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4th International Conference on Information and Computer Technologies (ICICT)

作者： Fu, Meiming Wang, Xiang Liu, Qingyang Liu, Jiayi Shao, Menghan Shenzhen Smartchip Microelect Technol CO LTD State Grid Lab Power Line Commun Applicat Technol Shenzhen Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China

ISBN: (纸本)9781665413992

The number of connected Internet of Things (IoT) devices has increasing rapidly due to the benefits and various use cases of IoT, which results in network congestion in traditional cloud. Fog computing has been recognized as a promising technology to meet the requirements of IoT devices by bringing computing, storage, and networking resources to the edge of the network. Fog computing is a dispersed architecture consisting of geographically distributed fog nodes. The selection for locations of fog nodes is an essential precondition for fog computing implementation since an effective fog nodes deployment method can reduce the service response time and improve the efficiency of energy consumption. In this work, regarding the space-time characteristics of sensed data of IoT devices, we formulate the fog nodes deployment as an uncertain programming problem with the aim to reduce the energy consumption of the devices. A learning -based algorithm is proposed to solve this problem with neural network embedded to speed up the solving process. Finally, we evaluate the effectiveness of the algorithm by a set of simulations and the results show the advantages of the algorithm compared to other baseline methods.

关键词： Fog computing fog nodes deployment uncertain programming learning-based algorithm

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Review of Traveling Salesman Problem Solution Methods 18th

Review of Traveling Salesman Problem Solution Methods

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18th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)

作者： Yang, Longrui Wang, Xiyuan He, Zhaoqi Wang, Sicong Lin, Jie Lanzhou Univ Technol Coll Elect Engn & Informat Engn Lanzhou 730050 Peoples R China

ISBN: (纸本)9789819722747;9789819722754

The Traveling Salesman Problem (TSP) is a key focus in the fields of computer science and operations research, widely applied in areas such as data collection, search and rescue, robot task allocation and scheduling, etc. This paper, by reviewing recent literature, first introduces the definition and mathematical model of the TSP, followed by an exposition of the concepts of classical TSP. Subsequently, an analysis of solving algorithms for the classical Traveling Salesman Problem is conducted, categorizing them into exact algorithms, heuristic algorithms, and learning-based algorithms. The paper then provides an assessment of the advantages and disadvantages associated with these three categories of algorithms, accompanied by an elaborate overviewof the research advancements made in recent years. Future research on TSP will focus on exploring undeveloped algorithms and integrating stable ones to address larger-scale problems, enhance solution quality, avoid local optima, and improve solution efficiency. Breakthroughs are anticipated in the application of learning-based methods for solving the Traveling Salesman Problem (TSP).

关键词： Traveling Salesman Problem Exact algorithms Heuristic algorithm learning-based algorithm

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A Comparative Study of 1D-Convolutional Neural Networks with Modified Possibilistic c-Mean algorithm for Mapping Transplanted Paddy Fields Using Temporal Data

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2022年第2期50卷 227-238页

作者： Rawat, Anuvi Kumar, Anil Upadhyay, Priyadarshi Kumar, Shashi PRSD IIRS ISRO Indian Inst Remote Sensing 4 Kalidas Rd Dehra Dun 248001 Uttarakhand India Uttarakhand Space Applicat Ctr Dehra Dun Uttarakhand India

With increasing availability of satellite data of high temporal resolution, a more robust classifier is needed which can exploit the temporal information along with the spectral information of the remote sensing images. Specific fuzzy-based and learning-based algorithms are two broad categories and have the potential to perform well in spectral-temporal domain. In the present study, for mapping paddy fields as a specific class two classification algorithms, viz. fuzzy-based modified possibilistic c-mean (MPCM) algorithm and learning-based 1D-convolutional neural networks (CNN), were tested using Sentinel-2A/2B temporal data. The overall accuracy for learning-based 1D-CNN and fuzzy-based MPCM classifiers was found to be 96% and 93%, respectively. The F-measure values were found to be 0.95 and 0.92 for 1D-CNN- and MPCM-based classifier, respectively. Thus, it can be inferred from this study that the 1D-CNN classifier performed better than the traditional fuzzy-based classifier and can handle heterogeneity within class.

关键词： Image classification Temporal 1D-CNN MPCM Fuzzy-based algorithm learning-based algorithm

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A novel learning-based global path planning algorithm for planetary rovers

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NEUROCOMPUTING 2019年 361卷 69-76页

作者： Zhang, Jiang Xia, Yuanqing Shen, Ganghui Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

Autonomous path planning algorithm is significant to planetary exploration rovers, since relying on commands from Earth will heavily reduce their efficiency of executing exploration missions. In this paper, a novel learning-based algorithm is proposed to deal with global path planning problem for planetary exploration rovers. Specifically, a novel deep convolutional neural network with dual branches (DB-CNN) is designed and trained, which can plan path directly from orbital images of planetary surfaces without implementing environment mapping. Moreover, the planning procedure requires no prior knowledge about planetary surface terrains. Finally, experimental results demonstrate that DB-CNN achieves better performance on global path planning and faster convergence during training compared with the existing Value Iteration Network (VIN). (C) 2019 Elsevier B.V. All rights reserved.

关键词： Planetary exploration rovers learning-based algorithm Global path planning Orbital images

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A Survey of Security Challenges, Attacks Taxonomy and Advanced Countermeasures in the Internet of Things

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IEEE ACCESS 2020年 8卷 219709-219743页

作者： Khanam, Shapla Ahmedy, Ismail Bin Idna Idris, Mohd Yamani Jaward, Mohamed Hisham Bin Md Sabri, Aznul Qalid Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst & Technol Kuala Lumpur 50603 Malaysia Univ Malaya Fac Comp Sci & Informat Technol Ctr Mobile Cloud Comp Kuala Lumpur 50603 Malaysia Monash Univ Malaysia Sch Engn Bandar Sunway 47500 Malaysia Univ Malaya Fac Comp Sci & Informat Technol Dept Artificial Intelligence Kuala Lumpur 50603 Malaysia

Internet of Things (IoT) facilitates the integration between objects and different sensors to provide communication among them without human intervention. However, the extensive demand for IoT and its various applications has continued to grow, coupled with the need to achieve foolproof security requirements. IoT produces a vast amount of data under several constraints such as low processor, power, and memory. These constraints, along with the invaluable data produced by IoT devices, make IoT vulnerable to various security attacks. This paper presents an overview of IoT, its well-known system architecture, enabling technologies, and discusses security challenges and goals. Furthermore, we analyze security vulnerabilities and provide state-of-the-art security taxonomy. The taxonomy of the most relevant and current IoT security attacks is presented for application, network, and physical layers. While most other surveys studied one of the areas of security measures, this study considers and reports on the most advanced security countermeasures within the areas of autonomic, encryption, and learning-based approaches. Additionally, we uncover security challenges that may be met by the research community regarding security implementation in heterogeneous IoT environment. Finally, we provide different visions about possible security solutions and future research directions.

关键词： Security Internet of Things Encryption Taxonomy Organizations Protocols Privacy Attacks countermeasures encryption Internet of Things IoT architecture learning-based algorithm privacy security secure communications taxonomy

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learning an Alternating Bergman Network for Non-convex and Non-smooth Optimization Problems 7th

Learning an Alternating Bergman Network for Non-convex and N...

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7th International Conference on Intelligence Science and Big Data Engineering (IScIDE)

作者： Wang, Yiyang Liu, Risheng Su, Zhixun Dalian Univ Technol Sch Math Sci Dalian Peoples R China Dalian Univ Technol DUT RU Int Sch Informat Sci & Engn Dalian Peoples R China Shenzhen Univ Shenzhen Key Lab Media Secur Shenzhen Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian Peoples R China Natl Engn Res Ctr Digital Life Guangzhou Guangdong Peoples R China

ISBN: (纸本)9783319677774;9783319677767

Recently, non-convex and non-smooth problems have received considerable interests in the fields of image processing and machine learning. The proposed conventional algorithms rely on carefully designed initializations, and the parameters can not be tuned adaptively during iterations with corresponding to various real-world data. To settle these problems, we propose an alternating Bregman network (ABN), which discriminatively learns all the parameters from training pairs and then is directly applied to test data without additional operations. Specifically, parameters of ABN are adaptively learnt from training data to force the objective value drop rapidly toward the optimal and then obtain a desired solution in practice. Furthermore, the basis algorithm of ABN is an alternating method with Bregman modification (AMBM), which solves each subproblem with a designated Bregman distance. This AMBM is more general and flexible than previous approaches;at the same time it is proved to receive the best convergence result for general non-convex and non-smooth optimization problems. Thus, our proposed ABN is an efficient and converged algorithm which rapidly converges to desired solutions in practice. We applied ABN to sparse coding problem with l(0) penalty and the experimental results verify the efficiency of our proposed algorithm.

关键词： Non-convex optimization Alternating direction method Sparse approximation learning-based algorithm Optimization network

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learning-based Opportunistic Spectrum Access with Adaptive Hopping Transmission Strategy

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2012年第11期11卷 3957-3967页

作者： Derakhshani, Mahsa Tho Le-Ngoc McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 0E9 Canada

This paper presents an adaptive hopping transmission strategy for secondary users (SUs) to access temporarily idle frequency-slots of a licensed frequency band in consideration of the random return of primary users (PUs), aiming to maximize the overall SU throughput. A SU dynamically hops over multiple idle frequency-slots, each with an adaptive activity factor to avoid high-risk data loss due to possible PU return. SU activity factor optimization problems are formulated to develop the optimal opportunistic spectrum access (OSA) algorithms for SUs based on the Lagrange dual decomposition method. Subsequently, a fully distributed learning-based OSA algorithm is developed in which each SU independently adapts its activity factors to the optimal values over time by learning other SUs' behavior from locally available information. The convergence and convergence rate that characterize its asymptotic behavior and efficiency are analyzed. It is shown that the proposed learning-based OSA algorithm converges with probability of 1 to the optimal solution. Illustrative results confirm its effectiveness and performance gain as compared to existing OSA schemes.

关键词： Opportunistic spectrum access hopping transmission dual optimization learning-based algorithm stochastic gradient descent method

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Face super-resolution using multiple occluded images of different resolutions

Face super-resolution using multiple occluded images of diff...

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IEEE Conference on Advanced Video and Signal based Surveillance

作者： Jia, K Gong, S Queen Mary Univ London Dept Comp Sci London E1 4NS England

ISBN: (纸本)0780393856

In this paper, we present a novel learning-based algorithm to super-resolve multiple partially occluded low-resolution face images. By integrating hierarchical patch-wise alignment and inter-frame constraints into a Bayesian framework, we can probabilistically align multiple input images at different resolutions and recursively infer the high-resolution face image. We address the problem of fusing partial imagery information through multiple frames and discuss the new algorithm's effectiveness when encountering occluded low-resolution face images. We show promising results compared to that of existing face hallucination methods.

关键词： belief networks image resolution Bayesian framework face hallucination methods face super-resolution hierarchical patch-wise alignment interframe constraints learning-based algorithm multiple occluded images Bayesian networks image resolution super-resolution occluding load capacity based algorithm evolutionary algorithm Hash based algorithms

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