A small-sized three-stage GaAs power module has been developed for portable digital radios using M-16QAM modulation. This module has exhibited typical P1dB of 10 W with PAE of 48% and a power gain of 35 dB at a low supply voltage of 6.5 V in 1.453-1.477 GHz band. The volume of the module is only 1.5 cc, which is one of the smallest value in 10 W class modules ever reported. In order to realize the reduced size and the high power performances simultaneously, the module has employed new power divider/combiner circuits with significant features of the reduced occupation area, the improved isolation properties and the function of second-harmonic control.

The dipole-dipole interaction in the quantum mechanical treatment of the matter-radiation dynamics, is shown to give rise to split energy levels reminiscent of the nonlinear coupled spectral features as well as a self-sustained coherent modes. Wiener's theory of nonlinear random processes is applied to the second harmonic generation (SHG), leading also to coupled spectral pulling and dipping features, due to the dual noise sources in the fundamental and the harmonic polarizations. Furthermore, the nonlinear spectral features are suggested to be applied to implement quantum (qubit) gates for computation.

It is important to analyze a tracking or synchronizing process in Spread Spectrum (SS) receiving system. The most common SS tracking system considered here consists of pseudorandom (PN) generator, Lowpass Filter (LPE) and Voltage Controlled Oscillator (VCO). The SS receiver is to track or synchronize its local PN generator to the received PN waveform by VCO. The fundamental equation of the system is known by a second order nonlinear differential equation in terms of phase difference between local PN generator and received PN waveform. The differential equation is nonautonoumous due to PN function of time t with period T. Picking up the gain of VCO as the main parameter in the system we show that the system has bifurcation from the normal oscillation through subharmonic oscillation to finally chaos. In the final case, chaos is confirmed by investigating maximum Liapunov number and both stable and unstable manifolds.

This paper proposes a technique for estimating the harmonic frequencies based on instantaneous frequency (IF) of speech signals. The main problem is how to decompose the speech signal into the harmonic components. For this purpose, we use a set of bandpass-filters, each of whose center frequencies changes with time in order to track the instantaneous freuency of its output. As a result, the outputs of the band-pass filters become the harmonic components, and the instantaneous frequencies of the harmonics are accurately estimated. To evaluate the effectiveness of the approach, we apply it to pitch determination of speech. Pitch determination is simply accomplished by selecting the correct fundamental frequency out of the harmonic components. It is confirmed that the pitch extraction using the proposed pitch determination algorithm (PDA) is stable and accurate. The most significant feature of the PDA is that the extracted pitch contour is smooth and it requires no post-processing such as nonlinear filtering or any smoothing processes. Several examples are presented to demonstrate the capability of the harmonics estimation technique and the PDA.

A subharmonic injection-locked oscillator (ILO) MMIC chain is proposed for the local oscillators and synthesizers used at millimeter-wave frequencies. A fabricated, primary 11-GHz-band injection-locked oscillator MMIC for the first stage ILO in the ILO-chain MMIC, achieves a wide subharmonic-injection-locking range at the subharmonic factors, 1/n (n=1, 2, 3, ), of 1/1, 1/2 and 1/3. The ILO MMIC abilities for synthesizer applications were confirmed with an injection-locking time of only 100-200 nsec, which is less than 1/100 that of PLL oscillators, and also with free-running oscillation performance and a wide injection locking range within a temperature range of -30 and 80.

The recursive least-squares filter and fixed-point smoother are designed in linear discrete-time systems. The estimators require the information of the system matrix, the observation vector and the variances of the state and white Gaussian observation noise in the signal generating model. By appropriate choices of the observation vector and the state variables, the state-space model corresponding to the ARMA (autoregressive moving average) model of order (n,m) is introduced. Here,some elements of the system matrix consist of the AR parameters. This paper proposes modified iterative technique to the existing one regarding the estimation of the variance of observation noise based on the estimation methods of ARMA parameters in Refs. [2],[3]. As a result, the system matrix, the ARMA parameters and the variances of the state and observation noise are estimated from the observed value and its sampled autocovariance data of finite number. The input noise variance of the ARMA model is estimated by use of the autocovariance data and the estimates of the AR parameters and one MA parameter.

This letter presents new estimation algorithm of ARMAX systems which do not always satisfy the strictly positive real (SPR) condition. We show how estimated parameters can converge to their true values based on the overparameterized system. Finally, the results of computer simulation are presented to illustrate the effectiveness of the proposed method.

Electromagnetic environments generated by power transmission system, possibilities of the interference and its mitigation method was introduced. In the frequency region below 10kHz, concern for DC and AC electric and magnetic field are described. In the frequency range above 10kHz, concern for discharges on power apparatus, electromagnetic emvironments generated by carrier system and fault locating system and passive interference are described. Electromagnetic environment caused by load equipments, that is harmonics, and undesirable electromagnetic emission from power converting units are described finally.

We introduce a procedure to determine the discrete Fourier spectra of the band-limited function from its irregularly distributed samples. The nonuniform data of the signal are represented by the non-orthogonal basis functions (non-harmonic Fourier functions) and discrete Fourier spectra of the signal. We construct a set of orthonormal basis functions from the above mentioned non-orthogonal basis functions using the Gram-Schmidt procedure. Based on the G-S procedure and the property of the orthogonalization, the spectral components of signal can be obtained by the conjugate transpose of orthonormal basis functions, their coefficients matrix and the nonuniform samples. Thus the desired signal can be obtained by the inverse Fourier transform of the determined discrete Fourier spectra. We apply this algorithm to reconstruct a band-limited low-pass and band-pass signal and show that our method provide more stable and better reconstruction than the matrix inversion method.

In the actual acoustic environment, the stochastic process exhibits various non-Gaussian distribution forms, and there exist potentially various nonlinear correlations in addition to the linear correlation between time series. In this study, a nonlinear ARMA model is proposed, based on the Bayes' theorem, where no artificially pre-established regression function model is assumed between time series, while reflecting hierarchically all of those various correlation informations. The proposed method is applied to the actual data of road traffic noise and its practical usefulness is verified.

In this paper, an ARMA order selection method is proposed with a fuzzy reasoning method. In order to identify the reference model with the ARMA model, we need to determine its ARMA order. A less or more ARMA order, other than a suitable order causes problems such as; lack of spectral information, increasing calculation cost, etc. Therefore, ARMA order selection is significant for a high accurate ARMA model identification. The proposed method attempts to select an ARMA order of a time-varying model with the following procedures: (1) Suppose the parameters of the reference model change slowly, by introducing recursive fuzzy reasoning method, the estimated order is selected. (2) By introducing a fuzzy c-mean clustering methed, the period of the time during which the reference model is changing is detected and the forgetting factor of the recursive fuzzy reasoning method is set. Further, membership functions used in our algorithm are original, which are realized by experiments. In this paper, experiments are documented in order to validate the performance of the proposed method.

Efficiency of Cerenkov-radiation-type second harmonic generation with absorption loss for second harmonic wave is analytically estimated. Output power reduction for attenuation coefficient of 2.0104 cm1 is calculated 37% (63% output of lossless case). Blue SHG at 443.5 nm is observed by a poled polymer pNAn-PVA waveguide. The wavelength is shorter than the cut-off wavelength of 480 nm.

In many applications involving the processing of noisy signals, it is desired to know the noise variance. This paper proposes a new method for estimating the noise variance from the signals of autoregressive (AR) and autoregressive moving-average (ARMA) systems corrupted by additive white noise. The method proposed here uses the low-order Yule-Walker (LOYW) equations and the lattice filter (LF) algorithm for the estimation of noise variance from the noisy output measurements of AR and ARMA systems, respectively. Two techniques are proposed here: iterative technique and recursive one. The accuracy of the methods depends on SNR levels, more specifically on the inherent accuracy of the Yule-Walker and lattice filter methods for signal plus noise system. The estimated noise variance is used for the blind indentification of AR and ARMA systems. Finally, to demonstrate the effectiveness of the method proposed here many numerical results are presented.

In this paper, the relationship between the recursive least square (RLS) method with a U-D decomposition algorithm and ARMA lattice filter realization algorithm is presented. Both the RLS method and the lattice filter realization algorithm are used for the same applications, such as model identification, etc., therefore, it is expected that the lattice filter algorithm is in some ways related to the RLS. Though some of the proposed lattice filter algorithms have been derived by the RLS method, they do not express the relationship between RLS snd ARMA lattice filter realization algorithm. In order to describe the relation clearly, a new structure of ARMA lattice filter is proposed. Further, based on the relationship, a method of model identification with frequency weighting (MIFW), which is different from a previous method, is derived. The new MIFW method modifies the lattice parameters which are acquired without a frequency weighting and obtain the parameters of an ARMA model, which is identified with frequency weighting. The proposed MIFW method has the following restrictions: (1) The used frequency weighting is FIR filter with a low order. (2) By using the parameters of the ARMA lattice filter with ARMA (N,M) order and the frequency weighting with L order, the new ARMA parameter with the frequency weignting is with ARMA(N-L,M-L) order. By using the proposed MIFW method, the ARMA parameters estimated with the frequency weighting can be obtained without starting the computation again.

An adaptive method analyzing analytic speech signals is proposed in this paper. The method decreases the errors of finite precision on calculation in a method with real coefficients. It is shown from the results of experiments that the proposed method is more useful than adaptive methods with real coefficients.

This paper introduces some modelling methods of time-varying stochastic process and its linear/nonlinear adaptive identification. Time-varying models are often identified by using a least square criterion. However the criterion should assume a time invariant stochastic model and infinite observed data. In order to adjust these serious different assumptions, some windowing techniques are introduced. Although the windows are usually applied to a batch processing of parameter estimates, all adaptive methods should also consider them at difference point of view. In this paper, two typical windowing techniques are explained into adaptive processing. In addition to the use of windows, time-varying stochastic ARMA models are built with these criterions and windows. By using these criterions and models, this paper explains nonlinear parameter estimation and the property of estimation convergence. On these discussions, some approaches are introduced, i.e., sophisticated stochastic modelling and multi-rate processing.

This paper describes blue second harmonic generation (SHG) by an organic crystal of 2-furyl methacrylic anhydride (FMA). It has short cut-off wavelength of 380 nm and SHG coefficients at 1064 nm. d3324 pm/V and d3116 pm/V. In 900 nm region 90-degree phase-matched blue SHG is observed using a Ti: Sapphire laser as a fundamental source. This crystal is not hygroscopic and does not exhibit sublimation at room temperature. Fine polishing is also possible.

This letter extends the Yule-Walker method to the estimation of ARMA parameters from output measurements corrupted by noise. In the proposed method it is assumed that the noise variance and the input are unknown. An algorithm for the estimation of noise variance is, therefore, given. The use of the variance estimation method proposed here together with the Yule-Walker equations allow the estimation of the parameters of a minimum phase ARMA model based only on noisy measurements of its output. Moreover, using this method it is not necessary to slove a set of nonlinear equations for MA parameter estimation as required in the conventional correlation based methods.

Poled polymer films doped with novel nonlinear organic materials, α-cyano unsaturated carboxylic acid (α-CUCA) derivatives, are prepared. Linear and second-order nonlinear optical properties are investigated. It is found that as the value of hyperpolarizability of the derivatives increases, the second-order nonlinear susceptibility of the film increases. Cerenkov-type second harmonic generation (SHG) of Nd: YAG laser is realized in a poled polymer waveguide doped with the α-CUCA material with a slight absorption at doubled wavelength.

A speech analysis/synthesis system that relies on a time-varying Auto Regressive Moving Average (ARMA) process and the Short-Time Fourier Transform (STFT) is proposed. The narrowband components in speech are represented in the frequency domain by a set of harmonic components, while the broadband random components are represented by a time-varying ARMA process. The time-varying ARMA model has a dual function, namely, it creates a spectral envelope that fits accurately the harmonic STFT components, and provides for the spectral representation of the broadband components of speech. The proposed model essentially combines the features of waveform coders by employing the STFT and the features of traditional vocoders by incorporating an appropriately shaped noise sequence.