Abstract: The aerodynamic noise radiation from a side view mirror (SVM) in the high-speed airflow is calculated by the combination of unsteady incompressible fluid flow analysis and acoustic analysis. The transient flow past the generic SVM is simulated with variable turbulence model, namely DES Detached Eddy Simulation and LES (Large Eddy Simulation). Detailed velocity vectors and contour plots of the time-varying velocity and pressure fields are presented along cut planes in the flow-field. Mean and transient pressure are also monitored at several points in the flow field and compared to corresponding experimentally data published in literature. The acoustic predictions made using the Ffowcs-Williams-Hawkins acoustic analogy (FW-H) and the boundary element (BEM).
Abstract: Discovering new biological knowledge from the highthroughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a new approach for protein classification. Proteins that are evolutionarily- and thereby functionally- related are said to belong to the same classification. Identifying protein classification is of fundamental importance to document the diversity of the known protein universe. It also provides a means to determine the functional roles of newly discovered protein sequences. Our goal is to predict the functional classification of novel protein sequences based on a set of features extracted from each protein sequence. The proposed technique used datasets extracted from the Structural Classification of Proteins (SCOP) database. A set of spectral domain features based on Fast Fourier Transform (FFT) is used. The proposed classifier uses multilayer back propagation (MLBP) neural network for protein classification. The maximum classification accuracy is about 91% when applying the classifier to the full four levels of the SCOP database. However, it reaches a maximum of 96% when limiting the classification to the family level. The classification results reveal that spectral domain contains information that can be used for classification with high accuracy. In addition, the results emphasize that sequence similarity measures are of great importance especially at the family level.
Abstract: In this paper, the statistical properties of filtered or convolved signals are considered by deriving the resulting density functions as well as the exact mean and variance expressions given a prior knowledge about the statistics of the individual signals in the filtering or convolution process. It is shown that the density function after linear convolution is a mixture density, where the number of density components is equal to the number of observations of the shortest signal. For circular convolution, the observed samples are characterized by a single density function, which is a sum of products.
Abstract: A frequency grouping approach for multi-channel
instantaneous blind source separation (I-BSS) of convolutive
mixtures is proposed for a lower net residual inter-symbol
interference (ISI) and inter-channel interference (ICI) than the
conventional short-time Fourier transform (STFT) approach. Starting
in the time domain, STFTs are taken with overlapping windows to
convert the convolutive mixing problem into frequency domain
instantaneous mixing. Mixture samples at the same frequency but
from different STFT windows are grouped together forming unique
frequency groups.
The individual frequency group vectors are input to the I-BSS
algorithm of choice, from which the output samples are dispersed
back to their respective STFT windows. After applying the inverse
STFT, the resulting time domain signals are used to construct the
complete source estimates via the weighted overlap-add method
(WOLA). The proposed algorithm is tested for source deconvolution
given two mixtures, and simulated along with the STFT approach to
illustrate its superiority for fairly motionless sources.
Abstract: With increasing utilization of the wireless devices in
different fields such as medical devices and industrial fields, the
paper presents a method for simplify the Bluetooth packets with
throughput enhancing. The paper studies a vital issue in wireless
communications, which is the throughput of data over wireless
networks. In fact, the Bluetooth and ZigBee are a Wireless Personal
Area Network (WPAN). With taking these two systems competition
consideration, the paper proposes different schemes for improve the
throughput of Bluetooth network over a reliable channel. The
proposition depends on the Channel Quality Driven Data Rate
(CQDDR) rules, which determines the suitable packet in the
transmission process according to the channel conditions. The
proposed packet is studied over additive White Gaussian Noise
(AWGN) and fading channels. The Experimental results reveal the
capability of extension of the PL length by 8, 16, 24 bytes for classic
and EDR packets, respectively. Also, the proposed method is suitable
for the low throughput Bluetooth.
Abstract: This paper studies a vital issue in wireless
communications, which is the transmission of images over Wireless
Personal Area Networks (WPANs) through the Bluetooth network. It
presents a simple method to improve the efficiency of error control
code of old Bluetooth versions over mobile WPANs through
Interleaved Error Control Code (IECC) technique. The encoded
packets are interleaved by simple block interleaver. Also, the paper
presents a chaotic interleaving scheme as a tool against bursts of
errors which depends on the chaotic Baker map. Also, the paper
proposes using the chaotic interleaver instead of traditional block
interleaver with Forward Error Control (FEC) scheme. A comparison
study between the proposed and standard techniques for image
transmission over a correlated fading channel is presented.
Simulation results reveal the superiority of the proposed chaotic
interleaving scheme to other schemes. Also, the superiority of FEC
with proposed chaotic interleaver to the conventional interleavers
with enhancing the security level with chaotic interleaving packetby-
packet basis.