Search Results for "signal processing" - Page 8

Showing 192 open source projects for "signal processing"

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  • 1
    SATELLITE (Systems Analysis Total Environment for Laboratory-Language and InTeractive Execution) provides interactive C-like language processing system that supports modules in signal processing, numerical simulation, parameter estimation, etc.
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  • 2
    FreeSP is an infrastructure for programming platform-independend online signal processing applications. Ports to specific platforms (like DSPs and microcontrollers) are made by simply exchanging the freeSP System-module.
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  • 3
    A Digital Signal Processing Program for educational purposes, written in Java, tested on Windows, should work on Linux too. Plugin architecture for inputs, outputs, filters, analisys.
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  • 4
    Feature extraction toolkit based on a wavelet multiscale vision model. Includes 1D signal processing sample application for line identification and classification of astronomical spectra.
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  • 5
    miscelaneous c++ code, scripts, commands, in matters as radar, signal processing, realtime gnuplot, 2D/3D graphics
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  • 6
    "tdsp" is a C++ based framework for the simulation and evaluation of various signal processing aspects. It is completly written in C++ and is based on "root" framework (http://root.cern.ch). TDSP includes C++-classes which extents the root-class-f
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  • 7
    The Open Mind Speech project is part of the Open Mind Initiative and aims to develop free(GPL) speech recognition and signal processing (DSP) tools and applications, as well as collect speech data from "e-citizens" using the Internet.
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  • 8
    CECILIA is an environment for music and signal-processing that uses Csound as its underlying audio processing engine. One can think of CECILIA as a completely programmable sound-processing tool, with real-time interactive processing on soundfiles or live
    Downloads: 1 This Week
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  • 9
    A digital signal processing library for Microsoft .NET platform written in C#. Features complex number data types, a complex number math library and both real and complex number fast fourier transforms (FFT).
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  • 10
    A general tool for creating and processing sound in a general manner. The plug-in system makes it possible to create a complete software studio with all kinds of cool little boxes.
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  • 11
    Chemopy is a set of Python functions and classes that are useful for the field of chemometrics. This includes multivariate regression, pattern recognition, signal processing, and other useful preprocessing techniques. For active development of Python Che
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  • 12
    A digital signal processor for Home Theater PCs providing core functionality (bass management, external input signal processing, calibration of multiple channel levels, etc.) for a soundcard-independent audio control panel.
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  • 13
    sposim simulates systems of connected software SPOs. Signal Processing Objects (SPOs) are hardware abstractions that can be used to build software defined radios.
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  • 14
    pySPACE

    pySPACE

    Signal Processing and Classification Environment in Python using YAML

    pySPACE is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g.
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  • 15
    SigLab is a signal calculator for Scilab. Its goal is to ease the analysis of signal processing simulation results. It allows to view and to manipulate monodimensional signals in a interactive and easy way.
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  • 16
    A set of Matlab toolboxes to perform signal and image processing.
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  • 17
    DeepSeek-V4-Flash

    DeepSeek-V4-Flash

    Efficient MoE model for million-token reasoning and coding

    ...It has 284B total parameters with 13B activated and supports a 1M-token context window, making it suitable for long-document reasoning, complex coding, agentic workflows, and large-scale information processing. The model uses a hybrid attention architecture that combines Compressed Sparse Attention and Heavily Compressed Attention to improve long-context efficiency, while Manifold-Constrained Hyper-Connections strengthen signal stability across layers. It is trained on more than 32T tokens and refined through a post-training pipeline that includes supervised fine-tuning, reinforcement learning, domain-specific expert cultivation, and on-policy distillation. ...
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