ◽  
pp. 440-444
Author(s):  
He Yan ◽  
Xiu Feng Wang
Keyword(s):  
Information Technology ◽  
Wavelet Transform ◽  
Image Reconstruction ◽  
Image Quality ◽  
Image Compression ◽  
High Performance ◽  
Three Dimensional ◽  
Discrete Wavelet ◽  
Compression Algorithms ◽  
Color Conversion

JPEG2000 algorithm has been developed based on the DWT techniques, which have shown how the results achieved in different areas in information technology can be applied to enhance the performance. Lossy image compression algorithms sacrifice perfect image reconstruction in favor of decreased storage requirements. Wavelets have become a popular technology for information redistribution for high-performance image compression algorithms. Lossy compression algorithms sacrifice perfect image reconstruction in favor of improved compression rates while minimizing image quality lossy.

THE ADVANTAGE OFTHEWAVELET TRANSFORM IN PROCESSING OF SPEECH SIGNALS

2021 ◽  
Vol 4 (3) ◽  
pp. 37-41
Author(s):  
Sayora Ibragimova ◽  
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Speech Processing ◽  
High Performance ◽  
Speech Sound ◽  
Digital Signal ◽  
Speech Signals ◽  
Multi Scale ◽  
Processing Of Speech Signals

This work deals with basic theory of wavelet transform and multi-scale analysis of speech signals, briefly reviewed the main differences between wavelet transform and Fourier transform in the analysis of speech signals. The possibilities to use the method of wavelet analysis to speech recognition systems and its main advantages. In most existing systems of recognition and analysis of speech sound considered as a stream of vectors whose elements are some frequency response. Therefore, the speech processing in real time using sequential algorithms requires computing resources with high performance. Examples of how this method can be used when processing speech signals and build standards for systems of recognition.Key words: digital signal processing, Fourier transform, wavelet analysis, speech signal, wavelet transform

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