Abstract: In H.264/AVC video encoding, rate-distortion
optimization for mode selection plays a significant role to achieve
outstanding performance in compression efficiency and video quality.
However, this mode selection process also makes the encoding
process extremely complex, especially in the computation of the ratedistortion
cost function, which includes the computations of the sum
of squared difference (SSD) between the original and reconstructed
image blocks and context-based entropy coding of the block. In this
paper, a transform-domain rate-distortion optimization accelerator
based on fast SSD (FSSD) and VLC-based rate estimation algorithm
is proposed. This algorithm could significantly simplify the hardware
architecture for the rate-distortion cost computation with only
ignorable performance degradation. An efficient hardware structure
for implementing the proposed transform-domain rate-distortion
optimization accelerator is also proposed. Simulation results
demonstrated that the proposed algorithm reduces about 47% of total
encoding time with negligible degradation of coding performance.
The proposed method can be easily applied to many mobile video
application areas such as a digital camera and a DMB (Digital
Multimedia Broadcasting) phone.
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: 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: This paper introduces a low cost INS/GPS algorithm for
land vehicle navigation application. The data fusion process is done
with an extended Kalman filter in cascade configuration mode. In
order to perform numerical simulations, MATLAB software has been
developed. Loosely coupled configuration is considered. The results
obtained in this work demonstrate that a low-cost INS/GPS navigation
system is partially capable of meeting the performance requirements
for land vehicle navigation. The relative effectiveness of the kalman
filter implementation in integrated GPS/INS navigation algorithm is
highlighted. The paper also provides experimental results; field test
using a car is carried out.
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: 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: An experiment was conducted to examine the effect of the level of performance stabilization on the human adaptability to perceptual-motor perturbation in a complex coincident timing task. Three levels of performance stabilization were established operationally: pre-stabilization, stabilization, and super-stabilization groups. Each group practiced the task until reached its level of stabilization in a constant sequence of movements and under a constant time constraint before exposure to perturbation. The results clearly showed that performance stabilization is a pre-condition for adaptation. Moreover, variability before reaching stabilization is harmful to adaptation and persistent variability after stabilization is beneficial. Moreover, the behavior of variability is specific to each measure.
Abstract: In this experimental study, performance of a counter
flow Ranque-Hilsch vortex tube (RHVT) with threads cut on its inner
surface was investigated experimentally (pitch is 1 and 2 mm). The
inner diameter of the vortex tube used was D=9 mm and the ratio of
the tube’s length to diameter was L/D=12. The experimental system
was a thermodynamic open system. Flow was controlled by a valve
on the hot outlet side, where the valve was changed from a nearly
closed position to its nearly open position. Fraction of cold flow (ξ) =
0.1-0.9, was determined under 300 and 350 kPa pressurized air. All
experimental data were compared with each other, the maximum
heating performance of the RHVT system was found to be 38.2 oC
and the maximum cooling performance of the RHVT in this study
was found to be -30.9 oC at pitch 1 mm.
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: The usual correctness condition for a schedule of
concurrent database transactions is some form of serializability of
the transactions. For general forms, the problem of deciding whether
a schedule is serializable is NP-complete. In those cases other approaches
to proving correctness, using proof rules that allow the steps
of the proof of serializability to be guided manually, are desirable.
Such an approach is possible in the case of conflict serializability
which is proved algebraically by deriving serial schedules using
commutativity of non-conflicting operations. However, conflict serializability
can be an unnecessarily strong form of serializability restricting
concurrency and thereby reducing performance. In practice,
weaker, more general, forms of serializability for extended models of
transactions are used. Currently, there are no known methods using
proof rules for proving those general forms of serializability. In this
paper, we define serializability for an extended model of partitioned
transactions, which we show to be as expressive as serializability
for general partitioned transactions. An algebraic method for proving
general serializability is obtained by giving an initial-algebra specification
of serializable schedules of concurrent transactions in the
model. This demonstrates that it is possible to conduct algebraic
proofs of correctness of concurrent transactions in general cases.
Abstract: The frontal area in the brain is known to be involved in
behavioral judgement. Because a Kanji character can be discriminated
visually and linguistically from other characters, in Kanji character
discrimination, we hypothesized that frontal event-related potential
(ERP) waveforms reflect two discrimination processes in separate
time periods: one based on visual analysis and the other based
on lexcical access. To examine this hypothesis, we recorded ERPs
while performing a Kanji lexical decision task. In this task, either a
known Kanji character, an unknown Kanji character or a symbol was
presented and the subject had to report if the presented character was
a known Kanji character for the subject or not. The same response
was required for unknown Kanji trials and symbol trials. As a preprocessing
of signals, we examined the performance of a method
using independent component analysis for artifact rejection and found
it was effective. Therefore we used it. In the ERP results, there
were two time periods in which the frontal ERP wavefoms were
significantly different betweeen the unknown Kanji trials and the
symbol trials: around 170ms and around 300ms after stimulus onset.
This result supported our hypothesis. In addition, the result suggests
that Kanji character lexical access may be fully completed by around
260ms after stimulus onset.
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: Music Information Retrieval (MIR) and modern data mining techniques are applied to identify style markers in midi music for stylometric analysis and author attribution. Over 100 attributes are extracted from a library of 2830 songs then mined using supervised learning data mining techniques. Two attributes are identified that provide high informational gain. These attributes are then used as style markers to predict authorship. Using these style markers the authors are able to correctly distinguish songs written by the Beatles from those that were not with a precision and accuracy of over 98 per cent. The identification of these style markers as well as the architecture for this research provides a foundation for future research in musical stylometry.
Abstract: The conventional GA combined with a local search
algorithm, such as the 2-OPT, forms a hybrid genetic algorithm(HGA)
for the traveling salesman problem (TSP). However, the geometric
properties which are problem specific knowledge can be used to
improve the search process of the HGA. Some tour segments (edges)
of TSPs are fine while some maybe too long to appear in a short tour.
This knowledge could constrain GAs to work out with fine tour
segments without considering long tour segments as often.
Consequently, a new algorithm is proposed, called intelligent-OPT
hybrid genetic algorithm (IOHGA), to improve the GA and the 2-OPT
algorithm in order to reduce the search time for the optimal solution.
Based on the geometric properties, all the tour segments are assigned
2-level priorities to distinguish between good and bad genes. A
simulation study was conducted to evaluate the performance of the
IOHGA. The experimental results indicate that in general the IOHGA
could obtain near-optimal solutions with less time and better accuracy
than the hybrid genetic algorithm with simulated annealing algorithm
(HGA(SA)).
Abstract: Text Mining is around applying knowledge discovery
techniques to unstructured text is termed knowledge discovery in text
(KDT), or Text data mining or Text Mining. In decision tree
approach is most useful in classification problem. With this
technique, tree is constructed to model the classification process.
There are two basic steps in the technique: building the tree and
applying the tree to the database. This paper describes a proposed
C5.0 classifier that performs rulesets, cross validation and boosting
for original C5.0 in order to reduce the optimization of error ratio.
The feasibility and the benefits of the proposed approach are
demonstrated by means of medial data set like hypothyroid. It is
shown that, the performance of a classifier on the training cases from
which it was constructed gives a poor estimate by sampling or using a
separate test file, either way, the classifier is evaluated on cases that
were not used to build and evaluate the classifier are both are large. If
the cases in hypothyroid.data and hypothyroid.test were to be
shuffled and divided into a new 2772 case training set and a 1000
case test set, C5.0 might construct a different classifier with a lower
or higher error rate on the test cases. An important feature of see5 is
its ability to classifiers called rulesets. The ruleset has an error rate
0.5 % on the test cases. The standard errors of the means provide an
estimate of the variability of results. One way to get a more reliable
estimate of predictive is by f-fold –cross- validation. The error rate of
a classifier produced from all the cases is estimated as the ratio of the
total number of errors on the hold-out cases to the total number of
cases. The Boost option with x trials instructs See5 to construct up to
x classifiers in this manner. Trials over numerous datasets, large and
small, show that on average 10-classifier boosting reduces the error
rate for test cases by about 25%.
Abstract: Modularized design approach can facilitate the
modeling of complex systems and support behavior analysis and
simulation in an iterative and thus complex engineering process, by
using encapsulated submodels of components and of their interfaces.
Therefore it can improve the design efficiency and simplify the
solving complicated problem. Multi-drivers off-road vehicle is
comparatively complicated. Driving-line is an important core part to a
vehicle; it has a significant contribution to the performance of a
vehicle. Multi-driver off-road vehicles have complex driving-line, so
its performance is heavily dependent on the driving-line. A typical
off-road vehicle-s driving-line system consists of torque converter,
transmission, transfer case and driving-axles, which transfer the
power, generated by the engine and distribute it effectively to the
driving wheels according to the road condition. According to its main
function, this paper puts forward a modularized approach for
designing and evaluation of vehicle-s driving-line. It can be used to
effectively estimate the performance of driving-line during concept
design stage. Through appropriate analysis and assessment method, an
optimal design can be reached. This method has been applied to the
practical vehicle design, it can improve the design efficiency and is
convenient to assess and validate the performance of a vehicle,
especially of multi-drivers off-road vehicle.
Abstract: Chloride induced corrosion of steel reinforcement is
the main cause of deterioration of reinforced concrete marine
structures. This paper investigates the relative performance of
alternative repair options with respect to the deterioration of
reinforced concrete bridge elements in marine environments. Focus is
placed on the initiation phase of reinforcement corrosion. A
laboratory study is described which involved exposing concrete
samples to accelerated chloride-ion ingress. The study examined the
relative efficiencies of two repair methods, namely Ordinary Portland
Cement (OPC) concrete and a concrete which utilised Ground
Granulated Blastfurnace Cement (GGBS) as a partial cement
replacement. The mix designs and materials utilised were identical to
those implemented in the repair of a marine bridge on the South East
coast of Ireland in 2007. The results of this testing regime serve to
inform input variables employed in probabilistic modelling of
deterioration for subsequent reliability based analysis to compare the
relative performance of the studied repair options.