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: EPC Class-1 Generation-2 UHF tags, one of Radio
frequency identification or RFID tag types, is expected that most
companies are planning to use it in the supply chain in the short term
and in consumer packaging in the long term due to its inexpensive
cost. Because of the very cost, however, its resources are extremely
scarce and it is hard to have any valuable security algorithms in it. It
causes security vulnerabilities, in particular cloning the tags for
counterfeits. In this paper, we propose a product authentication
solution for anti-counterfeiting at application level in the supply chain
and mobile RFID environment. It aims to become aware of
distribution of spurious products with fake RFID tags and to provide a
product authentication service to general consumers with mobile
RFID devices like mobile phone or PDA which has a mobile RFID
reader. We will discuss anti-counterfeiting mechanisms which are
required to our proposed solution and address requirements that the
mechanisms should have.
Abstract: This paper presents software tools that convert the C/Cµ floating point source code for a DSP algorithm into a fixedpoint simulation model that can be used to evaluate the numericalperformance of the algorithm on several different fixed pointplatforms including microprocessors, DSPs and FPGAs. The tools use a novel system for maintaining binary point informationso that the conversion from floating point to fixed point isautomated and the resulting fixed point algorithm achieves maximum possible precision. A configurable architecture is used during the simulation phase so that the algorithm can produce a bit-exact output for several different target devices.
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: Wireless LAN technologies have picked up
momentum in the recent years due to their ease of deployment, cost
and availability. The era of wireless LAN has also given rise to
unique applications like VOIP, IPTV and unified messaging.
However, these real-time applications are very sensitive to network
and handoff latencies. To successfully support these applications,
seamless roaming during the movement of mobile station has become
crucial. Nowadays, centralized architecture models support roaming
in WLANs. They have the ability to manage, control and
troubleshoot large scale WLAN deployments. This model is managed
by Control and Provision of Wireless Access Point protocol
(CAPWAP). This paper covers the CAPWAP architectural solution
along with its proposals that have emerged. Based on the literature
survey conducted in this paper, we found that the proposed
algorithms to reduce roaming latency in CAPWAP architecture do
not support seamless roaming. Additionally, they are not sufficient
during the initial period of the network. This paper also suggests
important design consideration for mobility support in future
centralized IEEE 802.11 networks.
Abstract: Feature selection has recently been the subject of intensive research in data mining, specially for datasets with a large number of attributes. Recent work has shown that feature selection can have a positive effect on the performance of machine learning algorithms. The success of many learning algorithms in their attempts to construct models of data, hinges on the reliable identification of a small set of highly predictive attributes. The inclusion of irrelevant, redundant and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. In this paper, a novel feature search procedure that utilizes the Ant Colony Optimization (ACO) is presented. The ACO is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by considering both local heuristics and previous knowledge. When applied to two different classification problems, the proposed algorithm achieved very promising results.
Abstract: Segmentation, filtering out of measurement errors and
identification of breakpoints are integral parts of any analysis of
microarray data for the detection of copy number variation (CNV).
Existing algorithms designed for these tasks have had some successes
in the past, but they tend to be O(N2) in either computation time or
memory requirement, or both, and the rapid advance of microarray
resolution has practically rendered such algorithms useless. Here we
propose an algorithm, SAD, that is much faster and much less thirsty
for memory – O(N) in both computation time and memory requirement
-- and offers higher accuracy. The two key ingredients of SAD are the
fundamental assumption in statistics that measurement errors are
normally distributed and the mathematical relation that the product of
two Gaussians is another Gaussian (function). We have produced a
computer program for analyzing CNV based on SAD. In addition to
being fast and small it offers two important features: quantitative
statistics for predictions and, with only two user-decided parameters,
ease of use. Its speed shows little dependence on genomic profile.
Running on an average modern computer, it completes CNV analyses
for a 262 thousand-probe array in ~1 second and a 1.8 million-probe
array in 9 seconds
Abstract: In field of Computer Science and Mathematics,
sorting algorithm is an algorithm that puts elements of a list in a
certain order i.e. ascending or descending. Sorting is perhaps the
most widely studied problem in computer science and is frequently
used as a benchmark of a system-s performance. This paper
presented the comparative performance study of four sorting
algorithms on different platform. For each machine, it is found that
the algorithm depends upon the number of elements to be sorted. In
addition, as expected, results show that the relative performance of
the algorithms differed on the various machines. So, algorithm
performance is dependent on data size and there exists impact of
hardware also.
Abstract: One of the essential components of much of DSP
application is noise cancellation. Changes in real time signals are
quite rapid and swift. In noise cancellation, a reference signal which
is an approximation of noise signal (that corrupts the original
information signal) is obtained and then subtracted from the noise
bearing signal to obtain a noise free signal. This approximation of
noise signal is obtained through adaptive filters which are self
adjusting. As the changes in real time signals are abrupt, this needs
adaptive algorithm that converges fast and is stable. Least mean
square (LMS) and normalized LMS (NLMS) are two widely used
algorithms because of their plainness in calculations and
implementation. But their convergence rates are small. Adaptive
averaging filters (AFA) are also used because they have high
convergence, but they are less stable. This paper provides the
comparative study of LMS and Normalized NLMS, AFA and new
enhanced average adaptive (Average NLMS-ANLMS) filters for noise
cancelling application using speech signals.
Abstract: In general dynamic analyses, lower mode response is
of interest, however the higher modes of spatially discretized
equations generally do not represent the real behavior and not affects
to global response much. Some implicit algorithms, therefore, are
introduced to filter out the high-frequency modes using intended
numerical error. The objective of this study is to introduce the
P-method and PC α-method to compare that with dissipation method
and Newmark method through the stability analysis and numerical
example. PC α-method gives more accuracy than other methods
because it based on the α-method inherits the superior properties of the
implicit α-method. In finite element analysis, the PC α-method is more
useful than other methods because it is the explicit scheme and it
achieves the second order accuracy and numerical damping
simultaneously.
Abstract: This paper proposes a bi-objective model for the
facility location problem under a congestion system. The idea of the
model is motivated by applications of locating servers in bank
automated teller machines (ATMS), communication networks, and so
on. This model can be specifically considered for situations in which
fixed service facilities are congested by stochastic demand within
queueing framework. We formulate this model with two perspectives
simultaneously: (i) customers and (ii) service provider. The
objectives of the model are to minimize (i) the total expected
travelling and waiting time and (ii) the average facility idle-time.
This model represents a mixed-integer nonlinear programming
problem which belongs to the class of NP-hard problems. In addition,
to solve the model, two metaheuristic algorithms including nondominated
sorting genetic algorithms (NSGA-II) and non-dominated
ranking genetic algorithms (NRGA) are proposed. Besides, to
evaluate the performance of the two algorithms some numerical
examples are produced and analyzed with some metrics to determine
which algorithm works better.
Abstract: In the recent past, there has been an increasing interest
in applying evolutionary methods to Knowledge Discovery in
Databases (KDD) and a number of successful applications of Genetic
Algorithms (GA) and Genetic Programming (GP) to KDD have been
demonstrated. The most predominant representation of the
discovered knowledge is the standard Production Rules (PRs) in the
form If P Then D. The PRs, however, are unable to handle
exceptions and do not exhibit variable precision. The Censored
Production Rules (CPRs), an extension of PRs, were proposed by
Michalski & Winston that exhibit variable precision and supports an
efficient mechanism for handling exceptions. A CPR is an
augmented production rule of the form:
If P Then D Unless C, where C (Censor) is an exception to the rule.
Such rules are employed in situations, in which the conditional
statement 'If P Then D' holds frequently and the assertion C holds
rarely. By using a rule of this type we are free to ignore the exception
conditions, when the resources needed to establish its presence are
tight or there is simply no information available as to whether it
holds or not. Thus, the 'If P Then D' part of the CPR expresses
important information, while the Unless C part acts only as a switch
and changes the polarity of D to ~D.
This paper presents a classification algorithm based on evolutionary
approach that discovers comprehensible rules with exceptions in the
form of CPRs.
The proposed approach has flexible chromosome encoding, where
each chromosome corresponds to a CPR. Appropriate genetic
operators are suggested and a fitness function is proposed that
incorporates the basic constraints on CPRs. Experimental results are
presented to demonstrate the performance of the proposed algorithm.
Abstract: Principle component analysis is often combined with
the state-of-art classification algorithms to recognize human faces.
However, principle component analysis can only capture these
features contributing to the global characteristics of data because it is a
global feature selection algorithm. It misses those features
contributing to the local characteristics of data because each principal
component only contains some levels of global characteristics of data.
In this study, we present a novel face recognition approach using
non-negative principal component analysis which is added with the
constraint of non-negative to improve data locality and contribute to
elucidating latent data structures. Experiments are performed on the
Cambridge ORL face database. We demonstrate the strong
performances of the algorithm in recognizing human faces in
comparison with PCA and NREMF approaches.
Abstract: This paper and its companion (Part 2) deal with
modeling and optimization of two NP-hard problems in production
planning of flexible manufacturing system (FMS), part type selection
problem and loading problem. The part type selection problem and
the loading problem are strongly related and heavily influence the
system-s efficiency and productivity. The complexity of the problems
is harder when flexibilities of operations such as the possibility of
operation processed on alternative machines with alternative tools are
considered. These problems have been modeled and solved
simultaneously by using real coded genetic algorithms (RCGA)
which uses an array of real numbers as chromosome representation.
These real numbers can be converted into part type sequence and
machines that are used to process the part types. This first part of the
papers focuses on the modeling of the problems and discussing how
the novel chromosome representation can be applied to solve the
problems. The second part will discuss the effectiveness of the
RCGA to solve various test bed problems.
Abstract: Radio propagation from point-to-point is affected by
the physical channel in many ways. A signal arriving at a destination
travels through a number of different paths which are referred to as
multi-paths. Research in this area of wireless communications has
progressed well over the years with the research taking different
angles of focus. By this is meant that some researchers focus on
ways of reducing or eluding Multipath effects whilst others focus on
ways of mitigating the effects of Multipath through compensation
schemes. Baseband processing is seen as one field of signal
processing that is cardinal to the advancement of software defined
radio technology. This has led to wide research into the carrying out
certain algorithms at baseband. This paper considers compensating
for Multipath for Frequency Modulated signals. The compensation
process is carried out at Radio frequency (RF) and at Quadrature
baseband (QBB) and the results are compared. Simulations are
carried out using MatLab so as to show the benefits of working at
lower QBB frequencies than at RF.
Abstract: The paper presents an optimization study based on
genetic algorithms (GA-s) for a radio-frequency applicator used in
heating dielectric band products. The weakly coupled electro-thermal
problem is analyzed using 2D-FEM. The design variables in the
optimization process are: the voltage of a supplementary “guard"
electrode and six geometric parameters of the applicator. Two
objective functions are used: temperature uniformity and total active
power absorbed by the dielectric. Both mono-objective and multiobjective
formulations are implemented in GA optimization.
Abstract: Heart disease (HD) is a major cause of morbidity and mortality in the modern society. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. All doctors are unfortunately not equally skilled in every sub specialty and they are in many places a scarce resource. A system for automated medical diagnosis would enhance medical care and reduce costs. In this paper, a new approach based on coactive neuro-fuzzy inference system (CANFIS) was presented for prediction of heart disease. The proposed CANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach which is then integrated with genetic algorithm to diagnose the presence of the disease. The performances of the CANFIS model were evaluated in terms of training performances and classification accuracies and the results showed that the proposed CANFIS model has great potential in predicting the heart disease.
Abstract: Image restoration involves elimination of noise. Filtering techniques were adopted so far to restore images since last five decades. In this paper, we consider the problem of image restoration degraded by a blur function and corrupted by random noise. A method for reducing additive noise in images by explicit analysis of local image statistics is introduced and compared to other noise reduction methods. The proposed method, which makes use of an a priori noise model, has been evaluated on various types of images. Bayesian based algorithms and technique of image processing have been described and substantiated with experimentation using MATLAB.
Abstract: Many factors affect the success of Machine Learning
(ML) on a given task. The representation and quality of the instance
data is first and foremost. If there is much irrelevant and redundant
information present or noisy and unreliable data, then knowledge
discovery during the training phase is more difficult. It is well known
that data preparation and filtering steps take considerable amount of
processing time in ML problems. Data pre-processing includes data
cleaning, normalization, transformation, feature extraction and
selection, etc. The product of data pre-processing is the final training
set. It would be nice if a single sequence of data pre-processing
algorithms had the best performance for each data set but this is not
happened. Thus, we present the most well know algorithms for each
step of data pre-processing so that one achieves the best performance
for their data set.
Abstract: Wireless Sensor Network (WSN) comprises of sensor
nodes which are designed to sense the environment, transmit sensed
data back to the base station via multi-hop routing to reconstruct
physical phenomena. Since physical phenomena exists significant
overlaps between temporal redundancy and spatial redundancy, it is
necessary to use Redundancy Suppression Algorithms (RSA) for sensor
node to lower energy consumption by reducing the transmission
of redundancy. A conventional algorithm of RSAs is threshold-based
RSA, which sets threshold to suppress redundant data. Although
many temporal and spatial RSAs are proposed, temporal-spatial RSA
are seldom to be proposed because it is difficult to determine when
to utilize temporal or spatial RSAs. In this paper, we proposed a
novel temporal-spatial redundancy suppression algorithm, Codebookbase
Redundancy Suppression Mechanism (CRSM). CRSM adopts
vector quantization to generate a codebook, which is easily used to
implement temporal-spatial RSA. CRSM not only achieves power
saving and reliability for WSN, but also provides the predictability
of network lifetime. Simulation result shows that the network lifetime
of CRSM outperforms at least 23% of that of other RSAs.