Discussing embedded systems in general is difficult, because each embedded system is unique. Rather than presenting a list of general principles for handling embedded development issues, this book presents examples of problems encountered and solutions to those problems using real hardware and software. In that sense, it is a “cookbook” for developers that offers design “recipes” that can be elaborated on or modified as needed to solve other design problems.
The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
Because WDM networks are circuit switched loss networks blocking may occur because of lack of resources. Also in circuit switched networks many paths use the same links. This toolbox answers the question how different paths with different loads influence on each other and what is the blocking on each of the defined path. Toolbox is capable of computing blocking for three different WDM network types: with no wavelength conversion, with full wavelength conversion and with limited range wavelength conversion. It is worth noting that case for full conversion can be usefull for any circuit switched network without additional constraints (i.e. wavelength continuity constraint in WDM), for example telephone network.
Toolbox contains also scripts for defining network structures (random networks, user defined networks) and traffic matrixes. Three graph algorithms for shortest path computation are also in this toolbox (they are used for traffic matrix creation).
Functions are mappings from one Manifold to another. Discrete Functions are functions which can be represented using a finite number of values. Given the finite extent of computer memory, algorithms which compute a function that satisfies some special properties are computing a discrete function which approximates a continuous function. Computing the function involves writing a set of equations that may be solved for the values representing the function.
The software and hardware development fields evolved along separate paths through the end of the 20th century. We seem to have come full circle, however. The previously rigid hardware on which our programs run is softening in many ways. Embedded systems are largely responsible for this softening. These hidden computing systems drive the electronic products around us, including consumer products like digital cameras and personal digital assistants, office automation equipment like copy machines and printers, medical devices like heart monitors and ventilators, and automotive electronics like cruise controls and antilock brakes.
Embedded systems force designers to work under incredibly tight time-tomarket, power consumption, size, performance, flexibility, and cost constraints.
Many technologies introduced over the past two decades have sought to help satisfy these constraints. To understand these technologies, it is important to first distinguish the underlying embedded systems elements.
David Vernon is the Coordinator of the European Network for the Advancement of Artificial Cognitive Systems and he is a Visiting Professor of Cognitive Systems at the University of Genoa. He is also a member of the management team of the RobotCub integrated working on the development of open-source cognitive humanoid robot.
Over the past 27 years, he has held positions at Westinghouse Electric, Trinity College Dublin, the European Commission, the National University of Ireland Maynooth, Science Foundation Ireland, and Etisalat University College.
He has authored two and edited three books on computer vision and has published over eighty papers in the fields of Computer Vision, Robotics, and Cognitive Systems. His research interests include Fourier-based computer vision and enactive approaches to cognition.
He is currently a Professor at Etisalat University College in Sharjah-United Arab Emirates, focusing on Masters programs by research in Computing fields.".[1]
matlab有限元網格劃分程序
DistMesh is a simple MATLAB code for generation of unstructured triangular and tetrahedral meshes. It was developed by Per-Olof Persson (now at UC Berkeley) and Gilbert Strang in the Department of Mathematics at MIT. A detailed description of the program is provided in our SIAM Review paper, see documentation below.
One reason that the code is short and simple is that the geometries are specified by Signed Distance Functions. These give the shortest distance from any point in space to the boundary of the domain. The sign is negative inside the region and positive outside. A simple example is the unit circle in 2-D, which has the distance function d=r-1, where r is the distance from the origin. For more complicated geometries the distance function can be computed by interpolation between values on a grid, a common representation for level set methods.
For the actual mesh generation, DistMesh uses the Delaunay triangulation routine in MATLAB and tries to optimize the node locations by a force-based smoothing procedure. The topology is regularly updated by Delaunay. The boundary points are only allowed to move tangentially to the boundary by projections using the distance function. This iterative procedure typically results in very well-shaped meshes.
Our aim with this code is simplicity, so that everyone can understand the code and modify it according to their needs. The code is not entirely robust (that is, it might not terminate and return a well-shaped mesh), and it is relatively slow. However, our current research shows that these issues can be resolved in an optimized C++ code, and we believe our simple MATLAB code is important for demonstration of the underlying principles.
To use the code, simply download it from below and run it from MATLAB. For a quick demonstration, type "meshdemo2d" or "meshdemond". For more details see the documentation.
