Adaptive Filter. This script shows the BER performance of several types of equalizers in a static channel with a null in the passband. The script constructs and implements a linear equalizer object and a decision feedback equalizer (DFE) object. It also initializes and invokes a maximum likelihood sequence estimation (MLSE) equalizer. The MLSE equalizer is first invoked with perfect channel knowledge, then with a straightforward but imperfect channel estimation technique.
This function returns NULL when called for an MDI main frame window (CMDIFrameWnd). In an MDI application, the MDI main frame window does not have a view associated with it. Instead, each individual child window (CMDIChildWnd) has one or more associated views. The active view in an MDI application can be obtained by first finding the active MDI child window and then finding the active view for that child window.
The first task at hand is to set up the endpoints appropriately for this example. The following code switches the CPU clock speed
to 48 MHz (since at power-on default it is 12 MHz), and sets up EP2 as a Bulk OUT endpoint, 4x buffered of size 512, and EP6
as a Bulk IN endpoint, also 4x buffered of size 512. This set-up utilizes the maximum allotted 4-KB FIFO space. It also sets up
the FIFOs for manual mode, word-wide operation, and goes through a FIFO reset and arming sequence to ensure that they are
ready for data operations
【下載說明】這里提供的是《》一書的CHm格式電子書下載。【內(nèi)容簡介】Ruby has been readily adopted by programmers in Japan and has had much documentation written for it in Japanese. As programmers outside of Japan learn about the benefits of Ruby, there is a growing need for documentation in English. The first book I wrote for O Reilly, Ruby Pocket Reference, was in Japanese. Since then Ruby has changed significantly. To meet the needs of non-Japanese programmers, we translated, updated, and expanded Ruby Pocket Reference into Ruby in a Nutshell.
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.
There are two files in the zip folder. bpsk_spread.m and jakesmodel.m
Steps for simulation:
1] Run jakesmodel.m first
2] Then run bpsk_spread.m .
3] Note that during the first run bpsk_spread.m has no rayleigh fading.This is because the corresponding code has been commented
4] The resulting performance is stored in BER_awgn.
5] Now uncomment the Rayleigh Fading code in bpsk_spread.m file.
6] Same time comment BER_awgn (line 112) and uncomment BER_ray variable.
7] Run the simulation.
To compare the perfromances of the receiver using DSSS plot the BER_awgn and BER_ray
This the first release of a free TCP/IP/PPP protocol stack for the uC/OS Real-Time Operating System. As a first release it s still rather rough and very much larger than desired however it is working well in an embedded product and therefore may be useful to others. Much of the code is ported from public sources including BDS Lite and KA9Q (and its derivatives) and carries copyright notices from those sources. Essentially TCP is based on KA9Q but with ideas from BSD, IP is a simple implementation supporting BSD structures, ICMP is ported from BSD, and PPP is ported from the BSD version of the distribution common to BSD, Linux, and many other systems
FASTICA can be called with numerous optional arguments. Optional arguments are given in parameter pairs, so that first argument is the name of the parameter and the next argument is the value for that parameter. Optional parameter pairs can be given in any order.
Recent advances in experimental methods have resulted in the generation
of enormous volumes of data across the life sciences. Hence clustering and
classification techniques that were once predominantly the domain of ecologists
are now being used more widely. This book provides an overview of these
important data analysis methods, from long-established statistical methods
to more recent machine learning techniques. It aims to provide a framework
that will enable the reader to recognise the assumptions and constraints that
are implicit in all such techniques. Important generic issues are discussed first
and then the major families of algorithms are described. Throughout the focus
is on explanation and understanding and readers are directed to other resources
that provide additional mathematical rigour when it is required. Examples
taken from across the whole of biology, including bioinformatics, are provided
throughout the book to illustrate the key concepts and each technique’s
potential.
Description The MUSIC algorithm, proposed by Schmidt, first estimates a basis for the noise subspace and then determines the peaks the associated angles provide the DOA estimates.
The MATLAB code for the MUSIC algorithm is sampled by creating an array of steering vectors corresponding to the angles in the vector angles.
