Abstract: The efficiency of an image watermarking technique depends on the preservation of visually significant information. This is attained by embedding the watermark transparently with the maximum possible strength. The current paper presents an approach for still image digital watermarking in which the watermark embedding process employs the wavelet transform and incorporates Human Visual System (HVS) characteristics. The sensitivity of a human observer to contrast with respect to spatial frequency is described by the Contrast Sensitivity Function (CSF). The strength of the watermark within the decomposition subbands, which occupy an interval on the spatial frequencies, is adjusted according to this sensitivity. Moreover, the watermark embedding process is carried over the subband coefficients that lie on edges where distortions are less noticeable. The experimental evaluation of the proposed method shows very good results in terms of robustness and transparency.
Abstract: Quantum cryptography offers a way of key agreement,
which is unbreakable by any external adversary. Authentication is
of crucial importance, as perfect secrecy is worthless if the identity
of the addressee cannot be ensured before sending important information.
Message authentication has been studied thoroughly, but no
approach seems to be able to explicitly counter meet-in-the-middle
impersonation attacks. The goal of this paper is the development of
an authentication scheme being resistant against active adversaries
controlling the communication channel. The scheme is built on top
of a key-establishment protocol and is unconditionally secure if built
upon quantum cryptographic key exchange. In general, the security
is the same as for the key-agreement protocol lying underneath.
Abstract: The objective of this paper is to investigate a new
approach based on the idea of pictograms for food portion size. This
approach adopts the model of the United States Pharmacopeia- Drug
Information (USP-DI). The representation of each food portion size
composed of three parts: frame, the connotation of dietary portion
sizes and layout. To investigate users- comprehension based on this
approach, two experiments were conducted, included 122 Taiwanese
people, 60 male and 62 female with ages between 16 and 64 (divided
into age groups of 16-30, 31-45 and 46-64). In Experiment 1, the mean
correcting rate of the understanding level of food items is 48.54%
(S.D.= 95.08) and the mean response time 2.89sec (S.D.=2.14). The
difference on the correct rates for different age groups is significant
(P*=0.00
Abstract: With the advance of multimedia and diagnostic
images technologies, the number of radiographic images is increasing
constantly. The medical field demands sophisticated systems for
search and retrieval of the produced multimedia document. This
paper presents an ongoing research that focuses on the semantic
content of radiographic image documents to facilitate semantic-based
radiographic image indexing and a retrieval system. The proposed
model would divide a radiographic image document, based on its
semantic content, and would be converted into a logical structure or
a semantic structure. The logical structure represents the overall
organization of information. The semantic structure, which is bound
to logical structure, is composed of semantic objects with
interrelationships in the various spaces in the radiographic image.
Abstract: The evolutionary tree is an important topic in bioinformation. In 2006, Chen and Lindsay proposed a new method to build the mixture tree from DNA sequences. Mixture tree is a new type evolutionary tree, and it has two additional information besides the information of ordinary evolutionary tree. One of the information is time parameter, and the other is the set of mutated sites. In 2008, Lin and Juan proposed an algorithm to compute the distance between two mixture trees. Their algorithm computes the distance with only considering the time parameter between two mixture trees. In this paper, we proposes a method to measure the similarity of two mixture trees with considering the set of mutated sites and develops two algorithm to compute the distance between two mixture trees. The time complexity of these two proposed algorithms are O(n2 × max{h(T1), h(T2)}) and O(n2), respectively
Abstract: Contractor selection in Saudi Arabia is very important due to the large construction boom and the contractor role to get over construction risks. The need for investigating contractor selection is due to the following reasons; large number of defaulted or failed projects (18%), large number of disputes attributed to contractor during the project execution stage (almost twofold), the extension of the General Agreement on Tariffs and Trade (GATT) into construction industry, and finally the few number of researches. The selection strategy is not perfect and considered as the reason behind irresponsible contractors. As a response, this research was conducted to review the contractor selection strategies as an integral part of a long advanced research to develop a good selection model. Many techniques can be used to form a selection strategy; multi criteria for optimizing decision, prequalification to discover contractor-s responsibility, bidding process for competition, third party guarantee to enhance the selection, and fuzzy techniques for ambiguities and incomplete information.
Abstract: Segmentation in ultrasound images is challenging due to the interference from speckle noise and fuzziness of boundaries. In this paper, a segmentation scheme using fuzzy c-means (FCM) clustering incorporating both intensity and texture information of images is proposed to extract breast lesions in ultrasound images. Firstly, the nonlinear structure tensor, which can facilitate to refine the edges detected by intensity, is used to extract speckle texture. And then, a spatial FCM clustering is applied on the image feature space for segmentation. In the experiments with simulated and clinical ultrasound images, the spatial FCM clustering with both intensity and texture information gets more accurate results than the conventional FCM or spatial FCM without texture information.
Abstract: A clustering is process to identify a homogeneous
groups of object called as cluster. Clustering is one interesting topic
on data mining. A group or class behaves similarly characteristics.
This paper discusses a robust clustering process for data images with
two reduction dimension approaches; i.e. the two dimensional
principal component analysis (2DPCA) and principal component
analysis (PCA). A standard approach to overcome this problem is
dimension reduction, which transforms a high-dimensional data into
a lower-dimensional space with limited loss of information. One of
the most common forms of dimensionality reduction is the principal
components analysis (PCA). The 2DPCA is often called a variant of
principal component (PCA), the image matrices were directly treated
as 2D matrices; they do not need to be transformed into a vector so
that the covariance matrix of image can be constructed directly using
the original image matrices. The decomposed classical covariance
matrix is very sensitive to outlying observations. The objective of
paper is to compare the performance of robust minimizing vector
variance (MVV) in the two dimensional projection PCA (2DPCA)
and the PCA for clustering on an arbitrary data image when outliers
are hiden in the data set. The simulation aspects of robustness and
the illustration of clustering images are discussed in the end of
paper
Abstract: Wireless sensor networks (WSN) consists of many
sensor nodes that are placed on unattended environments such as
military sites in order to collect important information.
Implementing a secure protocol that can prevent forwarding forged
data and modifying content of aggregated data and has low delay
and overhead of communication, computing and storage is very
important. This paper presents a new protocol for concealed data
aggregation (CDA). In this protocol, the network is divided to
virtual cells, nodes within each cell produce a shared key to send
and receive of concealed data with each other. Considering to data
aggregation in each cell is locally and implementing a secure
authentication mechanism, data aggregation delay is very low and
producing false data in the network by malicious nodes is not
possible. To evaluate the performance of our proposed protocol, we
have presented computational models that show the performance
and low overhead in our protocol.
Abstract: Recognizing human action from videos is an active
field of research in computer vision and pattern recognition. Human
activity recognition has many potential applications such as video
surveillance, human machine interaction, sport videos retrieval and
robot navigation. Actually, local descriptors and bag of visuals words
models achieve state-of-the-art performance for human action
recognition. The main challenge in features description is how to
represent efficiently the local motion information. Most of the
previous works focus on the extension of 2D local descriptors on 3D
ones to describe local information around every interest point. In this
paper, we propose a new spatio-temporal descriptor based on a spacetime
description of moving points. Our description is focused on an
Accordion representation of video which is well-suited to recognize
human action from 2D local descriptors without the need to 3D
extensions. We use the bag of words approach to represent videos.
We quantify 2D local descriptor describing both temporal and spatial
features with a good compromise between computational complexity
and action recognition rates. We have reached impressive results on
publicly available action data set
Abstract: The amount and heterogeneity of data in biomedical research, notably in interdisciplinary research, requires new methods for the collection, presentation and analysis of information. Important data from laboratory experiments as well as patient trials are available but come out of distributed resources. The Charite Medical School in Berlin has established together with the German Research Foundation (DFG) a new information service center for kidney diseases and transplantation (Open European Nephrology Science Centre - OpEN.SC). The system is based on a service-oriented architecture (SOA) with main and auxiliary modules arranged in four layers. To improve the reuse and efficient arrangement of the services the functionalities are described as business processes using the standardised Business Process Execution Language (BPEL).
Abstract: This paper presents an economic game for sybil
detection in a distributed computing environment. Cost parameters
reflecting impacts of different sybil attacks are introduced in the sybil
detection game. The optimal strategies for this game in which both
sybil and non-sybil identities are expected to participate are devised.
A cost sharing economic mechanism called Discriminatory
Rewarding Mechanism for Sybil Detection is proposed based on this
game. A detective accepts a security deposit from each active agent,
negotiates with the agents and offers rewards to the sybils if the latter
disclose their identity. The basic objective of the detective is to
determine the optimum reward amount for each sybil which will
encourage the maximum possible number of sybils to reveal
themselves. Maintaining privacy is an important issue for the
mechanism since the participants involved in the negotiation are
generally reluctant to share their private information. The mechanism
has been applied to Tor by introducing a reputation scoring function.
Abstract: The emergence of the Internet has brewed the
revolution of information storage and retrieval. As most of the
data in the web is unstructured, and contains a mix of text,
video, audio etc, there is a need to mine information to cater to
the specific needs of the users without loss of important
hidden information. Thus developing user friendly and
automated tools for providing relevant information quickly
becomes a major challenge in web mining research. Most of
the existing web mining algorithms have concentrated on
finding frequent patterns while neglecting the less frequent
ones that are likely to contain outlying data such as noise,
irrelevant and redundant data. This paper mainly focuses on
Signed approach and full word matching on the organized
domain dictionary for mining web content outliers. This
Signed approach gives the relevant web documents as well as
outlying web documents. As the dictionary is organized based
on the number of characters in a word, searching and retrieval
of documents takes less time and less space.
Abstract: Medical image registration is the key technology in image guided radiation therapy (IGRT) systems. On the basis of the previous work on our IGRT prototype with a biorthogonal x-ray imaging system, we described a method focused on the 2D/2D rigid-body registration using multiresolution pyramid based mutual information in this paper. Three key steps were involved in the method ： firstly, four 2D images were obtained including two x-ray projection images and two digital reconstructed radiographies（DRRs ） as the input for the registration ； Secondly, each pair of the corresponding x-ray image and DRR image were matched using multiresolution pyramid based mutual information under the ITK registration framework ； Thirdly, we got the final couch offset through a coordinate transformation by calculating the translations acquired from the two pairs of the images. A simulation example of a parotid gland tumor case and a clinical example of an anthropomorphic head phantom were employed in the verification tests. In addition, the influence of different CT slice thickness were tested. The simulation results showed that the positioning errors were 0.068±0.070, 0.072±0.098, 0.154±0.176mm along three axes which were lateral, longitudinal and vertical. The clinical test indicated that the positioning errors of the planned isocenter were 0.066, 0.07, 2.06mm on average with a CT slice thickness of 2.5mm. It can be concluded that our method with its verified accuracy and robustness can be effectively used in IGRT systems for patient setup.
Abstract: The stereophotogrammetry modality is gaining more widespread use in the clinical setting. Registration and visualization of this data, in conjunction with conventional 3D volumetric image modalities, provides virtual human data with textured soft tissue and internal anatomical and structural information. In this investigation computed tomography (CT) and stereophotogrammetry data is acquired from 4 anatomical phantoms and registered using the trimmed iterative closest point (TrICP) algorithm. This paper fully addresses the issue of imaging artifacts around the stereophotogrammetry surface edge using the registered CT data as a reference. Several iterative algorithms are implemented to automatically identify and remove stereophotogrammetry surface edge outliers, improving the overall visualization of the combined stereophotogrammetry and CT data. This paper shows that outliers at the surface edge of stereophotogrammetry data can be successfully removed automatically.
Abstract: Cross layer optimization based on utility functions has
been recently studied extensively, meanwhile, numerous types of
utility functions have been examined in the corresponding literature.
However, a major drawback is that most utility functions take a fixed
mathematical form or are based on simple combining, which can
not fully exploit available information. In this paper, we formulate a
framework of cross layer optimization based on Adaptively Weighted
Utility Functions (AWUF) for fairness balancing in OFDMA networks.
Under this framework, a two-step allocation algorithm is
provided as a sub-optimal solution, whose control parameters can be
updated in real-time to accommodate instantaneous QoS constrains.
The simulation results show that the proposed algorithm achieves
high throughput while balancing the fairness among multiple users.
Abstract: This paper describes a newly designed decentralized
nonlinear control strategy to control a robot manipulator. Based on the
concept of the nonlinear state feedback theory and decentralized
concept is developed to improve the drawbacks in previous works
concerned with complicate intelligent control and low cost effective
sensor. The control methodology is derived in the sense of Lyapunov
theorem so that the stability of the control system is guaranteed. The
decentralized algorithm does not require other joint angle and velocity
information. Individual Joint controller is implemented using a digital
processor with nearly actuator to make it possible to achieve good
dynamics and modular. Computer simulation result has been
conducted to validate the effectiveness of the proposed control scheme
under the occurrence of possible uncertainties and different reference
trajectories. The merit of the proposed control system is indicated in
comparison with a classical control system.
Abstract: The history of money is described in relationship to the history of computing. With the transformation and acceptance of money as information, major challenges to the security of money have involved engineering, computer science, and management. Research opportunities and challenges are described as money continues its transformation into information.
Abstract: A direct connection between ElectroEncephaloGram
(EEG) and the genetic information of individuals has been
investigated by neurophysiologists and psychiatrists since 1960-s;
and it opens a new research area in the science. This paper focuses on
the person identification based on feature extracted from the EEG
which can show a direct connection between EEG and the genetic
information of subjects. In this work the full EO EEG signal of
healthy individuals are estimated by an autoregressive (AR) model
and the AR parameters are extracted as features. Here for feature
vector constitution, two methods have been proposed; in the first
method the extracted parameters of each channel are used as a
feature vector in the classification step which employs a competitive
neural network and in the second method a combination of different
channel parameters are used as a feature vector. Correct classification
scores at the range of 80% to 100% reveal the potential of our
approach for person classification/identification and are in agreement
to the previous researches showing evidence that the EEG signal
carries genetic information. The novelty of this work is in the
combination of AR parameters and the network type (competitive
network) that we have used. A comparison between the first and the
second approach imply preference of the second one.
Abstract: While the explosive increase in information published
on the Web, researchers have to filter information when searching for
conference related information. To make it easier for users to search
related information, this paper uses Topic Maps and social information
to implement ontology since ontology can provide the formalisms and
knowledge structuring for comprehensive and transportable machine
understanding that digital information requires. Besides enhancing
information in Topic Maps, this paper proposes a method of
constructing research Topic Maps considering social information.
First, extract conference data from the web. Then extract conference
topics and the relationships between them through the proposed
method. Finally visualize it for users to search and browse. This paper
uses ontology, containing abundant of knowledge hierarchy structure,
to facilitate researchers getting useful search results. However, most
previous ontology construction methods didn-t take “people" into
account. So this paper also analyzes the social information which helps
researchers find the possibilities of cooperation/combination as well as
associations between research topics, and tries to offer better results.