A NEW ALGORITHM FOR ADAPTIVE LATTICE FILTERAND ITS APPLICATION IN THE SPEECH LINEARPREDICTIVE SYNTHESIS

In this paper, an adaptive line spectral pair filter is derived from an adaptive lattice filter. A least-mean-square(LMS) type adaptive algorithm used to calculate directly the line spectral pair(LSP) coefficients on a stage-by-stage basis is presented. Experimental results show that the algorithm has higher convergence rate and lower misadjustment as compared with the other algorithms. The LSP coefficients calculated by the algorithm have been used to carry out speech linear predictive synthesis, resulting in better results than PARCOR coefficients.     

高阻带抑制有源声表面波滤波组件

介绍有源声表面波滤波组件的设计思路。报道最新研制的高性能滤波组件，其中心频率４０ＭＨｚ，３ｄＢ相对带宽０．８％，阻带抑制优于１００ｄＢ，矩形系数（Δｆ１００ｄＢ／Δｆ３ｄＢ）小于３，输入输出端口电压驻波比接近１。组件体积８５ｍｍ×３５ｍｍ×２４ｍｍ，重量小于１１５ｇ     

Direct preparation of particles from liquid suspension for ESEM or SEM analysis

A simplified method for the preparation of particles from liquid suspensions has been developed. Particles are deposited directly on carbon planchets for rapid analysis by environmental scanning electron microscopy or by conventional scanning electron microscopy after an additional drying step. This is accomplished by filtering the liquid through thin carbon planchets. Three different grades of graphite were investigated for their suitability as the source material for these planchets. The high quality isomolded graphite is recommended for the filtration and direct observation of particles by electron microscopy. This technique is demonstrated for particles in hydraulic fluid and aquatic suspended particulate material from a natural water source.     

An attempt to compare the performance of bioscrubbers and biotrickling filters for degradation of ethyl acetate in gas streams

This study presents a comparison of the efficiency of a bioscrubber and a biotrickling filter (BTF) for the removal of ethyl acetate (EA) vapour from a waste gas stream, under the same operating conditions. The maximum EA elimination capacity achieved in the bioscrubber was 550 g m^?3 h^?1 with removal efficiency higher than 96%. For higher EA loadings the bioscrubber was oxygen limited, which caused incomplete EA biodegradation. When pure oxygen was fed to the bioscrubber at a rate of 0.02 L min^?1, the bioscrubber recovered and could treat higher EA loadings without any oxygen limitation. The BTF achieved EA elimination capacity of 600 g m^?3 h^?1 with removal efficiency higher than 97% and the dissolved oxygen concentration remained substantially higher than in the bioscrubber. However, severe channelling and blockage of the spray nozzle occurred due to the excessive biomass growth. Overall, the bioscrubber system was easier to operate and control than the BTF, while an enhancement of the oxygen mass transfer in the bioscrubber could potentially increase its performance by up to three times. Copyright © 2005 Society of Chemical Industry     

A New Algorithm for the Elimination of Common Subexpressions in Hardware Implementation of Digital Filters by Using Genetic Programming

A new algorithm based on Genetic Programming (GP) for the problem of optimization of Multiple constant Multiplication (MCM) by Common Subexpression Elimination(CSE) is developed. This method is used for hardware optimization for DSP systems. A solution based on Genetic Programming is shown in this paper. The performance of the technique is demonstrated in one- and multi-dimensional digital filters with constant coefficients.     

指状椭圆函数滤波器的研究与设计

阐述了指状椭圆函数滤波器的特点、适用范围，基本理论以及设计方法。用本文导出的公式进行设计有工程应用价值，介绍了一个L波段滤波器的例子。     

一种适用于有限长度混合相位未知脉冲的最小平方反褶积新方法

普通的最小平方反褶积只有在子波为最小相位脉冲和反射序列为白噪声的假设下才是成立的,而对于混合相位未知脉冲来说,这种最小平方反褶积就不再适用了。本文提出一种适合于有限长度混合相位未知脉冲的最小平方反褶积方法。运用此法只需预先估计一个混合相位子波的长度。合成数据的试验结果表明,该方法有较好的效果,且对预先估计出的子波长度不敏感。文中还证明混合相位未知脉冲最小平方反褶积等效于一种有间隙的平滑误差滤波器的作用,而这种有间隙的平滑误差滤波又可分解为一个前向多步预测误差滤波与一个后向多步预测误差滤波之和。可以预计这种新的反滤积方法将在地震数据处理中得到有效的应用。     

A genetic‐based neuro‐fuzzy controller for blind equalization of time‐varying channels

This paper presents a neuro‐fuzzy network (NFN) where all its parameters can be tuned simultaneously using genetic algorithms (GAs). The approach combines the merits of fuzzy logic theory, neural networks and GAs. The proposed NFN does not require a priori knowledge about the system and eliminates the need for complicated design steps such as manual tuning of input–output membership functions, and selection of fuzzy rule base. Although, only conventional GAs have been used, convergence results are very encouraging. A well‐known numerical example derived from literature is used to evaluate and compare the performance of the network with other equalizing approaches. Simulation results show that the proposed neuro‐fuzzy controller, all parameters of which have been tuned simultaneously using GAs, offers advantages over existing equalizers and has improved performance. From the perspective of application and implementation, this paper is very interesting as it provides a new method for performing blind equalization. The main contribution of this paper is the use of learning algorithms to train a feed‐forward neural network for M‐ary QAM and PSK signals. This paper also provides a platform for researchers of the area for further development. Copyright © 2008 John Wiley & Sons, Ltd.     

运用PLC技术改造385m~2凯利式叶滤机控制系统

根据生产实践 ,结合PLC的技术特点和 385m2 凯利式叶滤机的电气联锁特性 ,阐述了应用PLC技术 ,可进一步提高 385m2 凯利式叶滤机的运转率和自动化水平。     

Sediment Concentration and Its Prediction by Perceptron Kalman Filtering Procedure

Predictions of the discharge and the associated sediment concentration are very useful ingredients in any water resources reservoir design, planning, maintenance, and operation. Although there are many empirical relationships between the discharge and sediment concentration amounts, they need estimation of model parameters. Generally, parameter estimations are achieved through the regression method (RM), which has several restrictive assumptions. Such models are locally valid and their structures and parameter values are questionable from region to others. This paper proposes a new approach for sediment concentration prediction provided that there are measurements of discharge and sediment concentration. The basis of the methodology is a dynamic transitional model between successive time instances based on two variables, namely, discharge and sediment concentration measurements. The transition matrix elements are estimated from the measurements through a special form of the artificial neural networks as perceptrons. The sediment concentration predictions from discharge measurements are achieved through a perceptron Kalman filtering (PKF) technique. In the meantime, this technique also provides temporal predictions. A certain portion of the measurement sequence is employed for the model parameter estimations through training and the remaining part is used for the model verification. Detailed comparisons between RM and PKF approaches are presented and, finally, it is shown that the latter model works dynamically by simulating the observation scatter diagram in the best possible manner with smaller prediction errors. The application of the methodology is performed for the discharge and sediment concentration measurements obtained from the Mississippi River basin at St. Louis, Missouri. It is found that the PKF methodology has smaller average relative, root-mean-square, and absolute errors than RM. Furthermore, graphical representation, such as the scatter and frequency diagrams, indicated that the PKF approach has superiority over the RM.