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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Sharma, Sunita Verma, Umesh Kumar Jain, Sourabh Lohani, Bhanu Prakash Indian Institute of Information Technology Sonepat Haryana India Indian Institute of Information Technology Sonepat Department of Computer Science and Engineering Haryana India Amity University Greater Noida Uttar Pradesh India

ISBN: (纸本)9798331542344

Pulmonary malignancy remains the most prevalent and fatal type of cancer on a global scale playing major role on the yearly death from cancer. By using proficient and computerizes testing technologies, it is pursued by the project to lower mortality rate and improve early detection accuracy. Decreasing mortality rates needs early test results, but due to clinical presentation is so much depended upon by existing testing technologies like imaging and biopsies, late diagnoses frequently led to. Convolutional Neural Networks is one of the most proficient deep learning architectures for applications including image classification. ResNet50 is a version of Residual Networks architecture which is famous for its ability to reduce the fading change rate problem and enable challenging network training feasible. ResNet50 is pre-Trained on image ImageNet and in this project, pulmonary malignancy is detected by it. The results of the evaluation show that the ResNet50-based model is exceptionally precise in separating between malignant lung tissues and those that are not. Apart from accuracy, major advantages provided by the model's effectiveness in managing and evaluating images along with precision compared to traditional diagnostic techniques. The transformative potential of deep learning models in malignancy detection is demonstrated by the implementation of ResNet50 in respiratory malignancy diagnosis. A reliable, precise and valuable diagnosis tool is provided by this study, contributing to the ongoing efforts to enhance early identification and treatment results for pulmonary malignancy patients. The use of deep learning and convolutional neural networks has greatly benefited in the identification and diagnosis of lung tumor. The precision and effectiveness of medical imaging analysis are enhanced by these technologies through the employment of advanced image processing and pattern recognition capabilities. © 2024 IEEE.

关键词： Lung cancer

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Energy-efficiency Analysis of Different Scheduling Algorithms for Mobile Crowd Computing 15

Energy-efficiency Analysis of Different Scheduling Algorithm...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pramanik, Pijush Kanti Dutta Biswas, Tarun Galgotias University School of Computer Applications and Technology Greater Noida India Indian Institute of Information Technology Department of Computer Science & Engineering Ranchi India

ISBN: (纸本)9798350370249

Mobile crowd computing (MCC) has emerged as a promising paradigm to leverage the underutilised computational resources of smart mobile devices (SMDs). However, the energy constraints of these devices pose a significant challenge in efficiently executing computational tasks. This study analyses the energy efficiency of various scheduling algorithms for MCC environments. The study compares the performance of seven scheduling algorithms: PPIA, RAS, PSO, GA, MaxMin, MinMin, and MCT. These algorithms were evaluated in terms of their energy efficiency when executing different sets of tasks with varied instruction lengths. The experiment used a simulated MCC environment with real data from five SMDs. The results demonstrate that the PSO algorithm consistently outperformed the other scheduling schemes regarding energy efficiency across all task sets. The GA algorithm also showed promising results. The MinMin algorithm exhibited the lowest energy efficiency. The findings of this study provide valuable insights for designing and implementing energy-efficient task scheduling strategies in MCC systems. Analysing the different algorithms under varying task loads can help MCC service providers make informed decisions when selecting appropriate scheduling techniques to optimise the energy utilisation of SMDs. © 2024 IEEE.

关键词： Crowd computing Energy-efficiency Mobile cloud Particle swarm optimisation Task scheduling

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SSF: Sparse point cloud object detection based on self-adaptive voxel encoding and focal-sparse convolution

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 11041-11054页

作者： Zhang, Yu Wang, Zilong Zhu, Yongjian Li, Jianxin School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai China College of Engineering Physics Shenzhen Technology University Guangdong Province Shenzhen China

Point cloud object detection is gradually playing a key role in autonomous driving tasks. To address the issue of insensitivity to sparse objects in point cloud object detection, we have made improvements to the voxel encoding and 3D backbone network of the PVRCNN++. We have introduced adaptive pooling operations during voxel feature encoding to expand the point cloud information within each voxel, followed by the utilization of multi-layer perceptrons to extract richer point cloud features. On the 3D backbone network, we have employed adaptive sparse convolution operations to make the backbone network's channel count more flexible, allowing it to accommodate a wider range of input data types. Furthermore, we have integrated Focal Loss to tackle the issue of class imbalance in detection tasks. Experimental results on the public KITTI dataset demonstrate significant improvements over the PVRCNN++, particularly in pedestrian and bicycle detection tasks. Specifically, we have observed 1% increase in detection accuracy for pedestrians and 2.1% improvement for bicycles. Our detection performance also surpasses that of other comparative detection algorithms. © 2024 - IOS Press. All rights reserved.

关键词： Object detection

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Automatic text inpainting and quality elevation in video sequences

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Multimedia Tools and Applications 2024年 1-34页

作者： Palivela, Lakshmi Harika Dharmalingam, Vivekanandan Gayathri, D. Bala School of Computer Science and Engineering Vellore Institute of Technology Chennai Vandalur-Kelambakkam Road Chennai600127 India Department of Information Technology Madras Institute of Technology Anna University Chennai India Department of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute Of Science and Technology Chennai India

Scene text removal is a recent development in computer vision that replaces text patches in natural images with the appropriate background. Text removal is a difficult process leading to faulty areas of text containing text strokes with their hazy backgrounds. Text in the real world uses a variety of font kinds, some of which are difficult to localize due to their chaotic shapes, varied shading degrees, and orientation *** text erasing may include the subtasks of text detection as well as text inpainting. Both subtasks require a large amount of data to be successful;but, existing approaches were limited by insufficient real-world data for scene-text elimination. Eventhough the existing works produced considerable performance improvement in scene text removal, they often leave many text remains like text strokes, thus producinglow-quality visual outcomes. Therefore, this paper proposes an automatic text inpainting and video quality elevation model by using the Improved Convolutional Network-based ***, the video samples are collected from the diverse datasets and then converted into frames. Next, the frames are deblurred using an enhanced Convolutional Neural Network (CNN) model that has three convolutional layers for accurately localizing the texts in frames. Subsequently, the texts are detected by utilizing the CLARA-based VGG-16 network. Afterward, the text strokes are removed using a convolutional Encoder and decoder network to eliminate the presence of text on complex backgrounds and textures. Here, the coordinates of text in the deblurred frames are used to crop out the text stroke regions. So, the texts are in-painted, and then, the text in-painted regions are pasted back to their original positions in the frames. Furthermore, the video quality is elevated with the help of the DenseNet-centric Enhancement network. The experimental outcomes demonstrate that the proposed model effectively removed scene texts and enhanced the video qu

关键词： Convolutional neural networks

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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science China(information sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Blockchain Based Secure Federated Learning With Local Differential Privacy and Incentivization

IEEE Transactions on Privacy

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IEEE Transactions on Privacy 2024年第1期1卷 31-44页

作者： Chaudhury, Saptarshi De Reddy, Likhith Varun, Matta Sengupta, Tirthankar Chakraborty, Sandip Sural, Shamik Vaidya, Jaideep Atluri, Vijay Indian Institute of Technology Department of Computer Science and Engineering Kharagpur721302 India Rutgers University Management Science and Information Systems Department NewarkNJ07102-3122 United States

Interest in supporting Federated Learning (FL) using blockchains has grown significantly in recent years. However, restricting access to the trained models only to actively participating nodes remains a challenge even today. To address this concern, we propose a methodology that incentivizes model parameter sharing in an FL setup under Local Differential Privacy (LDP). The nodes that share less obfuscated data under LDP are awarded higher quantum of tokens, which they can later use to obtain session keys for accessing encrypted model parameters updated by the server. If one or more of the nodes do not contribute to the learning process by sharing their data, or share only highly perturbed data, they earn less number of tokens. As a result, such nodes may not be able to read the new global model parameters if required. Local parameter sharing and updating of global parameters are done using the distributed ledger of a permissioned blockchain, namely HyperLedger Fabric (HLF). Being a blockchain-based approach, the risk of a single point of failure is also mitigated. Appropriate chaincodes, which are smart contracts in the HLF framework, have been developed for implementing the proposed methodology. Results of an extensive set of experiments firmly establish the feasibility of our approach. © 2024 IEEE.

关键词： Federated learning

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Efficient Framework with Sparse Acquisition in CMOS Image Sensors for Low-Power Edge Devices 10

Efficient Framework with Sparse Acquisition in CMOS Image Se...

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10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Kisku, Wilfred Kaur, Amandeep Mishra, Deepak Indian Institute of Technology Department of Electrical Engineering Jodhpur India Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331533229

Memory bandwidth and power consumption is of utmost importance in the design of low power edge devices. This makes it essential to conserve power both at the sensor node and the computational unit. Our paper proposes a system design methodology to save power by acquiring compressed data at the CMOS Image Sensor (CIS) and making the Convolutional Neural Network (CNN) models lightweight for edge applications. A detailed study highlights the effect of sensor directed sparsity at the CIS, and its effect on CNN models such as VGG-16, MobileNet, ResNet50, EfficientNet, and ResNext models. It provides empirical evidence that the performance is mostly maintained even after pruning and quantization of considered models. The imagenette dataset is used to curate the data with the proposed acquisition pipeline that introduces data sparsity, and the experimental results provide evidence that the proposed pipeline performs at par with conventional CIS acquisition mode. For example, the analysis of VGG-16 model for a sparsity threshold level of 0.2 shows compression of × 7.6, × 16, and × 61 using only pruning (P), only quantization (Q), and with both pruning+quantization (P+Q), respectively. It is also observed that the proposed acquisition pipeline can achieve significant parameter reduction through pruning for model compression, comparable to convectional pipeline. © 2024 IEEE.

关键词： CMOS integrated circuits

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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

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Neutrosophic Sets and Systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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Minimal Counters, Maximum Insight: Simplifying System Performance With HPC Clusters for Optimized Monitoring

IEEE Computer Architecture Letters

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IEEE computer Architecture Letters 2025年第1期24卷 177-180页

作者： Shukla, Shubhi Singh, Abhijeet Chakraborty, Rajdeep Chakraborty, Anirban Rathod, Tejas Mumbaikar, Harshal Munigala, Manoj Kumar Madhusudhan, K.N. Mitra, Pabitra Mukhopadhyay, Debdeep Indian Institute of Technology Kharagpur Centre for Computational Data Sciences India Indian Institute of Technology Kharagpur Computer Science and Engineering Department India Intel Corporation Bangalore India

As computer systems become more complex, evaluating performance requires tracking various hardware performance counters that capture the system's internal activities. While these counters provide valuable insights, their growing number makes it challenging to identify the most relevant ones for performance analysis. In this paper, we investigate the correlation between performance counter values and overall system performance, while also exploring the inter-correlation between different counters. Our findings demonstrate that specific counters are strongly correlated with key performance metrics and that significant redundancy exists among counters. By leveraging these relationships, we propose a method for selecting a small, representative set of performance counters. This streamlined set can further be used to accurately predict performance score across various workloads and system configurations. © 2002-2011 IEEE.

关键词： Complex networks

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Heuristic-based Multicasting Utilizing Network Coding over a Multi-Hop Wireless Mesh Network 18

Heuristic-based Multicasting Utilizing Network Coding over a...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Sharma, Neha Rathore, Prateek Shukla, Mahendra K. Pandey, Om Jee Indian Institute of Technology BHU Department of Electronics Engineering Varanasi India The LNM Institute of Information Technology Department of Communication and Computer Engineering Jaipur India ABV-Indian Institute of Information Technology Department of Information Technology Gwalior India

ISBN: (纸本)9798350391725

Multicasting over a multi-hop wireless mesh network is a challenging issue that recently received less attention. Various applications, such as distance learning, telemedicine, web radio, and online conferencing, require the same information to be sent to multiple recipients. In these applications, the destination nodes are often far from the source, necessitating the design of a multi-hop multicasting protocol. This paper proposes an efficient multi-hop multicasting approach for transmitting packets from the source to all intended destinations. By combining network coding with opportunistic routing, the proposed method significantly reduces the expected number of transmissions a packet must undergo in a lossy wireless network. Specifically, this work introduces a Heuristic-Based Multicasting (HBM) scheme that integrates network coding with opportunistic routing. First, it calculates the minimum route cost from the source node to all the destinations. Then, a novel packet transmission strategy is employed, where packets are combined using network coding and transmitted via a path derived from the Network Coding Assisted Multicasting (NCAM) route cost calculation. Extensive simulations demonstrate the effectiveness of the NCAM route cost calculation and the corresponding packet transmission strategy. The results show that the proposed HBM scheme significantly reduces the expected number of packet transmissions in the network, thereby increasing throughput compared to existing multicasting schemes such as MORE, Pacifier, and MLCAR. © 2024 IEEE.

关键词： Wireless mesh networks (WMN)

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