A shared buffer ATM switch loaded with bursty input traffic is modeled by a discrete-time queueing system. Also, the unbalanced and correlated routing traffic patterns are considered. An approximation method to analyze the queueing system under consideration is developed. To overcome the problem regarding the size of state space to be dealt with, the entire switching system is decomposed into several subsystems, and then each subsystem is analyzed in isolation. We first propose an efficient algorithm for superposing all the individual bursty cell arrival processes to the switch. And then, the maximum entropy method is applied to obtain the steady-state probability distribution of the queueing system. From the obtained steady-state probabilities, we can derive some performance measures such as cell loss probability and average delay. Numerical examples of the proposed approximation method are given, which are compared with simulation results.

Time spread (TS) pulse position modulation (PPM) signals have been proposed for CDMA applications, where the envelope detection is employed instead of coherent detection for easier synchronization of PPM. In this paper, a new method of deriving symbol error probability (SEP) of TS PPM signals in the presence of interference is introduced. The analysis is based on the moment technique. The maximum entropy criterion for estimating an unknown probability density function (PDF) from its moments is applied to the evaluation of PDF of envelope detector output. Numerical results of SEP are shown for 4, 8 and 16PPM in the practical range of signal-to-noise power ratio (SNR) and signal-to-interference power ratio (SIR) of 5, 10 and 20 dB. SEP by the union bound is also given for comparison. From the results it is noted that when PPM multilevel number is small, the union bound goes near to SEP by the proposed method, but when it increases the difference of the SEP by the bound and proposed method becomes larger. The effect of central frequency offset of TS-filter is evaluated as an illustrative example.

Analysis of electroencephalogram (EEG) is presented for sleep physiology. This analysis is performed by the Instantaneous Maximum Entropy Method (IMEM), which was given by the author. Appearance and continuation of featuristic waves are not steady in EEG. The characteristics of these waves responding to epoch of sleep are analyzed. The behaviours of waves were clarified by this analysis as follows; (a) time dependent frequency of continuous oscillations of alpha rhythm was observed precisely. Sleep spindles were detected clearly within NREM and these parameters of time, frequency, and peak energy were specified. (b) delta waves with very low frequencies and sleep spindles were observed simultaneously. And (c) the relationship of sleep spindles and delta waves was first detected with negative correlation along time-axis. The analysis by the IMEM was found effective comparing conventional analysis method of FFT, bandpass filter bank, etc.

An analysis method is proposed for nonstationary waveforms. Modelling of a nonstationary waveform is first given in this paper. A waveform is represented by multiple oscillations. The instantaneous phase angle of each oscillation is written by three terms, predictive component, residual component, and initial phase constant. By this modelling, waveform analysis results in estimations of frequency, calculation of residual pbase in instantaneous phase angle. The Instantaneous Maximum Entropy Methods (IMEN) is utilized for frequency estimation. The residual phase angle is obtained by the Vandermonde matrix and the condition of continuity of phase angle among n-neighbourhood. Another analysis method is also proposed by the normalization of waveform parameters. The evaluation of the proposed method is done using artificially composed waveform signals. Novel and useful knowledge was provided by this analysis.

An adaptive array antenna can be considered as a useful tool of combating with fading in mobile communications. We can directly obtain the optimal weight coefficients without updating in temporal sampling, if the arrival angles and signal-to-noise ratio (SNR) of the desired and the undesired signals can be accurately estimated. The Maximum Entropy Method (MEM) can estimate the arrival angles, and the SNR from spatially sampled signals by an array antenna more precisely than the Discrete Fourier Transform (DFT). Therefore, this paper proposes and investigates an adaptive array antenna based on spatial spectral estimation using MEM. We call it MEM array. In order to reduce complexity for implementation, we also propose a modified algorithm using temporal updating as well. Furthermore, we propose a method of both improving estimation accuracy and reducing the number of antenna elements. In the method, the arrival angles can be approximately estimated by using temporal sampling instead of spatial sampling. Computer simulations evaluate MEM array in comparison with DFT array and LMS array, and show improvement owing to its modified algorithm and performance of the improved method.