SPLASH is a c++ class library that implements many of the Perl constructs and data types, including extensive regex regular Expression pattern matching. For those not familiar with Perl, it is an excellent scripting language by Larry Wall and is available for most platforms. This Class library provides List, String, Regular Expression, and text manipulation handling capabilities based on those provided in Perl
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.
Text processing often involves matching text against a pattern. Although Java s character and assorted string classes offerlow- levelpattern- matching support, that support commonly leads to complex code. To help you write simplerpattern- matching code, Java provides regular Expressions. After introducing you to terminology and thejava.util. regex package, Jeff Friesen explores many regular Expression constructs supported by that package s Pattern class. Then he examines Pattern s methods and the additionaljava.util. regex classes. In conclusion, he presents a practical application of regular Expressions.
UC Library Extensions
UnderC comes with a pocket implementation of the standard C++ libraries, which is a reasonably faithful subset. This documentation describes those UnderC functions and classes which are not part of the C++ standard.
UC Library
Builtin functions:
Most of these are standard C functions, but there are a few unique to the UnderC system which give you runtime access to the compiler. You may evaluate Expressions, execute commands, compile code, etc.
* Expands the text in expr using the UnderC preprocessor, putting the result
into buff.
void uc_macro_subst(const char* expr, char* buff, int buffsize)
* Executes a UC #-command, like #l or #help.
uc_cmd() expects the name of the command, _without_ the hash,
e.g. uc_cmd("l fred.cpp") or uc_cmd("help").
void uc_cmd(const char* cmd)
* Evaluates any C++ Expression or statement will return non-zero if
unsuccessful.
We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation,
phase-shift keying, and pulse amplitude modulation
communications systems.We study the performance of a standard
CFO estimate, which consists of first raising the received signal to
the Mth power, where M is an integer depending on the type and
size of the symbol constellation, and then applying the nonlinear
least squares (NLLS) estimation approach. At low signal-to noise
ratio (SNR), the NLLS method fails to provide an accurate CFO
estimate because of the presence of outliers. In this letter, we derive
an approximate closed-form Expression for the outlier probability.
This enables us to predict the mean-square error (MSE) on CFO
estimation for all SNR values. For a given SNR, the new results
also give insight into the minimum number of samples required in
the CFO estimation procedure, in order to ensure that the MSE
on estimation is not significantly affected by the outliers.
This paper examines the asymptotic (large sample) performance
of a family of non-data aided feedforward (NDA FF) nonlinear
least-squares (NLS) type carrier frequency estimators for burst-mode
phase shift keying (PSK) modulations transmitted through AWGN and
flat Ricean-fading channels. The asymptotic performance of these estimators
is established in closed-form Expression and compared with the
modified Cram`er-Rao bound (MCRB). A best linear unbiased estimator
(BLUE), which exhibits the lowest asymptotic variance within the family
of NDA FF NLS-type estimators, is also proposed.
