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Design and Analysis of Dynamic Block-Setup Reservation Algorithm for 5G Network Slicing

IEEE Transactions on Mobile Computing 2023年第9期22卷 5140-5154页

作者： Hsieh, Cheng-Ying Phung-Duc, Tuan Ren, Yi Chen, Jyh-Cheng National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan University of Tsukuba Faculty of Engineering Information and Systems Tsukuba305-8577 Japan University of East Anglia School of Computing Sciences NorwichNR4 7TJ United Kingdom

In 5G, network functions can be scaled out/in dynamically to adjust the capacity for network slices. The scale-out/-in procedure, namely autoscaling, enhances performance by scaling out instances and reduces operational costs by scaling in instances. However, the autoscaling problems in 5G networks are different from those in traditional cloud computing. The 5G network functions must be considered the simultaneous deployment of multiple instances;moreover, the deployment of 5G network functions is more frequent than that of traditional cloud computing. Both the number and timing of deployment will substantially affect the cost-effectiveness of the system. In this paper, we first identify the autoscaling issues specifically based on the 3GPP standards. We develop a low-complexity analytical queuing model to formulate the problem and quantify a set of performance metrics with closed-form solutions. The proposed analytical model and closed-form solutions are cross-validated by extensive simulations. The analytical model offers design insights and theoretical guidelines, helping us study the effectiveness of reservations. We proposed a dynamic block-setup reservation algorithm (DBRA) to find the optimal reserved number and threshold value of network slices. Therefore, mobile operators can balance the system's cost-effectiveness without large-scaled testing and real deployment, saving cost on time and money. © 2002-2012 IEEE.

关键词： Heuristic algorithms

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Hybrid Schrödinger-Ginzburg-Landau (Sch-GL) approach in study of superconducting integrated structures

arXiv

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arXiv 2023年

作者： Stojewski, Bartosz Pomorski, Krzysztof Cracow University of Technology Faculty of Electrical and Computer Engineering Poland Cracow University of Technology Faculty of Computer Science and Telecommunications Poland Quantum Hardware Systems

Various superconducting lattices were simulated and can be treated as lattices of superconducting atoms with preimposed symmetry in 1, 2 and 3 dimensions. Hybrid Schrödinger-Ginzburg-Landau approach is based on the fact of the mathematical similarity of Ginzburg-Landau (GL) and Schrödinger formalisms. Starting from Schrödinger approach by change of term V (x) - E with term α(x) + β(x)|ψ(x)|2we arrived at the Ginzburg-Landau equation. In the presented relaxation algorithm we use one and two dimensional ground energy solutions of Schrödinger equation and placed them as starting trial solution for GL relaxation method. In consecutive steps we increase the nonlinear term in the GL equation which results in achieving a stable approach of solution of GL equation. The obtained numerical results and used methodology form simulation platform bases for study of superconducting integrated structures that can model various superconducting devices. In general, one can model time-dependent geometry of superconducting structures. © 2023, CC0.

关键词： Simulation platform

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Parallel Path Planning for Multi-Robot Coordination

Parallel Path Planning for Multi-Robot Coordination

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Jorge Ribeiro Marina Brilhante Diogo Miguel Matos Carlos A. Silva Héber Sobreira Pedro Costa CRIIS - Centre of Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto Porto Portugal Flowbotic Mobile Systems S.A Viseu Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Multi-robot coordination aims to synchronize robots for optimized, collision-free paths in shared environments, addressing task allocation, collision avoidance, and path planning challenges. The Time Enhanced A* (TEA*) algorithm addresses multi-robot pathfinding offering a centralized and sequential approach. However, its sequential nature can lead to order-dependent variability in solutions. This study enhances TEA* through multi-threading, using thread pooling and parallelization techniques via OpenMP, and a sensitivity analysis enabling parallel exploration of robot-solving orders to improve robustness and the likelihood of finding efficient, feasible paths in complex environments. The results show that this approach improved coordination efficiency, reducing replanning needs and simulation time. Additionally, the sensitivity analysis assesses TEA*'s scalability across various graph sizes and number of robots, providing insights into how these factors influence the efficiency and performance of the algorithm.

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Human Flow Measurement System Using Floor Estimation of Depth Images for Low-End IoT Devices

Human Flow Measurement System Using Floor Estimation of Dept...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Takuya Nagatoshi Michiharu Niimi Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka-shi Fukuoka Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Kawazu Iizuka-shi Fukuoka Japan

We propose a system that uses depth information, which represents the distance from the sensor, instead of color information to do both measure human flow and protect privacy for low-end IoT devices. The system is designed to detect the position and number of persons from depth information. Since administrative organizations or educational institutions have been reducing their budgets, this system should be implemented at as low a cost as possible. In order to realize human flow detection system on low-end Iot devices, we use low performance depth cameras as data acquisition device controlled by single board computers, such as Raspberry Pi. As one of our goals is all data processing are performed on single board computers, we adopt computational methods for the detection as possible as simple. The proposed method is based on the background subtraction method, which prepares a reference depth image and extracts moving regions from one frame extracted from the video depth image. Furthermore, we aim to achieve high-precision people flow measurement by combining the following three elements: segmentation of moving objects using edge information, identification of human areas using floor information, and human tracking using areas where people overlap in the direction of the time axis. Experiments were also conducted and evaluated in a real space using a program that implements the presented method.

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Classification of benign and malignant breast lesions in mammograms using dense-unified multiscale attention network and data-efficient image transformers

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The European Physical Journal Special Topics 2025年 1-19页

作者： Paavankumar, S. Karthik, R. Idayachandiran, Gangatharan Sri, Pulaparthi Penchala Deepthi Illakiya, T. School of Computer Science Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Chennai India

Breast cancer is a common and a serious health problem and it is the major cause of morbidity and mortality for women. Early detection of the disease is particularly challenging because abnormalities such as masses and microcalcifications exhibit subtle and diverse characteristics that are often difficult to identify in mammograms. In recent years, advancement in artificial intelligence, particularly deep learning (DL), has shown to improve diagnostic accuracy and early-stage tumor detection. This study aims to improve performance of DL models by considering both masses and microcalcifications in the proposed work to classify breast cancer abnormalities. The proposed work introduces a novel dual-track network that employs a combination of dense-unified multiscale attention fusion (UMAF) track and data-efficient image transformer (DeiT). The DeiT track processes the entire image simultaneously using patch embeddings, enabling them to capture multiscale representations and dependencies across the entire image. Simultaneously, the Dense-UMAF track focuses on extracting localized features while utilizing connectivity of DenseNet architecture to enable effective feature reuse. This approach generates relevant input features through residual connections of varying lengths, thereby effectively addressing the vanishing gradient problem. The UMAF improves feature extraction by capturing multiscale information, resulting in a better representation of the input data. This dual-track architecture is specifically designed to capture the characteristics of mass and calcification abnormalities in mammograms, which display both localized features and global contextual patterns. The proposed network was evaluated on the Curated Breast Imaging Subset of Digital Database for Screening Mammography dataset, obtaining a classification accuracy of 88.69%.

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28-m W Fully Embedded AI Techniques with On-site Learning for Low-Power Handy Tactile Sensing System

28-m W Fully Embedded AI Techniques with On-site Learning fo...

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2022 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2022

作者： Yano, Yuji Iwamoto, Hisashi Yoshimura, Takuma Nishida, Yoshihiro Mori, Tatsuya Komoku, Kiyotaka Takao, Hidekuni Arimoto, Kazutami Poco-apoco Networks Development Department Osaka Japan Kagawa University Faculty of Engineering and Design Takamatsu Japan FaMT Himeji Development Department Japan Okayama Prefectural University Faculty of Computer Science and Systems Engineering Sojya Japan

ISBN: (纸本)9781665409216

We have been studying an edge-AI learning-and-inference techniques for low-power handy battery operating tactile sensing system. It works in an event-driven of normally off power supply mechanism. The power consumption during classify operation at 10 inference per second is approximately 28m W. The proposed system adopts on-site embedded learning for the low power consumption and the high recognition accuracy, which is effective to operate in variety of environments. On-site learning function improves the recognition accuracy to classify the six kinds of paper from 50% to 91 % though humans can hardly identify them. © 2022 IEEE.

关键词： Learning systems

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Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

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SN computer science 2024年第5期5卷 546页

作者： Khan, Surbhi Bhatia Kumar, Ankit Mashat, Arwa Pruthviraja, Dayananda Imam Rahmani, Mohammad Khalid Mathew, Jimson School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Information Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh 11673 Saudi Arabia Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna India

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through

关键词： Cluster head Clustering Data aggregation Gateway Internet of things LEACH Lifetime

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Novel Rank-based Features of Atrial Potentials for the Classification Between Paroxysmal and Persistent Atrial Fibrillation

Novel Rank-based Features of Atrial Potentials for the Class...

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2022 Computing in Cardiology, CinC 2022

作者： Moghaddasi, Hanie Hendriks, Richard C. Van Der Veen, Alle-Jan De Groot, Natasja M.S. Hunyadi, Borbala Delft University of Technology Circuits and Systems Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Erasmus University Medical Center Department of Cardiology Rotterdam Netherlands

ISBN: (纸本)9798350300970

Atrial fibrillation (AF) is the most common arrhythmia. Although the exact cause is unclear, electropathology of atrial tissue is one contributing factor. Electropathological characteristics derived from intra-operative epicardial measurements, such as conduction block (CB) and continues conduction delay and block (cCDCB), can be used to assess the severity of AF. In sinus rhythm, however, these parameters do not indicate significant difference between different development stages of AF, such as paroxysmal and persistent AF. Therefore, we propose a methodology to improve AF severity detection using intra-operative electrograms. We propose a model that describes the spatial diversity of atrial potential waveforms during a single beat on the multi-channel electrograms. Based on this model, we derive two novel features. During sinus rhythm, we used 293 beats from patients with a history of paroxysmal or persistent AF. Using a random forest classifier, we achieved 78.42% classification accuracy, while classification based on the CB and cCDCB leads to an accuracy of 58.34%. © 2022 Creative Commons.

关键词： Cardiology

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Numerical evaluation on sub-Nyquist spectrum reconstruction methods

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Frontiers of computer science 2023年第6期17卷 199-201页

作者： Zihang SONG Han ZHANG Sean FULLER Andrew LAMBERT Zhinong YING Petri MAHONEN Yonina ELDAR Shuguang CUI Mark DPLUMBLEY Clive PARINI Arumugam NALLANATHAN Yue GAO Department of Engineering King’s College LondonStrandLondon WC2R 2LSUK School of Computer Science and Electronic Engineering University of SurreyGuildfordSurrey GU27XHUK National Instruments Corporation(UK)Ltd. NewburyBerkshire RG142PZUK Electronic Media Services Ltd. BordonHampshire GU350FJUK Sony Research Center Sony CorporationLund 22188Sweden Institute for Networked Systems RWTH Aachen UniversityKackertstrasseAachen 52072Germany Faculty of Math&CS Weizmann Institute of ScienceRehovot 7610001Israel School of Science and Engineering The Chinese University of Hong KongShenzhen 518172China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondon E14NSUK School of Computer Science Fudan UniversityShanghai 200433China

1 Introduction As wireless technology continues to expand,there is a growing concern about the efficient use of spectrum *** though a significant portion of the spectrum is allocated to licensed primary users(PUs),studies indicate that their actual utilization is often limited to between 5%to 10%[1].The underutilization of spectrum has given rise to cognitive radio(CR)technology,which allows secondary users(SUs)to opportunistically access these underused resources[2].However,wideband spectrum sensing,the key of CR,is limited by the need for high-speed analog-to-digital converters(ADCs),which are costly and *** spectrum sensing(CSS)addresses this challenge by employing sub-Nyquist rate *** efficiency of active transmission detection heavily depends on the quality of spectrum reconstruction.

关键词： spectrum continue Nyquist

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Optimizing Patient Satisfaction Surveys and Feedback with Cloud Computing and NLP Techniques 8

Optimizing Patient Satisfaction Surveys and Feedback with Cl...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Jayanthi, L.N. Shanmugam, Kannan Suma, Challapalli Sagar, B.S. Maniraj, S.P. Ganeshbabu, T.R. Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Department of Gaming Technology Madhapradesh Bhopal India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Reva University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Faculty of Engineering & Technology Department of Data Science and Business Systems Chennai Tamil Nadu Kattankulathur India Department of Electronics and Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350376425

Healthcare quality assessment and identification of opportunities for improvement are crucially facilitated by patient satisfaction surveys. Traditional approaches sometimes face difficulties in handling substantial amounts of written comments and obtaining real useful data. It presents a new method that combines cloud computing with Natural Language Processing (NLP) approaches to enhance the analysis of patient satisfaction data. By using cloud platforms, it can effectively manage large datasets and provide scalable, cost-efficient processing. The use of NLP methods, such as text mining and sentiment analysis, enables the conversion of unstructured input into structured insights, thereby uncovering the fundamental patient feelings and patterns. This connection enables the analysis of data in real-time also improving the accuracy of interpreting feedback and facilitating fast enhancements in healthcare services. Future research will prioritise the improvement of NLP algorithms and the investigation of supplementary cloud-based technologies to enhance the accuracy and efficiency of patient satisfaction analysis to improved patient care and experience. © 2024 IEEE.

关键词： Sentiment analysis

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