* CONSTRAINTS
* This module does not handle data which is considered out of range by the
* application(i.e. fixed constants which represent error condition)
*
* Maximum weight value must be limited to 128 to prevent an overflow
* condition during the calculation.
*
* The internal data type must be large enough to handle the calculations.
* The maximum possible internal value
* = Max Input Value * (weight - 1) + Max Input Value
* If a maximum weight of 128 is used, the internal data type should be 2
* times the size of the input data type.
Knowledge of the process noise covariance matrix
is essential for the application of Kalman filtering. However,
it is usually a difficult task to obtain an explicit expression of
for large time varying systems. This paper looks at an adaptive
Kalman filter method for dynamic harmonic state estimation and
harmonic injection tracking.
This code was used for making the practical measurements in section 2.3 of my thesis. This Matlab code allows an OFDM signal to be generated based on an input data file. The data can be random data, a grey scale image, a wave file, or any type of file. The generated OFDM signal is stored as a windows wave file, allowing it to be viewed, listened to and manipulated in other programs. The modified wave file can then be decoded by the receiver software to extract the original data. This code was developed for the experiments that I performed in my honours thesis, and thus has not been fully debugged.
This is the original code developed for the thesis and so has several problems with it. The BER performance given by the simulations is infact Symbol Error Rate.
Pre-designed and pre-verified hardware and software blocks can be combined on
chips for many different applicationsVthey promise large productivity gains.
real property management,information management system developed by VB, using very conveniently.good real property management has such large information
In 1960, R.E. Kalman published his famous paper describing a recursive solution
to the discrete-data linear filtering problem. Since that time, due in large part to advances
in digital computing, the Kalman filter has been the subject of extensive research
and application, particularly in the area of autonomous or assisted
navigation.
In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata
linear filtering problem [Kalman60]. Since that time, due in large part to advances in digital
computing, the
Kalman filter
has been the subject of extensive research and application,
particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the
general idea of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete
introductory discussion can be found in [Sorenson70], which also contains some interesting
historical narrative.
In this report
we provide an overview of several closely related methods developed during the last few yers, to smooth, denoise,
edit, compress, transmit, and animate very large polygonal models.
This paper studies the problem of categorical data clustering,
especially for transactional data characterized by high
dimensionality and large volume. Starting from a heuristic method
of increasing the height-to-width ratio of the cluster histogram, we
develop a novel algorithm – CLOPE, which is very fast and
scalable, while being quite effective. We demonstrate the
performance of our algorithm on two real world
