S

signal processing

Armadillo: fast C++ library for linear algebra (matrix maths) & scientific computing - https://arma.sourceforge.net

Signal Viewer, Analyzer and Recorder on GPL

Real-Time Automation Library for C++. Uses PdServ to export signals and parameters to the network.

This project implements a BPSK (Binary Phase Shift Keying) transmitter using Software Defined Radio (SDR). The code is written to generate and transmit BPSK-modulated signals through SDR hardware such as HackRF or USRP. It allows configurable parameters like carrier frequency and bit rate, making it suitable for learning, experimentation, and research in digital communication systems.

CUDA-accelerated implementation of Partial 3D Discrete Wavelet Transform, enabling efficient multi-resolution analysis of volumetric data. Designed optimized GPU kernels to support partial inverse transform, allowing reconstruction of arbitrary points in a 3D signal without processing the entire dataset.

The codebase used in the IN-NOVA project (position DC 12 - Directional microphone arrays for remote microphone virtual sensing)

Python bindings for MATLAB fsst function

A tutorial on game audio programming from the ground up with C++.

Python para el procesamiento de señales.

SoundTouch audio tempo/pitch control library

Physical Layer of AcubeSAT communication system.

OQPSK modulator, GMSK demodulator, ASM synchronization, Error Correction Codes (LDPC, Convolutional, BCH)

Trabajo de laboratorio que enseña la metodologı́a para oı́r las muestras de radiofrecuencia tomadas a través de la radio SDR durante un perı́odo de tiempo. Pretende adentrarse en los sucesivos pasos que se deben llevar a cabo para obtener la información de una señal modulada en frecuencia (FM).

Materia: Fundamentos de las comunicaciones. Facultad de Ingenieria, UNLP.

A series of experiments to measure the impact of RF signal degradation on output quality of VHS-Decode

My MSc thesis work. I essayed with a number of classifiers for two type of health-related time series: electrocardiogram and blood oxygen levels. The four approaches used on each type of signal were: (i) featured based approach, where integral and differential attributes where devised and used to classify the sets (ii) Direct Machine Learning application to the time series (Perceptron, Random Forest and others) (iii) 1D convolutional neural networks applied to the signals (iv) 2D convolutional neural networks applied to the signal spectrograms.

The thesis pdf file, included here, is written in Spanish.

Python package to analyze and process time-discrete, equidistant measured signals, and visualize with Plotly

Tap detection based on piezo. Once event detected (double tap), power output enable PWM, which can drive LED strip for example.

LEM speech recognition device, designed for Signal Processing lecture.