In addition to all the people who contributed to the first edition, we would like to thank the following individuals for their generous help in writing this edition. Very special thanks go to Jory Prather for verifying the code samples as well as fixing them for consistency. Thanks to Dave Thaler, Brian Zill, and Rich Draves for clarifying our IPv6 questions, Mohammad Alam and Rajesh Peddibhotla for help with reliable multicasting, and Jeff Venable for his contributions on the Network Location Awareness functionality. Thanks to Vadim Eydelman for his Winsock expertise. And finally we would like to thank the .NET Application Frameworks team (Lance Olson, Mauro Ottaviani, and Ron Alberda) for their help with our questions about .NET Sockets.
We have a group of N items (represented by integers from 1 to N), and we know that there is some total order defined for these items. You may assume that no two elements will be equal (for all a, b: a<b or b<a). However, it is expensive to compare two items. Your task is to make a number of comparisons, and then output the sorted order. The cost of determining if a < b is given by the bth integer of element a of costs (space delimited), which is the same as the ath integer of element b. Naturally, you will be judged on the total cost of the comparisons you make before outputting the sorted order. If your order is incorrect, you will receive a 0. Otherwise, your score will be opt/cost, where opt is the best cost anyone has achieved and cost is the total cost of the comparisons you make (so your score for a test case will be between 0 and 1). Your score for the problem will simply be the sum of your scores for the individual test cases.
Generate 100 samples of a zero-mean white noise sequence with variance , by using a uniform random number generator.
a Compute the autocorrelation of for .
b Compute the periodogram estimate and plot it.
c Generate 10 different realizations of , and compute the corresponding sample autocorrelation sequences , and . Compute the average autocorrelation sequence as and the corresponding periodogram for .
d Compute and plot the average periodogram using the Bartlett method.
e Comment on the results in parts (a) through (d).
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More details can be found in the publications on directed diffusion. Directed diffusion simulation code is available in ns-2. The latest release of the diffusion routing software is available on the Testbeds and Software page.
The 6.0 release of Visual C++ shows Microsoft s continued focus on Internet technologies and COM, which are key components of the new Windows Distributed interNet Application Architecture (DNA). In addition to supporting these platform initiatives, Visual C++ 6.0 also adds an amazing number of productivity-boosting features such as Edit And Continue, IntelliSense, AutoComplete, and code tips. These features take Visual C++ to a new level. We have tried to make sure that this book keeps you up to speed on the latest technologies being introduced into Visual C++.
Because of the poor observability of Inertial Navigation System on stationary base, the estimation
error of the azimuth will converge very slowly in initial alignment by means of Kalmari filtering, and making the
time initial alignment is longer. In this paper, a fast estimation method of the azimuth error is creatively proposed
for the initial alignment of INS on stationary base. On the basis of the the fast convergence of the leveling error,
the azimuth error can be directly calculated. By means of this fast initial alignment method, the time of initial
alignment is reduced greatly. The computer simulation results illustrate the efficiency of the method.
