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DEVLOPMENT LOG

2025 - NOV

Tiny-DSP Module Feature Overview

The tiny-dsp module currently provides the following main features:

Transform Module

  • FFT (tiny_fft): Fast Fourier Transform
  • DWT (tiny_dwt): Discrete Wavelet Transform
  • ICA (tiny_ica): Independent Component Analysis

Filter Module

  • FIR Filter (tiny_fir): Finite Impulse Response filter
  • IIR Filter (tiny_iir): Infinite Impulse Response filter

Signal Processing Module

  • Convolution (tiny_conv): Convolution operations with various padding and output modes
  • Correlation (tiny_corr): Signal correlation analysis functions
  • Resampling (tiny_resample): Signal sampling rate conversion

Support Module

  • Signal Visualization (tiny_view): Signal viewing and analysis tools

Tiny-Math Module Progress

Matrix Decomposition Functions

  • LU Decomposition (lu_decompose): Supports LU decomposition with and without pivoting for efficient linear system solving
  • Cholesky Decomposition (cholesky_decompose): Specialized decomposition method for symmetric positive definite matrices
  • QR Decomposition (qr_decompose): Orthogonal-triangular decomposition for least squares problems and numerically stable solving
  • SVD Decomposition (svd_decompose): Singular value decomposition supporting rank estimation and pseudo-inverse computation

Eigenvalue Calculation Functions

  • Power Iteration (power_iteration): Computes the dominant eigenvalue (largest magnitude) and corresponding eigenvector
  • Inverse Power Iteration (inverse_power_iteration): Computes the smallest eigenvalue, suitable for fundamental frequency detection in system identification
  • Jacobi Eigendecomposition (eigendecompose_jacobi): Complete eigendecomposition for symmetric matrices
  • QR Eigendecomposition (eigendecompose_qr): Eigendecomposition method for general matrices