embedded-dsp

A #![no_std] Rust Digital Signal Processing library designed for microcontrollers (Cortex-M, RISC-V, AVR, Xtensa), embedded systems, and real-time digital signal processing applications.

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Features

• #![no_std] First: Pure core compatibility for bare-metal targets with zero dynamic allocation required.
• libm Integration: Unified FloatMath trait providing floating-point math routines in #![no_std] environments via libm.
• Fixed-Point & Floating-Point: Complete support for f32, f64, q31, q15, q7, and q63 saturating arithmetic.
• 16 Core DSP Modules:
  1. Basic Math: Elementwise add, sub, mult, negate, offset, scale, shift, dot_prod, clip, bitwise operations.
  2. Complex Math: Complex vector addition, multiplication, magnitude, conjugate, dot product.
  3. Fast Math: Trigonometric sin, cos, sin_cos, sqrt, vsqrt, divide, log, exp, atan2.
  4. Filtering: FIR filters, Biquad IIR cascade, LMS adaptive filters, 1D convolution & correlation.
  5. Transforms: In-place Complex FFT (cfft), Real FFT (rfft), Discrete Cosine Transform (dct4).
  6. Matrix Operations: Matrix addition, subtraction, multiplication, scaling, transpose, Gauss-Jordan inversion.
  7. Controller: PID motor controller, Clarke and Park transforms.
  8. Statistics: Mean, variance, standard deviation, RMS, power, min/max, entropy, KL divergence, logsumexp.
  9. Support & Conversions: Array copy/fill, zero-allocation sorting (sort_f32), format conversions (q15 ↔ f32 ↔ q31).
  10. Interpolation: Linear, Bilinear, and Cubic Spline interpolation.
  11. Quaternions: Norm, normalization, quaternion product, conjugate, inverse, rotation matrix conversion.
  12. Window Functions: Hanning, Hamming, Blackman, Bartlett, Welch, Flat-top generators.
  13. Distance Metrics: Euclidean, Cosine, Chebyshev, Manhattan, Minkowski, Jaccard, Hamming, Canberra, Bray-Curtis.
  14. Machine Learning: Support Vector Machine (SvmInstanceF32) and Gaussian Naive Bayes (GaussianNaiveBayesInstanceF32).

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Quick Start

Add embedded-dsp to your Cargo.toml:

[dependencies]
# For bare-metal #![no_std] environments with libm
embedded-dsp = { version = "0.1", default-features = false, features = ["libm"] }

# For standard std environments
embedded-dsp = "0.1"

Basic Example

use embedded_dsp::*;

fn main() {
    // 1. Vector Operations
    let a = [1.0f32, 2.0, 3.0, 4.0];
    let b = [10.0f32, 20.0, 30.0, 40.0];
    let mut vec_out = [0.0f32; 4];
    add_f32(&a, &b, &mut vec_out);

    // 2. Q15 Fixed-Point Saturating Addition
    let q15_a = [20000i16, 25000];
    let q15_b = [15000i16, 10000];
    let mut q15_out = [0i16; 2];
    add_q15(&q15_a, &q15_b, &mut q15_out); // Output: [32767, 32767] (clamped at i16::MAX)

    // 3. 64-Point Complex FFT
    let mut fft_data = [0.0f32; 128]; // 64 complex pairs [re, im, ...]
    cfft_f32(&mut fft_data, 64, 0, 1);
}

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Running Included Examples

# Run basic usage example
cargo run --example basic_usage

# Run performance comparison benchmark (libm vs embedded-dsp)
cargo run --release --example perf_comparison

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License

The contents of this repository are dual-licensed under the MIT OR Apache 2.0 License. That means you can choose either the MIT license or the Apache 2.0 license when you re-use this code. See LICENSE, LICENSE-MIT, or LICENSE-APACHE for more information on each specific license.