Abstract: The paper deals with acoustic-spectrographic voice
identification method in terms of its performance in non-native
language speech. Performance evaluation is conducted by comparing
the result of the analysis of recordings containing native language
speech with recordings that contain foreign language speech. Our
research is based on Tajik and Russian speech of Tajik native
speakers due to the character of the criminal situation with drug
trafficking. We propose a pilot experiment that represents a primary
attempt enter the field.
Abstract: Conceiving and developing routing protocols for
wireless sensor networks requires considerations on constraints such
as network lifetime and energy consumption. In this paper, we propose
a hybrid hierarchical routing protocol named HHRP combining both
clustering mechanism and multipath optimization taking into account
residual energy and RSSI measures. HHRP consists of classifying
dynamically nodes into clusters where coordinators nodes with extra
privileges are able to manipulate messages, aggregate data and ensure
transmission between nodes according to TDMA and CDMA
schedules. The reconfiguration of the network is carried out
dynamically based on a threshold value which is associated with the
number of nodes belonging to the smallest cluster. To show the
effectiveness of the proposed approach HHRP, a comparative study
with LEACH protocol is illustrated in simulations.
Abstract: Speaker Identification (SI) is the task of establishing
identity of an individual based on his/her voice characteristics. The SI
task is typically achieved by two-stage signal processing: training and
testing. The training process calculates speaker specific feature
parameters from the speech and generates speaker models
accordingly. In the testing phase, speech samples from unknown
speakers are compared with the models and classified. Even though
performance of speaker identification systems has improved due to
recent advances in speech processing techniques, there is still need of
improvement. In this paper, a Closed-Set Tex-Independent Speaker
Identification System (CISI) based on a Multiple Classifier System
(MCS) is proposed, using Mel Frequency Cepstrum Coefficient
(MFCC) as feature extraction and suitable combination of vector
quantization (VQ) and Gaussian Mixture Model (GMM) together
with Expectation Maximization algorithm (EM) for speaker
modeling. The use of Voice Activity Detector (VAD) with a hybrid
approach based on Short Time Energy (STE) and Statistical
Modeling of Background Noise in the pre-processing step of the
feature extraction yields a better and more robust automatic speaker
identification system. Also investigation of Linde-Buzo-Gray (LBG)
clustering algorithm for initialization of GMM, for estimating the
underlying parameters, in the EM step improved the convergence rate
and systems performance. It also uses relative index as confidence
measures in case of contradiction in identification process by GMM
and VQ as well. Simulation results carried out on voxforge.org
speech database using MATLAB highlight the efficacy of the
proposed method compared to earlier work.
Abstract: The distribution of a single global clock across a chip
has become the major design bottleneck for high performance VLSI
systems owing to the power dissipation, process variability and multicycle
cross-chip signaling. A Network-on-Chip (NoC) architecture
partitioned into several synchronous blocks has become a promising
approach for attaining fine-grain power management at the system
level. In a NoC architecture the communication between the blocks is
handled asynchronously. To interface these blocks on a chip
operating at different frequencies, an asynchronous FIFO interface is
inevitable. However, these asynchronous FIFOs are not required if
adjacent blocks belong to the same clock domain. In this paper, we
have designed and analyzed a 16-bit asynchronous micropipelined
FIFO of depth four, with the awareness of place and route on an
FPGA device. We have used a commercially available Spartan 3
device and designed a high speed implementation of the
asynchronous 4-phase micropipeline. The asynchronous FIFO
implemented on the FPGA device shows 76 Mb/s throughput and a
handshake cycle of 109 ns for write and 101.3 ns for read at the
simulation under the worst case operating conditions (voltage =
0.95V) on a working chip at the room temperature.
Abstract: In this paper, we used data mining to extract
biomedical knowledge. In general, complex biomedical data
collected in studies of populations are treated by statistical methods,
although they are robust, they are not sufficient in themselves to
harness the potential wealth of data. For that you used in step two
learning algorithms: the Decision Trees and Support Vector Machine
(SVM). These supervised classification methods are used to make the
diagnosis of thyroid disease. In this context, we propose to promote
the study and use of symbolic data mining techniques.
Abstract: This paper proposes a rotational invariant texture
feature based on the roughness property of the image for psoriasis
image analysis. In this work, we have applied this feature for image
classification and segmentation. The fuzzy concept is employed to
overcome the imprecision of roughness. Since the psoriasis lesion is
modeled by a rough surface, the feature is extended for calculating
the Psoriasis Area Severity Index value. For classification and
segmentation, the Nearest Neighbor algorithm is applied. We have
obtained promising results for identifying affected lesions by using
the roughness index and severity level estimation.
Abstract: The goal of image segmentation is to cluster pixels
into salient image regions. Segmentation could be used for object
recognition, occlusion boundary estimation within motion or stereo
systems, image compression, image editing, or image database lookup.
In this paper, we present a color image segmentation using
support vector machine (SVM) pixel classification. Firstly, the pixel
level color and texture features of the image are extracted and they
are used as input to the SVM classifier. These features are extracted
using the homogeneity model and Gabor Filter. With the extracted
pixel level features, the SVM Classifier is trained by using FCM
(Fuzzy C-Means).The image segmentation takes the advantage of
both the pixel level information of the image and also the ability of
the SVM Classifier. The Experiments show that the proposed method
has a very good segmentation result and a better efficiency, increases
the quality of the image segmentation compared with the other
segmentation methods proposed in the literature.
Abstract: One image is worth more than thousand words.
Images if analyzed can reveal useful information. Low level image
processing deals with the extraction of specific feature from a single
image. Now the question arises: What technique should be used to
extract patterns of very large and detailed image database? The
answer of the question is: “Image Mining”. Image Mining deals with
the extraction of image data relationship, implicit knowledge, and
another pattern from the collection of images or image database. It is
nothing but the extension of Data Mining. In the following paper, not
only we are going to scrutinize the current techniques of image
mining but also present a new technique for mining images using
Genetic Algorithm.
Abstract: A Distributed Denial of Service (DDoS) attack is a
major threat to cyber security. It originates from the network layer or
the application layer of compromised/attacker systems which are
connected to the network. The impact of this attack ranges from the
simple inconvenience to use a particular service to causing major
failures at the targeted server. When there is heavy traffic flow to a
target server, it is necessary to classify the legitimate access and
attacks. In this paper, a novel method is proposed to detect DDoS
attacks from the traces of traffic flow. An access matrix is created
from the traces. As the access matrix is multi dimensional, Principle
Component Analysis (PCA) is used to reduce the attributes used for
detection. Two classifiers Naive Bayes and K-Nearest neighborhood
are used to classify the traffic as normal or abnormal. The
performance of the classifier with PCA selected attributes and actual
attributes of access matrix is compared by the detection rate and
False Positive Rate (FPR).
Abstract: Wireless mesh networking is rapidly gaining in
popularity with a variety of users: from municipalities to enterprises,
from telecom service providers to public safety and military
organizations. This increasing popularity is based on two basic facts:
ease of deployment and increase in network capacity expressed in
bandwidth per footage; WMNs do not rely on any fixed
infrastructure. Many efforts have been used to maximizing
throughput of the network in a multi-channel multi-radio wireless
mesh network. Current approaches are purely based on either static or
dynamic channel allocation approaches. In this paper, we use a
hybrid multichannel multi radio wireless mesh networking
architecture, where static and dynamic interfaces are built in the
nodes. Dynamic Adaptive Channel Allocation protocol (DACA), it
considers optimization for both throughput and delay in the channel
allocation. The assignment of the channel has been allocated to be codependent
with the routing problem in the wireless mesh network and
that should be based on passage flow on every link. Temporal and
spatial relationship rises to re compute the channel assignment every
time when the pattern changes in mesh network, channel assignment
algorithms assign channels in network. In this paper a computing
path which captures the available path bandwidth is the proposed
information and the proficient routing protocol based on the new path
which provides both static and dynamic links. The consistency
property guarantees that each node makes an appropriate packet
forwarding decision and balancing the control usage of the network,
so that a data packet will traverse through the right path.
Abstract: Image spam is a kind of email spam where the spam
text is embedded with an image. It is a new spamming technique
being used by spammers to send their messages to bulk of internet
users. Spam email has become a big problem in the lives of internet
users, causing time consumption and economic losses. The main
objective of this paper is to detect the image spam by using histogram
properties of an image. Though there are many techniques to
automatically detect and avoid this problem, spammers employing
new tricks to bypass those techniques, as a result those techniques are
inefficient to detect the spam mails. In this paper we have proposed a
new method to detect the image spam. Here the image features are
extracted by using RGB histogram, HSV histogram and combination
of both RGB and HSV histogram. Based on the optimized image
feature set classification is done by using k- Nearest Neighbor(k-NN)
algorithm. Experimental result shows that our method has achieved
better accuracy. From the result it is known that combination of RGB
and HSV histogram with k-NN algorithm gives the best accuracy in
spam detection.
Abstract: This paper describes a logical method to enhance
security on the grid computing to restrict the misuse of the grid
resources. This method is an economic and efficient one to avoid the
usage of the special devices. The security issues, techniques and
solutions needed to provide a secure grid computing environment are
described. A well defined process for security management among
the resource accesses and key holding algorithm is also proposed. In
this method, the identity management, access control and
authorization and authentication are effectively handled.
Abstract: Artificial Immune Systems (AIS), inspired by the
human immune system, are algorithms and mechanisms which are
self-adaptive and self-learning classifiers capable of recognizing and
classifying by learning, long-term memory and association. Unlike
other human system inspired techniques like genetic algorithms and
neural networks, AIS includes a range of algorithms modeling on
different immune mechanism of the body. In this paper, a mechanism
of a human immune system based on apoptosis is adopted to build an
Intrusion Detection System (IDS) to protect computer networks.
Features are selected from network traffic using Fisher Score. Based
on the selected features, the record/connection is classified as either
an attack or normal traffic by the proposed methodology. Simulation
results demonstrates that the proposed AIS based on apoptosis
performs better than existing AIS for intrusion detection.
Abstract: Wireless sensor network is vulnerable to a wide range
of attacks. Recover secrecy after compromise, to develop technique
that can detect intrusions and able to resilient networks that isolates
the point(s) of intrusion while maintaining network connectivity for
other legitimate users. To define new security metrics to evaluate
collaborative intrusion resilience protocol, by leveraging the sensor
mobility that allows compromised sensors to recover secure state
after compromise. This is obtained with very low overhead and in a
fully distributed fashion using extensive simulations support our
findings.
Abstract: Through this paper we present a method for automatic
generation of ontological model from any data source using Model
Driven Architecture (MDA), this generation is dedicated to the
cooperation of the knowledge engineering and software engineering.
Indeed, reverse engineering of a data source generates a software
model (schema of data) that will undergo transformations to generate
the ontological model. This method uses the meta-models to validate
software and ontological models.
Abstract: Multispectral screening systems are becoming more
popular because of their very interesting properties and applications.
One of the most significant applications of multispectral screening
systems is prevention of terrorist attacks. There are many kinds of
threats and many methods of detection. Visual detection of objects
hidden under clothing of a person is one of the most challenging
problems of threats detection. There are various solutions of the
problem; however, the most effective utilize multispectral
surveillance imagers. The development of imaging devices and
exploration of new spectral bands is a chance to introduce new
equipment for assuring public safety. We investigate the possibility
of long lasting detection of potentially dangerous objects covered
with various types of clothing. In the article we present the results of
comparative studies of passive imaging in three spectrums – visible,
infrared and terahertz.
Abstract: This study proposes the transformation of nonlinear
Magnetic Levitation System into linear one, via state and feedback
transformations using explicit algorithm. This algorithm allows
computing explicitly the linearizing state coordinates and feedback
for any nonlinear control system, which is feedback linearizable,
without solving the Partial Differential Equations. The algorithm is
performed using a maximum of N-1 steps where N being the
dimension of the system.
Abstract: In MANET, mobile nodes communicate with each
other using the wireless channel where transmission takes place with
significant interference. The wireless medium used in MANET is a
shared resource used by all the nodes available in MANET. Packet
reserving is one important resource management scheme which
controls the allocation of bandwidth among multiple flows through
node cooperation in MANET. This paper proposes packet reserving
and clogging control via Routing Aware Packet Reserving (RAPR)
framework in MANET. It mainly focuses the end-to-end routing
condition with maximal throughput. RAPR is complimentary system
where the packet reserving utilizes local routing information
available in each node. Path setup in RAPR estimates the security
level of the system, and symbolizes the end-to-end routing by
controlling the clogging. RAPR reaches the packet to the destination
with high probability ratio and minimal delay count. The standard
performance measures such as network security level,
communication overhead, end-to-end throughput, resource utilization
efficiency and delay measure are considered in this work. The results
reveals that the proposed packet reservation and clogging control via
Routing Aware Packet Reserving (RAPR) framework performs well
for the above said performance measures compare to the existing
methods.
Abstract: The success of any retail business is predisposed by its
swift response and its knack in understanding the constraints and the
requirements of customers. In this paper a conceptual design model
of an automated customer-friendly supermarket has been proposed.
In this model a 10-sided, space benefited, regular polygon shaped
gravity shelves have been designed for goods storage and effective
customer-specific algorithms have been built-in for quick automatic
delivery of the randomly listed goods. The algorithm is developed
with two main objectives, viz., delivery time and priority. For
meeting these objectives the randomly listed items are reorganized
according to the critical-path of the robotic arm specific to the
identified shop and its layout and the items are categorized according
to the demand, shape, size, similarity and nature of the product for an
efficient pick-up, packing and delivery process. We conjectured that
the proposed automated supermarket model reduces business
operating costs with much customer satisfaction warranting a winwin
situation.
Abstract: This research proposes a novel reconstruction protocol
for restoring missing surfaces and low-quality edges and shapes in
photos of artifacts at historical sites. The protocol starts with the
extraction of a cloud of points. This extraction process is based on
four subordinate algorithms, which differ in the robustness and
amount of resultant. Moreover, they use different -but
complementary- accuracy to some related features and to the way
they build a quality mesh. The performance of our proposed protocol
is compared with other state-of-the-art algorithms and toolkits. The
statistical analysis shows that our algorithm significantly outperforms
its rivals in the resultant quality of its object files used to reconstruct
the desired model.