数字音频变调算法的研究

变调是一种用途广泛的音频处理手段,但有关的技术资料很少,并且介绍得不详细。由此提出了一种频域实现的算法,能有效实现变调不变时间,并达到较好的相位连续性,实验证实效果良好。

一种语音变速不变调处理算法的FPGA实现

文章介绍了一种对语音进行变速不变调处理的系统。该系统与ADPCM编解码技术相结合,能更精确地检测和分割“音元”,并通过音元的复制和抽取实现了对播放语速的控制,达到了变速不变调的目的。该系统用FPGA实现,结果表明:采用改进后的音元处理算法,可以大大减少语音处理中所引入的噪声;该算法与ADPCM相结合实现的语音变速系统,具有速度快、占用资源少、芯片面积小和成本低等特点。

GLOBAL CONVERGENCE OF A CLASS OF OPTIMALLY CONDITIONED SSVM METHODS

This paper explores the convergence of a class of optimally conditioned self scaling variable metric (OCSSVM) methods for unconstrained optimization. We show that this class of methods with Wolfe line search are globally convergent for general convex functions.

INDUCTION MOTOR SPEED CONTROL SYSTEM BASED ON FUZZY NEURAL NETWORK

A fuzzy neural network controller with the teaching controller guidance and parameter regulations for vector-controlled induction motor is proposed. The design procedures of the fuzzy neural controller and the teaching controller are described. The parameters of the membership function are regulated by an on-line learning algorithm. The speed responses of the system under the condition, where the target functions are chosen as I qs and ω, are analyzed. The system responses with the variant of parameter moment of inertial J, viscous coefficients B and torque constant K tare also analyzed. Simulation results show that the control scheme and the controller have the advantages of rapid speed response and good robustness.

Comparison of two kinds of approximate proximal point algorithms for monotone variational inequalities

This paper proposes two kinds of approximate proximal point algorithms （APPA） for monotone variational inequalities, both of which can be viewed as two extended versions of Solodov and Svaiter＇s APPA in the paper ＂Error bounds for proximal point subproblems and associated inexact proximal point algorithms＂ published in 2000. They are both prediction- correction methods which use the same inexactness restriction; the only difference is that they use different search directions in the correction steps. This paper also chooses an optimal step size in the two versions of the APPA to improve the profit at each iteration. Analysis also shows that the two APPAs are globally convergent under appropriate assumptions, and we can expect algorithm 2 to get more progress in every iteration than algorithm 1. Numerical experiments indicate that algorithm 2 is more efficient than algorithm 1 with the same correction step size,

Improved adaptive filter and its application in acoustic emission signals

In order to de-noise and filter the acoustic emission(AE) signal, the adaptive filtering technology is applied to AE signal processing in view of the special attenuation characteristics of burst AE signal. According to the contradiction between the convergence speed and steady-state error of the traditional least mean square(LMS) adaptive filter, an improved LMS adaptive filtering algorithm with variable iteration step is proposed on the basis of the existing algorithms. Based on the Sigmoid function, an expression with three parameters is constructed by function translation and symmetric transformation.As for the error mutation, e(k) and e(k-1) are combined to control the change of the iteration step. The selection and adjustment process of each parameter is described in detail, and the MSE is used to evaluate the performance. The simulation results show that the proposed algorithm significantly increases the convergence speed, reduces the steady-state error, and improves the performance of the adaptive filter. The improved algorithm is applied to the AE signal processing, and the experimental signal is demodulated by an empirical mode decomposition(EMD) envelope to obtain the upper and lower envelopes. Then, the expected function related to the AE signal is established. Finally, the improved algorithm is substituted into the adaptive filter to filter the AE signal. A good result is achieved, which proves the feasibility of adaptive filtering technology in AE signal processing.

A predictor-corrector interior-point algorithmfor monotone variational inequality problems

Mehrotra's recent suggestion of a predictor corrector variant of primal dual interior point method for linear programming is currently the interior point method of choice for linear programming. In this work the authors give a predictor corrector interior point algorithm for monotone variational inequality problems. The algorithm was proved to be equivalent to a level 1 perturbed composite Newton method. Computations in the algorithm do not require the initial iteration to be feasible. Numerical results of experiments are presented.

Study of Software Radio Receiver Development Platform

The Paper introduces an IF software radio receiver development Platform based on high-speed monolithic A/D Converter AD6640, Progranunable Digital Down-converter AD6620 and high-speed DSP chip TMSC320C6701. The implementation method is described as well as AD6620 parameter setting analysis. It also presents a flow chart of the on-line programming with the help of PC. The algorithm for demodulation AM signal is discussed.

Comparison of two approximal proximal point algorithms for monotone variational inequalities

Proximal point algorithms （PPA） are attractive methods for solving monotone variational inequalities （MVI）. Since solving the sub-problem exactly in each iteration is costly or sometimes impossible, various approximate versions ofPPA （APPA） are developed for practical applications. In this paper, we compare two APPA methods, both of which can be viewed as prediction-correction methods. The only difference is that they use different search directions in the correction-step. By extending the general forward-backward splitting methods, we obtain Algorithm Ⅰ; in the same way, Algorithm Ⅱ is proposed by spreading the general extra-gradient methods. Our analysis explains theoretically why Algorithm Ⅱ usually outperforms Algorithm Ⅰ. For computation practice, we consider a class of MVI with a special structure, and choose the extending Algorithm Ⅱ to implement, which is inspired by the idea of Gauss-Seidel iteration method making full use of information about the latest iteration. And in particular, self-adaptive techniques are adopted to adjust relevant parameters for faster convergence. Finally, some numerical experiments are reported on the separated MVI. Numerical results showed that the extending Algorithm II is feasible and easy to implement with relatively low computation load.

Genetic Optimization Algorithm of PID Decoupling Control for VAV Air-Conditioning System

Variable-air-volume (VAV) air-conditioning system is a multi-variable system and has multi coupling control loops. While all of the control loops are working together, they interfere and influence each other. A multivariable decoupling PID controller is designed for VAV air-conditioning system. Diagonal matrix decoupling method is employed to eliminate the coupling between the loop of supply air temperature and that of thermal-space air temperature. The PID controller parameters are optimized by means of an improved genetic algorithm in floating point representations to obtain better performance. The population in the improved genetic algorithm mutates before crossover, which is helpful for the convergence. Additionally the micro mutation algorithm is proposed and applied to improve the convergence during the later evolution. To search the best parameters, the optimized parameters ranges should be amplified 10 times the initial ideal parameters. The simulation and experiment results show that the decoupling control system is effective and feasible. The method can overcome the strong coupling feature of the system and has shorter governing time and less over-shoot than non-optimization PID control.

Multi-variable Optimization of HVAC System Using a Genetic Algorithm

Geothermal is a fast-growing alternative heat source for HVAC systems, however, the initial cost of using a ground source HVAC system is higher compared to an air source system. Studies about system design and operation are necessary to reduce the initial cost and ensure that the ground source heat pump system has high efficiency, resulting in a lower total life-time cost. In this study, a multi-variable evolutionary computation algorithm is proposed for generating optimal parameters for a geothermal source HVAC system. The system was modeled and simulated using MATLAB. The design parameters were calculated by minimizing the energy consumption, Based on an experimental building, a case study was presented. Using this model, the optimal set points were calculated and used as a designed system. Energy consumption of this system was reduced by about 10% compared to the system operated with a fixed supply cold water temperature （7 ℃）.

Evolution of downsized crescent-shaped dune in wind tunnel experiment

The migration of a downsized crescent-shaped dune was investigated in a wind tunnel experiment.Quantified upwind influx and vertical oscillation of the sand bed were introduced to modulate the saturation level of the sand flux above the dune surface to affect dune evolution.The evolution was recorded by top-view photography and then abstracted as the evolution of self-defined characteristic quantities using a digital image processing algorithm.The results showed that,in contrast to the case for spanwise quantities,the evolution of streamwise quantities corresponds to a linear increase in the modulation magnitude more positively and in a monotonic and convergent manner.In contrast with quantities on the windward face,the changes in quantities with respect to the horns were nonmonotonic with time and almost uncorrelated with the variation in modulation strength,which reveals the distinctiveness of leeside evolution.

Solving Job-shop Scheduling Problem with an Ant Colony Optimization Algorithm Based on Mutation and Dynamic Pheromone Updating

The validity of the ant colony algorithm has been demonstrated as a powerful tool solving the optimization. An ant colony optimization algorithm based on mutation and dynamic pheromone updating in this paper was applied to settle job shop scheduling problem. Result of computer simulation shows that this method is effective.