The corporation of the 1990s: Information technology and organizational transformation

Thank you for downloading digital design and computer architecture. As you may know, people have search numerous times for their chosen novels like this digital design and computer architecture, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some infectious virus inside their laptop. digital design and computer architecture is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the digital design and computer architecture is universally compatible with any devices to read.

Digital Design And Computer Architecture

Reconfigurable Computing. Accelerating Computation with Field-Programmable Gate Arrays by Maya B. Gokhale and Paul S. Graham Springer, 2005, 238 pp. ISBN-13 978-0387-26105-8, $87.20 Reconfigurable Computing Accelerating Computation with Field-Programmable Gate Arrays is an expository and easy to digest book. The authors are recognized leaders with many years of experience on the field of reconfigurable computing. The book is written so that non-specialists can understand the principles, techniques and algorithms. Each chapter has many excellent references for interested readers. It surveys methods, algorithms, programming languages and applications targeted to reconfigurable computing. Automatic generation of parallel code from a sequential program on conventional micro-processor architectures remains an open problem. Nevertheless, a wide range of computationally intensive applications have benefited from many tools developed to tackle such a problem. For RC, it is even a much harder problem (perhaps 10x and up) and intense research is being devoted to make RC a common-place practical tool. The aim of the authors is threefold. First, guide the readers to know current issues on HLL for RC. Second, help the readers understand the intricate process of algorithmic-to-hardware compilation. And third, show that, even though this process is painful, if the application is suitable for RC the gains in performance are huge. The book is divided into two parts. The first part contains four chapters about reconfigurable computing and languages. Chapter 1 presents an introduction of RC, contrasting conventional fixed instruction microprocessors with RC architectures. This chapter also contains comprehensive reference material for further reading. Chapter 2 introduces reconfigurable logic devices by explaining the basic architecture and configuration of FPGAs. Chapter 3 deals with RC systems by discussing how parallel processing is achieved on reconfigurable computers and also gives a survey of RC systems today. Then, in chapter 4, languages, compilation, debugging and their related manual vs. automatic issues are discussed. The second part of the book comprises five chapters about applications of RC. Chapter 5 and 6 discuss digital signal and image processing applications. Chapter 7 covers the application of RC to secure network communications. The aim of Chapter 8 is to discuss some important bioinformatics applications for which RC is a good candidate, their algorithmic problems and hardware implementations. Finally, Chapter 9 covers two applications of reconfigurable supercomputers. The first one is a simulation of radiative heat transfer and the second one models large urban road traffic. This book is neither a technical nor a text book, but in the opinion of this reviewer, it is an excellent state-of-the-art review of RC and would be a worthwhile acquisition by anyone seriously considering speeding up a specific application. On the downside, it is somewhat disappointing that the book does not contain more information about HLL tools that could be used to help close the gap between traditional HPC community and the raw computing power of RC. Edusmildo Orozco, Department of Computer Science, University Of Puerto Rico at Rio Piedras.

Reconfigurable Computing. Accelerating Computation with Field-Programmable Gate Arrays

https://hal-supelec.archives-ouvertes.fr/hal-00876457/document

Receding horizon flight control for trajectory tracking of autonomous aerial vehicles

Floods are natural disasters which cause the most economic damage at the global level. Therefore, flood monitoring and damage estimation are very important for the population, authorities and insurance companies. The paper proposes an original solution, based on a hybrid network and complex image processing, to this problem. As first novelty, a multilevel system, with two components, terrestrial and aerial, was proposed and designed by the authors as support for image acquisition from a delimited region. The terrestrial component contains a Ground Control Station, as a coordinator at distance, which communicates via the internet with more Ground Data Terminals, as a fixed nodes network for data acquisition and communication. The aerial component contains mobile nodes—fixed wing type UAVs. In order to evaluate flood damage, two tasks must be accomplished by the network: area coverage and image processing. The second novelty of the paper consists of texture analysis in a deep neural network, taking into account new criteria for feature selection and patch classification. Color and spatial information extracted from chromatic co-occurrence matrix and mass fractal dimension were used as well. Finally, the experimental results in a real mission demonstrate the validity of the proposed methodologies and the performances of the algorithms.

https://www.mdpi.com/1424-8220/17/3/446/pdf

Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing.

This paper proposes a novel methodology that combines the differential flatness formalism for trajectory generation of nonlinear systems, and the use of a model predictive control (MPC) strategy for constraint handling The methodology consists of a trajectory generator that generates a reference trajectory parameterised by splines, and with the property that it satisfies performance objectives The reference trajectory is generated iteratively in accordance with information received from the MPC formulation This interplay with MPC guarantees that the trajectory generator receives feedback from present and future constraints for real-time trajectory generation

A Flatness-Based Iterative Method for Reference Trajectory Generation in Constrained NMPC

In this work, we discuss two methods to generate trajectories for a magnetic-levitation (Maglev) system in the presence of constraints and compare each method's performance. The methods are based on the notion of differential flatness and spline parametrisation of every signal. The first method uses the nonlinear model of the plant, which turns out to belong to the class of flat systems. The second method uses a linearised version of the plant model around an operating point. In every case, a continuous-time description is used. Experimental results on a real Maglev system, reported here, show that, in most scenarios, the nonlinear and linearised model produce almost similar, indistinguishable trajectories.

Methods for trajectory generation in a magnetic-levitation system under constraints

Research Doctorate - Doctor of Philosophy (PhD)%%%%This thesis provides a unified treatment of the notions of differential flatness, for the characterisation of continuous-time linear systems, and B-splines, a mathematical concept commonly used in computer graphics. Differential flatness is a property of some controlled (linear or nonlinear) dynamical systems, often encountered in applications, which allows for a complete parameterisation of all system variables (inputs and states) in terms of a finite number of variables, called flat outputs, and a finite number of their time derivatives. The notion of differential flatness for a system is especially useful in situations when explicit trajectory generation is required. In fact, under the differential flatness formalism the motion planning problem, as far as the differential equation is concerned, is trivialised. However, a very important limitation, ubiquitous in all practical applications, is the presence of constraints. The problem of constrained trajectory generation is intimately related to that of optimal control, where one wants to achieve certain objectives with limited resources, and time-optimal control, in which one seeks to perform a task as fast as possible while, at the same time, satisfying all system constraints. In the literature, trajectory generation and [time-] optimal control often use some parameterisation to represent the system's signals. Polynomials and B-splines are a natural choice since they have several desirable properties. However, there has not been much work exploiting the combined properties of differential flatness for linear systems and B-splines. The first focus of this thesis is, hence, to investigate the use of B-splines for constrained trajectory generation of continuous-time linear flat systems in such a way that their respective properties are jointly exploited and complemented. This synthesis offers new methods and insights to the fields of constrained trajectory optimisation, optimal control, and minimum-time trajectory generation. The differential flatness parameterisation also offers analytical redundancy relations. That is, the value of some variables can be algebraically inferred from some other measured variables. This fact can be used to perform algebraic estimation and fault detection in linear and nonlinear systems. The second focus of this thesis is, thus, to develop a method to perform algebraic estimation and fault detection, based structurally on the differential flatness notion, for linear and nonlinear systems, and using a numerical method based on B-splines. The methodology to tackle the focal problems of constrained trajectory generation and fault tolerant control, based on differential flatness and B-splines, is primarily developed for linear systems. Then, experimental validations of the methods, using a laboratory-scale magnetic levitation system, are provided. Finally, some extensions of the ideas to nonlinear systems are discussed.

Constrained trajectory generation and fault tolerant control based on differential flatness and B-splines

This article addresses the problem of trajectory planning for flat systems with constraints. Flat systems have the useful property that the input and the state can be completely characterised by the so-called flat output. We propose a spline parametrisation for the flat output, the performance output, the states and the inputs. Using this parametrisation the problem of constrained trajectory planning can be cast into a simple quadratic programming problem. An important result is that the B-spline parametrisation used gives exact results for constrained linear continuous-time system. The result is exact in the sense that the constrained signal can be made arbitrarily close to the boundary without having intersampling issues as one would have in sampled-data systems. Simulation examples are presented, involving the generation of rest-to-rest trajectories. In addition, an experimental result of the method is also presented, where two methods to generate trajectories for a magnetic-levitation maglev system in the presence of constraints are compared and each method's performance is discussed. The first method uses the nonlinear model of the plant, which turns out to belong to the class of flat systems. The second method uses a linearised version of the plant model around an operating point. In every case, a continuous-time description is used. The experimental results on a real maglev system reported here show that, in most scenarios, the nonlinear and linearised models produce almost similar, indistinguishable trajectories.

Splines and polynomial tools for flatness-based constrained motion planning

This paper discusses an open-loop trajectory planning problem for linear systems and polynomial systems with constraints. Flat systems have the nice property that the input and the state can be completely characterised by the so-called flat output. We propose a spline parameterisation for the flat output, the performance output, the states, and the inputs. Using this parameterisation the problem of constrained trajectory planning can be cast into a simple quadratic programming problem. An important result is that the B-spline parametrisation used gives exact results for constrained linear continuous-time system. The result is exact in the sense that the constrained signal can be made arbitrarily close to the boundary without having intersampling issues (as one would have in sampled-data systems). Numerical results are outlined, involving the generation of rest-to-rest trajectories.

Fajar Suryawan

Papers

A Flatness-Based Iterative Method for Reference Trajectory Generation in Constrained NMPC

Methods for trajectory generation in a magnetic-levitation system under constraints

Constrained trajectory generation and fault tolerant control based on differential flatness and B-splines

Splines and polynomial tools for flatness-based constrained motion planning

On splines and polynomial tools for constrained motion planning