112,791 research outputs found

    PulsatioMech: An Open-Source MATLAB Toolbox for Seismocardiography Signal Processing

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    This paper summarizes and presents PulsatioMech: an open-source MATLAB toolbox for seismocardiography (SCG) signal processing. The toolbox may be found here: https://github.com/nzavanelli/SCG_master_toolbox PulsatioMech is currently under development as a common tool to promote new studies and discoveries in the use of cardiac mechanical signal for wearable health monitoring. This toolbox is designed to assist users in analyzing SCG signals without the need to devote significant effort into signal processing and coding tasks. Simultaneously, it provides a uniform basis to assess the reproducibility of works based on this toolbox, including those cited here [1-6]. The referenced works contain a great deal more detail regarding the specific algorithms implemented here, whereas this paper will present a short overview of the PulsatioMech Toolbox

    System identification, time series analysis and forecasting:The Captain Toolbox handbook.

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    CAPTAIN is a MATLAB compatible toolbox for non stationary time series analysis, system identification, signal processing and forecasting, using unobserved components models, time variable parameter models, state dependent parameter models and multiple input transfer function models. CAPTAIN also includes functions for true digital control

    Information Theoretical Estimators Toolbox

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    We present ITE (information theoretical estimators) a free and open source, multi-platform, Matlab/Octave toolbox that is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities, and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems. ITE also includes a prototype application in a central problem class of signal processing, independent subspace analysis and its extensions.Comment: 5 pages; ITE toolbox: https://bitbucket.org/szzoli/ite

    The DESAM toolbox: spectral analysis of musical audio

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    International audienceIn this paper is presented the DESAM Toolbox, a set of Matlab functions dedicated to the estimation of widely used spectral models for music signals. Although those models can be used in Music Information Retrieval (MIR) tasks, the core functions of the toolbox do not focus on any specific application. It is rather aimed at providing a range of state-of-the-art signal processing tools that decompose music files according to different signal models, giving rise to different ``mid-level'' representations. After motivating the need for such a toolbox, this paper offers an overview of the overall organization of the toolbox, and describes all available functionalities

    SCSA based MATLAB pre-processing toolbox for 1H MR spectroscopic water suppression and denoising

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    In vivo 1H Magnetic Resonance Spectroscopy (MRS) is a useful tool in assessing neurological and metabolic disease, and to improve tumor treatment. Different pre-processing pipelines have been developed to obtain optimal results from the acquired data with sophisticated data fitting, peak suppression, and denoising protocols. We introduce a Semi-Classical Signal Analysis (SCSA) based Spectroscopy pre-processing toolbox for water suppression and data denoising, which allows researchers to perform water suppression using SCSA with phase correction and apodization filters and denoising of MRS data, and data fitting has been included as an additional feature, but it is not the main aim of the work. The fitting module can be passed on to other software. The toolbox is easy to install and to use: 1) import water unsuppressed MRS data acquired in Siemens, Philips and .mat file format and allow visualization of spectroscopy data, 2) allow pre-processing of single voxel and multi-voxel spectra, 3) perform water suppression and denoising using SCSA, 4) incorporate iterative nonlinear least squares fitting as an extra feature. This article provides information about how the above features have been included, along with details of the graphical user interface using these features in MATLAB

    Incorporating MATLAB\u27s Signal Processing Toolbox into a DSP course at an undergraduate E.E. program

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    This paper provides some suggestions for incorporating MATLAB\u27s Signal Processing Toolbox into a DSP course. Often, in a DSP course, students have difficulties understanding abstract and non-intuitive concepts and seeing their relevance to the practical part of their curriculum. The tools offered by MATLAB\u27s Signal Processing Toolbox, can help to make these concepts more tangible and provide a perspective for students. Some basic tools relevant to an undergraduate DSP course will be introduced, including examples of tool use and graphic results. The tools presented will be applied in the areas of synthesis and analysis of signals, FFT computation, impulse response and convolution, frequency response, the Z-transform, filter design and filter applications

    A Python-based Brain-Computer Interface Package for Neural Data Analysis

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    Anowar, Md Hasan, A Python-based Brain-Computer Interface Package for Neural Data Analysis. Master of Science (MS), December, 2020, 70 pp., 4 tables, 23 figures, 74 references. Although a growing amount of research has been dedicated to neural engineering, only a handful of software packages are available for brain signal processing. Popular brain-computer interface packages depend on commercial software products such as MATLAB. Moreover, almost every brain-computer interface software is designed for a specific neuro-biological signal; there is no single Python-based package that supports motor imagery, sleep, and stimulated brain signal analysis. The necessity to introduce a brain-computer interface package that can be a free alternative for commercial software has motivated me to develop a toolbox using the python platform. In this thesis, the structure of MEDUSA, a brain-computer interface toolbox, is presented. The features of the toolbox are demonstrated with publicly available data sources. The MEDUSA toolbox provides a valuable tool to biomedical engineers and computational neuroscience researchers
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