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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Prajjwal Nijhara Dishit Sharma Dip Sankar Banerjee Department of Computer Science and Engineering Indian Institute of Technology Jodhpur NH62 Karwar Jodhpur Rajasthan India

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

In network analysis, Katz centrality is widely used to measure node influence by considering both direct and indirect connections weighted by path length. While numerous studies have examined Katz centrality for static graphs, relatively few address the challenges posed by dynamic graphs. Katz centrality can utilize perturbation theory by exploiting iterative solvers to obtain updated Katz scores in dynamic graphs. However, these methods are limited to handling only small changes, as they generally accept only minor structural updates limited to only edge insertions or removals. Additionally, due to an iterative approach, the solutions can be sequential and provide approximate answers, which may reduce accuracy when the graph undergoes frequent updates over time. This paper introduces a novel algorithm that significantly improves the efficiency of Katz centrality calculations in dynamic graphs. After each update, we identify affected nodes using a Breadth First Search (BFS) frontier and then apply dynamic programming, which allows for both edge/node insertions and deletions. Our parallel implementation on a shared memory platform achieves a 5.29 x speedup on an average over the static version. Our algorithm can update a batch of 1 million edges on an existing graph of 1 billion edges in 24.73 seconds.

关键词： Weight measurement Runtime Heuristic algorithms Perturbation methods Directed graphs Network analyzers Parallel processing Length measurement Dynamic programming Iterative methods

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Energy-Efficient Over-the-Air Computation for Federated Generative Model Fine-Tuning in Unmanned Vehicle-Assisted Disaster Relief

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Zhao, Yikun Feng, Lei Zhou, Fanqin Li, Wenjing Xiong, Zehui Du, Hongyang Wu, Celimuge Guo, Song Quek, Tony Q.S Han, Zhu State Key Laboratory of Networking and Switching Technology Beijing100876 China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong University of Electro-Communications Meta-Networking Research Center Tokyo Japan Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The utilization of emergent generative artificial intelligence (GAI) within the realm of unmanned vehicles (UVs) can boost edge intelligence and potentially enhance the efficiency and effectiveness of rescue operations. However, to facilitate specialized generative edge intelligence in dynamic UV networks, GAI models need to tap into local data and conduct online fine-tuning, and the dispersed distribution of UVs makes distributed fine-tuning paradigms crucial. Although the federated generative model fine-tuning brings a potential solution, the large number of parameter transmissions involved in its fine-tuning process are often constrained by communication bottlenecks, which are more pronounced for resource-constrained UV networks. To cope with these challenges, we introduce an energy-efficient GAI model fine-tuning framework for the hierarchical UV networks, which employs over-the-air technology to save computational resource costs for unmanned aerial vehicle (UAV) during federated aggregation within a federated learning paradigm. Thereafter, we study the resource allocation problem in the fine-tuning process of the generative model and formulate a joint optimization problem for the bandwidth allocation, power control, computation resource allocation, and denoising factor control, aiming to minimize the system energy consumption. Then, we decouple the original problem into four tractable subproblems, and propose a block coordinate descent algorithm to solve them iteratively. Particularly, we derive a closed-form solution for the denoising factor to minimize the local model uploading transmission energy consumption under a specific AirComp communication error threshold. Simulation based on the state-of-the-art generative models shows that our AirComp-assisted federated generative model fine-tuning scheme can achieve satisfactory customized field-of-view image generation capability compared with the traditional fine-tuning scheme in an energy-efficient way. © 2015 I

关键词： Energy efficiency

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An Efficient Nodule Characterization Framework using Gabor Filter and Shape Features

An Efficient Nodule Characterization Framework using Gabor F...

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International Conference on computer, Electrical & Communication engineering (ICCECE)

作者： Amitava Halder Computer Science & Engineering Department Dr. Sudhir Chandra Sur Institute of Technology and Sports Complex Kolkata India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

Lung cancer accounts for a large percentage of cancer-related fatalities each year, making it one of the most common and deadliest cancer types in the world. Lung cancer early detection and characterization entails detecting the disease when it is more curable and has a better probability of being successfully treated. In order to characterize lung nodules, a new completely automated machine learning (ML) framework has been created in this work by introducing Gabor filter-based textural features combined with shape features. Using the publicly available LIDC-IDRI dataset for lung nodule characterization, the suggested framework achieved the highest performance indices of $\mathbf{9 0. 4 8 \%}$ sensitivity, 100% specificity, and 95% accuracy.

关键词： Deep learning Sensitivity Accuracy Shape Lungs Lung cancer Feature extraction Automated machine learning Gabor filters

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Monitoring of Speed Breaker Violations with RoadHAWK

Monitoring of Speed Breaker Violations with RoadHAWK

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Rudhir Chandra Mahalik Prasant Misra Punit Rathore Department of Computer Science and Engineering National Institute of Technology Rourkela India Robert Bosch Centre for CPS Indian Institute of Science Bangalore India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Unauthorized or improperly constructed speed breakers on city roads are a major contributor to slow moving traffic and road accidents in developing nations, especially in rapidly urbanizing regions. The existing practice of manual inspection by road safety personnel has not been effective due to the large scale of this problem. To this end, we present RoadHAWK: an edge-centric, automated, real-time system to detect and characterize speed breakers. We propose an efficient sensor informatics pipeline that leverages the accelerometer sensor of a vehicle mounted smartphone, and overcomes measurement and ego-noise to not only detect the features of interest but also their temporal span; both of which provide the necessary estimates to identify potential violations in the design or placement of speak breakers. On-the-road experiments show that RoadHAWK is (on an average) 75-80% accurate in estimating speaker breakers and associated violations.

关键词： Road accidents Pipelines Velocity control Urban areas Road safety Real-time systems Sensors Informatics Monitoring Traffic congestion

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Cloud-Based AI Robotics for Precision Pruning of Fruit Trees to Improve Sustainable Farming

Cloud-Based AI Robotics for Precision Pruning of Fruit Trees...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： R.C. Ilambirai Barkathulla. A G. Mahalakshmi J. Jagan Babu Prabu K C. Srinivasan Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.D Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Gojan School of Business and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The crucial agricultural practice of precision pruning fruit trees greatly impacts fruit production yield and quality. Traditional pruning techniques take a lot of time and money because they need skilled workers and are labor-intensive. It suggests a novel cloud-based AI robotics system intended to precisely prune fruit trees to address these issues. This system uses cutting-edge robotics fitted with Convolutional Neural Networks (CNNs) to carry out precise and effective tasks related to pruning. Cloud computing integration makes it possible to process and make real-time decisions, improving the system's flexibility in responding to changing tree conditions and pruning needs. The system's primary parts are robotic arms for accurate cuts, various sensors for analyzing tree structures, and CNN models for maximizing pruning techniques. Early field trials show noticeable gains in operational efficiency and pruning accuracy compared to conventional techniques. This study demonstrates how cloud-integrated AI robotics can transform agricultural practices and improve fruit farming's sustainability and productivity.

关键词： Training Cloud computing Accuracy Crops Robot sensing systems Real-time systems Sensor systems Convolutional neural networks Robots Farming

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Active Learning guided Gaussian Process Regressor for Non-Linear PDE Modeling for environmental simulations

Active Learning guided Gaussian Process Regressor for Non-Li...

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Computational Intelligence for Energy, Transport and Environmental Sustainability (CIETES), IEEE Symposia on

作者： Soumen Sinha Lara Santos Bastos Rajen Kumar Sinha Faculty Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Department of Mathematics Indian Institute of Technology Guwahati India

ISBN: (数字)9798331508258

ISBN: (纸本)9798331508265

In this research, we present a novel method for solving non-linear, time-dependent partial differential equations (PDEs) using active learning combined with Gaussian Process Regression (GPR). PDEs like Burger’s equation describe fluid and traffic flow, incorporating congestion and shock waves, whereas the Allen-Cahn equation contributes to environmental control by predicting pollutant diffusion. Other examples include the Stefan equation, which models heat transfer in materials, crucial for efficient energy storage, and the Korteweg-de Vries equation that eases waterway management through wave propagation modeling. Dense discretisation grids are necessary for traditional numerical techniques to these equations, which are computationally demanding. Our method reduces the requirement for big datasets and computer resources by using active learning to select the most informative data points intelligently. Solving these PDEs with machine learning instead of more conventional techniques is essential since this allows for quicker, more scalable solutions for intricate systems. By merging GPR with active learning, we anticipate the numerical solutions with high accuracy while decreasing the amount of labeled samples. The findings demonstrate that, in comparison to conventional numerical methods, the use of active learning in conjunction with the GPR approach greatly reduces the data needs. Accurately predicting the numerical answer, it presents a viable option for energy-related applications and environmental modelling.

关键词： Shock waves Computational modeling Partial differential equations Propagation Active learning Water heating Gaussian processes Predictive models Mathematical models Numerical models

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A Novel Automatic Text Document Classification Using Learning based Text Classification(LbTC) Approach

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Procedia computer science 2025年 258卷 4279-4290页

作者： Avinash N J Krishnaraj Rao Rama Moorthy H Raviprakash B Raghunadan K R Vasudeva Venkatadri M Department of Electronics and Communication Engineering Mangalore Institute of Technology and Engineering Moodabidre 574225 Karnataka India Department of Information Science and Engineering NMAM Institute of Technology Nitte (Deemed to be University) Karkala 574110 Karnataka India Department of Computer Applications Nitte Institute of Professional Education Nitte (Deemed to be University) Mangalore 575007 Karnataka India Department of Computer Science and Engineering NMAM Institute of Technology Nitte (Deemed to be University) Karkala 574110 Karnataka India Associate Dean MPSTME Shirpur Campus NMIMS (Deemed-to-be-University) Shirpur Maharashtra India

There are practical uses for text document classification. In actuality, it is crucial to categorize and parse documents with natural language. Utilizing applications like bogus news identification, query tagging, sentiment classification, and spam filtering needs this sort of study. However, because of their ambiguity, open-ended nature, and vastness, text documents provide a difficult Classification challenge. Machine learning (ML) methods became valuable alongside the growth of artificial intelligence (AI) due to their data-driven learning techniques. These methods are seen as highly effective at handling and analyzing vast data sets in depth. Topic segmentation, text categorization, entity identification, machine translation, and text summarization, to name a few, are just a few of the issues that may be resolved with ML approaches. In this study, we introduced an automatic Text Classification system (ATCF), a system that uses shallow and deep neural networks to classify text documents. To implement our system, we proposed an approach called Learning based Text Classification (LbTC). We investigated the performance of several models in comparison. Based on the training provided to ML models, the suggested framework assists in categorizing any type of document. It is compatible with practical applications where the categorization of documents is essential.

关键词： Machine learning shallow learning deep learning text classification document Classification

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IoT based Line of Control Monitoring System

IoT based Line of Control Monitoring System

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Taniya Petkar Department of Computer Science And Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This paper presents a novel line-of-control (LoC) monitoring system that leverages the Internet of Things (IoT) to improve border security. The system creates a strong infrastructure for real-time monitoring throughout critical border regions, especially the Line of Control, by utilizing IoT devices, sensors, and communication technologies. The coverage, timeliness, and data quality of conventional border surveillance systems are frequently compromised. As opposed to this, the suggested system makes use of a wirelessly networked network of intelligent sensors, such as infrared, acoustic, and video cameras. By utilizing sophisticated image processing algorithms and machine learning to analyze the gathered data, the system is able to differentiate between regular activities and any security risks. To help security staff make decisions quickly, real-time updates are transmitted to a centralized control center. Integration with cloud computing also makes data analysis, retrieval, and storage simpler and guarantees data accessibility for forensic investigations and trend research.

关键词： Data analysis Surveillance Scalability Machine learning Cameras Real-time systems Sensor systems Security Internet of Things Intelligent sensors

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SX-Stitch: An Efficient VMS-UNet Based Framework for Intraoperative Scoliosis X-Ray Image Stitching

SX-Stitch: An Efficient VMS-UNet Based Framework for Intraop...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yi Li Heting Gao Mingde He Jinqian Liang Jason Gu Wei Liu Department of Mechanical and Energy Engineering Southern University of Science and Technology Peking Union Medical College Hospital Electrical and Computer Engineering Dalhousie University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In scoliosis surgery, the limited field of view of the C-arm Xray machine restricts the surgeons’ holistic analysis of spinal structures. This paper presents an end-to-end efficient and robust intraoperative X-ray image stitching method for scoliosis surgery, named SX-Stitch. The method is divided into two stages: segmentation and stitching. In the segmentation stage, we propose a medical image segmentation model named Vision Mamba of Spine-UNet (VMS-UNet), which utilizes the state space Mamba to capture long-distance contextual information while maintaining linear computational complexity. Meanwhile, the proposed model incorporates the SimAM attention mechanism, significantly improving the segmentation performance. In the stitching stage, we simplify the alignment process between images to minimize a registration energy function. The total energy function is then optimized to order unordered images and a hybrid energy function is introduced to optimize the best seam, effectively eliminating parallax artifacts. On the clinical dataset, Sx-Stitch demonstrates superiority over SOTA schemes both qualitatively and quantitatively.

关键词： Image segmentation Computational modeling Scoliosis Surgery Signal processing Speech processing Computational complexity X-ray imaging Image stitching Context modeling

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Multi-Channel Multi-Frame Integration Detection Method for Weak Target with Coherent MIMO Radar

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Gao, Longji Li, Xiaolong Wang, Mingxing Sun, Zhi Cui, Guolong Yeo, Tat Soon University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China National University of Singapore Department of Electrical and Computer Engineering Singapore119077 Singapore

Coherent multiple-input multiple-output (MIMO) radar could significantly improve the weak moving target detection ability by accumulating multi-channel and multi-frame echo signal. However, due to the target motion and the geometric configuration differences of each node relative to the target, the multi-channel multi-frame accumulation of target echoes by coherent MIMO radar faces several difficulties, i.e., range migration (RM) and Doppler frequency migration (DFM) issues within each channel, spatial variations of target signal envelope and phase across different channels, time variations of target signal envelope and phase across different frames. To tackle these problems, two contributions are made in this paper. Firstly, by applying the generalized Radon Fourier transform (GRFT) to correct the RM/DFM and obtain the signal integration within each channel, we propose a multi-channel accumulation algorithm to compensate the spatial variations of target signal and realize the target energy accumulation across different channels, which takes full advantage of the GRFT output characteristics and the radar system topological structure. Secondly, a multi-frame accumulation algorithm is developed to compensate the time variations of target signal and achieve the further integration of target energy distributed in different frames. We also provide explicit expressions and analysis for performance metrics, including the output response, SNR improvement performance. The proposed method's effectiveness is confirmed through simulation experiments. © 1965-2011 IEEE.

关键词： Topology

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