Abstract: This paper focuses on testing database of existing
information system. At the beginning we describe the basic problems
of implemented databases, such as data redundancy, poor design of
database logical structure or inappropriate data types in columns of
database tables. These problems are often the result of incorrect
understanding of the primary requirements for a database of an
information system. Then we propose an algorithm to compare the
conceptual model created from vague requirements for a database
with a conceptual model reconstructed from implemented database.
An algorithm also suggests steps leading to optimization of
implemented database. The proposed algorithm is verified by an
implemented prototype. The paper also describes a fuzzy system
which works with the vague requirements for a database of an
information system, procedure for creating conceptual from vague
requirements and an algorithm for reconstructing a conceptual model
from implemented database.
Abstract: Electrocardiogram (ECG) segmentation is necessary to help reduce the time consuming task of manually annotating ECG's. Several algorithms have been developed to segment the ECG automatically. We first review several of such methods, and then present a new single lead segmentation method based on Adaptive piecewise constant approximation (APCA) and Piecewise derivative dynamic time warping (PDDTW). The results are tested on the QT database. We compared our results to Laguna's two lead method. Our proposed approach has a comparable mean error, but yields a slightly higher standard deviation than Laguna's method.
Abstract: This paper presents data annotation models at five levels of granularity (database, relation, column, tuple, and cell) of relational data to address the problem of unsuitability of most relational databases to express annotations. These models do not require any structural and schematic changes to the underlying database. These models are also flexible, extensible, customizable, database-neutral, and platform-independent. This paper also presents an SQL-like query language, named Annotation Query Language (AnQL), to query annotation documents. AnQL is simple to understand and exploits the already-existent wide knowledge and skill set of SQL.
Abstract: Organization of video databases is becoming difficult
task as the amount of video content increases. Video classification
based on the content of videos can significantly increase the speed of
tasks such as browsing and searching for a particular video in a
database. In this paper, a content-based videos classification system
for the classes indoor and outdoor is presented. The system is
intended to be used on a mobile platform with modest resources. The
algorithm makes use of the temporal redundancy in videos, which
allows using an uncomplicated classification model while still
achieving reasonable accuracy. The training and evaluation was done
on a video database of 443 videos downloaded from a video sharing
service. A total accuracy of 87.36% was achieved.
Abstract: Identity verification of authentic persons by their multiview faces is a real valued problem in machine vision. Multiview faces are having difficulties due to non-linear representation in the feature space. This paper illustrates the usability of the generalization of LDA in the form of canonical covariate for face recognition to multiview faces. In the proposed work, the Gabor filter bank is used to extract facial features that characterized by spatial frequency, spatial locality and orientation. Gabor face representation captures substantial amount of variations of the face instances that often occurs due to illumination, pose and facial expression changes. Convolution of Gabor filter bank to face images of rotated profile views produce Gabor faces with high dimensional features vectors. Canonical covariate is then used to Gabor faces to reduce the high dimensional feature spaces into low dimensional subspaces. Finally, support vector machines are trained with canonical sub-spaces that contain reduced set of features and perform recognition task. The proposed system is evaluated with UMIST face database. The experiment results demonstrate the efficiency and robustness of the proposed system with high recognition rates.
Abstract: On-line (near infrared) spectroscopy is widely used to support the operation of complex process systems. Information extracted from spectral database can be used to estimate unmeasured product properties and monitor the operation of the process. These techniques are based on looking for similar spectra by nearest neighborhood algorithms and distance based searching methods. Search for nearest neighbors in the spectral space is an NP-hard problem, the computational complexity increases by the number of points in the discrete spectrum and the number of samples in the database. To reduce the calculation time some kind of indexing could be used. The main idea presented in this paper is to combine indexing and visualization techniques to reduce the computational requirement of estimation algorithms by providing a two dimensional indexing that can also be used to visualize the structure of the spectral database. This 2D visualization of spectral database does not only support application of distance and similarity based techniques but enables the utilization of advanced clustering and prediction algorithms based on the Delaunay tessellation of the mapped spectral space. This means the prediction has not to use the high dimension space but can be based on the mapped space too. The results illustrate that the proposed method is able to segment (cluster) spectral databases and detect outliers that are not suitable for instance based learning algorithms.
Abstract: In this paper, we present an improved fast and robust
search algorithm for copy detection using histogram-based features for
short MPEG video clips from large video database. There are two
types of histogram features used to generate more robust features. The
first one is based on the adjacent pixel intensity difference quantization
(APIDQ) algorithm, which had been reliably applied to human face
recognition previously. An APIDQ histogram is utilized as the feature
vector of the frame image. Another one is ordinal histogram feature
which is robust to color distortion. Furthermore, by Combining with a
temporal division method, the spatial and temporal features of the
video sequence are integrated to realize fast and robust video search
for copy detection. Experimental results show the proposed algorithm
can detect the similar video clip more accurately and robust than
conventional fast video search algorithm.
Abstract: In this paper, we propose an improved fast search
algorithm using combined histogram features and temporal division
method for short MPEG video clips from large video database. There
are two types of histogram features used to generate more robust
features. The first one is based on the adjacent pixel intensity
difference quantization (APIDQ) algorithm, which had been reliably
applied to human face recognition previously. An APIDQ histogram is
utilized as the feature vector of the frame image. Another one is
ordinal feature which is robust to color distortion. Combined with
active search [4], a temporal pruning algorithm, fast and robust video
search can be realized. The proposed search algorithm has been
evaluated by 6 hours of video to search for given 200 MPEG video
clips which each length is 30 seconds. Experimental results show the
proposed algorithm can detect the similar video clip in merely 120ms,
and Equal Error Rate (ERR) of 1% is achieved, which is more
accurately and robust than conventional fast video search algorithm.
Abstract: The purpose is to study the model and characteristic of
participation of the suitable community to lead to develop permanent
water marketing in Bang Noi Floating Market, Bangkonti District,
Samutsongkhram Province. A total of 342 survey questionnaire was
administered to potential respondents. The researchers interviewed
the leader of the community. Appreciation Influence Control (AIC)
was used to talk with 20 villagers on arena. The findings revealed
that overall, most people had the middle level of the participation in
developing the durable Bang Noi Floating Market, Bangkonti,
Samutsongkhram Province and in aspects of gaining benefits from
developing it with atmosphere and a beautiful view for tourism. For
example, the landscape is beautiful with public utilities. The
participation in preserving and developing Bang Noi Floating Market
remains in the former way of life. The basic factor of person affects
to the participation of people such as age, level of education, career,
and income per month. Most participants are the original hosts that
have houses and shops located in the marketing and neighbor. These
people involve with the benefits and have the power to make a water
marketing strategy, the major role to set the information database. It
also found that the leader and the villagers play the important role in
setting a five-physical database. Data include level of information
such as position of village, territory of village, road, river, and
premises. Information of culture consists of a two-level of
information, interesting point, and Itinerary. The information occurs
from presenting and practicing by the leader and villagers in the
community.All of phases are presented for listening and investigating
database together in both the leader and villagers in the process of
participation.
Abstract: Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects (e.g. individuals, quadrats, species etc). While Kohonen's Self-Organizing Feature Map (SOFM) or Self-Organizing Map (SOM) networks have been successfully applied as a classification tool to various problem domains, including speech recognition, image data compression, image or character recognition, robot control and medical diagnosis, its potential as a robust substitute for clustering analysis remains relatively unresearched. SOM networks combine competitive learning with dimensionality reduction by smoothing the clusters with respect to an a priori grid and provide a powerful tool for data visualization. In this paper, SOM is used for creating a toroidal mapping of two-dimensional lattice to perform cluster analysis on results of a chemical analysis of wines produced in the same region in Italy but derived from three different cultivators, referred to as the “wine recognition data" located in the University of California-Irvine database. The results are encouraging and it is believed that SOM would make an appealing and powerful decision-support system tool for clustering tasks and for data visualization.
Abstract: In this paper we present an approach for 3D face
recognition based on extracting principal components of range
images by utilizing modified PCA methods namely 2DPCA and
bidirectional 2DPCA also known as (2D) 2 PCA.A preprocessing
stage was implemented on the images to smooth them using median
and Gaussian filtering. In the normalization stage we locate the nose
tip to lay it at the center of images then crop each image to a standard
size of 100*100. In the face recognition stage we extract the principal
component of each image using both 2DPCA and (2D) 2 PCA.
Finally, we use Euclidean distance to measure the minimum distance
between a given test image to the training images in the database. We
also compare the result of using both methods. The best result
achieved by experiments on a public face database shows that 83.3
percent is the rate of face recognition for a random facial expression.
Abstract: There are so many databases of various fields of life sciences available online. To find well-used databases, a survey to measure life science database citation frequency in scientific literatures is done. The survey is done by measuring how many scientific literatures which are available on PubMed Central archive cited a specific life science database. This paper presents and discusses the results of the survey.
Abstract: In this article we explore the application of a formal
proof system to verification problems in cryptography. Cryptographic
properties concerning correctness or security of some cryptographic
algorithms are of great interest. Beside some basic lemmata, we
explore an implementation of a complex function that is used in
cryptography. More precisely, we describe formal properties of this
implementation that we computer prove. We describe formalized
probability distributions (σ-algebras, probability spaces and conditional
probabilities). These are given in the formal language of the
formal proof system Isabelle/HOL. Moreover, we computer prove
Bayes- Formula. Besides, we describe an application of the presented
formalized probability distributions to cryptography. Furthermore,
this article shows that computer proofs of complex cryptographic
functions are possible by presenting an implementation of the Miller-
Rabin primality test that admits formal verification. Our achievements
are a step towards computer verification of cryptographic primitives.
They describe a basis for computer verification in cryptography.
Computer verification can be applied to further problems in cryptographic
research, if the corresponding basic mathematical knowledge
is available in a database.
Abstract: Main Memory Database systems (MMDB) store their
data in main physical memory and provide very high-speed access.
Conventional database systems are optimized for the particular
characteristics of disk storage mechanisms. Memory resident
systems, on the other hand, use different optimizations to structure
and organize data, as well as to make it reliable.
This paper provides a brief overview on MMDBs and one of the
memory resident systems named FastDB and compares the
processing time of this system with a typical disc resident database
based on the results of the implementation of TPC benchmarks
environment on both.
Abstract: This manuscript presents, palmprint recognition by
combining different texture extraction approaches with high accuracy.
The Region of Interest (ROI) is decomposed into different frequencytime
sub-bands by wavelet transform up-to two levels and only the
approximate image of two levels is selected, which is known as
Approximate Image ROI (AIROI). This AIROI has information of
principal lines of the palm. The Competitive Index is used as the
features of the palmprint, in which six Gabor filters of different
orientations convolve with the palmprint image to extract the orientation
information from the image. The winner-take-all strategy
is used to select dominant orientation for each pixel, which is
known as Competitive Index. Further, PCA is applied to select highly
uncorrelated Competitive Index features, to reduce the dimensions of
the feature vector, and to project the features on Eigen space. The
similarity of two palmprints is measured by the Euclidean distance
metrics. The algorithm is tested on Hong Kong PolyU palmprint
database. Different AIROI of different wavelet filter families are also
tested with the Competitive Index and PCA. AIROI of db7 wavelet
filter achievs Equal Error Rate (EER) of 0.0152% and Genuine
Acceptance Rate (GAR) of 99.67% on the palm database of Hong
Kong PolyU.
Abstract: A Ground Control System (GCS), which controls Unmanned Aerial Vehicles (UAVs) and monitors their missionrelated data, is one of the major components of UAVs. In fact, some traditional GCSs were built on an expensive, complicated hardware infrastructure with workstations and PCs. In contrast, a GCS on a portable device – such as an Android phone or tablet – takes advantage of its light-weight hardware and the rich User Interface supported by the Android Operating System. We implemented that kind of GCS and called it Ground System Software (GSS) in this paper. In operation, our GSS communicates with UAVs or other GSS via TCP/IP connection to get mission-related data, visualizes it on the device-s screen, and saves the data in its own database. Our study showed that this kind of system will become a potential instrument in UAV-related systems and this kind of topic will appear in many research studies in the near future.
Abstract: This paper presents a method to detect multiple cracks
based on frequency information. When a structure is subjected to
dynamic or static loads, cracks may develop and the modal
frequencies of the cracked structure may change. To detect cracks in a
structure, we construct a high precision wavelet finite element (EF)
model of a certain structure using the B-spline wavelet on the interval
(BSWI). Cracks can be modeled by rotational springs and added to the
FE model. The crack detection database will be obtained by solving
that model. Then the crack locations and depths can be determined
based on the frequency information from the database. The
performance of the proposed method has been numerically verified by
a rotor example.
Abstract: This paper proposes a new approach to perform the
problem of real-time face detection. The proposed method combines
primitive Haar-Like feature and variance value to construct a new
feature, so-called Variance based Haar-Like feature. Face in image
can be represented with a small quantity of features using this
new feature. We used SVM instead of AdaBoost for training and
classification. We made a database containing 5,000 face samples
and 10,000 non-face samples extracted from real images for learning
purposed. The 5,000 face samples contain many images which have
many differences of light conditions. And experiments showed that
face detection system using Variance based Haar-Like feature and
SVM can be much more efficient than face detection system using
primitive Haar-Like feature and AdaBoost. We tested our method on
two Face databases and one Non-Face database. We have obtained
96.17% of correct detection rate on YaleB face database, which is
higher 4.21% than that of using primitive Haar-Like feature and
AdaBoost.
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: 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.