Maximum-likelihood localization of narrow-band autoregressivesources via the EM algorithm

The authors derive an expectation-maximization algorithm for the maximum-likelihood estimation of the directions of arrival of multiple narrowband autoregressive (AR) signals embedded in noise. The proposed algorithm simultaneously estimates the location parameters, the AR coefficients, and the signals. The additional structural information that the sources are of the AR type enables one to resolve the case in which the number of sources is equal to or possibly larger than the number of sensors. The authors provide examples demonstrating that the proposed algorithm can resolve two spectrally and spatially closely spaced sources by using a two-sensor array     

An effective method for coupling single-mode fiber to thin-filmwaveguide

An effective method for coupling optical signals from single-mode fibers to thin-film waveguides is proposed. The authors show that this method provides high coupling efficiency for connecting fibers of different core diameters to thin-film waveguides of different film thicknesses or refractive indexes. The authors summarize the theoretical treatment of the coupler. Examples of couplers designed for SiO₂ -based materials as well as iron garnets are presented. Fabrication of the proposed coupler and its experimental results are described     

Reduction of sidelobe levels in multicarrier radar signals via the fusion of hill patterns and geometric progression

Multi-carrier waveforms have several advantages over single-carrier waveforms for radar communication. Employing multi-carrier complementary phase-coded (MCPC) waveforms in radar applications has recently attracted significant attention. MCPC radar signals take advantage of orthogonal frequency division multiplexing properties, and several authors have explored the use of MCPC signals and the difficulties associated with their implementation. The sidelobe level and peak-to-mean-envelope-power ratio (PMEPR) are the key issues that must be addressed to improve the performance of radar signals. We propose a scheme that applies pattern-based scaling and geometric progression methods to enhance sidelobe and PMEPR levels in MCPC radar signals. Numerical results demonstrate the improvement of sidelobe and PMEPR levels in the proposed scheme. Additionally, autocorrelations are obtained and analyzed by applying the proposed scheme in extensive simulation experiments.     

Unambiguous determination of fine structures in multicomponent time-varying signals

When analysed with simple methods, multicomponent time-varying signals may present, in the frequency-time plane, patterns which look similar even when the original structures of the signals are completely different. The authors study here the f–t patterns which are obtained when analysing two different types of signals. Type I signals are closely adjacent gliding tones (whose frequency varies linearly with time). Type II signals consist of a series of monochromatic lines of finite duration, slightly spaced in time. Depending on the bandwidth of the analysing filter B, different patterns are obtained, which they discuss as a function of B and of the intrinsic properties of the original signal. A method is proposed for distinguishing unambiguously between the two types of signals. A numerical simulation is done in order to demonstrate the validity of the theory.     

Speech recognition method using quantised LSP parameters inCELP-type coders

A low-complexity speech recognition method applicable to digital communication networks is proposed. A feature set suitable for speech recognition is obtained from quantised LSP parameters in CELP-type coders without reconstructing the speech signals. The authors present the effects of the speech coder on speaker-independent recognition performance, and show that the recognition accuracy of the proposed method is better than that of the recogniser using reconstructed speech signals     

On the statistical optimality of locally monotonic regression

Locally monotonic regression is a recently proposed technique for the deterministic smoothing of finite-length discrete signals under the smoothing criterion of local monotonicity. Locally monotonic regression falls within a general framework for the processing of signals that may be characterized in three ways: regressions are given by projections that are determined by semimetrics, the processed signals meet shape constraints that are defined at the local level, and the projections are optimal statistical estimates in the maximum likelihood sense. the authors explore the relationship between the geometric and deterministic concept of projection onto (generally nonconvex) sets and the statistical concept of likelihood, with the object of characterizing projections under the family of the p-semi-metrics as maximum likelihood estimates of signals contaminated with noise from a well-known family of exponential densities     

Partially blind source separation of continuous chaotic signals from linear mixture

The authors consider the problem of partially blind source separation of continuous chaotic signals from a linear mixture, in which the generating systems of chaotic signals are assumed to be available and the mixture matrix is unknown. Determination of the mixture matrix is firstly formulated as a problem of the synchronisationbased parameter estimation. Then an efficient parameter estimation method by exploiting the generating dynamics of chaotic signals is introduced. The estimated mixture matrix is used to design a signal separator to blindly separate the mixed chaotic signals. Three examples are given to illustrate the applicability of the proposed approach for the mixed chaotic signals generated by different and/or same dynamical systems and contaminated in measurement noise. In comparison with statistics-based methods, the new approach can solve the magnitude scaling indeterminacy and shows the separability of the mixed signals in strong noise background.     

A high-precision positioning servo-controller using non-sinusoidal two-phase type PLL

Most servomechanisms use encoders whose output signals are 90° phase-shifted sinusoidal waves for detecting angular position. These two-phase signals are not true sinusoidal waves and their distortion makes the positioning accuracy worsen. In this paper, the authors propose non-sinusoidal two-phase type PLL (Phase-Locked Loop) to increase positioning accuracy of servomechanisms. To obtain high-precision detection of angular position, an iterative learning method to compensate the wave distortion was tried, and a high-resolution and high-precision positioning controller that can use low-resolution encoders was obtained. This paper discusses the proposed method and gives experimental results.     

Filterbank design for oversampled filter banks without aliasing inthe subbands

The authors propose a new strategy for the design of real-valued filterbanks which reduces aliasing in the subbands (`inband aliasing') such that applications where two subband signals have to be compared become possible. One example for such an application is subband adaptive filtering, where, with the proposed filterbank structure, the traditionally required crossterm adaptive filters between adjacent subbands can be omitted. The proposed filterbank consists of at least three channels which are subsampled by different subsampling ratios     

Time-frequency digital filtering based on an invertible wavelettransform: an application to evoked potentials

Presents a method to analyze and filter digital signals of finite duration by means of a time-frequency representation. This is done by defining a purely invertible discrete transform, representing a signal either in the time or in the time-frequency domain, as simply as possible with the conventional discrete Fourier transform between the time and the frequency domains. The wavelet concept has been used to build this transform. To get a correct invertibility of this procedure, the authors have proposed orthogonal and periodic basic discrete wavelets. The properties of such a transform are described, and examples on brain-evoked potential signals are given to illustrate the time-frequency filtering possibilities     

Estimation of probability density function using spectral analysis

The authors present a new bound on the estimation of the probability density function of random signals, using Woodward's theorem, correlation techniques and spectral analysis. The proposed method is based on the spectral analysis of the random process     

Cascade neural networks for multichannel blind deconvolution

The authors present a new simple but efficient and powerful extension of Bussgang-type blind equalisation algorithms which can extract multiple source signals from their unknown convolutive mixtures. A cascade neural network is proposed, where each module consists of an equalisation subnetwork and a deflation subnetwork. This approach can adopt any blind equalisation algorithm (which has been developed for the equalisation of a single channel). It can also be applied when the number of source signals is not known in advance. Extensive computer simulation results confirm the validity and high efficiency of the proposed method     

Image sequence coding using quadtree-based block-matching motioncompensation and classified vector quantisation

The authors propose a new image sequence coding algorithm based on two crucial methods: quadtree segmentation and classified vector quantisation (CVQ). Overall coding rates are efficiently lowered by quadtree segmentation while visual quality is well preserved by a CVQ method. A moving-block extraction technique is employed to greatly improve the coding efficiency in the interframe coding mode. A quadtree efficiently segments the stationary background regions of interframe differential signals with various large-sized blocks, and the moving regions are extracted from the smallest blocks of 4×4 size during the growth of the quadtree. These moving regions are motion-compensated using a block-matching method based on 4×4 blocks and the residual signals of the motion-compensated moving regions are coded by CVQ. The stationary regions are simply replenished from the previous frame. The proposed coding scheme is effective for coding the sequential signals of video telephony or video conferencing at low bit rates     

A time-frequency distribution of Cohen's class with a compoundkernel and its application to speech signal processing

Time-frequency distributions belonging to Cohen's class have been discussed in deterministic nonstationary signal processing. The Wigner-Ville distribution is the first to be proposed among the class and is most widely studied and applied in the various fields. However, one of the main difficulties with the Wigner-Ville distribution is that it indicates spurious values in the intensity due to interference particularly prevalent for multicomponent signals. The authors propose a new type kernel function that is the product of the Choi-Williams kernel and the Margenau-Hill kernel. Specific types of signals: sinusoidal signals, chirp signals, and others are analyzed using the new distribution in comparison with the results by the Wigner-Ville and the Choi-Williams distributions. The present distribution does not indicate spurious intensity in the regions where the other two distributions do. In the authors' distribution, the spurious values are transferred to places where one would expect the signal's inherent intensity at least for a signal of pure sine waves. Thus correct values of the signal's intensity are slightly modulated due to cross talk. The three distributions are also compared numerically for these signals and for speech signals to show the advantages of the present distribution     

A new interpretation of nonlinear energy operator and its efficacyin spike detection

A nonlinear energy operator (NEO) gives an estimate of the energy content of a linear oscillator. This has been used to quantify the AM-FM modulating signals present in a sinusoid. Here, the authors give a new interpretation of NEO and extend its use in stochastic signals. They show that NEO accentuates the high-frequency content. This instantaneous nature of NEO and its very low computational burden make it an ideal tool for spike detection. The efficacy of the proposed method has been tested with simulated signals as well as with real electroencephalograms (EEGs)     

Subband adaptive filtering with real-valued subband signals foracoustic echo cancellation

Adaptive filtering in subbands was originally proposed to overcome the limitations of conventional least-mean-square (LMS) algorithms. In general, subband adaptive filters offer computational savings, as well as faster convergence over the conventional LMS algorithm. However, improvements to current subband adaptive filters could be further enhanced by a more elegant choice of their design/structure. Classical subband adaptive filters employ DFT-based analysis and synthesis filter banks which results in subband signals that are complex-valued. The authors modify the structure of subband adaptive filters by using single-sideband (SSB) modulated analysis and synthesis filter banks, which result in subband signals that are real-valued. This simplifies the realisation of subband adaptive filters     

Design of prefilters for discrete multiwavelet transforms

The pyramid algorithm for computing single wavelet transform coefficients is well known. The pyramid algorithm can be implemented by using tree-structured multirate filter banks. The authors propose a general algorithm to compute multiwavelet transform coefficients by adding proper premultirate filter banks before the vector filter banks that generate multiwavelets. The proposed algorithm can be thought of as a discrete vector-valued wavelet transform for certain discrete-time vector-valued signals. The proposed algorithm can be also thought of as a discrete multiwavelet transform for discrete-time signals. The authors then present some numerical experiments to illustrate the performance of the algorithm, which indicates that the energy compaction for discrete multiwavelet transforms may be better than the one for conventional discrete wavelet transforms     

Bidirectional unrepeatered 43 Gb/s WDM transmission with C/L band-separated Raman amplification

A novel wavelength arrangement using C- and L-band-separated Raman preamplification is proposed for application to bidirectional unrepeatered transmission systems operating with multiple 43 Gb/s channels. The proposed wavelength allocation makes it possible to greatly mitigate Raman gain depletion by the counter-propagating signals. The authors have achieved bidirectional unrepeatered transmission of 32 /spl times/ 43 Gb/s channels (= 1.28 Tb/s) over 200 km with Raman preamplifiers using the proposed technique. They found that the system performance of bidirectional transmission with C/L band-separated Raman preamplification is degraded by nonlinear interactions between the high power Raman pump lights and the WDM signals. The root cause can be described in terms of nondegenerate four-wave mixing induced by beating between the WDM signals and two longitudinal modes of the Raman pump light. A solution avoiding ND-FWM was demonstrated in a 32 /spl times/ 43 Gb/s transmission experiment.     

Array processing in correlated noise fields based on instrumentalvariables and subspace fitting

Accurate signal parameter estimation from sensor array data is a problem which has received much attention in the last decade. A number of parametric estimation techniques have been proposed in the literature. In general, these methods require knowledge of the sensor-to-sensor correlation of the noise, which constitutes a significant drawback. This difficulty can be overcome only by introducing alternative assumptions that enable separating the signals from the noise. In some applications, the raw sensor outputs can be preprocessed so that the emitter signals are temporally correlated with correlation length longer than that of the noise. An instrumental variable (IV) approach can then be used for estimating the signal parameters without knowledge of the spatial color of the noise. A computationally simple IV approach has recently been proposed by the authors. Herein, a refined technique that can give significantly better performance is derived. A statistical analysis of the parameter estimates is performed, enabling optimal selection of certain user-specified quantities. A lower bound on the attainable error variance is also presented. The proposed optimal IV method is shown to attain the bound if the signals have a quasideterministic character