Glottal inverse filtering is a technique by which the flow past the time varying glottal constriction is estimated by a filtering operation on the acoustic signal in human speech. The filtering operation removes the e...
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Glottal inverse filtering is a technique by which the flow past the time varying glottal constriction is estimated by a filtering operation on the acoustic signal in human speech. The filtering operation removes the effects of the vocal tract resonances to reveal the underlying voice source signal. A linear model of a glottal pulse waveform is described along with a procedure for jointly determining an AR model of the vocal tract response together with the parameters of the glottal pulse model. This technique is applied to inverse filtering in human speech and the results are compared to covariance method LPC analysis.
In this paper, a generalized inflation method which can adaptively and robustly converge to the noise-subspace is proposed to improve the performances of subspace algorithms used for tracking nonstationary sources. Th...
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In this paper, a generalized inflation method which can adaptively and robustly converge to the noise-subspace is proposed to improve the performances of subspace algorithms used for tracking nonstationary sources. This generalized inflation method, which includes an inflation factor developed in the view point of orthogonal projection, preserves the parallel structure for realizations and achieves better performances of convergence and initialization behavior than the inflation method, adaptive PHR algorithms, and other adaptive eigensubspace algorithms when the number of sources is not known. A bound of the inflation factor is also suggested to secure the noise-subspace-only adaptation. The general inflation method in use of weighted-subspace can further improve the tracking performances. Simulations for analyzing the tracking performances of the algorithms are also included.
The paper presents a systematic approach for the numerical kinematic analysis of general parallel robotic manipulators. This approach consists of two parts. The first part deals with the structural analysis. Based on ...
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The paper presents a systematic approach for the numerical kinematic analysis of general parallel robotic manipulators. This approach consists of two parts. The first part deals with the structural analysis. Based on graph theory and the Depth First Search algorithm, a method for identifying and orientating the independent kinematic loops of the robot is developed. This method not only facilitates the assignment of the local coordinate systems attached to the robot, but also arranges them in the correct order to allow efficient recursive coordinate transformation. The second part of this approach deals with the displacement analysis. A set of recursion formulae is first developed for computing the forward coordinate transformations, and these formulae am then adopted in a two-phase computational algorithm to obtain the numerical solutions to the direct and the inverse kinematics problems. The two-phase algorithm developed in this paper is not only insensitive to the initial approximation of the solution vector, but also gives a rapid convergence rate. Furthermore, it is also useful for finding multiple solutions for the robot as well as for continuous trajectory planning, as shown by the numerical examples presented in this paper.
An algorithm is presented which computes optimal weights for arbitrary linear arrays. The application of this algorithm to in situ optimal reshading of arrays with failed elements is discussed. It is shown that optima...
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An algorithm is presented which computes optimal weights for arbitrary linear arrays. The application of this algorithm to in situ optimal reshading of arrays with failed elements is discussed. It is shown that optimal reshading can often regain the original sidelobe level by slightly increasing the mainlobe beamwidth. Three examples are presented to illustrate the algorithm's effectiveness. Hardware and software issues are discussed. Execution time for a 25-element array is typically between 1 and 2 min on an HP9836C microcomputer.
Despite the importance of sparsity signal models and the increasing prevalence of high-dimensional streaming data, there are relatively few algorithms for dynamic filtering of time-varying sparse signals. Of the exist...
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Despite the importance of sparsity signal models and the increasing prevalence of high-dimensional streaming data, there are relatively few algorithms for dynamic filtering of time-varying sparse signals. Of the existing algorithms, fewer still provide strong performance guarantees. This paper examines two algorithms for dynamic filtering of sparse signals that are based on efficient l(1) optimization methods. We first present an analysis for one simple algorithm (BPDN-DF) that works well when the system dynamics are known exactly. We then introduce a novel second algorithm (RWL1-DF) that is more computationally complex than BPDN-DF but performs better in practice, especially in the case where the system dynamics model is inaccurate. Robustness to model inaccuracy is achieved by using a hierarchical probabilistic data model and propagating higher-order statistics from the previous estimate (akin to Kalman filtering) in the sparse inference process. We demonstrate the properties of these algorithms on both simulated data as well as natural video sequences. Taken together, the algorithms presented in this paper represent the first strong performance analysis of dynamic filtering algorithms for time-varying sparse signals as well as state-of-the-art performance in this emerging application.
The authors present a computer-aided designalgorithm for the analysis and design of an evanescent-mode bandpass filter with nontouching E-plane fins. The theoretical analysis is based on the generalized scattering ma...
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The authors present a computer-aided designalgorithm for the analysis and design of an evanescent-mode bandpass filter with nontouching E-plane fins. The theoretical analysis is based on the generalized scattering matrix technique in conjunction with the spectral-domain approach and mode-matching method. The technique used takes into account the dominant as well as the higher-order effects. The measured filter responses in the Ka-band are in good agreement with those obtained by this analysis.< >
The single-layer backpropagation algorithm is a gradient-descent method that adjusts the connection weights of a single-layer perceptron to minimize the mean-square error at the output. It is similar to the standard l...
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The single-layer backpropagation algorithm is a gradient-descent method that adjusts the connection weights of a single-layer perceptron to minimize the mean-square error at the output. It is similar to the standard least mean square algorithm, except the output of the linear combiner contains a differentiable nonlinearity. In this paper, we present a statistical analysis of the mean weight behavior of the single-layer backpropagation algorithm for Gaussian input signals. It is based on a nonlinear system identification model of the desired response which is capable of generating an arbitrary hyperplane decision boundary. It is demonstrated that, although the weights grow unbounded, the algorithm, on average, quickly learns the correct hyperplane associated with the system identification model.
In this paper, we study the facility location problems on the real line. Given a set of n customers on the real line, each customer having a cost for setting up a facility at its position, and an integer k, we seek to...
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In this paper, we study the facility location problems on the real line. Given a set of n customers on the real line, each customer having a cost for setting up a facility at its position, and an integer k, we seek to find at most k of the customers to set up facilities for serving all n customers such that the total cost for facility set-up and service transportation is minimized. We consider several problem variations including the k-median, the k-coverage, and the linear model. The previously best algorithms for these problems all take O(nk) time. Our new algorithms break the O(nk) time bottleneck and solve these problems in sub-quadratic time. Our algorithms are based on a new problem modeling and interesting algorithmic techniques, which may find other applications as well.
It is the function of the design of a fuzzy-logic controller to determine the universes of discourse of the antecedents and the consequents, number of membership labels, distribution and shape of membership functions,...
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It is the function of the design of a fuzzy-logic controller to determine the universes of discourse of the antecedents and the consequents, number of membership labels, distribution and shape of membership functions, rule formulation, etc. Much the information is usually extracted from expert knowledge, operator experience, or heuristic thinking. It is hence difficult to mechanize the first-stage design of fuzzy-logic controllers using linguistic labels whose performance is no worse than that of conventional multivariable linear controllers such as state-feedback controllers, PID controllers, etc. In this paper, an original systematic seven-step linear-to-fuzzy (LIN2FUZ) algorithm is proposed for generating the labels, universes of discourse of the antecedents and the consequents, and fuzzy rules of 'basically linear' fuzzy-logic controllers, given the reference design of available conventional multivariable linear controllers. The functionally equivalent fuzzy-logic controllers can thus provide the sound basis for the further development to achieve performance beyond the capability of the conventional controllers. The validity and effectiveness of the proposed LIN2FUZ algorithm are demonstrated by a four-input one-output inverted pendulum system.
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