Routine mar1psd: To compute the power spectum by AR-model parameters.
Input parameters:
ip : AR model order (integer)
ep : White noise variance of model input (real)
ts : Sample interval in seconds (real)
a : Complex array of AR parameters a(0) to a(ip)
Output parameters:
psdr : Real array of power spectral density values
psdi : Real work array
in chapter 12
This applet illustrates the prediction capabilities of the multi-layer perceptrons. It allows to define an input signal on which prediction will be performed. The user can choose the number of input units, hidden units and output units, as well as the delay between the input series and the predicted output series. Then it is possible to observe interesting prediction properties.
Wavelets have widely been used in many signal and image processing applications. In this paper, a new
serial-parallel architecture for wavelet-based image compression is introduced. It is based on a 4-tap wavelet
transform, which is realised using some FIFO memory modules implementing a pixel-level pipeline
architecture to compress and decompress images. The real filter calculation over 4 · 4 window blocks is
done using a tree of carry save adders to ensure the high speed processing required for many applications.
The details of implementing both compressor and decompressor sub-systems are given. The primarily analysis
reveals that the proposed architecture, implemented using current VLSI technologies, can process a
video stream in real time.
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 paper deals with the problem of speech enhancement when a
corrupted speech signal with an additive colored noise is the only
information available for processing. Kalman filtering is known as
an effective speech enhancement technique, in which speech signal
is usually modeled as autoregressive (AR) process and represented
in the state-space domain.
tServer task executes functions at low priority (254). serverSend
funtion is used to send a request to the tServer to execute a
function at the tServer s priority.
First tServer task executes the i() system call to print the summary
of tShell task. Next an address exception is caused in tServer
task by trying to execute a funtion at non-aligned word boundry.
As a result SIGBUS signal is raised automatically. Signals
(setjmp() and longjmp()) are used to allow the tServer task to
recover gracefully from an address error exception. For more
information about signals, please refer to sigLib manual pages.
CCS編程環境 使用的是匯編加C的混合編程方法：
The programme of the Correlation Algorithm.
Using INT2 to get the input signal.
Array x, in first step, is the input signal produced by programme,
in next step, is the input signal get from A/D,
the length is 128, 32-bit floating point.
Array y, in first step, is the input signal produced by programme,
in next step, is the input signal get from A/D,
the length is 128, 32-bit floating point.
Array cor is the Correlation result, the length is 255, 32-bit floating point.
The ability to write efficient, high-speed arithmetic routines ultimately depends
upon your knowledge of the elements of arithmetic as they exist on a computer. That
conclusion and this book are the result of a long and frustrating search for
information on writing arithmetic routines for real-time embedded systems.
With instruction cycle times coming down and clock rates going up, it would
seem that speed is not a problem in writing fast routines. In addition, math
coprocessors are becoming more popular and less expensive than ever before and are
readily available. These factors make arithmetic easier and faster to use and
implement. However, for many of you the systems that you are working on do not
include the latest chips or the faster processors. Some of the most widely used
microcontrollers used today are not Digital Signal Processors (DSP), but simple
eight-bit controllers such as the Intel 8051 or 8048 microprocessors.
water temperature control system uses the Single Chip Microcomputer to carry on temperature real-time gathering and controling. DS18B20, digitized temperature sensor, provides the temperature signal by "a main line". In -10～+85℃ the scope, DS18B20’s inherent measuring accuracy is 0.5 ℃. The water temperature real-time control system uses the electricity nichrome wire carring on temperature increiseament and operates the electric fan to realize the temperature decrease control. The system has the higher measuring accuracy and the control precision, it also can complete the elevation of temperature and the temperature decrease control.
To increase simulation speed, ModelSim® can apply a variety of optimizations to your design. These include, but are not limited to, mergingprocesses, pulling constants out of loops, clock suppression, and signal collapsing. You control the level of optimization by specifying certain switches when you invoke the compiler.
