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This paper provides an improved algorithm which is proposed for the deviating optimal solution, non-target and path non-smooth problem of the original Rapidly-exploring Random Trees (RRT). The improved algorithm is based on the new parent node selection method to ensure that the random tree extends the asymptotic optimality of the new node. On this basis, the heuristic value function is introduced according to the Euclidean shortest distance thought, which improves the planning efficiency of the algorithm. In addition, the method of regression analysis is adopted to improve the search ability of the algorithm and solve the local minimum problem caused by the heuristic value function. The Bezier curve fitting method is then used to generate a smooth path, which further reduces the jitter during the 6-DOF manipulator motion. The simulation experiment of improved RRT algorithm is established in MATLAB platform, and the effectiveness and rationality of the algorithm are verified.

In this paper a novel nonlinear logistic regression model based on a simplex basis function neural network is introduced that outputs probability of categorical variables in response to multiple predictors. It is shown that since a linear combination of the simplex basis functions can be represented as a piecewise linear model, the proposed nonlinear logistic regression model retains the main advantage of linear logistic regression model, that is, allowing probabilistic interpretation of the data sets from an identified model. The associated estimation problem is treated based on the principle of maximum likelihood by alternating over two algorithms; the iteratively reweighted least squares algorithm for linear parameters, while the simplex basis functions are fixed; then nonlinear parameters in each simplex basis function are adapted in turn based on gradient descent of the negative likelihood. The proposed algorithm is then extended to estimation of nonlinear multinomial logistic model. Numerical experiments are initially carried out to illustrate the advantage of nonlinear logistic regression model versus its linear counterpart in terms of approximation capability. Then we apply the proposed method for a difficult computer vision example of land-cover real data set

Automatic plant leave recognition using digital images and machine learning techniques is an important task. The disadvantage of supervised learning techniques is that they are limited to learn from labelled datasets which are often expensive to obtain. In this paper, a novel decision fusion framework is proposed by combining semi-supervised clustering with the well known image features analysis methods in computer vision. Initially the leave image features are generated by applying the Grey Level Co-occurrence Matrix analysis to the processed leave images transformed by Gabor or Laplacian of Gaussian filters. Then an on-line spherical k-means clustering technique, guided by a minimum number of labelled leaves, is used to train the base classifiers. The final decision of classification is produced by selecting classifier which produces the max-cosine value amongst the baseline classifiers. Comparative experiments have been carried out to demonstrate that proposed approaches are suited for automatic leave type recognition.

Dimensionality Reduction (DR) which tries to discover low-dimensional feature representation embedded into the high-dimensional observations are significant for data visualization and data preprocessing. However, most DR models are designed for vector-valued data while only few of them are for functional data where samples are considered as continuous data such as curves or surfaces compared to discrete vector-valued data. Motivated by Functional Principal Component Analysis (FPCA), which generalizes the idea of Principal Component Analysis (PCA) to the Hilbert space of square-integrable functions, in this paper we propose Functional Locality Preserving Projection (FLPP), where classic Locality Preserving Projection (LPP) is extended for functional data analysis. Different from FPCA which only focuses on the global structure, FLPP could preserve local manifold structure embedded into the functional data, thus FLPP is capable of dealing with noise data. Experimental results on both synthetic data and real-world data verify that FLPP outperforms FPCA and typical LPP.

A new adaptive sparse least squares support vector regression algorithm, referred to as SLSSVR has been introduced for the adaptive modeling of nonstationary systems. Using a sliding window of recent data set of size N to track t he non-stationary characteristics of the incoming data, our adaptive model is initially formulated based on least squares support vector regression with forgetting factor (without bias term). In order to obtain a sparse model in which some parameters are exactly zeros, a l 1 penalty was applied in parameter estimation in the dual problem. Furthermore we exploit the fact that since the associated system/kernel matrix in positive definite, the dual solution of least squares support vector machine without bias term, can be solved iteratively with guaranteed convergence. Furthermore since the models between two consecutive time steps there are (N-1) shared kernels/parameters, the online solution can be obtained efficiently using coordinate descent algorithm in the form of Gauss-Seidel algorithm with minimal number of iterations. This allows a very sparse model per time step to be obtained very efficiently, avoiding expensive matrix inversion. The real stock market dataset and simulated examples have shown that the proposed approaches can lead to superior performances in comparison with the linear recursive least algorithm and a number of online non-linear approaches in terms of modelling performance and model size.

The $l_{1}$ -norm sparsity constraint is a widely used technique for constructing sparse models. In this paper, two zero-attracting recursive least squares algorithms, which are referred to as ZA-RLS-I and ZA-RLS-II, are derived by employing the $l_{1}$ -norm of the parameter vector constraint to facilitate model sparsity. To achieve a closed-form solution, the $l_{1}$ -norm of the parameter vector is approximated by an adaptively weighted $l_{2}$ -norm in which the weighting factors are set as the inversion of the associated $l_{1}$ -norm of parameter estimates that are readily available in the adaptive learning environment. ZA-RLS-II is computationally more efficient than ZA-RLS-I by exploiting the known results from linear algebra and the sparsity of the system. The proposed algorithms are proven to converge, and adaptive sparse channel estimation is used to demonstrate the effectiveness of the proposed approach.

A novel estimation algorithm is introduced for logistic regression model with nonnegative coefficients constraints. The basic idea is to decompose the model parameters as a vector of convex coefficients (the multinomial manifold) and a scaling parameter, which are then optimized alternatively based on the maximum likelihood cost function. The first and second order Riemannian geometry of the multinomial manifold are utilized in the Riemannian trust-region algorithm. The scaling parameter is solved using the golden section algorithm. Numerical examples are employed to demonstrate that effectiveness of the proposed approach.

Complex-valued (CV) B-spline neural network approach offers a highly effective means for identification and inversion of Hammerstein systems. Compared to its conventional CV polynomial based counterpart, CV B-spline neural network has superior performance in identifying and inverting CV Hammerstein systems, while imposing a similar complexity. In this paper, we review the optimality of CV B-spline neural network approach and demonstrate its excellent approximation capability for a real-world application. More specifically, we develop a CV B-spline neural network based approach for the nonlinear iterative frequency-domain decision feedback equalization (NIFDDFE) of single-carrier Hammerstein channels. Advantages of B-spline neural network approach as compared to polynomial based modeling approach are extensively discussed, and the effectiveness of CV neural network based NIFDDFE is demonstrated in a simulation study.

A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion combining local component analysis for the finite mixture model. We start with a Parzen window estimator which has the Gaussian kernels with a common covariance matrix, the local component analysis is initially applied to find the covariance matrix using expectation maximization algorithm. Since the constraint on the mixing coefficients of a finite mixture model is on the multinomial manifold, we then use the well-known Riemannian trust-region algorithm to find the set of sparse mixing coefficients. The first and second order Riemannian geometry of the multinomial manifold are utilized in the Riemannian trust-region algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with competitive accuracy to existing kernel density estimators.

In this paper, we develop a novel constrained recursive least squares algorithm for adaptively combining a set of given multiple models. With data available in an online fashion, the linear combination coefficients of submodels are adapted via the proposed algorithm.We propose to minimize the mean square error with a forgetting factor, and apply the sum to one constraint to the combination parameters. Moreover an l 1 -norm constraint to the combination parameters is also applied with the aim to achieve sparsity of multiple models so that only a subset of models may be selected into the final model. Then a weighted l 2 -norm is applied as an approximation to the l 1 -norm term. As such at each time step, a closed solution of the model combination parameters is available. The contribution of this paper is to derive the proposed constrained recursive least squares algorithm that is computational efficient by exploiting matrix theory. The effectiveness of the approach has been demonstrated using both simulated and real time series examples.

Single-carrier (SC) block transmission with frequency-domain equalization (FDE) offers a viable transmission technology for combating the adverse effects of long dispersive channels encountered in high-rate broadband wireless communication systems. However, for high-bandwidth-efficiency and high-power-efficiency systems, the channel can generally be modeled by the Hammerstein system, which includes the nonlinear distortion effects of the high-power amplifier (HPA) at transmitter. For such nonlinear Hammerstein channels, the standard SC-FDE scheme no longer works. This paper advocates a complex-valued (CV) B-spline neural-network-based nonlinear SC-FDE scheme for Hammerstein channels. Specifically, we model the nonlinear HPA, which represents the CV static nonlinearity of the Hammerstein channel, by a CV B-spline neural network, and we develop two efficient alternating least squares schemes for estimating the parameters of the Hammerstein channel, including both the channel impulse response coefficients and the parameters of the CV B-spline model. We also use another CV B-spline neural network to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can easily be estimated using the standard least-squares algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. Equalization of the SC Hammerstein channel can then be accomplished by the usual one-tap linear equalization in the frequency domain as well as the inverse B-spline neural network model obtained in the time domain. Extensive simulation results are included to demonstrate the effectiveness of our nonlinear SC-FDE scheme for Hammerstein channels.

This paper is concerned with tensor clustering with the assistance of dimensionality reduction approaches. A class of formulation for tensor clustering is introduced based on tensor Tucker decomposition models. In this formulation, an extra tensor mode is formed by a collection of tensors of the same dimensions and then used to assist a Tucker decomposition in order to achieve data dimensionality reduction. We design two types of clustering models for the tensors: PCA Tensor Clustering model and Non-negative Tensor Clustering model, by utilizing different regularizations. The tensor clustering can thus be solved by the optimization method based on the alternative coordinate scheme. Interestingly, our experiments show that the proposed models yield comparable or even better performance compared to most recent clustering algorithms based on matrix factorization.

We develop an on-line Gaussian mixture density estimator (OGMDE) in the complex-valued domain to facilitate adaptive minimum bit-error-rate (MBER) beamforming receiver for multiple antenna based space-division multiple-access systems. Specifically, the novel OGMDE is proposed to adaptively model the probability density function of the beamformer's output by tracking the incoming data sample by sample. With the aid of the proposed OGMDE, our adaptive beamformer is capable of updating the beamformer's weights sample by sample to directly minimize the achievable bit error rate (BER). We show that this OGMDE based MBER beam-former outperforms the existing on-line MBER beamformer, known as the least BER beamformer, in terms of both the convergence speed and the achievable BER.

A practical single-carrier (SC) block transmission with frequency domain equalisation (FDE) system can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. For such nonlinear Hammerstein channels, the standard SC-FDE scheme no longer works. In this paper, we propose a novel B-spline neural network based nonlinear SC-FDE scheme for Hammerstein channels. In particular, We model the nonlinear HPA, which represents the complex-valued static nonlinearity of the Hammerstein channel, by two real-valued B-spline neural networks, one for modelling the nonlinear amplitude response of the HPA and the other for the nonlinear phase response of the HPA. We then develop an efficient alternating least squares algorithm for estimating the parameters of the Hammerstein channel, including the channel impulse response coefficients and the parameters of the two B-spline models. Moreover, we also use another real-valued B-spline neural network to model the inversion of the HPA's nonlinear amplitude response, and the parameters of this inverting B-spline model can easily be estimated using the standard least squares algorithm based on the pseudo training data obtained as a byproduct of the Hammerstein channel identification. Equalisation of the SC Hammerstein channel can then be accomplished by the usual one-tap linear equalisation in frequency domain as well as the inverse B-spline neural network model obtained in time domain. The effectiveness of our nonlinear SC-FDE scheme for Hammerstein channels is demonstrated in a simulation study.

For the problem of worse performance induced by improper weighted coefficient matrix in data reconciliation algorithm, an innovated indirect method to determine the weighted matrix for data reconciliation is proposed. The method deduces the measurement variances with the estimated constraint residual errors based on the redundancy character. When singular matrix appears in the calculation process of matrix inversion, block matrix together with the known variances from empirical or direct method are applied to compensate the vacant elements of the weighted matrix. Thus the known information is utilized as more as possible to deduce the unknown measurement variances. Compared with the empirical method or direct method, the innovated indirect method shows obvious advantages, and it solves the problem of singular matrix inversion in indirect method. The result of data reconciliation for the flow rate measurement of steam network in an iron& steel corporation shows the method can largely improve the effect of data reconciliation.

A new sparse kernel density estimator is introduced. Our main contribution is to develop a recursive algorithm for the selection of significant kernels one at time using the minimum integrated square error (MISE) criterion for both kernel selection. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with competitive accuracy to existing kernel density estimators.

According to the distribution of faults in coal face, deep-hole loose blasting scheme is formulated and vibration monitoring is conducted. Obtained seismic wave attenuation rule of deep-hole loose blasting, HHT method is adopted for vibration signal decomposition to research the characteristic of spectrum and the energy distribution of the signal. Research results show that seismic wave propagation characteristic of deep-hole loose blasting accords with hard rock spread rule in Safety Regulation for Blasting, the linear component of the ligature between survey point and explosion source is the largest in the three direction components of vibration velocity, which is resulted from the difference of energy loss between different kinds of seismic wave in propagation process, the duration of blasting vibration is about 150 ms, main energy is concentrated in the low-frequency area of less than 100 Hz, and energy can be divided into several "sub-bands" with frequency distribution. According to attenuation law, the biggest single shot charge of deep-hole loose blasting in the site can be calculated inversely, and measures for reducing blasting seismic hazard in construction are proposed, which would provide a reference for the construction.

By the finite element method, blasting vibration about deep roadway is simulated in the blast driving process. By regression analysis about the effective stress peak value of the element the results show that the numerical simulation not only reveals the situation of blasting in rock roadway but also shows surrounding loose scope. By detecting instrument of the sonic wave, the change law of loose circle is obtained under dynamic loading of tunnel blasting around the ahead of tunneling in coal mining. Result of the simulation is agreement with the result of the experiment, which provides theory base for the choice of blasting parameter in soft rock driving. That has engineering significance to the blasting driving construction and supporting design.

A neurofuzzy classifier identification algorithm is introduced for two class problems. The initial fuzzy base construction is based on fuzzy clustering utilizing a Gaussian mixture model (GMM) and the analysis of covariance (ANOVA) decomposition. The expectation maximization (EM) algorithm is applied to determine the parameters of the fuzzy membership functions. Then neurofuzzy model is identified via the supervised subspace orthogonal least square (OLS) algorithm. Finally a logistic regression model is applied to produce the class probability. The effectiveness of the proposed neurofuzzy classifier has been demonstrated using a real data set.

To solve the problem that traditional RFID interrogator has no operating system and can only identify a short distance, this paper designed one UHF RFID interrogator based on S3C2440A controller and AS3992 UHF RFID chip. It uses the closed-loop network to control the output power, uses the antenna impedance match tuning network to reduce the reflected power. The maximum output power is 31d Bm. The identification distance is more than 10 meters. It solves the problem of misreading between the multi interrogators with the help of Linux operating system and host server. It has the advantages of long identification distance, high stability and no misreading. So it is more suitable for using in the mining logistics. It reduces the logistics cost and improves the logistics accuracy.

A novel probability density function (PDF) estimation based over-sampling approach is proposed for two-class imbalanced classification problems. The Parzen-window kernel function is applied to estimate the PDF of the positive class, from which synthetic instances are generated as additional training data to re-balance the class distribution. Utilising the re-balanced over-sampled training data, a radial basis function (RBF) classifier is constructed by applying an orthogonal forward regression, in which the classifier's structure and the parameters of RBF kernels are determined using a particle swarm optimisation algorithm based on the criterion of minimising the leave-one-out misclassification rate. The effectiveness of the proposed approach is demonstrated by an empirical study on several imbalanced data sets.

Floating point multiplier is one of the most important floating-point calculations. The difficulty of high performance multiplier is the rounding operation after multiplying of the mantissa. After an in-depth study of the rounding algorithm based on Injection-based algorithm [1] proposed by Guy. Even and Peter-Michael. Seidel and the fast sticky bit generation algorithm [2] proposed by Robert K. Yu and Gregory B. Zyner, an efficient rounding algorithm optimization program which can meet any precision in rounding algorithm is proposed in this paper. The optimization has been verified in circuit design. In this paper, the principles and formulas of two algorithms are derived from the perspective of circuit design and the latter is proved by mathematical induction. The speed of multiplier is increased effectively.

This paper studies the testability system structure, hierarchy partition, test mode and engineering method of digital nuclear power plant. Putting forward the module testability programming method based on system function division and the testability model design based on multi-signal model, improved the information fusion efficiency of the instrument control system for nuclear power plant.