Abstract: Most fingerprint recognition techniques are based on minutiae matching and have been well studied. However, this technology still suffers from problems associated with the handling of poor quality impressions. One problem besetting fingerprint matching is distortion. Distortion changes both geometric position and orientation, and leads to difficulties in establishing a match among multiple impressions acquired from the same finger tip. Marking all the minutiae accurately as well as rejecting false minutiae is another issue still under research. Our work has combined many methods to build a minutia extractor and a minutia matcher. The combination of multiple methods comes from a wide investigation into research papers. Also some novel changes like segmentation using Morphological operations, improved thinning, false minutiae removal methods, minutia marking with special considering the triple branch counting, minutia unification by decomposing a branch into three terminations, and matching in the unified x-y coordinate system after a two-step transformation are used in the work.
Abstract: A feature weighting and selection method is proposed
which uses the structure of a weightless neuron and exploits the
principles that govern the operation of Genetic Algorithms and
Evolution. Features are coded onto chromosomes in a novel way
which allows weighting information regarding the features to be
directly inferred from the gene values. The proposed method is
significant in that it addresses several problems concerned with
algorithms for feature selection and weighting as well as providing
significant advantages such as speed, simplicity and suitability for
real-time systems.
Abstract: Recently, Jia et al. proposed a remote user authentication scheme using bilinear pairings and an Elliptic Curve Cryptosystem (ECC). However, the scheme is vulnerable to privileged insider attack at their proposed registration phase and to forgery attack at their proposed authentication phase. In addition, the scheme can be vulnerable to server spoofing attack because it does not provide mutual authentication between the user and the remote server. Therefore, this paper points out that the Jia et al. scheme is vulnerable to the above three attacks.
Abstract: Association rules are an important problem in data
mining. Massively increasing volume of data in real life databases
has motivated researchers to design novel and incremental algorithms
for association rules mining. In this paper, we propose an incremental
association rules mining algorithm that integrates shocking
interestingness criterion during the process of building the model. A
new interesting measure called shocking measure is introduced. One
of the main features of the proposed approach is to capture the user
background knowledge, which is monotonically augmented. The
incremental model that reflects the changing data and the user beliefs
is attractive in order to make the over all KDD process more
effective and efficient. We implemented the proposed approach and
experiment it with some public datasets and found the results quite
promising.
Abstract: Although e-mail is the most efficient and popular communication method, unwanted and mass unsolicited e-mails, also called spam mail, endanger the existence of the mail system. This paper proposes a new algorithm called Dynamic Weighted Majority Concept Drift Detection (DWM-CDD) for content-based filtering. The design purposes of DWM-CDD are first to accurate the performance of the previously proposed algorithms, and second to speed up the time to construct the model. The results show that DWM-CDD can detect both sudden and gradual changes quickly and accurately. Moreover, the time needed for model construction is less than previously proposed algorithms.
Abstract: Each year many people are reported missing in most of the countries in the world owing to various reasons. Arrangements have to be made to find these people after some time. So the investigating agencies are compelled to make out these people by using manpower. But in many cases, the investigations carried out to find out an absconding for a long time may not be successful. At a time like that it may be difficult to identify these people by examining their old photographs, because their facial appearance might have changed mainly due to the natural aging process. On some occasions in forensic medicine if a dead body is found, investigations should be held to make sure that this corpse belongs to the same person disappeared some time ago. With the passage of time the face of the person might have changed and there should be a mechanism to reveal the person-s identity. In order to make this process easy, we must guess and decide as to how he will look like by now. To address this problem this paper presents a way of synthesizing a facial image with the aging effects.
Abstract: Dynamic bandwidth allocation in EPONs can be
generally separated into inter-ONU scheduling and intra-ONU scheduling. In our previous work, the active intra-ONU scheduling
(AS) utilizes multiple queue reports (QRs) in each report message to cooperate with the inter-ONU scheduling and makes the granted
bandwidth fully utilized without leaving unused slot remainder (USR).
This scheme successfully solves the USR problem originating from the
inseparability of Ethernet frame. However, without proper setting of
threshold value in AS, the number of QRs constrained by the IEEE
802.3ah standard is not enough, especially in the unbalanced traffic
environment. This limitation may be solved by enlarging the threshold
value. The large threshold implies the large gap between the adjacent QRs, thus resulting in the large difference between the best granted bandwidth and the real granted bandwidth. In this paper, we integrate
AS with a cooperative prediction mechanism and distribute multiple
QRs to reduce the penalty brought by the prediction error.
Furthermore, to improve the QoS and save the usage of queue reports,
the highest priority (EF) traffic which comes during the waiting time is
granted automatically by OLT and is not considered in the requested
bandwidth of ONU. The simulation results show that the proposed
scheme has better performance metrics in terms of bandwidth
utilization and average delay for different classes of packets.
Abstract: Compression algorithms reduce the redundancy in
data representation to decrease the storage required for that data.
Lossless compression researchers have developed highly
sophisticated approaches, such as Huffman encoding, arithmetic
encoding, the Lempel-Ziv (LZ) family, Dynamic Markov
Compression (DMC), Prediction by Partial Matching (PPM), and
Burrows-Wheeler Transform (BWT) based algorithms.
Decompression is also required to retrieve the original data by
lossless means. A compression scheme for text files coupled with
the principle of dynamic decompression, which decompresses only
the section of the compressed text file required by the user instead of
decompressing the entire text file. Dynamic decompressed files offer
better disk space utilization due to higher compression ratios
compared to most of the currently available text file formats.
Abstract: This article presents a computationally tractable probabilistic model for the relation between the complex wavelet coefficients of two images of the same scene. The two images are acquisitioned at distinct moments of times, or from distinct viewpoints, or by distinct sensors. By means of the introduced probabilistic model, we argue that the similarity between the two images is controlled not by the values of the wavelet coefficients, which can be altered by many factors, but by the nature of the wavelet coefficients, that we model with the help of hidden state variables. We integrate this probabilistic framework in the construction of a new image registration algorithm. This algorithm has sub-pixel accuracy and is robust to noise and to other variations like local illumination changes. We present the performance of our algorithm on various image types.
Abstract: This paper suggests a new Affine Projection (AP) algorithm with variable data-reuse factor using the condition number as a decision factor. To reduce computational burden, we adopt a recently reported technique which estimates the condition number of an input data matrix. Several simulations show that the new algorithm has better performance than that of the conventional AP algorithm.
Abstract: This paper presents a new strategy of identification
and classification of pathological voices using the hybrid method
based on wavelet transform and neural networks. After speech
acquisition from a patient, the speech signal is analysed in order to
extract the acoustic parameters such as the pitch, the formants, Jitter,
and shimmer. Obtained results will be compared to those normal and
standard values thanks to a programmable database. Sounds are
collected from normal people and patients, and then classified into
two different categories. Speech data base is consists of several
pathological and normal voices collected from the national hospital
“Rabta-Tunis". Speech processing algorithm is conducted in a
supervised mode for discrimination of normal and pathology voices
and then for classification between neural and vocal pathologies
(Parkinson, Alzheimer, laryngeal, dyslexia...). Several simulation
results will be presented in function of the disease and will be
compared with the clinical diagnosis in order to have an objective
evaluation of the developed tool.
Abstract: The purpose of this work is to present a method for
rigid registration of medical images using 1D binary projections
when a part of one of the two images is missing. We use 1D binary
projections and we adjust the projection limits according to the
reduced image in order to perform accurate registration. We use the
variance of the weighted ratio as a registration function which we
have shown is able to register 2D and 3D images more accurately and
robustly than mutual information methods. The function is computed
explicitly for n=5 Chebyshev points in a [-9,+9] interval and it is
approximated using Chebyshev polynomials for all other points. The
images used are MR scans of the head. We find that the method is
able to register the two images with average accuracy 0.3degrees for
rotations and 0.2 pixels for translations for a y dimension of 156 with
initial dimension 256. For y dimension 128/256 the accuracy
decreases to 0.7 degrees for rotations and 0.6 pixels for translations.
Abstract: In this paper, a method for matching image segments
using triangle-based (geometrical) regions is proposed. Triangular
regions are formed from triples of vertex points obtained from a
keypoint detector (SIFT). However, triangle regions are subject to
noise and distortion around the edges and vertices (especially acute
angles). Therefore, these triangles are expanded into parallelogramshaped
regions. The extracted image segments inherit an important
triangle property; the invariance to affine distortion. Given two
images, matching corresponding regions is conducted by computing
the relative affine matrix, rectifying one of the regions w.r.t. the other
one, then calculating the similarity between the reference and
rectified region. The experimental tests show the efficiency and
robustness of the proposed algorithm against geometrical distortion.
Abstract: I/O workload is a critical and important factor to
analyze I/O pattern and file system performance. However tracing I/O
operations on the fly distributed parallel file system is non-trivial due
to collection overhead and a large volume of data. In this paper, we
design and implement a parallel file system logging method for high
performance computing using shared memory-based multi-layer
scheme. It minimizes the overhead with reduced logging operation
response time and provides efficient post-processing scheme through
shared memory. Separated logging server can collect sequential logs
from multiple clients in a cluster through packet communication.
Implementation and evaluation result shows low overhead and high
scalability of this architecture for high performance parallel logging
analysis.
Abstract: This study proposes a new recommender system based on the collaborative folksonomy. The purpose of the proposed system is to recommend Internet resources (such as books, articles, documents, pictures, audio and video) to users. The proposed method includes four steps: creating the user profile based on the tags, grouping the similar users into clusters using an agglomerative hierarchical clustering, finding similar resources based on the user-s past collections by using content-based filtering, and recommending similar items to the target user. This study examines the system-s performance for the dataset collected from “del.icio.us," which is a famous social bookmarking website. Experimental results show that the proposed tag-based collaborative and content-based filtering hybridized recommender system is promising and effectiveness in the folksonomy-based bookmarking website.
Abstract: Graph transformation has recently become more and
more popular as a general visual modeling language to formally state
the dynamic semantics of the designed models. Especially, it is a
very natural formalism for languages which basically are graph (e.g.
UML). Using this technique, we present a highly understandable yet
precise approach to formally model and analyze the behavioral
semantics of UML 2.0 Activity diagrams. In our proposal, AGG is
used to design Activities, then using our previous approach to model
checking graph transformation systems, designers can verify and
analyze designed Activity diagrams by checking the interesting
properties as combination of graph rules and LTL (Linear Temporal
Logic) formulas on the Activities.
Abstract: Packet switched data network like Internet, which has
traditionally supported throughput sensitive applications such as email
and file transfer, is increasingly supporting delay-sensitive
multimedia applications such as interactive video. These delaysensitive
applications would often rather sacrifice some throughput
for better delay. Unfortunately, the current packet switched network
does not offer choices, but instead provides monolithic best-effort
service to all applications. This paper evaluates Class Based Queuing
(CBQ), Coordinated Earliest Deadline First (CEDF), Weighted
Switch Deficit Round Robin (WSDRR) and RED-Boston scheduling
schemes that is sensitive to delay bound expectations for variety of
real time applications and an enhancement of WSDRR is proposed.
Abstract: AAM (active appearance model) has been successfully
applied to face and facial feature localization. However, its performance is sensitive to initial parameter values. In this paper, we propose a two-stage AAM for robust face alignment, which first fits an
inner face-AAM model to the inner facial feature points of the face and then localizes the whole face and facial features by optimizing the
whole face-AAM model parameters. Experiments show that the proposed face alignment method using two-stage AAM is more reliable to the background and the head pose than the standard
AAM-based face alignment method.
Abstract: In online context, the design and implementation of
effective remote laboratories environment is highly challenging on
account of hardware and software needs. This paper presents the
remote laboratory software framework modified from ilab shared
architecture (ISA). The ISA is a framework which enables students to
remotely acccess and control experimental hardware using internet
infrastructure. The need for remote laboratories came after
experiencing problems imposed by traditional laboratories. Among
them are: the high cost of laboratory equipment, scarcity of space,
scarcity of technical personnel along with the restricted university
budget creates a significant bottleneck on building required
laboratory experiments. The solution to these problems is to build
web-accessible laboratories. Remote laboratories allow students and
educators to interact with real laboratory equipment located
anywhere in the world at anytime. Recently, many universities and
other educational institutions especially in third world countries rely
on simulations because they do not afford the experimental
equipment they require to their students. Remote laboratories enable
users to get real data from real-time hand-on experiments. To
implement many remote laboratories, the system architecture should
be flexible, understandable and easy to implement, so that different
laboratories with different hardware can be deployed easily. The
modifications were made to enable developers to add more
equipment in ISA framework and to attract the new developers to
develop many online laboratories.
Abstract: This Paper presents an on-going research in the area of Model-Driven Engineering (MDE). The premise is that UML is too unwieldy to serve as the basis for model-driven engineering. We need a smaller, simpler notation with a cleaner semantics. We propose some ideas for a simpler notation with a clean semantics. The result is known as μML, or the Micro-Modelling Language.