Search results for: Automated Image alignment

Authors: Sanghyeok Oh, Yunli Lee, Kwangjin Hong, Kirak Kim, Keechul Jung

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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.

Keywords: Computer vision, neural network, pose recognition, view-point insensitive.

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Authors: W. Kultangwattana, K. Somkantha, P. Phuangsuwan

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This paper presents a technique for diagnosis of the abdominal aorta aneurysm in magnetic resonance imaging (MRI) images. First, our technique is designed to segment the aorta image in MRI images. This is a required step to determine the volume of aorta image which is the important step for diagnosis of the abdominal aorta aneurysm. Our proposed technique can detect the volume of aorta in MRI images using a new external energy for snakes model. The new external energy for snakes model is calculated from Law-s texture. The new external energy can increase the capture range of snakes model efficiently more than the old external energy of snakes models. Second, our technique is designed to diagnose the abdominal aorta aneurysm by Bayesian classifier which is classification models based on statistical theory. The feature for data classification of abdominal aorta aneurysm was derived from the contour of aorta images which was a result from segmenting of our snakes model, i.e., area, perimeter and compactness. We also compare the proposed technique with the traditional snakes model. In our experiment results, 30 images are trained, 20 images are tested and compared with expert opinion. The experimental results show that our technique is able to provide more accurate results than 95%.

Keywords: Adbominal Aorta Aneurysm, Bayesian Classifier, Snakes Model, Texture Feature.

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Authors: Karel Frajtak, Miroslav Bures, Ivan Jelinek

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Software testing is important stage of software development cycle. Current testing process involves tester and electronic documents with test case scenarios. In this paper we focus on new approach to testing process using automated test case generation and tester guidance through the system based on the model of the system. Test case generation and model-based testing is not possible without proper system model. We aim on providing better feedback from the testing process thus eliminating the unnecessary paper work.

Keywords: Model based testing, test automation, test generating, tester support.

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Authors: Ali Zifan, Mohammad Hassan Moradi, Sohrab Saberi, Farzad Towhidkhah

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.

Keywords: Adaptive Piecewise Constant Approximation, Dynamic programming, ECG segmentation, Piecewise DerivativeDynamic Time Warping.

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Authors: Mahmoud Elmezain, Ayoub Al-Hamadi, Bernd Michaelis

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Hand gesture is an active area of research in the vision community, mainly for the purpose of sign language recognition and Human Computer Interaction. In this paper, we propose a system to recognize alphabet characters (A-Z) and numbers (0-9) in real-time from stereo color image sequences using Hidden Markov Models (HMMs). Our system is based on three main stages; automatic segmentation and preprocessing of the hand regions, feature extraction and classification. In automatic segmentation and preprocessing stage, color and 3D depth map are used to detect hands where the hand trajectory will take place in further step using Mean-shift algorithm and Kalman filter. In the feature extraction stage, 3D combined features of location, orientation and velocity with respected to Cartesian systems are used. And then, k-means clustering is employed for HMMs codeword. The final stage so-called classification, Baum- Welch algorithm is used to do a full train for HMMs parameters. The gesture of alphabets and numbers is recognized using Left-Right Banded model in conjunction with Viterbi algorithm. Experimental results demonstrate that, our system can successfully recognize hand gestures with 98.33% recognition rate.

Keywords: Gesture Recognition, Computer Vision & Image Processing, Pattern Recognition.

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Authors: D. A. K. S. Gunaratna, N. D. Kodikara, H. L. Premaratne

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Automatic currency note recognition invariably depends on the currency note characteristics of a particular country and the extraction of features directly affects the recognition ability. Sri Lanka has not been involved in any kind of research or implementation of this kind. The proposed system “SLCRec" comes up with a solution focusing on minimizing false rejection of notes. Sri Lankan currency notes undergo severe changes in image quality in usage. Hence a special linear transformation function is adapted to wipe out noise patterns from backgrounds without affecting the notes- characteristic images and re-appear images of interest. The transformation maps the original gray scale range into a smaller range of 0 to 125. Applying Edge detection after the transformation provided better robustness for noise and fair representation of edges for new and old damaged notes. A three layer back propagation neural network is presented with the number of edges detected in row order of the notes and classification is accepted in four classes of interest which are 100, 500, 1000 and 2000 rupee notes. The experiments showed good classification results and proved that the proposed methodology has the capability of separating classes properly in varying image conditions.

Keywords: Artificial intelligence, linear transformation and pattern recognition.

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Authors: Jeong-Tae Kim, Tchanghee Hyun

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The number of intrusions and attacks against critical infrastructures and other information networks is increasing rapidly. While there is no identified evidence that terrorist organizations are currently planning a coordinated attack against the vulnerabilities of computer systems and network connected to critical infrastructure, and origins of the indiscriminate cyber attacks that infect computers on network remain largely unknown. The growing trend toward the use of more automated and menacing attack tools has also overwhelmed some of the current methodologies used for tracking cyber attacks. There is an ample possibility that this kind of cyber attacks can be transform to cyberterrorism caused by illegal purposes. Cyberterrorism is a matter of vital importance to national welfare. Therefore, each countries and organizations have to take a proper measure to meet the situation and consider effective legislation about cyberterrorism.

Keywords: Cyberterrorism, cyber attack, information security, legislation

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Authors: T. K. Mondal, N C Shivaprakash

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A piston cylinder based high pressure differential thermal analyzer system is developed to investigate phase transformations, melting, glass transitions, crystallization behavior of inorganic materials, glassy systems etc., at ambient to 4 GPa and at room temperature to 1073 K. The pressure is calibrated by the phase transition of bismuth and ytterbium and temperature is calibrated by using thermocouple data chart. The system developed is calibrated using benzoic acid, ammonium nitrate and it has a pressure and temperature control of ± 8.9 x 10 -4 GPa , ± 2 K respectively. The phase transition of Asx Te100-x chalcogenides, ferrous oxide and strontium boride are studied using the indigenously developed system.

Keywords: double stage crystallization, Phase transition, Quasi hydrostatic, Rigidity percolation

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Authors: M. Samaka

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This paper presents the development of a software application for Off-line robot task programming and simulation. Such application is designed to assist in robot task planning and to direct manipulator motion on sensor based programmed motion. The concept of the designed programming application is to use the power of the knowledge base for task accumulation. In support of the programming means, an interactive graphical simulation for manipulator kinematics was also developed and integrated into the application as the complimentary factor to the robot programming media. The simulation provides the designer with useful, inexpensive, off-line tools for retain and testing robotics work cells and automated assembly lines for various industrial applications.

Keywords: Robot programming, task-level programming, robot languages, robot simulation, robotics software.

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Authors: Andreas Körtge, Susanne Stählke, Regina Lange, Mario Birkholz, Mirko Fraschke, Katrin Schulz, Barbara Nebe, Patrick Elter

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A major challenge in biomaterials research is the regulation of protein adsorption which is a key factor for controlling the subsequent cell adhesion at implant surfaces. The aim of the present study was to control the adsorption of fibronectin (FN) and the attachment of MG-63 osteoblasts with an electronic nanostructure. Shallow doping line lattices with a period of 260 nm were produced for this purpose by implantation of phosphorous in silicon wafers. Protein coverage was determined after incubating the substrate with FN by means of an immunostaining procedure and the measurement of the fluorescence intensity with a TECAN analyzer. We observed an increased amount of adsorbed FN on the nanostructure compared to control substrates. MG-63 osteoblasts were cultivated for 24h on FN-incubated substrates and their morphology was assessed by SEM. Preferred orientation and elongation of the cells in direction of the doping lattice lines was observed on FN-coated nanostructures.

Keywords: Cell adhesion, electronic nanostructures, doping lattice, fibronectin, MG-63 osteoblasts, protein adsorption.

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Authors: AlNPhannee Saengkaew, Armin Dadgar, Juergen Blaesing, Thomas Hempel, Sakuntam Sanorpim, Chanchana Thanachayanont, Visittapong Yordsri, Watcharee Rattanasakulthong, Alois Krost

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Group-III nitride material as particularly AlxGa1-xN is one of promising optoelectronic materials to require for shortwavelength devices. To achieve the high-quality AlxGa1-xN films for a high performance of such devices, AlN-nucleation layers are the important factor. To improve the AlN-nucleation layers with a variation of Ga-addition, XRD measurements were conducted to analyze the crystalline quality of the subsequent Al0.1Ga0.9N with the minimum ω-FWHMs of (0002) and (10-10) reflections of 425 arcsec and 750 arcsec, respectively. SEM and AFM measurements were performed to observe the surface morphology and TEM measurements to identify the microstructures and orientations. Results showed that the optimized Ga-atoms in the Al(Ga)Nnucleation layers improved the surface diffusion to form moreuniform crystallites in structure and size, better alignment of each crystallite, and better homogeneity of island distribution. This, hence, improves the orientation of epilayers on the Si-surface and finally improves the crystalline quality and reduces the residual strain of subsequent Al0.1Ga0.9N layers.

Keywords: AlGaN, UV-LEDs, seed layers, AFM, TEM

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Authors: N. Rezaee, M Nayeripour, A. Roosta, T. Niknam

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With the prevalence of computer and development of information technology, Geographic Information Systems (GIS) have long used for a variety of applications in electrical engineering. GIS are designed to support the analysis, management, manipulation and mapping of spatial data. This paper presents several usages of GIS in power utilities such as automated route selection for the construction of new power lines which uses a dynamic programming model for route optimization, load forecasting and optimizing planning of substation-s location and capacity with comprehensive algorithm which involves an accurate small-area electric load forecasting procedure and simulates the different cost functions of substations.

Keywords: Geographic information systems (GIS), optimallocation and capacity, power distribution planning, route selection, spatial load forecasting.

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Authors: Ali Zifan, Sohrab Saberi, Mohammad Hassan Moradi, Farzad Towhidkhah

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.

Keywords: Adaptive Piecewise Constant Approximation, Dynamic programming, ECG segmentation, Piecewise Derivative Dynamic Time Warping.

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Authors: Mitja Dečman

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The ever-increasing presence and use of information and communication technology (ICT) influences the different social relationships of today's society. Gender differences are especially important from the viewpoint of modern society since ICT can either deepen the existing inequalities or diminish them. In a developed Western world, gender equality has been a well-focused area for decades in many parts of society including education, employment or politics and has led to a decrease in the inequality of women and men in these and other areas. The area of digital equality, or inequality for that matter, is one of the areas where gender differences still exist in many countries of the world. The research presented in this paper focuses on Slovenia, one of the smallest EU member states, being an average achiever in the area of e-society according to the many different European benchmarking indexes. On the other hand, Slovenia is working in an alignment with many European gender equality guidelines and showing good results. The results of our research are based on the analysis of survey data from 2014 to 2017 dealing with Slovenian citizens and their households and the use of ICT. Considering gender issues, the synthesis showed that cultural differences influence some measured ICT indicators but on the other hand the differences are low and only sometimes statistically significant.

Keywords: Digital divide, e-society, gender inequality, Slovenia.

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Authors: Sandeep Chandana, Rene V. Mayorga, Christine W. Chan

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This article outlines conceptualization and implementation of an intelligent system capable of extracting knowledge from databases. Use of hybridized features of both the Rough and Fuzzy Set theory render the developed system flexibility in dealing with discreet as well as continuous datasets. A raw data set provided to the system, is initially transformed in a computer legible format followed by pruning of the data set. The refined data set is then processed through various Rough Set operators which enable discovery of parameter relationships and interdependencies. The discovered knowledge is automatically transformed into a rule base expressed in Fuzzy terms. Two exemplary cancer repository datasets (for Breast and Lung Cancer) have been used to test and implement the proposed framework.

Keywords: Knowledge Extraction, Fuzzy Sets, Rough Sets, Neuro–Fuzzy Systems, Databases

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Authors: Petr Šimek, Kamil Kostruk

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This paper deals with the problem of testing the Electronic Control Unit (ECU) for the specified function validation. Modern ECUs have many functions which need to be tested. This process requires tracking between the test and the specification. The technique discussed in this paper explores the system for automating this process. The paper focuses on the introduction to the problem in general, then it describes the proposed test system concept and its principle. It looks at how the process of the ECU interface specification file for automated interface testing and test tracking works. In the end, the future possible development of the project is discussed.

Keywords: Electronic control unit testing, embedded system, test generate, test automation, process automation, CAN bus, Ethernet.

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Authors: Christian A. Romero, Tanja Grkovic, John. R. J. French, D. İpek. Kurtböke, Ronald J. Quinn

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A high-throughput and automated 1H NMR metabolic fingerprinting dereplication approach was used to accelerate the discovery of unknown bioactive secondary metabolites. The applied dereplication strategy accelerated the discovery of new natural products, provided rapid and competent identification and quantification of the known secondary metabolites and avoided time-consuming isolation procedures. The effectiveness of the technique was demonstrated by the isolation and elucidation of arglecins B (1), C (2) and actinofuranosin A (3) from a termite-gut associated Streptomyces sp. (USC 597) grown under solid state fermentation. The structures of these compounds were elucidated by extensive interpretation of 1H, 13C and 2D NMR spectroscopic data. These represent the first report of arglecin analogues isolated from a termite gut-associated Streptomyces species.

Keywords: Actinomycetes, actinofuranosin, antibiotics, arglecins, NMR spectroscopy.

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Authors: Arun Prasad Jaganathan

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In today's rapidly evolving technological landscape, the need for efficient and proactive maintenance management solutions has become increasingly evident across various industries. Traditional approaches often suffer from drawbacks such as reactive strategies, leading to potential downtime, increased costs, and decreased operational efficiency. In response to these challenges, this paper proposes an AI-enabled approach to object-based maintenance management aimed at enhancing reliability and efficiency. The paper contributes to the growing body of research on AI-driven maintenance management systems, highlighting the transformative impact of intelligent technologies on enhancing object reliability and operational efficiency.

Keywords: AI, machine learning, predictive maintenance, object-based maintenance, expert team scheduling.

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Authors: Cherry Oo, Hnin Min Oo

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Software bug localization is one of the most costly tasks in program repair technique. Therefore, there is a high claim for automated bug localization techniques that can monitor programmers to the locations of bugs, with slight human arbitration. Spectrum-based bug localization aims to help software developers to discover bugs rapidly by investigating abstractions of the program traces to make a ranking list of most possible buggy modules. Using the Apache Commons Math library project, we study the diagnostic accuracy using our spectrum-based bug localization metric. Our outcomes show that the greater performance of a specific similarity coefficient, used to inspect the program spectra, is mostly effective on localizing of single line bugs.

Keywords: Software testing, fault localization, program spectra.

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Authors: Benjamin Kuester, Bjoern Eilert, Malte Stonis, Ludger Overmeyer

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Increasing quality requirements make reliable and effective quality management indispensable. This includes the complaint handling in which the 8D method is widely used. The 8D report as a written documentation of the 8D method is one of the key quality documents as it internally secures the quality standards and acts as a communication medium to the customer. In practice, however, the 8D report is mostly faulty and of poor quality. There is no quality control of 8D reports today. This paper describes the use of natural language processing for the automated evaluation of 8D reports. Based on semantic analysis and text-mining algorithms the presented system is able to uncover content and formal quality deficiencies and thus increases the quality of the complaint processing in the long term.

Keywords: 8D report, complaint management, evaluation system, text-mining.

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Authors: Nariman O. Kandil, Ehab K. Abou-Elkheir, Amr M. Kotb

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Effective IT governance guarantees the strategic alignment of IT and business goals, risk mitigation control, and better IT and business performance. This study seeks to examine empirically the extent of IT governance implementation within the firms listed on the Egyptian Stock Exchange (EGX30) and its determinants. Accordingly, 18 semi-structured interviews, face to face, phone, and video-conferencing, using various tools (e.g., WebEx, Zoom, and Microsoft Teams) were undertaken at the interviewees’ offices in Egypt between the end of November 2019 and the end of August 2020. Results suggest that there are variances in the extent of IT Governance (ITG) implementation within the firms listed on the EGX30, mainly caused by the industry type and internal and external triggers. The results also suggest that the organization size, the type of auditor, the criticality of the industry, the effective processes & key performance indicators (KPIs), and the information intensity expertise of the chief information officers (CIOs) have a significant impact on ITG implementation within the firms.

Keywords: Effective IT governance, Egyptian Market, information security, risk controls.

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Authors: Yao-Hong Tsai

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Due to the sensor technology, video surveillance has become the main way for security control in every big city in the world. Surveillance is usually used by governments for intelligence gathering, the prevention of crime, the protection of a process, person, group or object, or the investigation of crime. Many surveillance systems based on computer vision technology have been developed in recent years. Moving target tracking is the most common task for Unmanned Aerial Vehicle (UAV) to find and track objects of interest in mobile aerial surveillance for civilian applications. The paper is focused on vision-based collision avoidance for UAVs by recurrent neural networks. First, images from cameras on UAV were fused based on deep convolutional neural network. Then, a recurrent neural network was constructed to obtain high-level image features for object tracking and extracting low-level image features for noise reducing. The system distributed the calculation of the whole system to local and cloud platform to efficiently perform object detection, tracking and collision avoidance based on multiple UAVs. The experiments on several challenging datasets showed that the proposed algorithm outperforms the state-of-the-art methods.

Keywords: Unmanned aerial vehicle, object tracking, deep learning, collision avoidance.

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Authors: Hariton N. Costin, Iulian Ciocoiu, Tudor Barbu, Cristian Rotariu

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In this paper three different approaches for person verification and identification, i.e. by means of fingerprints, face and voice recognition, are studied. Face recognition uses parts-based representation methods and a manifold learning approach. The assessment criterion is recognition accuracy. The techniques under investigation are: a) Local Non-negative Matrix Factorization (LNMF); b) Independent Components Analysis (ICA); c) NMF with sparse constraints (NMFsc); d) Locality Preserving Projections (Laplacianfaces). Fingerprint detection was approached by classical minutiae (small graphical patterns) matching through image segmentation by using a structural approach and a neural network as decision block. As to voice / speaker recognition, melodic cepstral and delta delta mel cepstral analysis were used as main methods, in order to construct a supervised speaker-dependent voice recognition system. The final decision (e.g. “accept-reject" for a verification task) is taken by using a majority voting technique applied to the three biometrics. The preliminary results, obtained for medium databases of fingerprints, faces and voice recordings, indicate the feasibility of our study and an overall recognition precision (about 92%) permitting the utilization of our system for a future complex biometric card.

Keywords: Biometry, image processing, pattern recognition, speech analysis.

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Authors: Mohammad Y. Abualhoul, Edgar Talavera Munoz, Fawzi Nashashibi

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The new emerging Visible Light Communication (VLC) technology has been subjected to intensive investigation, evaluation, and lately, deployed in the context of convoy-based applications for Intelligent Transportations Systems (ITS). The technology limitations were defined and supported by different solutions proposals to enhance the crucial alignment and mobility limitations. In this paper, we propose the incorporation of VLC technology and Lane-Centering (LC) technique to assure the VLC-connectivity by keeping the autonomous vehicle aligned to the lane center using vision-based lane detection in a convoy-based formation. Such combination can ensure the optical communication connectivity with a lateral error less than 30 cm. As soon as the road lanes are detectable, the evaluated system showed stable behavior independently from the inter-vehicle distances and without the need for any exchanged information of the remote vehicles. The evaluation of the proposed system is verified using VLC prototype and an empirical result of LC running application over 60 km in Madrid M40 highway.

Keywords: VLC, lane-centering, platoon, ITS, road safety applications.

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Authors: Suraiya Jabin, Kamal K. Bharadwaj

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In the recent past Learning Classifier Systems have been successfully used for data mining. Learning Classifier System (LCS) is basically a machine learning technique which combines evolutionary computing, reinforcement learning, supervised or unsupervised learning and heuristics to produce adaptive systems. A LCS learns by interacting with an environment from which it receives feedback in the form of numerical reward. Learning is achieved by trying to maximize the amount of reward received. All LCSs models more or less, comprise four main components; a finite population of condition–action rules, called classifiers; the performance component, which governs the interaction with the environment; the credit assignment component, which distributes the reward received from the environment to the classifiers accountable for the rewards obtained; the discovery component, which is responsible for discovering better rules and improving existing ones through a genetic algorithm. The concatenate of the production rules in the LCS form the genotype, and therefore the GA should operate on a population of classifier systems. This approach is known as the 'Pittsburgh' Classifier Systems. Other LCS that perform their GA at the rule level within a population are known as 'Mitchigan' Classifier Systems. The most predominant representation of the discovered knowledge is the standard production rules (PRs) in the form of IF P THEN D. The PRs, however, are unable to handle exceptions and do not exhibit variable precision. The Censored Production Rules (CPRs), an extension of PRs, were proposed by Michalski and Winston that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: IF P THEN D UNLESS C, where Censor C is an exception to the rule. Such rules are employed in situations, in which conditional statement IF P THEN D holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence are tight or there is simply no information available as to whether it holds or not. Thus, the IF P THEN D part of CPR expresses important information, while the UNLESS C part acts only as a switch and changes the polarity of D to ~D. In this paper Pittsburgh style LCSs approach is used for automated discovery of CPRs. An appropriate encoding scheme is suggested to represent a chromosome consisting of fixed size set of CPRs. Suitable genetic operators are designed for the set of CPRs and individual CPRs and also appropriate fitness function is proposed that incorporates basic constraints on CPR. Experimental results are presented to demonstrate the performance of the proposed learning classifier system.

Keywords: Censored Production Rule, Data Mining, GeneticAlgorithm, Learning Classifier System, Machine Learning, PittsburgApproach, , Reinforcement learning.

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Authors: Mohammad Javad Shariatzadeh, Dana Grecov

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The tribological tests were performed on a new tribometer, in order to measure the coefficient of friction of a gland seal packing material on stainless steel shafts in presence of Cellulose Nanocrystal (CNC) suspension as a sustainable, environmentally friendly, water-based lubricant. To simulate the real situation from the slurry pumps, silica sands were used as slurry particles. The surface profiles after tests were measured by interferometer microscope to characterize the surface wear. Moreover, the coefficient of friction and surface wear were measured between stainless steel shaft and chrome steel ball to investigate the tribological effects of CNC in boundary lubrication region. Alignment of nanoparticles in the CNC suspensions are the main reason for friction and wear reduction. The homogeneous concentrated suspensions showed fingerprint patterns of a chiral nematic liquid crystal. These properties made CNC a very good lubricant additive in water.

Keywords: Gland seal, lubricant additives, nanocrystalline cellulose, water-based lubricants.

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Authors: Babak Darvish Rouhani, Mohd Naz’ri Mahrin, Fatemeh Nikpay, Pourya Nikfard, Maryam Khanian Najafabadi

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Enterprise Architecture (EA) is a strategy that is employed by enterprises in order to align their business and Information Technology (IT). EA is managed, developed, and maintained through Enterprise Architecture Implementation Methodology (EAIM). Effectiveness of EA implementation is the degree in which EA helps to achieve the collective goals of the organization. This paper analyzes the results of a survey that aims to explore the factors that affect the effectiveness of EAIM and specifically the relationship between factors and effectiveness of the output and functionality of EA project. The exploratory factor analysis highlights a specific set of five factors: alignment, adaptiveness, support, binding, and innovation. The regression analysis shows that there is a statistically significant and positive relationship between each of the five factors and the effectiveness of EAIM. Consistent with theory and practice, the most prominent factor for developing an effective EAIM is innovation. The findings contribute to the measuring the effectiveness of EA implementation project by providing an indication of the measurement implementation approaches which is used by the Enterprise Architects, and developing an effective EAIM.

Keywords: Enterprise Architecture, Enterprise Architecture Implementation Methodology, EA, Effectiveness, Factors, Implementation Methodology.

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Authors: Adam Sobey, James Blake, Ajit Shenoi

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The leisure boatbuilding industry has tight profit margins that demand that boats are created to a high quality but with low cost. This requirement means reduced design times combined with increased use of design for production can lead to large benefits. The evolutionary nature of the boatbuilding industry can lead to a large usage of previous vessels in new designs. With the increase in automated tools for concurrent engineering within structural design it is important that these tools can reuse this information while subsequently feeding this to designers. The ability to accurately gather this materials and parts data is also a key component to these tools. This paper therefore aims to develop an architecture made up of neural networks and databases to feed information effectively to the designers based on previous design experience.

Keywords:

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Authors: Min Kook Choi, Hyun Gyu Lee, Ryan You, Byeong-Seok Shin, Sang-Chul Lee

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Wireless capsule Endoscopy (WCE) has rapidly shown its wide applications in medical domain last ten years thanks to its noninvasiveness for patients and support for thorough inspection through a patient-s entire digestive system including small intestine. However, one of the main barriers to efficient clinical inspection procedure is that it requires large amount of effort for clinicians to inspect huge data collected during the examination, i.e., over 55,000 frames in video. In this paper, we propose a method to compute meaningful motion changes of WCE by analyzing the obtained video frames based on regional optical flow estimations. The computed motion vectors are used to remove duplicate video frames caused by WCE-s imaging nature, such as repetitive forward-backward motions from peristaltic movements. The motion vectors are derived by calculating directional component vectors in four local regions. Our experiments are performed on small intestine area, which is of main interest to clinical experts when using WCEs, and our experimental results show significant frame reductions comparing with a simple frame-to-frame similarity-based image reduction method.

Keywords: Wireless capsule endoscopy, optical flow, duplicated image, duplicated frame.

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Authors: R. Simutis, V. Galvanauskas, D. Levisauskas, J. Repsyte

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This paper proposes a bioprocess optimization procedure based on Relevance Vector Regression models and evolutionary programming technique. Relevance Vector Regression scheme allows developing a compact and stable data-based process model avoiding time-consuming modeling expenses. The model building and process optimization procedure could be done in a half-automated way and repeated after every new cultivation run. The proposed technique was tested in a simulated mammalian cell cultivation process. The obtained results are promising and could be attractive for optimization of industrial bioprocesses.

Keywords: Bioprocess optimization, Evolutionary programming, Relevance Vector Regression.

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