The code performs a number (ITERS) of iterations of the
Bailey s 6-step FFT algorithm (following the ideas in the
CMU Task Parallel suite).
1.- Generates an input signal vector (dgen) with size
n=n1xn2 stored in row major order
In this code the size of the input signal
is NN=NxN (n=NN, n1=n2=N)
2.- Transpose (tpose) A to have it stored in column
major order
3.- Perform independent FFTs on the rows (cffts)
4.- Scale each element of the resulting array by a
factor of w[n]**(p*q)
5.- Transpose (tpose) to prepair it for the next step
6.- Perform independent FFTs on the rows (cffts)
7.- Transpose the resulting matrix
The code requires nested Parallelism.
Written for engineering and computer science students and practicing engineers, this book provides the fundamental applications and mathematical techniques of signal processing. Topics covered include programming in MATLAB, filters, networking, and Parallel processing.
MATLAB is introduced and used to solve numerous examples in the book.
Companion software available
In addition, a set of MATLAB M-files is available on a CD bound in the book.
This document specifies a collection of compiler directives, library routines, and
environment variables that can be used to specify shared-memory Parallelism in C, C++
and Fortran programs. This functionality collectively defines the specification of the
OpenMP Application Program Interface (OpenMP API). This specification provides a
model for Parallel programming that is portable across shared memory architectures
from different vendors. Compilers from numerous vendors support the OpenMP API.
More information about OpenMP can be found at the following web site:
Supplemental information for a high-speed serial bus that integrates well with most IEEE standard
32-bit and 64-bit Parallel buses is specified. It is intended to extend the usefulness of a low-cost interconnect
between external peripherals, IEEE Std 1394-1995. This standard follows the ISO/IEC 13213:1994 Command
and Status Register (CSR) architecture.
This paper presents a Hidden Markov Model (HMM)-based speech
enhancement method, aiming at reducing non-stationary noise from speech
signals. The system is based on the assumption that the speech and the noise
are additive and uncorrelated. Cepstral features are used to extract statistical
information from both the speech and the noise. A-priori statistical
information is collected from long training sequences into ergodic hidden
Markov models. Given the ergodic models for the speech and the noise, a
compensated speech-noise model is created by means of Parallel model
combination, using a log-normal approximation. During the compensation, the
mean of every mixture in the speech and noise model is stored. The stored
means are then used in the enhancement process to create the most likely
speech and noise power spectral distributions using the forward algorithm
combined with mixture probability. The distributions are used to generate a
Wiener filter for every observation. The paper includes a performance
evaluation of the speech enhancer for stationary as well as non-stationary
noise environment.
Power Electronics is one of modern and key technologies in Electrical and
Electronics Engineering for green power, sustainable energy systems, and smart
grids. Especially, the transformation of existing electric power systems into smart
grids is currently a global trend. The gradual increase of distributed generators in
smart grids indicates a wide and important role for power electronic converters in
the electric power system, also with the increased use of power electronics devices
(nonlinear loads) and motor loadings, low cost, low-loss and high-performance
shunt current quality compensators are highly demanded by power customers to
solve current quality problems caused by those loadings.
If you are acquainted with neural networks, automatic control problems
are good industrial applications and have a dynamic or evolutionary nature
lacking in static pattern-recognition; control ideas are also prevalent in the
study of the natural neural networks found in animals and human beings.
If you are interested in the practice and theory of control, artificial neu-
ral networks offer a way to synthesize nonlinear controllers, filters, state
observers and system identifiers using a Parallel method of computation.
This book will discuss the topic of Control Systems, which is an interdisciplinary engineering
topic. Methods considered here will consist of both "Classical" control methods, and
"Modern" control methods. Also, discretely sampled systems (digital/computer systems) will
be considered in Parallel with the more common analog methods. This book will not focus
on any single engineering discipline (electrical, mechanical, chemical, etc.), although readers
should have a solid foundation in the fundamentals of at least one discipline.
