1.3.1 Open Source
* The idea behind Open Source software is rather simple: when programmers can read, distribute and change code, the code will mature.
Monday, November 2, 2009
Daily Study Keywords
-- Introduction to Linux: 20/223
-- Bash Guide for Beginners: 9/173
-- Bash Programming - Introduction HOW-TO: 8/29
Mon, Nov 2, 2009
-- POSIX
-- rms, Richard Matthew Stallman,
-- xterm, eterm, aterm
-- echo, grep, ls
Q: Linux is a full UNIX clone. Why is this clone needed? How to decide if a Linux or a UNIX is needed for an application?
A: With every standard Linux distribution, the C-compiler is included for free - as opposed to many UNIX distributions demanding licensing fees for this tool.
Q: Is PC a computer with Windows OS?
-- Bash Guide for Beginners: 9/173
-- Bash Programming - Introduction HOW-TO: 8/29
Mon, Nov 2, 2009
-- POSIX
-- rms, Richard Matthew Stallman,
-- xterm, eterm, aterm
-- echo, grep, ls
Q: Linux is a full UNIX clone. Why is this clone needed? How to decide if a Linux or a UNIX is needed for an application?
A: With every standard Linux distribution, the C-compiler is included for free - as opposed to many UNIX distributions demanding licensing fees for this tool.
Q: Is PC a computer with Windows OS?
LD_LIBRARY_PATH=/home/Yang/Download/v6_2_1-00167L/lib/Linux-x86-32-gcc:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH
Tuesday, July 21, 2009
VALUABLE REFERENCES
Quantization: Yao Wang's Video Processing and Communications
VQ: Yao Wang's Video Processing and Communications
Convolutional Codes: Shu Lin's Error Control Coding (2nd Edition)
VQ: Yao Wang's Video Processing and Communications
Convolutional Codes: Shu Lin's Error Control Coding (2nd Edition)
Tuesday, July 14, 2009
Sunday, July 12, 2009
TO DO
Quantization in Wang Y's book, then the 1-D W-Z Ps.
DISCUS paper part C
Consider Prof.'s chapter (how to decide c_j)
Gray's VQ paper
DISCUS paper part C
Consider Prof.'s chapter (how to decide c_j)
Gray's VQ paper
QUANTIZATION AND R-D FUNCTION
Data Compression: http://www.data-compression.com/theory.shtml
Quantization has two important properties: 1) a Distortion resulting from the approximation and 2) a Bit-Rate resulting from binary encoding of its levels. Therefore the Quantizer design problem is a Rate-Distortion optimization type. (http://en.wikipedia.org/wiki/Quantization_(signal_processing))
A good tutorial on Vector Quantization: http://www.data-compression.com/vq.html. In 1980, Linde, Buzo, and Gray (LBG) proposed a VQ design algorithm based on a training sequence.
Some VQers:
Rate-Distortion:
A good tutorial by Bernd Girod: http://www.stanford.edu/class/ee368b/Handouts/04-RateDistortionTheory.pdf
"Mutual Information" I(U;V) is the information that symbol U and symbol V convey about each other. Equivalently, I(U;V) is the communicated amount of information.
"Channel Capacity" C is the maximum mutual information between the transmitter and the receiver.
It is known that the Gaussian source is the most "difficult" source to encode: for a given mean square error, it requires the greatest number of bits. The performance of a practical compression system working on—say—images, may well be below the R(D) lower bound shown. ( http://en.wikipedia.org/wiki/Rate%E2%80%93distortion_theory )
Wyner and Ziv's paper "The Rate-Distortion Function for Source Coding with Side Information at the Decoder" provides the R-D function for the lossy DSC as well as its derivation. The proof procedure is crazy. I will read it when necessary.
Quantization has two important properties: 1) a Distortion resulting from the approximation and 2) a Bit-Rate resulting from binary encoding of its levels. Therefore the Quantizer design problem is a Rate-Distortion optimization type. (http://en.wikipedia.org/wiki/Quantization_(signal_processing))
A good tutorial on Vector Quantization: http://www.data-compression.com/vq.html. In 1980, Linde, Buzo, and Gray (LBG) proposed a VQ design algorithm based on a training sequence.
Some VQers:
- Stanley Ahalt
- Jim Fowler
- Allen Gersho
- Robert M. Gray
- Batuhan Ulug
Rate-Distortion:
A good tutorial by Bernd Girod: http://www.stanford.edu/class/ee368b/Handouts/04-RateDistortionTheory.pdf
"Mutual Information" I(U;V) is the information that symbol U and symbol V convey about each other. Equivalently, I(U;V) is the communicated amount of information.
"Channel Capacity" C is the maximum mutual information between the transmitter and the receiver.
It is known that the Gaussian source is the most "difficult" source to encode: for a given mean square error, it requires the greatest number of bits. The performance of a practical compression system working on—say—images, may well be below the R(D) lower bound shown. ( http://en.wikipedia.org/wiki/Rate%E2%80%93distortion_theory )
Wyner and Ziv's paper "The Rate-Distortion Function for Source Coding with Side Information at the Decoder" provides the R-D function for the lossy DSC as well as its derivation. The proof procedure is crazy. I will read it when necessary.
Thursday, July 9, 2009
ALE'S HOUSE
Diane took us to Ale's House last night,
in the community where she grew up.
It's very old and very America.
It was Mexico Night,
so we got some Mexican food.
After that,
we got great ice cream in Lathem.
Even a small vanilla corn was too much for me.
Qi got a large Hard Coffee,
which tasted very good,
and it was huge!
Chicken Quesadilla: chicken, cheese, tomato between two pieces of Tortila.
Chicken Fajita: grilled chicken and vegetables with Tortila.
in the community where she grew up.
It's very old and very America.
It was Mexico Night,
so we got some Mexican food.
After that,
we got great ice cream in Lathem.
Even a small vanilla corn was too much for me.
Qi got a large Hard Coffee,
which tasted very good,
and it was huge!
Chicken Quesadilla: chicken, cheese, tomato between two pieces of Tortila.
Chicken Fajita: grilled chicken and vegetables with Tortila.
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