Search

Generally, it is difficult to find constant patterns on identifiers in a container image, since the identifiers are not normalized in color, size, and position, etc. and their shapes are damaged by external environmental factors. This paper distinguishes identifier areas from background noises and removes noises by using an ART2-based quantization method and general morphological information on the identifiers such as color, size, ratio of height to width, and a distance from other identifiers. Individual identifier is extracted by applying the 8-directional contour tracking method to each identifier area. This paper proposes a refined ART2-based RBF network and applies it to the recognition of identifiers. Through experiments with 300 container images, the proposed algorithm showed more improved accuracy of recognizing container identifiers than the others proposed previously, in spite of using shorter training time. [ABSTRACT FROM AUTHOR]

This paper proposed an automatic recognition system of shipping container identifiers using fuzzy-based noise removal method and ART2-based self-organizing supervised learning algorithm. Generally, identifiers of a shipping container have a feature that the color of characters is black or white. Considering such a feature, in a container image, all areas excepting areas with black or white colors are regarded as noises, and areas of identifiers and noises are discriminated by using a fuzzy-based noise detection method. Noise areas are replaced with a mean pixel value of the whole image and areas of identifiers are extracted by applying the edge detection by Sobel masking operation and the vertical and horizontal block extraction in turn to the noise-removed image. Extracted areas are binarized by using the iteration binarization algorithm, and individual identifiers are extracted by applying 8-directional contour tacking method. This paper proposed an ART2-based self-organizing supervised learning algorithm for the identifier recognition, which creates nodes of the hidden layer by applying ART2 between the input and the hidden layers and improves the performance of learning by applying generalized delta learning and Delta-bar-Delta algorithm between the hidden and the output layers. Experiments using many images of shipping containers showed that the proposed identifier extraction method and the ART2-based self-organizing supervised learning algorithm are more improved compared with the methods previously proposed. [ABSTRACT FROM AUTHOR]

It presents an approach to the automatic generation of pencil sketching with the effects of paper texture. First, proposes a texture mapping of strokes in the aspect of skeleton, the filter algorithm and the standard deviation algorithm for rendering image, the near distances recover algorithm for real-time browsing, and finally implement the Pen-and-Ink Style of pencil sketching Rendering System. In the static rendering, it needn't adjust the threshold for the convenient on rendering and the outlines are more precise and exact. Besides, attaching the graftal and sketching shadow makes the composition of an image more attractive. Through a series of clever image processes, the system finally presents excellent colored pencil style drawings. Because the proposed algorithm is not complicated, the rendering time is quite short compared to other past related studies. Compared with other research works and Photoshop on a set of benchmarks, the system demonstrates its strength in the aspects of full automation, stability of sketching quality and higher visual satisfaction, all achieved in a considerably shorter time. [ABSTRACT FROM AUTHOR]

The storage capacity of the conventional neural network is 0.14 times of the number of neurons (P=0.14N). Due to the huge difficulty in recognizing large number of images or patterns,researchers are looking for new methods at all times. Quantum Neural Network (QNN), which is a young and outlying science built upon the combination of classical neural network and quantum computing,is a candidate to solve this problem.This paper presents Quantum Probability Distribution Network (QPDN) whose elements of the storage matrix are distributed in a probabilistic way on the base of quantum linear superposition and applies QPDN on image recognition. Contrasting to the conventional neural network, the storage capacity of the QPDN is increased by a factor of 2N,where N is the number of neurons. Besides,the case analysis and simulation tests have been carried out for the recognition of images in this paper, and the result indicates that QPDN can recognize the images or patterns effectively and its working process accords with quantum evolvement process. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of global stability for a class of neural networks with time-varying delays. A new sufficient condition for global stability is proposed by using some slack matrix variables to express the relationship between the system matrices. The restriction on the derivative of the delay function to be less than unit is removed. A numerical example shows that the result obtained in this paper improves the upper bound of the delay over some existing ones. [ABSTRACT FROM AUTHOR]

Traditional indexing methods face the difficulty of"curse of dimensionality" at high dimensionality. In this paper, the traditional vector approximation is improved. Firstly, it decreases the dimensions by LLE (locally-linear-embedding). As a result of it, a set of absolute low dimensions are gotten. Then, this paper uses the Gaussian mixture distribution and estimates the distribution through EM (expectation-maximization) method. The original data vectors are replaced by vector approximation. This approach gains higher efficiency and less run time. The experiments show a remarkable reduction of I/O. They also show an improvement on the indexing performance and then speed the image retrieval. [ABSTRACT FROM AUTHOR]

In this paper, selecting line segments as matching features in stereo vision, the orientation difference (O.D.) of the line segments is more deeply evaluated than the previous studies and two new constraints i.e. ordering and collinearity are proposed. The O.D. was supposed to be used for an indoor application, reducing candidates without elimination of the actual matches, especially if the line is horizontal or vertical with respect to the floor. The findings of this paper are as follows: 1) Applying a threshold on O.D. would result in a missing probability for the actual matches and this probability can be calculated for any given threshold, 2) The upper limit of O.D. for horizontal and vertical lines is a function of geometric parameters of the system, 3) An optimal tilt angle, whose results is the minimum upper limit for O.D., can be computed, 4) for disambiguation process, ordering and collinearity constraints are proposed. These are applied in a matching algorithm and their effectiveness is investigated on real stereo images. [ABSTRACT FROM AUTHOR]

In this paper, the line segment tracking is considered for an imaging system with translational motions (T.M.), rotational motions (R.M.) and arbitrary motions. Assuming a CCD camera with an arbitrary tilt angle installed on a mobile robot equipped with odometry system, to reduce the search space in the correspondence problem, two constraints are developed. These constraints are location and orientation differences (O.D.) for line segments in consecutive images. The findings of this paper include: 1)The development of the effect of camera tilt on the location constraint for T.M., 2)Illustrating that the upper bound of O.D. for both horizontal and vertical lines with respect to the floor, is a function of tilt in T.M., which can considerably be reduced and thus providing a tight constraint, 3)The development of the location constraint for R.M., 4) The development of the PDF and upper bound of O.D. for R.M., 5) The development of the location and O.D. constraint for arbitrary motions. Furthermore, the efficiency of these developed constraints in a line tracking algorithm was verified. [ABSTRACT FROM AUTHOR]

This paper presents a new description of image edge in the form of asymptote equation utilizing knowledge of differential geometry, and modifies a popular parlance in the traditional image processing. This paper develops the gradient concept and defines a generalized grads operator. The operator is a novel nonlinear transform, which inherits the strongpoint of noise suppression from the classical Gaussian differential filter and has a better edge extraction function. The experimental results show the proposed edge detection algorithm is powerful and effective. [ABSTRACT FROM AUTHOR]

The paper proposes a method to control the motion orbit of a cartoon object based on chaos theory. We first propose an adaptive delaying feedback controlling method which can control the Lorenz system to generate periodic, quasi-periodic and chaotic orbits. Then we apply our method to the topic of cartoon object motion controlling. It can enrich the motion type of the object thus enhances the visual effect of the cartoon video. The experiments validate the controlling results of the proposed method. Our research leads to a new area in which the chaotic theory is applied in the field of multimedia processing. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for facial expression analysis and recognition. The main contributions of the paper are as follows. First, we propose an efficient facial expression recognition scheme based on the detection of keyframes in videos where the recognition is performed using a temporal classifier. Second, we use the proposed method for extending the human-machine interaction functionality of the AIBO robot. More precisely, the robot is displaying an emotional state in response to the recognized user's facial expression. Experiments using unseen videos demonstrated the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

Pattern recognition and classification problems are most popular issue in machine learning, and it seem that they meet their second golden age with bioinformatics. However, the dataset of bioinformatics has several distinctive characteristics compared to the data set in classical pattern recognition and classification research area. One of the most difficulties using this theory in bioinformatics is that raw data of DNA or protein sequences cannot be directly used as input data for machine learning because every sequence has different length of its own code sequences. Therefore, this paper introduces one of the methods to overcome this difficulty, and also argues that the capability of generalization in this method is very poor as showing simple experiments. Finally, this paper suggests different approach to select the fixed number of effective features by using Support Vector Machine, and noise whitening method. This paper also defines the criteria of this suggested method and shows that this method improves the precision of diagnosing abnormal protein sequences with experiment of classifying ovarian cancer data set. [ABSTRACT FROM AUTHOR]

To improve the safety of drivers and walkers in a city, several traffic monitors are usually set on lanes. These traffic monitors can also improve the security of communities. In this paper, we integrate the so-called linear/circular consecutive-k-out-of-n:F systems into our proposed traffic-monitor system. The objective is to find the optimal design of monitors under limited budget for the system. The main purposes of this paper are : (1) to propose a new traffic-monitor system, (2) to present an immune algorithm (IA) for the optimal design of traffic monitors, and (3) to report numerical results of various parameters by the proposed algorithm. It is shown that the proposed immune algorithm performs well for all test problems. [ABSTRACT FROM AUTHOR]

In this paper, we originally propose a music recommendation agent in a smart office to recommend music for users. Personal tastes are diverse, even the same person may have different preferences in different situations. Hence, our music recommendation is not only based on the user favorite genres but also the current mood of users. By collecting and analyzing the contextual information of users, the agent can automatically senses the mood of users and recommends music. [ABSTRACT FROM AUTHOR]

In this paper, we propose face feature selection and classifier selection method for face image group according illuminant. In knowledge based, we stored context and weight for feature points and selected classifier for context. This context is distinguished the face images having varying illumination. This context knowledge can be accumulated and used later. Therefore we designed the face recognition system by using evolution method and efficient face feature point selection. It can improve its performance incrementally using proposed algorithm. And we proposed efficient context modeling method by using SOM. For context awareness, we made artificial face images from FERET fa dataset and divided several group. Therefore we improved face recognition ratio using adaptable classifier, feature and weight for feature points. [ABSTRACT FROM AUTHOR]

One of the most crucial and complex steps of object-oriented system design lies in the transition needed to go from system behavior (defined by means of scenario models) to component behavior (described by means of communicating hierarchical state machine models).This paper presents a re-verse approach for generating hierarchical UML statechart diagrams. Firstly, we put forward a generation algorithm for a flat statechart diagram based on the BK-algorithm, which is validated useful in our reverse engineering tool XDRE by generating UML statechart diagrams from a set of UML sequence diagrams. Secondly, according to UML composite state, an automatic approach of introducing hierarchy to the generated flat statechart diagrams is proposed and implemented. Finally, systematic experiment is conducted in the paper in order to verify the validity of this approach. [ABSTRACT FROM AUTHOR]

A novel contourlet transform based fusion algorithm for nighttime driving image is proposed in this paper. Because of advantages of the contourlet transform in dealing with the two or higher dimensions singularity or the image salient features, such as line, curve, edge and etc., each of the accurately registered images is decomposed into a low frequency subband image and a sets of high frequency subband images with various multiscale, multidirectional local salient features. By using different fusion rules for the low frequency subband image and high frequency subband images, respectively, the fused coefficients are obtained. Then, the fused image is generated by the inverse contourlet transform. The simulation results indicate that the proposed method outperforms the traditional wavelet packet transform based image fusion method. [ABSTRACT FROM AUTHOR]

Rough set theory has been proposed by Pawlak as a tool for dealing with the vagueness and granularity in information systems. The core concepts of classical rough sets are lower and upper approximations based on equivalence relations. This paper studies arbitrary binary relation based generalized rough sets. In this setting, a binary relation can generate a lower approximation operation and an upper approximation operation. We prove that such a binary relation is unique, since two different binary relations will generate two different lower approximation operations and two different upper approximation operations. This paper also explores the relationships between the lower or upper approximation operation generated by the intersection of two binary relations and those generated by these two binary relations, respectively. Keyword: Rough set, Lower approximation, Upper approximation, Binary relation, Fuzzy set, Granular computing. [ABSTRACT FROM AUTHOR]

Semiconductor Technology and multimedia communication has been developed to interest in high quality video and multi channels audio. Especially, it is trend to important technology so that DVD market grows rapidly. Mpeg audio standard is available support for any bit rate. In this paper, the design of Field Programmable Gate Array(FPGA) is presented mpeg audio synthesis filter by Distributed Arithmetic Look Up Table(DALUT) method. In order to design for high speed filter that operated from 10MHz to 30MHz and DALUT is used instead of multiplier. In this paper, the output is designed by VHDL and is verification using C language. VHDL simulation used Model-sim SE 5.7f and synthesis is used Synplify Pro 7.2Ver. Target library is used XC4010E, XC4020EX, XC4052XL and P&R tool used XACT M1.4. [ABSTRACT FROM AUTHOR]

There are restrictions of complete rationality and information symmetry, which is difficult to work in reality, in the traditional game theory. However, the evolutionary game theory, based on bounded rationality, can compensate these defects. In this paper, we set up an asymmetric model of the evolutionary game between a supplier and a retailer with asymmetric information. The evolutionary stable strategies and their premises were obtained with the replicator dynamics mechanism. The analysis in this paper has significance of explanation and direction for the supply chain management. [ABSTRACT FROM AUTHOR]

The variance minimization dual adaptive control approach with unknown parameters for stochastic system is studied in this paper. First,the problem is proposed for solving the optimal dual control law. Furthermore, the differential equation is transferred into state space model, the suboptimal dual control law is obtained through DUL algorithm. Finally, the example is given to verify the method developed in this paper. It is shown that the method is effective and practical. Keywords: dual control; adaptive control; stochastic systems. [ABSTRACT FROM AUTHOR]

The paper presents a new framework of oligopolistic market simulation based on coevolutionary computation. The coevolutionary compu-tation architecture can be regarded as a special model of the agent-based computational economics (ACE), which is a computational study of economies modeled as dynamic systems of interacting agents. The supply function equilibrium (SFE) model of an oligopolistic market is used in simulation. The piece-wise affine and continuous supply functions which have a large number of pieces are used to numerically estimate the equilibrium supply functions of any shapes. An example based on the cost data from the real-world electricity industry is used to validate the approach presented in this paper. Simulation results show that the coevolutionary approach robustly converges to SFE in different cases. The approach is robust and flexible and has the potential to be used to solve the complicated equilibrium problems in real-world oligopolistic markets. [ABSTRACT FROM AUTHOR]

This paper proposes a new blind watermarking scheme in which a watermark is embedded into the discrete wavelet transform (DWT) domain. The method uses the HVS model, and radial basis function neural networks (RBF). RBF will be implemented while embedding and extracting watermark. The human visual system (HVS) model is used to determine the watermark insertion strength. The inserted watermark is a random sequence. The secret key determines the beginning position of the image where the watermark is embedded. This process prevents possible pirates from removing the watermark easily. Experimental results show that the proposed method has good imperceptibility and high robustness to common image processing attacks. [ABSTRACT FROM AUTHOR]

According to the basic emotions theory, the paper presents personality,mood, and emotion space. The mapping relationship among personality , mood and emotion is built. The equations for updating the affective and mood states are induced and a generic computing model for personality ,mood and emotion simulation for virtual human is constructed. The simulation results demonstrate that the affective model can better simulate the dynamic process of emotion and mood change under various environment stimulus. The model provides a valid method to the emotional robot modelling and affective decision system. [ABSTRACT FROM AUTHOR]

A new robust controller based on linear-paremeter-neural-networks is designed for a class of nonlinear unkonwn chaotic systems which could be turned to "standard block control type" by using backstepping method. It was proved by constructing Lyapunov function step by step that all signals of the system are bounded and exponentially converge to the neighborhood of the origin globally and the weights of neural network converge to the optimal weights eventually. The assumption for unknown control function is reduced which stand for the innovation of our method compared with the traditional method. Also the unknown control function needn't to be positive or negative strictly in our paper. This assumption in the other papers is so strict that it couldn't be satisfied by many practical systems. So our method can be applied to a more extensive nonlinear systems. At last, take the unknown Duffing chaotic system for example, simulation study is given to demonstrate that the proposed method is effective. [ABSTRACT FROM AUTHOR]

This paper presents an approach for speech information hiding based on G.721 scheme. This approach proposes an Analysis-By-Synthesis (ABS)-based speech information hiding and extracting algorithm, is called ABS algorithm, which form the theoretical basis for designing a secure speech communication system. The ABS algorithm adopts a speech synthesizer in a speech coder. Speech embedding and coding are synchronous, i.e. fusing of secret speech information data into speech coding. Dynamic secret speech information data bits can be embedded into original carrier speech data, with high efficiency in steganography and good quality in output speech. This method is superior to available classical algorithms on hiding capacity and robustness. This paper implements the proposed approach based on speech coding scheme G.721 and the experiments show that this approach meets the requirements of information hiding, satisfies the constraints of speech quality for secure communication, and achieves high hiding capacity of 1.6Kbps with an excellent speech quality and complicating speakers' recognition. [ABSTRACT FROM AUTHOR]

In this paper, an efficient approach to search for the global threshold of image using Gaussian mixture model is proposed. Firstly, a gray-level histogram of an image is represented as a function of the frequencies of gray-level. Then to fit the Gaussian mixtures to the histogram of image, the Expectation Maximisation (EM) algorithm is developed to estimate the number of Gaussian mixture of such histograms. Finally, the optimal threshold which is the average of these means is chosen. The paper compares the new method with the classical discriminate analysis method of Otsu's. And the experimental results show that the new algorithm performs better than that of Otsu's. [ABSTRACT FROM AUTHOR]

In this paper, we proposed image processing technique for automatic real time fire and smoke detection in tunnel environment. To avoid the large scale of damage of fire occurred in the tunnel, it is necessary to have a system to minimize and to discover the incident as fast as possible. However it is impossible to keep the human observation of Closed-Circuit Television (CCTV) in tunnel for 24 hour. So if the fire and smoke detection system through image processing can warn fire state, it will be very convenient, and it can be possible to minimize damage even when people is not in front of monitor. The fire and smoke detection is different from the forest fire detection as there are elements such as car and tunnel lights and others that are different from the forest environment so that an indigenous algorithm has to be developed. The two algorithms proposed in this paper, are able to detect the exact position, at the earlier stage of incident. In addition, by comparing properties of each algorithm throughout experiment, we have proved the validity and efficiency of proposed algorithm. [ABSTRACT FROM AUTHOR]

In speaker recognition fields, score normalization is a widely used and effective technique to enhance the recognition performances and is developing further. In this paper, we are focused on the comparison among many kinds of candidates of score normalization methods and a new implementation of the speaker adaptive test normalization (ATnorm) based on a cross similarity measurement is presented which doesn't need an extra corpus for speaker adaptive impostor cohort selection. The use of ATnorm for the language robustness of the multi-language speaker verification is also investigated. Experiments are conducted on the core task of the 2006 NIST Speaker Recognition Evaluation (SRE) corpus. The experimental results indicate that all the score normalization methods mentioned can improve the recognition performances and ATnorm behaves best. Moreover, ATnorm can further contribute to the performance as a means of language robustness. [ABSTRACT FROM AUTHOR]

Based on neural network with favorable adaptability to handwritten Chinese character multi-features, in this paper a new method is proposed, using existing multi-features as inputs to structure multi neural network recognition subsystems and these subsystems are integrated with parallel connection mode. The integrated system has the lowest false recognition rate. When using traditional von Neumann architecture computer to implement this system, the system response time is longer as a result of serial computation. This paper introduces a kind of parallel computation method of using pc cluster to implement multi subsystems. It can reduce effectively recognition system's response time. [ABSTRACT FROM AUTHOR]

According to Cachin's steganography security criterion, if the statistical distributions of cover and stego images are identical, the hidden message is assumed undetectable. However, any steganographic method will surely cause some statistical distortions, which gives steganalyst a hint. This paper presents a steganalysis method for JPEG images based on Cachin criterion. It estimates the cover image from the stego one by using a small-scale geometrical transform, and then detects the statistical distortions between the cover and stego images based on some features, which are sensitive to the steganographic modifications. Then a classifier is trained on these features. Three different modern steganographic schemes are tested. Experimental results show that the proposed steganalysis scheme has better performance compared to the current steganalysis methods. [ABSTRACT FROM AUTHOR]

Traditional speech recognition systems have relied on power spectral densities, Mel-frequency cepstral, linear prediction coding and formant analysis. This paper introduces two novel input feature sets and their extraction methods for intelligent phoneme identification. These input sets are based on intrinsic phonetic characteristics of Arabic speech comprising of the dimensionally reduced Power Spectral Densities (DPSD) and Location, Trend, Gradient (LTG) values of the captured speech signal spectrum. These characteristics have been subsequently utilized as inputs to four different neural network based recognition classifiers. The classifiers have been tested for twenty-eight Arabic phonemes utterances from over one hundred non-native speakers. The results obtained using the proposed feature sets have been compared and it has been observed that LTG based input feature set provides an average phoneme identification accuracy of 86% as compared to 70% obtained through applying DPSD based inputs for similar classifiers. It is worthwhile to note that the methods proposed in this paper are generic and are equally applicable to other regional languages such as Persian and Urdu. [ABSTRACT FROM AUTHOR]

In this paper, two new properties of singular value decomposition (SVD) on images are proved. The first property demonstrates the quantitative relationship between singular values and power spectrum. The second one proves that under the condition of losing equal power spectrum, the square-error of the reconstructed image is much smaller when we reduce all singular values proportionally instead of neglect the smaller ones. Based on the two properties, a new data-hiding scheme is proposed. It performs well as for robustness, for it satisfies power-spectrum condition (PSC), and PSC-compliant watermarks are proven to be most robust. Besides, the proposed scheme has a good performance as for capacity and adaptability. [ABSTRACT FROM AUTHOR]

With the growth of critical worm threats, many researchers have studied worm-related topics and internet anomalies. The researches are mainly concentrated on worm propagation and detection more than the fundamental characteristics of worms. It is very important to know worms' characteristics because the characteristics provide basic resource for worm prevention. Unfortunately, this kind of research cases are very few until now. Moreover the existing researches only focus on understanding the function structure of the worm propagation or the taxonomy of the worm according to a sequence of worm attacks. Thus, in this paper, we try to confirm the formalized pattern of the worm action from the existing researches and analyze the report of the anti-virus companies. Finally, we define the formalized actions based on temporal behaviors and worm packet flows, and we apply our proposed method for the new worm classification. [ABSTRACT FROM AUTHOR]

Recent advances in networking technology have increased the potential collaborations for virtual enterprises (VEs) on a global scale. Due to the dynamic nature of collaborations, building and evolving trust is essential to support the formation of VEs. There is a critical need for the new approach suited to this environment. This paper presents a trust-based approach for partner selection problem in the VEs. The proposed model explores the dynamic properties of trustworthy index, which provides a multi-perspective and interactive overview of potential partners to the decision-makers. The model uses the hybrid learning algorithm of fuzzy cognitive map (FCM). As FCM has the excellent ability in modeling complex systems, the proposed model can support historical data mining automatically, and revise the existing model according to the new requirements. Results of three experiments show that this model provides reasonable performance and high adaptability for diverse partner selection problems. [ABSTRACT FROM AUTHOR]

This paper proposes obstacle collision avoidance algorithm for a mobile robot. To navigate without colliding with obstacles the mobile robot uses both the vision system approach and edge detection approach using ultrasonic sensor. The vision system approach is used when the obstacle is located more than 2 meters away from the mobile robot, and the edge detection approach using ultrasonic sensor is used for detecting obstacle remains within 2 meters range. The collision avoidance algorithm proposed in this paper utilizes both the limit-cycle method and the nearness diagram navigation method. The proposed method is tested using Pioneer 2-DX mobile robot. Through experiment, the effectiveness of proposed methods for navigating a mobile robot in the dynamic environment is demonstrated. [ABSTRACT FROM AUTHOR]

The are various definitions of instantaneous frequency (IF) but most of them have short comings. The most popular of these definitions, is the one given by Cohen. To estimate IF accuratly, techniques become more and more complex. This paper introduces the idea of aggregate spectrogram (AS). When we are not interested in the entire spectrum (e.g., finding IF), it is better to find out AS which gives an aggregate view of the spectrogram rather than calculating the individual frequencies. Some properties of AS are also defined. The AS lowers the computational complexity. This paper also presents a technique to construct an AS and finding the IF from the AS. [ABSTRACT FROM AUTHOR]

Conventional hysteresis control schemes for direct torque control (DTC) of dual-three-phase induction machine (DTPIM) usually result highly distorted current waveforms. In this paper, fuzzy space voltage modulation technique is presented for DTPIM. Using two fuzzy controllers, amplitude and space angle of desired stator voltage vector, are obtained dynamically. Combined with unified pulse width modulation method, direct torque control is applied to DTPIM. Simulation results show that fast dynamic responses are achieved. The ripple of torque and the harmonics of stator current in steady state can be reduced remarkably compared with conventional DTC method. [ABSTRACT FROM AUTHOR]

The focus of situation-aware ubiquitous computing has increased lately. An example of situation-aware applications is a multimedia education system. The development of multimedia computers and communication techniques has made it possible for a mind to be transmitted from a teacher to a student in distance environment. This paper proposed a new model of dynamic window binding by analyzing the window and attributes of the attributes of the object, and based on this, a mechanism that offers a seamless view without interfering with concurrency control is also suggested. As the result of this system's experimental implementation and analysis of the increase of delay factors accordant with the size of collaboration increase, the simulation results clearly showed that the seam increases seriously in that case, and that the dynamic window binding mechanism proposed in this paper is worth implementation and effective when a large scale of collaboration is required. [ABSTRACT FROM AUTHOR]

The real-time information access of goods plays an increasingly important role for decision-making in operations management of supply chain. Based on mobile agent and radio-frequency identification (RFID) technology, this paper proposes a novel solution to track in transit goods. Firstly, mobile RFID reader integrated in a smart phone retrieves information automatically from RFID tags bound to goods. Then, the front-end system that works on the smart phone send information back to the back-end system via short message system of mobile telecommunication platform. Because the solution is built on mobile agent platform, it enables the back-end system to dispatch mobile agents to the front-end system to process raw RFID data locally, and only the filtered results are fed back. Therefore, the solution promises to reduce time and cost of data transfer between the front-end system and the back-end system. Finally, a prototype system, GTSys named, is implemented and verified on simulation platform. [ABSTRACT FROM AUTHOR]

This paper explores the attributes that drive company wealth creation in the Miscellaneous Industrials sector of the Australian Stock Market. It looks at how the company's wealth creation changes in comparison to the changes in the Miscellaneous Industrial Index. We examine traditional and artificial intelligent (AI) feature selection techniques, to select attributes that drive company wealth and observe if a multiple domain model outperforms a single domain model with regards to predicting company wealth. Using a large number of calculated attributes, our empirical findings suggest that a multiple domain model was most effective. We found that WACC, Funds from Operation / EBITDA and EPS assist in guiding the direction of change in shareholder wealth. Whereas ROA, Capital Turnover and Gross Debt / Cashflow are key attributes in understanding the behaviour of the relative shareholder growth. We observed that ROIC, Ordinary Share Price, EVA, EPS and Trading Revenue / Total Assets are the important attributes that drive relative shareholder wealth in this industry. [ABSTRACT FROM AUTHOR]

With the rapid development of WWW, HTML documents become one of the main file formats on the Web. However, blind people find difficulty in accessing the HTML documents for their complex structure and visual reliability. The main methods for the blind to browse the web pages are through screen reader and text web browser with TTS engine. These methods can only read text on the screen without knowing the relationship among the texts. It's very difficult and time consuming to find out some information from a bunch of texts. In this paper, a special web browser called eGuideDog is designed for the visually impaired people. This web browser can extract the structure and the content of an HTML document and represent it in the form of audio. It helps the blind finding out information they concern more quickly. [ABSTRACT FROM AUTHOR]

This paper shows a strategy based on simulated annealing for the optimal tuning of a PID controller to deal with time-varying delay. The main goal is to minimize the integral time absolute error (ITAE) performance index and the overshoot for a drilling-force control system. The proposed strategy is compared with other classic tuning rules (the Ziegler-Nichols and Cohen-Coon tuning formulas). Other tuning laws derived from genetic algorithms and the Simplex search algorithm for unconstrained optimization are also included in the comparative study. The results demonstrate that simulated annealing provides an optimal tuning of the PID controller, which means better transient response (less overshoot) and less ITAE than with other methods. [ABSTRACT FROM AUTHOR]

This paper presents an approach to extracting stock market trading rules from stock market data. Trading rules are based on two multi-layer perceptrons, one generating buy signals and one generating sell signals. Inputs of these perceptrons are fed with values of technical indicators computed on historical stock quotations. Results of a large number of experiments on real-life data from the Paris Stock Exchange confirm that the model of trading rules is reasonable and the trading rules are able to generate reasonable trading signals, not only over a training period, used in the training process, but also over a test period, unknown during constructing trading rules. Moreover, trading strategies defined by such trading rules are profitable and often outperform the simple Buy&Hold strategy. [ABSTRACT FROM AUTHOR]

This paper describes an integrated clinical support system combining data entry and access with prognostic modelling, for use by the clinician, complemented by a patient information system tailored to the particular characteristics of the individual patient. The core of the system comprises a modelling methodology based on the PLANN-ARD neural network which combines risk staging with automatic rule generation to derive an explanation facility for the risk group allocation of each patient. The aim of the system is to promote better informed decision making on the part of both the clinician and the patient, exploiting the combined potential of analytical methodologies and the internet. [ABSTRACT FROM AUTHOR]

In this paper the utility of using the Self Organizing Maps (SOM), in conjunction with U-matrix, to visualize the evolution of a social network community formed by a set of blogs is shown. Weblogs are dynamic websites updated via easy-to-use content management systems whose links tend to mirror or in some cases establish new types of social relations, thereby creating a social network. Analyzing the evolution of this network allows the discovery of emerging social structures and their trends in growth. Here we apply this method to Blogalia, a blog hosting site from which we have a complete set of data. The proposed procedure not only gives some insight on how communities form and evolve, but would also enable to predict the future paths that their members will take. [ABSTRACT FROM AUTHOR]

In this paper we explore the interest of computational intelligence tools in the management of heterogeneous communication networks, specifically to predict congestion, failures and other anomalies in the network that may eventually lead to degradation of the quality of offered services. We show two different applications based on neural and neurofuzzy systems for Quality of Service (QoS) management in next generation networks for V2oIP services. The two examples explained in this paper attempt to predict the communication network resources for new incoming calls, and visualizing by means of self-organizing maps the QoS of a communication network. [ABSTRACT FROM AUTHOR]

The topology verification is an important problem in power system engineering. The paper presents the solution for this problem with use of the method, that is independent of state estimation. The method combines utilization of knowledge about power system and radial basis function networks. It allows to perform the power system topology verification as a series of verification processes for particular nodes of a power network. In the paper the possibility of such decomposition is proved. A principle of the method is described. Next, the computational example of topology verification with use of the characterized method is presented. At the end, the features of the method are analyzed, paying special attention to its efficiency. [ABSTRACT FROM AUTHOR]

This paper describes a new platform for FPGA implementation of the multilayer perceptron (MLP) back propagation algorithm (BP). The three implementation figures of the algorithm are considered. These are the off type implementation, the on chip global implementation and the dynamic reconfiguration of the ANN. To achieve our goal, a design for reuse strategy has been applied. To validate our approach, three case studies are considered using the Virtex-II and Virtex-4 FPGA devices. A comparative study is done and new conclusions are given. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology for the adjustment of fuzzy inference systems, which uses technique based on error back-propagation method. The free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules, are automatically adjusted. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through estimation of time series and by a mathematical modeling problem. More specifically, the Mackey-Glass chaotic time series is used for the validation of the proposed methodology. [ABSTRACT FROM AUTHOR]