Vijay K Chakka

Bio: Vijay K Chakka is an academic researcher from Shiv Nadar University. The author has contributed to research in topics: Demodulation & Computer science. The author has an hindex of 1, co-authored 3 publications receiving 4 citations.

Graph Signal Processing Based Cross-Subject Mental Task Classification Using Multi-Channel EEG Signals

Abstract: Classification of mental tasks from electroencephalogram (EEG) signals play a crucial role in designing various brain-computer interface (BCI) applications. Most of the current techniques consider each channel as independent, neglecting the functional connectivity of the brain during mental activity and are primarily subject specific. This paper proposes a graph signal representation to classify a pair of mental tasks using multi-channel EEG signals (MTMC-EEG) with cross subject classification within the database. Here, each channel of EEG signal corresponds to nodes of the task based graph whose EEG time series resides on the respective nodes. Functional connectivity of the brain between these nodes is obtained using smoothness constraint based Graph Signal Processing (GSP) technique. Graph spectral features namely, two-norm total variation of eigen vector (TNTV) corresponding to weighted adjacency matrix, graph Laplacian energy (GLE) using eigenvalues of Laplacian matrix and convex sum of TNTV and GLE in the form of joint total variation energy (JTVE) are proposed in this paper. The performance of the proposed methodology is evaluated on publicly available two different databases of MTMC EEG signals using benchmark classifiers and compared with the state of the art. Further, the superiority of the proposed metric obtained from the smoothened graph of GSP technique is validated by comparing it with Pearson correlation and Gaussian radial basis function (RBF) based functional connectivity in terms of accuracy, F-Score, and information transfer rate (ITR). The robustness of the proposed method is validated by adding white Gaussian noise (AWGN) to the EEG signals using different SNRs.

Experimental Implementation of Molecular Communication System using Sampling based Adaptive Threshold Variation Demodulation Algorithm

Abstract: In this paper, we consider a tabletop molecular communication (MC) system for exchange of information through flow assisted diffusion of ethanol chemical molecules. Designing modulation and demodulation algorithms for such setup is an important research problem. We propose a demodulation algorithm using Sampling based Adaptive Threshold Variation (S-ATV) for Pulse Position Modulation (PPM) and ON/OFF keying (OOK) based modulation techniques. The performance of BER with bit duration is plotted for proposed demodulation algorithm as well as Increase detection algorithm (IDA). It is found that the proposed S-ATV demodulation algorithm has better performance than IDA for smaller bit durations.

Weighted Vector Visibility based Graph Signal Processing (WVV-GSP) for Neural Decoding of Motor Imagery EEG signals

Abstract: The paper deals with weighted vector visibility-based graph signal processing (WVV-GSP) to decode motor imagery tasks from multi-channel EEG signals, which plays a key role in developing brain-computer interface (BCI) systems. Initially, multichannel EEG data at each time state is mapped into a vector which is defined as a node of the graph. Functional connectivity between different temporal states is determined by the visibility between the vector’s norm and the norm of its projections onto subsequent time states. To create a weighted vector visibility graph using the connections among the nodes, it presents edge weights between the nodes using Gaussian kernels. The MI task-based WVV graph is then used to obtain GSP-based spectral features of Laplacian energy (LE) and Fiedler vector energy (FE). On a publicly accessible Physio-net database, the performance of the suggested technique is evaluated with a subject-specific and subject-independent basis using an SVM classifier. Average subject-specific accuracy of 98.99% with AUC of 99.8% and subject independent accuracy of 95.8% with AUC 96.4% is achieved for decoding left and right hand MI tasks. Extensive comparative analysis with the current literature exhibits the efficacy of the proposed WVV-GSP-based method for the neural decoding of multichannel MI EEG signals.

Sub-band filtering in compressive domain

Abstract: This paper proposes a novel and computationally efficient method of compressive domain sub-band filtering. The technique utilizes the conjugate symmetry property of DFT matrix to achieve sub-band filtering of a wide-band multi-band input signal sensed by the Modulated Wide-band Converter (MWC) architecture. The proposed technique has flexibility of filtering single or multiple sub-bands of a wide-band input signal, which is sparse in frequency domain, without increase in additional computational cost. The filter bandwidth can also be controlled by increasing the number of channels in sensing architecture. The simulation result shown in this paper includes sub-band filtering of a frequency domain sparse multi-band input signal with or without noise. BER performance of a BPSK modulated signal after filtering and demodulation using the proposed technique is also presented to analyze the impact of sub-band filtering.

Performance Analysis of OSTBC in NOMA Assisted Downlink System with SIC Errors

Abstract: This paper investigates the error performance of the orthogonal space-time block coding (OSTBC) in non-orthogonal multiple access (NOMA) assisted downlink system in the presence of successive interference cancellation (SIC) errors. In the proposed system model, the base station communicates with multiple users to exploit the benefits of OSTBC in NOMA, like transmit diversity gain and low complexity. The system performance of the proposed OSTBC-NOMA system is analyzed in terms of the average symbol error rate (ASER) over the Rayleigh fading channel model. Further, in order to get a better insight to the system performance, the analysis is conducted over a high SNR regime to obtain diversity order and asymptotic ASER. The analytical results corroborated with Monte-Carlo simulations illustrate that the proposed OSTBC-NOMA system outperforms the conventional NOMA system over different combinations of the modulation scheme. Also, the power allocation coefficient’s impact on the system performance is numerically analyzed.

Nanorobots, NEMS, and Nanoassembly

Abstract: Author(s): Requicha, Ari | Abstract: Nanorobotics encompasses the design, fabrication, and programming of robots with overall dimensions below a few micrometers, and the programmable assembly of nanoscale objects. Nanorobots are quintessential nanoelectromechanical systems (NEMS) and raise all the important issues that must be addressed in NEMS design: sensing, actuation, control, communications, power, and interfacing across spatial scales and between the organic/inorganic and biotic/abiotic realms. Nanorobots are expected to have revolutionary applications in such areas as environmental monitoring and health care.This paper begins by discussing nanorobot construction, which is still at an embryonic stage. The emphasis is on nanomachines, an area which has seen a spate of rapid progress over the last few years. Nanoactuators will be essential components of future NEMS.The paper's focus then changes to nanoassembly by manipulation with scanning probe microscopes (SPMs), which is a relatively well established process for prototyping nanosystems. Prototyping of nanodevices and systems is important for design validation, parameter optimization and sensitivity studies. Nanomanipulation also has applications in repair and modification of nanostructures built by other means. High-throughput SPM manipulation may be achieved by using multitip arrays.Experimental results are presented which show that interactive SPM manipulation can be used to accurately and reliably position molecular-sized components. These can then be linked by chemical or physical means to form subassemblies, which in turn can be further manipulated. Applications in building wires, single-electron transistors, and nanowaveguides are presented.

Deep Common Spatial Pattern based Motor Imagery Classification with Improved Objective Function

Abstract: Survey/review study Deep Common Spatial Pattern Based Motor Imagery Classification with Improved Objective Function Nanxi Yu 1,2, Rui Yang 1, and Mengjie Huang 1,* 1 School of Electrical Engineering, Electronics & Computer Science, University of Liverpool, Liverpool, L69 3BX, United Kingdom 2 Department of Biostatistics, Graduate School of Arts and Sciences, Yale University, New Haven, CT 06511, United States * Correspondence: Mengjie.Huang@liverpool.ac.uk Received: 12 October 2022 Accepted: 28 November 2022 Published: 22 December 2022 Abstract: Common spatial pattern (CSP) technique has been very popular in terms of electroencephalogram (EEG) features extraction in motor imagery (MI)-based brain-computer interface (BCI). Through the simultaneous diagonalization of the covariance matrices, CSP intends to transform data into another mapping with data of different categories having maximal differences in their measures of dispersion. This paper shows the objective function realized by original CSP method could be inaccurate by regularizing the estimated spatial covariance matrix from EEG data by trace, leading to some flaws in the features to be extracted. In order to deal with this problem, a novel deep CSP (DCSP) model with optimal objective function is proposed in this paper. The benefits of the proposed DCSP method over original CSP method are verified with experiments on two EEG based MI datasets where the classification accuracy is effectively improved.

Understanding and embracing the complexities of the molecular communication channel in liquids

Abstract: Molecular communication has recently gained a lot of interest due to its potential to enable micro-implants to communicate by releasing molecules into the bloodstream. In this paper, we aim to explore the molecular communication channel through theoretical and empirical modeling in order to achieve a better understanding of its characteristics, which tend to be more complex in practice than traditional wireless and wired channels. Our study reveals two key new characteristics that have been overlooked by past work. Specifically, the molecular communication channel exhibits non-causal inter-symbol-interference and a long delay spread, that extends beyond the channel coherence time, which limit decoding performance. To address this, we design, μ-Link a molecular communication protocol and decoder that accounts for these new insights. We build a testbed to experimentally validate our findings and show that μ-Link can improve the achievable data rates with significantly lower bit error rates.

Molecular Signal Tracking and Detection Methods in Fluid Dynamic Channels

Abstract: This method and data paper sets out the macro-scale experimental techniques to acquire fluid dynamic knowledge to inform molecular communication performance and design. Fluid dynamic experiments capture latent features that allow the receiver to detect coherent signal structures and infer transmitted parameters for optimal decoding. This paper reviews two powerful fluid dynamical measurement methodologies that can be applied beneficially in the context of molecular signal tracking and detection techniques. The two methods reviewed are Particle Image Velocimetry (PIV) and Planar Laser-Induced Fluorescence (PLIF). Step-by-step procedures for these techniques are outlined as well as comparative evaluation in terms of performance accuracy and practical complexity is offered. The relevant data is available on IEEE DataPort to help in better understanding of these methods.

Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis

Abstract: Electroencephalogram (EEG) signals are effective tools towards seizure analysis where one of the most important challenges is accurate detection of seizure events and brain regions in which seizure happens or initiates. However, all existing machine learning-based algorithms for seizure analysis require access to the labeled seizure data while acquiring labeled data is very labor intensive, expensive, as well as clinicians dependent given the subjective nature of the visual qualitative interpretation of EEG signals. In this paper, we propose to detect seizure channels and clips in a self-supervised manner where no access to the seizure data is needed. The proposed method considers local structural and contextual information embedded in EEG graphs by employing positive and negative sub-graphs. We train our method through minimizing contrastive and generative losses. The employ of local EEG sub-graphs makes the algorithm an appropriate choice when accessing to the all EEG channels is impossible due to complications such as skull fractures. We conduct an extensive set of experiments on the largest seizure dataset and demonstrate that our proposed framework outperforms the state-of-the-art methods in the EEG-based seizure study. The proposed method is the only study that requires no access to the seizure data in its training phase, yet establishes a new state-of-the-art to the field, and outperforms all related supervised methods.