In general, analyses of phenomena In a stochastic environment showing various types of complex fluctuation form can be classified into two categories. One is the analysis method from the bottom-up viewpoint, and the other is the method based on the top-down viewpoint. The former is a method structurally based on the physical mechanism existing in the phenomena. The latter method lays the stress on a functional viewpoint connected with the human response to environmental phenomena, in order to obtain an evaluation of the phenomena even in cases of an unknown or uncertain structural mechanism. In this study, from the latter viewpoint, we propose a signal processing method utilizing fuzzy inference to be applied to actual stochastic environmental phenomena. The validity of the proposed method is experimentally confirmed by applying it to observed data in the electromagnetic environment for a near held and in the living noise environment, as two typical examples of phenomena difficult to partition into crisp sets by means of a threshold.

Recently, for the purpose of reducing a residential environmental noise, many sound insulation systems are often improved acoustically by changing their geometrical scales and/or acoustical characteristics. In this paper, new functional evaluation and probabilistic prediction methods for the energy acoustic system are theoretically and experimentally proposed in practical expression forms by introducing a few functional parameters. These functional parameters are supported by many of physically structural factors in close relation to the well-known statistical energy analysis method, and are easily estimated in a preliminary experiment. The estimation procedures are found out, especially using the actual overall frequency band data, based on an Lxevaluation criterion which matches to the actual situation of estimating the representative evaluation indices, Finally, by using the actual musical sound as an input noise, the effectiveness of the proposed method is experimentally confirmed by applying it to some actual problems.

As is well-known, it is impossible to model the actual time series data of acoustic signals by a simple equation. From the engineering viewpoint, effective prediction algorithm of the above time series signal is very important in the regulation and control problems for acoustic noise. It is also useful in saving the frequency band width in the communication line for acoustic information. From the above practical points of view, this paper describes a new trial for predicting the fluctuations of time series data by use of adaptive functions. The present prediction method is established based on not only the information of linear correlation but also that of higher order nonlinear correlations in the actual time series data. The validity of the proposed method has been experimentally confirmed by applying it to actual random noise data.

Upper and lower bounds on the probability of error of binary CPSK system subject to additive non-Gaussian noise and intersymbol interference are derived and the bounds are evaluated for square-wave signaling when the signal is pasSed through filters of Butterworth and Chebyshev transmission characteristics. It is assumed that the distribution of the non-Gaussian noise is characterized by skewness and kurtosis. These parameters express the effect of the limiter on the channel noise. For square-wave signaling it is shown that the skewness has no effect on the performance of the system, and it is also shown that the kurtosis always degrades the probability of error of the system and it increases linearly with the kurtosis.