cardlin Goto Github PK
Name: CardLin
Type: User
Bio: I am doing multi-dimensional data analysis which decomposition data into different spatial frequency data.
Location: Hsinchu City, Taiwan
Name: CardLin
Type: User
Bio: I am doing multi-dimensional data analysis which decomposition data into different spatial frequency data.
Location: Hsinchu City, Taiwan
This is the Exact 432Hz Converter with tkinter GUI provide user convert the audio to exact 432Hz Tone.
Holo-Hilbert Spectral Analysis (HHSA) OpenMP version: This program can doing Hilbert–Huang Transform and then doing HHSA.
This code is Spatial Frequency Extraction using Gradient-liked Operator in Three-Dimension (SFEGO_3D) that use gradient and integral to mimic the Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) and Three-dimensional Empirical Mode Decomposition (TEMD) in different way. Our code can get 6 Spatial Data (128*128*128) within 1 minutes with modern GPU.
This code is Spatial Frequency Extraction using Gradient-liked Operator in Three-Dimension (SFEGO_3D) that use gradient and integral to mimic the Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) and Three-dimensional Empirical Mode Decomposition (TEMD) in different way. Our code can get 6 Spatial Data (128*128*128) within 1 minutes with modern GPU.
This is image spectrum analysis write in PyOpenCL and PyCUDA code which can doing the SFEGO on multispectral image which can see the different spatial frequency info in different wavelength channel.
This project is mimic of Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) and this project achieve 10000x faster than MEEMD. Also the result is better than Bi-dimensional Empirical Mode Decomposition. (BEMD)
This python binding for run CUDA kernel code of SFEGO is mimic of Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) and this project achieve 10000x faster than MEEMD. Also the result is better than Bi-dimensional Empirical Mode Decomposition. (BEMD)
This python binding for run OpenCL kernel code of SFEGO is mimic of Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) and this project achieve 10000x faster than MEEMD. Also the result is better than Bi-dimensional Empirical Mode Decomposition. (BEMD)
This Server.py and Client.py can provide send job to multiple GPU Server to run SFEGO in GPU which can make better throughput.
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