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Intelligent Holographic Computing Fusion Network for IOT Consumer Technology

IEEE Transactions on Consumer Electronics 2025年

作者： Byeon, Haewon Khalaf, Mohammed I. Ramesh, Janjhyam Venkata Naga Chaudhary, Ankur Ahanger, Tariq Ahamed Rao, K. B. V. Brahma Nimma, Divya Shavkatov, Navruzbek Gupta, Rupesh Korea University of Technology and Education Convergence Department Cheonan South Korea university of Al Maarif College of science Department of computer science Al Anbar 31001 Iraq Graphic Era Hill University Department of CSE Dehradun 248002 India Graphic Era Deemed to Be University Department of CSE Uttarakhand Dehradun 248002 India Department of information technology Noida institute of Engineering and Technology Greater Noida India Department of Management Information Systems CoBA Prince Sattam Bin Abdulaziz University Saudi Arabia Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram 522302 India University of Southern Mississippi United States Tashkent State University of Economics Department of Corporate finance and securities Tashkent Uzbekistan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura 140401 India

With the rapid evolution of holographic technology, its integration into IoT-based consumer electronics is revolutionizing user experiences in areas such as smart entertainment systems, immersive education platforms, and remote healthcare solutions. However, existing network resource scheduling approaches fall short of meeting the stringent requirements of holographic communication, which demands ultra-high bandwidth, low latency, and synchronized data transmission. To address these challenges, this paper introduces an intelligent computing-integrated network architecture tailored for consumer electronics, leveraging in-network computing to dynamically process and optimize data during transmission. We propose a novel identification mapping framework based on service, resource chain, network function, and network component identifiers to enable adaptive and efficient scheduling of holographic services across heterogeneous resources, including compute, storage, and forwarding elements. Building on this, an intelligent computing-integrated network system is developed to support seamless holographic communication in IoT consumer electronics. Experimental evaluations reveal that the proposed system reduces network bandwidth consumption by up to 60% and transmission latency by 45%, outperforming conventional edge computing approaches and significantly enhancing the responsiveness and efficiency of consumer electronic devices. © 1975-2011 IEEE.

关键词： Consumer Electronics Data Synchronization Holographic Technology Intelligent Computing IoT

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Multivariate bounded support Kotz mixture model with semi-supervised projected model-based clustering

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Information Fusion 2025年 124卷

作者： Tsega Weldu Araya Muhammad Azam Nizar Bouguila Jamal Bentahar Concordia Institute for Information Systems Engineering Concordia University Montreal Quebec Canada Faculty of Computer Science and Technology Algoma University Sault Ste. Marie Ontario Canada Department of Computer Science 6G Research Center Khalifa University Abu Dhabi United Arab Emirates

Data clustering is a crucial technique in data analysis, aimed at identifying and grouping similar data points to uncover underlying structures within a dataset. We propose a new unsupervised clustering approach using a multivariate bounded Kotz mixture model (BKMM) for data modeling when the data lie within a bounded support region. In many real applications, BKMM effectively handles observed data that fall within these limits, accurately modeling and clustering the observations. In BKMM, parameter estimation is performed by maximizing the log-likelihood using Expectation–Maximization (EM) algorithm and the Newton–Raphson method. Additionally, we explore the enhancements in clustering performance through semi-supervised learning by incorporating a small amount of labeled data to guide the clustering process. Thus, we propose a bounded Kotz mixture model using a semi-supervised projected model-based clustering method (BKMM-SeSProC) to obtain hidden cluster labels. Model selection in mixtures is essential for determining the optimal number of mixture components, and we introduce a minimum message length (MML) model selection criterion to find the best number of clusters in the BKMM-SeSProC approach. A greedy forward search is applied to estimate the optimal number of clusters. We use the same datasets to evaluate our proposed models, BKMM and BKMM-SeSProC, for data clustering. Additionally, we utilize MML model selection with BKMM-SeSProC to determine the number of components. Initially, we validate both proposed models and the model selection process in various medical applications. Furthermore, to assess their broader performance, we test the models on image datasets, including Alzheimer’s disease, lung tissue, and gastrointestinal tract images for disease recognition, and the CIFAR-100 dataset for object categorization. BKMM is compared with the Kotz mixture model (KMM), Student’s t mixture model (SMM), Laplace mixture model (LMM), bounded Gaussian mixture model (

关键词： Multivariate bounded Kotz mixture model (BKMM) Semi-supervised learning Model-based clustering Semi-supervised projected model-based clustering (SeSProC) Minimum message length (MML) Data clustering Histogram of oriented gradients (HOG)

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Nonlinear optimal control for DC industrial microgrids

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Cyber-Physical systems 2019年第4期5卷 231-253页

作者： Rigatos, G. Zervos, N. Siano, P. Abbaszadeh, M. Wira, P. Onose, B. Unit of Industrial Automation Industrial Systems Institute Rion Patras Greece Department of Innovation Systems University of Salerno Fisciano Italy GE Global Research General Electric NiskayunaNY United States IRIMAS Université de Haute Alsace Mulhouse France ICPE-CA Institute For Research Development and Testing in Electrical Engineering Bucharest Romania

DC industrial microgrids can assure uninterrupted and free-of-perturbations power supply to industrial units. Besides the solution of the related optimal control problem can result in minimisation of electric power consumption by such units. In this article, the problem of nonlinear optimal (H-infinity) control for industrial microgrids is treated. The dynamic model of an indicative microgrid that comprises photovoltaic units, batteries and supercapacitors is considered. This model undergoes approximate linearisation around a temporary operating point that is recomputed at each time-step of the control method. The linearisation relies on Taylor series expansion and on the computation of the associated Jacobian matrices. For the linearised state-space model of the system, a stabilising optimal (H-infinity) feedback controller is designed. This controller stands for the solution to the nonlinear optimal control problem under model uncertainty and external perturbations. To compute the controller’s feedback gains an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. Finally, to implement state estimation-based control without the need to measure the entire state vector of the DC microgrid the H-infinity Kalman Filter is used as a robust state estimator. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Riccati equations

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A two-stage framework and reinforcement learning-based optimization algorithms for complex scheduling problems

arXiv

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arXiv 2021年

作者： He, Yongming Wu, Guohua Chen, Yingwu Pedrycz, Witold College of Systems Engineering National University of Defense Technology Changsha410073 China School of Traffic and Transportation Engineering Central South University Changsha410075 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2V4 Canada Systems Research Institute Polish Academy of Sciences Warsaw01447 Poland

There hardly exists a general solver that is efficient for scheduling problems due to their diversity and complexity. In this study, we develop a two-stage framework, in which reinforcement learning (RL) and traditional operations research (OR) algorithms are combined together to efficiently deal with complex scheduling problems. The scheduling problem is solved in two stages, including a finite Markov decision process (MDP) and a mixed-integer programming process, respectively. This offers a novel and general paradigm that combines RL with OR approaches to solving scheduling problems, which leverages the respective strengths of RL and OR: The MDP narrows down the search space of the original problem through an RL method, while the mixed-integer programming process is settled by an OR algorithm. These two stages are performed iteratively and interactively until the termination criterion has been met. Under this idea, two implementation versions of the combination methods of RL and OR are put forward. The agile Earth observation satellite scheduling problem is selected as an example to demonstrate the effectiveness of the proposed scheduling framework and methods. The convergence and generalization capability of the methods are verified by the performance of training scenarios, while the efficiency and accuracy are tested in 50 untrained scenarios. The results show that the proposed algorithms could stably and efficiently obtain satisfactory scheduling schemes for agile Earth observation satellite scheduling problems. In addition, it can be found that RL-based optimization algorithms have stronger scalability than non-learning algorithms. This work reveals the advantage of combining reinforcement learning methods with heuristic methods or mathematical programming methods for solving complex combinatorial optimization problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Scheduling

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Automated Injuries Classification on Postmortem Images: A Deep Learning Approach with Different Neural Networks Architectures

SSRN

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SSRN 2023年

作者： Kalinowska, Kamila Barbara Zawieska, Dorota Puchala, Sebastian Markiewicz, Jakub Stefan Kot, Patryk Wilkowski, Artur Krajewski, Pawel Fudalej, Marcin Soltyszewski, Ireneusz Department of Photogrammetry Remote Sensing and Spatial Information Systems Warsaw University of Technology Politechniki 1 Square Warsaw00-661 Poland Institute of Computer Science Warsaw University of Technology Politechniki 1 Square Warsaw00-661 Poland Research Institute Liverpool John Moores University Byrom Street LiverpoolL3 3AF United Kingdom Institute of Control and Computation Engineering Warsaw University of Technology Politechniki 1 Square Warsaw00-661 Poland Forensic Medicine Department Medical University of Warsaw 61 Żwirki and Wigury Street Warsaw02-091 Poland

The paper discusses a deep learning approach for automatically detecting the two most important classes of injuries (bruises and abrasions) with the use of semantic segmentation. First, a dataset of postmortem images of injuries is completed and fitting data pre-processing, and annotation methods for algorithm training and evaluation are developed. Second, end-to-end U-Net, FPN and LinkNet semantic segmentation network-based injuries detection algorithms are implemented on EfficientNetB3 and ResNet50 backbones. Third, an optimisation strategy for the network is employed to improve the algorithm's detection performance based on the data features of injuries. This technique entails data augmentation utilising a custom loss function and a combination of image transformation and class balancing. The experimental results demonstrated promising performance with high sensitivity and specificity for our best model called TIPPI (92,7% and 98,9%, respectively). TIPPI may be used to automatically analyse postmortem images. © 2023, The Authors. All rights reserved.

关键词： Metadata

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Fast learning of graph neural networks with guaranteed generalizability: One-hidden-layer case 37

Fast learning of graph neural networks with guaranteed gener...

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37th International Conference on Machine Learning, ICML 2020

作者： Zhang, Shuai Wang, Meng Liu, Sijia Chen, Pin-Yu Xiong, Jinjun Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute NY United States MIT-IBM Waston AI Lab CambridgeMA United States IBM Thomas J. Watson Research Center Yorktown HeightsNY United States

ISBN: (纸本)9781713821120

Although graph neural networks (GNNs) have made great progress recently on learning from graph-structured data in practice, their theoretical guarantee on generalizability remains elusive in the literature. In this paper, we provide a theoretically-grounded generalizability analysis of GNNs with one hidden layer for both regression and binary classification problems. Under the assumption that there exists a groundtruth GNN model (with zero generalization error), the objective of GNN learning is to estimate the ground-Truth GNN parameters from the training data. To achieve this objective, we propose a learning algorithm that is built on tensor initialization and accelerated gradient descent. We then show that the proposed learning algorithm converges to the ground-Truth GNN model for the regression problem, and to a model sufficiently close to the ground-Truth for the binary classification problem. Moreover, for both cases, the convergence rate of the proposed learning algorithm is proven to be linear and faster than the vanilla gradient descent algorithm. We further explore the relationship between the sample complexity of GNNs and their underlying graph properties. Lastly, we provide numerical experiments to demonstrate the validity of our analysis and the effectiveness of the proposed learning algorithm for GNNs. © 2020 by the Authors All rights reserved.

关键词： Graph neural networks

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Anchor channels under 3D-load interaction – New approaches to load distributions and design

Anchor channels under 3D-load interaction – New approaches ...

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fib Symposium 2019: Concrete - Innovations in Materials, Design and Structures

作者： Konertz, Dustin Kocur, Georg Karl Clauß, Felix Häusler, Frank Mark, Peter Institute of Concrete Structures Ruhr University Bochum Bochum Germany HALFEN GmbH Research – Development – Engineering Anchoring Systems Langenfeld Germany

ISBN: (纸本)9782940643004

In practice anchor channels are widely applied to transfer loads into concrete structures. In addition to tensile and shear loads, serrated anchor channels are capable to transfer longitudinal loads when toothed channel bolts are used. If multiple loads act on a channel at the same time and in different directions, complex interactions arise. In this paper, 3D-load interactions are analysed with emphasis on the general load bearing behaviour. A new approach to determine the load distribution from the channel bolt via the channel profile to its anchors, where the load is induced into the concrete, is presented. Therefore, a bedded beam model for three-dimensional loads was set-up and verified by experimental tests, wherein the steel strains were continuously recorded with fibre optical sensors. The test program included cast-in anchor channels in non-cracked concrete with up to five anchors, various channel sizes and load positions. New insight is gained especially regarding longitudinal loads that were seldom investigated before. The studies show that the current rules of practice are strongly conservative. Consideration of all anchors involved in the load transfer would significantly extend the applicability to new fields, i.e. the anchoring of facades or in mechanized tunnelling. © Federation Internationale du Beton (fib) - International Federation for Structural Concrete, 2019.

关键词： Anchors

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A framework for a practical nurse scheduling approach: A case of operating room of a hospital in thailand 1

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AHFE Virtual Conference on the Human Side of Service engineering, 2020

作者： Rerkjirattikal, Pavinee Huynh, Van-Nam Olapiriyakul, Sun Supnithi, Thepchai School of Knowledge Science Japan Advanced Institute of Science and Technology Ishikawa923-1211 Japan School of Manufacturing Systems and Mechanical Engineering Sirindhorn International Institute of Technology Thammasat University Pathumthani12120 Thailand Language and Semantic Research Team National Electronics and Computer Technology Center Pathumthani12120 Thailand

ISBN: (数字)9783030510572

ISBN: (纸本)9783030510565

Nurse Schedule Problem (NSP) is the assignment of nurses to fulfill hospitals operational goal and regulations. The major aim of NSP is to minimize the total cost, maximizing nurse’s satisfaction, or maintain balanced distribution among preferable assignments and workloads. This paper is the preliminary research in development of NSP for a case of an operating room at a private hospital in Pathumthani, Thailand. An interview with the head nurse was conducted to capture insights and emerging issues that the head nurse wants to resolve. Then, a framework is proposed as a guideline for the development stochastic optimization model to tackle uncertainties in patients’ arrival and surgery duration which are the main concerns. This paper aims to serve as a fundamental stage for a further case based NSP development research. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

关键词： Job satisfaction

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A Novel Chaotic Time Hopping TH-NRDCSK System for Anti-jamming Communications

A Novel Chaotic Time Hopping TH-NRDCSK System for Anti-jammi...

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2019 IEEE Wireless Communications and Networking Conference, WCNC 2019

作者： Kim, Jungmin Van Nguyen, Binh Jung, Hyoyoung Lee, Jong-Hwan Kim, Kiseon School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju Korea Republic of 2nd Research and Development Institute Agency for Defense Development Daejeon Korea Republic of

ISBN: (纸本)9781538676462

We propose a novel TH-NRDCSK system to enjoy the advantages of both TH and chaotic systems for AJ communication purpose and alleviate the strict synchronization issue of TH systems. We then simulate the performance of the proposed system to reveal the effects of the system parameters on AJ performance. In addition, the performance of the proposed system is compared to that of the TH-BPSK system to look at the advantages of non-coherent system. The simulation results show that the proposed system significantly outperforms conventional counterparts in a practical environment. © 2019 IEEE.

关键词： Chaotic systems

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Learning Spatial and Temporal Information with Complex-Valued Operation for High-Performance of Spiking Neural Networks

SSRN

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SSRN 2022年

作者： Liu, Fang Pedrycz, Witold Xu, Qi Xu, Jialin Yang, Jie Wu, Wei School of Mathematical Sciences Dalian University of Technology Dalian116024 China Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province Dalian116024 China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland School of Artificial Intelligence Dalian University of Technology Dalian116024 China

Every neuron in traditional spiking neural networks (SNNs) only transmits temporal information, namely spiking time, to its postsynaptic neurons while all other underlying information is discarded. In view that brain pathways for temporal and spatial cognition involve overlapping and interacting systems that converge on the hippocampal region, in this study, we show for the first time that spatial and temporal information can move forward simultaneously, interact, and finally fuse in the output layer as the brain system works by implementing the complex-valued operations in SNNs. The spatial information is extracted with the derivative of membrane potential function of spiking neuron at the moment of excitation and the temporal information is the spiking time. Accuracy and generalization ability of neural network outputs from seven benchmark classification datasets are found to be excellent. The networks are also able to process regression tasks, which is unachievable by most of the existing SNNs. © 2022, The Authors. All rights reserved.

关键词： Neurons

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