A video image analysis method for the velocity distribution measurement of the flood flow in a river was developed. Water surface images taken from the tall building facing to the river was used for the Particle ImageVelocimetry by use of a personal computer. The accuracy of the velocity measurement was found to be dependent on the angle between a video camera and water surface by laboratory study. The field application was successfully made to the flood flow in the Yodo River in July, 1993; the discharge of the flood peak was well estimated; and the result was also examined by the 2-D numerical simulation in BFC.

A B S T R A C TThe first five parameters the shape of the oral and pha This paper discusses an application of neural networks to the problem of estimating the motion of articulatory organs from speech waves.Recently, neural networks (NN) have been studied extensively. It has been proved that a three-or four-layer feed-forwardIn this paper, we apply this feature of NN to the articulatory parameter estimation problem.The evaluation test is performed using the vowel data in 5201) tokens in the ATR word database. Our results show that the difference in estimated articulatory parameter values between the conventional method (MM) and NN is only 0.1, which is about 3 % of value range, on average. For a few data, big differences arise between MM and NN, but this is due to mis-estimation in MM rather than NN. The percentage of misestimates in NN is less than 50 % of MM. As for calculation time, NN is 10 times faster than MM.Thus, a high speed and stable articulatory parameter estimation technique is realized using neural networks. latory For that purpose, the neural networks can approximate arbitrary nodinear functions. I. I N T R O D U C T I O NIt is well known that the coarticulation compensation and speaker adaptation are two major difficulties in speech recognition. These problems might be considered most clearly and fundamentally from the view point of the speech production mechanism. On the basis of this idea, the model matching (MM) method was proposed to extract articulatory parameters, which represent articulatory movements, from speech waves [1,2] and the estimated parameters were used for speech recognition [3,4,5]. These parameters are effective for the above two problems but the conventional estimation method has some problems: one is calculation cost and the other is instability of estimation. Since this method is constructed on the basis of hill climbing methods, it requires many iterations to converge and sometimes finds only local minimum. For a speech recognition system, a faster and more stable estima tion method is desired.Recently, neural networks (NN) have been studied extensively [7]. It has been proved that a three or four-layer feedforward neural networks can approximate arbitrary nonlinear functions [b]. In this paper, we apply this feature of NN to the articulatory parameter estimation problem. Fig.1 Articulatory m A R T I C U L A T O R Y M O D E LThe total configuration of the model and the characteristics of the articulatory parameters are shown in Fig.1 and Table 1.-489 -

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This paper presents data coding techniques for a stable single-bit noise-shaping quantizer, which has a cascade structure of a multi-bit CA modulator (CAM) and a binary interpolator. The binary interpolator chooses a pre-optimized binary vector for each input sample and successively generates the chosen binary vector as an output binary sequence. The binary vectors can have different lengths. The paper also proposes two methods to evaluate and bound output errors of a binary interpolator. A multi-bit CAM is designed to cause no overload for all possible input signals whose amplitudes are bounded to a specified level, and thus the CAM rigorously guarantees the stability condition. In a design example, we have evaluated SNDRs and a noise spectrum and then confirmed that our stable quantizer can sharply shape output noise spectra.