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: 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: 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: The visualization of geographic information on mobile devices has become popular as the widespread use of mobile Internet. The mobility of these devices brings about much convenience to people-s life. By the add-on location-based services of the devices, people can have an access to timely information relevant to their tasks. However, visual analysis of geographic data on mobile devices presents several challenges due to the small display and restricted computing resources. These limitations on the screen size and resources may impair the usability aspects of the visualization applications. In this paper, a variable-scale visualization method is proposed to handle the challenge of small mobile display. By merging multiple scales of information into a single image, the viewer is able to focus on the interesting region, while having a good grasp of the surrounding context. This is essentially visualizing the map through a fisheye lens. However, the fisheye lens induces undesirable geometric distortion in the peripheral, which renders the information meaningless. The proposed solution is to apply map generalization that removes excessive information around the peripheral and an automatic smoothing process to correct the distortion while keeping the local topology consistent. The proposed method is applied on both artificial and real geographical data for evaluation.
Abstract: In the study the influence of the physical-chemical properties of a liquid, the width of a channel gap and the superficial liquid and gas velocities on the patterns formed during two phase flows in vertical, narrow mini-channels was investigated. The research was performed in the channels of rectangular cross-section and of dimensions: 15 x 0.65 mm and 7.5 x 0.73 mm. The experimental data were compared with the published criteria of the transitions between the patterns of two-phase flows.
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: 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: The study is aimed to test causal relationship between
growth and unemployment, using time series data for Pakistan from
1972 to 2006. Growth is considered to be a pathway to decrease the
level of unemployment. Unemployment is a social and political
issue. It is a phenomenon where human resources are wasted leading
to deacceleration in growth. Johanson Cointegration shows that there
is long run relationship between growth and unemployment. For
short run dynamics and causality, the study utilizes Vector Error
Correction Model (VECM). The results of VECM indicate that there
is short and long run causal relation between growth and
unemployment including capital, labor and human capital as
explanatory variables.
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: 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: Performing High Voltage (HV) tasks with a multi craft
work force create a special set of safety circumstances. This paper
aims to present vital information relating to when it is acceptable to
use a single or a two-layer soil structure. Also it discusses the
implication of the high voltage infrastructure on the earth grid and the
safety of this implication under a single or a two-layer soil structure.
A multiple case study is investigated to show the importance of using
the right soil resistivity structure during the earthing system design.
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: Time series models have been used to make predictions of academic enrollments, weather, road accident, casualties and stock prices, etc. Based on the concepts of quartile regression models, we have developed a simple time variant quantile based fuzzy time series forecasting method. The proposed method bases the forecast using prediction of future trend of the data. In place of actual quantiles of the data at each point, we have converted the statistical concept into fuzzy concept by using fuzzy quantiles using fuzzy membership function ensemble. We have given a fuzzy metric to use the trend forecast and calculate the future value. The proposed model is applied for TAIFEX forecasting. It is shown that proposed method work best as compared to other models when compared with respect to model complexity and forecasting accuracy.
Abstract: Precise frequency estimation methods for pulseshaped echoes are a prerequisite to determine the relative velocity between sensor and reflector. Signal frequencies are analysed using three different methods: Fourier Transform, Chirp ZTransform and the MUSIC algorithm. Simulations of echoes are performed varying both the noise level and the number of reflecting points. The superposition of echoes with a random initial phase is found to influence the precision of frequency estimation severely for FFT and MUSIC. The standard deviation of the frequency using FFT is larger than for MUSIC. However, MUSIC is more noise-sensitive. The distorting effect of superpositions is less pronounced in experimental data.
Abstract: To reduce accidents in the industry, WSNs(Wireless Sensor
networks)- sensor data is used. WSNs- sensor data has the persistence and
continuity. therefore, we design and exploit the buffer management system that
has the persistence and continuity to avoid and delivery data conflicts. To
develop modules, we use the multi buffers and design the buffer management
modules that transfer sensor data through the context-aware methods.
Abstract: Currently, many types of no-reversible compressed
sound source, represented by MP3 (MPEG Audio Layer-3) are
popular in the world and they are widely used to make the music file
size smaller. The sound data created in this way has less information as
compared to pre-compressed data. The objective of this study is by
analyzing EEG to determine if people can recognize such difference as
differences in sound. A measurement system that can measure and
analyze EEG when a subject listens to music were experimentally
developed. And ten subjects were studied with this system. In this
experiment, a WAVE formatted music data and a MP3 compressed
music data that is made from the WAVE formatted data were
prepared. Each subject was made to hear these music sources at the
same volume. From the results of this experiment, clear differences
were confirmed between two wound sources.
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: 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%.