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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： D.V. Manjunatha Meenakshi Maindola Reny Jose S. Kaliappan Mitul Patel Ramya Maranan Department of Electronics & Communication Engineering Alvas Institute of Engineering & Technology Moodabidri India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Applications Marian College Kuttikkanam (Autonomous) Idukki India Division of Research and Development Lovely Professional University Phagwara India Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Vadodara India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

In order to improve the visual quality of low-resolution video frames, this study introduces a new super-resolution method for real-time video processing that is powered by artificial intelligence. With little computational overhead, the suggested approach uses a hybrid deep learning model to reconstruct high-resolution frames. This model mixes Convolutional Neural Networks (CNNs) with Generative Adversarial Networks (GANs). The suggested method outperforms state-of-the-art methods by 12.3% in PSNR and 8.7% in SSIM, according to experimental results on benchmark video datasets. The PSNR is 34.56 dB and the SSIM is 0.94. Also, running on a GTX 1080Ti GPU, the system shows an average processing speed of 30 fps, which is great for real-time apps. The suggested strategy is effective in eliminating artifacts, improving visual clarity, and retaining fine details in various real-time video processing contexts, according to both quantitative and qualitative results.

关键词： Visualization Interpolation Computational modeling Superresolution Streaming media Mathematical models Real-time systems Kernel Spatial resolution Faces

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Data Freshness Optimization Under CAA in the UAV-Aided MECN: A Potential Game Perspective

Data Freshness Optimization Under CAA in the UAV-Aided MECN:...

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作者： Wang, Weizheng Srivastava, Gautam Lin, Jerry Chun-Wei Yang, Yaoqi Alazab, Mamoun Gadekallu, Thippa Reddy City University of Hong Kong Department of Computer Science Hong Kong Brandon University Department of Mathematics and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung406040 Taiwan Western Norway University of Applied Sciences Department of Computer Science Bergen5063 Norway Army Engineering University of Pla Graduate School Nanjing210000 China Melbourne Polytechnic Prahran Campus Department of Information Technology MelbourneVIC3181 Australia Vellore Institute of Technology School of Information Technology Vellore632014 India

As a promising enabler for edge intelligence, Unmanned Aerial Vehicles (UAV) have become more and more important in Mobile Edge Computing Networks (MECN), such as communication, computation, collection and control service supply. Although Age of Information (AoI) minimization is indispensable for fresh information collection and computation in the UAV-aided MECN, some attackers can launch attacks to deteriorate the availability of precious channel resources, such as revealed channel access attacks (CAAs). Moreover, recent research has not considered the system's active probability and security issues concurrently, e.g., CAA, in the average AoI minimization process. In this paper, to deal with this problem, we consider an AoI-oriented channel access problem under CAA with a game theory viewpoint. Firstly, to obtain a MECN-based AoI indicator under CAA, the system model with active probability consideration is established. Next, the channel access-based AoI minimization problem is formulated from the viewpoint of the Ordinary Potential Game (OPG). Furthermore, two algorithms called AACSD and DCASD are proposed to determine channel access strategies, by which the Nash Equilibrium (NE) solution of the OPG could be reached. Finally, experiments are conducted under homogeneous and heterogeneous parameter settings, and the simulation results evaluate the correctness and effectiveness of our proposals. © 2000-2011 IEEE.

关键词： Game theory

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A Residual LSTM based Multi-Label Classification Framework for Proactive SLA Management in a Latency Critical NFV Application Use-Case 19

A Residual LSTM based Multi-Label Classification Framework f...

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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Jalodia, Nikita Taneja, Mohit Davy, Alan Dezfouli, Behnam Waterford Institute of Technology Walton Institute for Information and Communication Systems Science Department of Computing and Mathematics Waterford Ireland Sfi Connect Research Centre for Future Networks and Communications Ireland Santa Clara University Department of Computer Science and Engineering Santa ClaraCA United States

ISBN: (纸本)9781665431620

We are witnessing an emergence of a new era of applications delivered via a paradigm of flexible and softwarized communication networks. This has opened the market to a wider movement towards virtualized applications and services in key verticals such as automated vehicles, smart grid, virtual reality (VR), Internet of Things (IoT), industry 4.0, telecommunications, etc. With an increasing emergence of verticals driven by the vision of low latency and high reliability, there is a wide gap to efficiently bridge the Quality of Service (QoS) constraints for the end-user experience. Most latency-critical services are over-provisioned on all fronts to offer reliability, which is inefficient in the long run. In this work, we present a Residual Long Short-Term Memory (LSTM) based multi-label classification framework for proactive SLA management in a latency-critical Network Function Virtualization (NFV) application use case. We compose a multivariate time-series forecasting model with multiple time-step predictions in a multi-output scenario, and associate a multi-label classifier for a granular prediction of individual Service Level Objective (SLO) violations for each step in the forecast horizon. The Residual LSTM approach achieves an improvement of 31.1% over the baseline on the forecast classification accuracy, and a 2.65% improvement on the interpolated average precision over the standard LSTM methodology. © 2022 IEEE.

关键词： Long short-term memory

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Structured Tensor Decomposition Based Channel Estimation and Double Refinements for Active RIS Empowered Broadband Systems

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IEEE Transactions on Vehicular technology 2025年

作者： Wang, Yirun Wang, Yongqing Shen, Yuyao Wang, Gongpu Tellambura, Chintha Beijing Institute of Technology School of Information and Electronics Beijing100081 China Beijing Jiaotong University Engineering Research Center of Network Management Technology for High Speed Railway Ministry of Education School of Computer Science and Technology Beijing100044 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Channel parameter recovery is critical for the next-generation reconfigurable intelligent surface (RIS)-empowered communications and sensing. Tensor-based mechanisms are particularly effective, inherently capturing the multi-dimensional nature of wireless channels. However, existing studies assume either a line-of-sight (LOS) scenario or a blocked TX-RX channel. This paper solves a novel problem: tensor-based channel parameter estimation for active RIS-aided multiple-antenna broadband connections in fully multipath environments with the TX-RX link. System settings are customized to construct a fifth-order canonical polyadic (CP) signal tensor that matches the five-dimensional channel. Four tensor factors contain redundant columns, rendering the classical Kruskal's condition for decomposition uniqueness unsatisfied. The fifth-order Vandermonde structured CP decomposition (VSCPD) is developed to address this challenge, making the tensor factorization problem solvable using only linear algebra and offering a relaxed general uniqueness condition. With VSCPD as a perfect decoupling scheme, a sequential triple-stage channel estimation algorithm is proposed based on one-dimensional parameter estimation. The first stage enables multipath identification and algebraic coarse estimation. The following two stages offer optional successive refinements at the cost of increased complexity. The closed-form Cramér-Rao lower bound (CRLB) is derived to assess the estimation performance. Herein, the noise covariance matrix depends on multipath parameters in our active-RIS scenario. Numerical results are provided to verify the effectiveness of proposed algorithms under various evaluation metrics. Our results also show that active RIS can significantly improve channel estimation performance compared to passive RIS. © 2025 IEEE.

关键词： Tensors

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An Exploration of the Use Cases for Artificial Intelligence in Data Access Optimization

An Exploration of the Use Cases for Artificial Intelligence ...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Sharmila. P Snehal R. Rathi Manpreet Singh Preeti Naval Krishnan Batri Bhavesh Agnihotri Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India School of Computer Science and Engineering Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India

This paper provides an exploration of the use cases for artificial intelligence (AI) in information access optimization. AI-based tactics to statistics optimization have the ability to enhance performance and reduce cost drastically. The paper discusses the characteristics of AI-pushed optimization techniques, together with fusion of heterogeneous facts assets, predictive modeling, forecasting, anomaly detection, and clustering. It also explores the numerous statistics assets that can be optimized using AI, which includes conventional database systems, disbursed information lakes, and streaming event statistics. Moreover, the paper considers the demanding situations related to AI-enabled facts get admission to optimization, such as loss of availability of labeled data and issue in imposing commercial enterprise good judgment. Finally, excellent practices and considerations for implementation are mentioned. Through exploring the use instances for AI in records get entry to optimization, this paper provides important insights for builders and businesses considering the usage of AI for data get entry to optimization.

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Development of an application for recognizing mathematical formulas using convolutional neural networks

Development of an application for recognizing mathematical f...

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Innovative Trends in Information technology (ICITIIT), International Conference on

作者： Afshan Zareen Kuljeet Kaur Mayur V. Savsani Swapnil Parikh Harshal Patil Ramya Maranan Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University Mandya Karnataka India Department of Management Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Statistics Dr. Yagnik Road H & H. B. Kotak Institute of Science Rajkot Gujarat India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Waghodia Gujarat India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

In the ever-expanding landscape of digital content, the ability to recognize and understand mathematical formulas is crucial for various applications, including academic research, document processing, and educational technology. This project introduces a prototype system designed to recognize mathematical formulas using the YOLOv5 (You Only Look Once) object detection framework. YOLOv5’s real-time object detection capabilities make it a promising choice for identifying and extracting mathematical expressions seamlessly from digital documents. The proposed prototype system addresses a critical gap in the field of OCR by focusing on the specialized domain of mathematical notation recognition. The YOLOv5-based approach offers real-time processing capabilities, making it suitable for applications that require quick and accurate identification of mathematical content in various digital contexts.

关键词： YOLO Neural networks Optical character recognition Symbols Prototypes Training data Real-time systems

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A Segmentation Framework for Acoustic Sidescan Sonar Images Using Improved Smallest of Constant False Alarm Rate and MAP-MRF 16

A Segmentation Framework for Acoustic Sidescan Sonar Images ...

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16th International Conference on Underwater Networks and Systems, WUWNet 2022

作者： Tang, Yiteng Liu, Jun Song, Shanshan Guan, Wenxue Cui, Jun-Hong College of Computer Science and Technology Jilin University Jilin Province Changchun China School of Electronic and Information Engineering Beihang University Haidian Qu Beijing Shi China Smart Ocean Science and Technology Research Institute Jilin University Jilin Province Changchun China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Guangdong Province Shenzhen China

ISBN: (纸本)9781450399524

Segmentation of sidescan sonar image is a significant issue in underwater object detection and recognition. However, most prior methods only consider segmentation accuracy, ignoring false alarm rate, which plays a vital role in object detection and recognition. In this paper, a robust and accurate segmentation framework for sidescan sonar image is proposed, which balances a preferred tradeoff between accuracy and false alarm rate. The proposed method integrates an improved Smallest Of Constant False Alarm Rate (SO-CFAR) algorithm and a Maximum A Posteriori probability and Markov Random Field model (MAP-MRF). The part of innovations segments acoustical highlight region accurately while preserving edge features, which can make segmentation results obtain preferred false alarm rate. After that, MAP-MRF is employed for overcoming drawbacks associated with higher threshold value in continuous acoustical highlight areas. Besides, to better deal with intensity inhomogeneity, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is incorporated into this method, which can locate Region Of Interest (ROI) in sonar images as well as improve segmentation effect. Experimental and comparative results on actual side-scan sonar images demonstrate that our method provides superior denoising, precision, and robustness performance. © 2022 ACM.

关键词： Object detection

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A STABLE, FAST, AND FULLY AUTOMATIC LEARNING ALGORITHM FOR PREDICTIVE CODING NETWORKS 12

A STABLE, FAST, AND FULLY AUTOMATIC LEARNING ALGORITHM FOR P...

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12th International Conference on Learning Representations, ICLR 2024

作者： Salvatori, Tommaso Song, Yuhang Yordanov, Yordan Millidge, Beren Emde, Cornelius Xu, Zhenghua Sha, Lei Bogacz, Rafal Lukasiewicz, Thomas VERSES AI Research Lab Los AngelesCA90016 United States MRC Brain Network Dynamics Unit University of Oxford United Kingdom Department of Computer Science University of Oxford United Kingdom School of Health Sciences and Biomedical Engineering Hebei University of Technology China Institute of Logic and Computation Vienna University of Technology Austria Fractile Ltd. London United Kingdom

Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. Training such models, however, is quite inefficient and unstable. In this work, we show how by simply changing the temporal scheduling of the update rule for the synaptic weights leads to an algorithm that is much more efficient and stable than the original one, and has theoretical guarantees in terms of convergence. The proposed algorithm, which we call incremental predictive coding (iPC), is also more biologically plausible than the original one, as it is fully automatic. In an extensive set of experiments, we show that iPC constantly performs better than the original formulation on a large number of benchmarks for image classification, as well as for the training of both conditional and masked language models, in terms of test accuracy, efficiency, and convergence with respect to a large set of hyperparameters. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Bayesian networks

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Bayesian Optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction Model 5

Bayesian Optimization with Stacked Sparse Autoencoder based ...

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5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Shaikh, Irfan Abdul Karim Krishna, P. Vamsi Biswal, Swagat Gourav Kumar, A. Sasi Baranidharan, S. Singh, Kamlesh Faculty of Business Bahrain Polytechnic Isa Town Bahrain School of Management Malla Reddy University Telangana Hyderabad India Odisha University of Tecnology and Research Department of Electrical Engineering Orissa Bhubaneswar India Institute of Engineering and Technology Srinivas University Mentor-IT-Inurture Education Solutions Pvt Ltd Bangalore Department of Cloud Technology and Data Science Karnataka Dakshina Kannada India Department of Mba and Research Center Karnataka Bangalore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun Campus Dehradun India

ISBN: (纸本)9781665474672

Digital currency is a way of currency utilized in the digital world namely electronic devices or digital forms. Many terms are alternative words for digital currency such as cyber cash, digital money, and electronic money. Cryptocurrency is a type of asset that has developed due to the progression of financial technology and it has made a tremendous chance for research workers. Cryptocurrency price prediction is challenging because of the dynamism and price volatility. The electronic economy is severely hazardous and should be advanced with greater caution, to minimize or avoid the risk that occurs in this case. Therefore, this study develops a new Bayesian optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction (BOSSAE-CPP) model. The major intention of the BOSSALCPP technique lies in the effectual prediction of cryptocurrency prices. To attain this, the BOSSAE-CPP technique exploits SSAE model for price prediction process. Moreover, the BO technique is used to optimally choose the hyperparameter values of the SSAE model and results in enhanced predictive outcomes. To deliberate the enhanced outcomes of the BOSSALCPP technique, extensive experimentation study is made. The comparison study highlighted the improved performance of the BOSSAE-CPP technique. © 2023 IEEE.

关键词： Electronic money

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Improving Zero-Shot Coordination with Diversely Rewarded Partner Agents

Improving Zero-Shot Coordination with Diversely Rewarded Par...

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International Joint Conference on Neural Networks (IJCNN)

作者： Peilin Wu Zhenhua Yang Peng Yang Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Statistics and Data Science Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Zero-shot coordination studies the training of well-generalizing human-AI coordination agents in the scenario where human data is unavailable. To obtain a coordination agent generalize to unseen humans, prevailing methods generate a population of partner agents as proxy models of human partners and then train a coordination agent with these partner agents. Constructed partner agents are expected to be as diverse as possible to cover a wide range of human behaviors, preventing a distribution shift between training and testing stages. Recent works concentrate on studying effective methods of creating a group of high-reward while diverse partner agents to model unseen human partners. However, the resulting high-reward partner agents do not accurately reflect real-world situations, considering that human decisions are not always optimal and may sometimes even hinder the progression of coordination. Therefore, these studies still struggle to capture the potential characteristics of human partners. In this work, reinforcement learning (RL) and supervised learning (SL) are integrated to train a reward-conditioned policy. By conditioned on different desired rewards, a reward-conditioned policy simulates both low-reward and high-reward partners. Additionally, a reward-bucketed replay buffer and curriculum learning are applied to enhance reward diversity and boost the training of coordination agents. Experiments demonstrate that the proposed reward-conditioned policy is capable of generating agents with different rewards. Moreover, the zero-shot coordination performance of agents trained with these partners surpasses previous methods in the majority of scenarios within the Overcooked human-AI coordination benchmark.

关键词： Training Impedance matching Supervised learning Neural networks Reinforcement learning Benchmark testing Trajectory

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