I need to finish writing the paper on the Python toolbox for the Hilbert-Huang transform by the 28th of this month. That's almost two weeks away. Since PyHHT is an ambitiously chosen project that is
1. a big deal in signal processing
2. computationally expensive
3. not exactly easy to code for,
I conclude that I really need to maintain a proper record of what I work on. By not doing this, I have lost the ability to maintain a bird's eye view of my work on the toolbox. I often get stuck in just one layer of abstraction for weeks.
The following few blog posts are intended to be journal entries for the PyHHT project, and will be my primary resources when I submit my paper to SciPy India 2011. The abstract can be found here.
Bringing together different heuristics and interpretations of the Hilbert-Huang transform will require programming that ranges from simple numerical methods (for instance, as required in cubic spline interpolation) to complex machine learning and signal processing tools (for screening of the IMFs and further) to rendering plots (visualizing spectrograms for the time-frequency analysis). This calls for a comprehensive study of basic signal processing (especially the Fourier, Wavelet, and Hilbert transforms along with time-frequency analysis). On the other hand I need to develop high-performance algorithms for these techniques which complement the theory as neatly as possible.
Thus, beginning here, are my journal entries for the development of the PyHHT project. In the following posts I will break the project down into smaller development tasks.
1. a big deal in signal processing
2. computationally expensive
3. not exactly easy to code for,
I conclude that I really need to maintain a proper record of what I work on. By not doing this, I have lost the ability to maintain a bird's eye view of my work on the toolbox. I often get stuck in just one layer of abstraction for weeks.
The following few blog posts are intended to be journal entries for the PyHHT project, and will be my primary resources when I submit my paper to SciPy India 2011. The abstract can be found here.
Bringing together different heuristics and interpretations of the Hilbert-Huang transform will require programming that ranges from simple numerical methods (for instance, as required in cubic spline interpolation) to complex machine learning and signal processing tools (for screening of the IMFs and further) to rendering plots (visualizing spectrograms for the time-frequency analysis). This calls for a comprehensive study of basic signal processing (especially the Fourier, Wavelet, and Hilbert transforms along with time-frequency analysis). On the other hand I need to develop high-performance algorithms for these techniques which complement the theory as neatly as possible.
Thus, beginning here, are my journal entries for the development of the PyHHT project. In the following posts I will break the project down into smaller development tasks.
