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Efficient Framework for Real-Time Color Cast Correction and Dehazing Using online algorithms to Approximate Image Statistics

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IEEE ACCESS 2024年 12卷 72813-72827页

作者： Bilal, Muhammad Masud, Shahid Hanif, Muhammad Shehzad King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst Jeddah 21589 Saudi Arabia King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia Lahore Univ Management Sci Dept Elect Engn Lahore 54792 Pakistan

Image dehazing is a crucial task in the early stages of real-time image processing pipelines used in applications such as surveillance and advance driver assistance systems. Dehazing algorithms mitigate the spatially selective degradation of image details caused by natural phenomenon such as fog and sandstorms. The problem is exacerbated in the presence of color cast which can affect the color-sensitive processing in the downstream tasks. In order to correct these two aberrations, we have proposed a light-weight algorithm which is not only quantitively more effective than the state-of-the-art works but also uses minimal computational resources. Specifically, it has been proposed to tackle both aforementioned problems by processing luminance and chrominance channels separately through a custom resource-efficient colorspace transform. Moreover, it has been proposed to employ online calculation of the relevant video stream statistics to estimate the degradation model over several temporally adjacent frames. This approach not only reduces the hardware resource utilization when implemented inside the video processing pipeline but also reduces the flicker effect observed when frames are processed individually. It has been demonstrated through quantitative analysis on standard datasets that the proposed approach either works at par or better than the reference works in terms of image quality metrics. Furthermore, the proposed framework has been developed as a real-time video processing system on an FPGA platform. The synthesis results of this implementation suggest that the proposed framework achieves this performance using minimal logic resources.

关键词： Image color analysis Real-time systems Hardware Estimation Streaming media Task analysis Approximate computing Field programmable gate arrays Image processing algorithm design and analysis color cast correction dehazing FPGA high-level synthesis online algorithm real-time image processing

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A Dynamic Programming algorithm for the online Cutting Problem with Defects and Quality Grades

A Dynamic Programming Algorithm for the Online Cutting Probl...

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8th IFAC Conference on Manufacturing Modelling, Management and Control (MIM)

作者： Aksu, Dilek Tuzun Durak, Bahadir Yeditepe Univ Dept Ind & Syst Engn TR-34755 Istanbul Turkey

In this article, we propose a dynamic programming based algorithm for an online glass cutting problem that arises in the glass industry. In flat glass production. a continuous ribbon of flat glass is cut into glass products of various sizes as it flows on a conveyor belt. The glass ribbon contains defects of different types based on their severity and each glass product belongs to a quality class that indicates the maximum number of defects of each type that can be tolerated. Since the location and type of defects are detected in real time using a camera, the cutting problem has to be solved online and each cutting decision is made within a few seconds. The primary goal of this cutting problem is to minimize the amount of leftover (scrap) glass resulting from the cut products. The proposed algorithm uses a look-ahead strategy to solve a static cutting problem at each iteration to determine the products to be cut within a fixed horizon length. but implements only the first one or few of these cuts per iteration. We evaluated the algorithm on a set of realistic problems reflecting the parameters of current flat glass production lines. Computational results indicate that the proposed algorithm can reduce the amount of scrap glass by an average of 26.1%. (C) 2016, IFAC (International Federation of Automatic Control) Hosting-by Elsevier Ltd. All rights reserved,

关键词： cutting stock problems dynamic programming flat glass production defects online algorithm

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ROPW: An online Trajectory Compression algorithm

ROPW: An Online Trajectory Compression Algorithm

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26th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Li, Sirui Zhang, Kaiqi Yin, Hongbo Yin, Dan Zu, Hongquan Gao, Hong Harbin Inst Technol Harbin 150001 Heilongjiang Peoples R China

ISBN: (纸本)9783030732158;9783030732165

In smart phones, vehicles and wearable devices, GPS sensors are ubiquitous, which can collect a large amount of valuable trajectory data by tracking moving objects. Analysis of this valuable trajectory data can benefit many practical applications, such as route planning and transportation optimization. However, unprecedented large-scale GPS data poses a challenge to the effective storage of trajectories. Therefore, the necessity of trajectory compression (also called trajectory sampling) is reflected. However, the latest compression methods usually perform unsatisfactorily in terms of space-time complexity or compression rate, which leads to rapid exhaustion of memory, computing, storage, and energy. In response to this problem, this paper proposes an online trajectory compression algorithm (ROPW algorithm) with error bounded that traverses the sliding window backwards. This algorithm has significantly improved the trajectory compression rate, and its average time complexity and space complexity is O(NlogN) and 0(1) respectively. Finally, we conducted experiments on three real data sets to verify that the ROPW algorithm performed very well in terms of compression rate and time efficiency.

关键词： online algorithm Trajectory compression Sliding window Reverse traversal

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Competitive Analysis of Repeated Greedy Auction algorithm for online Multi-Robot Task Assignment

Competitive Analysis of Repeated Greedy Auction Algorithm fo...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Luo, Lingzhi Chakraborty, Nilanjan Sycara, Katia Carnegie Mellon Univ Sch Comp Sci Inst Robot Pittsburgh PA 15213 USA

ISBN: (纸本)9781467314053

We study an online task assignment problem for multi-robot systems where robots can do multiple tasks during their mission and the tasks arrive dynamically in groups. Each robot can do at most one task from a group and the total number of tasks a robot can do is bounded by its limited battery life. There is a payoff for assigning each robot to a task and the objective is to maximize the total payoff. A special case, where each group has one task and each robot can do one task is the online maximum weighted bipartite matching problem (MWBMP). For online MWBMP, it is known that, under some assumptions on the payoffs, a greedy algorithm has a competitive ratio of 1/3. Our key result is to prove that for the general problem, under the same assumptions on the payoff as in MWBMP and an assumption on the number of tasks arising in each group, a repeated auction algorithm, where each group of tasks is (near) optimally allocated to the available group of robots has a guaranteed competitive ratio. We also prove that (a) without the assumptions on the payoffs, it is impossible to design an algorithm with any performance guarantee and (b) without the assumption on the task profile, the algorithms that can guarantee a feasible allocation (if one exists) have arbitrarily bad performance in the worst case. Additionally, we present simulation results depicting the average case performance of the repeated greedy auction algorithm.

关键词： Multi-robot assignment Task allocation Auction algorithm online algorithm Competitive analysis

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AnomalyDetect: An online Distance-Based Anomaly Detection algorithm 1

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26th International Conference on Web Services (ICWS) Held as Part of the Services Conference Federation (SCF)

作者： Huo, Wunjun Wang, Wei Li, Wen Tongji Univ Dept Comp Sci & Engn Shanghai Peoples R China East China Normal Univ Sch Data Sci & Engn Shanghai Peoples R China

ISBN: (数字)9783030234997

ISBN: (纸本)9783030234997;9783030234980

Anomaly detection is a key challenge in data mining, which refers to finding patterns in data that do not conform to expected behavior. It has a wide range of applications in many fields as diverse as finance, medicine, industry, and the Internet. In particular, intelligent operation has made great progress in recent years and has an urgent need for this technology. In this paper, we study the problem of anomaly detection in the context of intelligent operation and find the practical need for high-accuracy, online and universal anomaly detection algorithms in time series database. Based on the existing algorithms, we propose an innovative online distance-based anomaly detection algorithm. K-means and time-space trade-off mechanism are used to reduce the time complexity. Through the experiments on Yahoo! Web-scope S5 dataset we show that our algorithm can detect anomalies accurately. The comparative study of several anomaly detectors verifies the effectiveness and generality of the proposed algorithm.

关键词： Anomaly detection Time series online algorithm Euclidean distance Intelligent operation

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A Comprehensive Study of an online Packet Scheduling algorithm

A Comprehensive Study of an Online Packet Scheduling Algorit...

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5th Annual International Conference on Combinatorial Optimization and Applications (COCOA 2011)

作者： Li, Fei George Mason Univ Dept Comp Sci Fairfax VA 22030 USA

ISBN: (纸本)9783642226151

We study the bounded-delay model for Qualify-of-Service buffer management. Time is discrete. There is a buffer. Unit-length jobs (also called packets) arrive at the buffer over time. Each packet has an integer release time, an integer deadline, and a positive real value. A packet's characteristics are not known to an online algorithm until the packet actually arrives. In each time step, at most one packet can be sent out of the buffer. The objective is to maximize the total value of the packets sent by their respective deadlines in an online manner. An online algorithm's performance is usually measured in terms of competitive ratio, when this online algorithm is compared with a clairvoyant algorithm achieving the best total value. In this paper, we study a simple and intuitive online algorithm. We analyze its performance in terms of competitive ratio for the general model and a few important variants.

关键词： online algorithm competitive analysis buffer management packet scheduling

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An online Cost Optimization algorithm for IaaS Instance Releasing in Cloud Environments 11

An Online Cost Optimization Algorithm for IaaS Instance Rele...

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IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Zheng, Bingbing Pan, Li Liu, Shijun Shandong Univ Sch Software Jinan Peoples R China

ISBN: (纸本)9781665414906

Benefiting from the development of cloud computing, service trades among IaaS (Infrastructure-as-a-Service) providers, SaaS (Software-as-a-Service) providers, and users are more and more common, where SaaS providers purchase on-demand instances in a pay-per-use way from IaaS providers to execute users' jobs. Given the pay-as-you-go mode of IaaS instances, SaaS providers can acquire and release instances whenever they need. However, considering the preparation time of acquiring new instances and the penalty functions of users, from the perspective of cost optimization, SaaS providers need to decide whether and when to release an idle instance. To make optimal decisions, future information about job arrivals and execution time is generally required, which is actually difficult to accurately predict. To address this problem, we propose an online cost optimization algorithm for SaaS providers to help them make better decisions on whether to release instances. Theoretical analysis shows that our online algorithm achieves a competitive ratio of 2 - alpha for different penalty functions, where alpha is an element of (0, 1). Extensive experiments on account of realistic Google cluster data demonstrate the effectiveness and efficiency of our online algorithm.

关键词： online algorithm Cost Optimization IaaS Instance Releasing Cloud Computing

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An Efficient Approximation algorithm for online Multi-Tier Multi-Cell User Association 16

An Efficient Approximation Algorithm for Online Multi-Tier M...

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17th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)

作者： Ao, Weng Chon Psounis, Konstantinos Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781450341844

The ever growing wireless bandwidth demand is pushing WiFi and cellular networks to dense multi-cell deployments, as well as to multi-tier architectures consisting of macrocells and small cells. In such a multi-tier multi-cell environment, the classic problem of associating users to base stations becomes both more challenging and more critical to the overall network performance. Most previous analytical work is focused on offline/static user-cell association, where the users' arrivals and their rates are assumed to be known in advance and thus has little practical relevance. On the other hand, practical online algorithms based on heuristics are often suboptimal and may not provide any performance guarantees. In this paper, we propose an online algorithm for the multi-tier multi-cell user association problem that has a provable performance guarantee which improves previously known bounds by a sizable amount. The proposed algorithm is motivated by online combinatorial auctions, while capturing and leveraging the relative sparsity of choices in wireless networks as compared to auction setups. Specifically, it is a 1/2-a-1 approximation algorithm, where a is the maximum number of feasible associations for a user and is, in general, small due to path loss. In addition to establishing formal performance bounds, we also conduct simulations under realistic assumptions which establish the superiority of the proposed algorithm over existing approaches under real-world scenarios.

关键词： User association Load balancing Heterogeneous networks online algorithm Randomized approximation algorithm

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Complexity and online algorithms for minimum skyline coloring of intervals 11th

Complexity and online algorithms for minimum skyline colorin...

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11th Annual International Conference on Combinatorial Optimization and Applications (COCOA)

作者： Erlebach, Thomas Liu, Fu-Hong Liu, Hsiang-Hsuan Shalom, Mordechai Wong, Prudence W. H. Zaks, Shmuel Univ Leicester Dept Informat Leicester Leics England Natl Tsing Hua Univ Dept Comp Sci Hsinchu Taiwan Univ Wroclaw Inst Comp Sci Wroclaw Poland TelHai Coll IL-12210 Upper Galilee Israel Univ Liverpool Dept Comp Sci Liverpool Merseyside England Technion Dept Comp Sci Haifa Israel Ruppin Acad Ctr Sch Engn Netanya Israel

ISBN: (纸本)9783319711461

Motivated by applications in optical networks and job scheduling, we consider the interval coloring problem in a setting where an increasing cost is associated with using a higher color index. The cost of a coloring at any point of the line is the cost of the maximum color index used at that point, and the cost of the overall coloring is the integral of the cost over all points on the line. A coloring of minimum cost is called a minimum skyline coloring. We prove that the problem of computing a minimum skyline coloring is NP-hard and initiate the study of the online setting, where intervals arrive one by one. We give an asymptotically optimal online algorithm for the case of linear color costs and present further results for some variations and generalizations of the problem. Furthermore, we consider the variant of the minimum skyline coloring problem where the intervals are already partitioned into color classes and we only need permute the colors so as to minimize the cost of the coloring. We show that this problem variant is NP-hard and present a 2-approximation algorithm for it. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Skyline coloring of intervals online algorithm NP-hardness Approximation algorithm

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Real-time peer-to-peer energy trading for networked multi-energy systems with hybrid energy storage

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JOURNAL OF ENERGY STORAGE 2025年 105卷

作者： Zheng, Boshen Wei, Wei Tsinghua Univ Dept Elect Engn Beijing Peoples R China

Integrated use of electricity and heat is an effective way to improve energy efficiency, precipitating the advent of multi-energy systems. Ina network of multi-energy systems connected via power electronics devices, the power flow through soft-open points can be directly controlled, which allows peer-to-peer (P2P) energy trading. This paper proposes an online distributed decision scheme for this scenario. We begin with the deterministic centralized model of P2P energy trading problem, aiming to minimize long-term energy cost. Then the problem is decomposed temporally and spatially. To handle uncertainty, Lyapunov optimization is employed to decompose inter-temporal constraints in storage operation model, thus the problem in each period can be solved independently, yielding an online prediction-free policy. It is proven that, by properly selecting parameters, online policy generates a strategy that satisfies inter-temporal constraints and enjoys guaranteed performance. To protect privacy, an alternating direction method of multipliers (ADMM) based algorithm is employed to decompose the one-time-slot problem spatially, so that the subproblem of each peer admits a solver-free solution. The P2P trading price is intrinsically generated during the consensus-reaching process. Case studies demonstrate the prediction-free, performance-competitive, solver-free advantages of the proposed method. Both total and individual energy costs are reduced, and close to offline optimum.

关键词： Multi-energy system Energy hub Peer-to-peer trading Lyapunov optimization online algorithm ADMM

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