Abstract: An electrocardiogram (ECG) feature extraction system
based on the calculation of the complex resonance frequency
employing Prony-s method is developed. Prony-s method is applied
on five different classes of ECG signals- arrhythmia as a finite sum
of exponentials depending on the signal-s poles and the resonant
complex frequencies. Those poles and resonance frequencies of the
ECG signals- arrhythmia are evaluated for a large number of each
arrhythmia. The ECG signals of lead II (ML II) were taken from
MIT-BIH database for five different types. These are the ventricular
couplet (VC), ventricular tachycardia (VT), ventricular bigeminy
(VB), and ventricular fibrillation (VF) and the normal (NR). This
novel method can be extended to any number of arrhythmias.
Different classification techniques were tried using neural networks
(NN), K nearest neighbor (KNN), linear discriminant analysis (LDA)
and multi-class support vector machine (MC-SVM).
Abstract: The speech signal conveys information about the
identity of the speaker. The area of speaker identification is
concerned with extracting the identity of the person speaking the
utterance. As speech interaction with computers becomes more
pervasive in activities such as the telephone, financial transactions
and information retrieval from speech databases, the utility of
automatically identifying a speaker is based solely on vocal
characteristic. This paper emphasizes on text dependent speaker
identification, which deals with detecting a particular speaker from a
known population. The system prompts the user to provide speech
utterance. System identifies the user by comparing the codebook of
speech utterance with those of the stored in the database and lists,
which contain the most likely speakers, could have given that speech
utterance. The speech signal is recorded for N speakers further the
features are extracted. Feature extraction is done by means of LPC
coefficients, calculating AMDF, and DFT. The neural network is
trained by applying these features as input parameters. The features
are stored in templates for further comparison. The features for the
speaker who has to be identified are extracted and compared with the
stored templates using Back Propogation Algorithm. Here, the
trained network corresponds to the output; the input is the extracted
features of the speaker to be identified. The network does the weight
adjustment and the best match is found to identify the speaker. The
number of epochs required to get the target decides the network
performance.
Abstract: Due to the ever growing amount of publications about
protein-protein interactions, information extraction from text is
increasingly recognized as one of crucial technologies in
bioinformatics. This paper presents a Protein Interaction Extraction
System using a Link Grammar Parser from biomedical abstracts
(PIELG). PIELG uses linkage given by the Link Grammar Parser to
start a case based analysis of contents of various syntactic roles as
well as their linguistically significant and meaningful combinations.
The system uses phrasal-prepositional verbs patterns to overcome
preposition combinations problems. The recall and precision are
74.4% and 62.65%, respectively. Experimental evaluations with two
other state-of-the-art extraction systems indicate that PIELG system
achieves better performance. For further evaluation, the system is
augmented with a graphical package (Cytoscape) for extracting
protein interaction information from sequence databases. The result
shows that the performance is remarkably promising.
Abstract: In this paper a new approach to face recognition is
presented that achieves double dimension reduction, making the
system computationally efficient with better recognition results and
out perform common DCT technique of face recognition. In pattern
recognition techniques, discriminative information of image
increases with increase in resolution to a certain extent, consequently
face recognition results change with change in face image resolution
and provide optimal results when arriving at a certain resolution
level. In the proposed model of face recognition, initially image
decimation algorithm is applied on face image for dimension
reduction to a certain resolution level which provides best
recognition results. Due to increased computational speed and feature
extraction potential of Discrete Cosine Transform (DCT), it is
applied on face image. A subset of coefficients of DCT from low to
mid frequencies that represent the face adequately and provides best
recognition results is retained. A tradeoff between decimation factor,
number of DCT coefficients retained and recognition rate with
minimum computation is obtained. Preprocessing of the image is
carried out to increase its robustness against variations in poses and
illumination level. This new model has been tested on different
databases which include ORL , Yale and EME color database.
Abstract: Understanding the cell's large-scale organization is an
interesting task in computational biology. Thus, protein-protein
interactions can reveal important organization and function of the
cell. Here, we investigated the correspondence between protein
interactions and function for the yeast. We obtained the correlations
among the set of proteins. Then these correlations are clustered using
both the hierarchical and biclustering methods. The detailed analyses
of proteins in each cluster were carried out by making use of their
functional annotations. As a result, we found that some functional
classes appear together in almost all biclusters. On the other hand, in
hierarchical clustering, the dominancy of one functional class is
observed. In brief, from interaction data to function, some correlated
results are noticed about the relationship between interaction and
function which might give clues about the organization of the
proteins.
Abstract: In image processing and visualization, comparing two
bitmapped images needs to be compared from their pixels by matching
pixel-by-pixel. Consequently, it takes a lot of computational time
while the comparison of two vector-based images is significantly
faster. Sometimes these raster graphics images can be approximately
converted into the vector-based images by various techniques. After
conversion, the problem of comparing two raster graphics images
can be reduced to the problem of comparing vector graphics images.
Hence, the problem of comparing pixel-by-pixel can be reduced to
the problem of polynomial comparisons. In computer aided geometric
design (CAGD), the vector graphics images are the composition of
curves and surfaces. Curves are defined by a sequence of control
points and their polynomials. In this paper, the control points will be
considerably used to compare curves. The same curves after relocated
or rotated are treated to be equivalent while two curves after different
scaled are considered to be similar curves. This paper proposed an
algorithm for comparing the polynomial curves by using the control
points for equivalence and similarity. In addition, the geometric
object-oriented database used to keep the curve information has also
been defined in XML format for further used in curve comparisons.
Abstract: In this paper, we present a new and effective image indexing technique that extracts features directly from DCT domain. Our proposed approach is an object-based image indexing. For each block of size 8*8 in DCT domain a feature vector is extracted. Then, feature vectors of all blocks of image using a k-means algorithm is clustered into groups. Each cluster represents a special object of the image. Then we select some clusters that have largest members after clustering. The centroids of the selected clusters are taken as image feature vectors and indexed into the database. Also, we propose an approach for using of proposed image indexing method in automatic image classification. Experimental results on a database of 800 images from 8 semantic groups in automatic image classification are reported.
Abstract: The quest of providing more secure identification
system has led to a rise in developing biometric systems. Dorsal
hand vein pattern is an emerging biometric which has attracted the
attention of many researchers, of late. Different approaches have
been used to extract the vein pattern and match them. In this work,
Principle Component Analysis (PCA) which is a method that has
been successfully applied on human faces and hand geometry is
applied on the dorsal hand vein pattern. PCA has been used to obtain
eigenveins which is a low dimensional representation of vein pattern
features. Low cost CCD cameras were used to obtain the vein
images. The extraction of the vein pattern was obtained by applying
morphology. We have applied noise reduction filters to enhance the
vein patterns. The system has been successfully tested on a database
of 200 images using a threshold value of 0.9. The results obtained are
encouraging.
Abstract: Business rules and data warehouse are concepts and
technologies that impact a wide variety of organizational tasks. In
general, each area has evolved independently, impacting application
development and decision-making. Generating knowledge from data
warehouse is a complex process. This paper outlines an approach to
ease import of information and knowledge from a data warehouse
star schema through an inference class of business rules. The paper
utilizes the Oracle database for illustrating the working of the
concepts. The star schema structure and the business rules are stored
within a relational database. The approach is explained through a
prototype in Oracle-s PL/SQL Server Pages.
Abstract: The main mission of Ezilla is to provide a friendly
interface to access the virtual machine and quickly deploy the high
performance computing environment. Ezilla has been developed by
Pervasive Computing Team at National Center for High-performance
Computing (NCHC). Ezilla integrates the Cloud middleware,
virtualization technology, and Web-based Operating System (WebOS)
to form a virtual computer in distributed computing environment. In
order to upgrade the dataset and speedup, we proposed the sensor
observation system to deal with a huge amount of data in the
Cassandra database. The sensor observation system is based on the
Ezilla to store sensor raw data into distributed database. We adopt the
Ezilla Cloud service to create virtual machines and login into virtual
machine to deploy the sensor observation system. Integrating the
sensor observation system with Ezilla is to quickly deploy experiment
environment and access a huge amount of data with distributed
database that support the replication mechanism to protect the data
security.
Abstract: The draw solute separation process in Forward
Osmosis desalination was simulated in Aspen Plus chemical process
modeling software, to estimate the energy consumption and compare
it with other desalination processes, mainly the Reverse Osmosis
process which is currently most prevalent. The electrolytic chemistry
for the system was retrieved using the Elec – NRTL property method
in the Aspen Plus database. Electrical equivalent of energy required
in the Forward Osmosis desalination technique was estimated and
compared with the prevalent desalination techniques.
Abstract: After allowing direct flights from Mainland China to
Taiwan, Chinese tourists increased according to Tourism
Bureaustatistics. There are from 0.19 to 2 million tourists from 2008 to
2011. Mainland China has become the main source of Taiwan
developing tourism industry. Taiwanese government should know
more about comments from Chinese tourists to Taiwan in order
toproperly market Taiwan tourism and enhance the overall quality of
tourism. In order to understand Chinese visitors’ comments, this study
adopts content analysis to analyze electronic word-of-mouth on Web.
This study collects 375 blog articles of Chinese tourists from
Ctrip.com as a database during 2009 to 2011. Through the qualitative
data analysis the traveling destination imagesis divided into seven
dimensions, such as senic spots, shopping, food and beverages,
accommodations, transportation, festivals and recreation activities.
Finally, this study proposes some practical managerial implication to
know both positive and negative images of the seven dimensions from
Chinese tourists, providing marketing strategies and suggestions to
traveling agency industry.
Abstract: This study reports an empirical investigation of
fatigue crack initiation and propagation in 2024 T351 aluminium
alloy using constant amplitude loading. In initiation stage, local
strain approach at the notch was used and in stable propagation stage
NASGRO model was applied.
In this investigation, the flat plate of double through crack at hole
is used. Based on experimental results (AFGROW Database), effect
of stress ratio, R, is highlights on fatigue initiation life (FIL) and
fatigue crack growth rate (FCGR). The increasing of dimension of
hole characterizing the notch effect decrease the fatigue life.
Abstract: Current tools for data migration between documentoriented
and relational databases have several disadvantages. We
propose a new approach for data migration between documentoriented
and relational databases. During data migration the relational
schema of the target (relational database) is automatically created
from collection of XML documents. Proposed approach is verified on
data migration between document-oriented database IBM Lotus/
Notes Domino and relational database implemented in relational
database management system (RDBMS) MySQL.
Abstract: This paper proposes view-point insensitive human
pose recognition system using neural network. Recognition system
consists of silhouette image capturing module, data driven database,
and neural network. The advantages of our system are first, it is
possible to capture multiple view-point silhouette images of 3D human
model automatically. This automatic capture module is helpful to
reduce time consuming task of database construction. Second, we
develop huge feature database to offer view-point insensitivity at pose
recognition. Third, we use neural network to recognize human pose
from multiple-view because every pose from each model have similar
feature patterns, even though each model has different appearance and
view-point. To construct database, we need to create 3D human model
using 3D manipulate tools. Contour shape is used to convert silhouette
image to feature vector of 12 degree. This extraction task is processed
semi-automatically, which benefits in that capturing images and
converting to silhouette images from the real capturing environment is
needless. We demonstrate the effectiveness of our approach with
experiments on virtual environment.
Abstract: Finger spelling is an art of communicating by signs
made with fingers, and has been introduced into sign language to serve
as a bridge between the sign language and the verbal language.
Previous approaches to finger spelling recognition are classified into
two categories: glove-based and vision-based approaches. The
glove-based approach is simpler and more accurate recognizing work
of hand posture than vision-based, yet the interfaces require the user to
wear a cumbersome and carry a load of cables that connected the
device to a computer. In contrast, the vision-based approaches provide
an attractive alternative to the cumbersome interface, and promise
more natural and unobtrusive human-computer interaction. The
vision-based approaches generally consist of two steps: hand
extraction and recognition, and two steps are processed independently.
This paper proposes real-time vision-based Korean finger spelling
recognition system by integrating hand extraction into recognition.
First, we tentatively detect a hand region using CAMShift algorithm.
Then fill factor and aspect ratio estimated by width and height
estimated by CAMShift are used to choose candidate from database,
which can reduce the number of matching in recognition step. To
recognize the finger spelling, we use DTW(dynamic time warping)
based on modified chain codes, to be robust to scale and orientation
variations. In this procedure, since accurate hand regions, without
holes and noises, should be extracted to improve the precision, we use
graph cuts algorithm that globally minimize the energy function
elegantly expressed by Markov random fields (MRFs). In the
experiments, the computational times are less than 130ms, and the
times are not related to the number of templates of finger spellings in
database, as candidate templates are selected in extraction step.
Abstract: Non-Destructive evaluation of in-service power
transformer condition is necessary for avoiding catastrophic failures.
Dissolved Gas Analysis (DGA) is one of the important methods.
Traditional, statistical and intelligent DGA approaches have been
adopted for accurate classification of incipient fault sources.
Unfortunately, there are not often enough faulty patterns required for
sufficient training of intelligent systems. By bootstrapping the
shortcoming is expected to be alleviated and algorithms with better
classification success rates to be obtained. In this paper the
performance of an artificial neural network, K-Nearest Neighbour
and support vector machine methods using bootstrapped data are
detailed and shown that while the success rate of the ANN algorithms
improves remarkably, the outcome of the others do not benefit so
much from the provided enlarged data space. For assessment, two
databases are employed: IEC TC10 and a dataset collected from
reported data in papers. High average test success rate well exhibits
the remarkable outcome.
Abstract: The scientific achievements coming from molecular
biology depend greatly on the capability of computational
applications to analyze the laboratorial results. A comprehensive
analysis of an experiment requires typically the simultaneous study
of the obtained dataset with data that is available in several distinct
public databases. Nevertheless, developing a centralized access to
these distributed databases rises up a set of challenges such as: what
is the best integration strategy, how to solve nomenclature clashes,
how to solve database overlapping data and how to deal with huge
datasets. In this paper we present GeNS, a system that uses a simple and yet innovative approach to address several biological data integration issues. Compared with existing systems, the main
advantages of GeNS are related to its maintenance simplicity and to its coverage and scalability, in terms of number of supported
databases and data types. To support our claims we present the current use of GeNS in two concrete applications. GeNS currently contains more than 140 million of biological relations and it can be
publicly downloaded or remotely access through SOAP web services.
Abstract: In this paper, we present a new method for
incorporating global shift invariance in support vector machines.
Unlike other approaches which incorporate a feature extraction stage,
we first scale the image and then classify it by using the modified
support vector machines classifier. Shift invariance is achieved by
replacing dot products between patterns used by the SVM classifier
with the maximum cross-correlation value between them. Unlike the
normal approach, in which the patterns are treated as vectors, in our
approach the patterns are treated as matrices (or images). Crosscorrelation
is computed by using computationally efficient
techniques such as the fast Fourier transform. The method has been
tested on the ORL face database. The tests indicate that this method
can improve the recognition rate of an SVM classifier.
Abstract: Medical images require special safety and confidentiality because critical judgment is done on the information provided by medical images. Transmission of medical image via internet or mobile phones demands strong security and copyright protection in telemedicine applications. Here, highly secured and robust watermarking technique is proposed for transmission of image data via internet and mobile phones. The Region of Interest (ROI) and Non Region of Interest (RONI) of medical image are separated. Only RONI is used for watermark embedding. This technique results in exact recovery of watermark with standard medical database images of size 512x512, giving 'correlation factor' equals to 1. The correlation factor for different attacks like noise addition, filtering, rotation and compression ranges from 0.90 to 0.95. The PSNR with weighting factor 0.02 is up to 48.53 dBs. The presented scheme is non blind and embeds hospital logo of 64x64 size.