Search results for: Structure-preserving doubling algorithm
3122 Efficient Scheduling Algorithm for QoS Support in High Speed Downlink Packet Access Networks
Authors: MohammadReza HeidariNezhad, Zuriati Ahmad Zukarnain, Nur Izura Udzir, Mohamed Othman
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In this paper, we propose APO, a new packet scheduling scheme with Quality of Service (QoS) support for hybrid of real and non-real time services in HSDPA networks. The APO scheduling algorithm is based on the effective channel anticipation model. In contrast to the traditional schemes, the proposed method is implemented based on a cyclic non-work-conserving discipline. Simulation results indicated that proposed scheme has good capability to maximize the channel usage efficiency in compared to another exist scheduling methods. Simulation results demonstrate the effectiveness of the proposed algorithm.Keywords: Scheduling Algorithm, Quality of Service, HSDPA.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15803121 Enhanced Genetic Algorithm Approach for Security Constrained Optimal Power Flow Including FACTS Devices
Authors: R.Narmatha Banu, D.Devaraj
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This paper presents a genetic algorithm based approach for solving security constrained optimal power flow problem (SCOPF) including FACTS devices. The optimal location of FACTS devices are identified using an index called overload index and the optimal values are obtained using an enhanced genetic algorithm. The optimal allocation by the proposed method optimizes the investment, taking into account its effects on security in terms of the alleviation of line overloads. The proposed approach has been tested on IEEE-30 bus system to show the effectiveness of the proposed algorithm for solving the SCOPF problem.Keywords: Optimal Power Flow, Genetic Algorithm, FlexibleAC transmission system (FACTS) devices, Severity Index (SI), Security Enhancement, Thyristor controlled series capacitor (TCSC).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17503120 Variable Step-Size APA with Decorrelation of AR Input Process
Authors: Jae Wook Shin, Ju-man Song, Hyun-Taek Choi, Poo Gyeon Park
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This paper introduces a new variable step-size APA with decorrelation of AR input process is based on the MSD analysis. To achieve a fast convergence rate and a small steady-state estimation error, he proposed algorithm uses variable step size that is determined by minimising the MSD. In addition, experimental results show that the proposed algorithm is achieved better performance than the other algorithms.
Keywords: adaptive filter, affine projection algorithm, variable step size.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18813119 Lane Detection Using Labeling Based RANSAC Algorithm
Authors: Yeongyu Choi, Ju H. Park, Ho-Youl Jung
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In this paper, we propose labeling based RANSAC algorithm for lane detection. Advanced driver assistance systems (ADAS) have been widely researched to avoid unexpected accidents. Lane detection is a necessary system to assist keeping lane and lane departure prevention. The proposed vision based lane detection method applies Canny edge detection, inverse perspective mapping (IPM), K-means algorithm, mathematical morphology operations and 8 connected-component labeling. Next, random samples are selected from each labeling region for RANSAC. The sampling method selects the points of lane with a high probability. Finally, lane parameters of straight line or curve equations are estimated. Through the simulations tested on video recorded at daytime and nighttime, we show that the proposed method has better performance than the existing RANSAC algorithm in various environments.
Keywords: Canny edge detection, k-means algorithm, RANSAC, inverse perspective mapping.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11843118 A Branch and Bound Algorithm for Resource Constrained Project Scheduling Problem Subject to Cumulative Resources
Authors: A. Shirzadeh Chaleshtari, Sh. Shadrokh
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Renewable and non-renewable resource constraints have been vast studied in theoretical fields of project scheduling problems. However, although cumulative resources are widespread in practical cases, the literature on project scheduling problems subject to these resources is scant. So in order to study this type of resources more, in this paper we use the framework of a resource constrained project scheduling problem (RCPSP) with finish-start precedence relations between activities and subject to the cumulative resources in addition to the renewable resources. We develop a branch and bound algorithm for this problem customizing precedence tree algorithm of RCPSP. We perform extensive experimental analysis on the algorithm to check its effectiveness and performance for solving different instances of the problem in question.
Keywords: Resource constrained project scheduling problem, cumulative resources, branch and bound algorithm, precedence tree.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 28953117 Emission Constrained Hydrothermal Scheduling Algorithm
Authors: Sayeed Salam
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This paper presents an efficient emission constrained hydrothermal scheduling algorithm that deals with nonlinear functions such as the water discharge characteristics, thermal cost, and transmission loss. It is then incorporated into the hydrothermal coordination program. The program has been tested on a practical utility system having 32 thermal and 12 hydro generating units. Test results show that a slight increase in production cost causes a substantial reduction in emission.Keywords: Emission constraint, Hydrothermal coordination, and Hydrothermal scheduling algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13633116 Optimal Planning of Ground Grid Based on Particle Swam Algorithm
Authors: Chun-Yao Lee, Yi-Xing Shen
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This paper presents an application of particle swarm optimization (PSO) to the grounding grid planning which compares to the application of genetic algorithm (GA). Firstly, based on IEEE Std.80, the cost function of the grounding grid and the constraints of ground potential rise, step voltage and touch voltage are constructed for formulating the optimization problem of grounding grid planning. Secondly, GA and PSO algorithms for obtaining optimal solution of grounding grid are developed. Finally, a case of grounding grid planning is shown the superiority and availability of the PSO algorithm and proposal planning results of grounding grid in cost and computational time.Keywords: Genetic algorithm, particle swarm optimization, grounding grid.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20483115 Distributed Estimation Using an Improved Incremental Distributed LMS Algorithm
Authors: Amir Rastegarnia, Mohammad Ali Tinati, Azam Khalili
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In this paper we consider the problem of distributed adaptive estimation in wireless sensor networks for two different observation noise conditions. In the first case, we assume that there are some sensors with high observation noise variance (noisy sensors) in the network. In the second case, different variance for observation noise is assumed among the sensors which is more close to real scenario. In both cases, an initial estimate of each sensor-s observation noise is obtained. For the first case, we show that when there are such sensors in the network, the performance of conventional distributed adaptive estimation algorithms such as incremental distributed least mean square (IDLMS) algorithm drastically decreases. In addition, detecting and ignoring these sensors leads to a better performance in a sense of estimation. In the next step, we propose a simple algorithm to detect theses noisy sensors and modify the IDLMS algorithm to deal with noisy sensors. For the second case, we propose a new algorithm in which the step-size parameter is adjusted for each sensor according to its observation noise variance. As the simulation results show, the proposed methods outperforms the IDLMS algorithm in the same condition.
Keywords: Distributes estimation, sensor networks, adaptive filter, IDLMS.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14303114 3D Mesh Coarsening via Uniform Clustering
Authors: Shuhua Lai, Kairui Chen
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In this paper, we present a fast and efficient mesh coarsening algorithm for 3D triangular meshes. Theis approach can be applied to very complex 3D meshes of arbitrary topology and with millions of vertices. The algorithm is based on the clustering of the input mesh elements, which divides the faces of an input mesh into a given number of clusters for clustering purpose by approximating the Centroidal Voronoi Tessellation of the input mesh. Once a clustering is achieved, it provides us an efficient way to construct uniform tessellations, and therefore leads to good coarsening of polygonal meshes. With proliferation of 3D scanners, this coarsening algorithm is particularly useful for reverse engineering applications of 3D models, which in many cases are dense, non-uniform, irregular and arbitrary topology. Examples demonstrating effectiveness of the new algorithm are also included in the paper.Keywords: Coarsening, mesh clustering, shape approximation, mesh simplification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13903113 Investigation on Bio-Inspired Population Based Metaheuristic Algorithms for Optimization Problems in Ad Hoc Networks
Authors: C. Rajan, K. Geetha, C. Rasi Priya, R. Sasikala
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Nature is a great source of inspiration for solving complex problems in networks. It helps to find the optimal solution. Metaheuristic algorithm is one of the nature-inspired algorithm which helps in solving routing problem in networks. The dynamic features, changing of topology frequently and limited bandwidth make the routing, challenging in MANET. Implementation of appropriate routing algorithms leads to the efficient transmission of data in mobile ad hoc networks. The algorithms that are inspired by the principles of naturally-distributed/collective behavior of social colonies have shown excellence in dealing with complex optimization problems. Thus some of the bio-inspired metaheuristic algorithms help to increase the efficiency of routing in ad hoc networks. This survey work presents the overview of bio-inspired metaheuristic algorithms which support the efficiency of routing in mobile ad hoc networks.
Keywords: Ant colony optimization algorithm, Genetic algorithm, naturally inspired algorithms and particle swarm optimization algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 35983112 Fractional Delay FIR Filters Design with Enhanced Differential Evolution
Authors: Krzysztof Walczak
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Fractional delay FIR filters design method based on the differential evolution algorithm is presented. Differential evolution is an evolutionary algorithm for solving a global optimization problems in the continuous search space. In the proposed approach, an evolutionary algorithm is used to determine the coefficients of a fractional delay FIR filter based on the Farrow structure. Basic differential evolution is enhanced with a restricted mating technique, which improves the algorithm performance in terms of convergence speed and obtained solution. Evolutionary optimization is carried out by minimizing an objective function which is based on the amplitude response and phase delay errors. Experimental results show that the proposed algorithm leads to a reduction in the amplitude response and phase delay errors relative to those achieved with the Least-Squares method.Keywords: Fractional Delay Filters, Farrow Structure, Evolutionary Computation, Differential Evolution
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18463111 IBFO_PSO: Evaluating the Performance of Bio-Inspired Integrated Bacterial Foraging Optimization Algorithm and Particle Swarm Optimization Algorithm in MANET Routing
Authors: K. Geetha, P. Thangaraj, C. Rasi Priya, C. Rajan, S. Geetha
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This paper presents the performance of Integrated Bacterial Foraging Optimization and Particle Swarm Optimization (IBFO_PSO) technique in MANET routing. The BFO is a bio-inspired algorithm, which simulates the foraging behavior of bacteria. It is effectively applied in improving the routing performance in MANET. In results, it is proved that the PSO integrated with BFO reduces routing delay, energy consumption and communication overhead.Keywords: Ant Colony Optimization, Bacterial Foraging Optimization, Hybrid Routing Intelligent Algorithm, Naturally inspired algorithms, Particle Swarm Optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 27153110 Grid Based and Random Based Ant Colony Algorithms for Automatic Hose Routing in 3D Space
Authors: Gishantha Thantulage, Tatiana Kalganova, Manissa Wilson
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Ant Colony Algorithms have been applied to difficult combinatorial optimization problems such as the travelling salesman problem and the quadratic assignment problem. In this paper gridbased and random-based ant colony algorithms are proposed for automatic 3D hose routing and their pros and cons are discussed. The algorithm uses the tessellated format for the obstacles and the generated hoses in order to detect collisions. The representation of obstacles and hoses in the tessellated format greatly helps the algorithm towards handling free-form objects and speeds up computation. The performance of algorithm has been tested on a number of 3D models.Keywords: Ant colony algorithm, Automatic hose routing, tessellated format, RAPID.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15593109 A New Derivative-Free Quasi-Secant Algorithm For Solving Non-Linear Equations
Authors: F. Soleymani, M. Sharifi
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Most of the nonlinear equation solvers do not converge always or they use the derivatives of the function to approximate the root of such equations. Here, we give a derivative-free algorithm that guarantees the convergence. The proposed two-step method, which is to some extent like the secant method, is accompanied with some numerical examples. The illustrative instances manifest that the rate of convergence in proposed algorithm is more than the quadratically iterative schemes.Keywords: Non-linear equation, iterative methods, derivative-free, convergence.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17603108 Genetic Algorithm Based Optimal Control for a 6-DOF Non Redundant Stewart Manipulator
Authors: A. Omran, G. El-Bayiumi, M. Bayoumi, A. Kassem
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Applicability of tuning the controller gains for Stewart manipulator using genetic algorithm as an efficient search technique is investigated. Kinematics and dynamics models were introduced in detail for simulation purpose. A PD task space control scheme was used. For demonstrating technique feasibility, a Stewart manipulator numerical-model was built. A genetic algorithm was then employed to search for optimal controller gains. The controller was tested onsite a generic circular mission. The simulation results show that the technique is highly convergent with superior performance operating for different payloads.
Keywords: Stewart kinematics, Stewart dynamics, task space control, genetic algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18793107 New Hybrid Algorithm for Task Scheduling in Grid Computing to Decrease missed Task
Authors: Z. Pooranian, A. Harounabadi, M. Shojafar, N. Hedayat
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The purpose of Grid computing is to utilize computational power of idle resources which are distributed in different areas. Given the grid dynamism and its decentralize resources, there is a need for an efficient scheduler for scheduling applications. Since task scheduling includes in the NP-hard problems various researches have focused on invented algorithms especially the genetic ones. But since genetic is an inherent algorithm which searches the problem space globally and does not have the efficiency required for local searching, therefore, its combination with local searching algorithms can compensate for this shortcomings. The aim of this paper is to combine the genetic algorithm and GELS (GAGELS) as a method to solve scheduling problem by which simultaneously pay attention to two factors of time and number of missed tasks. Results show that the proposed algorithm can decrease makespan while minimizing the number of missed tasks compared with the traditional methods.Keywords: Grid Computing, Genetic Algorithm, Gravitational Emulation Local Search (GELS), missed task
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19323106 Controlling the Angle of Attack of an Aircraft Using Genetic Algorithm Based Flight Controller
Authors: S. Swain, P. S Khuntia
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In this paper, the unstable angle of attack of a FOXTROT aircraft is controlled by using Genetic Algorithm based flight controller and the result is compared with the conventional techniques like Tyreus-Luyben (TL), Ziegler-Nichols (ZN) and Interpolation Rule (IR) for tuning the PID controller. In addition, the performance indices like Mean Square Error (MSE), Integral Square Error (ISE), and Integral Absolute Time Error (IATE) etc. are improved by using Genetic Algorithm. It was established that the error by using GA is very less as compared to the conventional techniques thereby improving the performance indices of the dynamic system.Keywords: Angle of Attack, Genetic Algorithm, Performance Indices, PID Controller.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17543105 The Load Balancing Algorithm for the Star Interconnection Network
Authors: Ahmad M. Awwad, Jehad Al-Sadi
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The star network is one of the promising interconnection networks for future high speed parallel computers, it is expected to be one of the future-generation networks. The star network is both edge and vertex symmetry, it was shown to have many gorgeous topological proprieties also it is owns hierarchical structure framework. Although much of the research work has been done on this promising network in literature, it still suffers from having enough algorithms for load balancing problem. In this paper we try to work on this issue by investigating and proposing an efficient algorithm for load balancing problem for the star network. The proposed algorithm is called Star Clustered Dimension Exchange Method SCDEM to be implemented on the star network. The proposed algorithm is based on the Clustered Dimension Exchange Method (CDEM). The SCDEM algorithm is shown to be efficient in redistributing the load balancing as evenly as possible among all nodes of different factor networks.
Keywords: Interconnection networks, Load balancing, Star network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20983104 Design and Simulation of Low Speed Axial Flux Permanent Magnet (AFPM) Machine
Authors: Ahmad Darabi, Hassan Moradi, Hossein Azarinfar
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In this paper presented initial design of Low Speed Axial Flux Permanent Magnet (AFPM) Machine with Non-Slotted TORUS topology type by use of certain algorithm (Appendix). Validation of design algorithm studied by means of selected data of an initial prototype machine. Analytically design calculation carried out by means of design algorithm and obtained results compared with results of Finite Element Method (FEM).Keywords: Axial Flux Permanent Magnet (AFPM) Machine, Design Algorithm, Finite Element Method (FEM), TORUS
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 32843103 Levenberg-Marquardt Algorithm for Karachi Stock Exchange Share Rates Forecasting
Authors: Syed Muhammad Aqil Burney, Tahseen Ahmed Jilani, C. Ardil
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Financial forecasting is an example of signal processing problems. A number of ways to train/learn the network are available. We have used Levenberg-Marquardt algorithm for error back-propagation for weight adjustment. Pre-processing of data has reduced much of the variation at large scale to small scale, reducing the variation of training data.
Keywords: Gradient descent method, jacobian matrix.Levenberg-Marquardt algorithm, quadratic error surfaces,
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24573102 A Multi-objective Fuzzy Optimization Method of Resource Input Based on Genetic Algorithm
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With the increasing complexity of engineering problems, the traditional, single-objective and deterministic optimization method can not meet people-s requirements. A multi-objective fuzzy optimization model of resource input is built for M chlor-alkali chemical eco-industrial park in this paper. First, the model is changed into the form that can be solved by genetic algorithm using fuzzy theory. And then, a fitness function is constructed for genetic algorithm. Finally, a numerical example is presented to show that the method compared with traditional single-objective optimization method is more practical and efficient.Keywords: Fitness function, genetic algorithm, multi-objectivefuzzy optimization, satisfaction degree membership function.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13433101 Adaptive Total Variation Based on Feature Scale
Authors: Jianbo Hu, Hongbao Wang
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The widely used Total Variation de-noising algorithm can preserve sharp edge, while removing noise. However, since fixed regularization parameter over entire image, small details and textures are often lost in the process. In this paper, we propose a modified Total Variation algorithm to better preserve smaller-scaled features. This is done by allowing an adaptive regularization parameter to control the amount of de-noising in any region of image, according to relative information of local feature scale. Experimental results demonstrate the efficient of the proposed algorithm. Compared with standard Total Variation, our algorithm can better preserve smaller-scaled features and show better performance.
Keywords: Adaptive, de-noising, feature scale, regularizationparameter, Total Variation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12213100 A Modified Fuzzy C-Means Algorithm for Natural Data Exploration
Authors: Binu Thomas, Raju G., Sonam Wangmo
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In Data mining, Fuzzy clustering algorithms have demonstrated advantage over crisp clustering algorithms in dealing with the challenges posed by large collections of vague and uncertain natural data. This paper reviews concept of fuzzy logic and fuzzy clustering. The classical fuzzy c-means algorithm is presented and its limitations are highlighted. Based on the study of the fuzzy c-means algorithm and its extensions, we propose a modification to the cmeans algorithm to overcome the limitations of it in calculating the new cluster centers and in finding the membership values with natural data. The efficiency of the new modified method is demonstrated on real data collected for Bhutan-s Gross National Happiness (GNH) program.Keywords: Adaptive fuzzy clustering, clustering, fuzzy logic, fuzzy clustering, c-means.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19643099 A Meta-Heuristic Algorithm for Vertex Covering Problem Based on Gravity
Authors: S. Raja Balachandar, K.Kannan
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A new Meta heuristic approach called "Randomized gravitational emulation search algorithm (RGES)" for solving vertex covering problems has been designed. This algorithm is found upon introducing randomization concept along with the two of the four primary parameters -velocity- and -gravity- in physics. A new heuristic operator is introduced in the domain of RGES to maintain feasibility specifically for the vertex covering problem to yield best solutions. The performance of this algorithm has been evaluated on a large set of benchmark problems from OR-library. Computational results showed that the randomized gravitational emulation search algorithm - based heuristic is capable of producing high quality solutions. The performance of this heuristic when compared with other existing heuristic algorithms is found to be excellent in terms of solution quality.
Keywords: Vertex covering Problem, Velocity, Gravitational Force, Newton's Law, Meta Heuristic, Combinatorial optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19963098 Algorithm Design and Performance Evaluation of Equivalent CMOS Model
Authors: Parvinder S. Sandhu, Iqbaldeep Kaur, Amit Verma, Inderpreet Kaur, Birinderjit S. Kalyan
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This work is a proposed model of CMOS for which the algorithm has been created and then the performance evaluation of this proposition has been done. In this context, another commonly used model called ZSTT (Zero Switching Time Transient) model is chosen to compare all the vital features and the results for the Proposed Equivalent CMOS are promising. In the end, the excerpts of the created algorithm are also includedKeywords: Dual Capacitor Model, ZSTT, CMOS, SPICEMacro-Model.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13163097 A Fuzzy Classifier with Evolutionary Design of Ellipsoidal Decision Regions
Authors: Leehter Yao, Kuei-Song Weng, Cherng-Dir Huang
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A fuzzy classifier using multiple ellipsoids approximating decision regions for classification is to be designed in this paper. An algorithm called Gustafson-Kessel algorithm (GKA) with an adaptive distance norm based on covariance matrices of prototype data points is adopted to learn the ellipsoids. GKA is able toadapt the distance norm to the underlying distribution of the prototypedata points except that the sizes of ellipsoids need to be determined a priori. To overcome GKA's inability to determine appropriate size ofellipsoid, the genetic algorithm (GA) is applied to learn the size ofellipsoid. With GA combined with GKA, it will be shown in this paper that the proposed method outperforms the benchmark algorithms as well as algorithms in the field.
Keywords: Ellipsoids, genetic algorithm, classification, fuzzyc-means (FCM)
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16833096 A Hybrid CamShift and l1-Minimization Video Tracking Algorithm
Authors: Clark Van Dam, Gagan Mirchandani
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The Continuously Adaptive Mean-Shift (CamShift) algorithm, incorporating scene depth information is combined with the l1-minimization sparse representation based method to form a hybrid kernel and state space-based tracking algorithm. We take advantage of the increased efficiency of the former with the robustness to occlusion property of the latter. A simple interchange scheme transfers control between algorithms based upon drift and occlusion likelihood. It is quantified by the projection of target candidates onto a depth map of the 2D scene obtained with a low cost stereo vision webcam. Results are improved tracking in terms of drift over each algorithm individually, in a challenging practical outdoor multiple occlusion test case.Keywords: CamShift, l1-minimization, particle filter, stereo vision, video tracking.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20333095 An Efficient Algorithm for Reliability Lower Bound of Distributed Systems
Authors: Mohamed H. S. Mohamed, Yang Xiao-zong, Liu Hong-wei, Wu Zhi-bo
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The reliability of distributed systems and computer networks have been modeled by a probabilistic network or a graph G. Computing the residual connectedness reliability (RCR), denoted by R(G), under the node fault model is very useful, but is an NP-hard problem. Since it may need exponential time of the network size to compute the exact value of R(G), it is important to calculate its tight approximate value, especially its lower bound, at a moderate calculation time. In this paper, we propose an efficient algorithm for reliability lower bound of distributed systems with unreliable nodes. We also applied our algorithm to several typical classes of networks to evaluate the lower bounds and show the effectiveness of our algorithm.Keywords: Distributed systems, probabilistic network, residual connectedness reliability, lower bound.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16653094 A Novel In-Place Sorting Algorithm with O(n log z) Comparisons and O(n log z) Moves
Authors: Hanan Ahmed-Hosni Mahmoud, Nadia Al-Ghreimil
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In-place sorting algorithms play an important role in many fields such as very large database systems, data warehouses, data mining, etc. Such algorithms maximize the size of data that can be processed in main memory without input/output operations. In this paper, a novel in-place sorting algorithm is presented. The algorithm comprises two phases; rearranging the input unsorted array in place, resulting segments that are ordered relative to each other but whose elements are yet to be sorted. The first phase requires linear time, while, in the second phase, elements of each segment are sorted inplace in the order of z log (z), where z is the size of the segment, and O(1) auxiliary storage. The algorithm performs, in the worst case, for an array of size n, an O(n log z) element comparisons and O(n log z) element moves. Further, no auxiliary arithmetic operations with indices are required. Besides these theoretical achievements of this algorithm, it is of practical interest, because of its simplicity. Experimental results also show that it outperforms other in-place sorting algorithms. Finally, the analysis of time and space complexity, and required number of moves are presented, along with the auxiliary storage requirements of the proposed algorithm.
Keywords: Auxiliary storage sorting, in-place sorting, sorting.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18983093 Orthogonal Regression for Nonparametric Estimation of Errors-in-Variables Models
Authors: Anastasiia Yu. Timofeeva
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Two new algorithms for nonparametric estimation of errors-in-variables models are proposed. The first algorithm is based on penalized regression spline. The spline is represented as a piecewise-linear function and for each linear portion orthogonal regression is estimated. This algorithm is iterative. The second algorithm involves locally weighted regression estimation. When the independent variable is measured with error such estimation is a complex nonlinear optimization problem. The simulation results have shown the advantage of the second algorithm under the assumption that true smoothing parameters values are known. Nevertheless the use of some indexes of fit to smoothing parameters selection gives the similar results and has an oversmoothing effect.
Keywords: Grade point average, orthogonal regression, penalized regression spline, locally weighted regression.
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