Browsing by Author "Aceros-Moreno, Cesar A."
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PublicationA computational modelling framework for time-frequency signal representations( 2010) Aceros-Moreno, Cesar A. ; Rodríguez, Domingo ; College of Engineering ; Rodríguez, Néstor J.|Lu, Kejie ; Department of Electrical and Computer Engineering ; Hajek, DarrellThis thesis presents an open source computational tool framework for the visualization and analysis of signals with time-dependent spectral content. SIRLAB (SIgnal Representation LABoratory) is the name given to this tool framework written in C- language for a Linux environment and using the OpenCV (Open Source Computer Vision) platform, a software library of programming functions for near-real-time computer vision application development. SIRLAB was initially developed as an application tool kit for environmental surveillance operations pertaining to acoustic monitoring of birds, amphibians, and aquatic animals. In this setting, it receives acoustic raw signal-data and it produces ordered sets of spectrogram frames which may be presented in a streaming video format due to its fast computation. Computer speed ups of more than 30 times have been reached when compared with MATLAB implementations utilizing the same computational resources and algorithm formulations. This allows to produce streaming video with a frame rate of 30 frames per second, for some applications, reaching the ATSC digital television frame rate standard.
PublicationFast signal transforms for radar information processing( 2005) Aceros-Moreno, Cesar A. ; Rodríguez, Domingo ; College of Engineering ; Rodríguez-Solís, Rafael ; Parsiani, Hamed ; Department of Electrical and Computer Engineering ; Colón, SilvestreThis work describes a useful processing tool for time-frequency signals known as the Discrete Chirp Fourier Transform (DCFT). Chirp signals are time-frequency signals which are linearly frequency modulated. DCFT implementations for RADAR systems provide important information about the nature of radar signals and allow to determine certain spectral characteristics. This work concentrates on the analysis, design, and implementation of efficient algorithms for the computation of the DCFT. The algorithms involve mathematical tools, in order to express DCFT in terms of factored composition of sparse matrices. A DCFT takes a one-dimensional signal in the object domain and returns a two- dimensional signal in the spectral domain. Important issue in this work is the study of algorithm arithmetic complexity. The main goal of this work is to generate implementations in MATLAB® and MPI-based clusters. The work addresses the issue of scalability as it pertains to the processing of high bandwidth data.
PublicationTensor products algebra applied to time frequency analysis of auditory encephalographic signal( 2019-05-15) Aceros-Moreno, Cesar A. ; Manian, Vidya ; College of Engineering ; Rodriguez, Domingo ; Rodriguez, Nestor ; Vega-Riveros, Jose Fernando ; Department of Electrical and Computer Engineering ; Baiges-Valentin, IvanTime-frequency methods (TFM) increase the dimensionality of signal spaces. In this thesis, a computational signal processing framework using tensor products algebra is developed to map Electroencephalographic (EEG) signals to time-frequency space, extract robust features and classify them. EEG signal acquisition is a modality to record brain signals using electrodes placed on the scalp of a subject. The signals are recorded with 32 electrodes while the subject listens to an auditory stimulus such as repeated tones, vowels, or words. The TFM used are cyclic short time Fourier transform (CSTFT) and continuous wavelet transform (CWT). The framework takes as input the raw EEG signals. The time-frequency sparse representations using the STFT and CWT are computed on the noise free channels at di erent time steps. The source localization using event related potentials (ERP) can be compared with time-frequency based localization. Results of multichannel EEG nonnegative matrix factorization (NMF) and reconstruction is also presented. The algorithms are implemented as part of a multichannel signal analysis toolbox (MSAT) using the Python programming language. The algorithms presented in this thesis can be extended to more general algorithms for analyzing and classifying the sources of multichannel biosensory signals.