*** *** *** *** *** *** *****
** Two wire/I2C Bus READ/WRITE Sample Routines of Microchip s
** 24Cxx / 85Cxx serial CMOS EEPROM interfacing to a
** PIC16C54 8-bit CMOS single chip microcomputer
** Revsied Version 2.0 (4/2/92).
**
** Part use = PIC16C54-XT/JW
** Note: 1) All timings are based on a reference crystal frequency of 2MHz
** which is equivalent to an instruction cycle time of 2 usec.
** 2) Address and literal values are read in octal unless otherwise
** specified.
We address the problem of predicting a word from previous words in a sample of text. In particular,
we discuss n-gram models based on classes of words. We also discuss several statistical algorithms
for assigning words to classes based on the frequency of their co-occurrence with other words. We
find that we are able to extract classes that have the flavor of either syntactically based groupings
or semantically based groupings, depending on the nature of the underlying statistics.
ABC_FDTD_Die(T) Implements simulation of a Gaussian Pulse
over T time steps. ABC are for free space. If boundaries are in
the Dielectric medium then the ABC fail. Dielectric medium begin and
end can be specified with the code
ABC_FDTD_Die(T) Implements simulation of a Gaussian Pulse
over T time steps. ABC are for free space. If boundaries are in
the Dielectric medium then the ABC fail. Dielectric medium begin and
end can be specified with the code
A "code-what"? Unless you have spent some time working in the area of reverse engineering, chances are you have not heard of the term "codecave" before. If you have heard of it, you might not have read a clear definition of it or quite understand what it is or why it is useful. I have even asked seasoned assembly programmers about the term before and most of them had not heard of it. If it is new to you, do not worry, you are not the only one. It is a term that is scarcely used and is only useful in a reverse engineering context. Furthermore, is it "codecave" or "code cave"? I am not quite sure, but I will try my best to refer to it consistently as a "codecave". A space may sneak in there from time to time
The basic principle using the branchand-
bound strategy to solve the traveling
salesperson optimization problem (TSP)
consists of two parts.
There is a way to split the solution space.
There is a way to predict a lower bound for a
class of solutions.
There is also a way to find an upper bound of
an optimal solution.
If the lower bound of a solution exceeds this
upper bound, this solution cannot be optimal.
Thus, we should terminate the branching
associated with this solution.
Huffman codes
In telecommunication, how do we represent a
set of messages, each with an access
frequency, by a sequence of 0’s and 1’s?
To minimize the transmission and decoding
costs, we may use short strings to represent
more frequently used messages.
This problem can by solved by using an
extended binary tree which is used in the 2-
way merging problem.
documentation for optimal filtering toolbox for mathematical software
package Matlab. The methods in the toolbox include Kalman filter, extended Kalman filter
and unscented Kalman filter for discrete time state space models. Also included in the toolbox
are the Rauch-Tung-Striebel and Forward-Backward smoother counter-parts for each filter, which
can be used to smooth the previous state estimates, after obtaining new measurements. The usage
and function of each method are illustrated with five demonstrations problems.
1
documentation for optimal filtering toolbox for mathematical software
package Matlab. The methods in the toolbox include Kalman filter, extended Kalman filter
and unscented Kalman filter for discrete time state space models. Also included in the toolbox
are the Rauch-Tung-Striebel and Forward-Backward smoother counter-parts for each filter, which
can be used to smooth the previous state estimates, after obtaining new measurements. The usage
and function of each method are illustrated with five demonstrations problems.
1