This book is intended for "hands-on" developers or advanced students interested in understanding the strategies and tactics of concurrent network programming using C++ and object-oriented design. We describe the key design dimensions, patterns, and principles needed to develop flexible and efficient concurrent networked applications quickly and easily. Our numerous C++ code examples reinforce the design concepts and illustrate concretely how to use the core classes in ACE right away. We also take you "behind the scenes" to understand how and why the IPC and concurrency mechanisms in the ACE toolkit are designed the way they are. This material will help to enhance your design skills and to apply C++ and patterns more effectively in your own object-oriented networked applications.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
This paper addresses a stochastic-#ow network in which each arc or node has several capacities and may
fail. Given the demand d, we try to evaluate the system reliability that the maximum #ow of the network is
not less than d. A simple algorithm is proposed "rstly to generate all lower boundary points for d, and then
the system reliability can be calculated in terms of such points. One computer example is shown to illustrate
the solution procedure.
Foreword
The four case studies that follow each have a number of common features. They each illustrate the birth of an idea and show how that idea can be realised into a marketable product. Each case study deals with engineering design and development issues and each highlights the importance of developing sound marketing strategies including market research. The importance of appropriate support mechanisms for young entrepreneurs is also covered. The case studies illustrate how successful entrepreneurs deploy a range of entrepreneurial skills and know-how. Above all, the entrepreneurs are seen to have the capacity to innovate and exercise vision.
We are grateful to Liz Read, Development Manager for Enterprise and Entrepreneurship (Students) at Coventry University for providing these case studies.
％％％ Demos for PUMA algorithms ％％％
We present four matlab demos for PUMA. demo1, demo2, demo3, and demo4
illustrate PUMA working with different parameters and with four
different images.
All you need to do is to run each of the demos. Please be sure that
all the files are put on an accessible path for matlab.
Notice that this code is intended for research purposes only.
For further reference see "Phase Unwrapping via Graph Cuts,
IEEE Transactions on Image Processing, 2007
This program is a new way to estimate the coherence function. It s based on the MVDR and is much more reliable than the classical Welch s method implemented in MATLAB.
There are 2 programs: the main program called coherence_MVDR.m and and an example, called illustrate.m, that calls the main function to show how it works. There also included 2 papers that we published on this algorithm.
Abstract—Wireless networks in combination with image
sensors open up a multitude of previously unthinkable sensing
applications. Capable tools and testbeds for these wireless image
sensor networks can greatly accelerate development of complex,
yet efficient algorithms that meet application requirements. In this
paper, we introduce WiSNAP, a Matlab-based application
development platform intended for wireless image sensor
networks. It allows researchers and developers of such networks
to investigate, design, and evaluate algorithms and applications
using real target hardware. WiSNAP offers standardized and
easy-to-use Application Program Interfaces (APIs) to control
image sensors and wireless motes, which do not require detailed
knowledge of the target hardware. Nonetheless, its open system
architecture enables support of virtually any kind of sensor or
wireless mote. Application examples are presented to illustrate the
usage of WiSNAP as a powerful development tool.
用于汽車巡航控制系統(tǒng)的模糊控制算法，以及如何利用梯度下降法和卡爾曼濾波來優(yōu)化模糊控制器的算法。The files illustrate a simple fuzzy control algorithm as applied to an automobile cruise control system. The files also illustrate how gradient descent and Kalman filtering can be used to optimize the fuzzy controller .
