Search results for: Image security

Authors: Irshad Ahmad, Abdul Muhamin Naeem, Muhammad Islam

Abstract:

Information on weed distribution within the field is necessary to implement spatially variable herbicide application. Since hand labor is costly, an automated weed control system could be feasible. This paper deals with the development of an algorithm for real time specific weed recognition system based on Histogram Analysis of an image that is used for the weed classification. This algorithm is specifically developed to classify images into broad and narrow class for real-time selective herbicide application. The developed system has been tested on weeds in the lab, which have shown that the system to be very effectiveness in weed identification. Further the results show a very reliable performance on images of weeds taken under varying field conditions. The analysis of the results shows over 95 percent classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds.

Keywords: Image Processing, real-time recognition, Weeddetection.

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Authors: Z. Barzegar, R. Deedari, B. Pourgharib

Abstract:

This study examines how metamorphosis in nature is depicted in Charles Tomlinson's poetry through Lawrence Buell's mimesis and referential strategy of adéquation. This study aims to answer questions about the relationship between Tomlinson's selected poems and nature, and examines how his poetry brings the reader closer to the natural environment. Adéquation is a way that brings the reader close to nature, not by imitating nature but by referring to it imaginatively and creating a stylized image. Using figurative language, namely imagery, metaphor, and analogy, adéquation creates a stylized image of metamorphosis in a nature scene that acts as a middle way between the reader and nature. This paper proves that adéquation reinvents the metamorphosis in natural occurrences in Charles Tomlinson's selected poems. Thus, a reader whose imagination is addressed achieves closeness with nature and a caring outlook toward natural happenings. This article confirms that Tomlinson's poems have the potential to represent metamorphosis in nature through adéquation. Therefore, the reader understands nature beyond the poem as they present a gist of nature through adéquation.

Keywords: adéquation, metamorphosis, nature, referentiality

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Authors: Hazem M. El-Bakry, Qiangfu Zhao

Abstract:

Recently, fast neural networks for object/face detection were presented in [1-3]. The speed up factor of these networks relies on performing cross correlation in the frequency domain between the input image and the weights of the hidden layer. But, these equations given in [1-3] for conventional and fast neural networks are not valid for many reasons presented here. In this paper, correct equations for cross correlation in the spatial and frequency domains are presented. Furthermore, correct formulas for the number of computation steps required by conventional and fast neural networks given in [1-3] are introduced. A new formula for the speed up ratio is established. Also, corrections for the equations of fast multi scale object/face detection are given. Moreover, commutative cross correlation is achieved. Simulation results show that sub-image detection based on cross correlation in the frequency domain is faster than classical neural networks.

Keywords: Conventional Neural Networks, Fast Neural Networks, Cross Correlation in the Frequency Domain.

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Authors: Phyu Phyu Mar, Khin Maung Latt

Abstract:

S-boxes (Substitution boxes) are keystones of modern symmetric cryptosystems (block ciphers, as well as stream ciphers). S-boxes bring nonlinearity to cryptosystems and strengthen their cryptographic security. They are used for confusion in data security An S-box satisfies the strict avalanche criterion (SAC), if and only if for any single input bit of the S-box, the inversion of it changes each output bit with probability one half. If a function (cryptographic transformation) is complete, then each output bit depends on all of the input bits. Thus, if it were possible to find the simplest Boolean expression for each output bit in terms of the input bits, each of these expressions would have to contain all of the input bits if the function is complete. From some important properties of S-box, the most interesting property SAC (Strict Avalanche Criterion) is presented and to analyze this property three analysis methods are proposed.

Keywords: S-boxes, cryptosystems, strict avalanche criterion, function, analysis methods.

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Authors: Revathi Venkataraman, M. Pushpalatha, T. Rama Rao

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The lack of any centralized infrastructure in mobile ad hoc networks (MANET) is one of the greatest security concerns in the deployment of wireless networks. Thus communication in MANET functions properly only if the participating nodes cooperate in routing without any malicious intention. However, some of the nodes may be malicious in their behavior, by indulging in flooding attacks on their neighbors. Some others may act malicious by launching active security attacks like denial of service. This paper addresses few related works done on trust evaluation and establishment in ad hoc networks. Related works on flooding attack prevention are reviewed. A new trust approach based on the extent of friendship between the nodes is proposed which makes the nodes to co-operate and prevent flooding attacks in an ad hoc environment. The performance of the trust algorithm is tested in an ad hoc network implementing the Ad hoc On-demand Distance Vector (AODV) protocol.

Keywords: AODV, Flooding, MANETs, trust estimation

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Authors: S. Loumi, H. Merrad, F. Alilat, B. Sansal

Abstract:

In this article, we propose a methodology for the characterization of the suspended matter along Algiers-s bay. An approach by multi layers perceptron (MLP) with training by back propagation of the gradient optimized by the algorithm of Levenberg Marquardt (LM) is used. The accent was put on the choice of the components of the base of training where a comparative study made for four methods: Random and three alternatives of classification by K-Means. The samples are taken from suspended matter image, obtained by analytical model based on polynomial regression by taking account of in situ measurements. The mask which selects the zone of interest (water in our case) was carried out by using a multi spectral classification by ISODATA algorithm. To improve the result of classification, a cleaning of this mask was carried out using the tools of mathematical morphology. The results of this study presented in the forms of curves, tables and of images show the founded good of our methodology.

Keywords: Classification K-means, mathematical morphology, neural network MLP, remote sensing, suspended particulate matter

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Authors: Juhan Kim, Howon Kim

Abstract:

EPC Class-1 Generation-2 UHF tags, one of Radio frequency identification or RFID tag types, is expected that most companies are planning to use it in the supply chain in the short term and in consumer packaging in the long term due to its inexpensive cost. Because of the very cost, however, its resources are extremely scarce and it is hard to have any valuable security algorithms in it. It causes security vulnerabilities, in particular cloning the tags for counterfeits. In this paper, we propose a product authentication solution for anti-counterfeiting at application level in the supply chain and mobile RFID environment. It aims to become aware of distribution of spurious products with fake RFID tags and to provide a product authentication service to general consumers with mobile RFID devices like mobile phone or PDA which has a mobile RFID reader. We will discuss anti-counterfeiting mechanisms which are required to our proposed solution and address requirements that the mechanisms should have.

Keywords: EPC, RFID, Anti-Counterfeiting, Mobile RFIDenvironment.

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Authors: M. M. Abutaleb, A. Hamdy, M. E. Abuelwafa, E. M. Saad

Abstract:

Optical flow is a research topic of interest for many years. It has, until recently, been largely inapplicable to real-time applications due to its computationally expensive nature. This paper presents a new reliable flow technique which is combined with a motion detection algorithm, from stationary camera image streams, to allow flow-based analyses of moving entities, such as rigidity, in real-time. The combination of the optical flow analysis with motion detection technique greatly reduces the expensive computation of flow vectors as compared with standard approaches, rendering the method to be applicable in real-time implementation. This paper describes also the hardware implementation of a proposed pipelined system to estimate the flow vectors from image sequences in real time. This design can process 768 x 576 images at a very high frame rate that reaches to 156 fps in a single low cost FPGA chip, which is adequate for most real-time vision applications.

Keywords: Optical flow, motion detection, real-time systems, FPGA.

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Authors: Jaroslav Polec, Lenka Krulikovská, Natália Helešová, Tomáš Hirner

Abstract:

In this paper we present a novel approach for face image coding. The proposed method makes a use of the features of video encoders like motion prediction. At first encoder selects appropriate prototype from the database and warps it according to features of encoding face. Warped prototype is placed as first I frame. Encoding face is placed as second frame as P frame type. Information about features positions, color change, selected prototype and data flow of P frame will be sent to decoder. The condition is both encoder and decoder own the same database of prototypes. We have run experiment with H.264 video encoder and obtained results were compared to results achieved by JPEG and JPEG2000. Obtained results show that our approach is able to achieve 3 times lower bitrate and two times higher PSNR in comparison with JPEG. According to comparison with JPEG2000 the bitrate was very similar, but subjective quality achieved by proposed method is better.

Keywords: Triangulation, H.264, Model-based coding, Average face

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Authors: Hashem Dehghanniri

Abstract:

Crime scripting is a simple and effective crime modeling technique that aims to improve understanding of security analysts about security and crime incidents. Low-quality scripts provide a wrong, incomplete, or sophisticated understanding of the crime commission process, which oppose the purpose of their application, e.g., identifying effective and cost-efficient situational crime prevention (SCP) measures. One important and overlooked factor in generating quality scripts is the crime scripting method. This study investigates the problems within the existing crime scripting practices and proposes a crime scripting approach that contributes to generating quality crime scripts. It was validated by experienced crime scripters. This framework helps analysts develop better crime scripts and contributes to their effective application, e.g., SCP measures identification or policy-making.

Keywords: Attack modeling, crime commission process, crime script, situational crime prevention.

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Authors: Simon Lessard, Pascal Bigras, Caroline Lau, Daniel Roy, Gilles Soulez, Jacques A. de Guise

Abstract:

The segmentation of endovascular tools in fluoroscopy images can be accurately performed automatically or by minimum user intervention, using known modern techniques. It has been proven in literature, but no clinical implementation exists so far because the computational time requirements of such technology have not yet been met. A classical segmentation scheme is composed of edge enhancement filtering, line detection, and segmentation. A new method is presented that consists of a vector that propagates in the image to track an edge as it advances. The filtering is performed progressively in the projected path of the vector, whose orientation allows for oriented edge detection, and a minimal image area is globally filtered. Such an algorithm is rapidly computed and can be implemented in real-time applications. It was tested on medical fluoroscopy images from an endovascular cerebral intervention. Ex- periments showed that the 2D tracking was limited to guidewires without intersection crosspoints, while the 3D implementation was able to cope with such planar difficulties.

Keywords: Edge detection, Line Enhancement, Segmentation, Fluoroscopy.

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Authors: Mounir Ait kerroum, Ahmed Hammouch, Driss Aboutajdine

Abstract:

Texture information plays increasingly an important role in remotely sensed imagery classification and many pattern recognition applications. However, the selection of relevant textural features to improve this classification accuracy is not a straightforward task. This work investigates the effectiveness of two Mutual Information Feature Selector (MIFS) algorithms to select salient textural features that contain highly discriminatory information for multispectral imagery classification. The input candidate features are extracted from a SPOT High Resolution Visible(HRV) image using Wavelet Transform (WT) at levels (l = 1,2). The experimental results show that the selected textural features according to MIFS algorithms make the largest contribution to improve the classification accuracy than classical approaches such as Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA).

Keywords: Feature Selection, Texture, Mutual Information, Wavelet Transform, SVM classification, SPOT Imagery.

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Authors: Li Qiang, Yang Ze-Ming, Liu Bao-Xu, Jiang Zheng-Wei

Abstract:

With the increasing complexity of cyberspace security, the cyber-attack attribution has become an important challenge of the security protection systems. The difficult points of cyber-attack attribution were forced on the problems of huge data handling and key data missing. According to this situation, this paper presented a reasoning method of cyber-attack attribution based on threat intelligence. The method utilizes the intrusion kill chain model and Bayesian network to build attack chain and evidence chain of cyber-attack on threat intelligence platform through data calculation, analysis and reasoning. Then, we used a number of cyber-attack events which we have observed and analyzed to test the reasoning method and demo system, the result of testing indicates that the reasoning method can provide certain help in cyber-attack attribution.

Keywords: Reasoning, Bayesian networks, cyber-attack attribution, kill chain, threat intelligence.

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Authors: A. Ambia, T. Takemae, Y. Kosugi, M. Hongo

Abstract:

Electric impedance imaging is a method of reconstructing spatial distribution of electrical conductivity inside a subject. In this paper, a new method of electrical impedance imaging using eddy current is proposed. The eddy current distribution in the body depends on the conductivity distribution and the magnetic field pattern. By changing the position of magnetic core, a set of voltage differences is measured with a pair of electrodes. This set of voltage differences is used in image reconstruction of conductivity distribution. The least square error minimization method is used as a reconstruction algorithm. The back projection algorithm is used to get two dimensional images. Based on this principle, a measurement system is developed and some model experiments were performed with a saline filled phantom. The shape of each model in the reconstructed image is similar to the corresponding model, respectively. From the results of these experiments, it is confirmed that the proposed method is applicable in the realization of electrical imaging.

Keywords: Back projection algorithm, electrical impedancetomography, eddy current, magnetic inductance tomography.

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Authors: M A Hannan, A. Hussain, S. A. Samad, K. A. Ishak, A. Mohamed

Abstract:

This paper presents a general trainable framework for fast and robust upright human face and non-human object detection and verification in static images. To enhance the performance of the detection process, the technique we develop is based on the combination of fast neural network (FNN) and classical neural network (CNN). In FNN, a useful correlation is exploited to sustain high level of detection accuracy between input image and the weight of the hidden neurons. This is to enable the use of Fourier transform that significantly speed up the time detection. The combination of CNN is responsible to verify the face region. A bootstrap algorithm is used to collect non human object, which adds the false detection to the training process of the human and non-human object. Experimental results on test images with both simple and complex background demonstrate that the proposed method has obtained high detection rate and low false positive rate in detecting both human face and non-human object.

Keywords: Algorithm, detection of human and non-human object, FNN, CNN, Image training.

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Authors: Mongkorn Klingajay, Tawatchai Jitson

Abstract:

The pipe inspection operation is the difficult detective performance. Almost applications are mainly relies on a manual recognition of defective areas that have carried out detection by an engineer. Therefore, an automation process task becomes a necessary in order to avoid the cost incurred in such a manual process. An automated monitoring method to obtain a complete picture of the sewer condition is proposed in this work. The focus of the research is the automated identification and classification of discontinuities in the internal surface of the pipe. The methodology consists of several processing stages including image segmentation into the potential defect regions and geometrical characteristic features. Automatic recognition and classification of pipe defects are carried out by means of using an artificial neural network technique (ANN) based on Radial Basic Function (RBF). Experiments in a realistic environment have been conducted and results are presented.

Keywords: Artificial neural network, Radial basic function, Curve fitting, CCTV, Image segmentation, Data acquisition.

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Authors: F. Meskine, N. Taleb, M. Chikr El-Mezouar, K. Kpalma, A. Almhdie

Abstract:

Image registration is the process of establishing point by point correspondence between images obtained from a same scene. This process is very useful in remote sensing, medicine, cartography, computer vision, etc. Then, the task of registration is to place the data into a common reference frame by estimating the transformations between the data sets. In this work, we develop a rigid point registration method based on the application of genetic algorithms and Hausdorff distance. First, we extract the feature points from both images based on the algorithm of global and local curvature corner. After refining the feature points, we use Hausdorff distance as similarity measure between the two data sets and for optimizing the search space we use genetic algorithms to achieve high computation speed for its inertial parallel. The results show the efficiency of this method for registration of satellite images.

Keywords: Feature extraction, Genetic algorithms, Hausdorff distance, Image registration, Point registration.

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Authors: P. Visutsak

Abstract:

The study of the interaction between humans and computers has been emerging during the last few years. This interaction will be more powerful if computers are able to perceive and respond to human nonverbal communication such as emotions. In this study, we present the image-based approach to emotion classification through lower facial expression. We employ a set of feature points in the lower face image according to the particular face model used and consider their motion across each emotive expression of images. The vector of displacements of all feature points input to the Adaptive Support Vector Machines (A-SVMs) classifier that classify it into seven basic emotions scheme, namely neutral, angry, disgust, fear, happy, sad and surprise. The system was tested on the Japanese Female Facial Expression (JAFFE) dataset of frontal view facial expressions [7]. Our experiments on emotion classification through lower facial expressions demonstrate the robustness of Adaptive SVM classifier and verify the high efficiency of our approach.

Keywords: emotion classification, facial expression, adaptive support vector machines, facial expression classifier.

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Authors: Shaya Alshahrani

Abstract:

Perceived risks play a major role in consumer intentions, behaviors, attitudes, and decisions about online shopping in the KSA. This paper investigates the influence of six perceived risk dimensions on Saudi consumers: product risk, information risk, financial risk, privacy and security risk, delivery risk, and terms and conditions risk empirically. To ensure the success of this study, a random survey was distributed to reflect the consumers’ perceived risk and to enable the generalization of the results. Data were collected from 323 respondents in the Kingdom of Saudi Arabia (KSA): 50 who had never shopped online and 273 who had done so. The results indicated that all six risks influenced the respondents’ perceptions of online shopping. The non-online shoppers perceived financial and delivery risks as the most significant barriers to online shopping. This was followed closely by performance, information, and privacy and security risks. Terms and conditions were perceived as less significant. The online consumers considered delivery and performance risks to be the most significant influences on internet shopping. This was followed closely by information and terms and conditions. Financial and privacy and security risks were perceived as less significant. This paper argues that introducing adequate legal solutions to addressing related problems arising from this study is an urgent need. This may enhance consumer trust in the KSA online market, increase consumers’ intentions regarding online shopping, and improve consumer protection.

Keywords: Perceived risk, consumer protection, online shopping, Saudi Arabia, online contracts, e-commerce.

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Authors: Irshad Ahmad, Abdul Muhamin Naeem, Muhammad Islam, Shahid Nawaz

Abstract:

Information on weed distribution within the field is necessary to implement spatially variable herbicide application. Since hand labor is costly, an automated weed control system could be feasible. This paper deals with the development of an algorithm for real time specific weed recognition system based on Histogram Maxima with threshold of an image that is used for the weed classification. This algorithm is specifically developed to classify images into broad and narrow class for real-time selective herbicide application. The developed system has been tested on weeds in the lab, which have shown that the system to be very effectiveness in weed identification. Further the results show a very reliable performance on images of weeds taken under varying field conditions. The analysis of the results shows over 95 percent classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds.

Keywords: Image processing, real-time recognition, weeddetection.

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Authors: Tarun Chand, Michael Jurczyk

Abstract:

With the increasing need and popularity of IoT devices and how integrated they are becoming in our daily lives and industries; these devices make for a very lucrative target for malicious actors. And since these devices have such limited resources, the implementation of robust security features is a tradeoff to be made for the actual functionality the device was intended for. This makes them an easy target with high returns. Several frameworks for the secure firmware update of these devices have been recently proposed in the literature. They focus on methods such as blockchains and distributed file systems to secure firmware updates, but do not go into the details of the actual implementation of these frameworks and the lower-level interactions among these methods used. This work integrates some of these security measures into one overall framework and details the actual lower-level implementation of this framework in a virtual dockerized testbed running on AWS.

Keywords: Blockchain, Ethereum, Geth, IPFS, secure IoT-firmware update, virtual testbed development

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Authors: Shahin Zanbaghi, Saeed Samet

Abstract:

The internet has transformed the way we shop. Previously, most of our purchases came in the form of shopping trips to a nearby store. Now, it is as easy as clicking a mouse. We have to be constantly vigilant about our personal information. In this work, our proposed approach is to encrypt the information printed on the physical packages, which include personal information in plain text using a symmetric encryption algorithm; then, we store that encrypted information into a Blockchain network rather than storing them in companies or corporations centralized databases. We present, implement and assess a blockchain-based system using Ethereum smart contracts. We present detailed algorithms that explain the details of our smart contract. We present the security, cost and performance analysis of the proposed method. Our work indicates that the proposed solution is economically attainable and provides data integrity, security, transparency and data traceability.

Keywords: Blockchain, Ethereum, smart contract, commit-reveal scheme.

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Authors: Hiren Mewada, Suprava Patnaik

Abstract:

Object identification and segmentation application requires extraction of object in foreground from the background. In this paper the Bhattacharya distance based probabilistic approach is utilized with an active contour model (ACM) to segment an object from the background. In the proposed approach, the Bhattacharya histogram is calculated on non-linear structure tensor space. Based on the histogram, new formulation of active contour model is proposed to segment images. The results are tested on both color and gray images from the Berkeley image database. The experimental results show that the proposed model is applicable to both color and gray images as well as both texture images and natural images. Again in comparing to the Bhattacharya based ACM in ICA space, the proposed model is able to segment multiple object too.

Keywords: Active Contour, Bhattacharya Histogram, Structure tensor, Image segmentation.

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Authors: Rosmini Yanti, Safira Aviolita, Marsetio

Abstract:

Indonesia has several straits that are very important as a shipping lane, including the Sunda Strait and the Lombok Strait, which are the part of the Indonesian Archipelagic Sea Lane (IASL). An increase in traffic on the Marine Archipelago makes the task of monitoring sea routes increasingly difficult. Indonesia has proposed the establishment of a Traffic Separation Scheme (TSS) in the Sunda Strait and the Lombok Strait and the country now has the right to be able to conceptualize the TSS as well as the obligation to regulate it. Indonesia has the right to maintain national safety and sovereignty. In setting the TSS, Indonesia needs to issue national regulations that are in accordance with international law and the general provisions of the IMO (International Maritime Organization) can then be used as guidelines for maritime safety and security in the Sunda Strait and the Lombok Strait. The research method used is a qualitative method with the concept of linguistic and visual data collection. The source of the data is the analysis of documents and regulations. The results show that the determination of TSS was justified by International Law, in accordance with article 22, article 41, and article 53 of the United Nations Convention on the Law of the Sea (UNCLOS) 1982. The determination of TSS by the Indonesian government would be in accordance with COLREG (International Convention on Preventing Collisions at Sea) 10, which has been designed to follow IASL. Thus, TSS can provide a function as a safety and monitoring medium to minimize ship accidents or collisions, including the warship and aircraft of other countries that cross the IASL.

Keywords: Archipelago State, maritime law, maritime security, traffic separation scheme.

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Authors: A. Vani, M. N. Mamatha

Abstract:

Visual signal processing in human beings occurs in the occipital lobe of the brain. The signals that are generated in the brain are universal for all the human beings and they are called Visual Evoked Potential (VEP). Generally, the visually impaired people lose sight because of severe damage to only the eyes natural photo sensors, but the occipital lobe will still be functioning. In this paper, a technique of artificially generating VEP is proposed to enhance the visual ability of the subject. The system uses the electrical photoreceptors to capture image, process the image, to detect and recognize the subject or object. This voltage is further processed and can transmit wirelessly to a BIOMEMS implanted into occipital lobe of the patient’s brain. The proposed BIOMEMS consists of array of electrodes that generate the neuron potential which is similar to VEP of normal people. Thus, the neurons get the visual data from the BioMEMS which helps in generating partial vision or sight for the visually challenged patient. 

Keywords: Visual evoked potential, OpenViBe, BioMEMS, Neuro prosthesis.

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Authors: Abdelouahab Moussaoui, Nabila Ferahta, Victor Chen

Abstract:

The development of aid's systems for the medical diagnosis is not easy thing because of presence of inhomogeneities in the MRI, the variability of the data from a sequence to the other as well as of other different source distortions that accentuate this difficulty. A new automatic, contextual, adaptive and robust segmentation procedure by MRI brain tissue classification is described in this article. A first phase consists in estimating the density of probability of the data by the Parzen-Rozenblatt method. The classification procedure is completely automatic and doesn't make any assumptions nor on the clusters number nor on the prototypes of these clusters since these last are detected in an automatic manner by an operator of mathematical morphology called skeleton by influence zones detection (SKIZ). The problem of initialization of the prototypes as well as their number is transformed in an optimization problem; in more the procedure is adaptive since it takes in consideration the contextual information presents in every voxel by an adaptive and robust non parametric model by the Markov fields (MF). The number of bad classifications is reduced by the use of the criteria of MPM minimization (Maximum Posterior Marginal).

Keywords: Clustering, Automatic Classification, SKIZ, MarkovFields, Image segmentation, Maximum Posterior Marginal (MPM).

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Authors: Sungjin Lee, Donghyun Choi, Choonsik Park, Seungjoo Kim

Abstract:

A SCADA (Supervisory Control And Data Acquisition) system is an industrial control and monitoring system for national infrastructures. The SCADA systems were used in a closed environment without considering about security functionality in the past. As communication technology develops, they try to connect the SCADA systems to an open network. Therefore, the security of the SCADA systems has been an issue. The study of key management for SCADA system also has been performed. However, existing key management schemes for SCADA system such as SKE(Key establishment for SCADA systems) and SKMA(Key management scheme for SCADA systems) cannot support broadcasting communication. To solve this problem, an Advanced Key Management Architecture for Secure SCADA Communication has been proposed by Choi et al.. Choi et al.-s scheme also has a problem that it requires lots of computational cost for multicasting communication. In this paper, we propose an enhanced scheme which improving computational cost for multicasting communication with considering the number of keys to be stored in a low power communication device (RTU).

Keywords: SCADA system, SCADA communication, Key management, Distributed networks.

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Authors: Denis Trcek

Abstract:

Trust is essential for further and wider acceptance of contemporary e-services. It was first addressed almost thirty years ago in Trusted Computer System Evaluation Criteria standard by the US DoD. But this and other proposed approaches of that period were actually solving security. Roughly some ten years ago, methodologies followed that addressed trust phenomenon at its core, and they were based on Bayesian statistics and its derivatives, while some approaches were based on game theory. However, trust is a manifestation of judgment and reasoning processes. It has to be dealt with in accordance with this fact and adequately supported in cyber environment. On the basis of the results in the field of psychology and our own findings, a methodology called qualitative algebra has been developed, which deals with so far overlooked elements of trust phenomenon. It complements existing methodologies and provides a basis for a practical technical solution that supports management of trust in contemporary computing environments. Such solution is also presented at the end of this paper.

Keywords: internet security, trust management, multi-agent systems, reasoning and judgment, modeling and simulation, qualitativealgebra

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Authors: Mi Sook Lee

Abstract:

The number of the companies accepting RFID in Korea has been increased continuously due to the domestic development of information technology. The acceptance of RFID by companies in Korea enabled them to do business with many global enterprises in a much more efficient and effective way. According to a survey[33, p76], many companies in Korea have used RFID for inventory or distribution manages. But, the use of RFID in the companies in Korea is in the early stages and its potential value hasn-t fully been realized yet. At this time, it would be very important to investigate the factors that affect RFID acceptance. For this study, many previous studies were referenced and some RFID experts were interviewed. Through the pilot test, four factors were selected - Security Trust, Employee Knowledge, Partner Influence, Service Provider Trust - affecting RFID acceptance and an extended technology acceptance model(e-TAM) was presented with those factors. The proposed model was empirically tested using data collected from employees in companies or public enterprises. In order to analyze some relationships between exogenous variables and four variables in TAM, structural equation modeling(SEM) was developed and SPSS12.0 and AMOS 7.0 were used for analyses. The results are summarized as follows: 1) security trust perceived by employees positively influences on perceived usefulness and perceived ease of use; 2) employee-s knowledge on RFID positively influences on only perceived ease of use; 3) a partner-s influence for RFID acceptance positively influences on only perceived usefulness; 4) service provider trust very positively influences on perceived usefulness and perceived ease of use 5) the relationships between TAM variables are the same as the previous studies.

Keywords: RFID, TAM, Security Trust, Employee Knowledge, Partner Influence, Service Provider Trust.

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Authors: Muhammad R. Ahmed, Xu Huang, Dharmendra Sharma

Abstract:

Despite extensive study on wireless sensor network security, defending internal attacks and finding abnormal behaviour of the sensor are still difficult and unsolved task. The conventional cryptographic technique does not give the robust security or detection process to save the network from internal attacker that cause by abnormal behavior. The insider attacker or abnormally behaved sensor identificationand location detection framework using false massage detection and Time difference of Arrival (TDoA) is presented in this paper. It has been shown that the new framework can efficiently identify and detect the insider attacker location so that the attacker can be reprogrammed or subside from the network to save from internal attack.

Keywords: Insider Attaker identification, Abnormal Behaviour, Location detection, Time difference of Arrival (TDoA), Wireless sensor network

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