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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Pokkuluri, Kiran Sree Soni, Nidhi Sahu, Amit Mukherjee, Tilak Prasanthi, Y. Sharma, Disha Shri Vishnu Engineering College for Women Department of Computer Science and Engineering Bhimavaram India Ies Institute of Technology and Management Ies University Department of Computer Science & Engineering M.P Bhopal462044 India Gyan Ganga Institute of Technology & Sciences Dept of Cse Jabalpur India Haldia Institute of Technology Electronics and Communication Engg. Dept West Bengal Haldia721657 India Kkr & Ksr Institute of Technology and Sciences Dept of CSE-Data Science Guntur India Ggits MP Jabalpur India

ISBN: (纸本)9798331518981

Congenital heart problems are the most common primary cause of death that can be attributed to birth defects. It is possible to identify cardiac defects by analyzing the geomorphology of cardiac electric indications. Additionally, the examination of fetal cardiac signals, which does not involve any intrusive procedures, can be an efficient method for monitoring the health of the fetal heart and identifying cardiac abnormalities at an early stage. One method that can be utilized to evaluate the health and condition of the fetus is the ECG signal, which is a graphical copy of the electric potential that is generated in conjunction with the action of the heart. However, there are several technical issues that arise during the process of non-invasive FECG extraction. These issues include interference from maternal electrocardiograms and maternal electromyograms, interference from power lines operating at 50 Hz, and baseline drift. Extraction and analysis of the fetal electrocardiogram signal are the key goals of electronic fetal monitoring;they are the primary objectives. In the process of extracting features from the fetal ECG indication, which is a nonlinear mix of the gentle ECG, the fetal ECG indication, and other interfering signals, digital signal processing techniques constitute a key part of the process. It is possible to find solutions to problems involving abnormality detection by employing data mining techniques and soft computing approaches for categorization. © 2024 IEEE.

关键词： Fetal monitoring

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Scheduling in Multi-Hop Wireless Networks using a Distributed Learning Algorithm 7

Scheduling in Multi-Hop Wireless Networks using a Distribute...

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7th International Conference on Trends in electronics and Informatics, ICOEI 2023

作者： Chinchawade, Amit Jaykumar Rajyalaxmi, S. Singh, Shashank Kumar, R.Anil Rastogi, Ravi Shah, Mohd Asif Sharad Institute of Technology College of Engineering Department of Electronics and Computer Engineering Maharashtra Yadrav Ichalkaranji India Vasavi College of Engineering Department of Information Technology Telangana Hyderabad India Integral University Department of Computer Science and Engineering Uttar Pradesh Lucknow India Aditya College of Engineering and Technology Andhra Pradesh Surampalem India Scientist C Nielit Uttar Pradesh Gorakhpur India University School of Business Chandigarh University Gharuan Adjunct Faculty Punjab Mohali India

ISBN: (纸本)9798350397284

This study takes on the challenge of learning and scheduling in a Multi-Hop Wireless Network (MHWN) without having any prior knowledge of connection charges. Earlier scheduling methods needed knowledge of the connection rates, whereas Techniques for training often required a centralized authority and had an exponential performance. These represent a significant barrier to creating a reliable distributed strategy for resource allocation that relies on learning in massive multi-hop networks. Management of multihop control It is a tough task to implement wireless networks in a distributed fashion while meeting end-to-end timing constraints for a variety of flows. Using the notions of Draining Time and Continuous Review, which originate from the notion of fluid boundaries of queues, an algorithm is constructed that meets delay needs for various streams in a system. This study proposes a regulated maximal matching, a completely distributed scheduling algorithm that ensures at least 50% of the performance of a centralized method. The approach employs a distributed optimization procedure called iterative gradient ascent, which is carried out in a cyclic fashion between nodes with little data interchange. The system prioritizes flows using weights that change over time. The effectiveness of the algorithm is analyzed in a network setting where interruption is symbolized by isolated nodes. © 2023 IEEE.

关键词： Wireless networks

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Estimating finger joint angles by surface EMG signal using feature extraction and transformer-based deep learning model

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Biomedical Signal Processing and Control 2024年第PartB期87卷

作者： Putro, Nur Achmad Sulistyo Avian, Cries Prakosa, Setya Widyawan Mahali, Muhammad Izzuddin Leu, Jenq-Shiou Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Indonesia Department of Electronics and Informatics Engineering Faculty of Engineering Universitas Negeri Yogyakarta Indonesia

Human-machine interfaces frequently use electromyography (EMG) signals. Based on previous work, feature extraction has a great deal of influence on the performance of EMG pattern recognition. Furthermore, the Deep Learning method is supposed to increase performance and not depend on feature engineering. However, directly processing raw signals will require a higher computation rate. This study proposed a new method that combines feature extraction and Deep Learning to address those issues while improving performance, reducing architecture size, and producing a more representative output. The proposed architecture employs the Transformer model as the backbone to get the correlation between elements and focus on the important information for estimating the flexion-extension of finger joint angles. This study uses experiment three of the NinaPro (Non-Invasive Adaptive Hand Prosthetics) DB5 dataset. Each experiment produces 16 Surface EMG data streams from two Myo Armbands devices representing 22 finger joint angles as output. This study compares the windowing process, feature extraction, execution time, and results with previous studies. The results show that the proposed model outperforms the previous study, from 0.957 in the previous study to become 0.970 in this study for the R-Square score. This result is obtained using 100 data points for the windowing process and Median Frequency for the best feature extraction method. © 2023

关键词： Feature extraction

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Enhanced Mseec routing protocol involving tabu search with static and mobile nodes in Wsns

Recent Advances in Computer Science and Communications

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Recent Advances in computer Science and Communications 2021年第4期14卷 1321-1335页

作者： Sahni, Varsha Bala, Manju Kumar, Manoj Department of Computer Science and Engineering I. K.G PTU Regional Center Jalandhar Punjab India Department of Computer Science and Engineering Khalsa College of Engineering & Technology Amritsar Punjab India Department of Electronics and Computer Engineering DAV Institute of Engineering Management & Technology Jalandhar Punjab India

Background: Background of this paper has taken place in mainly heterogeneous network in which three types of nodes are present like normal node, advance node and super node with different amount of energy. The energy of super node is greater than that of advance and normal nodes and the energy of advance nodes are also greater than that of normal nodes in the designed network. The optimization techniques have to be studied from the swarm intelligence based on the different aspects of routing. Objective: The objective of this paper is to propose a new heterogeneous protocol with the help of hybrid meta-heuristic technique. In this technique, the shortest route has been selected and forwarded the data to the sink in a minimal time span to save the energy and make the network more stable. Methods: To evaluate the technique, a new hybrid technique has been created, where the data transmission is implemented from the beginning. This technique contains the route process of the algorithm which was made available through a hybrid meta-heuristic technique. Results: Simulation results show that the hybrid meta-heuristic technique has high throughput with less number of dead nodes with existing methods and also show that the efficiency and stability of new proposed protocol. Conclusion: The conclusion to this paper is a novel, energy-efficient technique applied for randomly deployed sensor nodes over the wireless sensor network and enhancement has been done in stability and throughput of a new proposed algorithm in case of static as well as moving nodes. © 2021 Bentham Science Publishers.

关键词： MATLAB

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An Optimized Technique For Developing Optical Band Pass Filter

An Optimized Technique For Developing Optical Band Pass Filt...

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2024 IEEE Calcutta Conference, CALCON 2024

作者： Ghosh, Arup Kumar Garai, Gautam Bhattacharjee, Subhankar University of Calcutta A.K.C. School of Information Technology Kolkata India Saha Institute of Nuclear Physics Computer Section Kolkata India Techno International New Town Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798331516666

In this paper we have introduced an innovative method for designing a one dimensional photonic crystal (1D PhC) based Optical Band Pass Filter (OBPF) using the Differential Evolution (DE) algorithm. These filters are essential in optical communication systems because of their ability to control light propagation. The DE algorithm efficiently optimizes the PhC structure to achieve the desired optical band pass characteristics and simplifying the design process. The OBPFs are made of multiple dielectric layers with varying thicknesses and refractive indices to enable high transmission at specific wavelengths and low transmission elsewhere. Optimization helps to find the optimal thickness and refractive index. MATLAB implementation shows encouraging results with high transmittance (99.2214%) and a narrow full width at half maximum (FWHM) of 0.6 nm. The experimental results demonstrate the superiority of this approach over the existing well-known traditional methods. © 2024 IEEE.

关键词： MATLAB

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Real-Time Detection of Vehicles on South Asian Roads 3rd

Real-Time Detection of Vehicles on South Asian Roads

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3rd International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2021

作者： Pawar, Rutuparn Gujar, Shubham Chougule, Suyash Pote, Rutuja Pandit, Dipti Dandawate, Yogesh Department of Electronics and Telecommunication Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Mechanical Engineering Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Computer Science Vishwakarma Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9789811958670

Great advances have been made in the field of computer vision especially for object detection in the last two decades. Vehicle detection is fundamentally a task of object detection which assists in traffic control and management via traffic flow analysis. Models trained for vehicle detection on popular datasets containing vehicles as classes perform poorly while detecting vehicles in South Asian countries since the make and model of vehicles in the geographical region is different from those present in most popular datasets. Furthermore, some kinds of vehicles are found only in South Asian countries. In this paper, we propose a method that uses two YOLO models for the detection of various kinds of vehicles found on roads in South Asian countries. The first YOLO model detects vehicles while the second YOLO model detects wheels in case a truck is detected to further classify a truck based on the number of axles. A dataset consisting of images of bicycles, motorcycles, cars, buses, trucks, tempos and rickshaws was collected from surveillance videos, web scraping and subsetting the Indian Driving Dataset (IDD) as well as the Common Objects in Context (COCO) dataset. Another dataset was made which consists of images of trucks with their wheels annotated. YOLOv4 models trained on the latter mentioned datasets performed better than YOLOv3 and achieved a mAP of 71.08 per cent and 94.33 per cent for detection of vehicles and wheels, respectively. The proposed method to determine the number of axles of a truck gave an overall accuracy of 95 per cent. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Trucks

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Digital-Twin Enabled Time Ahead Resource Allocation for Integrated Fiber-Wireless Connected Vehicular Network

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Journal of Communications and Information Networks 2024年第3期9卷 296-308页

作者： Akshita Gupta Saurabh Jaiswal Martin Maier Vivek Ashok Bohara Anand Srivastava Wirocomm Research Group Department of Electronics&Communication EngineeringIndraprastha Institute of Information Technology Delhi(IIITD)New Delhi 110020India Department of Computer Science&Applied Mathematics Indraprastha Institute of Information Technology Delhi(IIITD)New Delhi 110020India Optical Zeitgeist Laboratory Institute National de la Recherche ScientifiqueMontreal H5A 1K6Canada

The digital twin(DT)is envisaged as a catalyst for pioneering ecosystems of service provision within an immersive environment born from the convergence of virtual and physical ***,DT could enhance the performance of edge-intelligent connected vehicular networks by allocating network resources efficiently based on the key performance indicators(KPIs)of vehicular data ***,this work addresses the key challenge of computation and spectrum resource allocation for vehicular *** allocate the optimal resource allocation,we subdivided the problem into:traffic classification,collective learning,and resource allocation *** order to do so,this paper concentrates on two crucial vehicular applications:brake application and lane-change *** utilize a random forest model to collectively learn vehicular data traffic in the upcoming time ***,a time-ahead resource allocation algorithm is proposed to improve the quality of service(QoS)by intelligently offloading vehicular data traffic to a DT-based integrated fiber-wireless(Fi-Wi)connected vehicular *** evaluate the performance of the resource allocation strategy in terms of resources required by the network alongside the packet loss *** was observed that there was a 44.74%increase in cost as the total computation resources increased from F=50 to 100 GHz,whereas the PLR of the network decreased by 71.43%.

关键词： connected vehicles digital twin edgeintelligence fiber-wireless machine learning resource allocation

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MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion

arXiv

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arXiv 2024年

作者： Wu, Lehong Lin, Lilang Zhang, Jiahang Ma, Yiyang Liu, Jiaying Wangxuan Institute of Computer Technology Peking University China School of Electronics Engineering and Computer Science Peking University China

Self-supervised learning has proved effective for skeleton-based human action understanding. However, previous works either rely on contrastive learning that suffers false negative problems or are based on reconstruction that learns too much unessential low-level clues, leading to limited representations for downstream tasks. Recently, great advances have been made in generative learning, which is naturally a challenging yet meaningful pretext task to model the general underlying data distributions. However, the representation learning capacity of generative models is under-explored, especially for the skeletons with spacial sparsity and temporal redundancy. To this end, we propose Masked Conditional Diffusion (MacDiff) as a unified framework for human skeleton modeling. For the first time, we leverage diffusion models as effective skeleton representation learners. Specifically, we train a diffusion decoder conditioned on the representations extracted by a semantic encoder. Random masking is applied to encoder inputs to introduce a information bottleneck and remove redundancy of skeletons. Furthermore, we theoretically demonstrate that our generative objective involves the contrastive learning objective which aligns the masked and noisy views. Meanwhile, it also enforces the representation to complement for the noisy view, leading to better generalization performance. MacDiff achieves state-of-the-art performance on representation learning benchmarks while maintaining the competence for generative tasks. Moreover, we leverage the diffusion model for data augmentation, significantly enhancing the fine-tuning performance in scenarios with scarce labeled data. Our project is available at https://***/ECCV24MacDiff/. © 2024, CC BY.

关键词： Self-supervised learning

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Entropy and likelihood-based detection of DGA generated domain names and their families

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International Journal of Security and Networks 2022年第3期17卷 147-192页

作者： Bhatia, Ashutosh Vishvakarma, Deepak Kumar Kaushik, Rekha Agrawal, Ankit Department of Computer Science BITS Rajasthan Pilani India DRDO Karnataka Bangalore India Department of Electronics and Communication Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal India

Botnet is a network of hosts (bots) infected by a common malware and controlled by command and control (C&C) servers. Once the malware is found in an infected host, it is easy to get the domain of its C&C server and block it. To counter such detection, many malware families use probabilistic algorithms, known as domain generation algorithms (DGAs), to generate domain names for the C&C servers. In this paper, we propose a probabilistic approach to identify the domain names that are likely to be generated by malware using DGAs. The proposed solution is based on the hypothesis that the entropy of human-generated domain names should be lesser than the entropy of DGA generated domain names. Results show that the percentage of false negatives in the detection of DGA generated domain names using the proposed method is less than 29% across 39 DGA families considered by us in our experimentation. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Botnet

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Financial Big Data Analysis Using Anti-tampering Blockchain-Based Deep Learning 22nd

Financial Big Data Analysis Using Anti-tampering Blockchain-...

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22nd International Conference on Hybrid Intelligent Systems, HIS 2022 and the 18th International Conference on Information Assurance and Security, IAS 2022

作者： Praghash, K. Yuvaraj, N. Peter, Geno Stonier, Albert Alexander Priya, R. Devi Department of Electronics and Communication Engineering Christ University Bengaluru India Department of Research and Publications ICT Academy IIT Madras Research Park ManagerChennai India CRISD School of Engineering and Technology University of Technology Sarawak Sibu Malaysia School of Electrical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9783031274084

This study recommends using blockchains to track and verify data in financial service chains. The financial industry may increase its core competitiveness and value by using a deep learning-based blockchain network to improve financial transaction security and capital flow stability. Future trading processes will benefit from blockchain knowledge. In this paper, we develop a blockchain model with a deep learning framework to prevent tampering with distributed databases by considering the limitations of current supply-chain finance research methodologies. The proposed model had 90.2% accuracy, 89.6% precision, 91.8% recall, 90.5% F1 Score, and 29% MAPE. Choosing distributed data properties and minimizing the process can improve accuracy. Using code merging and monitoring encryption, critical blockchain data can be obtained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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