# ABV-Indian Institute of Information Technology and Management Gwalior

• Gwalior, MP, India
Recent publications
The development of robust nano-electronic devices, rely on efficient nano-interconnects to minimize the delay and Schottky barrier issues. Here, we performed first-principles investigations to study the sp2/sp3 fluorine functionalized armchair graphene nanoribbons (AGNR) for nanoscale interconnects. The combinations of sp2/sp3 functionalization were investigated in six different possibilities having 25% to 100%sp3 functionalization. It is revealed that all considered configurations are energetically favorable on the basis of binding energy analysis. In addition, a few of the considered structures exhibit size independent metallic behavior whereas the remaining ones are found to be semiconductor with a band gap depending upon their widths. Further, transport calculations reveal that 25%sp3 functionalization exhibits a negative differential resistance while an Ohmic I–V characteristics has been obtained at low bias for 50% and 75%sp3 functionalization. Present investigations confirm that 75%sp3 functionalized edges with highest Fermi velocity, can serve as potential interconnect material in future organic nano-electronic devices.
In today’s real-world scenarios’ of computer vision applications, enhancing low-resolution (LR) facial images corrupted with unwanted noise effects is very challenging as the uneven noise distribution severely distorts these images’ local structure. This paper proposes a novel noise-robust face super-resolution (SR) method, namely structural similarity-based Bi-representation SR (SS-BRSR), to tackle this problem. It firstly estimates the true noise level in the corrupted LR face through the novel noise-level estimation algorithm. Afterward, it employs a robust deep-convolutional neural network, namely DnCNN, to separate the pixel-wise noise from the noisy LR face image. This network produces two outputs: (i) a residual image and (ii) a smooth LR face image. We utilize the first output for pixel-wise updating the entire LR training images, making the structural similarity between the test and the training LR images. Further, for SR reconstruction, the SS-BRSR consists of two patch representation components that individually reconstruct the HR faces corresponding to the initial noisy LR and smooth LR face images. Besides, in both the components, the Gradient and Laplacian features-based learning scheme is incorporated to preserve the discriminative facial features in the SR reconstruction. Here, the first component substantially minimizes the reconstruction error due to noise, and the second component compensates for the lost detail in the LR face image. The target HR face image is restored by taking the appropriate proportions of obtained HR face images from each component. The experimental results on different face datasets justify the SS-BRSR method’s superiority over the state-of-the-art face SR methods. For instance, the quantitative performance (in terms of PNSR and SSIM) of the proposed method over the state-of-the-art RLENR and DFDNet methods gained an improvement of [1%, 1.5%, 2.5%, 2.5%] under [10, 15, 20, 30] noise-level densities, and [1%, 1.5%, 2%, 1.5%] under [10, 15, 20, 30] noise-level densities, respectively, for the standard CelebA and FEI datasets.
It is well known that water-borne diseases occur seasonally and are strongly associated with the temperature. In this paper, a non-autonomous mathematical model for water-borne disease is proposed and analyzed. Three temperature-dependent parameters are included in the proposed model, related to the growth rate and death rate of pathogen in aquatic environment, and disease transmission rate from pathogen to human. In this paper, a threshold condition in terms of RC(t) is obtained to account for the extinction or the persistence of the disease. The impact of temperature variability on the spread of disease over the study region is also shown from the analysis of the temperature-dependent threshold RC(t). It is also shown that the proposed non-autonomous system has a non-trivial disease-free periodic state which is globally asymptotically stable whenever RC(t)<1. The autonomous version of the proposed model is also analyzed. Results show that the autonomous system is locally and globally asymptotically stable for the disease-free state. The endemic equilibrium of the autonomous model in terms of R0 is also derived, and it is shown that the endemic solution for the autonomous system is globally asymptotically stable. Numerical simulation has been carried out to illustrate our theoretical results and also predict the effectiveness of the control strategies.
Filters plays vital role in digital signal processing for filtering the signal and get filtered signal from the noisy signal Measured signal is damaged by noise and this signal cannot be utilized for analysis and for this reason it is required to restore it. Noise source was not detected. Therefore, the only conceivable solution is to filter the observed signal. We designed the IIR and FIR filter combined circuit after that we add random noise in the signal and in MATLAB software we get filtered signal from the noisy signal and we get the desired result by MATLAB software in simulation environment in the lab.
This chapter focuses on the division and location of brain deformities such as tumors in magnetic resonance imaging (MRI) through Chan-Vese active contour segmentation. Brain tumor division and identification is a major test in the area of biomedical picture processing. To detect the size and location of the tumor, various techniques are available, but active contour gives accurate knowledge of the region for segmentation. Chan-Vese Active contour method provides independent, robust and more flexible segmentation. In this chapter, firstly we used preprocessing technique in which noise and unused parts of the brain and skull are removed, for this we proposed the skull stripping method. Then, we applied feature extraction to enhance the image intensity and quality, and lastly, used Chan-Vese active contour with a level set image segmentation technique to detect the tumor. The tumor area was calculated after tumor detection.KeywordsBrain tumorMRIPreprocessingSkull strippingFeature extractionActive contour segmentation
This paper investigates Dynamic Spectrum Access (DSA) paradigm with imperfect feedback for multiuser wireless network. Each user selects an orthogonal channel in particular time slot to transmit packet with a certain transmission probability. In next time slot, the user who has transmitted a packet receives an ACK signal based on local observation. Bearing in mind the dynamic nature of wireless networks, it is appealing to develop a blended strategy to perform effective DSA. This paper aims to design a distributed Deep Reinforcement Learning (DRL) based scheme with an objective of maximizing network utility. In conventional DRL framework, it is assumed that the feedback received (ACK packet) is always correct but in wireless networks, it may be lost or corrupted due to noise. Furthermore, it is challenging to promise that the multiple agents will cooperate to make coherent decisions in order to accomplish the same objective, particularly under imperfect feedback. To tackle these challenges, this work proposes (i) Deep Recurrent Reinforcement Learning network with integrated GRU layer to optimize the network utility function and (ii) a feedback recovery mechanism using complete and incomplete replay buffers. Extensive simulations corroborate the success of proposed scheme in complex multiuser scenario and exhibit robustness against the detrimental effects of the imperfect feedback.
In the present scenario, Machine Learning techniques are used in much ongoing research as a powerful tool. This paper proposes the applications of machine learning in antenna design optimization by implementing different machine learning algorithms like KNN, ANN, Random Forest, XGBoost and Decision Tree. A Double ring Cylindrical Di-electric Resonator Antenna is designed using High-Frequency Structure Simulator (HFSS). For the proposed antenna design, the frequency range is 2–3.5 GHz, while the range of height and radius is 6.5–19.5 mm and 12–18 mm respectively. The data set is generated for the proposed antenna design and S11 parameter is optimized using machine learning algorithms. Out of the five algorithms, the models for KNN, XGBoost and Artificial Neural Network perform almost similarly and Random Forest has the highest performance.
Hardware Trojan (HT) intrusion at different integrated circuit (IC) phases is the most important concern for the semiconductor industries. Recently, machine learning (ML) models have been used to detect HT from the pre-silicon IC phase, which utilizes either structural or SCOAP gate level netlist features. However, the main concern is that an adversary may poison the training dataset by flipping the target labels to malign the ML model training, which further provides an incorrect prediction on the test dataset. Thus, due to the malicious training of ML models, the Trojan-inserted ICs are missed out and can easily perform their malicious activities. Hence, it is of utmost importance to scan the training dataset and identify the poisoned input samples before applying ML models for HT detection. Therefore, this paper proposes a new technique that first identifies the poisoned training samples, which consist of SCOAP features, and then detects HTs from the unseen gate-level netlist. The proposed technique employs a robust ensemble Categorical Boosting (CatBoost) model, which avoids the problem of target leakage by using the concept of ordered boosting. Further, a label flipping poisoning attack based on a stochastic hill-climbing search is proposed, which flips the labels of the handful of samples that maximizes the validation dataset loss by deteriorating the model performance. Moreover, a defense method is proposed which utilizes CatBoost object importance and k-nearest neighbor to detect malicious training samples and restore their original labels. Finally, the CatBoost model is trained on the clean dataset to detect the HT nets from the unseen gate-level netlist accurately. Experimental results shows that the proposed attack method increases the on-an-average loss up to 58%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$58\%$$\end{document} and 54%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$54\%$$\end{document} on Trust-Hub and DeTrust benchmarks. Whereas the proposed defense method accurately identifies the poisoned input labels from the training dataset with on-an-average 99%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$99\%$$\end{document} accuracy on these benchmarks.
In the modern era, the Internet of Things (IoT) plays a vital role in connecting physical objects to the internet. Therefore, an IoT technology can monitor and control any physical object from remote locations. In IoT-based systems, Wireless Sensor Network (WSN) is used to monitor a large number of heterogeneous physical objects. The IoT-enabled WSNs suffer from congestion problems that significantly reduce the network lifetime and performance of the network. This paper present a green hybrid congestion control mechanism for IoT-enabled WSNs. It uses an unequal clustering mechanism which saves the energy of battery-limited sensor nodes and resolves energy hole issues. Furthermore, a novel two-class priority-based congestion avoidance mechanism is proposed which significantly reduces communication delay. Extensive simulations and real-life experiments are performed to demonstrate the effectiveness of the proposed scheme. Experimental result shows that the proposed scheme outperforms the state-of-the-art algorithms in terms of network lifetime, successful packet delivery, average throughput, and other parameters. Furthermore, the proposed scheme is also tested in a real testbed that demonstrates its effectiveness for disaster management in smart city applications.
The ever-increasing number of wireless users (or devices) and their varied demand require the need for an advanced architecture for the future wireless network. To support massive connectivity, non-orthogonal multiple access (NOMA) has been recognized as a promising solution. NOMA increases the number of simultaneous connections using available resources for users with varying demands. Furthermore, recent measurements and experiments suggest that wide underutilized bandwidth available at millimeter-wave (mmWave) frequencies provide high data rate and therefore are capable of addressing the issue of spectrum scarcity at sub-6 GHz bands utilized by the 4G network. Consequently, co-existence of multi-radio access technologies (RATs) for 5G and beyond networks has been of interest to both industries and academia. In this context, this work studies the co-existence of the two RATs, namely, sub-6 GHz and mmWave communication using NOMA-enabled hybrid heterogeneous network (NOMA-HHN) for massive connectivity. The application of NOMA requires ordering users, which in turn requires the knowledge of users' channel state information (CSI). However, gathering and processing CSI of such a large number of users is difficult to implement in practice. Thus, a solution based on partial CSI is proposed. Additionally, a feedback scheme for user scheduling and RAT selection using dual association is proposed to reduce the initial access delay in beam-training at the mmWave network. Moreover, utilizing directional nature of the mmWave communication, random beamforming is used to reduce system overhead in a network with massive users. The analytical results are confirmed using Monte-Carlo simulation, and various significant advantages are noted for the proposed NOMA-HHN over existing architectures.
Multimodal interaction enables multiple modes for users to interact with the system. Whereas the multimodal interaction with IoT applications depends on multimedia systems’ input/output which may limit their scalability and expressiveness. Furthermore, multimodal interaction with IoT applications could also be limited by input/output integration, security, privacy, and portability issues. This special issue focuses on recent state-of-the-art advances in multimodal interaction and IoT applications that could address these challenges. We aim at bringing together the latest industrial and academic progress, research, and development efforts within the rapidly maturing multimodal interaction and IoT applications. This article summarizes the research contribution of the accepted papers (27.94% acceptance rate) along with possible future directions emanating from these papers.
In this article, a four-port MIMO (Multiple Input Multiple Output) Dielectric Resonator Antenna (DRA) integrated cognitive radio (CR) concept is designed and analyzed. It is the first time when CR concept is integrated with a dielectric resonator-based MIMO antenna. The use of a multipurpose reconfigurable filter design within the feeding structure makes it suitable for both interweave and underlay operations of CR. The proposed antenna is designed over an FR4 substrate. After doing the rigorous simulation process, it is confirmed that the DR-based MIMO antenna (when the feeding structure behaves as all-pass filters) operates over a wide frequency range i.e., 2.5-5.8 GHz. In the case of interweave operation; the proposed antenna shows narrow pass band tunability between 3.2-4.3 GHz. In the case of underlay operation; it shows band rejection tunability between 2.5-4.5 GHz. The proposed antenna can be efficiently used for a Sub-6.0 GHz 5G Communication system.
The grey-wolf optimizer (GWO) is a comparatively recent and competent algorithm in Swarm Intelligence (SI) to solve numerical and real-world optimization problems. However, the biggest challenge is the quick stabilization of its search agents to the local optima. Therefore, to bring effectiveness in the global search, it is imperative to relocate the leading agents through the procreation of their positions in the search space. This paper proposes GL-GWO, a genetic learning (GL)-based GWO, which imitates the genetic offspring generation scheme to improve the intelligence of GWO’s leading agents. The GL scheme expedites the global effectiveness of leading agents by constructing the exemplars for them through genetic operators using their historical information. The obtained exemplars are well diversified and highly intelligent; therefore, the rest of the population’s global searchability and search efficiency are enhanced under their guidance. The GL-GWO is tested on widely adopted 20 benchmark functions from the IEEE-CEC-2005 dataset and 38 functions from the IEEE-CEC-2014 dataset. The efficacy of GL-GWO is tested on four real-world engineering problems, namely recommendation systems, face image super-resolution, tension/compression spring, and welded beam. The obtained results on benchmark functions and considered engineering problems conclude that the GL-GWO is an efficient, effective, and reliable algorithm for solving real-world optimization problems.
This article shows how the international nuclear disaster in Fukushima affected the antinuclear movement in Koodankulam by using the cross-national diffusion model proposed by Kriesi, Koopmans, Duyvendak and Giugni (1995) . It examines the impact of the international disaster on the antinuclear movement and its subsequent expansion in terms of protest events and organizational trajectories. It also describes the new participants and actors in this antinuclear power issue. The research questions are addressed through archives, handbills, unpublished documents, and semi-structured interviews. I argue that diffusion of information and domestic opportunities helped the antinuclear groups erect a protest camp that offered manufactured vulnerability. This induced several meso and micro level social movement organizations and political parties to join the antinuclear movement, leading to expansion at the organizational level and the formation of coalitions. Further, the participation of newly joined social movement organizations and political parties in the mobilization helped the movement expand its protest events and led to an increase in the level of contention. The study contributes to the study of antinuclear movements and cross-national diffusion.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.
925 members
• Computer Science and Engineering
• Computational Nanoscience and Technology Laboratory
• Behavioural Economics Experients and Analytics Labartory
• Information and Communications Technology (ICT)
• M.Tech Program in Information Communication Technology (ICT)
Information