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exact432hzconverter icon exact432hzconverter

This is the Exact 432Hz Converter with tkinter GUI provide user convert the audio to exact 432Hz Tone.

hhsa_cpu_openmp icon hhsa_cpu_openmp

Holo-Hilbert Spectral Analysis (HHSA) OpenMP version: This program can doing Hilbert–Huang Transform and then doing HHSA.

sfego_3d_pycuda icon 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 icon 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 icon 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 icon 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 icon 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 icon 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 icon 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 icon spatialframenetwork

Spatial Frame Network can be a Layer of Deep Neural Network or Preprocessing of any Machine Learning input.

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