Search

This paper is concerned with a multi-resolution tool for analysis of a time series aiming to detect abnormalities in various frequency regions. The task is treated as a kernel based novelty detection applied to a multi-level time series representation obtained from the discrete wavelet transform. Having a priori knowledge that the abnormalities manifest themselves in several frequency regions, a committee of detectors utilizing data dependent aggregation weights is build by combining outputs of detectors operating in those regions. [ABSTRACT FROM AUTHOR]

This paper presents a middleware system for multi-agents on a distributed system as a general test-bed for bio-inspired approaches. The middleware is unique to other approaches, including distributed object systems, because it can maintain and migrate a dynamic federation of multiple agents on different computers. It enables each agent to explicitly define its own deployment policy as a relocation between the agent and another agent. This paper describes a prototype implementation of the middleware built on a Java-based mobile agent system and its practical applications that illustrates the utility and effectiveness of the approach in real distributed systems. [ABSTRACT FROM AUTHOR]

Resource replication is a crucial technique for improving system performance of distributed applications with shared resources. A larger number of replicas require shorter time to reach a replica of the requested resource, but consume more storage of hosts. Therefore, it is indispensable to adjust the number of replicas appropriately for its application. This paper considers the problem for controlling the density of replicas adaptively in dynamic networks. The goal of the problem is to adjust the number of replicas to a constant fraction of the current network size. This paper proposes algorithm inspired by the single species population model, which is a well-known population ecology model. The simulation results show that the proposed algorithm realize self-adaptation of the replica density in dynamic networks. [ABSTRACT FROM AUTHOR]

This paper describes how a distributed neural architecture for the general control of robots has been applied for the generation of a walking behaviour in the Aibo robotic dog. The architecture described has been already demonstrated useful for the generation of more simple behaviours like standing or standing up. This paper describes specifically how it has been applied to the generation of a walking pattern in a quadruped with twelve degrees of freedom, in both simulator and real robot. The main target of this paper is to show that our distributed architecture can be applied to complex dynamic tasks like walking. Nevertheless, by showing this, we also show how a completely neural and distributed controller can be obtained for a robot as complex as Aibo on a task as complex as walking. This second result is by itself a new and interesting one since, to our extent, there are no other completely neural controllers for quadruped with so many DOF that allow the robot to walk. Bio-inspiration is used in three ways: first we use the concept of central pattern generators in animals to obtain the desired walking robot. Second we apply evolutionary processes to obtain the neural controllers. Third, we seek limitations in how real dogs do walk in order to apply them to our controller and limit the search space. [ABSTRACT FROM AUTHOR]

An all-optical neural network based on coupled ring lasers is proposed in this paper. Each laser in the network has a different wavelength, representing one neuron. The network status is determined by the wavelength of the network's light output. Inputs to the network are in the optical power domain. The nonlinear threshold function required for neural-network operation is achieved optically by interaction between the lasers. A simple laser model developed in the paper has illuminated the behavior of the coupled lasers. An experimental system is implemented using single mode fiber optic components at wavelengths near 1550 nm. A number of functions are implemented to demonstrate the practicality of the new network. From the experiment, a conclusion can be obtained that the neural network is particularly robust against input wavelength variations. [ABSTRACT FROM AUTHOR]

This paper proposes a reconfigurable architecture for VLSI implementation of BP neural networks with on-chip learning. Basing on systolic arrays, this architecture can flexibly adapt to neural networks with different scales, transfer functions or learning algorithms by reconfiguration of basic processing components,. Three kinds of reconfigurable processing units (RPU) are proposed firstly basing on the analysis of neural network's reconfiguration. Secondly, the paper proposes a reconfigurable systolic architecture and the method of mapping BP networks into this architecture. The implementation of an instance on FPGA is introduced in the last. The results show that this flexible architecture can also achieve a high learning speed of 432M CUPS (Connection Updated Per Second) at 100MHz using 22 multipliers. [ABSTRACT FROM AUTHOR]

In this paper there is a natural language interface presented, which consists of the intelligent mechanisms of human identification, speech recognition, word and command recognition, command syntax and result analysis, command safety assessment, technological process supervision as well as human reaction assessment. In this paper there is also a review of the selected issues on recognition of speech commands in natural language given by the operator of the technological device. A view is offered of the complexity of the recognition process of the operator's words and commands using neural networks made of a few layers of neurons. The paper presents research results of speech recognition and automatic recognition of commands in natural language using artificial neural networks. [ABSTRACT FROM AUTHOR]

New applications of artificial neural networks are capable of recognition and verification of effects and safety of commands given by the operator of the technological device. In this paper there is a review of the selected issues on estimation of results and safety of the operator's commands as well as supervision of the technological process. A view is offered of the complexity of effect analysis and safety assessment of commands given by the operator using neural networks. The first part of the paper introduces a new concept of modern supervising systems of the technological process using a natural language human-machine interface and discusses the general topics and issues. The second part is devoted to a discussion of more specific topics of the automatic command verification that have led to interesting new approaches and techniques. [ABSTRACT FROM AUTHOR]

It is well known, the Job-Shop Scheduling Problem (JSSP) is the most complicated and typical problem of all kinds of production scheduling problems, the allocation of resources over time to perform a collection of tasks. The current method has several shortcomings in solving the JSSP. In this paper, we correct these deficiencies by introducing a dynamic model that is based on an analysis of the run-time behavior of CSANN algorithm. At the same time, this paper proposes several new heuristics in order to improve the performance of CSANN. The computational simulations have shown that the proposed hybrid approach has good performance with respect to the quality of solution and the speed of computation. [ABSTRACT FROM AUTHOR]

As more and more industries experience the globalization of business activities, measuring productivity performance has become an area of concern for companies and policy makers in Europe, the United States, Japan and so on. A novel way about nonlinear regression modeling of high-technology manufacturing (HTM) productivity with the support vector machines (SVM) is presented in this paper. Optimization of labor productivity (LP) is also presented in this paper, which is based on chaos and uses the SVM regression model as the objective function. [ABSTRACT FROM AUTHOR]

In this paper posture control of a human arm in the sagittal plane is investigated by means of model simulations. The arm is modeled by a nonlinear neuromusculoskeletal model with two degrees of freedom and six muscles. A multilayer perceptron network is used in this paper, and effectively adapted by Levenberg-Marquardt training algorithm. The duration of next movement is regulated according as current feedback states. Simulation Results indicate that this method can maintain two joints at different location in allowable bound. The control scheme provides novel insight into neural prosthesis control and robotic control. [ABSTRACT FROM AUTHOR]

In this paper, we aim to address the cell phase identification problem, and two important aspects, the feature extraction methods and the classifier design, are discussed. In our study, we first propose extracting high frequency information of different orientations using Steerable filters. Next, we employ a multi-layer neural network using the back-propagation algorithm to replace K-Nearest Neighbor (KNN) classifier which has been implemented in the Cellular Image Quantitator (CELLIQ) system [3]. Experimental results provide a comparison between the proposed steerable filter features and existing regular features which have been used in published papers [3, 5]. From the comparison, it can be concluded that Steerable filter features can effectively represent the cells in different phases and improve the classification accuracy. Neural network also has a better performance than KNN currently deployed in CELLIQ system [3]. [ABSTRACT FROM AUTHOR]

Many interaction data still exist only in the biomedical literature and they require much effort to construct well-structured data. Discovering useful knowledge from large collections of papers is becoming more important for efficient biological and biomedical researches as genomic research advances. In this paper, we present a relation kernel-based interaction extraction method to extract knowledge efficiently. We extract protein interactions of from text documents with relation kernel and Yeast was used as an example target organism. Kernel for relation extraction is constructed with predefined interaction corpus and set of interaction patterns. The proposed method only exploits shallow parsed documents. Experimental results show that the proposed kernel method achieves a recall rate of 79.0% and precision rate of 80.8% for protein interaction extraction from biomedical document without full parsing efforts. [ABSTRACT FROM AUTHOR]

In self and non-self discrimination (SNSD) model, it is very important to generate a desirable detector set since it decides the performance and scale of the SNSD model based task. By using the famous principle of negative selection in natural immune system, a novel generating algorithm of detector, multiple-point bit mutation method, is proposed in this paper. It utilizes random multiple-point mutation to look for non-self detectors in a large range in the whole space of detectors, such that we can obtain a required detector set in a reasonable computation time. This paper describes the work procedure of the proposed detector generating algorithm. We tested the algorithm by using many datasets and compared it with the Exhaustive Detector Generating Algorithm in details. The experimental results show that the proposed algorithm outperforms the Exhaustive Detector Generating Algorithm both in computational complexities and detection performance. [ABSTRACT FROM AUTHOR]

This paper addresses the important issues of error control for video transmission over 3G, considering the fact that wireless video delivery faces the huge challenge of the high error rate and time variability in wireless channel. This paper proposes a real world statistics based event-trigger bit error rate (BER) model, which can describe and handle the time-varying wireless channel error characteristics better. Moreover, a recurrent neural network is employed to decide the state transfer as a mechanism. Simulation results and comparisons demonstrate effectiveness and efficiency of the proposed method in term of visual performance and transmission efficiency over a variety of wireless channel conditions. [ABSTRACT FROM AUTHOR]

The recommendation G.728 depends on the Levinson-Durbin (L-D) algorithm to update its gain filter coefficients. In this paper, it is contrasted with BP neural network method. Because quantizer has not existed at optimizing gain filter, the quantization SNR can not be used to evaluate its performance. This paper proposes a scheme to estimate SNR so that gain predictor can be separately optimized with quantizer. Using BP neural network filter, the calculation quantity is only 6.7 percent of L-D method's and its average segment SNR is about 0.156dB higher than G.728. It is also used to evaluate the case that excitation vector is 16 or 20 samples, respectively, the BP neural network algorithm has similarly good result. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid neural network model based on the Generalized Learning Vector Quantization(GLVQ) learning algorithm and applies this proposed method to the BIT system of More-Electric Aircraft Electrical Power System (MEAEPS). This paper first discusses the feasibility of application unsupervised neural networks to the BIT system and the representative Generalized LVQ (GLVQ) neural network is selected due to its good performance in clustering analysis. Next, we adopt a new form of loss factor to modify the original GLVQ algorithm in order to make it more suitable for our application. Since unsupervised networks cannot distinguish the similar classes, we add a LVQ layer to the GLVQ network to construct a hybrid neural network model. Finally, the proposed method has been applied to the intelligent BIT system of the MEAEPS, and the results show that the proposed method is promising to improve the performance of the BIT system. [ABSTRACT FROM AUTHOR]

This paper presents a power system stabilizer design that uses a generalized minimum variance-inverse dynamic neuro controller, which is the combination of the inverse dynamic neural model, the generalized minimum variance, and the neuro compensator. An inverse dynamic neural model represents the inverse dynamics of the system. The inverse dynamic neural model is trained to provide control input into the system, which makes the plant output reach the target value at the next sampling time. Once the inverse dynamic neural model is trained, it does not require retuning for cases with other types of disturbances. In this paper, a generalized minimum variance control scheme is adapted to prevent unstable system performance caused by non-minimum phase characteristics. In addition, a neural compensator is designed to compensate for modeling errors. The proposed control scheme is tested in a multimachine power system. [ABSTRACT FROM AUTHOR]

The distribution system problems, such as planning, loss minimization, and energy restoration, usually involve the phase balancing or network reconfiguration procedures. The determination of an optimal phase balance is, in general, a combinatorial optimization problem. This paper proposes optimal reconfiguration of the phase balancing using the neural network, to switch on and off the different switches, allowing the three phases supply by the transformer to the end-users to be balanced. This paper presents the application examples of the proposed method using the real and simulated test data. [ABSTRACT FROM AUTHOR]

Electric Power Load Forecasting is important for the economic and secure operation of power system, and highly accurate forecasting result leads to substantial savings in operating cost and increased reliability of power supply. Conventional load forecasting techniques, including time series methods and stochastic methods, are widely used by electric power companies for forecasting load profiles. However, their accuracy is limited under some conditions. In this paper, neural networks have been successfully applied to load forecasting. Forecasting model with Neural Networks is set up based on the analysis of the characteristics of electric power load, and it works well even with rapidly changing weather conditions. This paper also proposes a novel method to improve the generalization ability of the Neural Networks, and this leads to further increasing accuracy of load forecasting. [ABSTRACT FROM AUTHOR]

In this paper, we propose a grasping control method for robot hand using fuzzy theory and partially- linearized neural network. The robot hand has Double-Octagon Tactile Sensor (D.O.T.S), which has been proposed in our previous papers, to detect grasping force between the grasped object and the robot fingers. Because the measured forces are fluctuant due to the measuring error and vibration of the hand, the tactile information is ambiguous. In order to quickly control the grasping force to prevent the grasped object sliding out off the robot fingers, we apply the possibility theory to deal with the ambiguous problem of the tactile information, and use the partially- linearized neural network (P.L.N.N) to construct a fuzzy neural network. The method proposed in this paper is verified by applying it to practical grasping control of breakable objects, such as eggs, fruits, etc. [ABSTRACT FROM AUTHOR]

This paper considers the problem of PD control of overhead crane in the presence of uncertainty associated with crane dynamics. By using radial basis function neural networks, these uncertainties can be compensated effectively. This new neural control can resolve the two problems for overhead crane control: 1) decrease steady-state error of normal PD control. 2) guarantee stability via neural compensation. By Lyapunov method and input-to-state stability technique, we prove that these robust controllers with neural compensators are stable. Real-time experiments are presented to show the applicability of the approach presented in this paper. [ABSTRACT FROM AUTHOR]

A novel least squares support vector machines based on Mexican hat wavelet kernel is presented in the paper. The wavelet kernel which is admissible support vector kernel is characterized by its local analysis and approximate orthogonality, and we can well obtain estimates for regression by applying a least squares wavelet support vector machines (LS-WSVM). To test the validity of the proposed method, this paper demonstrates that LS-WSVM can be used effectively for the identification and adaptive control of nonlinear dynamical systems. Simulation results reveal that the identification and adaptive control schemes suggested based on LS-WSVM gives considerably better performance and show faster and stable learning in comparison to neural networks or fuzzy logic systems. LS-WSVM provides an attractive approach to study the properties of complex nonlinear system modeling and adaptive control. [ABSTRACT FROM AUTHOR]

Presented in this paper is neural network based adaptive control for a class of affine nonlinear systems in the strict-feedback form with unknown nonlinearities. A popular recursive design methodology - backstepping is employed to systematically construct feedback control laws and associated Lyapunov functions. The significance of this paper is to make best use of available signals, avoid unnecessary parameterization, and minimize the node number of neural networks as on-line approximators. The design assures that all the signals in the closed loop are semi-globally uniformly, ultimately bounded and the outputs of the system converges to a tunable small neighborhood of the desired trajectory. Novel parameter tuning algorithms are obtained on a more practical basis. [ABSTRACT FROM AUTHOR]

In this paper, the topic of fractional order digital differentiators (FODD) is designed using neural networks approximation method. First, FODD amplitude response is given in the form of sum of exponential basis functions. Then, the exponential basis function neural network is used to approximate FODD amplitude response. Finally, some examples compared with others' method are given to illustrate the advantages of this paper approach. [ABSTRACT FROM AUTHOR]

A new approach to object detection using image icons based on Unit-linking PCNN (Pulse Coupled Neural Network) is introduced in this paper. Unit-linking PCNN, which has been developed from PCNN exhibiting synchronous pulse bursts in cat and monkey visual cortexes, is a kind of time-space-coding SNN (Spiking Neural Network). We have used Unit-linking PCNN to produce the global image icons with translation and rotation invariance. Unit-linking PCNN image icon (namely global image icons) is the 1-dimentional time series, and is a kind of image feature extracted from the time information that Unit-linking PCNN code the 2-dimentional image into. Its translation and rotation invariance is a good property in object detection. In addition to translation, rotation invariance, the object detection approach in this paper is also independent of scale variation. [ABSTRACT FROM AUTHOR]

This paper presents an evolutionary cellular automata based neural systems (Evolutionary CANS) for visual servoing of RV-M2 robot manipulator. The architecture of CANS consist of a two-dimensional (2-D) array of basic neurons. Each neuron of CANS has local connections only with contiguous neuron and acts as a form of pulse according to the dynamics of the chaotic neuron model. CANS are generated from initial cells according to the cellular automata (CA) rule. Therefore neural architecture is determined by both initial pattern of cells and production rule of CA. Production rules of CA are evolved based on a DNA coding. DNA coding has the redundancy and overlapping of gene and is apt for representation of the rule. In this paper we show the general expression of CA rule and propose translating method from DNA code to CA rule. In addition, we present visual servoing application using evolutionary CANS. [ABSTRACT FROM AUTHOR]

This paper first introduces the system requirement and the system flow of the auto-plotting system. As the data points needed by the auto-plotting system coming from the remote speech signals, to reach high recognition accuracy, the Hidden Markov Model (HMM) approach was chosen as the speech recognition approach. Then the paper is detailed on the speaker dependent (SD), speaker independent (SI) and speaker adaptive (SA) speech recognition methods. We proposed the n-speech models SD system as the recognition system to gain the highest recognition performance in varying speech environments. However the system required that searching for the optimal model from the database should finish in 5 minutes, so the paper finally describes how the Self-Organizing Map (SOM) was used to pre clustering to the n-speech models, to decrease the time for speech recognition and results evaluation and decrease matching time, Experiments show the n-speech models SD system can select the best-matching model in the limited time and improve the average speech recognition accuracy to 97.2. It ideally suits the system requirements. [ABSTRACT FROM AUTHOR]

Linear Discriminant Analysis (LDA) is a very powerful method in pattern recognition. But it is difficult to realize online processing for data stream. In this paper, a new adaptive LDA method is proposed. We decompose the online LDA problem into two adaptive PCA problems and develop a fixed point adaptive PCA to implement adaptive LDA. Online updating of in-class scatter matrix Sw(t) and covariance matrix Cx(t) are derived in this paper. Simulation results show that the proposed method has no learning rate, fast convergence and less time-consuming. [ABSTRACT FROM AUTHOR]

Locally linear embedding (LLE) is one of the methods intended for dimensionality reduction, which relates to the number K of nearest-neighbors points to be initially chosen. So, in this paper, we want that the parameter K has little influence on the dimension reduction, that is to say, the parameter K can be widely chosen while not influence the effect of dimension reduction. Therefore, we propose a method of improved LLE, which uses new distance computing for weight of K nearest-neighbors points in LLE. Thus, even when the number K is little, the improved LLE can get good results of dimension reduction, while the traditional LLE needs a larger number of K to get the same results. When the number K of the nearest neighbors gets larger, test in this paper has proved that the improved LLE can still get correct results. [ABSTRACT FROM AUTHOR]

Constructive fuzzy neural networks (i.e., CFNN) proposed in [1] cannot be used for non-numerical data. In order to use CFNN to deal with non-numerical complicated data, rough set theory is adopted to improve the CFNN in this paper. First of all, we use rough set theory to extract core set of non-numerical attributes and decrease number of dimension of samples by reducing redundancy. Secondly, we can pre-classify the samples according to non-numerical attributes. Thirdly, we use CFNN to classify the samples according to numerical attributes. The proposed method not only increases classification accuracy but also speeds up classification process. Finally, the classification of wireless communication signals is given as an example to illustrate the validation of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

For a well-trained radial basis function neural network, this paper proposes a novel input stochastic sensitivity definition and gives its computational formula assuming the inputs are modelled by normal distribution random variables. Based on this formula, one can calculate the magnitude of sensitivity for each input (i.e. feature), which indicates the degree of importance of input to the output of neural network. When there are redundant inputs in the training set, one always wants to remove those redundant features to avoid a large network. This paper shows that removing redundant features or selecting significant features can be completed by choosing features with sensitivity over a predefined threshold. Numerical experiment shows that the new approach to feature selection performs well. [ABSTRACT FROM AUTHOR]

Blind identification of mixing matrix approach and the corresponding algorithm are proposed in this paper. Usually, many conventional Blind Source Separation (BSS) methods separate the source signals by estimating separated matrix. Different from this way, we present a new BSS approach in this paper, which achieves BSS by directly identifying the mixing matrix, especially for underdetermined case. Some experiments are conducted to check the validity of the theory and availability of the algorithm in this paper. [ABSTRACT FROM AUTHOR]

Comparative study of extreme learning machine (ELM) and support vector machine (SVM) is investigated in this paper. A cross validation method for determining the appropriate number of neurons in the hidden layer is also proposed in this paper. ELM proposed by Huang, et al [3] is a novel machine-learning algorithm for single hidden-layer feedforward neural network (SLFN), which randomly chooses the input weights and hidden-layer bias, and analytically determines the output weights optimally instead of tuning them. This algorithm tends to produce good generalization ability and obtain least experience risk simultaneously with solid foundations. Benchmark tests of a real Tennessee Eastman Process (TEP) are carried out to validate its superiority. Compared with SVM, this proposed algorithm is much faster and has better generalization performance than SVM in the case studied in this paper. [ABSTRACT FROM AUTHOR]

This paper presents an SVM classification algorithm with predesigned error correction ability by incorporating the error control coding schemes used in digital communications into the classification algorithm. The algorithm is applied to face recognition problems in the paper. Simulation experiments are conducted for different SVM-based classification algorithms using both PCA and Fisherface features as input vectors respectively to represent the images with dimensional reduction, and performance analysis is made among different approaches. Experiment results show that the error correction SVM classifier of the paper outperforms other commonly used SVM-based classifiers both in recognition rate and error tolerance. [ABSTRACT FROM AUTHOR]

In this paper, a clustering algorithm based on Gradient Based Fuzzy C-Means with a Mercer Kernel, called GBFCM (MK), is proposed. The kernel method adopted in this paper implicitly performs nonlinear mapping of the input data into a high-dimensional feature space. The proposed GBFCM(MK) algorithm is capable of dealing with nonlinear separation boundaries among clusters. Experiments on a synthetic data set and several real MPEG data sets show that the proposed algorithm gives better classification accuracies than both the conventional k-means algorithm and the GBFCM. [ABSTRACT FROM AUTHOR]

In view of the character of saturation linearity of output functions of neurons of the cellular neural networks, the method decomposing the state space to sub-regions is adopted to study almost sure exponential stability on delayed cellular neural networks which are in the noised environment. When perturbed terms in the model of the neural network satisfy Lipschitz condition, some algebraic criteria are obtained. The results obtained in this paper show that if an equilibrium of the neural network is the interior point of a sub-region, and an appropriate matrix related to this equilibrium has some stable degree to stabilize the perturbation, then the equilibrium of the delayed cellular neural network can still remain the property of exponential stability. All results in the paper is only to compute eigenvalues of matrices. [ABSTRACT FROM AUTHOR]

In this paper, the global exponential stability of a class of recurrent cellular neural networks with reaction-diffusion and variable time delays was studied. When neural networks contain unbounded activation functions, it may happen that equilibrium point does not exist at all. In this paper, without assuming the boundedness, monotonicity and differentiability of the active functions, the algebraic criteria ensuring existence, uniqueness and global exponential stability of the equilibrium point of neural networks are obtained. [ABSTRACT FROM AUTHOR]

In this paper, we discuss delayed Cohen-Grossberg neural networks with time-varying and distributed delays and investigate their global asymptotical stability of the equilibrium point. The model proposed in this paper is universal. A set of sufficient conditions ensuring global convergence and globally exponential convergence for the Cohen-Grossberg neural networks with time-varying and distributed delays are given. Most of the existing models and global stability results for Cohen-Grossberg neural networks, Hopfield neural networks and cellular neural networks can be obtained from the theorems given in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the global uniform asymptotical stability is studied for delayed neutral-type neural networks by constructing appropriate Lyapunov functional and using the linear matrix inequality (LMI) approach. The main condition given in this paper is dependent on the size of the measure of the space, which is usually less conservative than space-independent ones. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed criteria. [ABSTRACT FROM AUTHOR]

In this paper we studied a new dynamic evolution model on phase encoding in population of neuronal oscillators under condition of different phase, and investigated neural information processing in cerebral cortex and dynamic evolution under action of different stimulation signal. It is obtained that evolution of the averaging number density along with time in space of three dimensions is described in different cluster of neuronal oscillators firing action potential at different phase space by means of method of numerical analysis. The results of numerical analysis show that the dynamic model proposed in this paper can be used to describe mechanism of neurodynamics on attention and memory. [ABSTRACT FROM AUTHOR]

A simple and effective way of coordinating distributed, mobile, and parallel applications is to use a virtual shared memory (VSM), such as a Linda tuple-space. In this paper, we propose a new kind of VSM, called a tagged set. Each element in the VSM is a value with an associated tag, and values are read or removed from the VSM by matching the tag. Tagged sets exhibit three properties useful for VSMs: 1Ease of use. A tagged value naturally corresponds to the notion that data has certain attributes, expressed by the tag, which can be used for later retrieval. 2Flexibility. Tags are implemented as propositional logic formulae, and selection as logical implication, so the resulting system is quite powerful. Tagged sets naturally support a variety of applications, such as shared data repositories (e.g., for media or e-mail), message passing, and publish/subscribe algorithms; they are powerful enough to encode existing VSMs, such as Linda spaces.3Security. Our notion of tags naturally corresponds to keys, or capabilities: a user may not select data in the set unless she presents a legal key or keys. Normal tags correspond to symmetric keys, and we introduce asymmetric tags that correspond to public and private key pairs. Treating tags as keys permits users to easily specify protection criteria for data at a fine granularity. This paper motivates our approach, sketches its basic theory, and places it in the context of other data management strategies. [ABSTRACT FROM AUTHOR]

Composition of a concurrent system out of components involves coordination of their mutual interactions. In component-based construction, this coordination becomes the responsibility of the glue-code language and its underlying run-time middle-ware. Reo offers an expressive glue-language for construction of coordinating component connectors out of primitive channels. In this paper we consider the problem of synthesizing Reo coordination code from a specification of a behavior as a relation on scheduled-data streams. The specification is given as a constraint automaton that describes the desired input/output behavior at the ports of the components. The main contribution in this paper is an algorithm that generates Reo code from a given constraint automaton. [ABSTRACT FROM AUTHOR]

In this paper the obscene images first primarily recognizes through the human skin color detection and key point model matching. The other images that are not confirmed extract characteristic of obscene images through edge detection, posture estimation and wavelet compression, and then recognized using the optimizing broaden weighted fuzzy neural network, which is called two-phase recognizing method. The experiment indicates the method that this paper present can recognize the obscene images effectively. [ABSTRACT FROM AUTHOR]

In this paper, a digital image encryption scheme based on the hybrid chaotic system is proposed. A three-order Cellular Neural Network (CNN) with complex dynamical behavior and Logistic chaotic map are employed in the encryption scheme. The output sequences of two chaotic systems are combined effectively by using a special method, and a fast image encryption is realized by means of the bit-wise XOR operations. The results of the security analyses indicate that this encryption scheme not only has a large key space but also has a very sensitivity with respect to the encryption key. The encryption scheme proposed in this paper possesses perfect diffusion and confusion properties and it can resist the know-plaintext attacks and chosen-plaintext attacks. [ABSTRACT FROM AUTHOR]

This paper studied a model that is able to extract occluding surfaces of subjective contour figures based on the mechanism of feature extraction found in a visual system. A common factor in all such subjective contour figures, such as the Kanizsa triangle is having a surface occluding part of a background, i.e. subjective contours are always accompanied by subjective surfaces. In this paper we propose a neural network model that predicts the shape of subjective surfaces. This model employed an important two-stage process of the Induced Stimuli Extraction System (ISES) and Subjective Surfaces Perception System (SSPS). The former system extracted the induced stimuli for the perception of subjective surfaces, and the latter formed the subjective surfaces from the induced stimuli. The proposed model is demonstrated on a variety of Kanizsa-type subjective contour displays. The results of the experiment showed that the proposed model was successful not only in extracting the induced stimuli for the perception of subjective contours, but also in perceiving the subjective surface from the induced stimuli. [ABSTRACT FROM AUTHOR]

In this paper, a new filter, η-LM, which is based on Levenberg-Marquardt Artificial Neural Networks, is proposed for the impulsive noise suppression from highly distorted images. The η-LM uses Anderson-Darling goodness-of-fit test in order to find corrupted pixels more accurately. The extensive simulation results show that the proposed filter achieves a superior performance to the other filters mentioned in this paper in the cases of being effective in detail preservation and noise suppression, especially when the noise density is very high. [ABSTRACT FROM AUTHOR]

A new method is brought forward to predict multivariate time series in this paper. Related time series instead of a single time series are applied to obtain more information about the input signal. The input data are embedded as the phase space points. By the Principle Component Analysis (PCA) the most useful information is extracted form the input signal and the embedding dimension of the phase space is reduced, consequently, the input of the neural networks is simplified. The recurrent neural network has a number of advantages for predicting nonlinear time series. Therefore, Elman neural network is adopted to predict multivariate time series in this paper. Simulations of nonlinear multivariate time series from nature and industry process show the validity of the method proposed. [ABSTRACT FROM AUTHOR]

The focus of this paper is to propose an explanation for how biological auditory mechanism is able to use spiking neurons to code high bandwidth information using information channels with very slow sampling rates (< 20 Hz). The general approach described in this paper is to decompose the signal into narrow band channels, each of which can be sampled at a frequency that is much lower than the center frequency of the corresponding narrow band filter. The new idea here is that the system can use non-uniform sampling to capture both the amplitude of the modulation and the phase of the carrier signal. In this paper, we first describe a system based on FFT analysis combined with overlap-add and a sampling process where magnitude is digitized but phase is represented using a temporal code of spiking neurons. The coding/decoding mechanism is based on the properties of the refractory period. We demonstrate that it is possible to reduce the bit rate to 50% by coding the carrier phase using the timing of the pulses. In the second part of this paper we show how a biological system may approximate the broadband auditory signal using spiking neurons in conjunction with a simple model of neural refractory period. [ABSTRACT FROM AUTHOR]

This paper discusses blind identification and deconvolution of two-input two-output channels corrupted by noises based on second-order statistics. First, the identifiability of channel is analyzed. By constructing an new criterion, the channel parameters can be identified precisely in the present of noises. Second, the cost function of identification is established and the corresponding algorithm is presented. Next, a feedback model is used for deconvolution, and several important problems, such as the effect of noises in the blind deconvolution of mixed sources and the stability of deconvolution model, are discussed. At last, simulation results are given to illustrate the theoretical results of this paper. [ABSTRACT FROM AUTHOR]