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TechRxiv 2022年

作者： Singh, Pushpendra Singhal, Amit Fatimah, Binish Gupta, Anubha The Department of Electronics and Communication Engineering National Institute of Technology Hamirpur HP Hamirpur177005 India The Department of Electronics & Communication Engineering Netaji Subhas University of Technology Delhi India The Department of Electronics & Communication Engineering CMR Institute of Technology Bengaluru India The Department of Electronics and Communication Engineering IIIT-Delhi Delhi110020 India

This work presents a novel perfect reconstruction filter bank decomposition (PRFBD) for nonlinear and nonstationary time series and image data representation and analysis. The Fourier decomposition method (FDM), an adaptive approach wholly based on the Fourier representation, is shown to be a special case of the proposed PRFBD. The adaptive Fourier–Gauss decomposition (FGD) proposed in this work is a variation of the FDM, which is based on the FR and Gaussian filtering. Similarly, we also consider Butterworth filtering to develop adaptive Fourier–Butterworth decomposition (FBD). The proposed theory can decompose any signal (time series, image, or other data) into a set of the desired number of Fourier intrinsic band functions (FIBFs), which follow the amplitude-modulation and frequency-modulation (AM-FM) representations. A generic filterbank representation is also provided, where perfect reconstruction can be ensured for any given set of lowpass or highpass filters. We have performed an extensive analysis of both simulated and real-life data (COVID-19 pandemic, Earthquake and Gravitational waves) to demonstrate the efficacy of the proposed method. The resolution results in the time-frequency representation demonstrate that the proposed method is more promising than the state-of-the-art approaches. © 2022, CC BY-NC-SA.

关键词： COVID-19

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A Probabilistic Routing-Based Secure Approach for Opportunistic IoT Network Using Blockchain 17

A Probabilistic Routing-Based Secure Approach for Opportunis...

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17th IEEE India Council International Conference, INDICON 2020

作者： Rani, Poonam Balyan, Arnav Jain, Vibha Sangwan, Devesh Singh, Pushpinder Pal Shokeen, Jyoti Netaji Subhas University of Technology Department of Computer Engineering New Delhi India Uiet Maharshi Dayanand University Department of Computer Science and Engg Rohtak Haryana India

ISBN: (纸本)9781728169163

With expanding applications of the opportunistic Internet of Things (IoT), there has been ever-increasing enthusiasm in integrating blockchain with the IoT for enhancing privacy and security. However, due to high resource consumption by the blockchain, its implementation becomes challenging task for IoT networks. A traditional opportunistic IoT network sends multiple copies of messages for communicating information from source to destination, which gives rise to two risky situations: communication overhead and network security. Therefore, this paper proposes a probabilistic-based blockchain algorithm for opportunistic IoT network. The algorithm presents a secure routing protocol based on the delivery predictability of messages by integrating blockchain to ensure security and privacy. The simulation result shows that our proposed protocol outperforms some traditional routing schemes like epidemic routing and PRoPHET (Probabilistic Routing Protocol using History of Encounters and Transitivity) routing with more secure transmission. © 2020 IEEE.

关键词： Routing protocols

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On the convergence of Fourier representations and Schwartz distributions

Franklin Open

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Franklin Open 2024年 8卷

作者： Singh, Pushpendra Singhal, Amit Fatimah, Binish Gupta, Anubha Joshi, Shiv Dutt School of Engineering Jawaharlal Nehru University Delhi India Department of Electronics and Communication Engineering National Institute of Technology Hamirpur Hamirpur (HP) 177005 India Department of Electronics & Communication Engineering Netaji Subhas University of Technology Delhi India MaxEye Technologies Private Ltd Bengaluru India Department of Electronics and Communication Engineering IIIT-Delhi Delhi 110020 India Department of EE IIT Delhi Delhi India

While Fourier theory remains a cornerstone for analyzing and interpreting the spectral content of diverse signals, its limitations often surface in practical applications. Notably, popular signals like sinusoids, Dirac deltas, signums, and unit steps lack convergent Fourier representations within the conventional framework. This discrepancy necessitates distribution theory to build and interpret suitable representations for such signals. However, existing literature in signal processing and communication engineering often glosses over these intricacies, leaving researchers grappling with obscure concepts regarding the very existence of Fourier representations for non-conforming signals. This work bridges this critical gap by offering a comprehensive exploration of the conditions guaranteeing the existence of Fourier representations. We introduce a novel linear space – the Gauss–Schwartz (GS) function space – and its corresponding class of tempered superexponential (TSE) distributions. We demonstrate that the Fourier transform (FT) acts as an isomorphism on the GS space of test functions and, by duality, on TSE distributions. Crucially, the GS space proves to be minimal in the sense that its dual, encompassing TSE distributions, represents the largest possible linear space over which the FT can be defined through duality. This theoretical advancement signifies a twofold contribution. Firstly, it clarifies the existence and interpretation of Fourier representations for prevalent signals that evade conventional analysis. Secondly, the introduction of the GS-TSE framework expands the reach of the FT to encompass a broader spectrum of functions, enabling novel applications in diverse fields. Ultimately, this work paves the way for a more robust and accessible understanding of Fourier analysis, empowering researchers and practitioners to leverage its full potential in exploring and processing a wider range of signals. © 2024 The Author(s)

关键词： Fourier series Fourier transform Gauss functions Schwartz distributions Tempered superexponential distribution

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Human Detecting Sensors and End-To-End Security Model for Design and Manufacturing of IoT-Based Disinfectant Sanitizer Tunnel: An Innovation Against Covid-19

Human Detecting Sensors and End-To-End Security Model for De...

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International Conference on Innovations in Cyber Physical Systems, ICICPS 2020

作者： Panda, Ramesh Chandra Chakraborty, Rajdeep Choudhury, Tanupriya Mathivanan, K.E. Chakraborty, Sudeshna Synergy Institute of Technology Bhubaneswar Odisha India Netaji Subhash Engineering College Kolkata West Bengal India Dehradun India Madras Institute of Technology Chennai Tamilnadu India Department of Computer Science Engineering Sharda University Greater Noida India

ISBN: (纸本)9789811641480

COVID-19 pandemic affected many people world-wide. It has become a global issue. The major priority of the scientific community is to control this pandemic. The infection can spread through various ways, mainly by touching a contaminated or infected surface or person-to-person contact (droplets from the oral and nasal passage from an infected person). Increase in the rate of infection can by caused by inadequate hygiene practices and cleanliness. The virus is active up to few hours on various surfaces and time may vary according to the nature of the surface. The virus can continue to spread rapidly by contact surfaces and air. Prevention of further viral spread can be attained by effective disinfection of surface and air. It is suggested that sanitizers (0.1% sodium hypochlorite) can effectively arrest COVID-19 virus within a minute. This paper describes a strong IoT-based disinfectant tunnel which is used to disinfect external surface of objects, clothes, or even human skin to provide protection against COVID-19 in social or public places such as malls, schools, hospitals, airports, and offices. Primarily focusing on the significance, structural design, and functioning of the tunnel. To make the tunnel cost-effective and sustainable, solar panels and steel rods have been used. The disinfectant tunnel is highly efficient as it offers no contact disinfection or sanitization. This sensitization tunnel must detect human to sanitize, this paper illustrates a novel approach of using sensors for human detection, and since this project is based on IoT as backbone, so security model must be there to guard against malicious user. This paper also illustrates end-to-end security model. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Reinforcement Learning Based Congestion Control Mechanism for Opportunistic Networks

Reinforcement Learning Based Congestion Control Mechanism fo...

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IEEE Globecom Workshops

作者： Jagdeep Singh Sanjay Kumar Dhurandher Isaac Woungang Periklis Chatzimisios Joel J. P. C. Rodrigues Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal Punjab India Department of Information Technology Netaji Subhas University of Technology New Delhi India Department of Computer Science Ryerson University Toronto ON Canada International Hellenic University Thessaloniki Greece College of Computer Science and Technology China University of Petroleum (East China) Qingdao China Instituto de Telecomunicações Covilhã Portugal

ISBN: (纸本)9781665459761

This work proposes a Reinforcement Learning-based Congestion Control protocol (called (RLCC)) for Opportunistic Networks to find the optimal number of message counts based on the real-time density in the network. (RLCC) jointly uses Q-learning and fuzzy logic to make the routing decision based on real attributes such as social status, centrality, activeness, message lifetime, hop count, and battery status. In the proposed (RLCC) scheme, the message priority is required to maintain the optimal count of messages in the network, and the fuzzy inference rules perform well in predicting the best hop for message transmission. All network nodes get inputs from the environment and take action accordingly. If the message is transferred successfully to the intended node, then the node receives the reward for the action, otherwise, the penalty will be assigned. Based on this, network nodes only select those nodes, which are capable to transmit the message from one node to another node. Simulation results demonstrate that RLCC is superior to the MARLCC and F-GSAF routing protocols using the infocom2006 real mobility data trace, in terms of delivery probability, latency, and overhead ratio.

关键词： Fuzzy logic Measurement Q-learning Mobility models Simulation Routing Routing protocols

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A RSA-Biometric Based User Authentication Scheme for Smart Homes Using Smartphones 34th

A RSA-Biometric Based User Authentication Scheme for Smart H...

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34th International Conference on Advanced Information Networking and Applications, AINA 2020

作者： Bagha, Amir Mohammadi Woungang, Isaac Dhurandher, Sanjay Kumar Traore, Issa Department of Computer Science Ryerson University Toronto Canada Department of Information Technology Netaji Subhas University of Technology New Delhi India Department of Electrical and Computer Engineering University of Victoria VictoriaBC Canada

ISBN: (纸本)9783030440404

In the recent years, the Internet of Things (IoT) has become one of the leading technologies that allow the mainstreaming of smart homes and smart cities by creating a communication system for physical objects over the Internet. In a smart home, devices are not necessarily homogeneous in terms of topology, security protocols, computational power and communication. This nature of the devices causes some incompatibilities with conventional authentication methods and the security requirements of IoT standards. This paper proposes a RSA-Biometric based user Authentication Scheme for Smart Homes using Smartphones (denoted RSA-B-ASH-S). A formal security analysis of the proposed scheme using the Burrows-Abadi-Needham (BAN) logic is provided, showing that it achieves perfect forward secrecy by utilizing a fresh encryption key for each session. The performance evaluation of the proposed scheme in terms of computational time, storage requirements, and communication overhead, is also presented. © 2020, Springer Nature Switzerland AG.

关键词： Biometrics

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A Guided Learning Approach for Generative Adversarial Networks

A Guided Learning Approach for Generative Adversarial Networ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Sidhant Nagpal Siddharth Verma Shikhar Gupta Swati Aggarwal Department of Computer Engineering Netaji Subhas Institute of Technology Delhi India

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In this paper, we propose a novel technique for training Generative Adversarial Networks (GANs) using autoencoders. GANs, in recent years, have emerged as one of the most popular generative models. Despite their success, there are several challenges in maintaining the trade-off between diversity and quality of the generated distribution. Our idea stems from the fact that deeper layers of an autoencoder contain high-level feature representation of the input data distribution. Reusing these layers provides GAN with information about the representative characteristics of real data and hence can guide its adversarial training. We call our model Guided GAN since the autoencoder (guiding network) provides a direction to train the GAN (generative network). Guided GAN also minimizes both the forward and reverse Kullback-Leibler (KL) divergence in a single model, exploiting the complementary statistical properties of the two. We conduct extensive experiments and use various metrics for assessing the quality, diversity of generated images and convergence of the model. Our model is evaluated on two standard datasets: CIFAR-10 and CelebA demonstrating either superior or competitive performance compared to baseline GANs, especially in the earlier training stages. Our guided training procedure has been tested on different baseline GANs without any changes to their hyper-parameter configuration or architecture.

关键词： Gallium nitride Training Generators Decoding Generative adversarial networks Convolution Data models

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A Trend Analysis of Significant Topics Over Time in Machine Learning Research

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SN computer Science 2021年第6期2卷 469页

作者： Sharma, Deepak Kumar, Bijendra Chand, Satish Shah, Rajiv Ratn Department of Computer Engineering Netaji Subash Institute of Technology University of Delhi Sector-3 Dwarka New Delhi 110078 India School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi 110067 India Multimodal Digital Media Analysis Lab Indraprastha Institute of Information Technology New Delhi 110020 India

A vast number of research papers on numerous topics publish every year in different conferences and journals. Thus, it is difficult for new researchers to identify research problems and topics manually, which research community is currently focusing on. Since such research problems and topics help researchers to be updated with new topics in research, it is essential to know trends in research based on topic significance over time. Therefore, in this paper, we propose a method to identify the trends in machine learning research based on significant topics over time automatically. Specifically, we apply a topic coherence model with latent Dirichlet allocation (LDA) to evaluate the optimal number of topics and significant topics for a dataset. The LDA model results in topic proportion over documents where each topic has its probability (i.e., topic weight) related to each document. Subsequently, the topic weights are processed to compute average topic weights per year, trend analysis using rolling mean, topic prevalence per year, and topic proportion per journal title. To evaluate our method, we prepare a new dataset comprising of 21,906 scientific research articles from top six journals in the area of machine learning published from 1988 to 2017. Extensive experimental results on the dataset demonstrate that our technique is efficient, and can help upcoming researchers to explore the research trends and topics in different research areas, say machine learning. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Information retrieval Latent Dirichlet allocation Machine learning Research trend analysis

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Tools and Techniques for Teaching computer Programming: A Review

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Journal of Educational technology Systems 2020年第2期49卷 170 - 198页

作者： Kanika Shampa Chakraverty Pinaki Chakraborty Department of Computer Science and Engineering Netaji Subhas University of Technology New Delhi India

Courses on computer programming are included in the curricula of almost all engineering disciplines. We surveyed the research literature and identified the techniques that are commonly used by instructors for teaching these courses. We observed that visual programming and game-based learning can enhance computational thinking and problem-solving skills in students and may be used to introduce them to programming. Robot programming may be used to attract students to programming, but the success of this technique is subjected to the availability of robots. Pair and collaborative programming allows students to learn from one another and write efficient programs. Assessment systems help instructors in evaluating programs written by students and provide them with timely feedback. Furthermore, an analysis of citations showed that Scratch is the most researched tool for teaching programming. We discuss how these techniques may be used to teach introductory courses, advanced courses, and massive open online courses on programming.

关键词： computer science education visual programming game-based learning pair programming collaborative learning robot programming assessment system

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Correction to: Automatic detection of osteosarcoma based on integrated features and feature selection using binary arithmetic optimization algorithm

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Multimedia Tools and Applications 2023年第26期82卷 41433-41433页

作者： Bansal, Priti Gehlot, Kshitiz Singhal, Abhishek Gupta, Abhishek Department of Information Technology Netaji Subhas University of Technology New Delhi India Department of Information Technology Netaji Subhas Institute of Technology New Delhi India School of Computer Science and Engineering Shri Mata Vaishno Devi University Jammu and Kashmir India

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