This paper reports on the properties of thin-film thermal switches that are monolithically fabricated on high-Tc superconductive filter. Operating at a wide temperature range of 50-77 K, it was found that the switch could control the center frequency by -10 MHz with an increased insertion loss of less than 0.7 dB. In an on-off switching operation of filter characteristics using thin-film switches, power consumption was approximately 20 mW at 77 K, and the signal decay time as a switching speed was 30 ms at 76 K with a switch current of 70 mA. The decay time decreased exponentially as the switch current or the temperature setting increased.

In this paper we propose a method for increasing the reliability in multiprocessor realization of lowpass and highpass FIR digital filters possessing a maximally flat magnitude response. This method is based on the use of array realization of the filter which has been proposed earlier by the authors. It is shown that if a processing module of the array functions erroneously, it is possible to exclude the module and still obtain a lowpass FIR filter. However, as a price we should tolerate a slight degradation in the magnitude response of the filter that is equivalent to a wider transition band. We also analyze the behavior of the filter when our proposed schemes are implemented on more than one module. The justification of our approach is based on that a slight degradation of the spectral characteristics of a filter may be well tolerated in most filtering applications and thus a graceful degradation in the frequency domain can sufficiently reduce the vulnerability to errors.

The factored state space approach (FSS) can be a powerful mathematical tool for the synthesis and analysis of non state space digital filters. In the following letter, this technique is used for the rederivation of some classes of low sensitivity filters described by a II-cascade two-pair structure. This method leads to a simplified synthesis algorithm (with applications to automated synthesis procedures for many classes of non state space digital filters) as well as a straightforward analysis of roundoff noise and norm scaling problems.

In direct connection with the signal information processing, a practical method of identification and probabilistic prediction for sound insulation systems is theoretically proposed in the object-oriented expression forms by introducing a few functional system parameters. Concretely, a trial of identification of the above functional system parameters and the output probabilistic prediction for a panel thickness change of double-wall type sound insulation system, especially, under the existence of a strong background noise inside of the reception room, is newly proposed based on one of wide sense digital filters and SEA (Statistical Energy Analysis) method. Finally, by using the actual music sound of an arbitrary distribution type, the effectiveness of the proposad method is confirmed experimentally by applying it to some problems of predicting the cumulative probability distribution of the transmitted sound level fluctuation.

A bounded complex (BC) digital transfer function realized with a II-cascade structure of Lossless Bounded Complex (LBC) two-pairs is known to have low magnitude sensitivity. In this letter, it is shown that the two-pairs parameters depend directly on some invariants of the transfer function corresponding to the transmission zeros of the structure. An analysis of the existence and the numbering of these invariants leads to a simplified automated LBC filter structure design avoiding the need for polynomial manipulations. These results are also easily applied for the real filtering case.

An analog circuit is devised which selects and outputs the kth largest element among n input voltages. The circuit is composed of n basic transconductance amplifiers connected mutually with an O(n) length wire, thus the complexity of the circuit is O(n). The circuit becomes particularly simple for the case of the selection of the median of inputs.

This paper proposes high-performance multiprocessor implementation for real-time one-dimensional (1-D) statespace digital filters (SSDFs). The block-state realization of SSDFs (BSRDF) is suitable for their high speed realization and gives the characteristics of high accuracy. Previously we proposed a VLSI-oriented highly parallel architecture for BSRDF. For the purpose of speeding up and reducing hardware complexity, the distributed arithmetic, of which processing time depends only on word length, is applied to this architecture. It is implemented as a 2-D SIMD processor array, and the processor consists of n homogeneous processing elements (PEs), n being filter order. The high sampling rate of one or more hundred MHz becomes possible for high filter order. Moreover, the number of I/O data per processor can be a small fixed value for any filter order, and the number of gates can also be smaller than that in the case of using multiplier. Consequently, this proposed system can be implemented easily even in the present VLSI environment.

In this paper, a new structure which is useful for the detection of multiple sinusoids is presented. The proposed structure is based on the direct form second-order IIR notch filter using simplified adaptive algorithm. It has been shown that the convergence characteristics of the proposed structure are much improved compared with the previously proposed structure. A cascaded adaptive notch filter using the proposed second-order section is also shown. It takes multiple sinusoids corrupted by white Gaussian noise and produces the individual sinusoids at each of the outputs. The results of computer simulation are shown which confirm the theoretical prediction.

It is important to obtain a low coefficient sensitivity digital filter. This paper presents a new low coefficient sensitivity network structure that consists of a second order digital filter and a feedback path. This network structure is based on the effectiveness of the feedback path in an analog system. The coefficient sensitivity of the proposed digital filter can be control with the coefficient of the feedback path. Using this property, the digital filter with the low coefficient sensitivity is obtained. To add the feedback path makes the frequency response deviate from the characteristic of the original second order digital filter, but the deviation can be compensated with the other coefficients. A nonlinear optimization technique is employed to determine the coefficients of the digital filter. The proposed method is not effective only to narrow-band low-pass but wide-band low-pass filters.

Myoelectric (ME) signals during dynamic movement suffer from motion arifact noise caused by mechanical friction between electrodes and the skin. It is difficult to reject artifact noises using linear filters, because the frequency components of the artifact noise include those of ME signals. This paper describes a nonlinear method of eliminating artifacts. It consists of an inverse autoregressive (AR) filter, a nonlinear filter, and an AR filter. To deal with ME signals during dynamic movement, we introduce an adaptive procedure and fuzzy rules that improve the performance of the nonlinear filter for local features. The result is the best ever reported elimination performance. This fuzzy rule based adaptive nonlinear artifact elimination filter will be useful in measurement of ME signals during dynamic movement.

Reduction of the complexity of the NLMS algorithm has received attention in the area of adaptive filtering. A processing cost reduction method, in which the component of the weight vector is updated when the absolute value of the sample is greater than or equal to the average of the absolute values of the input samples, has been proposed. The convergence analysis of the processing cost reduction method has been derived from a low-pass filter expression. However, in this analysis the effect of the weignt vector components whose adaptations are skipped is not considered in terms of the direction of the gradient estimation vector. In this paper, we use an arbitrary value instead of the average of the absolute values of the input samples as a threshold level, and we derive the convergence characteristics of the processing cost reduction method with arbitrary threshold level for zero-mean white Gaussian samples. From the analytical results, it is shown that the range of the gain constant to insure convergence and the misadjustment are independent of the threshold level. Moreover, it is shown that the convergence rate is a function of the threshold level as well as the gain constant. When the gain constant is small, the processing cost is reduced by using a large threshold level without a large degradation of the convergence rate.

This paper describes a new method of approximating ideal Hilbert transformers by using time reversal techniques. As is well known, an ideal Hilbert transformer is not physically realizable because it is not causal. Nevertheless, it is extremely imprortant conceptually in the area of digital signal processing. In this paper, we propose a method to approximately implement such a Hilbert transformer. The method divides the impulse response of the ideal Hilbert transformer into two parts, i.e., causal and noncausal parts. Although a causal filter is physically realizable, a noncausal filter is not realizable. A noncausal filter is realized using time reversal techniques for input signals to the filter, and then the Hilbert transformer can be approximately implemented by the parallel connection of causal and noncausal filters.

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.

This paper proposes a fast convergence algorithm for adaptive FIR filters with sparse taps. Coefficient values and positions are simultaneously controlled. The proposed algorithm consists of two stages： flat-delay estimation and tapposition control with a constraint. The flat-delay estimation is carried out by estimating the significant dispersive region of the impulse response. The constrained tap-position control is achieved by imposing a limit on the new-tap-position search. Simulation results show that the proposed algorithm reduces the convergence speed by up to 85% over the conventional algorithms for a white signal input. For a colored signal, it also converges in 40% of the convergence time by the conventional algorithms. The proposed algorithm is applicable to adaptive FIR filters which are to model a path with long flat delay, such as echo cancelers for satellite-link communications.

Optical amplifier structures suitable for a 622Mbit/s repeater in an optical communication system containing one in-line amplifier have been investigated. Two wavelengths of 1.533µm and 1.549µm are considered for two cases, i.e., single-channel transmission and two-channel wavelength division multiplexing transmission. The basic amplifier structure is of a two-stage type where forward pumped and backward pumped erbium-doped fibers are connected with each other through intermediate optical filters and an optical isolator. First, the effect of the intermediate optical filters was clarified in optical gain and bit error rate characteristics. Then, the erbium-doped fiber length was optimized on the basis of the allowable optical loss of the optical system which was operated at a bit error rate of 10-9. As a result, the appropriate length of the forward pumped erbium-doped fiber was found to be about 20m for both cases of single-channel and two-channel wavelength multiplexing amplifiers. With the designed amplifier used in the system, the calculated allowable optical line loss was more than 90dB for both the cases.

The timing jitter reductions with differently shaped optical bandpass filters are discussed and the transmission distance achievable against the timing jitter is evaluated using optical bandpass filters in several tens of Gb/s soliton transmission. Experimental confirmation of timing jitter reduction with optical bandpass filters is demonstrated in 10Gb/s optical soliton recirculating loop experiments by measuring the timing jitter and the bit error rates.

The scope of this paper is the realization of FIR digital filters with an emphasis on linear phase and maximally flat cases. The transfer functions of FIR digital filters are polynomials and polynomial evaluation algorithms can be utilized as realization schemes of these filters. In this paper we investigate the application of a class of polynomial evaluation algorithms called "recursive triangles" to the realization of FIR digital filters. The realization of an arbitrary transfer function using De Casteljau algorithm, a member of the recursive triangles used for evaluating Bernstein polynomials, is studied and it is shown that in some special and important cases it yields efficient modular structures. Realization of two dimensional filters based on Bernstein approximation is also considered. We also introduce recursive triangles for evaluating the power basis representation of polynomials and give a new multiplier-less maximally flat structure based on them. Finally, we generalize the structure further and show that Chebyshev polynomials can also be evaluated by the triangles. This is the triangular counterpart of the well-known Chebyshev structure. In general,the triangular structures yield highly modular digital filters that can be mapped to an array of concurrent processors resulting in high speed and effcient filtering specially for maximally flat transfer functions.

This paper proposes new algorithms for adaptive FIR filters. The proposed algorithms provide both fast convergence and small final misadjustment with an adaptive step size even under an interference to the error. The basic algorithm pays special attention to the interference which contaminates the error. To enhance robustness to the interference, it imposes a special limit on the increment/decrement of the step-size. The limit itself is also varied according to the step-size. The basic algorithm is extended for application to nonstationary signals. Simulation results with white signals show that the final misadjustment is reduced by up to 22 dB under severe observation noise at a negligible expense of the convergence speed. An echo canceler simulation with a real speech signal exhibits its potential for a nonstationary signal.

Although the perception of gloss is based on human visual perception, some methods for extracting glossiness, in contrast to human ability, have been proposed involving curved surfaces. Glossiness defined in these methods, however, does not correspond with psychological glossiness perceived by the human eye over the wide range from relatively low gloss to high gloss. In addition, the obtained glossiness in these methods changes remarkably when the curvature radius of the high-gloss object becomes larger than 10mm. In reality, psychological glossiness does not change. These methods, furthermore, are available only for spherical objects. A new method for extracting glossiness is proposed in this study. For the new definition of glossiness, a spatial filter which simulates human retina function is utilized. The light intensity distribution of the curved object is convoluted with the spatial filter. The maximum value Hmax of the convoluted distribution has a high correlation with psychological glossiness Gph. From the relationship between Gph and Hmax, new glossiness Gf is defined. The gloss-extraction equipment consists of a light source, TV camera, an image processor and a personal computer. Cylinders with the curvature radii of 3-30 mm are used as the specimens in addition to spherical balls. In all specimens, a strong correlation, with a correlation coefficient of more than 0.97, has been observed between Gf and Gph over a wide range. New glossiness Gf conforms to Gph even if the curvature radius in more than 10 mm. Based on these findings, it is found that this method for extracting glossiness is useful for the extraction of glossiness of spherical and cylindrical objects over a wide range from relatively low gloss to high gloss.

For IF direct sampling phase detection method (IFSM) which realizes the arithmetical operations with digital filters by direct A/D (Analog to Digital) conversion of IF (Intermediate Frequency) signal, the method to eliminate DC offset is proposed and developed by using the gate array. A principle of the proposed method and the results of the measurement are shown.