scholarly journals Comparative Study of Massively Parallel GPU Realizations of Wavelet Transform Computation with Lattice Structure and Matrix-Based Approach

2018 ◽  
Author(s):  
Dariusz Puchala ◽  
Kamil Stokfiszewski ◽  
Kamil Wieloch ◽  
Mykhaylo Yatsymirskyy
Keyword(s):  
Wavelet Transform ◽  
Comparative Study ◽  
Lattice Structure ◽  
Massively Parallel

scholarly journals Improving energy compaction of a wavelet transform using genetic algorithm and fast neural network

2010 ◽  
Vol 20 (4) ◽  
pp. 417-433 ◽  
Author(s):  
Jan Stolarek
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wavelet Transform ◽  
Energy Distribution ◽  
Lattice Structure ◽  
Optimization Method ◽  
New Method ◽  
Discrete Wavelet ◽  
Energy Compaction ◽  
High Pass

Improving energy compaction of a wavelet transform using genetic algorithm and fast neural networkIn this paper a new method for adaptive synthesis of a smooth orthogonal wavelet, using fast neural network and genetic algorithm, is introduced. Orthogonal lattice structure is presented. A new method of supervised training of fast neural network is introduced to synthesize a wavelet with desired energy distribution between output signals from low-pass and high-pass filters on subsequent levels of a Discrete Wavelet Transform. Genetic algorithm is proposed as a global optimization method for defined objective function, while neural network is used as a local optimization method to further improve the result. Proposed approach is tested by synthesizing wavelets with expected energy distribution between low- and high-pass filters. Energy compaction of proposed method and Daubechies wavelets is compared. Tests are performed using image signals.

Hardware Design of Seizure Detection Based on Wavelet Transform and Sample Entropy

2016 ◽  
Vol 25 (09) ◽  
pp. 1650101 ◽  
Author(s):  
Yuanfa Wang ◽  
Zunchao Li ◽  
Lichen Feng ◽  
Chuang Wang ◽  
Wen Jing ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Lattice Structure ◽  
Wavelet Coefficient ◽  
Distance Matrix ◽  
Seizure Detection ◽  
Sample Entropy ◽  
Classification Performance ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Detection Scheme

Detecting epileptic seizure is a very time consuming and costly task if a support vector machine (SVM) hardware processor is used. In this paper, an automated seizure detection scheme is developed by combining discrete wavelet transform (DWT), sample entropy (SampEn) and a novel classification algorithm based on each wavelet coefficient and voting strategy. In order to save circuit area, a Daubechies order 4 (db4) filter of lattice structure is introduced in DWT, only half elements of the symmetric distance matrix in the SampEn are stored and module reusing strategy is used. To speed up the detection, intermediate results are reused by reasonably organizing the SampEn calculation procedures. The seizure detection scheme is implemented in a field-programmable gate array (FPGA) and its classification performance is tested with publicly available epilepsy dataset.

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