Search results for: automatic classification

Authors: Vahid Anari, Mina Bakhshi

Abstract:

Visual search and identification of immunohistochemically stained tissue of meningioma was performed manually in pathologic laboratories to detect and diagnose the cancers type of meningioma. This task is very tedious and time-consuming. Moreover, because of cell's complex nature, it still remains a challenging task to segment cells from its background and analyze them automatically. In this paper, we develop and test a computerized scheme that can automatically identify cells in microscopic images of meningioma and classify them into positive (proliferative) and negative (normal) cells. Dataset including 150 images are used to test the scheme. The scheme uses Fuzzy C-means algorithm as a color clustering method based on perceptually uniform hue, saturation, value (HSV) color space. Since the cells are distinguishable by the human eye, the accuracy and stability of the algorithm are quantitatively compared through application to a wide variety of real images.

Keywords: Positive cell, color segmentation, HSV color space, immunohistochemistry, meningioma, thresholding, fuzzy c-means.

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Authors: A. Biran, P. Sobhe Bidari, K. Raahemifar

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Diabetic Retinopathy (DR) is an eye disease that leads to blindness. The earliest signs of DR are the appearance of red and yellow lesions on the retina called hemorrhages and exudates. Early diagnosis of DR prevents from blindness; hence, many automated algorithms have been proposed to extract hemorrhages and exudates. In this paper, an automated algorithm is presented to extract hemorrhages and exudates separately from retinal fundus images using different image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. Since Optic Disc is the same color as the exudates, it is first localized and detected. The presented method has been tested on fundus images from Structured Analysis of the Retina (STARE) and Digital Retinal Images for Vessel Extraction (DRIVE) databases by using MATLAB codes. The results show that this method is perfectly capable of detecting hard exudates and the highly probable soft exudates. It is also capable of detecting the hemorrhages and distinguishing them from blood vessels.

Keywords: Diabetic retinopathy, fundus, CHT, exudates, hemorrhages.

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Authors: Ozan Mut, Mehmet Göktürk

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Matching algorithms have significant importance in speaker recognition. Feature vectors of the unknown utterance are compared to feature vectors of the modeled speakers as a last step in speaker recognition. A similarity score is found for every model in the speaker database. Depending on the type of speaker recognition, these scores are used to determine the author of unknown speech samples. For speaker verification, similarity score is tested against a predefined threshold and either acceptance or rejection result is obtained. In the case of speaker identification, the result depends on whether the identification is open set or closed set. In closed set identification, the model that yields the best similarity score is accepted. In open set identification, the best score is tested against a threshold, so there is one more possible output satisfying the condition that the speaker is not one of the registered speakers in existing database. This paper focuses on closed set speaker identification using a modified version of a well known matching algorithm. The results of new matching algorithm indicated better performance on YOHO international speaker recognition database.

Keywords: Automatic Speaker Recognition, Voice Recognition, Pattern Recognition, Digital Audio Signal Processing.

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Authors: Nour Charara, Iman Jarkass, Maria Sokhn, Elena Mugellini, Omar Abou Khaled

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Intelligent Video-Surveillance (IVS) systems are being more and more popular in security applications. The analysis and recognition of abnormal behaviours in a video sequence has gradually drawn the attention in the field of IVS, since it allows filtering out a large number of useless information, which guarantees the high efficiency in the security protection, and save a lot of human and material resources. We present in this paper ADABeV, an intelligent video-surveillance framework for event recognition in crowded scene to detect the abnormal human behaviour. This framework is attended to be able to achieve real-time alarming, reducing the lags in traditional monitoring systems. This architecture proposal addresses four main challenges: behaviour understanding in crowded scenes, hard lighting conditions, multiple input kinds of sensors and contextual-based adaptability to recognize the active context of the scene.

Keywords: Behavior recognition, Crowded scene, Data fusion, Pattern recognition, Video-surveillance

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Authors: Moustapha Bande, Hakima Ould-Slimane, Hanifa Boucheneb

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Software development for complex systems requires efficient and automatic tools that can be used to verify the satisfiability of some critical properties such as security ones. With the emergence of Aspect-Oriented Programming (AOP), considerable work has been done in order to better modularize the separation of concerns in the software design and implementation. The goal is to prevent the cross-cutting concerns to be scattered across the multiple modules of the program and tangled with other modules. One of the key challenges in the aspect-oriented programs is to be sure that all the pieces put together at the weaving time ensure the satisfiability of the overall system requirements. Our paper focuses on this problem and proposes a formal property verification approach for a given property from the woven program. The approach is based on the control flow graph (CFG) of the woven program, and the use of a satisfiability modulo theories (SMT) solver to check whether each property (represented par one aspect) is satisfied or not once the weaving is done.

Keywords: Aspect-oriented programming, control flow graph, satisfiability modulo theories, property verification.

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Authors: Qurban A Memon, Mohammad S. Laghari

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Integration of system process information obtained through an image processing system with an evolving knowledge database to improve the accuracy and predictability of wear debris analysis is the main focus of the paper. The objective is to automate intelligently the analysis process of wear particle using classification via self-organizing maps. This is achieved using relationship measurements among corresponding attributes of various measurements for wear debris. Finally, visualization technique is proposed that helps the viewer in understanding and utilizing these relationships that enable accurate diagnostics.

Keywords: Relationship Network, Relationship Measurement, Self-organizing Clusters, Wear Debris Analysis, Kohonen Network

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Authors: Mansab Ali, Muhammad Khalid Khan

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The advances in wireless communication have opened unlimited horizons but there are some challenges as well. The Nature derived air medium between MS (Mobile Station) and BS (Base Station) is beyond human control and produces channel impairment. The impact of the natural conditions at the air medium is the biggest issue in wireless communication. Natural conditions make reliability more cumbersome; here reliability refers to the efficient recovery of the lost or erroneous data. The SR-ARQ (Selective Repeat-Automatic Repeat Request) protocol is a de facto standard for any wireless technology at the air interface with its standard reliability features. Our focus in this research is on the reliability of the control or feedback signal of the SR-ARQ protocol. The proposed mechanism, RSR-ARQ (Reliable SR-ARQ) is an enhancement of the SR-ARQ protocol that has ensured the reliability of the control signals through channel impairment sensitive mechanism. We have modeled the system under two-state discrete time Markov Channel. The simulation results demonstrate the better recovery of the lost or erroneous data that will increase the overall system performance.

Keywords: ISR-ARQ, MAA, RSR-ARQ, SAA.

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Authors: Wojciech Rejchel

Abstract:

The problem of ranking (rank regression) has become popular in the machine learning community. This theory relates to problems, in which one has to predict (guess) the order between objects on the basis of vectors describing their observed features. In many ranking algorithms a convex loss function is used instead of the 0-1 loss. It makes these procedures computationally efficient. Hence, convex risk minimizers and their statistical properties are investigated in this paper. Fast rates of convergence are obtained under conditions, that look similarly to the ones from the classification theory. Methods used in this paper come from the theory of U-processes as well as empirical processes.

Keywords: Convex loss function, empirical risk minimization, empirical process, U-process, boosting, euclidean family.

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Authors: C. Kwan, X. Li, D. Lao, Y. Deng, Z. Ren, B. Raj, R. Singh, R. Stern

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Automated operations based on voice commands will become more and more important in many applications, including robotics, maintenance operations, etc. However, voice command recognition rates drop quite a lot under non-stationary and chaotic noise environments. In this paper, we tried to significantly improve the speech recognition rates under non-stationary noise environments. First, 298 Navy acronyms have been selected for automatic speech recognition. Data sets were collected under 4 types of noisy environments: factory, buccaneer jet, babble noise in a canteen, and destroyer. Within each noisy environment, 4 levels (5 dB, 15 dB, 25 dB, and clean) of Signal-to-Noise Ratio (SNR) were introduced to corrupt the speech. Second, a new algorithm to estimate speech or no speech regions has been developed, implemented, and evaluated. Third, extensive simulations were carried out. It was found that the combination of the new algorithm, the proper selection of language model and a customized training of the speech recognizer based on clean speech yielded very high recognition rates, which are between 80% and 90% for the four different noisy conditions. Fourth, extensive comparative studies have also been carried out.

Keywords: Non-stationary, speech recognition, voice commands.

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Authors: A-Q. Nguyen. A. Amrhar, J. Zambrano, G. Brown, O.A. Yeste-Ojeda, R. Jr. Landry

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Since the presence of Next Generation Air Transportation System (NextGen), Automatic Dependent Surveillance-Broadcast (ADS-B) has raised specific concerns related to the privacy and security, due to its vulnerable, low-level of security and limited payload. In this paper, the authors introduce and analyze the combination of Pulse Amplitude Modulation (PAM) and Phase Shift Keying (PSK) Modulation in conventional ADS-B, forming Secure ADS-B (SADS-B) avionics. In order to demonstrate the potential of this combination, Hardware-in-the-loop (HIL) simulation was used. The tests' results show that, on the one hand, SADS-B can offer five times the payload as its predecessor. This additional payload of SADS-B can be used in various applications, therefore enhancing the ability and efficiency of the current ADS-B. On the other hand, by using the extra phase modulated bits as a digital signature to authenticate ADS-B messages, SADS-B can increase the security of ADS-B, thus ensure a more secure aviation as well. More importantly, SADS-B is compatible with the current ADS-B In and Out. Hence, no significant modifications will be needed to implement this idea. As a result, SADS-B can be considered the most promising approach to enhance the capability and security of ADS-B.

Keywords: ADS-B authentication, ADS-B security, NextGen ADS-B, PSK signature, secure ADS-B.

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Authors: Rossitza M. Setchi, Qiao Tang

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Nowadays, ontologies are the only widely accepted paradigm for the management of sharable and reusable knowledge in a way that allows its automatic interpretation. They are collaboratively created across the Web and used to index, search and annotate documents. The vast majority of the ontology based approaches, however, focus on indexing texts at document level. Recently, with the advances in ontological engineering, it became clear that information indexing can largely benefit from the use of general purpose ontologies which aid the indexing of documents at word level. This paper presents a concept indexing algorithm, which adds ontology information to words and phrases and allows full text to be searched, browsed and analyzed at different levels of abstraction. This algorithm uses a general purpose ontology, OntoRo, and an ontologically tagged corpus, OntoCorp, both developed for the purpose of this research. OntoRo and OntoCorp are used in a two-stage supervised machine learning process aimed at generating ontology tagging rules. The first experimental tests show a tagging accuracy of 78.91% which is encouraging in terms of the further improvement of the algorithm.

Keywords: Concepts, indexing, machine learning, ontology, tagging.

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Authors: George E. Tsekouras, Evi Sampanikou

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We compare three categorical data clustering algorithms with respect to the problem of classifying cultural data related to the aesthetic judgment of comics artists. Such a classification is very important in Comics Art theory since the determination of any classes of similarities in such kind of data will provide to art-historians very fruitful information of Comics Art-s evolution. To establish this, we use a categorical data set and we study it by employing three categorical data clustering algorithms. The performances of these algorithms are compared each other, while interpretations of the clustering results are also given.

Keywords: Aesthetic judgment, comics artists, cluster analysis, categorical data.

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Authors: Richard Elling Moe

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Computer languages are usually lumped together into broad -paradigms-, leaving us in want of a finer classification of kinds of language. Theories distinguishing between -genuine differences- in language has been called for, and we propose that such differences can be observed through a notion of expressive mode. We outline this concept, propose how it could be operationalized and indicate a possible context for the development of a corresponding theory. Finally we consider a possible application in connection with evaluation of language revision. We illustrate this with a case, investigating possible revisions of the relational algebra in order to overcome weaknesses of the division operator in connection with universal queries.

Keywords: Expressive mode, Computer language species, Evaluation of revision, Relational algebra, Universal database queries

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Authors: Fumito Yoshikawa, Takumi Kobayashi, Kenji Watanabe, Nobuyuki Otsu

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Extracting in-play scenes in sport videos is essential for quantitative analysis and effective video browsing of the sport activities. Game analysis of badminton as of the other racket sports requires detecting the start and end of each rally period in an automated manner. This paper describes an automatic serve scene detection method employing cubic higher-order local auto-correlation (CHLAC) and multiple regression analysis (MRA). CHLAC can extract features of postures and motions of multiple persons without segmenting and tracking each person by virtue of shift-invariance and additivity, and necessitate no prior knowledge. Then, the specific scenes, such as serve, are detected by linear regression (MRA) from the CHLAC features. To demonstrate the effectiveness of our method, the experiment was conducted on video sequences of five badminton matches captured by a single ceiling camera. The averaged precision and recall rates for the serve scene detection were 95.1% and 96.3%, respectively.

Keywords: Badminton, CHLAC, MRA, Video-based motiondetection

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Authors: Ayşe Onat, Ferruh Yildiz, Mesut Gündüz

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In this study, an OCR system for segmentation, feature extraction and recognition of Ottoman Scripts has been developed using handwritten characters. Detection of handwritten characters written by humans is a difficult process. Segmentation and feature extraction stages are based on geometrical feature analysis, followed by the chain code transformation of the main strokes of each character. The output of segmentation is well-defined segments that can be fed into any classification approach. The classes of main strokes are identified through left-right Hidden Markov Model (HMM).

Keywords: Chain Code, HMM, Ottoman Script Recognition, OCR

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Authors: Radim Farana, Bogdan Walek, Michal Janosek, Jaroslav Zacek

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This paper presents the advantages of fuzzy control use in technological processes control. The paper presents a real application of the Linguistic Fuzzy-Logic Control, developed at the University of Ostrava for the control of physical models in the Intelligent Systems Laboratory. The paper presents an example of a sensitive non-linear model, such as a magnetic levitation model and obtained results which show how modern information technologies can help to solve actual technical problems. A special method based on the LFLC controller with partial components is presented in this paper followed by the method of automatic context change, which is very helpful to achieve more accurate control results. The main advantage of the used system is its robustness in changing conditions demonstrated by comparing with conventional PID controller. This technology and real models are also used as a background for problem-oriented teaching, realized at the department for master students and their collaborative as well as individual final projects.

Keywords: Control, fuzzy logic, sensitive system, technological proves.

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Authors: Sarra Ben Hariz, Zied Elouedi, Khaled Mellouli

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The belief K-modes method (BKM) approach is a new clustering technique handling uncertainty in the attribute values of objects in both the cluster construction task and the classification one. Like the standard version of this method, the BKM results depend on the chosen initial modes. So, one selection method of initial modes is developed, in this paper, aiming at improving the performances of the BKM approach. Experiments with several sets of real data show that by considered the developed selection initial modes method, the clustering algorithm produces more accurate results.

Keywords: Clustering, Uncertainty, Belief function theory, Belief K-modes Method, Initial modes selection.

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Authors: Raida El Mansouri, Elhillali Kerkouche, Allaoua Chaoui

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The Petri net tool INA is a well known tool by the Petri net community. However, it lacks a graphical environment to cerate and analyse INA models. Building a modelling tool for the design and analysis from scratch (for INA tool for example) is generally a prohibitive task. Meta-Modelling approach is useful to deal with such problems since it allows the modelling of the formalisms themselves. In this paper, we propose an approach based on the combined use of Meta-modelling and Graph Grammars to automatically generate a visual modelling tool for INA for analysis purposes. In our approach, the UML Class diagram formalism is used to define a meta-model of INA models. The meta-modelling tool ATOM3 is used to generate a visual modelling tool according to the proposed INA meta-model. We have also proposed a graph grammar to automatically generate INA description of the graphically specified Petri net models. This allows the user to avoid the errors when this description is done manually. Then the INA tool is used to perform the simulation and the analysis of the resulted INA description. Our environment is illustrated through an example.

Keywords: INA, Meta-modelling, Graph Grammars, AToM3, Automatic Code Generation.

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Authors: Sung Ho Ha, Seong Hyeon Joo, Hyun U. Pae

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In terms of total online audience, newspapers are the most successful form of online content to date. The online audience for newspapers continues to demand higher-quality services, including personalized news services. News providers should be able to offer suitable users appropriate content. In this paper, a news article recommender system is suggested based on a user-s preference when he or she visits an Internet news site and reads the published articles. This system helps raise the user-s satisfaction, increase customer loyalty toward the content provider.

Keywords: Content classification, content recommendation, customer profiling, documents clustering.

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Authors: Silvia Tamayo-Moreno, Diana Pérez-Marín

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In this paper, the possibility of children studying by using an interactive learning technology called Pedagogic Conversational Agent is presented. The main benefit is that the agent is able to adapt the dialogue to each student and to provide automatic feedback. Moreover, according to Math teachers, in many cases students are unable to solve the problems even knowing the procedure to solve them, because they do not understand what they have to do. The hypothesis is that if students are helped to understand what they have to solve, they will be able to do it. Taken that into account, we have started the development of Dr. Roland, an agent to help students understand Math problems following a User-Centered Design methodology. The use of this methodology is proposed, for the first time, to design pedagogic agents to teach any subject from Secondary down to Pre-Primary education. The reason behind proposing a methodology is that while working on this project, we noticed the lack of literature to design and evaluate agents. To cover this gap, we describe how User-Centered Design can be applied, and which usability techniques can be applied to evaluate the agent.

Keywords: Pedagogic conversational agent, human-computer interaction, user-centered design, natural language interface.

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Authors: J. R. Wang, S. W. Chen, Y. Chiang, W. S. Hsu, J. H. Yang, Y. S. Tseng, C. Shih

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In this research, the HABIT analysis methodology was established for Maanshan nuclear power plant (NPP). The Final Safety Analysis Report (FSAR), reports, and other data were used in this study. To evaluate the control room habitability under the CO₂ storage burst, the HABIT methodology was used to perform this analysis. The HABIT result was below the R.G. 1.78 failure criteria. This indicates that Maanshan NPP habitability can be maintained. Additionally, the sensitivity study of the parameters (wind speed, atmospheric stability classification, air temperature, and control room intake flow rate) was also performed in this research.

Keywords: PWR, HABIT, habitability, Maanshan.

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Authors: Y. Chiang, W. Y. Li, J. R. Wang, S. W. Chen, W. S. Hsu, J. H. Yang, Y. S. Tseng, C. Shih

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In this research, the HABIT code was used to estimate the concentration under the CO₂ and H₂SO₄ storage burst conditions for Kuosheng nuclear power plant (NPP). The Final Safety Analysis Report (FSAR) and reports were used in this research. In addition, to evaluate the control room habitability for these cases, the HABIT analysis results were compared with the R.G. 1.78 failure criteria. The comparison results show that the HABIT results are below the criteria. Additionally, some sensitivity studies (stability classification, wind speed and control room intake rate) were performed in this study.

Keywords: BWR, HABIT, habitability, KUOSHENG.

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Authors: Essam Al Daoud

Abstract:

To improve the classification rate of the face recognition, features combination and a novel non-linear kernel are proposed. The feature vector concatenates three different radius of local binary patterns and Gabor wavelet features. Gabor features are the mean, standard deviation and the skew of each scaling and orientation parameter. The aim of the new kernel is to incorporate the power of the kernel methods with the optimal balance between the features. To verify the effectiveness of the proposed method, numerous methods are tested by using four datasets, which are consisting of various emotions, orientations, configuration, expressions and lighting conditions. Empirical results show the superiority of the proposed technique when compared to other methods.

Keywords: Face recognition, Gabor wavelet, LBP, Non-linearkerner

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Authors: Chih-Neng Liang, Huang-Bin Huang, Bo-Chiuan Chen

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Nowadays, keyless entry systems are widely adopted for vehicle immobilizer systems due to both advantages of security and convenience. Keyless entry systems could overcome brute-force key guessing attack, statistics attack and masquerade attack, however, they can't prevent from thieves stealing behavior. In this paper, we proposed a new architecture try to improve the existent flaws. The integration of the keyless entry system and the fingerprint identification technology is more suitable to implement on the portable transponder to achieve higher security needs. We also adopt and modify AES security protocol for life expectancy and security of the portable transponder. In addition, the identification of a driver's fingerprint makes the service of automatic reinstatement of a driver's preferences become possible. Our design can satisfy not only the three kinds of previous illegal attacks, but also the stealing situation. Furthermore, many practical factors, such as costs, life expectancy and performance, have been well considered in the design of portable transponder.

Keywords: Keyless entry-system, fingerprint identification, AES security protocol, vehicle immobilizer system.

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Authors: El Mahdi Barrah, Said Safi, Abdessamad Malaoui

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In this paper, we present an algorithm for reconstruction from incomplete 2D scans for tifinagh characters. This algorithm is based on using correlation between the lost block and its neighbors. This system proposed contains three main parts: pre-processing, features extraction and recognition. In the first step, we construct a database of tifinagh characters. In the second step, we will apply “shape analysis algorithm”. In classification part, we will use Neural Network. The simulation results demonstrate that the proposed method give good results.

Keywords: Tifinagh character recognition, Neural networks, Local cost computation.

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Authors: Dongliang Wang, Jun Han, Dianfu Ma, Xianqi Zhao

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Avionics software is safe-critical embedded software and its architecture is evolving from traditional federated architectures to Integrated Modular Avionics (IMA) to improve resource usability. ARINC 653 (Avionics Application Standard Software Interface) is a software specification for space and time partitioning in Safety-critical avionics Real-time operating systems. Arinc653 uses two-level scheduling strategies, but current modeling tools only apply to simple problems of Arinc653 two-level scheduling, which only contain time property. In avionics industry, we are always manually allocating tasks and calculating the timing table of a real-time system to ensure it-s running as we design. In this paper we represent an automatically generating strategy which applies to the two scheduling problems with dependent constraints in Arinc653 partition run-time environment. It provides the functionality of automatic generation from the task and partition models to scheduling policy through allocating the tasks to the partitions while following the constraints, and then we design a simulating mechanism to check whether our policy is schedulable or not

Keywords: Arinc653, scheduling, task allocation, simulation.

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Authors: Girija Chetty, Monica Singh

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In this paper we propose a novel approach for ascertaining human identity based on fusion of profile face and gait biometric cues The identification approach based on feature learning in PCA-LDA subspace, and classification using multivariate Bayesian classifiers allows significant improvement in recognition accuracy for low resolution surveillance video scenarios. The experimental evaluation of the proposed identification scheme on a publicly available database [2] showed that the fusion of face and gait cues in joint PCA-LDA space turns out to be a powerful method for capturing the inherent multimodality in walking gait patterns, and at the same time discriminating the person identity..

Keywords: Biometrics, gait recognition, PCA, LDA, Eigenface, Fisherface, Multivariate Gaussian Classifier

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Authors: C. Adam, G. Henri, T. Levent, J.-B. Mauro, A. -L. Mayet

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This paper is concerning the application of a deterministic decisional pattern to a multi-agent system which would provide intelligence to a distributed energy smart grid at local consumer level. Development of multi-agent application involves agent specifications, analysis, design and realization. It can be implemented by following several decisional patterns. The purpose of present article is to suggest a new approach to control the smart grid system in a decentralized competitive approach. The proposed algorithmic solution results from a deterministic dichotomous approach based on environment observation. It uses an iterative process to solve automatic learning problems. Through memory of collected past tries, the algorithm monotonically converges to very steep system operation point in attraction basin resulting from weak system nonlinearity. In this sense, system is given by (local) constitutive elementary rules the intelligence of its global existence so that it can self-organize toward optimal operating sequence.

Keywords: Decentralized Competitive System, Distributed Smart Grid, Multi-Agent System

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Authors: Harkishen Singh, Gavin Mangeni

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This project aims at building an efficient and automatic power monitoring SCADA system, which is capable of monitoring the electrical parameters of high voltage powered devices in real time for example RMS voltage and current, frequency, energy consumed, power factor etc. The system uses RS-485 serial communication interface to transfer data over longer distances. Embedded C programming is the platform used to develop two hardware modules namely: RTU and Master Station modules, which both use the CC2540 BLE 4.0 microcontroller configured in slave / master mode. The Si8900 galvanic ally isolated microchip is used to perform ADC externally. The hardware communicates via UART port and sends data to the user PC using the USB port. Labview software is used to design a user interface to display current state of the power loads being monitored as well as logs data to excel spreadsheet file. An understanding of the Si8900’s auto baud rate process is key to successful implementation of this project.

Keywords: SCADA, RS485, CC2540, Labview, Si8900.

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Authors: Benjapol Auprasert, Yachai Limpiyakorn

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Measuring the complexity of software has been an insoluble problem in software engineering. Complexity measures can be used to predict critical information about testability, reliability, and maintainability of software systems from automatic analysis of the source code. During the past few years, many complexity measures have been invented based on the emerging Cognitive Informatics discipline. These software complexity measures, including cognitive functional size, lend themselves to the approach of the total cognitive weights of basic control structures such as loops and branches. This paper shows that the current existing calculation method can generate different results that are algebraically equivalence. However, analysis of the combinatorial meanings of this calculation method shows significant flaw of the measure, which also explains why it does not satisfy Weyuker's properties. Based on the findings, improvement directions, such as measures fusion, and cumulative variable counting scheme are suggested to enhance the effectiveness of cognitive complexity measures.

Keywords: Cognitive Complexity Measure, Cognitive Weight of Basic Control Structure, Counting Rules, Cumulative Variable Counting Scheme.

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