Shahedul Haque Laskar

The redundant data in multichannel electroencephalogram (EEG) signals significantly reduces the performance of brain–computer interface (BCI) systems. By removing redundant channels, a channel selection strategy increases the classification accuracy of BCI systems. In this work, a novel channel selection method (stdWC) based on the standard deviation of wavelet coefficients across channels is proposed to identify Motor Imagery (MI) based EEG signals. The wavelet coefficients are calculated by employing a Continuous Wavelet Transform (CWT) filter bank to decompose each trial from the EEG channel. The wavelet coefficient's standard deviation values are obtained across the channels, and these values are then sorted to determine the EEG channels with the highest standard deviation values. The channels with the largest wavelet coefficient divergence are chosen. MI trials are then spatially filtered with the Common Spatial Pattern (CSP), and CWT filter bank‐based 2D images are generated from the spatially filtered trials. These images are then classified using a unique nine‐layered convolutional neural network (CNN) model that combines two feature maps acquired with differing filter sizes. The proposed framework (stdWC‐CSP‐CNN) is evaluated using kappa score and classification accuracy on two publically accessible datasets (BCI Competition III dataset IVa and BCI Competition IV dataset 2a). The suggested framework achieved a mean test classification accuracy of 88.8% for dataset IVa from BCI Competition III and 75.03% for dataset 2a from BCI Competition IV, according to the results. The proposed channel selection method outperforms the other channel selection methods examined, according to the results. By rejecting redundant channels, the whole framework can improve the performance of MI‐based BCIs.

The feature extraction technique plays a vital role in obtaining better classification accuracy. In this paper, a novel framework is proposed, which develops two‐dimensional (2D) images for convolutional neural network (CNN) to classify four (left hand, right hand, feet, and tongue) MI tasks. 2D image is formed by decomposing each trial using continuous wavelet transform (CWT) filter bank after pre‐processing the MI‐based EEG data by multi‐class common spatial pattern (CSP) method. Obtained images are used to train the CNN model for classification. The proposed framework is evaluated using publicly available BCI competition IV dataset 2a by calculating the classification accuracy for all subjects. Results show that the proposed framework has been giving better classification accuracy than some existing CNN‐based and conventional machine learning‐based approaches compared in this paper. The average time required to train CNN using the proposed framework is 12.67 s, acceptable for online MI‐based BCI applications.

A Double-layer banana leaves-based absorber is designed and can be treated as a potential alternative to conventional MAM. The dielectric relaxation parameters of the dry banana leaves in powder form and the composites of dry banana leaves and charcoal powder have are measured by OCP method using vector network analyzer in the frequency range of 1–16 GHz. Further, the return loss of two single-layers and a double-layer with a thickness of 4 and 8.02 mm, respectively, is investigated in the frequency range of 1–20 GHz. Results show that the electromagnetic properties along with absorption performance of the double layer absorber are appreciably ameliorated as compared to its single-layer counterparts.

Soft sensors in wastewater treatment plants are used to estimate the difficult to measure variables, predict the parameters of concern as per the instructions of the pollution control board or monitor the plant and effluent quality. However the soft sensor deterioration is a limitation of such soft sensors which occur due to various reasons like sudden change in process characteristic or involvement of faulty data in designing and updating the soft sensor. The paper overcome this limitation by using a deep learning based method for automatic maintenance. The proposed technique identify and rectify the faulty data involved in designing and updating of soft sensor based on a ten layered deep neural network. The comparative results show that the proposed method reduces the soft sensor deterioration of a total nitrogen soft sensor used in wastewater treatment plants.

Electric vehicles (EVs) are key players for transport oriented smart cities (TOSC) powered by smart grids (SG) because they help those cities to become greener by reducing vehicle emissions and carbon footprint. In this article, the authors analyze different use-cases to show how big data analytics (BDA) can play vital role for successful electric vehicle (EV) to smart grid (SG) integration. Followed by this, this article presents an edge computing model and highlights the advantages of employing such distributed edge paradigms towards satisfying the store, compute and networking (SCN) requirements of smart EV applications in TOSCs. This article also highlights the distinguishing features of the edge paradigm, towards supporting BDA activities in EV to SG integration in TOSCs. Finally, the authors provide a detailed overview of opportunities, trends, and challenges of both these computing techniques. In particular, this article discusses the deployment challenges and state-of-the-art solutions in edge privacy and edge forensics. Request access from your librarian to read this chapter's full text.

The Electroencephalogram (EEG) signals recorded during motor imagery (MI) tasks are often used as input in the brain-computer interface (BCI) applications. The adaptation of these EEG signals to control signals depend on the selection of an accurate feature that can provide good classification. The study compared various features of alpha frequency band adapting wavelet-based time-frequency analysis approach. The mean, variance, wavelet energy, Shannon entropy, log energy entropy, kurtosis, and skewness feature were extracted from the EEG signals recorded from symmetrical electrode locations of the motor cortex and their performance accuracies were evaluated using SVM and KNN classification algorithms. In results, the Shannon entropy showed the highest classification accuracy of 86.4 % with SVM classifier. Findings of this study establish Shannon entropy as a potential feature to use in EEG-based BCI analysis along with SVM classification algorithm.