Abstract: In this paper, we propose an optimized brain computer
interface (BCI) system for unspoken speech recognition, based on
the fact that the constructions of unspoken words rely strongly on the
Wernicke area, situated in the temporal lobe. Our BCI system has four
modules: (i) the EEG Acquisition module based on a non-invasive
headset with 14 electrodes; (ii) the Preprocessing module to remove
noise and artifacts, using the Common Average Reference method;
(iii) the Features Extraction module, using Wavelet Packet Transform
(WPT); (iv) the Classification module based on a one-hidden layer
artificial neural network. The present study consists of comparing
the recognition accuracy of 5 Arabic words, when using all the
headset electrodes or only the 4 electrodes situated near the Wernicke
area, as well as the selection effect of the subbands produced by
the WPT module. After applying the articial neural network on the
produced database, we obtain, on the test dataset, an accuracy of
83.4% with all the electrodes and all the subbands of 8 levels of the
WPT decomposition. However, by using only the 4 electrodes near
Wernicke Area and the 6 middle subbands of the WPT, we obtain
a high reduction of the dataset size, equal to approximately 19% of
the total dataset, with 67.5% of accuracy rate. This reduction appears
particularly important to improve the design of a low cost and simple
to use BCI, trained for several words.
Abstract: In this paper we presented a new method for tracking
flying targets in color video sequences based on contour and kernel.
The aim of this work is to overcome the problem of losing target in
changing light, large displacement, changing speed, and occlusion.
The proposed method is made in three steps, estimate the target
location by particle filter, segmentation target region using neural
network and find the exact contours by greedy snake algorithm. In
the proposed method we have used both region and contour
information to create target candidate model and this model is
dynamically updated during tracking. To avoid the accumulation of
errors when updating, target region given to a perceptron neural
network to separate the target from background. Then its output used
for exact calculation of size and center of the target. Also it is used as
the initial contour for the greedy snake algorithm to find the exact
target's edge. The proposed algorithm has been tested on a database
which contains a lot of challenges such as high speed and agility of
aircrafts, background clutter, occlusions, camera movement, and so
on. The experimental results show that the use of neural network
increases the accuracy of tracking and segmentation.
Abstract: Since the last decade, there has been a rapid growth in
digital multimedia, such as high-resolution media files and threedimentional
movies. Hence, there is a need for large digital storage
such as Hard Disk Drive (HDD). As such, users expect to have a
quieter HDD in their laptop. In this paper, a jury test has been
conducted on a group of 34 people where 17 of them are students
who are the potential consumer, and the remaining are engineers who
know the HDD. A total 13 HDD sound samples have been selected
from over hundred HDD noise recordings. These samples are
selected based on an agreed subjective feeling. The samples are
played to the participants using head acoustic playback system, which
enabled them to experience as similar as possible the same
environment as have been recorded. Analysis has been conducted and
the obtained results have indicated different group has different
perception over the noises. Two neural network-based acoustic
annoyance models are established based on back propagation neural
network. Four psychoacoustic metrics, loudness, sharpness,
roughness and fluctuation strength, are used as the input of the
model, and the subjective evaluation results are taken as the output.
The developed models are reasonably accurate in simulating both
training and test samples.
Abstract: It is well known that a linear dynamic system including
a delay will exhibit limit cycle oscillations when a bang-bang sensor
is used in the feedback loop of a PID controller. A similar behaviour
occurs when a delayed feedback signal is used to train a neural
network. This paper develops a method of predicting this behaviour
by linearizing the system, which can be shown to behave in a manner
similar to an integral controller. Using this procedure, it is possible
to predict the characteristics of the neural network driven limit cycle
to varying degrees of accuracy, depending on the information known
about the system. An application is also presented: the intelligent
control of a spark ignition engine.
Abstract: In this paper, a neural network technique is applied to
real-time classifying media while a projectile is penetrating through
them. A laboratory-scaled penetrating setup was built for the
experiment. Features used as the network inputs were extracted from
the acceleration of penetrator. 6000 set of features from a single
penetration with known media and status were used to train the neural
network. The trained system was tested on 30 different penetration
experiments. The system produced an accuracy of 100% on the
training data set. And, their precision could be 99% for the test data
from 30 tests.
Abstract: The application of Neural Network for disease
diagnosis has made great progress and is widely used by physicians.
An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which
was the great motivation towards our study. In our work, tachycardia
features obtained are used for the training and testing of a Neural
Network. In this study we are using Fuzzy Probabilistic Neural
Networks as an automatic technique for ECG signal analysis. As
every real signal recorded by the equipment can have different
artifacts, we needed to do some preprocessing steps before feeding it
to our system. Wavelet transform is used for extracting the
morphological parameters of the ECG signal. The outcome of the
approach for the variety of arrhythmias shows the represented
approach is superior than prior presented algorithms with an average
accuracy of about %95 for more than 7 tachy arrhythmias.