Practical design procedure of a four-pole dual-mode cavity filter is explained in the details. Coupling matrix M of an elliptic function filter is derived analytically. The effects of septum thickness is studied experimentally. The dimensions of the aperture have to be modified due to the effects. This attempt had made the filter design very elegant, because no complicated calculation is required. A four-pole filter and a multiplexer are designed and constructed experimentally. They show very excellent performances in the 23 GHz band.

This paper deals with a set of differential operators for calculating the differentials of an observed signal by the Daubechies wavelet and its application for the estimation of the transfer function of a linear system by using non-stationary step-like signals. The differential operators are constructed by iterative projections of the differential of the scaling function for a multiresolution analysis into a dilation subspace. By the proposed differential operators we can extract the arbitrary order differentials of a signal. We propose a set of identifiable filters constructed by the sum of multiple filters with the first order lag characteristics. Using the above differentials and the identifiable filters we propose an identification method for the transfer function of a linear system. In order to ensure the appropriateness and effectiveness of the proposed method some numerical simulations are presented.

In this paper, we consider the selection of analysis filters used in the delayless subband adaptive digital filter (SBADF) and propose to use simple analysis filters to reduce the computational complexity. The coefficients of filters are determined using the components of the first order Hadamard matrix. Because coefficients of Hadamard matrix are either 1 or -1, we can analyze signals without multiplication. Moreover, the conditions for convergence of the proposed method is considered. It is shown by computer simulations that the proposed method can converge to the Wiener filter.

Two-dimensional (2-D) adaptive digital filters (ADFs) for 2-D signal processing have become a fascinating area of the adaptive signal processing. However, conventional 2-D FIR ADF's require a lot of computations. For example, the TDLMS requires 2N2 multiplications per pixel. We propose a new 2-D adaptive filter using the FFTs. The proposed adaptive filter carries out the fast convolution using overlap-save method, and has parallel structure. Thus, we can reduce the computational complexity to O(log2N) per pixel.

The following, which is related to the design of the microwave filters, is mainly presented: (1) certain useful approximation which can be obtained by double-resistive- terminated 2-ports consisting of a cascade of two 1-variable 2-ports in different variables, and (2) an approach for filter design from 2-variable viewpoint. Approximations presented provide useful magnitude responses in 2-D domain. Hence it is discussed that how the provided 2-D responses can be used for the design of the microwave filters. Furthermore, properties of the 2-variable transfer functions resulting in such circuits are given.

The increasing activity at millimeter wave frequency band and the growing demand for waveguide components to be applied for integrated circuit purpose have promoted the need for applying the field-theory-based approaches to the design procedure. In this paper, genetic algorithms (GA's) are applied to accurately design the iris-coupled waveguide filters based on network-boundary element method (NBEM). GA's model the natural selection and evolve towards the global optimum, thus avoid being trapped in local minima. Network-boundary element method, which combines boundary element method with network analysis method, derives the network parameters of the guided wave structures with less storage location and central processing unit time. Therefore, NBEM is a feasible and efficient field-theory-based approach for the GA optimization of waveguide filters. With NBEM performing the task of evaluating the performance of the filter designs optimized by the GA, rigorous and optimal designs of the waveguide filters are realized. The obtained analysis and optimization results are compared to a number of reference solutions to demonstrate the validity and accuracy of the proposed approach.

In this paper, we propose an idea for intramedia synchronization control using a method of end-to-end delay monitoring to estimate future delay in delay compensation protocol. The estimated value by Kalman filtering at the presentation site is used for feedback control to adjust the retrieval schedule at the source according to the network conditions. The proposed approach is applicable for the real time retrieving application where `tightness' of temporal synchronization is required. The retrieval schedule adjustment is achieved by two resynchronization mechanisms-retrieval offset adjustment and data unit skipping. The retrieval offset adjustment is performed along with a buffer level check in order to compensate for the change in delay jitter, while the data unit skipping control is performed to accelerate the recovery of unsynchronization period under severe conditions. Simulations are performed to verify the effectiveness of the proposed scheme. It is found that with a limited buffer size and tolerable latency in initial presentation, using a higher efficient delay estimator in our proposed resynchronization scheme, the synchronization performance can be improved particularly in the critically congested network condition. In the study, Kalman filtering is shown to perform better than the existing estimation methods using the previous measured jitter or the average value as an estimate.

In this letter, a design method of linear-phase paraunitary filter banks is proposed for an odd number of channels. In the proposed method, a non-linear unconstrained optimization process is assumed to be applied to a lattice structure which makes the starting guess of design parameters simple. In order to avoid insignificant local minimum solutions, a recursive initialization procedure is proposed. The significance of our proposed method is verified by some design examples.

This paper proposes a new design method of a nonlinear filtering algorithm in continuous-time stochastic systems. The observed value consists of nonlinearly modulated signal and additive white Gaussian observation noise. The filtering algorithm is designed based on the same idea as the extended Kalman filter is obtained from the recursive least-squares Kalman filter in linear continuous-time stochastic systems. The proposed filter necessitates the information of the autocovariance function of the signal, the variance of the observation noise, the nonlinear observation function and its differentiated one with respect to the signal. The proposed filter is compared in estimation accuracy with the MAP filter both theoretically and numerically.

In this paper, we propose a new structure of the frequency domain adaptive filter (FDAF). The proposed structure is based on the modified DFT pair which consists of the FIR filters, so that un-delayed output signal can be obtained with stable convergence and without accumulated error which are problems for the conventional FDAFs. The convergence performance of the proposed FDAF is examined through the computer simulations in the adaptive line enhancer (ALE) comparing with the conventional FDAF and the DCT domain adaptive filter. Furthermore, in order to improve the error performance of the FDAF, we propose a composite algorithm which consists of the normalized step size algorithm for fast convergence and the variable step size one for small estimation error. The advantage of the proposed algorithm is also confirmed through simulations in the ALE. Finally, we propose a reduction method of the computational complexity of the proposed FDAF. The proposed method is to utilize a part of the FFT flow-graph, so that the computational complexity is reduced to O(N log N).

We propose a robust detection scheme by employing an order statistic filter as a preprocessor of the input signal. For ease of design, the variance of the order statistic filtered output is modeled by proposing an approximate upper bound. The detector is analytically designed using a fixed sample size (FSS) test scheme. The performance of the proposed detector is compared to that of other robust detectors in terms of the sample size required for given false alarm and miss detection probabilities. Finally, analytical results are verified by computer simulation.

FIR digital filters composed of parallel multiple subfilters are proposed. A binary expression of an input signal is decomposed into multiple shorter words, which drive the subfilters having different length. The output error is evaluated by mean squared and maximum spectra. A fast algorithm is also proposed to determine optimal filter lengths and coefficients of subfilters. Many examples confirm that the proposed filters generate smaller output errors than conventional filters under the condition of specified number of multiplications and additions in filter operations. Further, multiplier and adder structures (MAS) to perform the operations of the proposed filters are also presented. The number of gates used in the proposed MAS and its critical path are estimated. The effectiveness of the proposed MAS is confirmed.

Penumbral imaging is a technique which uses the facts that spatial information can be recovered from the shadow or penumbra that an unknown source casts through a simple large circular aperture. The technique is based on a linear deconvolution. In this paper, a two-step method is proposed for decoding penumbral images. First a local-statistic filter based on adaptive windowing is applied to smooth the noise; then, followed by the conventional linear deconvolution. The simulation results show that the reconstructed image is dramatically improved in comparison to that without the noise-smoothing filtering, and the proposed method is also applied to real experimental X-ray imaging.

In this paper, we compare the performance of evolutionary digital filters (EDFs) for IIR adaptive digital filters (ADFs) in terms of convergence behavior and stability, and discuss their advantages. The authors have already proposed the EDF which is controlled by adaptive algorithm based on the evolutionary strategies of living things. This adaptive algorithm of the EDF controls and changes the coefficients of inner digital filters using the cloning method or the mating method. Thus, the adaptive algorithm of the EDF is of a non-gradient and multi-point search type. Numerical examples are given to demonstrate the effectiveness and features of the EDF such that (1) they can work as adaptive filters as expected, (2) they can adopt various error functions such as the mean square error, the absolute sum error, and the maximum error functions, and (3) the EDF using IIR filters (IIR-EDF) has a higher convergence rate and smaller adaptation noise than the LMS adaptive digital filter (LMS-ADF) and the adaptive digital filter based on the simple genetic algorithm (SGA-ADF) on a multiple-peak surface.

Time series prediction is very important technology in a wide variety of fields. The actual time series contains both linear and nonlinear properties. The amplitude of the time series to be predicted is usually continuous value. For these reasons, we combine nonlinear and linear predictors in a cascade form. The nonlinear prediction problem is reduced to a pattern classification. A set of the past samples x(n-1),. . . ,x(n-N) is transformed into the output, which is the prediction of the next coming sample x(n). So, we employ a multi-layer neural network with a sigmoidal hidden layer and a single linear output neuron for the nonlinear prediction. It is called a Nonlinear Sub-Predictor (NSP). The NSP is trained by the supervised learning algorithm using the sample x(n) as a target. However, it is rather difficult to generate the continuous amplitude and to predict linear property. So, we employ a linear predictor after the NSP. An FIR filter is used for this purpose, which is called a Linear Sub-Predictor (LSP). The LSP is trained by the supervised learning algorithm using also x(n) as a target. In order to estimate the minimum size of the proposed predictor, we analyze the nonlinearity of the time series of interest. The prediction is equal to mapping a set of past samples to the next coming sample. The multi-layer neural network is good for this kind of pattern mapping. Still, difficult mappings may exist when several sets of very similar patterns are mapped onto very different samples. The degree of difficulty of the mapping is closely related to the nonlinearity. The necessary number of the past samples used for prediction is determined by this nonlinearity. The difficult mapping requires a large number of the past samples. Computer simulations using the sunspot data and the artificially generated discrete amplitude data have demonstrated the efficiency of the proposed predictor and the nonlinearity analysis.

We present a finite-difference time-domain (FD-TD) method with the perfectly matched layers (PMLs) absorbing boundary condition (ABC) based on the multidimensional wave digital filters (MD-WDFs) for discrete-time modelling of Maxwell's equations and show its effectiveness. First we propose modified forms of the Maxwell's equations in the PMLs and its MD-WDFs' representation by using the current-controlled voltage sources. In order to estimate the lower bound of numerical errors which come from the discretization of the Maxwell's equations, we examine the numerical dispersion relation and show the advantage of the FD-TD method based on the MD-WDFs over the Yee algorithm. Simultaneously, we estimate numerical errors in practical problems as a function of grid cell size and show that the MD-WDFs can obtain highly accurate numerical solutions in comparison with the Yee algorithm. Then we analyze several typical dielectric optical waveguide problems such as the tapered waveguide and the grating filter, and confirm that the FD-TD method based on the MD-WDFs can also treat radiation and reflection phenomena, which commonly done using the Yee algorithm.

In this paper, two universal building blocks for complex filter using CCIIs, CFCCIIs, grounded resistors and grounded capacitors are presented. These can be used to realize various complex bandpass filters with arbitrary order. The paper shows that the response error of the proposed circuit caused by nonideality of active components is more easily compensated than that of the conventional one employing op-amps, and that the sensitivities for all components are relatively small. Experimental results are used for verifying the validity of the proposed circuits.

Fundamental nonlinear filters including M-filters and order statistic filters are formulated generally by the maximum a-posteriori (MAP) estimation and some filters are derived with the aid of the Bayes formula. This MAP-filters reduces to M-filters if a-priori probability distribution is uniform, while the rank filters are derived when a-priori bias exists in the MAP estimation. This MAP-filters are implemented with an analog electronic circuit and the log-likelihood is shown to be a Liapunov function for the dynamics of this circuit.

New grounded capacitor realizations of second order and third order current mode Butterworth lowpass filters are given. The proposed circuits employ the current conveyor as the active element, and have the attractive property of using equal valued capacitors and equal valued resistors. PSpice simulation results are included.

In the literatures [5] and [10], a systematic discussion is presented with respect to the optimum interpolation of multi-dimensional signals. However, the measures of error in these literatures are defined only in each limited block separately. Further, in these literatures, most of the discussion is limited to theoretical treatment and, for example, realization of higher order linear phase FIR filter bank is not considered. In this paper, we will present the optimum interpolation functions minimizing various measures of approximation error simultaneously. Firstly, we outline necessary formulation for the time-limited interpolation functions ψm(t) (m=0,1,. . . ,M-1) realizing the optimum approximation in each limited block separately, where m are the index numbers for analysis filters. Secondly, under some assumptions, we will present analytic or piece-wise analytic interpolation functions φm(t) minimizing various measures of approximation error defined at discrete time samples n=0, 1, 2,. . . . In this discussion, φm(n) are equal to ψm(n) n=0, 1, 2,. . . . Since ψm(t) are time-limited, φm(n) vanish outside of finite set of n. Hence, in designing discrete filter bank, one can use FIR filters if one wants to realize discrete synthesis filters which impulse responses are φm(n). Finally, we will present one-dimensional linear phase M channel FIR filter bank with high attenuation characteristic in each stop band. In this design, we adopt the cosine-sine modulation initially, and then, use the iterative approximation based on the reciprocal property.