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Spectral Analysis and Validation of Parietal Signals for Different Arm Movements

Intelligent Automation & Soft Computing 2023年第6期36卷 2849-2863页

作者： Umashankar Ganesan a.vimala juliet R.Amala Jenith Joshi School of Electrical and Electronics Engineering Sathyabama Institute of Science and TechnologyChennai600119India School of Electronics and Instrumentation Engineering SRM Institute of Science and TechnologyChennai630203India Department of Biomedical and Mechanical Engineering School of EngineeringNational University of IrelandGalwayH91 CF50Ireland

Brain signal analysis plays a significant role in attaining data related to motor *** parietal region of the brain plays a vital role in muscular *** approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements;perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype *** can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease(PD).To play out this handling method,electroencepha-logram(EEG)signals are gained while the subject is performing different wrist and elbow ***,the frontal brain signals and just the parietal signals are separated from the obtained EEG signal by utilizing a band pass ***,feature extraction is carried out using Fast Fourier Transform(FFT).Subse-quently,the extraction process is done by Daubechies(db4)and Haar wavelet(db1)in MATLAB and classified using the Levenberg-Marquardt *** results of the frequency changes that occurred during various wrist move-ments in the parietal region are compared with the frequency changes that occurred in frontal EEG *** proposed algorithm also uses the deep learn-ing pattern analysis network to evaluate the matching sequence for each action that takes *** accuracy of 97.2%and maximum error range of 0.6684%are achieved during the *** of this research confirm that the Levenberg-Marquardt algorithm,along with the newly developed deep learn-ing hybrid PatternNet,provides a more accurate range of frequency changes than any other classifier used in previous works of *** on the analysis,the peak-to-peak value is used to define the threshold for the prototype arm,which performs all the intended degrees of freedom(DOF),verifying

关键词： Parietal EEG signals fast fourier transform Levenberg-Marquardt algorithm haar wavelet daubechies wavelet statistical analysis

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Computational Microuidic Channel for Separation of Escherichia coli from Blood-Cells

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Computers, Materials & Continua 2021年第5期67卷 1369-1384页

作者： Chinnapalli Likith Kumar a.vimala juliet Bandaru Ramakrishna Shubhangi Chakraborty Mazin Abed Mohammed Kalakanda Alfred Sunny Department of Electronics and Instrumentation Engineering SRM Institute of Science and TechnologyKattankulathur603203India Department of Electronics and Communication Engineering SRM Institute of Science and TechnologyKattankulathur603203India College of Computer Science and Information Technology University of AnbarAnbarIraq Department of Mechanical Engineering Karunya Institute of Technology and SciencesCoimbatore641114India

Microuidic channels play a vital role in separation of analytes of interest such as bacteria and platelet cells,etc.,in various biochemical diagnosis procedures including urinary tract infections(UTI)and bloodstream *** paper presents the multi physics computational model specifically designed to study the effects of design parameters of a microuidics channel for the separation of Escherichia coli(***)from various blood constituents including red blood cells(RBC)and platelets.A standard two inlet and a two outlet microchannel of length 805µm with a channel width of 40µm is *** effect of electrode potentials and the effect of electrode placement along the channel length and also the levitation of electrodes from the channel wall are studied to optimize the selective particle separation *** results show the efcient separation of E-coli with a mean diameter 0.68µm is achieved at low voltages(less than 20 V)when electrodes placed near to the micro channel and also noticed that the applied electric potential is inversely proportional to the number of electrodes placed along the microuidic *** computer aided multi physics simulations with multiple governing parameters could be advantage in design optimization of microuidics channels and support precise bioparticle separation for better diagnosis.

关键词： Microuidics dielectrophoresis cell separation electro kinetic force platelets E.coli

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