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SVM is a new general learning method based on the statistic learning system which can be used as an effective means to process small sample, nonlinear and high dimensional pattern recognition. This paper did research on the learning algorithm of support vector machine, extracted characteristic data of banknote which is on account of PCA according to the characteristics of the support vector machine (SVM), and proposed to put support vector machine (SVM) into banknotes denomination recognition by combining SMO training algorithm with one-to-many multi-value classification algorithm. Besides, this article used genetic algorithm in parameters optimization such as the punishment coefficient C of soft margin SVM and the width parameter of Gaussian kernel function. The ultimate purpose is to recognize the denomination of banknote efficiently and accurately. The experimental results verified that this kind of recognition method increases the recognition accuracy up to 90% or more.

The core of the relationship between science and technology in Europe can be traced back to Galileo Galilei (died 1642) or Leonardo da Vinci (died 1519). Thus progress in scientific knowledge to the heliocentric world system essentially advanced through improved Dutch telescope technology and excellent lens grinding and also conversely the then new scientific model of the cosmos could be used directly for maritime navigation. Science and technology appear to be closely interlinked. Owing to the connection — obvious even then — between technical change and economic prosperity, early innovations were in no doubt as the importance of science to economic and social advance was self.

Several lattice-based cryptosystems require to sample from a discrete Gaussian distribution over the integers. Existing methods to sample from such a distribution either need large amounts of memory or they are very slow. In this paper we explore a different method that allows for a flexible time-memory trade-off, offering developers freedom in choosing how much space they can spare to store precomputed values. We prove that the generated distribution is close enough to a discrete Gaussian to be used in lattice-based cryptography. Moreover, we report on an implementation of the method and compare its performance to existing methods from the literature. We show that for large standard deviations, the Ziggurat algorithm outperforms all existing methods.

An automatic method for rail inspection is introduced in this paper. The method detects rail flaws using computer vision algorithms. Unlike other methods designed for the same goal, we propose a method that automatically fits a 3D rail model to the observations. The proposed strategy is based on the novel combination of a simple but effective laser-camera calibration procedure with the application of an MCMC (Markov Chain Monte Carlo) framework. The proposed particle filter uses the efficient overrelation slice sampling method, which allows us to exploit the temporal coherence of observations and to obtain more accurate estimates than with other sampling techniques. The results show that the system is able to robustly obtain measurements of the wear of the rail. The two other contributions of the paper are the successfull introuction of the slice sampling technique into MCMC particle filters and the proposed online and flexible method for camera-laser calibration.

This paper addresses the problem of shape from shadings under perspective projection and turntable motion.Two-Frame-Theory is a newly proposed method for 3D shape recovery. It estimates shape by solving a first order quasi-linear partial differential equation through the method of characteristics. One major drawback of this method is that it assumes an orthographic camera which limits its application. This paper re-examines the basic idea of the Two-Frame-Theory under the assumption of a perspective camera, and derives a first order quasi-linear partial differential equation for shape recovery under turntable motion. The Dirichlet boundary condition is derived based on Dynamic programming. The proposed method is tested against synthetic and real data. Experimental results show that perspective projection can be used in the framework of Two-Frame-Theory, and competitive results can be achieved.

In order to inherit and develop the Couplet culture, one of the most famous language culture in China, this paper implements a creation system of couplets. In this paper, a noval way to generate the customized couplet is discussed from the name-embedded couplet. The 2-gram word graph technology is used to generate the first sentence which contained the given word. Then, combined with language model which based on statistical method, grammar model and mutual information model, improved Viterbi Algorithm based on HMM is used to decode the second sentence. It turned out that the system can realize the automatic generation of the name embedded couplet. According to the contents involved in the given couplet, the system can generate pictures to match the couplet. This system is aimed for Spring Festival couplets and Birthday couplets, both are name-embedded couplet that generated with the name of people. Users can choose the templates and relative special elements freely to create a satisfactory multimedia couplet picture.

Based on the measured data of IGS, the characteristics of QZSS signal, namely, carrier to noise ratio, noise and multipath characteristics, inter-frequency bias, were studied in this paper. At the same time, the results were compared with those of other satellite navigation systems, such as GPS and Galileo, to analyze the reason for the differences. Methods and results provided in this paper have important reference values for the study and construction of satellite navigation systems.

Large time-delay often occurs unexpectedly in data transmission via Internet. As a kind of typical non-Gaussian stochastic process cannot be described by traditional prediction models with Gaussian innovation. In this paper, FARIMA model with standard symmetrical α stable distribution is adopted to model the transmission time-delay in Internet, and the method for estimating the model parameters is presented. Numerical results show that the approach proposed in this paper is more accurate than the general FARIMA model with Gaussian innovation in modeling Internet time-delay.

Software project risk system is a nonlinear system and the chaos theory provides a new paradigm for understanding the development and evolution of its risk factors. From the angle of chaotic dynamics, this paper studies the software project risk system and based on the rational premise, builds a dynamical model of software project risk system dominated by three main parameters. According to Lyapunov exponent which can judge chaos characters, this paper calculates Lyapunov exponent of the software project risk system model, proves that this system is a chaos one and analyzes the chaos characters such as initial sensibility, inner randomness and strange attractor of this system. Therefore, it opens up a new thought for software project risk control.

Since human face is often not clear in the surveillance video, this paper proposes a retrieval method on the whole body with fine-grained feature extraction. This method first extracts foreground region of human movement based on Gaussian mixed model (GMM). The human body is divided into two parts, head and below the head, based on the human morphological features and skin color. There are three parts coupled with the whole body, and then, we extract color feature for each part. Secondly, the human body is divided into front and back feature samples according to the a priori knowledge. Calculate the gap between the retrieval eigenvectors and the eigenvectors of the target body, then determine whether match. The experimental results show that this method maintains better retrieval precision when the recall rate is high. This paper target retrieve is real time on video.

CAPTCHA is a completely automated public Turing test which has been broadly adopted to enhance the website security and strengthen its attack resistance ability. The research of recognition technology of CAPTCHA has a great benefit for increasing the machine learning ability and aiding to designing high security code. The work in this paper focuses on the most difficult adhesion code in picture. At present, the traditional methods which depend heavily on segmentation will become invalid for most adhesion picture code. To resolve the problem, an integrated recognition algorithm based on mask matching is presented in this paper. The recognition of picture code is completed regardless of segmentation and noise removal beforehand. Especially, the matching degree is controlled by a fuzzy factor which can be adjusted automatically to meet with the picture code. During mask creation, two kinds of methods are provided. Finally, a series of experiments from different websites demonstrate that the algorithm can crack all kinds of picture codes and has a satisfied performance in time and popularity.

Based on the characteristics of the Beidou navigation satellites, this paper introduce a RayTracking analytical solar radiation pressure modeling method which scatters the incident sunlights into a beam array, tracks the solar pressure of every discrete beam generated by reflection of satellite surface, superimposes the radiation pressures of all the discrete beams and gives an accurate solar radiation pressure model with analytical expressions using Fourier analysis. Considering the mutual occlusions of different parts of the satellite surface and the effect of sunlight beams reflected repeatedly by the satellite surface, this method can be used in solar radiation pressure modeling of satellites with complex surface structures. This paper also analyzes the effects of characteristics of satellite attitude and optical properties of the surface, then establishes an analytical solar radiation pressure model for BeiDou IGSO navigation satellites. With a unified form of expressions in the states of dynamic yaw control and zero yaw control of satellite attitude, this model can be used in orbital determination analysis in the period of crossing dynamic yaw and zero yaw control.

In this paper, we focus on the issue of characterizing microblogs' topics based on topic models. Different from dealing with traditional textual media (such as news documents), modeling microblogs has three challenges: 1) too much noise; 2) short text; and 3) content incompleteness. Previously, all these limitations have been investigated separately. Some work filters the noise through a prior classification; some enhances the text through the user's blog history; and some utilizes the social network. However, none of these work could solve all the above limitations simultaneously. To solve this problem, we make a combination of previous work in this paper, and propose a unified generative model for characterizing microblogs' topics. In the proposed unified approach, all the three limitations could be solved. A collapsed Gibbs-sampling optimization method is derived for estimating the parameters. Through both qualitative and quantitative analysis in Twitter, we demonstrate that our approach consistently outperforms previous methods at a significant scale.

The paper addresses development of a brain-like system based on Lamarckian view toward evolution. It describes a development of an artificial brain, from an artificial genome, through a neural stem cell. In the presented design a modulon level of genetic hierarchical control is used. In order to evolve such a system, two environments are considered, genetic and behavioral. The genome comes from the genetic environment, evolves into an artificial brain, and then updates the memory through interaction with the behavioral environment. The updated genome can then be sent back to the genetic environment. The memory units of the artificial brain are synaptic weights which in this paper represent achievement motivations of the agent, so they are updated in relation to particular achievement. A simulation of the process of learning by updating achievement motivations in the behavioral environment is also shown.

Given two pictures of the same scene captured using the same camera and the same lens, the one captured with a large aperture will appear partially blurred while the other captured with a small aperture will appear totally sharp. This paper investigates two possible ways of inferring depth of the scene from such an image pair with the constraint that both pictures are focused on the closest point of the scene. Our first method uses a series of Gaussian kernels to blur the image pair, and in the second method, the image pair will be shrunk to a series of smaller dimensions. In both methods, sharp areas in both images will always stay similar to each other, whereas the areas that appear sharp in one image but blurred in the other will not be similar until they are blurred using a large Gaussian kernel or shrunk to small dimensions. This observation enables us to roughly tell which objects in the scene are closer to us and which ones are farther away. At the end of this paper, we will discuss the limitations of our proposed approaches and some of the directions for future work.

Recent research on magnitude Magnetic Resonance Images (MRI) reconstruction from the Fourier inverse transform of complex (gaussian contaminated) data sets focuses on the proper modeling of the resulting Rician noise contaminated data. In this paper we consider a variational Rician denoising model for MRI data sets that we solve by a semi-implicit numerical scheme, which leads to the resolution of a sequence of Rudin, Osher and temi (ROF) models. The (iterated) resolution of these well posed numerical problems is then proposed for Total Variation (TV) Rician denoising. For numerical comparison we also consider a direct semi-implicit approach for the primal problem which amounts to consider some (regularizing) approximating problems. Synthetic and real MR brain images are then denoised and the results show the effectiveness of the new method in both, the accuracy and the speeding up of the algorithm.

The positioning accuracy with Global Navigation Satellite System (GNSS) depends on both the pseudorange error and the geometric dilution of precision (GDOP), and GDOP is depended on the geometry of user/satellites which are selected. Positioning with a GNSS needs at least four satellites which are visible for the user so that at least four pseudorange measurements can be provided to determine the user position in three dimensions and the receiver clock offset. In order to select as few as visible satellites whose geometry is better, a new fast satellite selection algorithm based on geometry is come up with by this paper through analyzing geometry characteristics of the optimal visible satellites combination. The expected visible satellites are selected based on the distribution characteristics of elevation angles and azimuth angles combined with tetrahedron volume. Taking GPS, GLONASS and GALILEO system for examples, this paper compares the algorithm proposed by this paper with the best geometry satellite selection algorithm through simulation. The simulation results show that the algorithm proposed by this paper solves the problem that there are a lot of matrix multiplications and matrix inversions in the best geometry satellite selection algorithm. So the new algorithm can reduce computational complexity and increase receiver processing speed. The theory of the algorithm is simple and easy to use. Meanwhile, the algorithm can satisfy the real time requirements for users.

This year marks the 40th anniversary of Charles Bennett's seminal paper on reversible computing. Bennett's contribution is remembered as one of the first to demonstrate how any deterministic computation can be simulated by a logically reversible Turing machine. Perhaps less remembered is that the same paper suggests the use of nucleic acids to realise physical reversibility. In context, Bennett's foresight predates Leonard Adleman's famous experiments to solve instances of the Hamiltonian path problem using strands of DNA -- a landmark date for the field of natural computing -- by more than twenty years. The ensuing time has seen active research in both reversible computing and natural computing that has been, for the most part, unrelated. Encouraged by new, experimentally viable DNA computing models, there is a resurgent interest in logically reversible computing by the natural computing community. We survey these recent results, and their underlying ideas, which demonstrate the potential for logically and physically reversible computation using nucleic acids.

Surface reconstruction using patch-based multi-view stereo commonly assumes that the underlying surface is locally planar. This is typically not true so that least-squares fitting of a planar patch leads to systematic errors which are of particular importance for multi-scale surface reconstruction. In a recent paper [12], we determined the modulation transfer function of a classical patch-based stereo system. Our key insight was that the reconstructed surface is a box-filtered version of the original surface. Since the box filter is not a true low-pass filter this causes high-frequency artifacts. In this paper, we propose an extended reconstruction model by weighting the least-squares fit of the 3D patch. We show that if the weighting function meets specified criteria the reconstructed surface is the convolution of the original surface with that weighting function. A choice of particular interest is the Gaussian which is commonly used in image and signal processing but left unexploited by many multi-view stereo algorithms. Finally, we demonstrate the effects of our theoretic findings using experiments on synthetic and real-world data sets.

In this paper we present one way of modeling candlestick patterns using interpolative Boolean algebra (IBA). This method shows a degree of fulfillment for observed patterns, thus giving traders easy interpretation of by how much candlesticks fit into different patterns. Candlestick patterns have been used for financial forecasting for a couple of decades on Western markets and they have become a mainstream trader’s tool. Since the need for automated candlestick patterns discovery arose, some papers proposed fuzzy approach as a solution. Our decision to use IBA for modeling candlestick patterns comes from the fact that fuzzy logic has it limits and cannot be applied to these models. Proposed method is another approach to the same problem, but it could not be modeled using conventional fuzzy logic, because it is necessary for it to be in the Boolean frame. Results obtained from our tests are satisfactory and also open the opportunity for combining this technique with existing ones.

This paper adapts a novel model, deep belief nets (DBN), to extract entity mentions in Chinese documents. Our experiments were designed to develop entity detection system and entity categorization system applying DBN, to complete entity extraction. Our results exhibit how the depth of architecture and quantity of unit in hidden layer of DBN influence the performance. In DBN Systems, token labels are produced independently and DBN does not concerned what the labels of surrounding token are. Viterbi algorithm is a good solution to overcome this issue. It can find the most likely probability label path to make DBN to be more suitable for entity detection. Furthermore, this paper demonstrates DBN is proper model for our tasks and its results are better than Support Vector Machine (SVM), Artificial Neural Network (ANN) and Conditional Random Field (CRF).

The paper introduces circular rotation testing system by which GPS receiver’s dynamic performance can be examined, analyzes the change rule of Doppler frequency shift caused by the circular rotation of antenna, compares the states of antenna circular rotation and the linear moving of airborne by investigating the Doppler shift and Doppler shift changing rate, then puts forward the basic method for detecting receiver dynamic positioning accuracy via antenna circular rotation to simulate the state of the airborne linear motion by investigating Doppler shift changing rate. As an example, by using a NovAtel RT2 GPS receiver, the paper gives accuracy analysis of the GPS receiver point positioning and carrier phase differential positioning under the condition of antenna circular rotation with the proposed method, and gives the Receiver’s accuracy of point positioning and carrier phase differential positioning under different moving states as well. Finally, points out the application and popularization of the proposed method in detecting dynamic accuracy and tracking performance of Compass and GPS of other types.

Recently, image classification has been an active research topic due to the urgent need to retrieve and browse digital images via semantic keywords. Based on the success of low-rank matrix recovery which has been applied to statistical learning, computer vision and signal processing, this paper presents a novel low-rank matrix recovery algorithm with discriminant regularization. Standard low-rank matrix recovery algorithm decomposes the original dataset into a set of representative basis with a corresponding sparse error for modeling the raw data. Motivated by the Fisher criterion, the proposed method executes low-rank matrix recovery in a supervised manner, i.e., taking the with-class scatter and between-class scatter into account when the whole label information is available. The paper shows that the formulated model can be solved by the augmented Lagrange multipliers, and provide additional discriminating ability to the standard low-rank models for improved performance. The representative bases learned by the proposed method are encouraged to be structural coherence within the same class, and as independent as possible between classes. Numerical simulations on face recognition tasks demonstrate that the proposed algorithm is competitive with the state-of-the-art alternatives.

In this paper, we provide a revised inference for correlated topic model (CTM) [3]. CTM is proposed by Blei et al. for modeling correlations among latent topics more expressively than latent Dirichlet allocation (LDA) [2] and has been attracting attention of researchers. However, we have found that the variational inference of the original paper is unstable due to almost-singularity of the covariance matrix when the number of topics is large. This means that we may be reluctant to use CTM for analyzing a large document set, which may cover a rich diversity of topics. Therefore, we revise the inference and improve its quality. First, we modify the formula for updating the covariance matrix in a manner that enables us to recover the original inference by adjusting a parameter. Second, we regularize posterior parameters for reducing a side effect caused by the formula modification. While our method is based on a heuristic intuition, an experiment conducted on large document sets showed that it worked effectively in terms of perplexity., 10th International Symposium on Neural Networks, ISNN 2013; Dalian; China; 4 July 2013 through 6 July 2013, Lecture Notes in Computer Science, 7952, pp.445-454; 2013

It is a challenging topic how to achieve the real-time tracking of fast video object under complex environment. In this paper, a scheme and its corresponding implementing algorithms of real-time tracking of fast video object are designed and perfected, which are characterized with high performances of real-time tracking and robustness. At first, a kind of scheme is designed for the real-time tracking of fast video object and corresponding implementing strategies for some key modules are proposed. Then the particle filter is employed to predict the pose state of fast video object and the motion object is discriminated from its background by Gaussian kernel and random projection. Moreover, an adaptive feature selection method is used to enhance the robustness and tracking efficiency. A series of experiment results demonstrate that the scheme and algorithms proposed in this paper outperform the current existing algorithms in the tracking efficiency, accuracy and robustness.

This paper proposes to add the multithreaded Graphic Processing Units (GPUs) to some virtual machines (VMs) in the existing cloud-based VM groups. To handle the multidimensional or multithreaded computing that a CPU cannot process quickly by a GPU that has hundreds of Arithmetic Logic Units (ALUs), and to regulate the time for initiating physical servers by real-time thermal migration, our proposed scheme can enhance the system performance and reduce the energy consumption of long-term computing. Four major techniques in this paper include: (1) GPU virtualization, (2) Hypervisor for GPU, (3) Thermal migration implementation, and (4) Estimation of multithreaded tasks. In no matter quantum mechanics, astronomy, fluid mechanics, or atmospheric simulation and prediction, a GPU suits not only parallel multithreaded computing for its tens of times performance than a CPU, but also multidimensional array operations for its excellent efficiency. Therefore, how to distribute the computing performance of CPUs and GPUs appropriately becomes a significant issue. In general cloud computing applications, it is rarely seen that GPUs can outperform CPUs. Furthermore, for groups of virtual servers, many tasks actually can be completed by CPUs without the support of GPUs. Thus, it is a waste of resources to implement GPUs to all physical servers. For this reason, by integrating with the migration characteristic of VMs, our proposed scheme can estimate whether to compute tasks by physical machines with GPUs or not. In estimating tasks, we use Amdahl’s law to estimate the overall performance include communication delays, Synchronization overhead and me possible additional burden.

Experiments on micro-specimens have shown that the deformation behavior of materials can be size dependent. The size dependence is, for example, reflected in a stiffer elastic response on the sub-microscale. A quantitative understanding of the size effect is important for the design of micro- and nanosize systems. In our paper higher-order theories of elasticity are used for the description of the bending behavior of micro-beams. These include additional material parameters in order to describe a size effect and they go beyond the limits of the classical Boltzmann continuum. In particular couple stress and strain gradient theory of linear elasticity are used in this work as special examples of higher gradient theories. Another objective of the paper is to determine the length scale parameters by measurements performed with extremely small cantilever beams. In particular, deflection measurements are performed and force data are recorded for submicron beams made of silicon and silicon nitride. The tests are performed by using a highly sensitive atomic force microscope. In addition Raman spectroscopy is used for the same purpose. The obtained data is fitted to the formulae of higher elasticity for the bending of slender beams and can be used for evaluation of higher gradient coefficients.

The paper contributes to the development of the theory of AI-Completeness by formalizing the notion of AI-Complete, C-Complete and AI-Hard problems. The intended goal is to provide a classification of problems in the field of Artificial General Intelligence. We prove Turing Test to be an instance of an AI-Complete problem and further show certain AI problems to be AI-Complete or AI-Hard via polynomial time reductions. Finally, the paper suggests some directions for future work on the theory of AI-Completeness.

This paper addresses the problem of using a fleet of autonomous watercraft to create models of various water quality parameters in complex environments using intelligent sampling algorithms. Maps depicting the spatial variation of these parameters can help researchers understand how certain ecological processes work and in turn help reduce the negative impact of human activities on the environment. In our domain of interest, it is infeasible to exhaustively sample the field to obtain statistically significant results. This problem is pertinent to autonomous water sampling where hysteresis in sensors causes delay in obtaining accurate measurements across a large field. In this paper, we present several different approaches to sampling with cooperative vehicles to quickly build accurate models of the environment. In addition, we describe a novel filter and a specialized planner that uses the gradient of sensor measurements to compensate for hysteresis while ensuring a fast sampling process. We validate the algorithms using results from both simulation and field experiments with four autonomous airboats measuring temperature and dissolved oxygen in a lake.

As the core technology of intelligent monitoring system, the moving target detection method plays an important role in the intelligent monitoring system, but the current moving target detection method still has some shortages such as lower anti-jamming performance on the environment, low judge accuracy and so on. In this paper, the improved background subtraction based on mixed Gaussian probability background model is used, local optical flow algorithm is introduced to achieve the unconstrained stable initialization of background model, the added items which are sensitive to global illumination conditions are adopted to assist the adaptive updating of model parameters, eventually the improved algorithm framework are formed and combined with processing method after testing to extract the information of effective movement area and realize the judgment of break in of foreign matters. Finally, the overall realization of intelligent monitoring system is expounded theoretically in the paper, based on the improved moving target detection method, the factors of characteristics of moving targets which affect the detecting precision are analyzed systematically, so it shows that this moving target detection method can improve the detecting precision of intelligent monitoring system and has good real time, accuracy and reliability.

Word boundary ambiguity is a major problem for the Thai morphological analysis since the Thai words are written consecutively with no word delimiters. However the part of speech (POS) tagged corpus which has been used is constructed from the academic papers and there are no researches that worked on the documents written in the informal language. This paper presents Thai morphological analysis with unknown word boundary detection using both POS tagged and untagged corpora. Viterbi algorithm and Maximum Entropy (ME) - Viterbi algorithm are employed separately to evaluate our methods. The unknown word problem is handled by making use of string's length in order to estimate word boundaries. The experiments are performed on documents written in formal language and documents written in informal language. The experiments show that the method we proposed to use untagged corpus in addition to tagged corpus is efficient for the text written in informal language.

It is challenging to count and analyze people in crowds due to the changes of lighting, occlusions, shadows, backgrounds, and weather conditions. Especially for the occlusion problem, until now, it is still ill-posed. To deal with the occlusion problem, the MCMC (Monte Carlo Markova Chain) scheme is used in this paper to estimate all possible pedestrian positions across different frames. However, it requires good initial head positions for parameter searching and people counting. Thus, an intelligent head-shoulder-region detector is then developed for detecting all possible pedestrian candidates from videos. One key problem in head-shoulder detection is that the feature contrast between the objects and their background should be larger. To tackle this problem, a Linear Discriminant Analysis (LDA) approach is then used to enhance the boundaries between objects and features. Three contributions are made in this paper: (1) Intelligent head-shoulder-region detector; (2) People detection under occlusions; (3) Integrated people counting system using LDA. Experimental results have proved the superiorities of the proposed method in people detection and counting.

Today’s modern human life-style is in a great uplift with respect to anything and everything. The requirement of road transportation and vehicles is increasing day-by-day which is proportional to the number of accidents happening eventually. At this juncture, building an intelligent vehicle system and other safety driven driver assistance systems have become the order-of-the-day. Despite the fact that considerable research has been carried out in detecting and tracking the traffic signs, still there is a huge lack in pertinent traffic sign recognition systems, especially in countries like India. This paper designates on the implementation of iReSign, next-generation two-tier identity classifier-based traffic sign recognition system architecture using Zernike and GFD algorithms for the future intelligent vehicle systems. Further, this paper details the results and its analysis, which is based on 840 images of 28 distinct traffic signs of India (collected manually). Using Zernike Moments and Generic Fourier Descriptors (GFD) region-based global shape recognition techniques, iReSign produced 78.33%, 90% cross validation accuracy and 77.85%, 91% testing recognition accuracy respectively. Using our new hybrid approach of combining Zernike Moments and GFD, iReSign produced 92% cross validation accuracy and 95% testing recognition accuracy.

The Coanda effect, invented by Henri Marie Coanda is an interesting phenomenon in fluid mechanics. The basics of the Coanda effect is based on the property of a jet flow to attach itself to a nearby surface and to remain attached even when the surface bends away from the initial jet direction. This effect has been applied in many areas since 1960. In the recent years, researches have been initiated towards the application of Coanda effect in robots. This paper is focused on the utilization of Coanda effect in the field of robotics. The research that the authors are carrying out in this area is also addressed at the end of this paper.

In this paper, we describe a model for extracting rules that describe enterprise resource planning (ERP) system upgrade process. The rules are extracted automatically by analyzing programming code from completed ERP systems upgrade projects. Later those rules are verified and tuned by experienced programmer. The rules that we are extracting are described in the language equivalent to the first-order logic (i.e. expressivity of Turing machine). Nevertheless, we put a constrain on the rules description language by defining how knowledge base is used to define these rules. We require that ERP system code and ERP upgrade knowledge base must be transformed to a series of aligned strings without lost of expressivity. Such strong requirement ensures that we are able to use existing machine learning algorithms in the process of software development and upgrade. These series of strings are compared by strings manipulation algorithms and then differences are resolved by merge algorithm presented in this paper. We used Microsoft Dynamics NAV as an example to test usefulness of presented method but other systems can be used as well if they can be presented as series of code strings.

In this paper, the induction heating power supply adopting cascaded multilevel inverter was the study object. For improving the quality of output voltage in induction heating power supply inverter, a harmonics control mean of output voltage by selecting switching angles were presented. The harmonics equation system of output voltage in two modules cascaded multilevel inverter were established based on the Fourier expression of the output voltage. Based on the work characteristic of induction hearting power supply, the calculation method of eliminating harmonics and the reasonable advice about switching angles were given. Simulation and experiment results shown the total harmonic distortion (THD) of output voltage can be reduced by adopting the harmonics control mean presented in this paper.

During the course of prediction and estimation to the target in opto-electronic tracking system, stochastic noises statistical character affects the precision badly. To solve this problem, set-membership estimation is introduced to the target prediction in this paper. Considering the non-linear feature of opto-electronic tracking system, the paper analyzes extend optimal bounding ellipsoid (EOBE). Result of simulation shows that, compared with extend kalman filter (EKF), the prediction precision of EOBE is almost as good as that of EKF. Especially when the noise is bounded but not white and Gaussian, EOBE has better robustness.

In order to solve the problems of deviations with large error occurred at the sharp corners of motion trajectories and poor stability of tracking algorithm presented in previous paper, and further make improvements to the tracking precision, in this paper the rather appropriate state model is established first of all, and then the effective observations are collected by using spatial-temporal detection and fusion in which the brightness information of the target included in the decision criteria for the first time. Under the different circumstances of Gaussian noise and non-Gaussian noise, the experimental results show that take the Gaussian particle filter as the tracking algorithm which has no use of re-sampling could have effectively improved the precision of algorithm for tracking dim moving point target in IR image sequences, and has a good real-time performances and good stability.

In this paper, the potential of unlocking wing sweep on aircraft drag reduction is explored in the context of recent development in shock control by three dimensional bumps on natural laminar flow(NLF) wings. Reduction of the wing sweep currently employed for large transport transonic aircraft is advocated for significant drag reduction, enabling natural laminar flow wing development with shock control. The aim is to unlock a normally fixed design variable (for large transonic aircraft), i.e. wing sweep, in order to achieve substantial drag reduction through extended natural laminar flow area while keeping the shock strength and its impact on wave drag under control. A decrease of the current transonic wing sweep angle is anticipated to accommodate the natural laminar flow target, because of the potential alleviation of attachment line transition and crossflow boundary layer instability. The paper demonstrates the effects of 3D shock control bumps from zero to 20sweep and from 2D sectional to 3D NLF wings for cruise Mach number from 0.68 to 0.85.

The process of segmentation consists of dividing the image in regions with similar characteristics. In other words, segmentation is the partitioning of digital images into several regions, according to a given criteria [6]. In addition, applying biometrics to daily scenarios involves difficult and challenging requirements in terms of software and hardware. On the contrary, current biometric techniques are also being adapted to present-day devices, like mobile phones, laptops and the like, which are considered to be unconstrained and contact-less. In fact, achieving a combination of both necessities is one of the most difficult problems at present in biometrics. Segmentation in these kind of environments require special effort and attention, since although there exist an obvious difficulty in providing accurate segmentation to these scenarios, the constraints in terms of software and hardware are important and affect the segmentation performance. Therefore, this paper presents a segmentation algorithm able to provide suitable solutions in terms of precision for hand biometric recognition, considering a wide range of backgrounds like carpets, glass, grass, mud, pavement, plastic, tiles or wood. Results highlight that segmentation accuracy is carried out with high rates of precision (F-measure≥ 88%), presenting competitive time results when compared to state-of-the-art segmentation algorithms time performance. Finally, the accuracy of the proposed method is compared to the performance of Normalized Cuts in terms of segmentation accuracy and time performance.

This paper investigates the hybrid chaos synchronization of identical Arneodo systems (1981), identical Rossler systems (1976) and non-identical Arneodo and Rossler systems. In hybrid synchronization of chaotic systems, one part of the systems is synchronized and the other part is anti-synchronized so that complete synchronization (CS) and anti-synchronization (AS) co-exist in the systems. The co-existence of CS and AS is very useful in secure communication and chaotic encryption schemes. Active nonlinear control is the method used for the hybrid synchronization of the chaotic systems addressed in this paper. Since the Lyapunov exponents are not required for these calculations, the active control method is effective and convenient to achieve hybrid synchronization of the two chaotic systems. Numerical simulations are shown to verify the results.

The short-term frequency stability of satellite on-board clock is of great significance for GNSS applications, such as satellite clock modeling, simulation and clock offset prediction. In this paper, four short-term frequency stability estimation methods are analyzed, which need observation data from only one station in contrast with ODTS needing continuous observation of large ground monitoring networks. Performance and accuracy of all these four methods is analyzed and compared using GPS and COMPASS observation data, and their advantage and disadvantage are discussed correspondingly. For COMPASS and Galileo systems which are currently in the early stage of construction, this paper proposes some meaningful suggestions of on-board clock short-term frequency stability estimation.

Mobile processors, a subclass of embedded processors, are increasingly employing multicore designs to improve performance. This often requires sacrificing resources in each CPU, degrading single thread performance which is still important according to Amdahl's law. The traditional technique for efficiently boosting serial performance in embedded processors, dedicated hardware acceleration, is unsuitable for modern mobile processors because of the heterogeneity and the diversity of applications they run. This paper proposes ‘general purpose' accelerators, reconfigured on an application-by-application basis, as a means of increasing single thread performance. These accelerators are placed within the datapath of CPUs and support dynamic compilation. This paper presents the design of an architecture with such accelerators and evaluates the cost/performance implications of the design.

This paper investigates the problem of designing active feedback controllers for achieving generalized projective synchronization (GPS) of identical hyperchaotic Lu systems (Chen et al. 2006) and non-identical hyperchaotic Cai system (Wang and Cai, 2009) and hyperchaotic Lu system. The synchronization results (GPS) derived in this paper have been established using Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the active feedback control method is very effective and convenient for achieving the general projective synchronization (GPS) of hyperchaotic Lu and hyperchaotic Cai systems. Numerical simulations are shown to demonstrate the effectiveness of the synchronization results derived in this paper.

CAPTCHA stands for Completely Automated Public Turing test to distinguish Computers and Humans apart. CAPTCHA is a program which can generate and grade the tests that it itself cannot pass. The security aspect of CAPTCHA should be such that none of the computer program should be able to pass the tests generated by it even if the knowledge of the exact working of the CAPTCHA is known. The effectiveness of CAPTCHA of a given strength is determined by how frequently the guesses of CAPTCHA can be tested by an attacker. This paper proposes a simple and uniform framework for the assessment of security and usability of CAPTCHA that arbitrary compositions of security measures can provide”. In this sentence instead of ”a simple and uniform framework”, use ”parameters”. This paper proposes parameters for the assessment of security and usability of CAPTCHA that arbitrary compositions of security measures can provide.The pre-processing attack on targeted CAPTCHA is demonstrated having success rate of approximately 97% which in turn helps to build more robust and human friendly CAPTCHA. The universal structure for segmentation attack is framed to analyze security of CAPTCHA.

This paper analyzes the Paris Metro Pricing (PMP) strategy for differentiating Internet service. PMP has several advantages over other pricing schemes that guarantee quality of service (QoS) such as simplicity and less bandwidth overhead. In this paper, we develop a simple analytical model for PMP. We first assume that there is only one network service provider (a monopolist) serving N users and model the user behavior and the provider’s profit. Then we derive the optimal ratio of dividing a given network capacity in order to maximize the profit of the service provider. Our results show that, by maximizing providers profit, the subscription is also maximized which can be interpreted as a higher satisfaction of users compared to that of not using PMP. In addition, by taking into account various network types, we show that in a monopoly environment, it is always better to implement PMP regardless of user populations we considered. We then further extend our model to a duopoly setting. We found that there exist no Nash equilibrium even when both providers do not differentiate the network service.

A detector is said to be superior if the information is extracted in a best way for some particular purpose. Here the objective of this paper is to analyze the detection performance of non-coherent detectors such as square law detector, linear detector and logarithmic detector in a background of white Gaussian noise (WGN) for Ultra Wide Band Short Range Radar in Automotive applications. It is assumed that the target is stationary and energy reflected from the target is distributed. The non-coherent detectors have almost similar detection probability in a background of white Gaussian noise. The performance of the detector is analyzed and simulation has been done in order to verify.

This paper deals with the development of an adaptive speed controller for autonomous mobile robot using a fuzzy logic controller.The analysis of performance between a system applying triangular membership and a system using gaussian membership is compared to distinguish their differences. The results have shown that the gaussian membership method has improved the tracking performance for the mobile robot to reach its target. This also suggest the same behavior even when a different mobile robot movements is assigned. This paper considers three, five and seven memberships for both techniques to determine their effectiveness and effects to the system performance. The investigation has leads to the conclusion that the gaussian membership has competently surpassed the triangular membership performances even when the robot has different movements to achieve its target.

This paper proposes a Dirichlet Process Mixture Model (DPMM) considering relevant topical information for adaptive topic tracking. The method has two characters: 1) It uses DPMM to implement topic tracking. Prior knowledge of known topics is combined in Gibbs sampling for model inference, and correlation between a story and each known topics can be estimated. 2) To alleviate topic excursion problem and topic deviation problem brought by existing adaptive tracking methods, the paper presents a new adaptive learning mechanism, the basic idea of which is to introduce tracking feedback with a reliability metric into the topic tracking procedure and make tracking feedback influence tracing computation under the condition of the reliability metric. The empirical results on TDT3 evaluation data show that the model, without a large scale of in-domain data, can solve topic excursion problem of topic tracking task and topic deviation problem brought by existing adaptive learning mechanisms significantly even with a few on-topic stories.

The representation and the thorough understanding of human motion is a crucial and challenging problem which has been raised in many scientific areas. This paper considers approaches in performing motion analysis with multi-resolution techniques based on rotations of joints over the time written in the form of a quaternion signal. The second generation wavelet transform constructed by the lifting scheme for the quaternion rotation representation can be used. Quaternions in terms of motion analysis are a more efficient representation of rotation than Euler angles. This paper presents the new quaternion lifting scheme building blocks for the smooth second degree transform based on the spherical cubic quaternion interpolation method (SQUAD). Also the possible applications of result multi-resolution representation as feature detection and compression are described.