This document is intended to serve as an introduction to Wavelet processing through a set of Matlab experiments. These experiments will gives an overview of three fundamental tasks in signal and image processing : signal, denoising and compression. These scripts are selfs contents (needed additional Matlab functions can be downloaded WHILE reading the lectures).
Each one of these five lectures should take between 1h and 2h in order to tests the various features of the scripts. One should copy/paste the provided code into a file names e.g. tp1.m, and launch the script directly from Matlab comand line > tp1 . Some of the scripts contains "holes" that you should try to fill on your own.
I also provide the complete correction of these lectures as a set of Matlab scripts, but you should try as much as possible to avoid using them.
JavaServer Pages, Third Edition is completely revised and updated to cover the substantial changes in the 2.0 version of the JSP specification. It also includes detailed coverage of the major revisions to the JSP Standard Tag Library (JSTL) specification. Combining plenty of practical advice with detailed coverage of JSP syntax and features and clear, useful examples, JavaServer Pages, Third Edition demonstrates how to embed server-side Java into Web pages, WHILE also covering important topics such as JavaBeans, Enterprise JavaBeans (EJB), and JDBC database access.
模式識別學習綜述.該論文的英文參考文獻為303篇.很有可讀價值.Abstract— Classical and recent results in statistical pattern
recognition and learning theory are reviewed in a two-class
pattern classification setting. This basic model best illustrates
intuition and analysis techniques WHILE still containing the essential
features and serving as a prototype for many applications.
Topics discussed include nearest neighbor, kernel, and histogram
methods, Vapnik–Chervonenkis theory, and neural networks. The
presentation and the large (thogh nonexhaustive) list of references
is geared to provide a useful overview of this field for both
specialists and nonspecialists.
* Explains process algebra and protocol specification using µ CRL, a language developed to combine process algebra and abstract data types
* Text is supported throughout with examples and exercises
* Full solutions are provided in an appendix, WHILE exercise sheets, lab exercises, example specifications and lecturer slides are available on the author s website
Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal
％ component subspace U of dimension PPCA_DIM using a centred covariance
matrix X. The variable VAR contains the off-subspace variance (which
is assumed to be spherical), WHILE the vector LAMBDA contains the
variances of each of the principal components. This is computed
using the eigenvalue and eigenvector decomposition of X.
EKF-SLAM Simulator
This version of the simulator uses global variables for
all large objects, such as the state covariance matrix.
WHILE bad programming practice, it is a necessary evil
for MatLab efficiency, as MatLab has no facility to avoid
gratuitous memory allocation and copying when passing
(and modifying) variables between functions. With this
concession, effort has been made to keep the code as
clean and modular as possible.
Data_Structures_and_Algorithms - These notes accompany Data Structures and Algorithms II. The course, to a large extent, follows on from
Data Structures and Algorithms I. However, WHILE DS&A I focused on fundamental datastructures, DS&A II
will focus on practical algorithms, applicable to a wide range of tasks. The approach will be somewhat less
formal, with a little more focus on applications.
