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A Machine Learning Approach for Artifact Removal from Brain Signal

Computer Systems Science & Engineering 2023年第5期45卷 1455-1467页

作者： Sandhyalati Behera mihir narayan mohanty Department of Electronics and Communication Engineering ITERSikhsha‘O’Anusandhan(Deemed to be University)Bhubaneswar751030India

Electroencephalography(EEG),helps to analyze the neuronal activity of a human brain in the form of electrical signals with high temporal resolution in the millisecond *** extract clean clinical information from EEG signals,it is essential to remove unwanted artifacts that are due to different causes including at the time of *** this piece of work,the authors considered the EEG signal contaminated with Electrocardiogram(ECG)artifacts that occurs mostly in cardiac *** clean EEG is taken from the openly available Mendeley database whereas the ECG signal is collected from the Physionet database to create artifacts in the EEG signal and verify the proposed *** the artifactual signal is non-linear and non-stationary the Random Vector Functional Link Network(RVFLN)model is used in this *** Machine Learning approach has taken a leading role in every field of current research and RVFLN is one of *** the proof of adaptive nature,the model is designed with EEG as a reference and artifactual EEG as *** peaks of ECG signals are evaluated for artifact estimation as the amplitude is higher than the EEG *** vary the weight and reduce the error,an exponentially weighted Recursive Least Square(RLS)algorithm is used to design the adaptive filter with the novel RVFLN *** random vectors are considered in this model with a radial basis function to satisfy the required signal *** is found that the result is excellent in terms of Mean Square Error(MSE),Normalized Mean Square Error(NMSE),Relative Error(RE),Gain in Signal to Artifact Ratio(GSAR),Signal Noise Ratio(SNR),Information Quantity(IQ),and Improvement in Normalized Power Spectrum(INPS).Also,the proposed method is compared with the earlier methods to show its efficacy.

关键词： Random vector functional link network(RVFLN) information quantity(IQ) constrained independent component analysis(cICA)

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Machine Learning-based Inverse Model for Few-Mode Fiber Designs

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Computer Systems Science & Engineering 2023年第4期45卷 311-328页

作者： Bhagyalaxmi Behera Gyana Ranjan Patra Shailendra Kumar Varshney mihir narayan mohanty ITER Siksha‘O’Anusandhan(Deemed to be University)Bhubaneswar751030India Department of Electronics and Electrical Communication Engineering IITKharagpur721302India

The medium for next-generation communication is considered as fiber for fast,secure communication and switching *** division and space division multiplexing provide an excellent switching capability with high data transmission *** this work,the authors have approached an inverse modeling technique using regression-based machine learning to design a weakly coupled few-mode fiber for facilitating mode division *** technique is adapted to predict the accurate profile parameters for the proposed few-mode fiber to obtain the maximum number of *** is for a three-ring-core few-mode fiber for guiding five,ten,fifteen,and twenty *** types of regression models namely ordinary least-square linear multi-output regression,k-nearest neighbors of multi-output regression,and ID3 algorithm-based decision trees for multi-output regression are used for predicting the multiple profile *** is observed that the ID3-based decision tree for multioutput regression is the robust,highly-accurate machine learning model for fast modeling of *** proposed fiber claims to be an efficient candidate for the next-generation 5G and 6G backhaul networks using mode division multiplexing.

关键词： Few-mode fibers inverse modeling machine learning regression ring-core

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Design of Z-Shape Microstrip Antenna with I- Slot for Wi-Max/Satellite Application

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通讯和计算机（中英文版） 2016年第5期13卷 261-265页

作者： Swamaprava Sahoo mihir narayan mohanty Department of Electronics and Communication Engineering ITER Siksha '0' Anusandhan University Bhubaneswar Odisha

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