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arXiv 2022年

作者： Mazloomi, Anahita Sami, Hani Bentahar, Jamal Otrok, Hadi Mourad, Azzam Concordia Institute for Information Systems Engineering Concordia University MontrealQCH3G 1M8 Canada The Center of Cyber-Physical Systems Department of EECS Khalifa University Abu Dhabi United Arab Emirates The Department of Computer Science and Mathematics Lebanese American University Beirut961 Lebanon

Cloud computing is a reliable solution to provide distributed computation power. However, real-time response is still challenging regarding the enormous amount of data generated by the IoT devices in 5G and 6G networks. Thus, multi-access edge computing (MEC), which consists of distributing the edge servers in the proximity of end-users to have low latency besides the higher processing power, is increasingly becoming a vital factor for the success of modern applications. This paper addresses the problem of minimizing both, the network delay, which is the main objective of MEC, and the number of edge servers to provide a MEC design with minimum cost. This MEC design consists of edge servers placement and base stations allocation, which makes it a joint combinatorial optimization problem (COP). Recently, reinforcement learning (RL) has shown promising results for COPs. However, modeling real-world problems using RL when the state and action spaces are large still needs investigation. We propose a novel RL framework with an efficient representation and modeling of the state space, action space and the penalty function in the design of the underlying Markov Decision Process (MDP) for solving our problem. This modeling makesthe Temporal Difference learning (TD) applicable for a large-scale real-world problem while minimizing the cost of network design. We introduce the TD(λ) with eligibility traces for Minimizing the Cost (TDMC) algorithm, in addition to Q-Learning for the same problem (QMC) when λ = 0. Furthermore, we discuss the impact of state representation, action space, and penalty function on the convergence of each model. Extensive experiments using a real-world dataset from Shanghai Telecommunication demonstrate that in the light of an efficient model, TDMC/QMC are able to find the actions that are the source of lower delayed penalty. The reported results show that our algorithm outperforms the other benchmarks by creating a trade-off among multiple objectives.

关键词： Reinforcement learning

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On stability of one-dimensional Hughes’ dynamics with affine costs

arXiv

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arXiv 2025年

作者： Andreianov, Boris Fagioli, Simone Rosini, Massimiliano D. Stivaletta, Graziano Institut Denis Poisson CNRS UMR7013 Université de Tours Université d’Orléans Parc Grandmont Tours37200 France 6 Miklukho-Maklaya St Moscow117198 Russia Department of Information Engineering Computer Science and Mathematics University of L’Aquila I-67100 Italy University of Marii Curie-Sklodowskiej Plac Marii Curie-Sklodowskiej 1 Lublin20-031 Poland Department of Management and Business Administration University of Chieti-Pescara Viale Pindaro 42 Pescara65127 Italy

We investigate stability issues for the one-dimensional variant of the celebrated Hughes model for pedestrian evacuation. The cost function is assumed to be affine, which is a setting where existence of solutions with BVloc in space regularity, away from the so-called turning curve, was recently established. We provide a uniqueness result for solutions having the special property that agents never cross the turning curve (which implies that they are BV globally). In the same setting, continuous dependence of solutions on the cost parameter is highlighted. On the other hand, numerical simulations using the many-particle approximation of the model, with more general initial conditions that allow the support of the solutions to intersect the turning curve, demonstrate the strong sensitivity of the evacuation time to the same cost parameter;this instability arises from interactions between agents and the turning curve. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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Efficient Algorithms for Approximating Quantum Partition Functions at Low Temperature

arXiv

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arXiv 2022年

作者： Helmuth, Tyler Mann, Ryan L. Department of Mathematical Sciences Durham University DurhamDH1 3LE United Kingdom Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology SydneyNSW2007 Australia School of Mathematics University of Bristol BristolBS8 1UG United Kingdom

We establish an efficient approximation algorithm for the partition functions of a class of quantum spin systems at low temperature, which can be viewed as stable quantum perturbations of classical spin systems. Our algorithm is based on combining the contour representation of quantum spin systems of this type due to Borgs, Kotecký, and Ueltschi with the algorithmic framework developed by Helmuth, Perkins, and Regts, and Borgs et al. © 2022, CC BY.

关键词： Temperature

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Handcrafted local feature descriptor-based point cloud registration and its applications: A review

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IEEE Transactions on Visualization and computer Graphics 2025年

作者： Tao, Wuyong Wang, Ruisheng Hua, Xianghong Liu, Jingbin Chen, Xijiang Zang, Yufu Chen, Dong Xu, Dong Zhao, Bufan Nanchang University School of Mathematics and Computer Sciences Nanchang330031 China Shenzhen University School of Architecture & Urban Planning Shenzhen518060 China University of Calgary Department of Geomatics Engineering CalgaryT2N 1N4 Canada Wuhan University School of Geodesy and Geomatics Wuhan430079 China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430079 China Finnish Geospatial Research Institute Department of Remote Sensing and Photogrammetry Masala02430 Finland Wuhan University of Technology School of Safety Science and Emergency Management Wuhan430079 China Nanjing University of Information Science & Technology School of Remote Sensing & Geomatics Engineering Nanjing210000 China Nanjing Forestry University College of Civil Engineering Nanjing210037 China Guangzhou Urban Planning & Design Survey Research Institute Guangzhou51100 China

Point cloud registration serves as a fundamental problem across multiple fields including computer vision, computer graphics, and remote sensing. While local feature descriptors (LFDs) have long been established as a cornerstone for point cloud registration and the LFD-based approach has been extensively studied, the field has witnessed significant advancements in recent years. Despite these developments, the research community lacks a systematic review to consolidate these contributions, leaving many researchers unaware of recent progress in LFD-based registration. To address this gap, we present a comprehensive review that critically examines both state-of-the-art and widely referenced methods across all subtasks of LFD-based registration. Our work provides: (1) an extensive survey of existing methodologies, (2) in-depth analysis of their respective strengths and limitations, (3) insightful observations and practical recommendations, and (4) a thorough summary of relevant applications and publicly available datasets. This systematic overview offers valuable guidance for researchers pursuing future investigations in this domain. © 1995-2012 IEEE.

关键词： Three dimensional computer graphics

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Stiffness ratio and the diffusion of fake news

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AIP Conference Proceedings 2022年第1期2425卷

作者： Raffaele D’Ambrosio Serena Mottola Beatrice Paternoster 1Department of Information Engineering and Computer Science and Mathematics University of L’Aquila Italy 2Department of Economic and Legal Studies University “Parthenope” of Naples Italy 3Department of Mathematics University of Salerno Italy

This paper emphasizes the role of the stiffness ratio that characterizes differential problems modeling the diffusion of fake news. In particular, we propose the employ of the stiffness ratio in order to understand how fast is the transit of fake news in a given population, providing a numerical evidence supporting our approach, based on real data.

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Employee performance assessment system based on smart method (case study: Banda Aceh Military Court I-01)

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AIP Conference Proceedings 2023年第1期2484卷

作者： Zalfie Ardian Arifah Che Alhadi Gusti Mas Panji 1Department of Information System Faculty of Computer Science Universitas Ubudiyah Indonesia Indonesia 2Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia 3Department of Informatics Engineering Faculty of Computer Science Universitas Ubudiyah Indonesia Indonesia

Employee performance assessment is the evaluation of the employee's performances recorded at the end of every month. This approach is currently used at the Military Court Office I-01 Banda Aceh. The main aim of this assessment is to measure employee's performances in achieving their targets. The results will be used as the measuring key, where an employee is only eligible to receive the allowance, if the targets are successfully fulfilled. At this moment, the assessment form has to be filled in manually by each employee, and they will be later assessed manually by the assessors. This manual process undeniably has many disadvantages, especially in terms of accuracy and efficiency. To solve this problem, the Employee Performance information System was designed using the SMART (Simple Multi Attribute Technique). The assessments were based on eight indicators, i.e; performance, attendance, the proportion of performance targets, behavioral scores, number of tasks, services, overtime, and monthly rewards earned. The weights for each variable were assessed, and keyed into the system. The values for each variable were compared, and results were generated. In SMART, the weighting is measured as a scale between 0 and 10, thus it is easier to make calculations and comparison. The obtained results (SI-TUKIN) indicated that the system is capable of evaluating and analysing the employee's performances, quickly and effectively.

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Consistent application of capacitive touch infrastructure in wireless reloadable sensor networks

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AIP Conference Proceedings 2023年第1期2523卷

作者： G. Radha Devi R. Uma A. Vishnuprasath D. Brindha 1Department of Computer Science and Engineering Malla Reddy College of Engineering Hyderabad Telangana India 2Departmentof Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu India 3Department of Information Technology Sri Ramakrishna Engineering Coimbatore Tamil Nadu India 4Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

Wireless technology evolves exponentially, creating revolutionary automotive energy applications and advancements through wireless communication. Wireless Reloadable Sensor Networks (WRSN) typically feature one or more wireless vehicles charging the sensor nodes in most existing systems. However, because of off-road and speed restrictions in cars, certain SNs are not charging in time, impacting network life adversely. These schemes should address the energy problem to a certain degree. We suggest a new WRSN concept with a single wireless charging drone by a restricted distance of flight attached with multiple inductive indicting mats used to load the drone when it cannot hit the following halt. Our concept successfully addresses these charging challenges and overcomes drone energy capacities. Thus, an issue is formulated with wireless loading pad deployment, which is intended to use a minimal number of pads to create a practicable routing route for the drone to meet each Sensor Node (SN) in a defined WRSN. Four potential heuristics are suggested for this problem, three based on graph theory and one based on geometry. In addition, the drone has been designed to attend charging requests with the aid of pads by a new drone programming angered, the shortest multi-hop route algorithm. We look into detailed simulations of the proposed schemes. The findings show the feasibility of the suggested models in terms of wear rate, the scale of the area, and overall flight duration.

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Centralized network infrastructure and virtualization of inter wireless networks

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AIP Conference Proceedings 2023年第1期2523卷

作者： Narendhar Mulugu Channapragada Rama Seshagiri Rao Yeligeti Raju L. N. C. Prakash K. 1Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India. 2Department of Computer Science and Engineering Vignana Bharathi Engineering College Hyderabad Telangana India. 3Department of Information Technology Vignana Bharathi Institute of Technology Hyderabad Telangana India. 4Department of Computer Science and Engineering CVR College of Engineering Hyderabad Telangana India.

Wireless network computing has been suggested as a voracious development by isolating and exchanging network infrastructure across various software systems in future wireless networks to counter the contradiction amongst the rapid development of the demand for information services besides the small bandwidth of resources. In this article, the multi-service virtualized wireless system consists of three broad-based situations and suggests a two-dimensional hierarchic resource distribution system. The resource division and allotment dilemma are composed of two-time scales with a large time frame before the equivalent lateral assets and a limited time frame for the intra-slice resource preparation. In the long run, the problem of pre-allotting inter-slice services is conceived by modelling the packets that arrive and serve each slice as a queuing device. In short, the source and energy management in each share is described as a stochastic optimization problem because of variable time and complex traffic arrivals. This problem aims to maximize overall output relative to various service quality criteria, including network stability, latency, throughput, and control restrictions. By applying the Lyapunov objective function, the stochastic optimization problem can be turned into a delay-aware optimization problematic methodology comprising a probabilistic and concave algorithm. The simulation findings show that the suggested systems will minimize uncertainty with an output loss of up to 0.017 percent relative to the optimum solution, which also allows for a balance in terms of the regulation coagulation Factor quality.

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Channel separation with CNN model for glaucoma color spectral detection

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AIP Conference Proceedings 2022年第1期2519卷

作者： Anisha Rebinth S. Mohan Kumar 1Department of Information Science and Engineering New Horizon College of Engineering Bangalore Karnataka India 2Department of Computer Science and Engineering Nagarjuna College of Engineering & Technology Beedaganahalli Karnataka India

Glaucoma is a leading type of eye disease that affects the optic nerve causing permanent vision loss. As this optic nerve is important for good vision safety, glaucoma can be prevented only if detected earlier. Optic disc to cup ratio is one of the key factors for glaucoma diagnosis with abnormally high eye pressure. A descriptive analysis of glaucoma diagnosis using Convolutional Neural Network (CNN) for spectral color detection of the eye is presented in this study. Initially, the color components are separated as red, green, and blue. Then, the region of Interest (ROI) is obtained from the green channel of the fundus image for the feature extraction. The Green channel is used for the fundus image analysis and detection because it is the only sensitive and high contrast color that the other two components for humans. Finally, the proposed system’s glaucoma color spectral diagnosis and its performance are analyzed using CNN with accuracy.

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Lo-Ra based covid patient health detecting system

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AIP Conference Proceedings 2023年第1期2523卷

作者： Darpan Majumder S. Mohan Kumar D. V. Ashoka A Shajin Naragunam 1Department of Information Science and Engineering New Horizon College of Engineering Research Centre Visvesvaraya Technological University (VTU) Bangalore Karnataka India. 2Department of Computer Science and Engineering Nagarjuna College of Engineering & Technology Bangalore Karnataka India. 3Department of Information Science and Engineering JSS Academy of Technical Education Bangalore Karnataka India. 4Faculty of Information and Computer Engineering Noorul Islam Centre for Higher Education Kanyakumari Tamil Nadu India.

Coronavirus is an authentic WHO-proclaimed pandemic disease. Qualified professionals and medical care organizations experienced significant conditions across the world. In addition, the governmental authority develops a significant technique to alleviate the disease, to lock the nations. In this article, we proposed the covid detection system among the people by using several sensors embedded in a device to detect the people's disease. This system uses the Lora module for communication for the doctor's unit. IoT-based health networks attempt to improve health services quality through real-time data collecting and analysis. However, there are significant disadvantages with standard IoT systems. For example, they cannot function efficiently in locations with poor or unstable Internet. The solution to the question of internet services is a feasible option with technology like LPWAN (Low Power Wider Network), such as full-scale order elements, viz. Lora. The monitoring system plays a crucial role in diagnostic procedures and treatment.

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