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Power control algorithm in CDMA systems using symmetric successive overrelaxation iteration

EUROPEAN TRANSACTIONS ON TELECOMMUNICATIONS 2005年第6期16卷 583-589页

作者： Soo, KK Siu, YM Zhao, L Yang, L Chen, RS Chan, WS City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China Nanjing Univ Sci & Technol Dept Commun Engn Nanjing Peoples R China

System capacity is the paramount requirement in the design of code division multiple access (CDMA) cellular systems. Power control to mobiles is an essential tool used to maximise the system capacity by minimising interference while at the same time maintaining transmission quality. The convergence speed of power control algorithm is critical in determining its practical application especially when the propagation and traffic condition are rapidly changing. In this letter, a new centralised power control algorithm is proposed to solve the power control problem by using symmetric successive overrelaxation (SSOR) preconditioned generalised product-type bi-conjugate gradient (GPBi-CG) iteration method. The simulation results show that our proposed algorithm has much faster convergent rate than the distributive constrained second-order power control (CSOPC) and constrained Gauss-Seidel (CGS) algorithm. The fast convergent speed can increase the overall network capacity. Copyright (c) 2005 AEIT.

关键词： network capacity CENTROSYMMETRIC system capacity transmission quality algorithms CDMA mobile communications iterative methods computer algorithms Power control Code division multiple access

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System identification for state feedback integral observer control of polymer plastic extrusion

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POLYMER-PLASTICS TECHNOLOGY AND ENGINEERING 1997年第5期36卷 749-775页

作者： Lin, YJ Lee, G Department of Mechanical Engineering University of Akron Akron OH 44325 United States

In this article, the system dynamics of a realistic plastic extrusion process is identified by least squares approximation associated with loss and covariance function criteria applied to a set of experimentally measured data of true single-screw extrusion processes. The system identification yields a third-order mathematical model of the process with a single-input/single-output nature. This modeling approach leads to a reliable and effective system model which facilitates the control design. In designing the control algorithm, the derived dynamic model is formulated in the state-space form to obtain the required Riccati equation. Then, an integral observer control methodology is performed. To reduce the computation time, a program which simulates deterministic model and controller design is proposed. In addition, a steady-state matrix based on the algebraic Riccati equations is utilized to design the controller gain matrices. Finally, the observer design methodology employing the observer characteristic equation corresponding to each system matrix is elaborated. The simulation results show that the two controller output data, namely pressure and temperature responses, track the desired values satisfactorily.

关键词： observational data Polymers and Plastics computational time design control State feedback Riccati equations computer algorithms Simulation results System identification System dynamics extrusion extrusion process controller design

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Improving updating rules in multiplicative algorithms for computing D-optimal designs

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2008年第2期53卷 312-320页

作者： Dette, Holger Pepelyshev, Andrey Zhigljavsky, Anatoly Ruhr Univ Bochum Fak Math D-44780 Bochum Germany St Petersburg State Univ Dept Math St Petersburg Russia Cardiff Univ Sch Math Cardiff CF24 4AC S Glam Wales

A class of multiplicative algorithms for computing D-optimal designs for regression models on a finite design space is discussed and a monotonicity result for a sequence of determinants obtained by the iterations is proved. As a consequence the convergence of the sequence of designs to the D-optimal design is established. The class of algorithms is indexed by a real parameter and contains two algorithms considered previously as special cases. Numerical results are provided to demonstrate the efficiency of the proposed methods. Finally, several extensions to other optimality criteria are discussed. (C) 2008 Elsevier B.V. All rights reserved.

关键词： computer algorithms OPTIMAL designs (Statistics) REGRESSION analysis MATHEMATICAL models MATHEMATICAL optimization

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Optimization algorithms for the design of digital microfluidic biochips: A survey

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computerS & ELECTRICAL ENGINEERING 2013年第1期39卷 112-121页

作者： Pan, Indrajit Mukherjee, Ritwik Rahaman, Hafizur Samanta, Tuhina Dasgupta, Parthasarathi Bengal Engn & Sci Univ Sibpur India Indian Inst Management Kolkata 700027 W Bengal India

Digital microfluidic biochip revolutionizes the medical diagnosis process rendering multiple tasks executed on a single chip. Incorporation of multiple functionality makes the design process complex and costly for digital microfluidic biochip. Physical simulation for the device components in a biochip is essential in todays manufacturing industry. In this paradigm, design automation and development of computer-aided-design tool that can perform physical level simulation and testing becomes crucial for a successful biochip design. This paper presents a comprehensive survey on design automation for biochip. Initially, a brief description on popular optimization techniques and some heuristic algorithms to solve various optimization problems is presented, followed by a review on biochip design automation works. Generally, architectural and geometry level synthesis for biochip design is performed using optimization techniques. Hence, some recent works on bioassay analysis, resource binding, and scheduling in geometry level are discussed. Finally the survey concludes with some possible future research directions. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： MATHEMATICAL optimization computer algorithms MICROFLUIDIC devices BIOCHIPS SURVEYS computer-aided design HEURISTIC algorithms BIOLOGICAL assay

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Histogram of Maximal Optical Flow Projection for Abnormal Events Detection in Crowded Scenes

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2015年第11期11卷 1-11页

作者： Li, Ang Miao, Zhenjiang Cen, Yigang Wang, Tian Voronin, Viacheslav Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Don State Tech Univ Dept Radioelect Syst Shakhty 346500 Russia

Abnormal events detection plays an important role in the video surveillance, which is a challenging subject in the intelligent detection. In this paper, based on a novel motion feature descriptor, that is, the histogram of maximal optical flow projection (HMOFP), we propose an algorithm to detect abnormal events in crowded scenes. Following the extraction of the HMOFP of the training frames, the one-class support vector machine (SVM) classification method is utilized to detect the abnormality of the testing frames. Compared with other methods based on the optical flow, experiments on several benchmark datasets show that our algorithm is effective with satisfying results.

关键词： HISTOGRAMS OPTICAL flow VIDEO surveillance FEATURE extraction (Data processing) DESCRIPTOR systems (computer science) computer algorithms SUPPORT vector machines

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Vector Lyapunov Functions for Stability and Stabilization of Differential Repetitive Processes

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JOURNAL OF computer AND SYSTEMS SCIENCES INTERNATIONAL 2016年第4期55卷 503-514页

作者： Galkowski, K. Emelianov, M. A. Pakshin, P. V. Rogers, E. Univ Zielona Gora Inst Control & Computat Engn Zielona Gora Poland RE Alekseev Nizhny Novgorod State Tech Univ Arzamas Polytech Inst Arzamas Russia Univ Southampton Dept Elect & Comp Sci Southampton Hants England

Differential repetitive processes arise in the analysis and design of iterative learning control algorithms. They belong to a class of mathematical models whose dynamic properties are defined by two independent variables, such as a time and a spatial coordinate, also known as 2D systems in the literature. Moreover, standard stability analysis methods cannot be applied to such processes. This paper develops a vector Lyapunov function-based approach to the exponential stability analysis of differential repetitive processes and applies the resulting conditions to develop linear matrix inequality based iterative learning control law design algorithms in the presence of model uncertainty.

关键词： Mathematical Model linear matrix inequalities model uncertainty Stability analysis exponential stability. Independent Variables spatial coordinates dynamic characteristics Repetitive computer algorithms

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An image segmentation method based on maximizing fuzzy correlation and its fast recursive algorithm

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computerS & ELECTRICAL ENGINEERING 2014年第3期40卷 833-843页

作者： Tang, Yinggan Mu, Weiwei Zhao, Lixing Zhao, Gang Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Hebei Peoples R China Natl Engn Res Ctr Equipment & Technol Cold Strip Qinhuangdao 066004 Hebei Peoples R China Rochester Inst Technol Rochester NY 14624 USA

In this paper, an image segmentation method is proposed that integrates fuzzy 2-partition into Yen's maximum correlation thresholding method. A fuzzy 2-partition of the image is obtained by transforming the image into fuzzy domain by means of two parameterized membership functions. Fuzzy correlation is defined to measure the appropriateness of the fuzzy 2-partition. An ideal threshold is calculated from the optimal membership functions' parameters, which make the corresponding fuzzy 2-partition have maximum fuzzy correlation. In the process of searching the optimal parameters of membership functions, a fast recursive algorithm is presented in order to reduce the computation complexity. Experimental results on synthetic image, brain magnetic resonance (MR) images and casting images show that the proposed method has a satisfactory performance. Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved.

关键词： SEGMENTATION (Image processing) FUZZY systems CORRELATION (Statistics) RECURSIVE functions computer algorithms PARTITIONS (Mathematics)

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A component overlapping attribute clustering (COAC) algorithm for single-cell RNA sequencing data analysis and potential pathobiological implications

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PLOS COMPUTATIONAL BIOLOGY 2019年第2期15卷 e1006772-e1006772页

作者： Peng, He Zeng, Xiangxiang Zhou, Yadi Zhang, Defu Nussinov, Ruth Cheng, Feixiong Xiamen Univ Dept Comp Sci Xiamen Fujian Peoples R China Ohio Univ Dept Chem & Biochem Athens OH 45701 USA NCI Canc & Inflammat Program Leidos Biomed Res Inc Frederick Natl Lab Canc Res Frederick MD 21701 USA Tel Aviv Univ Sackler Sch Med Dept Human Mol Genet & Biochem Tel Aviv Israel Cleveland Clin Genom Med Inst Lerner Res Inst Cleveland OH 44106 USA Case Western Reserve Univ Dept Mol Med Cleveland Clin Lerner Coll Med Cleveland OH 44106 USA Case Western Reserve Univ Sch Med Case Comprehens Canc Ctr Cleveland OH 44106 USA

Recent advances in next-generation sequencing and computational technologies have enabled routine analysis of large-scale single-cell ribonucleic acid sequencing (scRNA-seq) data. However, scRNA-seq technologies have suffered from several technical challenges, including low mean expression levels in most genes and higher frequencies of missing data than bulk population sequencing technologies. Identifying functional gene sets and their regulatory networks that link specific cell types to human diseases and therapeutics from scRNA-seq profiles are daunting tasks. In this study, we developed a Component Overlapping Attribute Clustering (COAC) algorithm to perform the localized (cell subpopulation) gene co-expression network analysis from large-scale scRNA-seq profiles. Gene subnetworks that represent specific gene co-expression patterns are inferred from the components of a decomposed matrix of scRNA-seq profiles. We showed that single-cell gene subnetworks identified by COAC from multiple time points within cell phases can be used for cell type identification with high accuracy (83%). In addition, COAC-inferred subnetworks from melanoma patients' scRNA-seq profiles are highly correlated with survival rate from The Cancer Genome Atlas (TCGA). Moreover, the localized gene subnetworks identified by COAC from individual patients' scRNA-seq data can be used as pharmacogenomics biomarkers to predict drug responses (The area under the receiver operating characteristic curves ranges from 0.728 to 0.783) in cancer cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) database. In summary, COAC offers a powerful tool to identify potential network-based diagnostic and pharmacogenomics biomarkers from large-scale scRNA-seq profiles. COAC is freely available at https://***/ChengF-Lab/COAC.

关键词： computer algorithms RNA-seq MELANOMA DATA analysis BIOLOGICAL tags COMPUTATIONAL biology

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Plane Registration Leveraged by Global Constraints for Context-Aware AEC Applications

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computer-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING 2013年第5期28卷 325-343页

作者： Feng, Chen Kamat, Vineet R. Univ Michigan Dept Civil & Environm Engn Ann Arbor MI 48109 USA

In this article, we propose a new registration algorithm and computing framework, the KEG tracker, for estimating a camera's position and orientation for a general class of mobile context-aware applications in Architecture, Engineering, and Construction (AEC). By studying two classic natural marker-based registration algorithms, Homography-from-detection and Homography-from-tracking, and by overcoming their specific limitations of jitter and drift, our method applies two global constraints (geometric and appearance) to prevent tracking errors from propagating between consecutive frames. The proposed method is able to achieve an increase in both stability and accuracy, while being fast enough for real-time applications. Experiments on both synthesized and real-world test cases demonstrate that our method is superior to existing state-of-the-art registration algorithms. The article also explores several AEC applications of our method in context-aware computing and desktop-augmented reality.

关键词： CONTEXT-aware computing AUGMENTED reality computer algorithms JACOBIAN matrices GLOBAL optimization MEAN square algorithms

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Congested Link Inference algorithms in Dynamic Routing IP Network

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MATHEMATICAL PROBLEMS IN ENGINEERING 2017年第1期2017卷 1-17页

作者： Chen, Yu Duan, Zhe-min Northwestern Polytech Univ Xian 710072 Peoples R China Zhengzhou Univ Aeronaut Zhengzhou 450015 Peoples R China

The performance descending of current congested link inference algorithms is obviously in dynamic routing IP network, such as the most classical algorithm CLINK. To overcome this problem, based on the assumptions of Markov property and time homogeneity, we build a kind of Variable Structure Discrete Dynamic Bayesian (VSDDB) network simplified model of dynamic routing IP network. Under the simplified VSDDB model, based on the Bayesian Maximum A Posteriori (BMAP) and Rest Bayesian Network Model (RBNM), we proposed an Improved CLINK (ICLINK) algorithm. Considering the concurrent phenomenon of multiple link congestion usually happens, we also proposed algorithm CLILRS (Congested Link Inference algorithm based on Lagrangian Relaxation Subgradient) to infer the set of congested links. We validated our results by the experiments of analogy, simulation, and actual Internet.

关键词： INFERENTIAL statistics DISCRETE systems MARKOV processes ROUTING (computer network management) BAYESIAN analysis computer algorithms

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