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1Design, Analysis And Evaluation Of Control Algorithms For Applications In Smart Grids

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In many countries, the currently observable transformation of the power supply system from a centrally controlled system towards a complex "system of systems", comprising lots of autonomously interacting components, leads to a significant amount of research regarding novel control concepts. To facilitate the structured development of such approaches regarding the criticality of the targeted system, the research and development of a distributed control concept is demonstrated by employing an integrated methodology comprising both the Smart Grids Architecture Model framework (SGAM) and the Smart Grid Algorithm Engineering process model (SGAE). Along the way, a taxonomy of evaluation criteria and evaluation methods for such approaches is presented. For the whole paper, the Dynamic Virtual Power Plants business case (DVPP) serves as motivating example.

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2New Parallel Algorithms For Structural Analysis And Design Of Aerospace Structures

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Subspace and Lanczos iterations have been developed, well documented, and widely accepted as efficient methods for obtaining p-lowest eigen-pair solutions of large-scale, practical engineering problems. The focus of this paper is to incorporate recent developments in vectorized sparse technologies in conjunction with Subspace and Lanczos iterative algorithms for computational enhancements. Numerical performance, in terms of accuracy and efficiency of the proposed sparse strategies for Subspace and Lanczos algorithm, is demonstrated by solving for the lowest frequencies and mode shapes of structural problems on the IBM-R6000/590 and SunSparc 20 workstations.

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3Introduction To Parallel Computing : Design And Analysis Of Algorithms

Subspace and Lanczos iterations have been developed, well documented, and widely accepted as efficient methods for obtaining p-lowest eigen-pair solutions of large-scale, practical engineering problems. The focus of this paper is to incorporate recent developments in vectorized sparse technologies in conjunction with Subspace and Lanczos iterative algorithms for computational enhancements. Numerical performance, in terms of accuracy and efficiency of the proposed sparse strategies for Subspace and Lanczos algorithm, is demonstrated by solving for the lowest frequencies and mode shapes of structural problems on the IBM-R6000/590 and SunSparc 20 workstations.

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4Analysis And Design Of Parallel Algorithms: Arithmetic And Matrix Problems.

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Subspace and Lanczos iterations have been developed, well documented, and widely accepted as efficient methods for obtaining p-lowest eigen-pair solutions of large-scale, practical engineering problems. The focus of this paper is to incorporate recent developments in vectorized sparse technologies in conjunction with Subspace and Lanczos iterative algorithms for computational enhancements. Numerical performance, in terms of accuracy and efficiency of the proposed sparse strategies for Subspace and Lanczos algorithm, is demonstrated by solving for the lowest frequencies and mode shapes of structural problems on the IBM-R6000/590 and SunSparc 20 workstations.

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5An Overview Of Engineering Concepts And Current Design Algorithms For Probabilistic Structural Analysis

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The article begins by examining the fundamentals of traditional deterministic design philosophy. The initial section outlines the concepts of failure criteria and limit state functions two traditional notions that are embedded in deterministic design philosophy. This is followed by a discussion regarding safety factors (a possible limit state function) and the common utilization of statistical concepts in deterministic engineering design approaches. Next the fundamental aspects of a probabilistic failure analysis are explored and it is shown that deterministic design concepts mentioned in the initial portion of the article are embedded in probabilistic design methods. For components fabricated from ceramic materials (and other similarly brittle materials) the probabilistic design approach yields the widely used Weibull analysis after suitable assumptions are incorporated. The authors point out that Weibull analysis provides the rare instance where closed form solutions are available for a probabilistic failure analysis. Since numerical methods are usually required to evaluate component reliabilities, a section on Monte Carlo methods is included to introduce the concept. The article concludes with a presentation of the technical aspects that support the numerical method known as fast probability integration (FPI). This includes a discussion of the Hasofer-Lind and Rackwitz-Fiessler approximations.

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6Analytic Methods In The Analysis And Design Of Number-theoretic Algorithms

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The article begins by examining the fundamentals of traditional deterministic design philosophy. The initial section outlines the concepts of failure criteria and limit state functions two traditional notions that are embedded in deterministic design philosophy. This is followed by a discussion regarding safety factors (a possible limit state function) and the common utilization of statistical concepts in deterministic engineering design approaches. Next the fundamental aspects of a probabilistic failure analysis are explored and it is shown that deterministic design concepts mentioned in the initial portion of the article are embedded in probabilistic design methods. For components fabricated from ceramic materials (and other similarly brittle materials) the probabilistic design approach yields the widely used Weibull analysis after suitable assumptions are incorporated. The authors point out that Weibull analysis provides the rare instance where closed form solutions are available for a probabilistic failure analysis. Since numerical methods are usually required to evaluate component reliabilities, a section on Monte Carlo methods is included to introduce the concept. The article concludes with a presentation of the technical aspects that support the numerical method known as fast probability integration (FPI). This includes a discussion of the Hasofer-Lind and Rackwitz-Fiessler approximations.

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7Design Of A Maximum Power Point Tracker With Simulation, Analysis, And Comparison Of Algorithms

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In this thesis, the advantages of three different maximum power point tracking (MPPT) algorithm are investigated. By simulation, the performance and efficiency of these algorithms was analyzed. By using MATLAB's SimPowerSystems block set, we created the model comprised of a Kyocera KD135GX-LP solar panel powering a buck converter controlled by the MPPT algorithms driving a resistive load. The main objective was to track the maximum power point (MPP) of the solar array by modulating the buck converter's duty cycle, thereby, optimizing the power output of the panel. The three algorithms observed performance was on par with other real world tests of these algorithms as seen in other published work. The Perturb and Observe (P&O) algorithm performed with a higher overall efficiency and was able to track the MPP quickly, while the Incremental Conductance (InC) algorithm had similar performance but requires more intensive calculations. The analysis of these algorithms led to a greater understanding of where the inefficiencies of this type of system are located, allowing improvement in future work on this subject.

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8DTIC ADA617569: Award #1 Title: Acoustic Communications 2011 Experiment: Deployment Support And Post Experiment Data Handling And Analysis. Award #2 Title: Exploiting Structured Dependencies In The Design Of Adaptive Algorithms For Underwater Communication. Award #3 Title: Coupled Research In Ocean Acoustics And Signal Processing For The Next Generation Of Underwater Acoustic Communication Systems

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A high performance, versatile, and reliable underwater communications capability is of strategic importance to the U.S. Navy. Operational scenarios involving the use, monitoring, and coordination of multiple undersea assets, both manned and unmanned and mobile and fixed, are significantly enhanced by the ability to communicate quickly, reliably, in a wide range of environmental conditions, and with minimal constraints on the actions of the platforms involved. The long-term goal of the effort in underwater acoustic communications is to integrate research in the areas of physical oceanography, ocean acoustics, signal processing, and communications theory to 1. develop a underwater communications capability that can be deployed on and used by a wide range of platforms with minimal required external infrastructure and which achieves reliable and high performance under a wide range of environmental and operational conditions, and 2. develop an communications performance prediction capability that enables commanders to reliably predict the performance of deployed communications systems as a function of environmental conditions and the location, velocity, and capabilities of the deployed assets.

“DTIC ADA617569: Award #1 Title: Acoustic Communications 2011 Experiment: Deployment Support And Post Experiment Data Handling And Analysis. Award #2 Title: Exploiting Structured Dependencies In The Design Of Adaptive Algorithms For Underwater Communication. Award #3 Title: Coupled Research In Ocean Acoustics And Signal Processing For The Next Generation Of Underwater Acoustic Communication Systems” Metadata:

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“DTIC ADA617569: Award #1 Title: Acoustic Communications 2011 Experiment: Deployment Support And Post Experiment Data Handling And Analysis. Award #2 Title: Exploiting Structured Dependencies In The Design Of Adaptive Algorithms For Underwater Communication. Award #3 Title: Coupled Research In Ocean Acoustics And Signal Processing For The Next Generation Of Underwater Acoustic Communication Systems” Subjects and Themes:

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9Design, Evaluation And Analysis Of Combinatorial Optimization Heuristic Algorithms

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Combinatorial optimization is widely applied in a number of areas nowadays. Unfortunately, many combinatorial optimization problems are NP-hard which usually means that they are unsolvable in practice. However, it is often unnecessary to have an exact solution. In this case one may use heuristic approach to obtain a near-optimal solution in some reasonable time. We focus on two combinatorial optimization problems, namely the Generalized Traveling Salesman Problem and the Multidimensional Assignment Problem. The first problem is an important generalization of the Traveling Salesman Problem; the second one is a generalization of the Assignment Problem for an arbitrary number of dimensions. Both problems are NP-hard and have hosts of applications. In this work, we discuss different aspects of heuristics design and evaluation. A broad spectrum of related subjects, covered in this research, includes test bed generation and analysis, implementation and performance issues, local search neighborhoods and efficient exploration algorithms, metaheuristics design and population sizing in memetic algorithm. The most important results are obtained in the areas of local search and memetic algorithms for the considered problems. In both cases we have significantly advanced the existing knowledge on the local search neighborhoods and algorithms by systematizing and improving the previous results. We have proposed a number of efficient heuristics which dominate the existing algorithms in a wide range of time/quality requirements. Several new approaches, introduced in our memetic algorithms, make them the state-of-the-art metaheuristics for the corresponding problems. Population sizing is one of the most promising among these approaches; it is expected to be applicable to virtually any memetic algorithm.

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10Analysis And Design Of Optimization Algorithms Via Integral Quadratic Constraints

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This manuscript develops a new framework to analyze and design iterative optimization algorithms built on the notion of Integral Quadratic Constraints (IQC) from robust control theory. IQCs provide sufficient conditions for the stability of complicated interconnected systems, and these conditions can be checked by semidefinite programming. We discuss how to adapt IQC theory to study optimization algorithms, proving new inequalities about convex functions and providing a version of IQC theory adapted for use by optimization researchers. Using these inequalities, we derive numerical upper bounds on convergence rates for the gradient method, the heavy-ball method, Nesterov's accelerated method, and related variants by solving small, simple semidefinite programming problems. We also briefly show how these techniques can be used to search for optimization algorithms with desired performance characteristics, establishing a new methodology for algorithm design.

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11Analysis And Design Of Irregular Graphs For Node-Based Verification-Based Recovery Algorithms In Compressed Sensing

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In this paper, we present a probabilistic analysis of iterative node-based verification-based (NB-VB) recovery algorithms over irregular graphs in the context of compressed sensing. Verification-based algorithms are particularly interesting due to their low complexity (linear in the signal dimension $n$). The analysis predicts the average fraction of unverified signal elements at each iteration $\ell$ where the average is taken over the ensembles of input signals and sensing matrices. The analysis is asymptotic ($n \rightarrow \infty$) and is similar in nature to the well-known density evolution technique commonly used to analyze iterative decoding algorithms. Compared to the existing technique for the analysis of NB-VB algorithms, which is based on numerically solving a large system of coupled differential equations, the proposed method is much simpler and more accurate. This allows us to design irregular sensing graphs for such recovery algorithms. The designed irregular graphs outperform the corresponding regular graphs substantially. For example, for the same recovery complexity per iteration, we design irregular graphs that can recover up to about 40% more non-zero signal elements compared to the regular graphs. Simulation results are also provided which demonstrate that the proposed asymptotic analysis matches the performance of recovery algorithms for large but finite values of $n$.

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12DTIC ADA231888: The Design And Analysis Of Efficient Learning Algorithms

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This thesis explores various theoretical aspects of machine learning with particular emphasis on techniques for designing and analyzing computationally efficient learning algorithms. Many of the results in this thesis are concerned with a model of concept learning proposed by Valiant. The thesis begins in Chapter 2 with a proof that any 'weak' learning algorithm in this model that performs slightly better than random guessing can be converted into one whose error can be made arbitrarily small. Several interesting consequences of this result are also described. Chapter 3 next explores in detail a simple but powerful technique for discovering the structure of an unknown read-once formula from random examples. An especially nice feature of this technique is its powerful resistance to noise. Chapter 4 considers a realistic extension of the PAC model to concepts that may exhibit uncertain or probabilistic behavior. A range of techniques are explored for designing efficient algorithms for learning such probabilistic concepts. In the last chapter, we present new algorithms for inferring an unknown finite-state automation from its input-output behavior. This problem is motivated by that faced by a robot in unfamiliar surroundings who must, through experimentation, discover the structure of its environment.

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13DESIGN AND ANALYSIS OF ALGORITHMS

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DESIGN AND ANALYSIS OF ALGORITHMS 

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14DTIC ADA276255: Design And Analysis Of Scalable Parallel Algorithms

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The objective of this research is to develop efficient parallel algorithms for a variety of problems and to analyze the scalability of new and existing parallel algorithms. Scalability analysis is an important tool used for predicting the performance of an algorithm-architecture combination when one or more of the hardware related parameters (interconnection network, speed of processors, speed of communication channels, number of processors) are changed. The problems studied as a part of this project come from diverse domains such as solution of differential equations, discrete optimization, neural network based learning, sorting and graph algorithms. In particular, we have studied parallel algorithms for solving linear systems using the preconditioned conjugate gradient method, partitioning of finite element meshes, balancing load in unstructured tree search arising in discrete optimization, the backpropagation neural network learning algorithm, dynamic programming, fast fourier transform, sorting, shortest-path computation for graphs, robot motion planning, and matrix multiplication. Parallel algorithms, Scalability analysis, Isoefficiency.

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15DTIC ADA576577: Design Of A Maximum Power Point Tracker With Simulation, Analysis, And Comparison Of Algorithms

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In this thesis, the advantages of three different maximum power point tracking (MPPT) algorithm are investigated. By simulation, the performance and efficiency of these algorithms was analyzed. By using MATLAB's SimPowerSystems block set, we created the model comprised of a Kyocera KD135GX-LP solar panel powering a buck converter controlled by the MPPT algorithms driving a resistive load. The main objective was to track the maximum power point (MPP) of the solar array by modulating the buck converter's duty cycle, thereby, optimizing the power output of the panel. The three algorithms observed performance was on par with other real world tests of these algorithms as seen in other published work. The Perturb and Observe (P&O) algorithm performed with a higher overall efficiency and was able to track the MPP quickly, while the Incremental Conductance (InC) algorithm had similar performance but requires more intensive calculations. The analysis of these algorithms led to a greater understanding of where the inefficiencies of this type of system are located, allowing improvement in future work on this subject.

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16The Design And Analysis Of Algorithms

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In this thesis, the advantages of three different maximum power point tracking (MPPT) algorithm are investigated. By simulation, the performance and efficiency of these algorithms was analyzed. By using MATLAB's SimPowerSystems block set, we created the model comprised of a Kyocera KD135GX-LP solar panel powering a buck converter controlled by the MPPT algorithms driving a resistive load. The main objective was to track the maximum power point (MPP) of the solar array by modulating the buck converter's duty cycle, thereby, optimizing the power output of the panel. The three algorithms observed performance was on par with other real world tests of these algorithms as seen in other published work. The Perturb and Observe (P&O) algorithm performed with a higher overall efficiency and was able to track the MPP quickly, while the Incremental Conductance (InC) algorithm had similar performance but requires more intensive calculations. The analysis of these algorithms led to a greater understanding of where the inefficiencies of this type of system are located, allowing improvement in future work on this subject.

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17Design And Analysis Of Algorithms

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Design and Analysis of Algorithms. Computer Science course material

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18NPTEL Lectures: Computer Science And Engineering - Design And Analysis Of Algorithms

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Lectures from the National Programme on Technology Enhanced Learning - Computer Science and Engineering - Design and Analysis of Algorithms

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19Analytic Methods In The Analysis And Design Of Number-theoretic Algorithms

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Lectures from the National Programme on Technology Enhanced Learning - Computer Science and Engineering - Design and Analysis of Algorithms

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20MIT 3 Divide & Conquer: FFT - Design And Analysis Of Algorithms - Introduction To Alogorithms

MIT Design and Analysis of Algorithms Prof. Erik Demaine Prof. Srini Devadas Prof. Nancy Lynch MIT Course Number 6.046J / 18.410J As Taught In Spring 2015 Level Undergraduate

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21Design And Analysis Of Randomized Algorithms : Introduction To Design Paradigms

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MIT Design and Analysis of Algorithms Prof. Erik Demaine Prof. Srini Devadas Prof. Nancy Lynch MIT Course Number 6.046J / 18.410J As Taught In Spring 2015 Level Undergraduate

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22The Design And Experimental Analysis Of Algorithms For Temporal Reasoning

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Many applications -- from planning and scheduling to problems in molecular biology -- rely heavily on a temporal reasoning component. In this paper, we discuss the design and empirical analysis of algorithms for a temporal reasoning system based on Allen's influential interval-based framework for representing temporal information. At the core of the system are algorithms for determining whether the temporal information is consistent, and, if so, finding one or more scenarios that are consistent with the temporal information. Two important algorithms for these tasks are a path consistency algorithm and a backtracking algorithm. For the path consistency algorithm, we develop techniques that can result in up to a ten-fold speedup over an already highly optimized implementation. For the backtracking algorithm, we develop variable and value ordering heuristics that are shown empirically to dramatically improve the performance of the algorithm. As well, we show that a previously suggested reformulation of the backtracking search problem can reduce the time and space requirements of the backtracking search. Taken together, the techniques we develop allow a temporal reasoning component to solve problems that are of practical size.

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23Linear-programming Design And Analysis Of Fast Algorithms For Max 2-Sat And Max 2-CSP

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The class $(r,2)$-CSP, or simply Max 2-CSP, consists of constraint satisfaction problems with at most two $r$-valued variables per clause. For instances with $n$ variables and $m$ binary clauses, we present an $O(n r^{5+19m/100})$-time algorithm which is the fastest polynomial-space algorithm for many problems in the class, including Max Cut. The method also proves a treewidth bound $\tw(G) \leq (13/75+o(1))m$, which gives a faster Max 2-CSP algorithm that uses exponential space: running in time $\Ostar{2^{(13/75+o(1))m}}$, this is fastest for most problems in Max 2-CSP. Parametrizing in terms of $n$ rather than $m$, for graphs of average degree $d$ we show a simple algorithm running time $\Ostar{2^{(1-\frac{2}{d+1})n}}$, the fastest polynomial-space algorithm known. In combination with ``Polynomial CSPs'' introduced in a companion paper, these algorithms also allow (with an additional polynomial-factor overhead in space and time) counting and sampling, and the solution of problems like Max Bisection that escape the usual CSP framework. Linear programming is key to the design as well as the analysis of the algorithms.

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24DTIC ADA461336: Design And Analysis Of Distributed Routing Algorithms

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Route assignment is one of the operational problems of communication network, and adaptive routing schemes are required to achieve real time performance. This thesis introduces, verifies and analyses two new distributed, shortest-path routing algorithms, which are called, Path-Finding Algorithm (PFA) and Loop-Free Path-Finding Algorithm (LPA). Both algorithms require each routing node to know only the distance and the second-to-last-hop (or predecessor) node to each destination. In addition to the above information, LPA uses an efficient inter-neighbor coordination mechanism spanning over a single hop. PFA reduces the formation of temporary loops significantly, while LPA achieves loop-freedom at every instant by eliminating temporary loops. The average performance of these two algorithms is compared with the Diffusing Update Algorithm (DUAL) and an ideal link state (ILS) using Dijkstra's shortest-path algorithm by simulation; this performance comparison is made in terms of time taken for convergence, number of packets exchanged and the total number of operations required for convergence by each of the algorithms. The simulations were performed using a C-based simulation tool called Drama, along with a network simulation library. The results indicated that the performance of PFA is comparable to that of DUAL and ILS and that a significant improvement in performance can be achieved with LPA over DUAL and ILS.

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25Design And Analysis Of Algorithms Laboratory 1 ( 1) 1( 0)

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26Adaptive Reduced-Rank LCMV Beamforming Algorithms Based On Joint Iterative Optimization Of Filters: Design And Analysis

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This paper presents reduced-rank linearly constrained minimum variance (LCMV) beamforming algorithms based on joint iterative optimization of filters. The proposed reduced-rank scheme is based on a constrained joint iterative optimization of filters according to the minimum variance criterion. The proposed optimization procedure adjusts the parameters of a projection matrix and an adaptive reducedrank filter that operates at the output of the bank of filters. We describe LCMV expressions for the design of the projection matrix and the reduced-rank filter. We then describe stochastic gradient and develop recursive least-squares adaptive algorithms for their efficient implementation along with automatic rank selection techniques. An analysis of the stability and the convergence properties of the proposed algorithms is presented and semi-analytical expressions are derived for predicting their mean squared error (MSE) performance. Simulations for a beamforming application show that the proposed scheme and algorithms outperform in convergence and tracking the existing full-rank and reduced-rank algorithms while requiring comparable complexity

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27NASA Technical Reports Server (NTRS) 19990004187: New Parallel Algorithms For Structural Analysis And Design Of Aerospace Structures

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Subspace and Lanczos iterations have been developed, well documented, and widely accepted as efficient methods for obtaining p-lowest eigen-pair solutions of large-scale, practical engineering problems. The focus of this paper is to incorporate recent developments in vectorized sparse technologies in conjunction with Subspace and Lanczos iterative algorithms for computational enhancements. Numerical performance, in terms of accuracy and efficiency of the proposed sparse strategies for Subspace and Lanczos algorithm, is demonstrated by solving for the lowest frequencies and mode shapes of structural problems on the IBM-R6000/590 and SunSparc 20 workstations.

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28Design And Analysis Of Algorithms

edition: 2021-22 Aktu B.tech semester 5 Branch :IT 

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29DTIC ADA400121: Design, Analysis And Simulation Of Memory-Based Control Algorithms For Dual Range Missiles

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The primary objective of this project is to design, analyze, and test new autopilot algorithms for trajectory tracking control of dual range missiles. The main contributions of this project can be summarized as follows. First, a low-order memory-based control method was developed. This method is based on observing and generalizing past system responses and control experience. It does not demand detailed information about the system dynamics. Second, the memory-based method is integrated with a partial inverse dynamics control and applied to achieve missile orientation tracking in the presence of actuator dynamics and aerodynamics. It is shown that this method is effective in dealing with system nonlinearities and uncertainties due to varying flight conditions. The method is also extended to position/orientation tracking. Finally, the developed autopilot algorithms were tested via computer simulation.

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30DTIC ADA055823: The Design And Analysis Of Algorithms For Asynchronous Multiprocessors.

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The characteristic of an asynchronous multiprocessor is that it is composed of several processors capable of carrying out the execution of their own programs in a completely independent fashion. As a consequence, parallel algorithms for asynchronous multiprocessors present some unique aspects in both their design and their analysis. This thesis explores the issues raised by the design and the analysis of parallel algorithms for asynchronous multiprocessors and illustrates the various notions and concepts involved with these algorithms by considering problems in diverse areas. The thesis demonstrates that asynchronous multiprocessors can be used efficiently in different problem domains, provided that appropriate algorithms are used. It also illustrates various techniques useful in the analysis of such algorithms. As evidenced by a series of experimental results, the computation time required by a process to execute several instances of the same task on an asynchronous multiprocessor cannot be regarded as constant and is actually subject to important fluctuations. These fluctuations in computation times have a negative effect on the performance of parallel algorithms when several processes cooperating in the solution of a problem communicate extensively among themselves. In this case, when synchronization is used, it tends to introduce a prohibitive overhead which decreases the parallelism. On the other hand, an algorithm is presented to illustrate that the fluctuations are not always a negative factor but can also be utilized advantageously.

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31Design And Analysis Of Algorithms Laboratory 1 ( 1) 1( 0)

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32The Design And Analysis Of Parallel Algorithms

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33Analysis And Design Of Algorithms For Combinatorial Problems

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34Design And Analysis Of Algorithms

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35NASA Technical Reports Server (NTRS) 19960007299: An Overview Of Engineering Concepts And Current Design Algorithms For Probabilistic Structural Analysis

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The article begins by examining the fundamentals of traditional deterministic design philosophy. The initial section outlines the concepts of failure criteria and limit state functions two traditional notions that are embedded in deterministic design philosophy. This is followed by a discussion regarding safety factors (a possible limit state function) and the common utilization of statistical concepts in deterministic engineering design approaches. Next the fundamental aspects of a probabilistic failure analysis are explored and it is shown that deterministic design concepts mentioned in the initial portion of the article are embedded in probabilistic design methods. For components fabricated from ceramic materials (and other similarly brittle materials) the probabilistic design approach yields the widely used Weibull analysis after suitable assumptions are incorporated. The authors point out that Weibull analysis provides the rare instance where closed form solutions are available for a probabilistic failure analysis. Since numerical methods are usually required to evaluate component reliabilities, a section on Monte Carlo methods is included to introduce the concept. The article concludes with a presentation of the technical aspects that support the numerical method known as fast probability integration (FPI). This includes a discussion of the Hasofer-Lind and Rackwitz-Fiessler approximations.

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36DTIC ADA139600: Modular Computing Networks: A New Methodology For Analysis And Design Of Parallel Algorithms/Architectures,

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Several methods for modeling and analysis of parallel algorithms and architecture have been proposed in the recent years. These include recursion-type methods, like recursion equations, z-transform descriptions and 'do-loops' in high-level programming languages, and precedence-graph-type methods like data-flow graphs (marked graphs) and related Petri-net derived models. This paper presents a new methodology for modeling and analysis of parallel algorithms and architectures. This methodology provides a unified conceptual framework that clearly displays the key properties of parallel systems. This methodology is largely based upon the theory of directed graphs and can, therefore, be expressed both informally, in pictorial fashion, and formally, in the language of precedence relations and compositions of functions. This duality will, hopefully, help to bridge the gap between the two schools of research in this field.

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37Design And Analysis Of Parallel Algorithms For Distributed In-situ Array Beamforming

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38Design And Analysis Of Algorithms Laboratory 1 ( 1) 1

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39DTIC AD1013911: Award 1 Title: Acoustic Communications 2011 Experiment: Deployment Support And Post Experiment Data Handling And Analysis. Award 2 Title: Exploiting Structured Dependencies In The Design Of Adaptive Algorithms For Underwater Communication Award. 3 Title: Coupled Research In Ocean Acoustics And Signal Processing For The Next Generation Of Underwater Acoustic Communication Systems

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“DTIC AD1013911: Award 1 Title: Acoustic Communications 2011 Experiment: Deployment Support And Post Experiment Data Handling And Analysis. Award 2 Title: Exploiting Structured Dependencies In The Design Of Adaptive Algorithms For Underwater Communication Award. 3 Title: Coupled Research In Ocean Acoustics And Signal Processing For The Next Generation Of Underwater Acoustic Communication Systems” Subjects and Themes:

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40DTIC AD1036014: Design And Demonstration Of Automated Data Analysis Algorithms For Ultrasonic Inspection Of Complex Composite Panels With Bonds

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To address the data review burden and improve the reliability of the ultrasonic inspection of large composite structures, automated data analysis (ADA) algorithms have been developed to make calls on indications that satisfy the detection criteria and minimize false calls. The original design followed standard procedures for analyzing signals for time-of-flight indications and backwall amplitude dropout. However, certain complex panels with varying shape, ply drops and the presence of bonds can complicate this interpretation process. In this paper, enhancements to the automated data analysis algorithms are introduced to address these challenges. To estimate the thickness of the part and presence of bonds without prior information, an algorithm tracks potential backwall or bond-line signals, and evaluates a combination of spatial, amplitude, and time-of-flight metrics to identify bonded sections. Once part boundaries, thickness transitions and bonded regions are identified, feature extraction algorithms are applied to multiple sets of through-thickness and backwall C-scan images.

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41Application And Analysis Of Machine Learning Algorithms For Design Of Concrete Mix With Plasticizer And Without Plasticizer

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The objective of this paper is to find an alternative to conventional method of concrete mix design. For finding the alternative, 4 machine learning algorithms viz. multi-variable linear regression, Support Vector Regression, Decision Tree Regression and Artificial Neural Network for designing concrete mix of desired properties. The multi-variable linear regression model is just a simplistic baseline model, support vector regression Artificial Neural Network model were made because past researchers worked heavily on them, Decision tree model was made by authors own intuition. Their results have been compared to find the best algorithm. Finally, we check if the best performing algorithm is accurate enough to replace the convention method. For this, we utilize the concrete mix designs done in lab for various on site designs. The models have been designed for both mixes types – with plasticizer and without plasticizer The paper presents detailed comparison of four models Based on the results obtained from the four models, the best one has been selected based on high accuracy and least computational cost. Each sample had 24 features initially, out of which, most significant features were chosen which were contributing towards prediction of a variable using f regression and p values and models were trained on those selected features. Based on the R squared value, best fitting models were selected among the four algorithms used. From the paper, the author(s) conclude that decision tree regression is best for calculating the amount of ingredients required with R squared values close to 0.8 for most of the models. DTR model is also computationally cheaper than ANN and future works with DTR in mix design is highly recommended in this paper.

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42DTIC ADA333351: Essential Elements Of Computational Algorithms For Aerodynamic Analysis And Design

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This paper traces the development of computational fluid dynamics as a tool for aircraft design. It addresses the requirements for effective industrial use, and trade-offs between modeling accuracy and computational costs. Essential elements of algorithm design are discussed in detail, together with a unified approach to the design of shock capturing schemes. Finally, the paper discusses the use of techniques drawn from control theory to determine optimal aerodynamic shapes. In the future multidisciplinary analysis and optimization should be combined to provide an integrated design environment.

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The book is available for download in "texts" format, the size of the file-s is: 104.65 Mbs, the file-s for this book were downloaded 84 times, the file-s went public at Mon Apr 09 2018.

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43Introduction To The Design And Analysis Of Algorithms

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This paper traces the development of computational fluid dynamics as a tool for aircraft design. It addresses the requirements for effective industrial use, and trade-offs between modeling accuracy and computational costs. Essential elements of algorithm design are discussed in detail, together with a unified approach to the design of shock capturing schemes. Finally, the paper discusses the use of techniques drawn from control theory to determine optimal aerodynamic shapes. In the future multidisciplinary analysis and optimization should be combined to provide an integrated design environment.

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44MIT 6.046J Design And Analysis Of Algorithms, Spring 2015

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View the complete course: http://ocw.mit.edu/6-046JS15 Instructors: Erik Demaine, Srinivas Devadas, Nancy Ann Lynch 6.046 introduces students to the design of computer algorithms, as well as analysis of sophisticated algorithms. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

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45Design And Analysis Of Algorithms CS 502 Handouts

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