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In this paper, we propose an active noise control (ANC) system without secondary path model (Ĉ filter). The proposed system is based on the simultaneous perturbation optimization method with block process. Consequently, the coefficients of the adaptive filter in the proposed system are updated by error signals only. The conventional ANC system using the Filtered-x algorithm becomes unstable due to the error between the secondary path from a secondary source to an error sensor and its model. On the other hand, the proposed ANC system has the advantage of becoming stable because of not using the model. In this paper, we show the principle of the proposed ANC system and also the effectiveness of this system by computer simulations. © 2000 Scripta Technica, Electron Comm Jpn Pt 3, 83(10): 47–55, 2000 [ABSTRACT FROM AUTHOR]

When synchronization phenomena are analyzed, the phase equations are known to often provide useful qualitative and quantitative information [1]. The target of this paper is to examine through comparisons of the derivation algorithm of the phase equations a realistic system including the case where the equations are not necessarily given explicitly. In other words, the equivalence of two different conventional algorithms [1, 4] is shown, and the advantages and cautions when specific phenomena use both in an example are each explained. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 90(12): 1– 9, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20284 [ABSTRACT FROM AUTHOR]

With the recent development of semiconductor integration technology, the amount of data that must be handled in the layout design of VLSI is increasing rapidly. Even if the improvement of the processing speed of the computer in the future is considered, it is desired to develop a high-speed layout algorithm compared to the conventional method. This paper discusses the k (> 2)-way graph partitioning problem, which is one of the most basic problems concerning the layout design. A parallel algorithm is proposed. The general method to solve this problem has been to apply hierarchically the two-way graph partitioning algorithm. In this method, the algorithm can easily be executed in parallel by operating a number of processors at each hierarchy. A problem then is the efficiency of the processor and the computation time. This paper considers the k-way graph partitioning and proposes a new method called nonhierarchical k-way graph partitioning, aiming at the education of the computation time by parallel processing. In general, it is considered difficult to improve the speed sufficiently by the parallel processing, while maintaining the same accuracy of the solution as that of the sequential algorithm. In this paper, the effectiveness of the proposed algorithm is shown by a simulation experiment on the sequential computer. [ABSTRACT FROM AUTHOR]

Understanding electromagnetic scattering phenomena at high frequencies is important in specifying the location of the local scattered wave (scattering center). This procedure also is available for the practical radar target recognition. Detection of the scattering center also is possible by means of the Fourier transform in the frequency data measured by a network analyzer. However, since its resolution is limited by the measured frequency bandwidth, practical applications may often be difficult. In this paper, the use of the MUSIC algorithm which has a higher resolution than the Fourier transform is proposed for the time-domain estimation method of the electromagnetic scattering. As an example, the detection of the scattering center in the backscattering measurement of a conductive sphere is discussed. It is pointed out that the algorithm can be applied without significant modifications. Most of the super-resolution methods require many snapshots. It is found by computer simulation that the noise suppression preprocessing by time domain gating is effective for reduction of the snapshots. The setting of the number of incident signals (scattering center) of the MUSIC algorithm in the time-gated data is discussed. Further, by experiments, these investigations were verified and the high-resolution nature of the present method is proven. [ABSTRACT FROM AUTHOR]

In this paper, a new algorithm for the detection of a single sinusoid corrupted by Gaussian noise using IIR adaptive bandpass/notch filter is presented. As the first step, the relation between the convergence speed and the filter Q-factor in IIR adaptive bandpass filter is analyzed. Based on the result of analysis, an algorithm is derived in which the filter Q-factor and the adaptive control signal generator Q-factor are made continuously variable so that they are inversely proportional to the distance between the filter center frequcncy f0 and the frequency f of the sinusoid to be detected. This makes both the convergence speed of f0 and f and the resolution high. Then it Is found that limiting the maximum amplitude response of the adaptive control signal generator to a value proportional to sin 2&pie;f can suppress the coefficient fluctuation after convergence effectively. By this approach, an IIR adaptive filter with a high Q can easily be realized. As the next step, an additional high-speed and highly stable adaptive algorithm is proposed in which the step size of the filter coefficient update is made to vary inversely with the fitter Q-factor. A realization of the IIR adaptive notch filter is also presented. Finally, the usefulness of the proposed adaptive algorithm is verified by a computer simulation. [ABSTRACT FROM AUTHOR]

Learning identification is one of the most widely utilized adaptive algorithms due to its small computational complexity. A problem with this algorithm is that a division by the square norm of the state vector is includ- ed in the coefficient update process which easily produces an instability when the nonstationary such as speech is given as the input. A method known to cope with such a problem and to realize the stable convergence involves not updating the coefficient when small input signals continue. This paper discusses this approach. As a first step, the situation is assumed where the square norm of the state vector is less than the specified threeshold and the effect of interrupting the coefficient update is analyzed. The convergence and the time-constant are formulated and the guarantee of the estimation accuracy for the convergence value is discussed. Then, the step gain is derived for the specified guarantee value so that the convergence speed is the fastest in the stochastic sense while ensuring stability. Lastly, the effectiveness of the proposed method is verified by computer simulation. It is shown that, even if the input signal is annotation, the fastest convergence speed is realized, together with the satisfactory estimation accuracy, which is always obtained according to the specified guarantee value after the convergence. [ABSTRACT FROM AUTHOR]

A novel adaptive filter is proposed that adapts to the sample spacing as well as to the tap weights. Although in adaptive digital processing, the tapped-delay line usually has uniformly spaced taps, with the spacing equal to symbol period or a fraction of symbol period, an adaptive filter with a few taps should be adaptively arranged for nonuniform spacing for better convergence as the sampling timing varies. The conventional non-uniformly spaced filters are not adaptively changed or not arranged under the constraint of the acceptable mean-square error. In this paper, a least-mean square (LMS)- type algorithm is derived in which an adaptive filter adaptively changes the amount of delay, as well as the filter coefficients. The performance of the proposed algorithm is investigated by computer simulation. [ABSTRACT FROM AUTHOR]

In this paper, a novel design technique for adaptive stack filters is proposed. The Boolean function of the proposed filters is selected by the controller based on pattern classification. This controller associates an input vector with some cluster according to its pattern and selects the Boolean function corresponding to the cluster. The Boolean functions used in this technique are adjusted by using the adaptive least mean absolute (ALMA) algorithm. As inputs of this controller, both binary and multilevel signals are considered. Finally, a simulation example is given to illustrate the utility of the proposed technique. © 1999 Scripta Technica, Electron Comm Jpn Pt 3, 82(4): 38–47, 1999 [ABSTRACT FROM AUTHOR]

This paper proposes a tonal component coding algorithm for a codec that employs a transform followed by Huffman coding, such as MPEG-2 Audio AAC (advanced audio coding) [1]. After the input audio signal is mapped onto the frequency domain, the proposed algorithm removes local maxima components that degrade the coding efficiency. As a result of this withdrawal, the flatness of the spectrum increases and the efficiency of Huffman coding is improved. The removed components are encoded separately as side information. When the frequency resolution of the time/frequency mapping is high, this algorithm works more effectively since local maximum samples appear more frequently with such a mapping. Simulation results show that this algorithm achieves as much as an 11% bit reduction per frame and improves the coding efficiency in 41% of all audio frames. ©1999 Scripta Technica, Electron Comm Jpn Pt 3, 82(4): 71–78, 1999 [ABSTRACT FROM AUTHOR]

This paper presents a new adaptive filter whose adaptive weights are modified via discrete cosine transform (DCT) bases. It is shown first that the principal axes of a mean-square error surface are identical to the eigenvectors of the autocorrelation matrix R with the Toeplitz form. It is well known that these eigenvectors of the Toeplitz matrix approximate the bases of the DCT so that then a new adaptation algorithm is presented which modifies the weight vectors along with the DCT bases. Ideally, the proposed algorithm converges (in the mean-square sense) adaptive weights in N iterations. Finally, soma computer simulations with additive noises are presented to show the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

This paper introduces a method for suppression of multiple access interference in the direct sequence/spread spectrum multiple access system (DS/SSMA). It is especially effective for the near-far problem. When an integration period of cross correlation is divided into subintervals in the DS/SSMA system, the partial cross correlation values in the presence of noise and interference vary considerably from subinterval to subinterval. In the proposed system, allocating small weight coefficients to subintervals with large cross-correlation values and large weight coefficients to subintervals with small cross-correlation values results in considerable, improvement in signal-to-noise ratio. Using this method, multiple access interference can be suppressed without knowing the PN sequences of other users. The principle of this system, a novel algorithm using partial correlation weight coefficient control techniques, will be illustrated with the help of computer simulations. [ABSTRACT FROM AUTHOR]

This paper proposes a speech recognition algorithm for large vocabulary continuous speech. The proposed algorithm is based on the hidden Markov model (HMM)- LR algorithm using a generalized predictive LR parser and phoneme HMMs. The following three techniques are applied to improve recognition performance and reduce processing time. (1) The forward and the backward likelihood are used to accurately determine the likelihood in the beam search. (2) To reduce the trellis computation in HMM speech recognition and for efficient search, only the speech frames in which the predicted phoneme seems to exist are used by the window for phoneme matching. (3) For efficient search, adjusting identical phoneme sequences are merged by checking the stack and the state of the LR parser. The algorithm was applied to a telephone directory assistance task involving more than 70,000 subscribers. A recognition experiment for continuous word utterance was done. The sentence recognition rate was 85 percent speech recognition. The sentence understanding rate was 59 percent for speaker-dependent speech recognition with spontaneous utterances. [ABSTRACT FROM AUTHOR]

Most of the general active noise control (ANC) systems are based on feedforward with adaptive filters. The adaptive algorithm is mainly the filtered-x LMS algorithm or the multiple error filtered-x LMS algorithm, and many examples of application are reported. Most past research on the multiple noise sources can be applied only to the case where multiple reference signals are available, each of which corresponds to one noise source, and the signal of each noise source can be observed separately. When independent reference signals are not available, only the possibility of adaptive processing has been shown and nothing has been reported on the convergence of the adaptive filters. This paper provides a theoretical analysis of the convergence of adaptive filters in the filtered-x LMS algorithm, and of the degradation of the convergence speed of the adaptive filter resulting from the existence of correlations among sensor outputs, so that each reference sensor receives only a mixture of all noise sources. A new algorithm is proposed to improve the convergence speed of the adaptive filter by insertion of a decorrelator filter, which can reduce the correlations among reference signals. The effectiveness of the proposed decorrelator filter is proved theoretically and by simulation experiments. © 1997 Scripta Technica, Inc. Electron Comm Jpn Pt 3, 80(12): 20–28, 1997 [ABSTRACT FROM AUTHOR]

The delayed least mean square (DLMS) algorithm has been known as a possible pipelined algorithm. DLMS can be considered as a special case of pipelined LMS (PIPLMS), but it has a faster convergence speed than PIPLMS. For the conventional pipelined architecture based on DLMS, in order to keep the same input signal clock rate, independent of the filter order, a one-tap filter is put into one cell. However, as the filter order increases, the convergence behavior degrades, latency increases, and the tracking capability deteriorates. This paper proposes an architecture, in which one cell includes a multitap filter. Thus, the convergence behavior and latency can be improved, and its effectiveness has been confirmed by computer simulation. In addition, computations within a cell can be scheduled, and the decrease of the signal clock rate can be restrained. It is demonstrated that about half the amount of hardware can be saved with this proposed method. © 1997 Scripta Technica, Inc. Electron Comm Jpn Pt 3, 80(12): 82-90, 1997 [ABSTRACT FROM AUTHOR]

Finite wordlength effects on the steady-state characteristics of finite impulse response adaptive digital echo cancellers are considered. Focusing on an FIR-type echo canceller based on the learning identification algorithm, the so-called normalized least mean square algorithm was investigated. Both fixed point and floating point arithmetic systems are discussed. The equivalent noise models for both systems are given, and the output errors caused by quantization and rounding noises were analyzed to allow explicit representation of echo canceller parameters (particularly wordlength) when the input signal is white Gaussian. Further, the relationship between echo cancellation and circuit parameters such as number of filter tap, loop-gain coefficient, etc., the optimal parameter values to minimize output error are also discussed. Computer simulation results were compared with the analytical values and found to be in good agreement. The proposed method is valid for obtaining the performance of echo cane cellers. [ABSTRACT FROM AUTHOR]