This gives examples of Session Description Protocol (SDP)
offer/answer exchanges. Examples include codec negotiation and
selection, hold and resume, and addition and deletion of media
streams. The examples show multiple media types, bidirectional,
unidirectional, inactive streams, and dynamic payload types. Common
Third Party Call Control (3pcc) examples are also given.
The TMS320C64x™ DSPs (including the TMS320DM642 device) are the highest-performance fixed-point
DSP generation in the TMS320C6000™ DSP platform. The TMS320DM642 (DM642) device is based on
the second-generation high-performance, advanced VelociTI™ very-long-instruction-word (VLIW)
architecture (VelociTI.2™ ) developed by Texas Instruments (TI), making these DSPs an excellent choice
for digital media applications. The C64x™ is a code-compatible member of the C6000™ DSP platform.
FIST可堆疊文件系統，linux文件系統修改相關的開源軟件，使得對linux文件系統功能的改進很簡單了
The FiST (File System Translator) system combines two methods to solve the above problems in a novel way: a set of stackable file system templates for each operating system, and a high-level language that can describe stackable file systems in a cross-platform portable fashion. Using FiST, stackable file systems need only be described once. FiST s code generation tool, fistgen, compiles a single file system description into loadable kernel modules for several operating systems (currently Solaris, Linux, and FreeBSD). The project demonstrates that with FiST, code size and development time are reduced significantly, while imposing a small performance overhead of only 1-2％. These benefits are achieved, as well as portability, without changing existing operating systems or file system.
// Copyright (c), Philips Semiconductors Gratkorn
// (C)PHILIPS Electronics N.V.2000
// All rights are reserved.
// Philips reserves the right to make changes without notice at any time.
// Philips makes no warranty, expressed, implied or statutory, including but
// not limited to any implied warranty of merchantibility or fitness for any
//particular purpose, or that the use will not infringe any third party patent,
// copyright or trademark. Philips must not be liable for any loss or damage
// arising from its use.
The algorm of viterbi. You talk to your friend three days in a row and discover that on the first day he went for a walk, on the second day he went shopping, and on the third day he cleaned his apartment. You have two questions: What is the overall probability of this sequence of observations? And what is the most likely sequence of rainy/sunny days that would explain these observations? The first question is answered by the forward algorithm the second question is answered by the Viterbi algorithm. These two algorithms are structurally so similar (in fact, they are both instances of the same abstract algorithm) that they can be implemented in a single function:
The first edition of this book ranks among the small (very small) number of books that I credit with significantly elevating my skills as a professional software devel-oper. Like the others, it was practical and easy to read, but loaded with important advice. Effective C++, Third Edition, continues that tradition. C++ is a very powerful programming language. If C gives you enough rope to hang yourself, C++ is a hard-ware store with lots of helpful people ready to tie knots for you. Mastering the points discussed in this book will definitely increase your ability to effectively use C++ and reduce your stress level.
This packet is a IS-95 baseband simulation for 1 data channel of 9.6 KBps rate.
The simulation is written for static channel and AWGN noise. The packet include:
1) Packet Builder (Viterbi Encoding, Interleaver, PN generation)
2) Modulator (RRC filter)
3) Demodulator (Matched Filter, RAKE receiver)
4) Receiver (HD or SD) (Deinterleaver, Viterbi Decoder).
JaNet: Java Neural Network Toolkit
resume: A well documented toolkit for designing and training, and a java library for inclusion in third party programs.
description: jaNet package is a java neural network toolkit, which you can use to design, test, train and optimize an ideal Neural Network for your private application. You can then include your saved network in your program using the jaNet.backprop package. The consequent documentation is only in french for the moment, but an english translation is planned. The java source code is released under GPL, and can be compiled with JDK, Symantec Cafe or MS Visual J
A .zip file contains a series of scripts that were used in the MathWorks webinar "Using MATLAB to Develop Portfolio Optimization Models." The scripts generate 3D efficient frontiers for a universe of 44 stocks with time as the third axis. Additional scripts perform various ex-ante and ex-post analyses. Results are generated with and without market adjustments in the data. A readme.txt. file in the .zip folder describes each script and how to use it
