Objectives
The purpose of this notebook is to give you a brief introduction to the
DiscreteWavelets Toolbox and show you how to use it to load
images. Some basic image manipulation is illustrated as well. You will
also learn how to use measures and tools such as cumulative energy,
entropy, PSNR, and Huffman coding.
Help on the DiscreteWavelets Toolbox
Help for the toolbox is available by clicking on Help and then Product
Help (or press F1) and then clicking on the DiscreteWavelets Toolbox.
Several demos and examples are available as well by clicking on the Demos
tab on the Help menu.
Image Basics
The DiscreteWavelets Toolbox comes with 18 grayscale images and 9 color
images for you to use. There are three functions available to tell you more about these images.
The first function is called |ImageList|. This function can tell you the
names and sizes of the digital images in the Toolbox.
KML 2.0介紹 KML全稱是Keyhole Markup Language KML，是一個基于XML語法和文件格式的文件，用來描述和保存地理信息如點、線、圖片、折線并在Google Earth客戶端之中顯示
The core of the project is the KMLCreator.cs. This has three classes, KMLCoordinates, KMLPoint and KMLLine
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).
This article discusses some issues that a typical Windows C++ programmer will encounter when approaching
Symbian OS for the first time. Our experience in developing for three successive versions of Symbian OS has
given us considerable perspective on what can be difficult when working in this otherwise rich and stable
environment. While one reason for Symbian s success may be the desire of many mobile phone manufacturers not
to be tied to Microsoft, the other reason is that Symbian has put together a lightweight, elegant system that
succeeds in providing a very impressive range of functionality. Here are some pointers to help ease the transition to
successful Symbian OS application development.
With the Wireless module, OPNET can model both
terrestrial and satellite radio systems. In this tutorial,
you will use Modeler and Wireless modeling to create a
radio network you will also observe variations in the
quality of received signal that results from radio noise
at the receiving node in a dynamic network topology.
ADIAL Basis Function (RBF) networks were introduced
into the neural network literature by Broomhead and
Lowe [1], which are motivated by observation on the local
response in biologic neurons. Due to their better
approximation capabilities, simpler network structures and
faster learning algorithms, RBF networks have been widely applied in many science and engineering fields. RBF network is three layers feedback network, where each hidden unit implements a radial activation function and each output unit implements a weighted sum of hidden units’ outputs.
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]
Implementation of Edmonds Karp algorithm that calculates maxFlow of graph.
Input:
For each test case, the first line contains the number of vertices (n) and the number of arcs (m). Then, there exist m lines, one for each arc (source vertex, ending vertex and arc weight, separated by a space). The nodes are numbered from 1 to n. The node 1 and node n should be in different sets. There are no more than 30 arcs and 15 nodes. The arc weights vary between 1 and 1 000 000.
Output:
The output is a single line for each case, with the corresponding minimum size cut.
Example:
Input:
7 11
1 2 3
1 4 3
2 3 4
3 1 3
3 4 1
3 5 2
4 6 6
4 5 2
5 2 1
5 7 1
6 7 9
Output:
5
