Showing papers by "Sarat Kumar Patra published in 2021"

First-principles investigation of F-functionalized ZGNR/AGNR for nanoscale interconnect applications

Abstract: Zigzag and armchair graphene nanoribbons (ZGNR and AGNR) have been investigated using the density functional theory (DFT) and nonequilibrium Green’s function (NEGF) framework. Based on binding energy calculations, both-edge-F-functionalized ZGNR emerges as the most thermostatically and energetically stable among various ZGNR and AGNR configurations. The band structures and density of states (DOS) reveal that all the examined configurations of ZGNR exhibit metallic behavior. The I–V characteristics of both-edge-F-functionalized ZGNR shows pure linear behavior among all the configurations of ZGNR and AGNR. For interconnect modeling, the small-signal dynamic performance parameters RBq, CBq, and Lkq are calculated using the standard two-probe model. Furthermore, both-edge-F-functionalized ZGNR shows lower values of CBq (98.07pF/cm), Lkq (45.38nH/ $$\mu $$ m) and quantum delay (42.17 $$\mu $$ s) due to the higher Fermi velocity. The impact of variation of the contact length and ribbon length on the both-edge-F-functionalized ZGNR interconnect model is also presented. F-functionalized ZGNR is a potential candidate for use in future low-power nanoscale high-speed interconnect applications.

A user independent hand gesture recognition system using deep CNN feature fusion and machine learning technique

Abstract: Hand gesture recognition is one of the active research areas in the field of human-computer interface due to its flexibility and user friendliness The gesture recognition technique is used to develop a system that can be used to convey information among disabled people or for controlling a device Major challenges for the development of an efficient hand gesture recognition technique are illumination variation, nonuniform backgrounds, diversities in the size and shape of a user's hand, and high interclass similarities between hand gesture poses In this chapter, a user independent static hand gesture recognition technique is analyzed using handcrafted features such as a histogram of oriented gradients (HOG) and a deep convolutional neural network (CNN) The deep features are extracted from fully connected layers of two different well-known pretrained CNNs such as AlexNet and VGG-16 The fusion of feature vectors extracted from different fully connected layers of both the CNNs is proposed for the enhancement of gesture recognition accuracy The proposed CNN-based feature does not require any hand segmentation or background subtraction technique to segment the hand region from the input image A support vector machine as a machine learning algorithm is used to classify the gesture poses A comparison of the HOG feature and the deep CNN feature is presented for the recognition of static hand gesture poses The proposed deep CNN feature fusion-based hand gesture recognition is robust to illumination variation, nonuniform backgrounds, and interclass similarities The performance of the proposed technique is evaluated using leave-one-subject-out cross-validation (LOO CV) and holdout CV tests The extensive analysis is performed on three benchmark static hand gesture datasets with uniform and nonuniform backgrounds on both the CV tests A significant improvement of user independent static hand gesture recognition performance using the LOO CV test is found using the proposed technique The experimental results show that the proposed technique is superior as compared to individual deep CNN features and state-of-the-art techniques A real-time application of the gesture recognition system is developed and tested using the proposed technique

Fe-functionalized zigzag GaN nanoribbon for nanoscale spintronic/interconnect applications

Abstract: The paper investigated the Zigzag GaN nanoribbons (ZGaNNR) using the density functional theory(DFT) and non equilibrium Green’s function(NEGF) framework. We have calculated the structural, electronic and transport properties of various Fe-ZGaNNR configurations. Based on the binding energy( $$E_{B}$$ ) calculations, Fe-doped@Ga_edge ZGaNNR(-6.51eV) is observed to be most structurally stable among different configurations. Our findings show the substitutional Fe passivation provides a stable bonding as compared to pristine configuration. The magnetic moment of different configurations depends upon the position of Fe atom. The discontinuity is observed in degenerative states of spin modes and same is follows by their respective density of states(DOS) and projected density of states(PDOS). Fe-termination@N_edge ZGaNNR is found to be a strong candidate for magnetic stabilization. High metallicity is observed in Fe-termination@both_the_edges ZGaNNR configuration. Further same is verified through current-voltage characteristics as current follow the pure linear behaviour. The practical application of the work on ZGaNNR can serve as a potential candidate for future low bias nanoscale spitronic devices and low power high speed interconnect applications.

Feature Engineering for Machine Learning and Deep Learning Assisted Wireless Communication

Abstract: Feature engineering involves extracting information from raw-data to use in machine learning or deep learning algorithms through feature transformation, feature generation or feature extraction, feature construction, feature selection, etc. Feature engineering optimizes the feature space dimensions, thereby reducing complexity. Hence, makes the data suitable for machine learning or deep learning applications: process prediction, detection accuracy, estimation accuracy, and quality of clustering and classi cation. Traditionally machine learning algorithms have been used in various wireless communication appli cations viz spectrum access and sharing, resource allocation, spectrum coverage, capacity optimization, signal intelligence, etc. This chapter presents the need for feature engineering and its application to machine learning and deep learning in wireless communication. The chapter also gives the details of the feature engineering assisted machine-learning algorithm for automatic modulation classi cation and path-loss prediction in wireless communication.

Massive MIMO in cooperative network with multiple relay under imperfect CSI

Abstract: This paper considers a cooperative network, where a base station (BS) has a very large number of antenna and multiple relays assist a large number of the mobile station (MS) in imperfect channel co...