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In this paper we describe a study carried out with SenToy: a tangible interface that has the shape of a doll and is used to capture emotions from its user whilst performing specific gestures. SenToy was used with an application named Fearnot!, which is a virtual storytelling environment, where characters act autonomously and in character, so that stories emerge from those autonomous actions. The integration of SenToy in FearNot! was evaluated in two ways: (1) if users were able to manipulate the tangible interface appropriately, even if engaged in storytelling situation and (2) if the emotions expressed by the users with SenToy were the same as the ones reported to have been felt after the session. The results of the study show that Sentoy can be used to understand how the viewers reacted to the stories portrayed, and at the same time that the emotions that were expressed with SenToy (apart from some exceptions, reported in the paper) are indeed similar as the ones reported to have been felt by the users. Keywords: Affective tangible interface, affective computing, synthetic characters. [ABSTRACT FROM AUTHOR]

Reticular Activating system which has a form of small neural networks in the brain is closely related system with the automatic nervous system. It takes charge of the function that distinguishes between memorizing one and the others, accepts the only selected information and discards the unnecessary things.In this paper, we propose Reticular Activating system which has Knowledge acquisition, selection, storing, reconfiguration and retrieving part. In this paper, On/Off Switching mechanism for flexible memory is specially designed and tested. [ABSTRACT FROM AUTHOR]

Human emotion is related to stimulus and cognitive appraisal. Emotion is very important to entertainment application of virtual reality. Emotion model of 3D virtual characters is a challenging branch of Intelligent Virtual Environment (IVE). A believable 3D character should be provided with emotion and perception. In general, a virtual character is regarded as an autonomous agent with sense, perception, behavior and action. An emotion model of 3D virtual characters on the basis of psychology theory is presented in this paper. Our work is to construct 3D virtual characters that have internal sensor and perception for external stimulus, and express emotion autonomously in real time. Firstly, architecture of a virtual character is set up by cognitive model; Secondly, emotion class is set up by OCC and Plutchik's emotion theory; Thirdly, some new concepts about emotion are presented with a general mathematical model which is relation among emotion, stimulus, motivation variable, personality variable. Fourthly, a perception model of 3D characters by Gibson's theory is introduced. As a result, an emotional animation demo system of 3D virtual character is implemented on PC. [ABSTRACT FROM AUTHOR]

The paper first builds a novel affective model based on rough sets, presents the static description of affective space. Meanwhile, the paper creatively combines rough sets with Markov chain, gives the dynamic forecast of human affective change. In this affective model, some concepts and states are defined such as affective description precision and so on. It is a fundamental work to more research. Simulations are done using Matlab software, and simulation results show that this affective model can well simulate the human emotion. The results of this paper are innovative. It is a new research direction of affective computing that rough sets and affective computing infiltrate each other. [ABSTRACT FROM AUTHOR]

In this paper, combined features of global and time-sequence were used as the characteristic parameters for speech emotional recognition. A new method based on formula of MMD (Modified Mahalanobis Distance) was proposed to decrease the estimated errors and simplify the calculation. Four emotions including happiness, anger, surprise and sadness are considered in the paper. 1000 recognizing sentences collected from 10 speakers were used to demonstrate the effectiveness of the new method. The average emotion recognition rate reached at 95%. Comparison with method of MQDF [1] (Modified quadratic discriminant function), Data analysis also displayed that the MMD is better than MQDF. [ABSTRACT FROM AUTHOR]

In this paper we introduce an improved binary correlation matrix memory (CMM) with better storage capacity when storing sparse fixed weight codes generated with the algorithm of Baum et al. [3]. We outline associative memory, and describe the binary correlation matrix memory– a specific example of a distributed associative memory. The importance of the representation used in a CMM for input and output codes is discussed, with specific regard to sparse fixed weight codes. We present an algorithm for generating of fixed weight codes, originally given by Baum et al. [3]. The properties of this algorithm are briefly discussed, including possible thresholding functions which could be used when storing these codes in a CMM; L-max and L-wta. Finally, results generated from a series of simulations are used to demonstrate that the use of L-wta as a thresholding function provides an increase in storage capacity over L-max. [ABSTRACT FROM AUTHOR]

This paper describes our work on practical stereo vision for mobile robots using commodity hardware. The approach described in this paper is based on line segments, since those provide a lot of information about the environment, provide more depth information than point features, and are robust to image noise and colour variations. However, stereo matching with line segments is a difficult problem due to poorly localized end points and perspective distortion. Our algorithm uses integral images and Haar features for line segment extraction. Dynamic programming is used in the line segment matching phase. The resulting line segments track accurately from one frame to the next, even in the presence of noise. [ABSTRACT FROM AUTHOR]

Based on three dimensional echocardiography sequences, a new technique using connected equi-length curve angle chain (CELCAC) model for reconstruction and dynamic analysis of mitral annulus is proposed in this paper. Firstly, the boundary points of mitral annulus of the mitral annulus is extracted by interactive method and ordered according to their positions. Then, the three dimensional mitral annulus visualization model is established based on non-uniform rational B - spline algorithm. Finally, dynamic analysis of mitral annulus presented by the CELCAC model is suggested. The presentation is invariant to rotation, scaling, and translation. Results show that the reconstruction and analysis method proposed in this paper is feasible to assess the geometry and analyze the motion of mitral annulus. [ABSTRACT FROM AUTHOR]

Now a days all medical imaging equipments give output as digital image and non-invasive techniques are becoming cheaper, the database of images is becoming larger. This archive of images increases up to significant size and in telemedicine-based applications the storage and transmission requires large memory and bandwidth respectively. There is a need for compression to save memory space and fast transmission over internet and 3G mobile with good quality decompressed image, even though compression is lossy. This paper presents a novel approach for designing enhanced vector quantizer, which uses Kohonen's Self Organizing neural network. The vector quantizer (codebook) is designed by training with a neatly designed training image and by selective training approach .Compressing; images using it gives better quality. The quality analysis of decompressed images is evaluated by using various quality measures along with conventionally used PSNR. [ABSTRACT FROM AUTHOR]

Dual-energy mammography can suppress the contrast between adipose and glandular tissues and improve the detectability of microcalcifications (MCs). In the published papers, MCs were calibrated by aluminum and identified by their thickness. However, the variety of compositions of MCs causes the variety of attenuation differences between MCs and MC calibration material which bring about huge calculation errors. In our study, we selected calcium carbonate and calcium phosphate as the most suitable MC calibration materials and the correction coefficient was reasonably determined. Area density was used as MC indicator instead of thickness. Therefore, the calculation errors from MC calibration materials can be reduced a lot and the determination of MCs will become possible. [ABSTRACT FROM AUTHOR]

In a health care context, the use of RFID (Radio Frequency Identi- fication) technology can be employed for not only bringing down health care costs but also to facilitate automatic streamlining patient identification processes in health centers and assist medical practitioners in quick and accurate diagnosis and treatments. In this paper, we outline a describe design and application of RFID-based Real-time Intelligent Clinical Diagnosis and Treatment Support System (ICDTS) in health care. [ABSTRACT FROM AUTHOR]

Breast cancer is the leading cancer among females, the key technology of preventing breast cancers is early detection. Based on the advantage of support vector machine (SVM), finding global solution and possessing higher generalization capability on dealing with the small sample, a new method of diagnosing breast cancer by CAD is proposed in this paper. Firstly, a principal component analysis is used to represent the information of ROI image, which account for most of the variance of the original data set while significantly reducing the data dimension. After the extraction of principal components, only those data of which account for most part of variance were retained as the feature vector and input into a SVM classification and BP neural network classification to classify. Finally, the results of experiment show that the accuracy and specificity for the diagnosis of breast cancer using SVM classification is good. [ABSTRACT FROM AUTHOR]

Pattern recognition involving large-scale associative memory applications, generally constitutes tightly coupled algorithms and requires substantial computational resources. Thus these schemes do not work well on large coarse grained systems such as computational grids and are invariably unsuited for fine grained environments such as wireless sensor networks (WSN). Distributed Hierarchical Graph Neuron (DHGN) is a single-cycle pattern recognising algorithm, which can be implemented from coarse to fine grained computational networks. In this paper we describe a two-level enhancement to DHGN, which enables it to act as a standard binary image recogniser. This paper demonstrates that our single-cycle learning approach can be successfully applied to denser patterns, such as black and white images. Additionally we are able to load-balance the pattern recognition processes, irrespective of the granularity of the underlying computational network. [ABSTRACT FROM AUTHOR]

Previous research has shown that human actions can be detected and classified using their motion patterns. However, simply labelling motion patterns is not sufficient in a cognitive system that requires the ability to reason about the agent's intentions, and also to account for how the environmental setting (e.g. the presence of nearby objects) affects the way an action is performed. In this paper, we develop a graphical model that captures how the low level movements that form a high level intentional action (e.g. reaching for an object) vary depending on the situation. We then present statistical learning algorithms that are able to learn characterisations of specific actions from video using this representation. Using object manipulation tasks, we illustrate how the system infers an agent's goals from visual information and compare the results with findings in psychological experiments. In particular we show that we are able to reproduce a key result from the child development literature on action learning in children. This provides support for our model having properties in common with action learning in humans. At the end of the paper we argue that our action representation and learning model is also suitable as a framework for understanding and learning about affordances. An important element of our framework is that it will allow affordances to be understood as indicative of possible intentional actions. [ABSTRACT FROM AUTHOR]

Perceptual grouping is an important part of many computer vision systems. When inferring a new grouping from the primitive features there is always an uncertainty degree on this detection, that might be useful in further reasonings. In this paper, we present a fuzzy logic based system for the computation of the certainties assigned to pairwise line segment relations and introduce its application to the detection of continuity, identity, junction, L-junction, incidence, T-junction and parallelism relations. The results presented show that the proposed method might be very promising for future applications. [ABSTRACT FROM AUTHOR]

This paper introduces a new benchmark study of evaluating landmark-based shape correspondence used for statistical shape analysis. Different from previous shape-correspondence evaluation methods, the proposed benchmark first generates a large set of synthetic shape instances by randomly sampling a specified ground-truth statistical shape model. We then run the test shape-correspondence algorithms on these synthetic shape instances to construct a new statistical shape model. We finally introduce a new measure to describe the difference between this newly constructed statistical shape model and the ground truth. This new measure is then used to evaluate the performance of the test shape-correspondence algorithm. By introducing the ground-truth statistical shape model, we believe the proposed benchmark allows for a more objective evaluation of the shape correspondence than those that do not specify any ground truth. [ABSTRACT FROM AUTHOR]

Inspired by the idea of multi-view, we proposed an image segmentation algorithm using co-EM strategy in this paper. Image data are modeled using Gaussian Mixture Model (GMM), and two sets of features, i.e. two views, are employed using co-EM strategy instead of conventional single view based EM to estimate the parameters of GMM. Compared with the single view based GMM-EM methods, there are several advantages with the proposed segmentation method using co-EM strategy. First, imperfectness of single view can be compensated by the other view in the co-EM. Second, employing two views, co-EM strategy can offer more reliability to the segmentation results. Third, the drawback of local optimality for single view based EM can be overcome to some extent. Fourth, the convergence rate is improved. The average time is far less than single view based methods. We test the proposed method on large number of images with no specified contents. The experimental results verify the above advantages, and outperform the single view based GMM-EM segmentation methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new face recognition approach based on local binary patterns (LBP). The proposed approach has the following novel contributions. (i) As compared with the conventional LBP, anisotropic structures of the facial images can be captured effectively by the proposed approach using elongated neighborhood distribution, which is called the elongated LBP (ELBP). (ii) A new feature, called Average Maximum Distance Gradient Magnitude (AMDGM), is proposed. AMDGM embeds the gray level difference information between the reference pixel and neighboring pixels in each ELBP pattern. (iii) It is found that the ELBP and AMDGM features are well complement with each other. The proposed method is evaluated by performing facial expression recognition experiments on two databases: ORL and FERET. The proposed method is compared with two widely used face recognition approaches. Furthermore, to test the robustness of the proposed method under the condition that the resolution level of the input images is low, we also conduct additional face recognition experiments on the two databases by reducing the resolution of the input facial images. The experimental results show that the proposed method gives the highest recognition accuracy in both normal environment and low image resolution conditions. [ABSTRACT FROM AUTHOR]

This paper describes a method that accelerates pattern matching. The distance between a pattern and a window is usually close to the distance of the pattern to the adjacement windows due to image smoothness. We show how to exploit this fact to reduce the running time of pattern matching by adaptively sliding the window often by more than one pixel. The decision how much we can slide is based on a novel rank we define for each feature in the pattern. Implemented on a Pentium 4 3GHz processor, detection of a pattern with 7569 pixels in a 640 × 480 pixel image requires only 3.4ms. [ABSTRACT FROM AUTHOR]

This paper presents a practical method for obtaining the global minimum to the least-squares (L2) triangulation problem. Although optimal algorithms for the triangulation problem under L ∞ -norm have been given, finding an optimal solution to the L2 triangulation problem is difficult. This is because the cost function under L2-norm is not convex. Since there are no ideal techniques for initialization, traditional iterative methods that are sensitive to initialization may be trapped in local minima. A branch-and-bound algorithm was introduced in [1] for finding the optimal solution and it theoretically guarantees the global optimality within a chosen tolerance. However, this algorithm is complicated and too slow for large-scale use. In this paper, we propose a simpler branch-and-bound algorithm to approach the global estimate. Linear programming algorithms plus iterative techniques are all we need in implementing our method. Experiments on a large data set of 277,887 points show that it only takes on average 0.02s for each triangulation problem. [ABSTRACT FROM AUTHOR]

Interactive Image Segmentation has attracted much attention in the vision and graphics community recently. A typical application for interactive image segmentation is foreground/background segmentation based on user specified brush labellings. The problem can be formulated within the binary Markov Random Field (MRF) framework which can be solved efficiently via graph cut [1]. However, no attempt has yet been made to handle segmentation of multiple regions using graph cuts. In this paper, we propose a multiclass interactive image segmentation algorithm based on the Potts MRF model. Following [2], this can be converted to a multiway cut problem first proposed in [2] and solved by expansion-move algorithms for approximate inference [2]. A faster algorithm is proposed in this paper for efficient solution of the multiway cut problem based on partial optimal labeling. To achieve this, we combine the one-vs-all classifier fusion framework with the expansion-move algorithm for label inference over large images. We justify our approach with both theoretical analysis and experimental validation. [ABSTRACT FROM AUTHOR]

Detecting the boundaries of protein domains has been an important and challenging problem in experimental and computational structural biology. In this paper the domain detection is first taken as an imbalanced data learning problem. A novel undersampling method using distance-based maximal entropy in the feature space of SVMs is proposed. On multiple sequence alignments that are derived from a database search, multiple measures are defined to quantify the domain information content of each position along the sequence. The overall accuracy is about 87% together with high sensitivity and specificity. Simulation results demonstrate that the utility of the method can help not only in predicting the complete 3D structure of a protein but also in the machine learning system on general imbalanced datasets. [ABSTRACT FROM AUTHOR]

Provide these intelligent medical services, it is necessary to understand the situation information generated in a hospital. There should be infra technologies that can classify and control the information for processing situation data, not mere collection of conceptual information, with clear standards. This paper, as a study to seize the information generated from medical situation more clearly, understood the property of data using neural network and applied the security ratings of information so that the system to provide the user appropriate to designated rating with analyzed medical information is established. It will be an effective measure to enhance the effectiveness of medical devices and backup data already introduced and understand the various medical data that will be generated from medical devices to be introduced. [ABSTRACT FROM AUTHOR]

An edge detection method by combining image entropy and Self -Organizing Map (SOM) is proposed in this paper. First, according to information theory image entropy is used to curve up the smooth region and the region of gray level abruptly changed. Then we transform the gray level image to ideal binary pattern of pixels. We define six classes' edge and six edge prototype vectors. These edge prototype vectors are fed into input layer of the Self-Organizing Map (SOM). Classifying the type of edge through this network, the edge image is obtained. At last, the speckle edges are discarded from the edge image. Experimental results show that it gained better edge image compared with Canny edge detection method. [ABSTRACT FROM AUTHOR]

With the development of Intelligent Transport Systems (ITS), automatic license plate recognition (LPR) plays an important role in numerous applications in reality. In this paper, a coarse to fine algorithm to detect license plates in images and video frames with complex background is proposed. First, the method based on Component Connect (CC) is used to detect the possible license plate regions in the coarse detection. Second, the method based on texture analysis is applied in the fine detection. Finally, a BP Neural Network is adopted as classifier, parts of the features is selected based on statistic diagram to make the network efficient. The average accuracy of detection is 95.3% from the images with different angles and different lighting conditions. [ABSTRACT FROM AUTHOR]

In this paper, synchronization is investigated for an array of nonlinearly coupled identical connected neural networks with delay. By employing the Lyapunov functional method and the Kronecker product technique, several sufficient conditions are derived. It is shown that global exponential synchronization of the coupled neural networks is guaranteed by a suitable design of the coupling matrix, the inner linking matrix and some free matrices representing the relationships between the system matrices. The conditions obtained in this paper are in the form of linear matrix inequalities, which can be easily computed and checked in practice. A typical example with chaotic nodes is finally given to illustrate the effectiveness of the proposed synchronization scheme. [ABSTRACT FROM AUTHOR]

Of the possible interactions between human and robot, touch is an important means of providing human beings with emotional relief. However, most previous studies have focused on interactions based on voice and images. In this paper, a method of recognizing human touching behaviors is proposed for developing a robot that can naturally interact with humans through touch. In this method, the recognition process is divided into pre-process phase and recognition phase. In the pre-process phase, recognizable characteristics are calculated from the data generated by the touch detector which was fabricated using force sensors. The force sensor used an FSR (force sensing register). The recognition phase classifies human touching behaviors using a multi-layer perceptron which is a neural network model. We measured three different human touching behaviors for six men. The human touching behaviors are ‘hitting,' 'stroking,' and ‘tickling'. In the test conducted with recognizers generated for each user, the average recognition rate was 93.8%, while the test conducted with a single recognizer showed a 79.8% average recognition rate. These results show the feasibility of the proposed human touching behavior recognition method. [ABSTRACT FROM AUTHOR]

This paper investigates the construction of a wide class of singlehidden layer neural networks (SLNNs) with or without tunable parameters in the hidden nodes. It is a challenging problem if both the parameter training and determination of network size are considered simultaneously. Two alternative network construction methods are considered in this paper. Firstly, the discrete construction of SLNNs is introduced. The main objective is to select a subset of hidden nodes from a pool of candidates with parameters fixed ‘a priori'. This is called discrete construction since there are no parameters in the hidden nodes that need to be trained. The second approach is called continuous construction as all the adjustable network parameters are trained on the whole parameter space along the network construction process. In the second approach, there is no need to generate a pool of candidates, and the network grows one by one with the adjustable parameters optimized. The main contribution of this paper is to show that the network construction can be done using the above two alternative approaches, and these two approaches can be integrated within a unified analytic framework, leading to potentially significantly improved model performance and/or computational efficiency. [ABSTRACT FROM AUTHOR]

At present, most of the algorithms for learning Bayesian Networks (BNs) use EM algorithm to deal with incomplete data. They are of low efficiency because EM algorithm has to perform iterative process of probability reasoning to complete the incomplete data. In this paper we present an efficient BN learning algorithm, which use the combination of EMI method and a scoring function based on mutual information theory. The algorithm first uses EMI method to estimate, from incomplete data, probability distributions over local structures of BNs, then evaluates BN structures with the scoring function and searches for the best one. The detailed procedure of the algorithm is depicted in the paper. The experimental results on Asia and Alarm networks show that when achieving high accuracy, the algorithm is much more efficient than two EM based algorithms, SEM and EM-EA algorithms. [ABSTRACT FROM AUTHOR]

Fuzzy Petri net (FPN) is a powerful modeling tool for fuzzy production rules based knowledge systems. But it is lack of learning mechanism, which is the main weakness while modeling uncertain knowledge systems. Fuzzy neural Petri net (FNPN) is proposed in this paper, in which fuzzy neuron components are introduced into FPN as a sub-net model of FNPN. For neuron components in FNPN, back propagation (BP) learning algorithm of neural network is introduced. And the parameters of fuzzy production rules in FNPN neurons can be learnt and trained by this means. At the same time, different neurons on different layers can be learnt and trained independently. The FNPN proposed in this paper is meaningful for Petri net models and fuzzy systems. [ABSTRACT FROM AUTHOR]

Consumer demand for intelligent battery charges is increasing as portable electronic applications continue to grow. Fast charging of battery packs is a problem which is difficult, and often expensive, to solve using conventional techniques. Conventional techniques only perform a linear approximation of a nonlinear behavior of a battery packs. The battery charging is a nonlinear electrochemical dynamic process and there is no exact mathematical model of battery. Better techniques are needed when a higher degree of accuracy and minimum charging time are desired. In this paper we propose soft computing approach based on fuzzy recurrent neural networks (RFNN) training by genetic algorithms to control batteries charging process. This technique does not require mathematical model of battery packs, which are often difficult, if not impossible, to obtain. Nonlinear and uncertain dynamics of the battery pack is modeled by recurrent fuzzy neural network. On base of this FRNN model, the fuzzy control rules of the control system for battery charging is generated. Computational experiments show that the suggested approach gives least charging time and least Tend-Tstart results according to the other intelligent battery charger works. [ABSTRACT FROM AUTHOR]

In this paper we will study the naïve Bayes, one of the popular machine learning algorithms, and improve its accuracy without seriously affecting its computational efficiency. Naïve Bayes assumes positional independence, which makes the computation of the joint probability value easier at the expense of the accuracy or the underlying reality. In addition, the prior probabilities of positive and negative instances are computed from the training instances, which often do not accurately reflect the real prior probabilities. In this paper we address these two issues. We have developed algorithms that automatically perturb the computed prior probabilities and search around the neighborhood to maximize a given objective function. To improve the prediction accuracy we introduce limited dependency on the underlying pattern. We have demonstrated the importance of these extensions by applying them to solve the problem in discriminating a TATA box from putative TATA boxes found in promoter regions of plant genome. The best prediction accuracy of a naïve Bayes with 10 fold cross validation was 69% while the second extension gave the prediction accuracy of 79% which is better than the best solution from an artificial neural network prediction. [ABSTRACT FROM AUTHOR]

This paper proposes a novel ranking function, called RFHOS by incorporating higher order cumulants into the ranking function for finding differentially expressed genes. Traditional ranking functions assume a data distribution (e.g., Normal) and use only first two cumulants for statistical significance analysis. Ranking functions based on second order statistics are often inadequate in ranking small sampled data (e.g., Microarray data). Also, relatively small number of samples in the data makes it hard to estimate the parameters accurately causing inaccuracies in ranking of the genes. The proposed ranking function is based on higher order statistics (RFHOS) that account for both the amplitude and the phase information by incorporating the HOS. The incorporation of HOS deviates from implicit symmetry assumed for Gaussian distribution. In this paper the performance of the RFHOS is compared against other well known ranking functions designed for ranking the genes in two sample microarray experiments. [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]

Bearing fault detection with the aid of the vibration signals is presented. In this paper, time-domain features are extracted to indicate bearing fault, which collected from tri-axial vibration signal. Decision tree is chosen as an effective diagnostic tool to obtain bearing status. The paper also introduces principal component analysis (PCA) algorithm to reduce training data dimension and remove irrelevant data. Both original data and PCA-based data are used to train C4.5 decision tree models. Then, the result of PCA-based decision tree is compared with normal decision tree to get the best performance of classification process. [ABSTRACT FROM AUTHOR]

This paper proposed a novel residual capacity estimation method for lead-acid battery. Generally, the battery residual capacity is related to open-circuit-voltage (OCV) and inner resistance. Due to complexities, delay, coupling and nonlinearity on the relation described above, the residual capacity estimation is difficult to be obtained by the voltage or inner resistance measurement only. In this paper, by observing and recording the constant current discharging process, we generated the residual capacity pattern for different capacity levels and built a CMAC (Cerebellar Model Articulation Controller) neural network to estimate the lead-acid battery residual capacity. The characteristic of self-learning and generalization, like the cerebellum of human being, a CMAC NN estimation scheme enables powerful, straightforward, and efficient battery residual capacity estimation. With application of this scheme to the experimental data test, the estimation results demonstrate the new scheme with high accuracy and high noise rejection ability. [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]