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This book explains how to write device drivers for the newest members of the MicrosoftWindows family of operating systems using the Windows Driver Model (WDM). In this Introduction, I ll explain who should be reading this book, the organization of the book, and how to use the book most effectively. You ll also find a note on errors and a section on other resources you can use to learn about driver programming. Looking ahead, Chapter 1 explains how the two main branches of the Windows family operate internally, what a WDM device driver is, and how it relates to the rest of Windows.
Very simple USB 1.1 PHY. Includes all the goodies: serial/parallel
conversion, bit stuffing/unstuffing, NRZI encoding decoding. Uses a
simplified UTMI interface. Currently doesn t do any error checking in
the RX section [should probably check for bit unstuffing errors].
Otherwise complete and fully functional.
There is currently no test bench available. This core is very simple
and is proven in hardware. I see no point of writing a test bench at
this time.
Here are the functions for Hamming code 7.4 and Extended Hamming code 8.4
encoding and decoding.
For 7.4 code, one error per 7-bit codeword can be corrected.
For 8.4 code, one error per 8-bit codeword can be corrected
and not less than 2 errors can be detected.
Learn how to:
*
Tokenize a null-terminated string
*
Create a search and replace function for strings
*
Implement subtraction for string objects
* Use the vector, deque, and list sequence containers
*
Use the container adaptors stack, queue, and priority_queue
* Use the map, multimap, set, and multiset associative containers
*
Reverse, rotate, and shuffle a sequence
*
Create a function object
*
Use binders, negators, and iterator adapters
*
Read and write files
*
Use stream iterators to handle file I/O
*
Use exceptions to handle I/O errors
*
Create custom inserters and extractors
*
Format date, time, and numeric data
* Use facets and the localization library
*
Overload the [ ], ( ), and -> operators
*
Create an explicit constructor
*
And much, much more
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information contained herein.
While every precaution has been taken in the preparation of this book, the publisher assumes no
responsibility for errors or omissions, or for damages resulting from the use of the information
contained herein.
These Release Notes describe the functionality of the AudioCodes’ TrunkPack Series Boards
and Digital Media Gateways supported by Software Release 4.8. Information contained in this
document is believed to be accurate and reliable at the time of printing. However, due to
ongoing product improvements and revisions, AudioCodes cannot guarantee the accuracy of
printed material after the Date Published nor can it accept responsibility for errors or omissions.
We consider the problem of target localization by a
network of passive sensors. When an unknown target emits an
acoustic or a radio signal, its position can be localized with multiple
sensors using the time difference of arrival (TDOA) information.
In this paper, we consider the maximum likelihood formulation
of this target localization problem and provide efficient convex
relaxations for this nonconvex optimization problem.We also propose
a formulation for robust target localization in the presence of
sensor location errors. Two Cramer-Rao bounds are derived corresponding
to situations with and without sensor node location errors.
Simulation results confirm the efficiency and superior performance
of the convex relaxation approach as compared to the
existing least squares based approach when large sensor node location
errors are present.
