This paper discusses the least-squares linear filtering and fixed-lag smoothing problems of discrete-time signals from uncertain observations when the random interruptions in the observation process are modelled by a sequence of not necessarily independent Bernoulli variables. It is assumed that the observations are perturbed by white noise and the autocovariance function of the signal is factorizable. Using an innovation approach we obtain the filtering and fixed-lag smoothing recursive algorithms, which do not require the knowledge of the state-space model generating the signal. Besides the observed values, they use only the matrix functions defining the factorizable autocovariance function of the signal, the noise autocovariance function, the marginal probabilities and the (2,2)-element of the conditional probability matrices of the Bernoulli variables. The algorithms are applied to estimate a scalar signal which may be transmitted through one of two channels.

This paper describes the effect of power line inductance and smoothing capacitance on a single-phase AC power supply system. Voltage fluctuations were calculated when the three main types of smoothing circuit (capacitor input, choke input, and power factor correction types) were used and the magnitude of power line inductance and smoothing capacitance were changed. First, we show the difference in voltage fluctuation in the case of constant resistance and negative resistance in a DC-DC converter. Second, we show the waveforms for which the power line inductance affects the voltage fluctuation of the AC power supply system. Finally, we propose the boundary condition for the power line inductance affecting the voltage fluctuation of the AC power supply system and estimate AC power supply system stability.

This paper treats the least-squares linear filtering and smoothing problems of discrete-time signals from uncertain observations when the random interruptions in the observation process are modelled by a sequence of independent Bernoulli random variables. Using an innovation approach we obtain the filtering algorithm and a general expression for the smoother which leads to fixed-point, fixed-interval and fixed-lag smoothing recursive algorithms. The proposed algorithms do not require the knowledge of the state-space model generating the signal, but only the covariance information of the signal and the observation noise, as well as the probability that the signal exists in the observed values.

This paper presents recursive algorithms for the least mean-squared error linear filtering and fixed-interval smoothing estimators, from uncertain observations for the case of white and white plus coloured observation noises. The estimators are obtained by an innovation approach and do not use the state-space model, but only covariance information about the signal and the observation noises, as well as the probability that the signal exists in the observed values. Therefore the algorithms are applicable not only to signal processes that can be estimated by the conventional formulation using the state-space model but also to those for which a realization of the state-space model is not available. It is assumed that both the signal and the coloured noise autocovariance functions are expressed in a semi-degenerate kernel form. Since the semi-degenerate kernel is suitable for expressing autocovariance functions of non-stationary or stationary signal processes, the proposed estimators provide estimates of general signal processes.

Solution space smoothing allows a local search heuristic to escape from a poor, local minimum. In this paper, we propose a technique that can smooth the rugged terrain surface of the solution space of a placement problem. We test the smoothing heuristics for MCNC benchmarks, and for VLSI placement with pre-placed modules and placement with consideration of congestion. Experiment results demonstrated that solution space smoothing is very efficient for VLSI module placement, and it can be applied to all floorplanning representations proposed so far.

In this paper, we consider the problem of estimating the time-varying directions-of-arrival (DOAs) of coherent narrowband cyclostationary signals impinging on a uniform linear array (ULA). By exploiting the cyclostationarity of most communication signals, we investigate a new computationally efficient subspace-based direction estimation method without eigendecomposition and spatial smoothing (SS) processes. The proposed method uses the inherently temporal property of incident signals and a subarray scheme to decorrelate the signal coherency and to suppress the noise and interfering signals, while the null subspace is obtained from the resulting cyclic correlation matrix through a linear operation. Then an on-line implementation of this method is presented for tracking the DOAs of slowly moving coherent signals. The proposed algorithm is computationally simple and has a good tracking performance. The effectiveness of the proposed method is verified through numerical examples.

The effect of subarray size (equal to the order of the prediction model plus one) on the estimation performance of a previously proposed forward-backward linear prediction (FBLP) based cyclic method is investigated. This method incorporates an overdetermined FBLP model with a subarray scheme and is used to estimate the directions-of-arrival (DOAs) of coherent cyclostationary signals impinging on a uniform linear array (ULA) from the corresponding polynomial or spectrum formed by the prediction coefficients. However, the decorrelation is obtained at the expense of a reduced working array aperture, as it is with the spatial smoothing (SS) technique. In this paper, an analytical expression of the mean-squared-error (MSE) of the spectral peak position is derived using the linear approximation for higher signal-to-noise ratio (SNR). Then the subarray size that minimizes this approximate MSE is identified. The effect of subarray size on the DOA estimation is demonstrated and the theoretical analysis is substantiated through numerical examples.

An optimization of the smoothing preprocessing for the correlated signal parameter estimation was considered. Although the smoothing factor (the number of subarrays) is a free parameter in the smoothing preprocessing, a useful strategy to determine it has not yet been established. In this paper, we investigated thoroughly about the smoothing factor and also proposed a new scheme to optimize it. The proposed method, using the smoothed equivalent diversity profile (SED profile), is able to evaluate the effect of smoothing preprocessing without any a priori information. Therefore, this method is applicable in the real multipath parameter estimation.

A systematic continuous data placement scheme on distributed multi-zone disks is developed for video on demand. The proposed scheme makes use of constant read time concept, i. e. , each video stream has the same access time in each service round when serving the request. The developed scheme maximizes not only the averaged data transmitted rate, but also the number of simultaneous accesses. The scheme consists of the following components. First we developed an algorithm that reorganizes the multi-zone disk into several logical zones in the sense that the averaged disk throughput is maximum. Second, a sequential data access method was developed that takes disk loading balance into account. Thus, at each service round, the total amount of data transmitted is a constant. Third, we introduce the idle round technique to reduce the buffer size required at the client site for VBR video stream. As a result, admission control is enforced in an efficient manner. Finally we perform experimental tests to evaluate the performance of the proposed scheme. The results indicate the value of the proposed scheme.

A timing synchronization is required in the mobile station to determine the correct transmission timing of the mobile-to-base bursts. In this letter, a timing synchronization technique using the reliability check and smoothing algorithm is proposed for the GSM receiver. The reliability check scheme extends the usefulness of this algorithm into low SNR region. And also smoothing algorithm is carried out by a first-order filter with an asymmetric step size. Simulation results show that the proposed algorithm is adequate for timing recovery of GSM modem.

Because of non-negligible ISI due to the Gaussian filter and delay spread in the GSM system, an equalizer is required. In this letter, a joint sliding window channel estimation and timing adjustment method is proposed for maximum likelihood sequence equalizer. And also a smoothing algorithm is presented in order to improve the equalizer performance. This smoothing scheme utilizes a variant of LMS algorithm to tune the channel coefficient estimates. Simulation results show that the proposed scheme is adequate for channel estimation of the adaptive equalizer.

In a variety of communication systems, the multipath propagation due to various reflections is often encountered. In this paper, the directions-of-arrival (DOA) estimation of the cyclostationary coherent signals is investigated. A new approach is proposed for estimating the DOA of the coherent signals impinging on a uniform linear array (ULA) by utilizing the spatial smoothing (SS) technique. In order to improve the robustness of the DOA estimation by exploiting the cyclic statistical information sufficiently and handling the coherence effectively, we give a cyclic algorithm with multiple lag parameters and the optimal subarray size. The performance of the presented method is verified and compared with the conventional methods through numerical examples.

In live multimedia applications with multiple videos, it is necessary to develop an efficient mechanism of multiplexing several MPEG video streams into a single stream and transmitting it over network without wasting excessive bandwidth. In this paper, we present an efficient multiplexing and traffic smoothing scheme for multiple variable bit rate (VBR) MPEG video streams in live video applications with finite buffer sizes. First, we describe the constraints imposed by the allowable delay bound for each elementary stream and by the multiplexer/receiver buffer sizes. Based on these constraints, a new multiplexing and traffic smoothing scheme is designed in such a way as to smooth maximally the multiplexed transmission rate by exploiting temporal and spatial averaging effects, while avoiding the buffer overflow and underflow. Through computer experiments based on an MPEG-coded video trace of Star-wars, it is shown that the proposed scheme significantly reduces the peak rate, coefficient of variation, and effective bandwidth of the multiplexed transmission rate.

In this paper we propose two methods, named the time smoothing and the scale smoothing respectively, to reduce the Gibbs overshoot in continuous wavelet transform. In is shown that for a large kind of wavelets the scale smoothing cannot remove the Gibbs overshoot completely as in the case of Fourier analysis, but it is possible to reduce the overshoot for any wavelets by choosing the smoothing window functions properly. The frequency behavior of scale smoothing is similar to that of the time smoothing. According to its frequency behavior we give the empirical conditions for selecting the smoothing window functions. Numerical examples are given for illustrations.

It is an important problem in fields of radar, sonar, and so on to estimate parameters of closely spaced multiple signals. The MUSIC algorithm with the forward-backward (FB) spatial smoothing is considered as the most effective technique at present for the problem with coherent signals in a variety of fields. We have applied this in Laser Microvision. Recently, Shimotahira has proposed the Kernel MUSIC algorithm, which is applicable to cases when signal vectors and noise vectors are orthogonal. It also utilizes Gaussian elimination of the covariance matrix instead of eigenvalue analysis to estimate noise vectors. Although the amount of computation by the Kernel MUSIC algorithm became lighter than that of the conventional MUSIC algorithm, the covariance matrix was formed to estimate noise vectors and also all noise vectors were used to analyze the MUSIC eigenspectrum. The heaviest amount of computation in the Kernel MUSIC algorithm exists in the transformation of the covariance matrix and the analysis of the MUSIC eigenspectrum. We propose a more straightforward algorithm to estimate noise vectors without forming a covariance matrix, easier algorithm to analyze the MUSIC eigenspectrum. The superior characteristics will be demonstrated by results of numerical simulation.

The MUSIC algorithm has proven to be an effective means of estimating parameters of multiple incoherent signals. Furthermore, the forward-backward (FB) spatial smoothing technique has been considered the best preprocessing method to decorrelate coherent signals. In this paper, we propose a novel preprocessing technique based upon ideas associated with the FB and adaptive spatial smoothing techniques and report on its superiority in numerical simulations.

An HDTV still-picture camera that uses four PAL CCD sensors has been developed for use as a high-speed, high-resolution image reader. The CCD sensors are optically coupled to a single lens by a pyramidal mirror. Each CCD sensor reads a quarter of the image and the four quarter-images are combined into one HDTV picture. Discontinuities at the lines where the four images join can be eliminated by white- and dark-level correction and gamma correction. Moreover, smoothing processing using a weighted-mean method is performed to produce a seamless picture. With this processing the camera can consistently produce seamless pictures.

A superresolution technique is considered for use in antenna gain measurements. A modification of the MUSIC algorithm is employed to resolve incident signals separately in the time domain. The modification involves preprocessing the received data using a spatial scheme prior to applying the MUSIC algorithm. Interference rejection in the antenna measurements using the fast Fourier transform (FFT) based techniques have been realized by a recently developed vector network analyzer, and its availability has been reported in the literature. However, response resolution in the time domain of these conventional techniques is limited by the antenna bandwidth. The MUSIC algorithm has the advantage of being able to eliminate unwanted responses when performing antenna measurements in situations where the antenna band-width is too narrow to support FFT based techniques. In this paper, experimental results of antenna gain measurements in a multipath environment show the accuracy and resolving power of this technique.