A Comparative Study and Implementation of Blind Source Separation Algorithm using MATLAB and TMS320c6713 DSK

Mekhfioui, Mohcin; Elgouri, Rachid; Satif, Amal; Benahmed, Aziz; Hamzaoui, El-Mehdi; Hadjoudja, Abdelkader; Hlou, Laamari

doi:10.36478/jeasci.2020.1074.1081

Cited by 3 publications

(7 citation statements)

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“…x The estimated sources are obtained from the vector s(k), and their related projections for the various sensors are determined from the estimated mixture array: H = W −1 [20,27].…”

Section: About Blind Source Separationmentioning

confidence: 99%

The Development and Implementation of Innovative Blind Source Separation Techniques for Real-Time Extraction and Analysis of Fetal and Maternal Electrocardiogram Signals

Mekhfioui,

Benahmed,

Chebak

et al. 2024

Bioengineering

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This article presents an innovative approach to analyzing and extracting electrocardiogram (ECG) signals from the abdomen and thorax of pregnant women, with the primary goal of isolating fetal ECG (fECG) and maternal ECG (mECG) signals. To resolve the difficulties related to the low amplitude of the fECG, various noise sources during signal acquisition, and the overlapping of R waves, we developed a new method for extracting ECG signals using blind source separation techniques. This method is based on independent component analysis algorithms to detect and accurately extract fECG and mECG signals from abdomen and thorax data. To validate our approach, we carried out experiments using a real and reliable database for the evaluation of fECG extraction algorithms. Moreover, to demonstrate real-time applicability, we implemented our method in an embedded card linked to electronic modules that measure blood oxygen saturation (SpO2) and body temperature, as well as the transmission of data to a web server. This enables us to present all information related to the fetus and its mother in a mobile application to assist doctors in diagnosing the fetus’s condition. Our results demonstrate the effectiveness of our approach in isolating fECG and mECG signals under difficult conditions and also calculating different heart rates (fBPM and mBPM), which offers promising prospects for improving fetal monitoring and maternal healthcare during pregnancy.

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“…x The estimated sources are obtained from the vector s(k), and their related projections for the various sensors are determined from the estimated mixture array: H = W −1 [20,27].…”

Section: About Blind Source Separationmentioning

confidence: 99%

The Development and Implementation of Innovative Blind Source Separation Techniques for Real-Time Extraction and Analysis of Fetal and Maternal Electrocardiogram Signals

Mekhfioui,

Benahmed,

Chebak

et al. 2024

Bioengineering

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“…The efficiency of the algorithms of the image blind separation is valued by their separability performance index (PI) which is determined with the FPGA processor. The index (PI) is given by the below equation [7], [8]:…”

Section: Evaluation Criteria For Image Separationmentioning

confidence: 99%

FPGA based Hardware Co-Simulation Implementation for RealTime Image Blind Separation using ICA Algorithms

Benahmed¹,

Mekhfioui²,

Guennoun³

2022

IJETAE

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The use of mixed image sources requires digital preprocessing to separate them, for making easier their storage and improve their quality. The ICALAB for Image Processing method is one of the solutions proposed for the blind efficient separation of signals without prior knowledge of the source images. Nevertheless, to choose the appropriate algorithm with the most efficient separation index, several tests are carried out on a certain number of algorithms, with off-line processing of image samples bases, by means of software tools which are generally installed on a computer, such as ICALAB, TIPICA, GIFT, NMFLAB, model identification UBSS, ... Thus, in order to perform the image separation processing in real time, this study aims to develop a rapid prototyping embedded system for implementing the blind source separation concept. It mainly provides a promising approach for implementing twelve efficient blind image separation algorithms on the ZYBO Z7 FPGA board, using Xilinx System Generator (XSG) for Real Time Hardware Co-simulation. As an application example, this study deals with the blind separation of two e-frames mixture, and then provides the adequate algorithm, basing on the calculation of the Performance Index (PI), and the separated images results. Keywords—Blind Image Separation; Hardware CoSimulation; Independent Component Algorithm (ICA); Xilinx System Generator (XSG); Zynq-7000; VHDL.

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“…The importance of this separation method justifies the different use of platforms such as Arduino Due [1], DSP [2] and FPGA [3]- [4].The Arduino Due is an open source electronic prototyping platform based low cost on the ARM Cortex-M3 microcontroller [5].…”

Section: Source Separation Techniques Have Been Applied In Various Scientificmentioning

confidence: 99%

“…This work presents a comparative study between the implementation of the SOBI (Second Order Blind Identification) [2] algorithm for the separation of two source signals in the Arduino Due board and in the TMS320c6713 board, using Matlab/Simulink and Code Composer Studio (CCS) [6].…”

Section: Source Separation Techniques Have Been Applied In Various Scientificmentioning

confidence: 99%

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A Comparative Approach of Blind Source Separation with Arduino Due and TMS320C6713

Mekhfioui¹

2020

IJETER

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This paper presents a comparison between two blind source separation methods: the first one based on a microcontroller board such as Arduino Due by the RTW (Real-Time Workshop) integrated into Matlab Simulink and the second one based on a DSP board (TMS320c6713). This study compared the space occupied, the execution speed and the separation index between the two platforms Arduino and DSP. The separation was practically simulated on the Arduino Due and TMS320c6713 board by two signal generators and an oscilloscope.

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A Comparative Study and Implementation of Blind Source Separation Algorithm using MATLAB and TMS320c6713 DSK

Cited by 3 publications

References 0 publications

The Development and Implementation of Innovative Blind Source Separation Techniques for Real-Time Extraction and Analysis of Fetal and Maternal Electrocardiogram Signals

The Development and Implementation of Innovative Blind Source Separation Techniques for Real-Time Extraction and Analysis of Fetal and Maternal Electrocardiogram Signals

FPGA based Hardware Co-Simulation Implementation for RealTime Image Blind Separation using ICA Algorithms

A Comparative Approach of Blind Source Separation with Arduino Due and TMS320C6713

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