Abstract: Everyday the usages of the Internet increase and simply a world of the data become accessible. Network providers do not want to let the provided services to be used in harmful or terrorist affairs, so they used a variety of methods to protect the special regions from the harmful data. One of the most important methods is supposed to be the firewall. Firewall stops the transfer of such packets through several ways, but in some cases they do not use firewall because of its blind packet stopping, high process power needed and expensive prices. Here we have proposed a method to find a discriminate function to distinguish between usual packets and harmful ones by the statistical processing on the network router logs. So an administrator can alarm to the user. This method is very fast and can be used simply in adjacent with the Internet routers.
Abstract: It is important to give input information without other device in AR system. One solution is using hand for augmented reality application. Many researchers have proposed different solutions for hand interface in augmented reality. Analyze Histogram and connecting factor is can be example for that. Various Direction searching is one of robust way to recognition hand but it takes too much calculating time. And background should be distinguished with skin color. This paper proposes a hand tracking method to control the 3D object in augmented reality using depth device and skin color. Also in this work discussed relationship between several markers, which is based on relationship between camera and marker. One marker used for displaying virtual object and three markers for detecting hand gesture and manipulating the virtual object.
Abstract: This paper proposes new hybrid approaches for face
recognition. Gabor wavelets representation of face images is an
effective approach for both facial action recognition and face
identification. Perform dimensionality reduction and linear
discriminate analysis on the down sampled Gabor wavelet faces can
increase the discriminate ability. Nearest feature space is extended to
various similarity measures. In our experiments, proposed Gabor
wavelet faces combined with extended neural net feature space
classifier shows very good performance, which can achieve 93 %
maximum correct recognition rate on ORL data set without any preprocessing
step.
Abstract: By taking advantage of both k-NN which is highly
accurate and K-means cluster which is able to reduce the time of classification, we can introduce Cluster-k-Nearest Neighbor as "variable k"-NN dealing with the centroid or mean point of all subclasses generated by clustering algorithm. In general the algorithm of K-means cluster is not stable, in term of accuracy, for that reason we develop another algorithm for clustering our space which gives a higher accuracy than K-means cluster, less
subclass number, stability and bounded time of classification with respect to the variable data size. We find between 96% and 99.7 % of accuracy in the lassification of 6 different types of Time series by using K-means cluster algorithm and we find 99.7% by using the new clustering algorithm.
Abstract: Face authentication for access control is a face
membership authentication which passes the person of the incoming
face if he turns out to be one of an enrolled person based on face
recognition or rejects if not. Face membership authentication belongs
to the two class classification problem where SVM(Support Vector
Machine) has been successfully applied and shows better performance
compared to the conventional threshold-based classification. However,
most of previous SVMs have been trained using image feature vectors
extracted from face images of each class member(enrolled
class/unenrolled class) so that they are not robust to variations in
illuminations, poses, and facial expressions and much affected by
changes in member configuration of the enrolled class
In this paper, we propose an effective face membership
authentication method based on SVM using class discriminating
features which represent an incoming face image-s associability with
each class distinctively. These class discriminating features are weakly
related with image features so that they are less affected by variations
in illuminations, poses and facial expression.
Through experiments, it is shown that the proposed face
membership authentication method performs better than the threshold
rule-based or the conventional SVM-based authentication methods and
is relatively less affected by changes in member size and membership.
Abstract: The use of solar control film on windows as one of
solar passive strategies for building have becoming important and is
gaining recognition. Malaysia located close to equator is having
warm humid climate with long sunshine hours and abundant solar
radiation throughout the year. Hence, befitting solar control on
windows is absolutely necessary to capture the daylight whilst
moderating thermal impact and eliminating glare problems. This is
one of the energy efficient strategies to achieve thermal and visual
comfort in buildings. Therefore, this study was carried out to
investigate the effect of window solar controls on thermal and visual
performance of naturally ventilated buildings. This was conducted via
field data monitoring using a test building facility. Four types of
window glazing systems were used with three types of solar control
films. Data were analysed for thermal and visual impact with
reference to thermal and optical characteristics of the films. Results
show that for each glazing system, the surface temperature of
windows are influenced by the Solar Energy Absorption property, the
indoor air temperature are influenced by the Solar Energy
Transmittance and Solar Energy Reflectance, and the daylighting by
Visible Light Transmission and Shading Coefficient. Further
investigations are underway to determine the mathematical relation
between thermal energy and visual performance with the thermal and
optical characteristics of solar control films.
Abstract: In this paper, a novel method for a biometric system based on the ECG signal is proposed, using spectral coefficients computed through linear predictive coding (LPC). ECG biometric systems have traditionally incorporated characteristics of fiducial points of the ECG signal as the feature set. These systems have been shown to contain loopholes and thus a non-fiducial system allows for tighter security. In the proposed system, incorporating non-fiducial features from the LPC spectrum produced a segment and subject recognition rate of 99.52% and 100% respectively. The recognition rates outperformed the biometric system that is based on the wavelet packet decomposition (WPD) algorithm in terms of recognition rates and computation time. This allows for LPC to be used in a practical ECG biometric system that requires fast, stringent and accurate recognition.
Abstract: One of the common problems encountered in software
engineering is addressing and responding to the changing nature of
requirements. While several approaches have been devised to address
this issue, ranging from instilling resistance to changing requirements
in order to mitigate impact to project schedules, to developing an
agile mindset towards requirements, the approach discussed in this
paper is one of conceptualizing the delta in requirement and
modeling it, in order to plan a response to it. To provide some
context here, change is first formally identified and categorized as
either formal change or informal change. While agile methodology
facilitates informal change, the approach discussed in this paper
seeks to develop the idea of facilitating formal change. To collect,
document meta-requirements that represent the phenomena of change
would be a pro-active measure towards building a realistic cognition
of the requirements entity that can further be harnessed in the
software engineering process.
Abstract: The identification and classification of weeds are of
major technical and economical importance in the agricultural
industry. To automate these activities, like in shape, color and
texture, weed control system is feasible. The goal of this paper is to
build a real-time, machine vision weed control system that can detect
weed locations. In order to accomplish this objective, a real-time
robotic system is developed to identify and locate outdoor plants
using machine vision technology and pattern recognition. The
algorithm is developed to classify images into broad and narrow class
for real-time selective herbicide application. The developed
algorithm has been tested on weeds at various locations, which have
shown that the algorithm to be very effectiveness in weed
identification. Further the results show a very reliable performance
on weeds under varying field conditions. The analysis of the results
shows over 90 percent classification accuracy over 140 sample
images (broad and narrow) with 70 samples from each category of
weeds.
Abstract: In this paper the problem of face recognition under variable illumination conditions is considered. Most of the works in the literature exhibit good performance under strictly controlled acquisition conditions, but the performance drastically drop when changes in pose and illumination occur, so that recently number of approaches have been proposed to deal with such variability. The aim of this work is to introduce an efficient local appearance feature extraction method based steerable pyramid (SP) for face recognition. Local information is extracted from SP sub-bands using LBP(Local binary Pattern). The underlying statistics allow us to reduce the required amount of data to be stored. The experiments carried out on different face databases confirm the effectiveness of the proposed approach.
Abstract: Facial recognition and expression analysis is rapidly
becoming an area of intense interest in computer science and humancomputer
interaction design communities. The most expressive way
humans display emotions is through facial expressions. In this paper
skin and non-skin pixels were separated. Face regions were extracted
from the detected skin regions. Facial expressions are analyzed from
facial images by applying Gabor wavelet transform (GWT) and
Discrete Cosine Transform (DCT) on face images. Radial Basis
Function (RBF) Network is used to identify the person and to classify
the facial expressions. Our method reliably works even with faces,
which carry heavy expressions.
Abstract: The private theme parks are gradually surpassing
public-owned scenic areas after many years of development and have
become a mainstream choice for domestic tourists. Previous studies
show that visitors from different backgrounds differ in consumer
behavior and satisfaction factors. An understanding of visitor
satisfaction is therefore of extreme importance to operators of
privately-owned theme parks. Importance-Performance Analysis (IPA)
is used to measure consumer's potential satisfaction with services and
has become a widely used management tool for strength and weakness
analysis for brands, products, services and point of sales. As IPA has
so far not been used to evaluate the visitor satisfaction with
privately-owned theme parks, in this study the IPA method is used to
analyze visitor satisfaction with Janfusun Fancyworld (one of the most
popular private theme parks in Taiwan) and to rank visitor focus and
satisfaction on/in theme park facilities and services. Results of the
analysis provide private theme park operators with an understanding
of user or consumer demands as well as an assessment of the quality of
services currently offered.
Abstract: Effectiveness of Artificial Neural Networks (ANN)
and Support Vector Machines (SVM) classifiers for fault diagnosis of
rolling element bearings are presented in this paper. The
characteristic features of vibration signals of rotating driveline that
was run in its normal condition and with faults introduced were used
as input to ANN and SVM classifiers. Simple statistical features such
as standard deviation, skewness, kurtosis etc. of the time-domain
vibration signal segments along with peaks of the signal and peak of
power spectral density (PSD) are used as features to input the ANN
and SVM classifier. The effect of preprocessing of the vibration
signal by Discreet Wavelet Transform (DWT) prior to feature
extraction is also studied. It is shown from the experimental results
that the performance of SVM classifier in identification of bearing
condition is better then ANN and pre-processing of vibration signal
by DWT enhances the effectiveness of both ANN and SVM classifier
Abstract: One of the approaches enabling people with amputated
limbs to establish some sort of interface with the real world includes
the utilization of the myoelectric signal (MES) from the remaining
muscles of those limbs. The MES can be used as a control input to a
multifunction prosthetic device. In this control scheme, known as the
myoelectric control, a pattern recognition approach is usually utilized
to discriminate between the MES signals that belong to different
classes of the forearm movements. Since the MES is recorded using
multiple channels, the feature vector size can become very large. In
order to reduce the computational cost and enhance the generalization
capability of the classifier, a dimensionality reduction method is
needed to identify an informative yet moderate size feature set. This
paper proposes a new fuzzy version of the well known Fisher-s
Linear Discriminant Analysis (LDA) feature projection technique.
Furthermore, based on the fact that certain muscles might contribute
more to the discrimination process, a novel feature weighting scheme
is also presented by employing Particle Swarm Optimization (PSO)
for estimating the weight of each feature. The new method, called
PSOFLDA, is tested on real MES datasets and compared with other
techniques to prove its superiority.
Abstract: An emotional speech recognition system for the
applications on smart phones was proposed in this study to combine
with 3G mobile communications and social networks to provide users
and their groups with more interaction and care. This study developed
a mechanism using the support vector machines (SVM) to recognize
the emotions of speech such as happiness, anger, sadness and normal.
The mechanism uses a hierarchical classifier to adjust the weights of
acoustic features and divides various parameters into the categories of
energy and frequency for training. In this study, 28 commonly used
acoustic features including pitch and volume were proposed for
training. In addition, a time-frequency parameter obtained by
continuous wavelet transforms was also used to identify the accent and
intonation in a sentence during the recognition process. The Berlin
Database of Emotional Speech was used by dividing the speech into
male and female data sets for training. According to the experimental
results, the accuracies of male and female test sets were increased by
4.6% and 5.2% respectively after using the time-frequency parameter
for classifying happy and angry emotions. For the classification of all
emotions, the average accuracy, including male and female data, was
63.5% for the test set and 90.9% for the whole data set.
Abstract: The nearly 21-year-old Jiujiang Bridge, which is suffering from uneven line shape, constant great downwarping of the main beam and cracking of the box girder, needs reinforcement and cable adjustment. It has undergone cable adjustment for twice with incomplete data. Therefore, the initial internal force state of the Jiujiang Bridge is identified as the key for the cable adjustment project. Based on parameter identification by means of static force test data, this paper suggests determining the initial internal force state of the cable-stayed bridge according to the cable force-displacement relationship parameter identification method. That is, upon measuring the displacement and the change in cable forces for twice, one can identify the parameters concerned by means of optimization. This method is applied to the cable adjustment, replacement and reinforcement project for the Jiujiang Bridge as a guidance for the cable adjustment and reinforcement project of the bridge.
Abstract: This paper describes the evolution of language
politics and the part played by political leaders with reference to
the Dravidian parties in Tamil Nadu. It explores the interesting
evolution from separatism to coalition in sustaining the values of
parliamentary democracy and federalism. It seems that the
appropriation of language politics is fully ascribed to the DMK
leadership under Annadurai and Karunanidhi. For them, the Tamil
language is a self-determining power, a terrain of nationhood, and
a perennial source of social and political powers. The DMK
remains a symbol of Tamil nationalist party playing language
politics in the interest of the Tamils. Though electoral alliances
largely determine the success, the language politics still has
significant space in the politics of Tamil Nadu. Ironically, DMK
moves from the periphery to centre for getting national recognition
for the Tamils as well as for its own maximization of power. The
evolution can be seen in two major phases as: language politics for
party building; and language politics for state building with three
successive political processes, namely, language politics in the
process of separatism, representative politics and coalition. The
much pronounced Dravidian Movement is radical enough to
democratize the party ideology to survive the spirit of
parliamentary democracy. This has secured its own rewards in
terms of political power. The political power provides the means to
achieve the social and political goal of the political party.
Language politics and leadership pattern actualized this trend
though the movement is shifted from separatism to coalition.
Abstract: Hand gesture is one of the typical methods used in
sign language for non-verbal communication. It is most commonly
used by people who have hearing or speech problems to
communicate among themselves or with normal people. Various sign
language systems have been developed by manufacturers around the
globe but they are neither flexible nor cost-effective for the end
users. This paper presents a system prototype that is able to
automatically recognize sign language to help normal people to
communicate more effectively with the hearing or speech impaired
people. The Sign to Voice system prototype, S2V, was developed
using Feed Forward Neural Network for two-sequence signs
detection. Different sets of universal hand gestures were captured
from video camera and utilized to train the neural network for
classification purpose. The experimental results have shown that
neural network has achieved satisfactory result for sign-to-voice
translation.
Abstract: Automated operations based on voice commands will become more and more important in many applications, including robotics, maintenance operations, etc. However, voice command recognition rates drop quite a lot under non-stationary and chaotic noise environments. In this paper, we tried to significantly improve the speech recognition rates under non-stationary noise environments. First, 298 Navy acronyms have been selected for automatic speech recognition. Data sets were collected under 4 types of noisy environments: factory, buccaneer jet, babble noise in a canteen, and destroyer. Within each noisy environment, 4 levels (5 dB, 15 dB, 25 dB, and clean) of Signal-to-Noise Ratio (SNR) were introduced to corrupt the speech. Second, a new algorithm to estimate speech or no speech regions has been developed, implemented, and evaluated. Third, extensive simulations were carried out. It was found that the combination of the new algorithm, the proper selection of language model and a customized training of the speech recognizer based on clean speech yielded very high recognition rates, which are between 80% and 90% for the four different noisy conditions. Fourth, extensive comparative studies have also been carried out.
Abstract: This paper describes a probabilistic method for
three-dimensional object recognition using a shared pool of surface
signatures. This technique uses flatness, orientation, and convexity
signatures that encode the surface of a free-form object into three
discriminative vectors, and then creates a shared pool of data by
clustering the signatures using a distance function. This method
applies the Bayes-s rule for recognition process, and it is extensible
to a large collection of three-dimensional objects.