Abstract：Noise frequency modulation(FM)jamming。which belongs to blanket jamming。is already become
the main form ofnoise jamming at present。because the wideband was gained by it．Tne spectnlnl ofnoise FM
jamming is analyzed by time domain autocorrelation method in this paper．It’S jamm g peculiarity and幾o(hù)ut—
putting signal’S jamming peculiarity ale explained．At last，these time series models ofnoise FM jalllIIling sig—
nal and幾o(hù)utputting signal ale built．
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 leon3 design is tailored to the Altera NiosII Startix2
Development board, with 16-bit DDR SDRAM and 2 Mbyte of SSRAM.
As of this time, the DDR interface only works up to 120 MHz.
At 130, DDR data can be read but not written.
NOTE: the test bench cannot be simulated with DDR enabled
because the Altera pads do not have the correct delay models.
* How to program the flash prom with a FPGA programming file
1. Create a hex file of the programming file with Quartus.
2. Convert it to srecord and adjust the load address:
objcopy --adjust-vma=0x800000 output_file.hexout -O srec fpga.srec
3. Program the flash memory using grmon:
flash erase 0x800000 0xb00000
flash load fpga.srec
The present paper deals with the problem of calculating mean delays in polling systems
with either exhaustive or gated service. We develop a mean value analysis (MVA) to
compute these delay figures. The merits of MVA are in its intrinsic simplicity and its
intuitively appealing derivation. As a consequence, MVA may be applied, both in an
exact and approximate manner, to a large variety of models.
In this article, we present an overview of methods for sequential simulation from posterior distributions.
These methods are of particular interest in Bayesian filtering for discrete time dynamic models
that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed
that unifies many of the methods which have been proposed over the last few decades in several
different scientific disciplines. Novel extensions to the existing methods are also proposed.We showin
particular how to incorporate local linearisation methods similar to those which have previously been
employed in the deterministic filtering literature these lead to very effective importance distributions.
Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of
the analytic structure present in some important classes of state-space models. In a final section we
develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
The need for accurate monitoring and analysis of sequential data arises in many scientic, industrial
and nancial problems. Although the Kalman lter is effective in the linear-Gaussian
case, new methods of dealing with sequential data are required with non-standard models.
Recently, there has been renewed interest in simulation-based techniques. The basic idea behind
these techniques is that the current state of knowledge is encapsulated in a representative
sample from the appropriate posterior distribution. As time goes on, the sample evolves and
adapts recursively in accordance with newly acquired data. We give a critical review of recent
developments, by reference to oil well monitoring, ion channel monitoring and tracking
problems, and propose some alternative algorithms that avoid the weaknesses of the current
methods.
Hidden_Markov_model_for_automatic_speech_recognition
This code implements in C++ a basic left-right hidden Markov model
and corresponding Baum-Welch (ML) training algorithm. It is meant as
an example of the HMM algorithms described by L.Rabiner (1) and
others. Serious students are directed to the sources listed below for
a theoretical description of the algorithm. KF Lee (2) offers an
especially good tutorial of how to build a speech recognition system
using hidden Markov models.
These Simulink blocks contain transfer functions that model the pressure and flow transients for axisymmetric 2D viscous flow of a compressible fluid in a straight rigid circular cross section pipelines. Three models are available:
(1) pressures at the ends
(2) flow rates at the ends
(3) pressure at one end and flow rate at the other
Filtering is incorporated to reduce numerical oscillation (Gibbs phenomenon). See J. Dyn. Systems, Meas. & Control vol 122 (2000) pp. 153-162.
