This paper deals with the joint estimation of Rayleigh fading channels and symbols in a MC-DS-CDMA system. Formerly, particle filtering has been introduced as a set of promising methods to solve communication problems. PF consists in simulating possible values of the unkwnown parameters and selecting the most likely candidates with regard to the received signal. Here, the Rao-Blackwellized particle filter (RBPF) is used to significantly decrease the variance of the channel/symbol estimates. Our contribution is twofold. Firstly, sinusoidal stochastic models have been shown to better represent the statistical properties of Rayleigh channels than classical autoregressive models. Therefore, the proposed RBPF estimator is based on these models which are expressed as the sum of two sinusoids in quadrature at the maximum Doppler frequency with autoregressive processes as amplitudes. The model parameters are unknown and need to be estimated. Since PFs are not well-suited to recover non-varying parameters, we propose to cross-couple the RBPF with a Kalman filter which makes use of the RBPF ouputs to sequentially update the parameters. Secondly, the choice of an efficient proposal distribution to simulate the particles is crucial for PF performance. We suggest using a suboptimal distribution which simulates likely values of the symbols at a reasonable computational cost.

This paper presents an algorithm for the estimation of the 2-D AR parameters. This new method is based on the functional Schur coefficients recently introduced by the authors. The algorithm is efficient in terms of computation and preserves in the 2-D case the advantages of the 1-D Schur algorithm vs 1-D Levinson algorithm. This approach is illustrated on several numerical examples.

In this paper, we deal with the problem of feature extraction in content-based image retrieval (CBIR) using statistical approach. A Gussianization procedure based on parametric density assumptions of steerable pyramid coefficients is proposed. The extraction method of features including the Gaussianization step allows us to limit the order of the statistical model used to characterize the image textures. The performances of the proposed method are analyzed on a database of texture images and compared with the performances of other textures proposed in previous.

This letter deals with a new Rayleigh channel simulator. In many cases, the channel is modeled by an autoregressive process, but this choice is not well suited due to the band limitation of the theoretical Rayleigh channel power spectrum density. Therefore, we suggest using a low-pass filtered version of the so-called stochastic sinusoidal process. It consists of sinusoids in quadrature with random magnitudes modeled by autoregressive processes. Unlike the autoregressive channel modeling, this simulator has the advantage of exhibiting the power spectrum density (PSD) peaks at the maximum Doppler frequency. Since estimating the autoregressive parameters of the amplitudes directly using a matrix approach from the theoretical Rayleigh channel autocorrelation leads to numerical errors, we propose two alternative methods using either the asymptotic behavior of the Bessel function or genetic algorithms.

In this paper, we propose a new pilot-aided channel estimator. Among the existing approaches, some are based on adaptive algorithms, but they are outperformed by methods where the channel is modeled by an AR or an ARMA process. In that case, estimating the model parameters from noisy observations and selecting the model orders are challenging problems. To avoid them, we propose to view the channel estimation as a realization issue. By taking advantage of the subspace methods for identification, the proposed estimator provides the system matrices in the state-space representation of the channel directly from the output observations. At that stage, the channel process can be estimated using a Kalman filter. This method has the advantage of being non-iterative and avoiding an a priori model for the channel.

In this paper, we propose a new Rayleigh channel simulator. Modeling the channel by an AR process leads to numerical problems due to the bandlimitation of the theoretical power density spectrum (PSD) of a Rayleigh channel. Therefore, we suggest modeling the channel by a low-pass filtered version of the so-called stochastic sinusoidal process. It consists of sinusoids in quadrature with random magnitudes modeled as AR processes. To estimate the AR parameters of the amplitudes, we take advantage of the asymptotic behavior of the first-kind zero-order Bessel function. We show that unlike an AR channel modeling, this simulator has the advantage of exhibiting the PSD peaks at the maximum Doppler frequency, for any AR process order.

In this paper a new necessary and sufficient criterion of BIBO stability of 3-D filters is given, based on a recent n-D extension of the Schur coefficients associated to a polynomial. The criterion relies upon the slice functions mechanism, and is given as an extended 3-D Jury table. Several examples are provided to show how the new procedure reduces the complexity of stability tests that are using the Anderson Jury criterion of stability.

The paper deals with the design of a receiver for synchronous multi-carrier direct-sequence code division multiple access (MC-DS-CDMA) systems, in time-varying frequency selective fading channels. We consider a structure operating in two steps; after channel compensation and time alignment along each carrier, the resulting signals are combined and define the input of the blind adaptive multiuser detector. Our contribution is twofold. First, we propose a generalization of the blind least mean squares (LMS) algorithm on the basis of multiple delayed input signal vectors, which has the advantage of improving the convergence features in a high multiple access interference (MAI) environment and time-varying fading scenario. In addition, we carry out a comparative study between the proposed algorithm and previously published blind LMS and blind Kalman filter algorithms. Simulation results show that the proposed algorithm can trade-off performance with complexity. In other words, it can have a better performance than the existing blind LMS algorithm and less computational complexity than the blind Kalman filter algorithm.

Among parametric methods for speech enhancement, one consists in combining an autoregressive model for speech and a Kalman filter. This filtering is optimal in the H/sub 2/ sense providing the initial state vector, the input and the observation vectors in the state space representation of the system are independent, white and Gaussian. However, these assumptions do not necessarily hold when processing speech. In this paper, we propose to investigate an alternative approach, which is based on H/sub /spl infin// filtering and hence does not depend on these restrictive assumptions. In that setting, the purpose is to minimize the worst possible effects of the noises and system uncertainties on the estimation error. A comparative study between Kalman and H/sub /spl infin// filtering is carried out, when the additive colored noise can be modeled by a moving average (MA) process.

This paper deals with the parametric 3D models for volumes and image blocks based on the so-called Wold decomposition theory. This decomposition raises purely indeterministic, harmonic and evanescent components. Based on this theory, we recently proposed new explicit parametric models able to describe both the spectral support and spatial structure of each component. When the purely indeterministic component is assumed Gaussian and the evanescent component vanishes, we state the problem of separating the 3D texture into a deterministic component and an indeterministic one such as a 3D harmonics retrieval problem in a noisy background. We propose to achieve such a task using a new 3D spectral method based on higher order statistics (HOS).

In the framework of speech enhancement, many approaches have been developed when the speech signal is only corrupted by additive noise. However, in an auditorium, when echoes appear, spatial transformations between the sources and the microphones must be considered. For this reason, we propose to deal with speech contaminated by both convolutive and additive coloured noise. The two-microphone based noise canceller we present operates as follows: firstly, a prewhitening step is carried out. Secondly, the blind deconvolution method we use makes it possible to estimate the finite impulse responses (FIR), their orders and the variances of the additive noise, which is a great advantage. Then, the filtered versions of speech, estimated by means of a subspace method, are used to retrieve the original speech.

When a single sequence of noisy observations is available, the autoregressive (AR)-model based methods using Kalman-filter make it possible to enhance speech. However, the estimation of the AR parameters is required, but is still a challenging problem as the signal is corrupted by an additive noise. In this paper, we propose to both estimate the signal and the AR parameters by developing a recursive instrumental variable-based approach. Avoiding a non linear approach such as the EKF, this method involves two conditionally linked Kalman filters running in parallel. Once a new observation is available, the first filter uses the latest estimated AR parameters to estimate the signal, while the second filter uses the estimated signal to update the AR parameters. A comparative study between existing speech enhancement methods is completed.

We investigate various adaptive filters to design a minimum mean square error (MMSE) receiver, when considering asynchronous uplink multi-carrier direct-sequence code division multiple access (MC-DS-CDMA) systems, over a frequency selective fading channel. Two design structures are considered: the so called "separate detection" consists in using a particular adaptive filter structure for each carrier, whereas the so called "joint detection" is based on a joint structure defined by the concatenation of the adaptive filter weights dedicated to each carrier. We carry out a comparative study between both structures with various adaptive filters such as recursive least square (RLS), Normalized least mean square (NLMS) and affirm projection algorithm (APA). The simulation results show that a compromise has to be found between performance and computational cost. The so called "joint detection" scheme provides the best performance in terms of bit error rate (BER). Although NLMS has the lowest computational cost, it is outperformed by APA and RLS when considering the convergence rate or/and excess mean square error. Besides, the RLS provides the best performance but at the price of high computational complexity. Therefore, we propose to use APA in a joint structure to design the receiver.

Kalman algorithms have been widely applied, for instance in single-channel speech enhancement. However, when carrying out Kalman smoothing, computational cost and data storage requirements are two specific problems. A dual-filter-based smoother is proposed and used in the framework of speech enhancement. Our approach comprises a forward-in-time Kalman filter and a backward-in-time Kalman filter. Both filters are based on their respective forward-in-time linear prediction (LP) model and backward-in-time LP model. This method does not require as large a storage space as a standard Kalman smoother does. The algorithm is evaluated by considering a speech signal embedded in a white Gaussian noise. Simulation results show that the proposed algorithm provides a higher improvement of signal-to-noise ratio (SNR) than Kalman filtering.

The Schur-Cohn test plays an essential role in checking the stability of one-dimensional (1D) random processes such as autoregressive (AR) models, via the so-called reflection coefficients, partial correlations, or Schur-Szego coefficients. In the context of two-dimensional (2D) random field modeling, one of the authors recently proposed a 2D AR quarter-plane model representation using 2D reflection coefficients estimated by a fast recursive adaptive algorithm. Based on such 2D reflection coefficients, we can therefore derive two necessary stability conditions for a 2D AR quarter-plane model. One of these conditions can be considered as an extension of the Schur-Cohn stability criterion based on the 2D reflection coefficients.

In this paper, we propose an efficient approach based on a fast convolution algorithm to reduce the computational complexity of the Least Mean Square (LMS) adaptive algorithm for the quadratic filter, i.e. the quadratic part of the second order Volterra filter (SOVF). The previous works using the fast convolution in the adaptive LMS filtering are limited to the linear case. We show that this approach reduces the multiplications number by close to 25%, at the expense of only 25% more additions. The steady-state performance of this algorithm is studied for gaussian inputs and in stationary setting. The Steady-State Excess Mean-Square-Error is evaluated. The theoretical performance predictions are shown to be in good agreement with simulation results, especially for small step-sizes.

This paper deals with a 2-D high resolution frequency estimation method dedicated to corrupted image by outliers. Outliers are particular data points that do not obey the assumed model. In this framework the well-known model of the sum of complex exponentials fails for the smallest fraction of a data set, which causes the classical estimators to produce inaccurate results. To alleviate this drawback, we propose a new robust iterative Levenberg-Marquardt (LM)-based method. The three main steps of the method we propose are as follows. First, we define a weight function based on the influence function which allows one to detect and correct the "wrong" data. The influence function measures the influence of a datum on the value of the parameter estimate. It is inspired from the so-called M-estimator. Second, a 2-D extension of the large sample approximation of the Maximum likelihood (ML) estimator is developed in order to estimate the image parameters. Third, the Levenberg-Marquardt (LM) technique is used to ensure the convergence of the ML estimator by performing the detection of "wrong" data for each iteration. The effectiveness of the proposed method is illustrated by some numerical simulations.

This paper deals with a new approach to detect the structure (i.e. determination of the number of hidden units) of a feedforward neural network (FNN). This approach is based on the principle that any FNN could be represented by a Volterra series such as a nonlinear input-output model. The new proposed algorithm is based on the following three steps: first, we develop the nonlinear activation function of the hidden layer's neurons in a Taylor expansion, secondly we express the neural network output as a NARX (nonlinear autoregressive with exogenous input) model and finally, by appropriately using the nonlinear order selection algorithm proposed by Kortmann-Unbehauen (1988), we select the most relevant signals on the NARX model obtained. Starting from the output layer, this pruning procedure is performed on each node in each layer. Using this new algorithm with the standard backpropagation (SBP) and over various initial conditions, we perform Monte Carlo experiments leading to a drastic reduction in the nonsignificant network hidden layer neurons.

In this paper, we propose a model to generate fundamental frequency (F0) contours using neural networks. A learning procedure is proposed as an alternative to synthesis-by-rules. The generation of correct fundamental frequency contour is one of the important issues in the naturalness of automatic text-to-speech conversion systems. The proposed approach is based on a standard feed-forward multi-layer network that produces global F0 contours of sentences, directly from encoded linguistic features of standard Arabic language. Our model does not need syntactic information to produce suitable declarative intonation. TD-PSOLA synthesizer is used for validation of our results.

In this letter, a new approach for the learning process of multilayer feedforward neural network is introduced. This approach minimizes a modified form of the criterion used in the standard backpropagation algorithm. This criterion is based on the sum of the linear and the nonlinear quadratic errors of the output neuron. The quadratic linear error signal is appropriately weighted. The choice of the weighted design parameter is evaluated via rank convergence series analysis and asymptotic constant error values. The new proposed modified standard backpropagation algorithm (MBP) is first derived on a single neuron-based net and then extended to a general feedforward neural network. Simulation results of the 4-b parity checker and the circle in the square problem confirm that the performance of the MBP algorithm exceed the standard backpropagation (SBP) in the reduction of the total number of iterations and in the learning time.

This paper presents a theoretical approach for the evaluation of a feedforward neural network covariance output error matrix. It is shown how the input signals errors and the different weights errors are linked together and spread over the neural network to form the output covariance matrix error which could may be used to determine an error bound. The formulas of the output covariance matrix error is derived arising the sensitivity of the additive weight perturbations or input perturbations. The analytical formulas is validated via simulation of a function approximation example showing that the theoretical result is in agreement with simulation result.

This paper deals with Kalman filter-based enhancement of a speech signal contaminated by a white noise, using a single microphone system. Such a problem can be stated as a realization issue in the framework of identification. For such a purpose we propose to identify the state space model by using subspace non-iterative algorithms based on orthogonal projections. Unlike estimate-maximize (EM)-based algorithms, this approach provides, in a single iteration from noisy observations, the matrices related to state space model and the covariance matrices that are necessary to perform Kalman filtering. In addition no voice activity detector is required unlike existing methods. Both methods proposed here are compared with classical approaches.

This paper deals with frequency estimation in the 2-D case when one has only few data points. We propose a method to estimate the frequencies of a sum of exponentials. This method is based on an original set of 2-D linear prediction models with new regions of support derived from the standard quarter plane support region. These models define various spectra which are finally combined by computing their harmonic mean. This method benefits from the subspace decomposition of the covariance matrix to perform well. It is demonstrated that the new regions of support improve the spectrum geometry and the estimation accuracy compared to the classical quarter plane (QP) support regions.

In the framework of parametric texture modeling, a question arises: are adaptive approaches based on higher order statistics (HOS) more appropriate to characterize texture models than those based on second order statistics (SOS)? In order to give some responses to this question, we have compared two fast adaptive filters for texture characterization: the 2-D FLRLS filter (2-D fast lattice recursive least square) based on SOS only and the 2-D OLRIV filter (2-D overdetermined lattice recursive instrumental variable) based on third order statistics. Extensive experiments to study the characterization performance of each filter are presented and interpreted. They show that the 2-D FLRLS filter provides a very good performance for texture characterization, even with important noise. Furthermore, the third order based algorithm presents higher variance than the second order one. We believe that for 2-D adaptive modeling, there is no advantage to using a HOS based adaptive algorithm for characterizing textures.

A great deal of attention has been paid to speech enhancement using a single microphone system. The various approaches, based on the Kalman filter, operate in two steps: (1) the noise variances and the parameters of the speech model are estimated, and (2) the speech signal is retrieved using standard Kalman filtering. This letter presents an alternative solution that does not require the explicit estimation of the noise and the driving process variances. This deals with a new formulation of the approach proposed within a control literature framework by Mehra (1970).

We take into account the non-Gaussian properties of textures and we propose a new approach for their characterization based on bidimensional adaptive modeling using higher order statistics. The 2D-OLRIV (bidimensional overdetermined lattice recursive instrumental variable) algorithm allows accurate texture model estimation. Sets of 2D-AR coefficients obtained from the 2D reflection coefficients of the lattice model are used to characterize the texture model. This algorithm has the advantage of yielding non-biased estimates of the 2D-AR model even when the texture image is disturbed by Gaussian noise. A multilayer neural network deals with these coefficients in order to classify different textures. In order to evaluate the performance of this approach, the classification sensitivity is evaluated on a set of eight different textures. This characterization approach gives very promising results.

In the framework of image modeling for texture analysis, we propose the combination of the new parametric 2-D spectrum estimation method called HMHV (harmonic mean horizontal vertical) and the Fourier-Mellin transform. This latter technique allows the calculation of a set of texture descriptors from a 2-D spectrum estimate which is invariant under rotation and scaling. A comparison of the HMHV and the "standard" parametric HM (harmonic mean) methods on synthetic and natural stochastic textures shows that the HMHV method presents almost no spurious peaks and is quite isotropic. By performing the classification of a set of 60 images divided in 12 texture classes, descriptors computed with the HMHV method provide better results than those computed with the HM method.

Classical high resolution spectral analysis methods for the estimation of complex sinusoids parameters require the a priori knowledge of the number of sinusoids. In most situations, the correctness of this knowledge is crucial. In this paper, we present a new high resolution sub-space method. Its novelty stems from the fact that the analysis of complex sinusoids is considered as a joint "detection-estimation" issue. Simulation results and an application on real radar signals are presented to illustrate the efficiency of this method.

In the framework of high resolution 2-D spectrum analysis, a new multichannel approach called harmonic mean horizontal vertical (HMHV) is proposed. It is based on 2-D fast recursive least squares (2-D FRLS) algorithms and their use for the computation of causal 2-D autoregressive (AR) parameters. This HMHV spectrum presents the following three main advantages on the 2-D spectrum estimated by the harmonic mean (HM) of the 2-D AR first and second quarter plane supports (QP1 and QP2) spectrum estimates: first, it presents the same biases and variances of estimation for the horizontal and vertical frequency components and improves in many cases the variances obtained with the HM method. Secondly, the single peak area (SPA) of the HMHV estimate is quite circular although the HM one looks like a skewed square indicating the existence of a best direction for the separation of two sinusoids. Thirdly, the new estimate presents less spurious peaks. This paper sums up the calculation of the different spectrum estimates and the experiments which lead to the conclusions.

Tomographic imaging deals with reconstructing an image from its projections. In the electromagnetic field, cross-range projections can be obtained from a spectral analysis of the radar echoes of a rotating target. However, the lack of resolution of classical methods like the Fourier transform leads to very poor quality images. We thus couple a proposed high resolution spectral analysis method with the backprojection algorithm. We illustrate the efficiency of the whole method on a measured radar echo of a target.

This paper presents a fast wavelet transform-domain LMS (FWTDLMS) algorithm whose computational complexity is very low as compared to what has been reported so far, because it is completely independent from the adaptive filter order. In our method, the reduction in the computational load has been achieved by exploiting the redundancy in the computation of the wavelet coefficients as the data window slides on by one sample at each new iteration. As a result, the complexity of the wavelet transform is reduced from O(mN) where m is the wavelet filter length and N is the adaptive filter order to O(m(m+M)/2) where M (M

This paper deals with bidimensional modeling using cumulants. Two main contributions are provided. Firstly, we show that the cumulant matrix involved in the 2D normal equations can be seen as the cumulant matrix of a multichannel process. Secondly, we use this equivalence to derive a 2D adaptive lattice algorithm based on cumulants, which can be seen as an extension of Swami's method (1988) to the 2D case. The proposed algorithm has the advantage of yielding non-biased estimates of the 2D-AR parameters even when the image under study is disturbed by Gaussian noise.

This paper presents a fast method for nonlinear filtering based on multichannel Chandrasekhar equations. By assuming that the adaptive second order Volterra filter may be transformed in a multichannel input linear filter, we present a new form of the second order Volterra filtering based a the fast multichannel Chandrasekhar algorithm. This method has a computational complexity of 3.N/sup 3/ multiplications per time instant, where N represents the memory span in number of samples of the nonlinear system model. This compares with 7.N/sup 3/ multiplications required for application of the fast Kalman filter with the same approach. A direct implementation of the RLS algorithm has a computational complexity of N/sup 6/. The adaptive filter is successfully used in a second order Volterra system identification in a stationary environment.

In the framework of a model based approach and using Bayesian estimation techniques, one can improve the results of image segmentation algorithms. In such cases, the texture field is modeled by a 2D autoregressive model. In previous works, segmentation algorithm derivation was based on the prediction error calculated from the first quadrant quarter plane support [Bouman and Liu, 1992]. In this paper, we introduce information extracted from the estimation of the four linear prediction errors calculated from the four quarter plane supports in order to solve boundary problems and to propose isotropic local criteria. Simulation results using the multiple resolution segmentation algorithm [Bouman and Liu] with single quarter plane and four quarter plane criteria are provided.

This paper is devoted to a new adaptive class of nonlinear contrast enhancement filters. These filters are referred to as nonstationary filters because the filter mask depends on the local pixel values. First we show how to find the adaptive filter mask, i.e. the set of surrounding pixels the more similar, in a sense which is defined, to the current pixel. Then we define a new class of quadratic filters based on the so defined filter mask. Finally, processing of blurred and noisy images by these algorithms demonstrates the contrast enhancement improvement in comparison to other quadratic filters.

In the framework of model based image processing, we propose a new parametric approach for classifying textured images. The image, considered as a two-dimensional stochastic process, is characterized by a set of reflection coefficients computed using a two-dimensional adaptive lattice filter based on the recursive least squares (RLS) criterion. The corresponding algorithm is named the two-dimensional fast lattice RLS. In order to evaluate this method, classification rates are calculated on a set of 8 different textures from the Brodatz album. We carry out performance comparisons with methods of characterization based on two-dimensional AR coefficients computed with two-dimensional transversal filters or based on statistical features calculated from co-occurrence matrices and neighbouring matrices.

The wavelet transform least mean squares algorithm (WTLMS) has been recently proposed as an alternative to the simple and transform based (DCT) LMS algorithms, because of its faster convergence. In this paper, we first show the influence of the regularity of the wavelet low-pass filter on the convergence behavior of the normalized WTLMS algorithm (NWTLMS). Then, we show that the subband decomposition of the input signal along a regular subband tree, which splits the signal frequency band uniformly, gives better results, i.e., a faster convergence rate than the dyadic subband tree, which splits the signal frequency band dyadically. Finally, we show that perfect reconstruction quadrature mirror filters (PR-QMFs), which are less regular, can lead to as good results while the multiplier-free PR-QMFs offer, furthermore, a very reduced computational complexity, and hence can be used as an alternative to the wavelet filters for accelerating the convergence rate of the NWTLMS algorithm.

The authors consider the problems which occur when implementing the stabilized fast transversal filter algorithm (SFTF), on a limited precision (16-bit) fixed point digital signal processor (DSP). It is shown that the crucial problem is the choice of initial values of the tracking parameters, which must be different from the optimal ones, in order to prevent divergence. The implementation of SFTF is illustrated with the TMS320C25 DSP.>

A new two-dimensional fast lattice recursive least squares algorithm is proposed. This algorithm can update the filter coefficients in growing-order form with a computational complexity O((M+1)K/sub 1/). By associating the previous 2-D data with the region of support, the causality is specified. After appropriately defining the partial order of 2-D data, the 1-D multichannel analogy, order recursion relations and the shift invariance property are derived. The geometrical approaches for the vector space and the orthogonal projection then can be used for solving this 2-D prediction problem. The performance of this new algorithm is examined in comparison with other fast algorithms.>

The two dimensional (2D) joint process lattice (TDJPL) and its implementations for image restoration applications are examined. A 2D adaptive lattice algorithm (TDAL) is first developed. Convergence properties of the algorithm are covered for the 2D adaptive lattice least mean squares (TDAL-LMS) case. The complexity of the normalized algorithm is slightly more than that of the TDAL-LMS, but it is a faster-converging algorithm. Implementations of the proposed TDJPL estimator as a 2D adaptive lattice noise canceler and as a 2D adaptive lattice line enhancer are then considered. The performance of both schemes is evaluated using artificially degraded image data at different signal-to-noise ratios (SNRs). The results show that substantial noise reduction has been achieved, and the high improvement in the mean square error, even at very low input SNR, is ensured. The results obtained consistently demonstrate the efficacy of the proposed TDJPL implementations, and illustrate the success in its use for adaptive restoration of images.

It is shown that the spectral difference between a short vowel and its long counterpart in the Arabic language can be neglected for isolated vowels. The analysis technique used is time-varying linear predictive coding. To obtain the set of parameters corresponding to an analysis duration T, the reconstruction of the same segment with different durations is performed automatically. The excitation of the LPC synthesis filter is provided by a modified multipulse algorithm. The intelligibility of the synthetic long vowels is satisfactory.>

Schistosomiasis is a parasitic infection affecting, actually, more than 200 millions persons in the developing countries. The parasite multiplication and transmission are very fast and constitute a heavy burden on the public health and medical services. The parasite is characterized by eggs in the urine (Haematobium), which are photographed under the microscope light. The specialist must visually examine the immense amounts of information, and proceeds to count the total eggs number which determines the disease gravity. This operation is time-consuming and gives rise to a great counting error when manually scanning the microscopic preparation. This paper presents an image processing technique for schistosome eggs detection and counting, as well as an intermidiate system based on a microcomputer interfaced to the microscope stage controller which allows the automatic scanning of the entire microscopic preparation and the creation of an information file to the affected persons.

In explosion seismology, the arrival time determination of reflected waves is the fundamental problem in relating seismograms to the subsurface structure. A robust method, combining both energitical and zero-crossings analyses of the signal, is proposed. The basic idea is to analyse the variations of the signal power computed between consecutive zero-crossings. In order to handle noisy signals, discrimination of the effects of noise on the so-computed power is performed by means of an unsupervised automatic classification procedure. Synthetic signals and field data, recorded in the south of Morocco, have been used to illustrate the performance of the procedure.

In explosion seismology, computer detection of reflected waves is the most important process in relating seismograms to subsurface structure. In this paper the analysis of the complex oscillations of the seimometers is undertaken in the phase plane. The variations of the "phase plane velocity", defined as the velocity of the point moving along the phase trajectory, reflect the changing properties of a dynamic model of the seismic signal. A procedure is presented that determines the arrival times of the waves by segmenting the signals into intervals where the "phase plane velocity" tends to vary in a monotonic manner. Field results show that the arrival times can be precisely identified.