CardLin
I am doing multi-dimensional data analysis which decomposition data into different spatial frequency data.
Hsinchu City, Taiwan
Pinned Repositories
Exact432HzConverter
This is the Exact 432Hz Converter with tkinter GUI provide user convert the audio to exact 432Hz Tone.
HHSA_CPU_OpenMP
Holo-Hilbert Spectral Analysis (HHSA) OpenMP version: This program can doing Hilbert–Huang Transform and then doing HHSA.
SFEGO_3D_PyCUDA
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.
SFEGO_3D_PyOpenCL
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.
SFEGO_Color_PyGPU
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.
SFEGO_OpenCL
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)
SFEGO_PyCUDA
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)
SFEGO_PyOpenCL
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)
SFEGO_PyOpenCL_Service
This Server.py and Client.py can provide send job to multiple GPU Server to run SFEGO in GPU which can make better throughput.
SpatialFrameNetwork
Spatial Frame Network can be a Layer of Deep Neural Network or Preprocessing of any Machine Learning input.
CardLin's Repositories
CardLin/Exact432HzConverter
This is the Exact 432Hz Converter with tkinter GUI provide user convert the audio to exact 432Hz Tone.
CardLin/SFEGO_PyCUDA
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)
CardLin/HHSA_CPU_OpenMP
Holo-Hilbert Spectral Analysis (HHSA) OpenMP version: This program can doing Hilbert–Huang Transform and then doing HHSA.
CardLin/SFEGO_3D_PyCUDA
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.
CardLin/SFEGO_Color_PyGPU
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.
CardLin/SFEGO_OpenCL
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)
CardLin/SFEGO_3D_PyOpenCL
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.
CardLin/SFEGO_PyOpenCL
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)
CardLin/SFEGO_PyOpenCL_Service
This Server.py and Client.py can provide send job to multiple GPU Server to run SFEGO in GPU which can make better throughput.
CardLin/SpatialFrameNetwork
Spatial Frame Network can be a Layer of Deep Neural Network or Preprocessing of any Machine Learning input.