Time-correlated single-photon technology is emerging as an important approach to 3D Imaging. This paper presents a reconstruction algorithm that exploits data statistics and multi-scale information to deliver clean depth and reflectivity images together with associated uncertainty maps. The statistical method has been implemented to run on graphics processing units (GPUs) that enable real-time reconstruction of moving scenes at more than 1000 depth frames per second on the 32 × 64 pixels real Quantic4x4 SPAD sensor array data. Comparisons with state-of-the-art algorithms on simulated and real data demonstrate the robust and efficient performance of the proposed method.

In this paper, we study the spectrum efficiency (SE), energy efficiency (EE), and economic efficiency (ECE) for a heterogeneous cellular architecture that separates the indoor and outdoor scenarios for beyond 5G (B5G) wireless communication systems. For outdoor scenarios, massive multiple-input-multiple-output (MIMO) technologies and distributed antenna systems (DASs) at sub-6 GHz frequency bands are used for long-distance communications. For indoor scenarios, millimeter-wave (mmWave) and beamforming communication technologies are deployed at wireless indoor access points (IAPs) to provide high-speed short-range services to indoor users. Mathematical expressions for the system capacity, SE, EE, and ECE are derived using a proposed realistic power consumption model. The results shed light on the fact that the proposed network architecture is able to improve SE and EE by more than three times compared to those conventional network architectures. The analysis of system performance in terms of SE, EE, ECE, and their trade-off results in the observation that the proposed network architecture offers a promising solution for future B5G communication systems.

We address the problem of pulse shape discrimination (PSD) for radiation sources characterization by leveraging a Gaussian mixture variational autoencoder (GMVAE). When using PSD to characterize radiation sources, the number of emission sources and types of pulses to be classified is usually known. Yet, the creation of labeled data can be challenging for some classes as it requires expensive expert annotation. In this context, GMVAE can learn the distinct features of pulses from only unlabeled data. We show that classification accuracy can be further enhanced by adopting a semi-supervised GMVAE with auxiliary loss functions when labeled data are available. The preliminary results on two datasets with different number of classes suggest superior performance of GMVAE compared to other classifiers such as Gaussian mixture model (GMM) for unsupervised and semi-supervised learning and random forest for supervised learning.

This paper presents a new Bayesian algorithm for the robust reconstruction of multispectral single-photon Lidar data acquired in extreme conditions. We focus on imaging through obscurants (i.e., fog, water) leading to high and possibly non-uniform background noise. The proposed hierarchical Bayesian method accounts for multiscale information to provide distribution estimates for the target’s depth and reflectivity, i.e., point and uncertainty measures of the estimates to improve decision making. The correlations between variables are enforced using a weighting scheme that allows the incorporation of guide information available from other sensors or state-of-the-art algorithms. Results on synthetic and real data show improved reconstruction of the scene in extreme conditions when compared to the state-of-the-art algorithms.

In this paper, a new Expectation Propagation (EP) algorithm using ℓ1-norm total variation (ℓ1-TV) prior is proposed for color image restoration in the low photon-count regime. Different from most color image restoration methods proposed for the restoration of color images from observations that are already color images with some missing pixels and/or are usually corrupted by Gaussian noise, the observations considered in this paper are only a single channel grayscale image without color information and are corrupted by Poisson noise, making the color image restoration problem more difficult. To address the problem, a new efficient EP algorithm is proposed to estimate the RGB values of each pixel from such observations and simultaneously provide uncertainty quantification of the estimates. Moreover, by coupling the EP algorithm with a variational Expectation Maximization (EM) approach, the ℓ1-TV prior hyperparameter can be adjusted automatically with-out user supervision. Experiments on color image inpainting and compressive sensing (CS) reconstruction are conducted to illustrate the potential benefits of the proposed EP algorithm for color image restoration in the low photon-count regime.

This paper presents a new Expectation Propagation (EP) framework for image restoration using patch-based prior distributions. While Monte Carlo techniques are classically used to sample from intractable posterior distributions, they can suffer from scalability issues in high-dimensional inference problems such as image restoration. To address this issue, EP is used here to approximate the posterior distributions using products of multivariate Gaussian densities. Moreover, imposing structural constraints on the covariance matrices of these densities allows for greater scalability and distributed computation. While the method is naturally suited to handle additive Gaussian observation noise, it can also be extended to non-Gaussian noise. Experiments conducted for denoising, inpainting, and deconvolution problems with Gaussian and Poisson noise illustrate the potential benefits of such a flexible approximate Bayesian method for uncertainty quantification in imaging problems, at a reduced computational cost compared to sampling techniques.

This article presents a novel Bayesian approach for hyperspectral image unmixing. The observed pixels are modeled by a linear combination of material signatures weighted by their corresponding abundances. A spike-and-slab abundance prior is adopted to promote sparse mixtures and an Ising prior model is used to capture spatial correlation of the mixture support across pixels. We approximate the posterior distribution of the abundances using the expectation-propagation (EP) method. We show that it can significantly reduce the computational complexity of the unmixing stage and meanwhile provide uncertainty measures, compared to expensive Monte Carlo strategies traditionally considered for uncertainty quantification. Moreover, many variational parameters within each EP factor can be updated in a parallel manner, which enables mapping of efficient algorithmic architectures based on graphics processing units (GPUs). Under the same approximate Bayesian framework, we then extend the proposed algorithm to semi-supervised unmixing, whereby the abundances are viewed as latent variables and the expectation-maximization (EM) algorithm is used to refine the endmember matrix. Experimental results on synthetic data and real hyperspectral data illustrate the benefits of the proposed framework over state-of-art linear unmixing methods.

This paper presents a scalable approximate Bayesian method for image restoration using Total Variation (TV) priors, with the ability to offer uncertainty quantification. In contrast to most optimization methods based on maximum a posteriori estimation, we use the Expectation Propagation (EP) framework to approximate minimum mean squared error (MMSE) estimates and marginal (pixel-wise) variances, without resorting to Monte Carlo sampling. For the classical anisotropic TV-based prior, we also propose an iterative scheme to automatically adjust the regularization parameter via Expectation Maximization (EM). Using Gaussian approximating densities with diagonal covariance matrices, the resulting method allows highly parallelizable steps and can scale to large images for denoising, deconvolution, and compressive sensing (CS) problems. The simulation results illustrate that such EP methods can provide a posteriori estimates on par with those obtained via sampling methods but at a fraction of the computational cost. Moreover, EP does not exhibit strong underestimation of posteriori variances, in contrast to variational Bayes alternatives.

Edge-resolved transient imaging (ERTI) is a method for non-line-of-sight imaging that combines the use of direct time of flight for measuring distances with the azimuthal angular resolution afforded by a vertical edge occluder. Recently conceived and demonstrated for the first time, no performance analyses or optimizations of ERTI have appeared in published papers. This paper explains how the difficulty of detection of hidden scene objects with ERTI depends on a variety of parameters, including illumination power, acquisition time, ambient light, visible-side reflectivity, hidden-side reflectivity, target range, and target azimuthal angular position. Based on this analysis, optimization of the acquisition process is introduced whereby the illumination dwell times are varied to counteract decreasing signal-to-noise ratio at deeper angles into the hidden volume. Inaccuracy caused by a coaxial approximation is also analyzed and simulated.

In this article, we present a new algorithm for fast, online 3D reconstruction of dynamic scenes using times of arrival of photons recorded by single-photon detector arrays. One of the main challenges in 3D imaging using single-photon lidar in practical applications is the presence of strong ambient illumination which corrupts the data and can jeopardize the detection of peaks/surface in the signals. This background noise not only complicates the observation model classically used for 3D reconstruction but also the estimation procedure which requires iterative methods. In this work, we consider a new similarity measure for robust depth estimation, which allows us to use a simple observation model and a non-iterative estimation procedure while being robust to mis-specification of the background illumination model. This choice leads to a computationally attractive depth estimation procedure without significant degradation of the reconstruction performance. This new depth estimation procedure is coupled with a spatio-temporal model to capture the natural correlation between neighboring pixels and successive frames for dynamic scene analysis. The resulting online inference process is scalable and well suited for parallel implementation. The benefits of the proposed method are demonstrated through a series of experiments conducted with simulated and real single-photon lidar videos, allowing the analysis of dynamic scenes at 325 m observed under extreme ambient illumination conditions.

The aim of spectral unmixing of hyperspectral images is to determine the component materials and their associated abundances from mixed pixels. In this paper, we present sparse linear unmixing via an Expectation-Propagation method based on the classical linear mixing model and a spike-and-slab prior promoting abundance sparsity. The proposed method, which allows approximate uncertainty quantification (UQ), is compared to existing sparse unmixing methods, including Monte Carlo strategies traditionally considered for UQ. Experimental results on synthetic data and real hyperspectral data illustrate the benefits of the proposed algorithm over state-of-art linear unmixing methods.

Energy efficiency (EE) is a major performance metric for fifth generation (5G) and beyond 5G(B5G) wireless communication systems, especially for ultra dense networks. This paper proposes an end-to-end (e2e) power consumption model and studies the energy efficiency for a heterogeneous B5G cellular architecture that separates the indoor and outdoor communication scenarios in ultra dense networks. In this work, massive multiple-input-multiple-output (MIMO) technologies at conventional sub-6 GHz frequencies are used for long-distance outdoor communications. Light-Fidelity (LiFi) and millimeter wave (mmWave) technologies are deployed to provide a high data rate service to indoor users. Whereas, in the referenced non-separated system, the indoor users communicate with the outdoor massive MIMO macro base station directly. The performance of these two systems are evaluated and compared in terms of the total power consumption and energy efficiency. The results show that the network architecture which separates indoor and outdoor communication can support a higher data rate transmission for less energy consumption, compared to non-separate communication scenario. In addition, the results show that deploying LiFi and mmWave IAPs can enable users to transmit at a higher data rate and further improve the EE.

In this paper, we present an algorithm for online 3D reconstruction of dynamic scenes using individual times of arrival (ToA) of photons recorded by single-photon detector arrays. One of the main challenges in 3D imaging using single-photon Lidar is the integration time required to build ToA histograms and reconstruct reliably 3D profiles in the presence of non-negligible ambient illumination. This long integration time also prevents the analysis of rapid dynamic scenes using existing techniques. We propose a new method which does not rely on the construction of ToA histograms but allows, for the first time, individual detection events to be processed online, in a parallel manner in different pixels, while accounting for the intrinsic spatiotemporal structure of dynamic scenes. Adopting a Bayesian approach, a Bayesian model is constructed to capture the dynamics of the 3D profile and an approximate inference scheme based on assumed density filtering is proposed, yielding a fast and robust reconstruction algorithm able to process efficiently thousands to millions of frames, as usually recorded using single-photon detectors. The performance of the proposed method, able to process hundreds of frames per second, is assessed using a series of experiments conducted with static and dynamic 3D scenes and the results obtained pave the way to a new family of real-time 3D reconstruction solutions.

This paper presents a new algorithm for the learning of spatial correlation and non-local restoration of single-photon 3-Dimensional Lidar images acquired in the photon starved regime (fewer or less than one photon per pixel) or with a reduced number of scanned spatial points (pixels). The algorithm alternates between three steps: (i) extract multi-scale information, (ii) build a robust graph of non-local spatial correlations between pixels, and (iii) the restoration of depth and reflectivity images. A non-uniform sampling approach, which assigns larger patches to homogeneous regions and smaller ones to heterogeneous regions, is adopted to reduce the computational cost associated with the graph. The restoration of the 3D images is achieved by minimizing a cost function accounting for the multi-scale information and the non-local spatial correlation between patches. This minimization problem is efficiently solved using the alternating direction method of multipliers (ADMM) that presents fast convergence properties. Various results based on simulated and real Lidar data show the benefits of the proposed algorithm that improves the quality of the estimated depth and reflectivity images, especially in the photon-starved regime or when containing a reduced number of spatial points.

The TOUCAN project proposed an ontology for telecommunication networks with hybrid technologies – the TOUCAN Ontology (ToCo), available at http://purl.org/toco/ , as well as a knowledge design pattern Device-Interface-Link (DIL) pattern. The core classes and relationships forming the ontology are discussed in detail. The ToCo ontology can describe the physical infrastructure, quality of channel, services and users in heterogeneous telecommunication networks which span multiple technology domains. The DIL pattern is observed and summarised when modelling networks with various technology domains. Examples and use cases of ToCo are presented for demonstration.

Single-photon light detection and ranging (Lidar) data can be used to capture depth and intensity profiles of a 3D scene. In a general setting, the scenes can have an unknown number of surfaces per pixel (semi-transparent surfaces or outdoor measurements), high background noise (strong ambient illumination), can be acquired by systems with a broad instrumental response (non-parallel laser beam with respect to the target surface) and with possibly high attenuating media (underwater conditions). The existing methods generally tackle only a subset of these problems and can fail in a more general scenario. In this paper, we propose a new 3D reconstruction algorithm that can handle all the aforementioned difficulties. The novel algorithm estimates the broadening of the impulse response, considers the attenuation induced by scattering media, while allowing for multiple surfaces per pixel. A series of experiments performed in real long-range and underwater Lidar datasets demonstrate the performance of the proposed method.

Pneumonia is a major cause of morbidity and mortality of patients in intensive care. Rapid determination of the presence and gram status of the pathogenic bacteria in the distal lung may enable a more tailored treatment regime. Optical Endomicroscopy (OEM) is an emerging medical imaging platform with preclinical and clinical utility. Pulmonary OEM via multi-core fibre bundles has the potential to provide in vivo, in situ, fluorescent molecular signatures of the causes of infection and inflammation. This paper presents a Bayesian approach for bacterial detection in OEM images. The model considered assumes that the observed pixel fluorescence is a linear combination of the actual intensity value associated with tissues or background, corrupted by additive Gaussian noise and potentially by an additional sparse outlier term modelling anomalies (bacteria). The bacteria detection problem is formulated in a Bayesian framework and prior distributions are assigned to the unknown model parameters. A Markov chain Monte Carlo algorithm based on a partially collapsed Gibbs sampler is used to sample the posterior distribution of the unknown parameters. The proposed algorithm is first validated by simulations conducted using synthetic datasets for which good performance is obtained. Analysis is then conducted using two ex vivo lung datasets in which fluorescently labelled bacteria are present in the distal lung. A good correlation between bacteria counts identified by a trained clinician and those of the proposed method, which detects most of the manually annotated regions, is observed.

Light detection and ranging (Lidar) data can be used to capture the depth and intensity profile of a 3D scene. This modality relies on constructing, for each pixel, a histogram of time delays between emitted light pulses and detected photon arrivals. In a general setting, more than one surface can be observed in a single pixel. The problem of estimating the number of surfaces, their reflectivity, and position becomes very challenging in the low-photon regime (which equates to short acquisition times) or relatively high background levels (i.e., strong ambient illumination). This paper presents a new approach to 3D reconstruction using single-photon, single-wavelength Lidar data, which is capable of identifying multiple surfaces in each pixel. Adopting a Bayesian approach, the 3D structure to be recovered is modelled as a marked point process, and reversible jump Markov chain Monte Carlo (RJ-MCMC) moves are proposed to sample the posterior distribution of interest. In order to promote spatial correlation between points belonging to the same surface, we propose a prior that combines an area interaction process and a Strauss process. New RJ-MCMC dilation and erosion updates are presented to achieve an efficient exploration of the configuration space. To further reduce the computational load, we adopt a multiresolution approach, processing the data from a coarse to the finest scale. The experiments performed with synthetic and real data show that the algorithm obtains better reconstructions than other recently published optimization algorithms for lower execution times.

Texture characterization of natural images using the mathematical framework of multifractal analysis (MFA) enables the study of the fluctuations in the regularity of image intensity. Although successfully applied in various contexts, the use of MFA has so far been limited to the independent analysis of a single image, while the data available in applications are increasingly multivariate. This paper addresses this limitation and proposes a joint Bayesian model and associated estimation procedure for multifractal parameters of multivariate images. It builds on a recently introduced generic statistical model that enabled the Bayesian estimation of multifractal parameters for a single image and relies on the following original key contributions: First, we develop a novel Fourier domain statistical model for a single image that permits the use of a likelihood that is separable in the multifractal parameters via data augmentation. Second, a joint Bayesian model for multivariate images is formulated in which prior models based on gamma Markov random fields encode the assumption of the smooth evolution of multifractal parameters between the image components. The design of the likelihood and of conjugate prior models is such that exploitation of the conjugacy between the likelihood and prior models enables an efficient estimation procedure that can handle a large number of data components. Numerical simulations conducted using sequences of multifractal images demonstrate that the proposed procedure significantly outperforms previous univariate benchmark formulations at a competitive computational cost.

This paper presents a novel nonlinear hyperspectral mixture model and its associated supervised unmixing algorithm. The model assumes a linear mixing model corrupted by an additive term which accounts for multiple scattering nonlinearities (NL). The proposed model generalizes bilinear models by taking into account higher order interaction terms. The inference of the abundances and nonlinearity coefficients of this model is formulated as a convex optimization problem suitable for fast estimation algorithms. This formulation accounts for constraints such as the sum-to-one and nonnegativity of the abundances, the non-negativity of the nonlinearity coefficients, and the spatial sparseness of the residuals. The resulting convex problem is solved using the alternating direction method of multipliers (ADMM) whose convergence is ensured theoretically. The proposed mixture model and its unmixing algorithm are validated on both synthetic and real images showing competitive results regarding the quality of the inference and the computational complexity when compared to the state-of-the-art algorithms.

In this paper, we consider ridge detection for multicomponent signal analysis. We introduce a new ridge detector based on a projection of the reassignment vector in a specific direction which is related to the geometry of the spectrogram magnitude. The ridge definition we introduce enables that of the basin of attraction associated with a ridge and then mode reconstruction. Simulations show better concentration of the information on the ridges obtained by our method compared to other existing ridge detectors that also make use of the reassignment vector.

This paper presents a new Bayesian estimation technique for hidden Potts-Markov random fields with unknown regularisation parameters, with application to fast unsupervised K -class image segmentation. The technique is derived by first removing the regularisation parameter from the Bayesian model by marginalisation, followed by a small-variance-asymptotic (SVA) analysis in which the spatial regularisation and the integer-constrained terms of the Potts model are decoupled. The evaluation of this SVA Bayesian estimator is then relaxed into a problem that can be computed efficiently by iteratively solving a convex total-variation denoising problem and a least-squares clustering ( K -means) problem, both of which can be solved straightforwardly, even in high-dimensions, and with parallel computing techniques. This leads to a fast fully unsupervised Bayesian image segmentation methodology in which the strength of the spatial regularisation is adapted automatically to the observed image during the inference procedure, and that can be easily applied in large 2D and 3D scenarios or in applications requiring low computing times. Experimental results on synthetic and real images, as well as extensive comparisons with state-of-the-art algorithms, confirm that the proposed methodology offer extremely fast convergence and produces accurate segmentation results, with the important additional advantage of self-adjusting regularisation parameters.

This paper presents a new Bayesian model and associated algorithm for depth and intensity profiling using full waveforms from time-correlated single-photon counting (TCSPC) measurements when the photon count in very low. The model represents each Lidar waveform as an unknown constant background level, which is combined in the presence of a target, to a known impulse response weighted by the target intensity and finally corrupted by Poisson noise. The joint target detection and depth imaging problem is expressed as a pixel-wise model selection problem which is solved using Bayesian inference. A Reversible Jump Markov chain Monte Carlo algorithm is proposed to compute the Bayesian estimates of interest. Finally, the benefits of the methodology are demonstrated through a series of experiments using real data.

Texture analysis is an image processing task that can be conducted using the mathematical framework of multifractal analysis to study the regularity fluctuations of image intensity and the practical tools for their assessment, such as (wavelet) leaders. A recently introduced statistical model for leaders enables the Bayesian estimation of multifractal parameters. It significantly improves performance over standard (linear regression based) estimation. However, the computational cost induced by the associated nonstandard posterior distributions limits its application. The present work proposes an alternative Bayesian model for multifractal analysis that leads to more efficient algorithms. It relies on three original contributions: A novel generative model for the Fourier coefficients of log-leaders; an appropriate reparametrization for handling its inherent constraints; a data-augmented Bayesian model yielding standard conditional posterior distributions that can be sampled exactly. Numerical simulations using synthetic multifractal images demonstrate the excellent performance of the proposed algorithm, both in terms of estimation quality and computational cost.

Texture analysis can be embedded in the mathematical framework of multifractal (MF) analysis, enabling the study of the fluctuations in regularity of image intensity and providing practical tools for their assessment, wavelet leaders. A statistical model for leaders was proposed permitting Bayesian estimation of MF parameters for images yielding improved estimation quality over linear regression based estimation. This present work proposes an extension of this Bayesian model for patch-wise MF analysis of images. Classical MF analysis assumes space homogeneity of the MF properties whereas here we assume MF properties may change between texture elements and we do not know where the changes are located. This paper proposes a joint Bayesian model for patches formulated using spatially smoothing gamma Markov Random Field priors to counterbalance the increased statistical variability of estimates caused by small patch sizes. Numerical simulations based on synthetic multi-fractal images demonstrate that the proposed algorithm outperforms previous formulations and standard estimators.

This paper presents a new Bayesian model and algorithm used for depth and intensity profiling using full waveforms from the time-correlated single photon counting (TCSPC) measurement in the limit of very low photon counts. The model proposed represents each Lidar waveform as a combination of a known impulse response, weighted by the target intensity, and an unknown constant background, corrupted by Poisson noise. Prior knowledge about the problem is embedded in a hierarchical model that describes the dependence structure between the model parameters and their constraints. In particular, a gamma Markov random field (MRF) is used to model the joint distribution of the target intensity, and a second MRF is used to model the distribution of the target depth, which are both expected to exhibit significant spatial correlations. An adaptive Markov chain Monte Carlo algorithm is then proposed to compute the Bayesian estimates of interest and perform Bayesian inference. This algorithm is equipped with a stochastic optimization adaptation mechanism that automatically adjusts the parameters of the MRFs by maximum marginal likelihood estimation. Finally, the benefits of the proposed methodology are demonstrated through a serie of experiments using real data.

This paper presents a Bayesian algorithm for linear spectral unmixing that accounts for outliers present in the data. The proposed model assumes that the pixel reflectances are linear mixtures of unknown endmembers, corrupted by an additional term modelling outliers and additive Gaussian noise. A Markov random field is considered for outlier detection based on the spatial and spectral structures of the anomalies. This allows outliers to be identified in particular regions and wavelengths of the data cube. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint linear unmixing and outlier detection algorithm. Simulations conducted with synthetic data demonstrate the accuracy of the proposed unmixing and outlier detection strategy for the analysis of hyperspectral images.

International audience

Texture characterization is a central element in many image processing applications. Multifractal analysis is a useful signal and image processing tool, yet, the accurate estimation of multifractal parameters for image texture remains a challenge. This is due in the main to the fact that current estimation procedures consist of performing linear regressions across frequency scales of the 2D dyadic wavelet transform, for which only a few such scales are computable for images. The strongly non-Gaussian nature of multifractal processes, combined with their complicated dependence structure, makes it difficult to develop suitable models for parameter estimation. Here, we propose a Bayesian procedure that addresses the difficulties in the estimation of the multifractality parameter. The originality of the procedure is threefold. The construction of a generic semiparametric statistical model for the logarithm of wavelet leaders; the formulation of Bayesian estimators that are associated with this model and the set of parameter values admitted by multifractal theory; the exploitation of a suitable Whittle approximation within the Bayesian model which enables the otherwise infeasible evaluation of the posterior distribution associated with the model. Performance is assessed numerically for several 2D multifractal processes, for several image sizes and a large range of process parameters. The procedure yields significant benefits over current benchmark estimators in terms of estimation performance and ability to discriminate between the two most commonly used classes of multifractal process models. The gains in performance are particularly pronounced for small image sizes, notably enabling for the first time the analysis of image patches as small as 64 × 64 pixels.

This paper presents a fast converging Riemannian steepest descent method for nonparametric statistical active contour models, with application to image segmentation. Unlike other fast algorithms, the proposed method is general and can be applied to any statistical active contour model from the exponential family, which comprises most of the models considered in the literature. This is achieved by first identifying the intrinsic statistical manifold associated with this class of active contours, and then constructing a steepest descent on that manifold. A key contribution of this paper is to derive a general and tractable closed-form analytic expression for the manifold's Riemannian metric tensor, which allows computing discrete gradient flows efficiently. The proposed methodology is demonstrated empirically and compared with other state of the art approaches on several standard test images, a phantom positron-emission-tomography scan and a B-mode echography of in-vivo human dermis.

This paper presents a nonlinear mixing model for joint hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are linear mixtures of endmembers, corrupted by an additional nonlinear term and an additive Gaussian noise. A Markov random field is considered for nonlinearity detection based on the spatial structure of the nonlinear terms. The observed image is segmented into regions where nonlinear terms, if present, share similar statistical properties. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint nonlinear unmixing and nonlinearity detection algorithm. Simulations conducted with synthetic and real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.

This paper presents a nonlinear mixing model for joint hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are linear combinations of known pure spectral components corrupted by an additional nonlinear term, affecting the end members and contaminated by an additive Gaussian noise. A Markov random field is considered for nonlinearity detection based on the spatial structure of the nonlinear terms. The observed image is segmented into regions where nonlinear terms, if present, share similar statistical properties. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint nonlinear unmixing and nonlinearity detection algorithm. The performance of the proposed strategy is first evaluated on synthetic data. Simulations conducted with real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.

This paper addresses the issue of the retrieval of the components of a multicomponent signal from its short-time Fourier transform. It recalls two popular reconstruction methods, and extends each of them for the case of strong frequency modulation, by taking into account the second derivative of the phase. Numerical experiments illustrate the improvement and compare the methods.

This paper deals with a novel class of set-theoretic adaptive sparsity promoting algorithms of linear computational complexity. Sparsity is induced via generalized thresholding operators, which correspond to nonconvex penalties such as those used in a number of sparse LMS based schemes. The results demonstrate the significant performance gain of our approach, at comparable computational cost.

This paper describes a Gaussian process based method for nonlinear hyperspectral image unmixing. The proposed model assumes a nonlinear mapping from the abundance vectors to the pixel reflectances contaminated by an additive white Gaussian noise. The parameters involved in this model satisfy physical constraints that are naturally expressed within a Bayesian framework. The proposed abundance estimation procedure is applied simultaneously to all pixels of the image by maximizing an appropriate posterior distribution which does not depend on the endmembers. After determining the abundances of all image pixels, the endmembers contained in the image are estimated by using Gaussian process regression. The performance of the resulting unsupervised unmixing strategy is evaluated through simulations conducted on synthetic data.

In this paper, a novel scheme for online, sparsity-aware learning is presented. A new theory is developed that allows for the incorporation, in a unifying way, of different thresholding rules to promote sparsity, that may even be of a nonconvex nature. The complexity of the algorithm exhibits a linear dependence on the number of free parameters.

It is the purpose of the note to point out that the Cumulative Distribution Function (CDF) (Eq. (23)) in Appendix A in the paper "Relay Selection for Secure Cooperative Networks with Jamming" by Krikidis et al. (IEEE Trans. Wireless Commun., vol. 8, no. 10, pp. 5003-5011, Oct. 2009) is not the exact expression but an approximation. We provide the exact solution of the CDF in two forms: one using Beta and hypergeometric functions and the second exploiting a recurrence relationship.

This paper studies a linear radial basis function network (RBFN) for unmixing hyperspectral images. The proposed RBFN assumes that the observed pixel reflectances are nonlinear mixtures of known end members (extracted from a spectral library or estimated with an end member extraction algorithm), with unknown proportions (usually referred to as abundances). We propose to estimate the model abundances using a linear combination of radial basis functions whose weights are estimated using training samples. The main contribution of this paper is to study an orthogonal least squares algorithm which allows the number of RBFN centers involved in the abundance estimation to be significantly reduced. The resulting abundance estimator is combined with a fully constrained estimation procedure ensuring positivity and sum-to-one constraints for the abundances. The performance of the nonlinear unmixing strategy is evaluated with simulations conducted on synthetic and real data.

In this paper, the source–filter model of speech production is adapted to represent the speech signal as the superposition and convolution of a dynamic source and resonant modes. The aim is to increase the resolution of the time-instantaneous-frequency representation of each of the individual contributions of different sections of the human phonatory system. We present a framework based on dynamic mode predictors and filters, which are adapted, using gradient-based techniques, to track the modal dynamics of speech yielding a representation which is free from quasi-stationary assumptions thus allowing flexible manipulation of the speech signal. Several examples are offered including intonation modifications to illustrate the potential of the proposed approach .

In this paper, we deal with the performance of nonorthogonal Amplify-and-Forward protocols over block-fading channels (BFNAF), where the source retransmits the same data during cooperation in order to increase spatial diversity. Despite the additional diversity degree that is offered by the channel, channel inversion amplification schemes are not always able to increase the diversity gain of the system due to the high correlation that can result in the two simultaneous transmissions. It is proven that this diversity loss is related to a poor source-relay link that via the relay amplification process affects the third available diversity branch corresponding to the second source transmission. In order to resolve this problem, we integrate a fixed gain amplification factor in the BFNAF scheme which efficiently uses the additional diversity degree of the channel and recovers the diversity loss associated with channel inversion schemes. This new BFNAF scheme offers spatial diversity benefits with high reliability and is an appropriate solution for Amplify-and- Forward scenarios in which the source-relay link is not stronger than the relay-destination link. The diversity analysis is based on some well-defined capacity bounds which follow the diversity order of the true capacity and enable theoretical derivations. The enhancements of the proposed schemes are verified through both theoretical results and computer simulations.

This paper deals with protocol design for cognitive cooperative systems with many secondary users. Appropriate relaying improves the throughput of the primary users and can increase the transmission opportunities for the cognitive users. Based on different multi-access protocols, the schemes investigated enable relaying either between the primary user and a selected secondary user or between two selected secondary users. This collaboration can be a simple distributed multiple-input single-output transmission of the primary data or a simultaneous transmission of primary and secondary data using dirty-paper coding (DPC). The parametrization of DPC as well as its combination with opportunistic relay selection yields an interesting trade-off between the primary and the secondary performance which is investigated by theoretical and simulation results under the perspective of a desired primary throughput.

In this paper, an amplify-and-forward (AF) cooperative strategy in interference limited networks is considered. In contrast to previously reported work, where the effect of interference is ignored, the effect of multi-user interference in AF schemes is analyzed. It is shown that the interference changes the statistical description of the conventional AF protocol and a statistical expression is subsequently derived. Asymptotic analysis of the expression shows that interference limits the diversity gain of the system and the related channel capacity is bounded by a stationary point. In addition, it is proven that previously proposed relay selection criteria for multi-relay scenarios become inefficient in the presence of interference. Based on a consideration of the interference term, two new selection criteria suitable for different system set-ups are proposed. A theoretical framework for selecting when to apply the proposed selection criteria is also presented.

This paper deals with protocol design for cognitive cooperative systems with many secondary users. In contrast with previous cognitive configurations, the channel model considered assumes a cluster of secondary users which perform both a sensing process for transmitting opportunities and can relay data for the primary user. Appropriate relaying improves the throughput of the primary users and can increase the transmission opportunities for the cognitive users. Based on different multi-access protocols, the schemes investigated enable relaying either between the primary user and a selected secondary user or between two selected secondary users. This collaboration can be a simple distributed multiple-input single-output transmission of the primary data or a simultaneous transmission of primary and secondary data using dirty-paper coding (DPC). The parametrization of DPC as well as its combination with opportunistic relay selection yields an interesting trade-off between the primary and the secondary performance which is investigated by theoretical and simulation results under the perspective of a desired primary throughput. The proposed protocols are studied from a networking point of view and the stable throughput for primary and secondary users is derived based on the principles of queueing theory.

This paper deals with relay selection in cooperative networks with secrecy constraints. The proposed scheme enables an opportunistic selection of two relay nodes to increase security against eavesdroppers. The first relay operates as a conventional mode and assists a source to deliver its data to a destination via a decode-and-forward strategy. The second relay is used in order to create intentional interference at the eavesdropper nodes. The proposed selection technique jointly protects the primary destination against interference and eavesdropping and jams the reception of the eavesdropper. The new approach is analyzed for different complexity requirements based on instantaneous and average knowledge of the eavesdropper channels. In addition an investigation of an hybrid security scheme which switches between jamming and non-jamming protection is discussed in the paper. It is proven that an appropriate application of these two modes further improves security. The enhancements of the proposed selection techniques are demonstrated analytically and with simulation results.

In this paper, an amplify-and-forward (AF) cooperative strategy for interference limited networks is considered. In contrast to previously reported work, where the effect of interference is ignored, the effect of multi-user interference in AF schemes is analyzed. It is shown that the interference changes the statistical description of the conventional AF protocol and a statistical expression is subsequently derived. Asymptotic analysis of the expression shows that interference limits the diversity gain of the system and the related channel capacity is bounded by a stationary point. In addition, it is proven that previously proposed relay selection criteria for multi-relay scenarios become inefficient in the presence of interference. Based on consideration of the interference term, two extensions to the conventional max-min selection scheme suitable for different system setups are proposed. The extensions investigated are appropriate for legacy architectures with limitations on their flexibility where the max-min operation is pre-designed. A theoretical framework for selecting when to apply the proposed selection criteria is also presented. The algorithm investigated is based on some welldefined capacity approximations and incorporates the outage probabilities averaged over the fading statistics. Analytical results and simulation studies reveal enhancements of the proposed algorithm.

In this paper, we deal with the amplify-and-forward (AF) cooperative strategy in slot-based block-fading environments. In contrast with previous schemes which assume a constant channel during the cooperative frame (several slots), here, we relax this constraint and assume a classical quasi-static block-fading channel (constant for one slot). This additional degree of freedom modifies the behavior of the conventional non-orthogonal (NAF) schemes and generates a new block-fading NAF (BFNAF) protocol where the source can usefully retransmit the same data during the cooperative slot. This new protocol is interesting at low spectral efficiencies where diversity against fading is more important. Another issue which is discussed throughout the paper is the optimal power allocation of the investigated schemes. The proposed power allocation strategy uses as an optimization criterion well-defined asymptotic expressions of the outage probabilities, averaged over the fading statistics.

In this paper, a simple and reliable technique is proposed to track vocal tract resonances in continuous speech. The approach is based on the use of predictor filters with adaptive zeros whose constrained trajectories guarantee the successful tracking of the frequency and the damping of each resonance. The zeros are adapted using a gradient-based algorithm to minimize an instantaneous prediction residual according to the principle of minimal disturbance yielding an adaptive structure capable of tracking fast-changing resonance parameters.

In this paper, new timing synchronization methods for optical wireless systems using asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM) are described. It is well known that OFDM systems are sensitive to timing synchronization errors. Existing synchronization methods cannot be used directly or do not perform well in ACO-OFDM systems because ACO-OFDM signals are unipolar. Therefore, new synchronization methods using a novel training symbol designed for the characteristics of ACO-OFDM are presented. The sensitivity of the new methods to the phase of the receiver sampling clock is considered and it is shown that if only the lower frequency subcarriers in the training symbol are non zero, the sensitivity can be reduced. Analysis and simulation results show that the new timing synchronization methods are effective in ACO-OFDM systems.

Empirical mode decomposition (EMD) has lately received much attention due to the many interesting features that exhibits. However it lacks a strong theoretical basis which would allow a performance analysis and hence the enhancement and optimization of the method in a systematic way. In this paper, an investigation of EMD is attempted in an alternative way. The interpolation points and the piecewise interpolating polynomials for the formation of the upper and lower envelopes of the signal are optimized based on a genetic algorithm framework revealing important characteristics of the method which where previously hidden. As a result, novel directions for both the performance enhancement and the theoretical investigation of the method are unveiling.

This paper considers a fading relay channel where the total transmit power used is constrained to be equal to that of the standard single-hop channel. The relay channel used operates in what is termed as MIMO cooperative diversity mode, where the source transmits to both relay and destination terminals in the first instance. Both the source and relay then transmit to the destination in the second instance. Initially the cooperative diversity framework is introduced to consider system constraints so a direct and fair comparison with the single-hop case can be made. In-particular a power constraint is placed on the system and the optimal transmit power levels are derived and presented. The derived technique for finding the optimal transmit power levels is then used to demonstrate the advantages of using cooperative diversity in a wireless network. The results presented show that MIMO cooperative diversity offers a 3.4 dB increase in spectral efficiency at 5 % outage, with no additional cost incurred in transmit time, power or bandwidth.

In this paper, we investigate different packet scheduling techniques applied in MIMO to maximize spectral efficiency and to provide fairness among users. We investigate the performance of various schemes based on exploiting the VBLAST (Vertical Bell Labs Space Time) architecture and proportional fair (PF) scheduling. New algorithms using MIMO antennas and the VBLAST architecture are proposed to improve the performance of the PF scheme. Computer simulations are conducted to compare the different scheduling schemes in terms of cell throughputs and outage capacities. The degree of fairness (in terms of time delay and data rates) among the users for these schemes is also investigated.

A novel iterative channel estimation approach is proposed for cyclic prefix–code division multiple access systems. Code-multiplexed pilots are used for channel estimation while maintaining bandwidth efficiency. The proposed method achieves a significant improvement when compared to the conventional correlation approach by reconstructing data signals for channel estimation. Simulation results demonstrate good estimation capability with an allocation of only 10% of the whole power to the pilot channel. In addition, an integrated channel estimator and parallel interference cancellation (PIC) detector are proposed. Data signals are reconstructed for channel estimation while the interference contributed by different data channels as well as the pilot channel are regenerated and subtracted from the received signal at the final stage. The channel estimation error reduces at each iteration and the PIC at the last stage enables further bit error rate performance improvement to be achieved for the system. The performance of the proposed scheme is studied through simulations and results verify its effectiveness.

A new SOS-expressed blind channel shortening algorithm is proposed. It is based on a necessary and sufficient condition that guarantees detection of all possible shortening equalizers before the best one is selected. The unique existing SOS-based blind channel shortening algorithm meets the same objective but has a much higher complexity

This paper introduces a new technique for efficiently incorporating multihop communications in a wideband code division multiple access (WCDMA) system with a clustered user distribution. For each cluster of users a mobile station (MS) is selected as a gateway to act as a relay between the other MSs and the base station (BS). The under-used resources from the FDD (frequency division duplex) uplink band are utilized for communication within the cluster (TDD underlay). This model is simulated for a number of scenarios and scheduling algorithms. It is demonstrated that the proposed enhancements lead to an improvement in the spectral efficiency of the system of up to 50%

This paper presents a family of cooperative diversity strategies for the fading relay channel in an initially frequency flat fading environment. The relay channel used in this paper operates in what is termed as MIMO cooperative diversity mode, where the source transmits to both relay and destination terminals in the first instance. Both the source and relay then transmit to the destination in the second instance. Initially the current cooperative diversity frameworks are extended to consider system constraints to make a direct and fair comparison with the single-hop case. In-particular a power constraint is put on the system and the optimal transmit power levels are presented. The framework is then extended to consider the frequency selective channel by consider an Orthogonal Frequency Division Multiplexing (OFDM) framework. The amount of collision in frequency can be varied and then analysed. This paper shows that full collision is the preferred transmission scheme.

Distributed Channel-Adaptive Fair Allocation (DCAFA) is proposed to extend the IEEE 802.11e Enhanced Distributed Coordination Function (EDCF), by halving the contention window (CW) after ζ consecutive successful transmissions to reduce the collision probability when channel is busy. The scheme computes an adaptive threshold function for each priority queue by taking into account the channel load. Simulation results show that DCAFA outperforms EDCF in-terms of global throughput, throughput of traffic class, frame discard probability and average delay in medium and high load cases. They also show that the proposed analytical model is accurate and achieves a high degree of fairness among applications of same priority level.

A new pre-FFT synchronization method for OFDM is proposed and assessed that gives improved performance in multipath channels. The technique can be used for a range of OFDM signal parameters, and channel environments. The relative increase in complexity over existing correlation based methods is less than 10%

The error rate of a coded multi-carrier system can be controlled by adjusting the modulation parameters (signal constellation and level) per sub-carrier, while at the same time, minimizing the transmission power. We modify existing techniques to allow for efficient error rate control and to take the encoder/decoder into account. In particular, we study the soft decision receiver for which no adaptive allocation technique has been proposed to-date.

In this paper, a new chip-level MMSE frequency domain equalizer (FDE) is investigated for the downlink frequency division duplex (FDD) mode in UMTS (Universal Mobile Telephone System). The multiple access interference (MAI) and inter chip interference (ICI) can be reduced at the chip level before despreading. For frequency domain equalization, the cyclicity of the received block signal needs to be reconstructed if neither cyclic prefix nor zero-padding are used at the transmitter. We exploit the relationship between the reconstructed part and the equalized signal itself and derive a self cyclic reconstruction scheme. Performance of the proposed MMSE-FDE is shown by computer simulations, compared with performance of FDE with perfect reconstructions. It is shown that the conventional time domain equalization can be implemented in the frequency domain, thus a multimode receiver that can handle both UMTS and OFDM systems in one equalization structure is feasible.

In this paper, a chip-level MMSE frequency domain equalizer (FDE) is investigated for a Cyclic-Prefix CDMA (CP-CDMA) system. The multiple access interference (MAI) and inter chip interference (ICI) is reduced at the chip level before despreading. One code multiplexed pilot channel carries a higher power than dedicated channels and is utilized for channel estimation. It also brings high MAI to the other users. In this paper, the impact of imperfect channel estimation is studied and a pilot interference cancellation algorithm is proposed. We deploy a high power pilot channel to obtain good channel estimates, subsequently, the high MAI along with the pilot channel is removed by the proposed algorithm. Performance of the proposed algorithm is shown by computer simulations.

A hybrid cellular and ad hoc network for high data rate wireless access is considered. An air-interface which uses TDD in combination with time division multiple access (TDMA) is assumed. Interference and resource reuse are important issues in hybrid networks. For this purpose analytical models for the reuse efficiency and a capacity bound of such a network are developed. These models are based on an exclusion range concept which is introduced to keep interference at an acceptable level. It is found that with this interference limiting measure, multi hop networks can benefit from a multi hop reuse gain compared to single hop networks. However, it is further found that this gain does not significantly increase for more than 5 hops. Furthermore, the pre-requisite for being able to exploit the multi hop reuse gain is a certain TS granularity. It was observed that with an increasing number of TS per frame, the mean and the variance of the number of supported hops increased almost linearly

In this paper, a network topology is investigated that allows both peer-to-peer and nonlocal traffic in a cellular-based time-division duplex code-division multiple-access (TDD-CDMA) system known as opportunity driven multiple access (ODMA). The key to offering appropriate performance of peer-to-peer communication in such a system relies on the use of a routing algorithm which minimizes interference. This paper presents a study of the constraints and limitations on the capacity of such a system using a variety of routing techniques. A congestion-based routing algorithm is presented that attempts to minimize the overall power of the system as well as providing a measure of feasibility. This technique provides the lowest required transmit power in all circumstances, and the highest capacity in nearly all cases studied. All of the routing algorithms studied here allocate TDD time slots on a first come first served basis according to a set of predefined rules. This fact is utilized to enable the development of a combined routing and resource allocation algorithm for TDD-CDMA relaying. A novel method of time slot allocation according to relaying requirements is then developed. Two measures of assessing congestion are presented based on matrix norms. One is suitable for current interior point solution, the other is more elegant but is not currently suitable for efficient minimization and, thus, practical implementation.

A novel stochastic wideband dynamic spatio-temporal indoor channel model which incorporates both the spatial and temporal domain properties as well as the dynamic evolution of paths when the mobile moves is proposed based on the concept of a Markov process. The derived model is based on dynamic measurement data collected at a carrier frequency of 5.2 GHz in typical indoor environments. Multipath components are estimated using the super-resolution frequency domain space-alternating generalized expectation maximization algorithm prior to identification of path "birth" and "death" using a new data analysis method. Analysis shows that multiple births and deaths are possible at any instant of time. Furthermore, correlation exists between the number of births and deaths. Thus, an M-step 4-state Markov channel model (MCM) is proposed in order to account for these two effects. The spatio-temporal variations of paths within their lifespans are taken into consideration by the spatio-temporal vector which was found to be well modeled by a Gaussian probability density function while the power variation can be modeled by a simple low-pass filter. In addition, the methodology used to extract the MCM parameters from the measurement data is also presented. Due to the distinction in the birth-death statistics, the model is generalized through segmentation of the measurement runs and can be completely parameterized by several sets of Markov parameters associated with the type of environment and scenario under consideration. The implementation of the model is also detailed and, finally, the model is evaluated by comparing key statistics of the simulation results with the measurement results.

Independent component analysis (ICA) has shown success in the separation of sources in lots of applications. Almost all of them assume that a set of recorded signals is the result of a linear mixture of independent sources. Although ICA methods were firstly designed to apply only to free-noise signals, numerous methods have extended it to deal with additive noise, using only higher order statistics. However, in speckle environment signals the noise is multiplicative, so the applicability of ICA is seriously reduced. This paper proposes an ICA method for speckle signals, taking into account the multiplicative nature of the noise and improving the results obtained by standard ICA methods.

Abstract In this paper, the effect of wireless internet traffic on the functionality of a radio resource metric estimation (RME) applicable to a TDD‐CDMA system which incorporates a multirate transmission scheme (such as multicode and multislot) with simple call admission control algorithm is investigated. A solution for efficient inter‐working between the physical layer and the higher layers is proposed, a resource metric mapping function (RMMF) that can deliver information about the state of the current channel load condition by the monitoring of signal‐to‐interference ratio (SIR) bursts and also the availability of a code pool by estimating the mean and the standard deviation of SIR bursts. This function is defined and demonstrated by means of an average raw BER mapping diagram as a function of the mean and the standard deviation of SIR bursts according to the multirate transmission method. By using this kind of mapping function combined with throughput estimation, the radio resource allocation algorithm can successfully reflect the current interference and mobile radio channel characteristics. Copyright © 2004 John Wiley & Sons, Ltd.

A novel stochastic wideband dynamic directional indoor channel model which incorporates both the spatial and temporal domain properties as well as the dynamic evolution of paths when the mobile moves is proposed based on the concept of a Markov process. The derived model is based on dynamic measurement data collected at a carrier frequency of 5.2 GHz in several typical indoor environments. Analysis shows that multiple births and deaths are possible at any instant of time. Furthermore, correlation exists between the number of births and deaths. Thus, an M-step, 4-state Markov channel model is proposed in order to account for these two effects. The spatio-temporal variations of paths within their lifespan are taken into consideration by the spatio-temporal vector which was found to be well-modeled by a Gaussian probability density function while the power variation can be modeled by a simple low pass filter. The implementation of the model is detailed and finally, the model validity is evaluated by comparing key statistics of the simulation results with the measurement results.

In this paper, the spatial and temporal dispersion and correlation properties of a wideband dynamic directional indoor channel at 5.2 GHz are presented based on extensive measurement campaigns in four different environments under various propagation scenarios. Channel spatio-temporal dispersions are assessed in terms of the rms delay spread (DS) and rms azimuth spread (AS), respectively, while the correlation properties are determined by the spatio-temporal correlation coefficient. A strong correlation is observed with the presence of the line-of sight (LOS) path but degraded when the LOS is obstructed. The variations of the spatio-temporal dispersions with mobility are also investigated where higher values of spatio-temporal dispersions were found when the transmitter and receiver separation increased particularly in a more cluttered environment. The average values of the rms DS and rms AS for all investigated environments and scenarios are also given. These parameters can be used as empirical values for the 5 GHz band WLAN systems.

In this paper, a new statistical wideband indoor channel model which incorporates both the clustering of multipath components (MPCs) and the correlation between the spatial and temporal domains is proposed. The model is derived based on measurement data collected at a carrier frequency of 5.2 GHz in three different indoor scenarios and is suitable for performance analysis of HIPERLAN/2 and IEEE 802.11a systems that employ smart antenna architectures. MPC parameters are estimated using the super-resolution frequency domain space-alternating generalized expectation maximization (FD-SAGE) algorithm and clusters are identified in the spatio-temporal domain by a nonparametric density estimation procedure. The description of the clustering observed within the channel relies on two classes of parameters, namely, intercluster and intracluster parameters which characterize the cluster and MPC, respectively. All parameters are described by a set of empirical probability density functions (pdfs) derived from the measured data. The correlation properties are incorporated in two joint pdfs for cluster and MPC positions, respectively. The clustering effect also gives rise to two classes of channel power density spectra (PDS)-intercluster and intracluster PDS-which are shown to exhibit exponential and Laplacian functions in the delay and angular domains, respectively. Finally, the model validity is confirmed by comparison with two existing models reported in the literature.

This paper considers the impact of interleaving and combining multiple DMT symbols in a single RS codeword on the performance of Asymmetrical Digital Subscriber Lines (ADSL) in the presence of impulse noise. In recent years there has been an increasing interest in xDSL technology due to its high data rate capability and relative ease of deployment. Impulse noise is one of the main impairments for xDSL systems and different framing techniques are aimed at mitigating the impact of impulse noise. In the ADSL standard techniques for interleaving and combining of several consecutive symbols into a single FEC codeword are used. This paper considers the performance of the above techniques at higher layers of the protocol stack. The results are based on simulations of a generic ADSL system with a new broadband-oriented impulse noise model. The conclusions include a comparison between the packet error rates of the two techniques at different bit rates and the interaction between them when used together. The aim of this research is to find a suitable set of framing parameters and help designers in their choice of single or dual latency ADSL systems.

This paper considers the performance of Digital Subscriber Lines (DSL) in the presence of impulse noise in terms of data errors. Impulse noise is one of the biggest impairments for xDSL systems. An analysis of error statistics of transmitted data for two popular versions of xDSL - ADSL and SHDSL - is reported here. On the basis of a recent novel impulse noise model a model of the impact of impulse noise on higher protocol levels has been developed. Specifics of framing for the different DSL versions have also been taken into account. Simulation results for statistics of data error due to impulse noise based on the new byte level models are presented.

In this paper, the serial interference cancellation (SIC) technique and the frequency domain SAGE (FD-SAGE) algorithm are jointly used to detect and estimate the radio channel parameters of interest. The implementation of the SAGE algorithm in the frequency domain is novel. Furthermore, the parallel interference cancellation (PIC) technique in the standard SAGE algorithm is replaced by the SIC technique. The SIC technique demonstrates more stable performance especially in a multipath rich environment. The two-dimensional (2-D) FD-SAGE algorithm and the SIC technique are introduced and their performance demonstrated by using real indoor channel measurement data to jointly estimate the number of multipath components (MPCs), their time-of-arrivals (TOAs), angle-of-arrivals (AOAs) and complex amplitudes. Their performance is evaluated and compared using synthetic data and results using 2-D unitary ESPRIT (another form of super-resolution algorithm).

In this paper we address the problem of how to measure the link quality in terms of signal-to-interference ratio (SIR) over rapidly time varying channels which are due to either fast variation of the interference or the bursty nature of the traffic. We propose a new SIR estimation approach for the UTRA-TDD system, that is based on a pre-selection tentative decision device which discards and selects the estimated symbol on the basis of MAP ratio. Simulation results show that the proposed SIR estimator works well in the context of rapidly time varying channels.

ODMA is a multi-hop relaying routing protocol, the use of which has been investigated in conventional cellular scenarios. This paper compares the performance of ODMA with direct transmission for cases where links may be required directly to other nodes, as well as to a controlling (backbone) node. For an interference-limited system, it is shown that whereas the topology is not supportable by a conventional (single-hop) system, a relayed system is able to provide service. A new admission control and routing algorithm based on receiver interference is presented which is shown to further enhance performance.

The interference in a TDD system is demonstrated as being comprised of two categories of interference: (1) same entity interference, which is base station (BS) to BS interference and mobile station (MS) to MS interference; (2) other entity interference which is BS/spl rarr/MS and MS/spl rarr/BS interference. Based on this specific TDD property, an equation for capacity in a CDMA/TDD system is derived. Moreover, it is shown that the capacity in a CDMA/TDD system can be significantly higher compared with an equivalent FDD system. This assumes use of a DCA algorithm. A novel DCA is presented which uses a newly developed method termed: time slot (TS) opposing technique. The main advantage of this technique is that a TDD system adopting different asymmetries for uplink and downlink in neighbouring cells does not necessarily suffer from capacity losses.

This paper proposes a suitable method for simulating impulses with appropriate amplitude, spectral, and inter-arrival characteristics. The statistics used to develop the parameters of this model are based on statistics derived from observations of impulse noise on the telephone networks of British Telecom (BT) and Deutsche Telekom (DT). This paper initially reviews the former DT approach to impulse noise generation for testing digital subscriber line systems, so called xDSL systems. Some problems are highlighted and an alternative technique is suggested that is capable of generating impulses with both appropriate amplitude an spectral characteristics.

This paper studies the statistical behavior of the affine projection (AP) algorithm for /spl mu/=1 for Gaussian autoregressive inputs. This work extends the theoretical results of Rupp (1998) to the numerical evaluation of the MSE learning curves for the adaptive AP weights. The MSE learning behavior of the AP(P+1) algorithm with an AR(Q) input (Q/spl les/P) is shown to be the same as the NLMS algorithm (/spl mu/=1) with a white input with M-P unity eigenvalues and P zero eigenvalues and increased observation noise. Monte Carlo simulations are presented which support the theoretical results.

This paper considers the cell error performance of ATM over digital subscriber lines (DSL) in the presence of impulse noise. In previous years there has been an increasing interest in xDSL technology due to its relatively broad bandwidth and ease of deployment. ATM is a preferred protocol over DSL. ATM, however, has been designed for low bit error rates. Despite error correction protocols, DSL cannot always overcome bursty errors, particularly impulse noise. Interest in DSL has triggered research into impulse noise. A new model has been developed based on surveys on experimental data. Using this model the impact of impulse noise on ATM cell error performance has been traced. Various DSL framings have been found to affect adversely the ATM stream. The interleave depth should be either 1 or large enough to correct the errors. The performance in terms of header and payload errors differs. Time between errored cells exhibits clustering like interarrival times. It is possible within one impulse event to have "good" cells between errored cells. Finally, for higher level applications error free cells is a more appropriate metric than error free seconds.

Linear filter theory based on Wiener filtering is well understood and used widely in many fields of image and signal processing. A field-programmable gate array (FPGA) implementation of a series of nonlinear filters is developed based on the concepts of Volterra series and these are applied to image interpolation problems. More explicitly, the aim is to interpolate one field of a frame of a television picture in real time to form an estimate of the second field. This is known as de-interlacing and is useful in many areas of video processing, for example standards conversion.

It has been demonstrated that code division multiple access (CDMA) provides great flexibility by enabling efficient multiuser access in a cellular environment. In addition, time division duplex (TDD) as compared to frequency division duplex (FDD) represents an appropriate method to cater for the asymmetric use of a duplex channel. However, the TDD technique is subject to additional interference mechanisms compared to an FDD system, in particular if neighboring cells require different rates of asymmetry. If TDD is combined with an interference limited multiple access technique such as CDMA, the additional interference mechanism represents an important issue. This issue poses the question of whether a CDMA/TDD air-interface can be used in a cellular environment. The problems are eased if a hybrid time division multiple access (TDMA)/CDMA interface (TD-CDMA) is used. The reason for this is that the TDMA component adds another degree of freedom which can be utilized to avoid interference. This, however, requires special channel assignment techniques. A notable example of a system which uses a TD-CDMA/TDD interface is the Universal Mobile Telecommunications System (UMTS). This paper presents a novel centralized dynamic channel assignment (DCA) algorithm for a TD-CDMA/TDD air-interface. The DCA algorithm exploits a new technique which is termed "TS-opposing technique." The key result is that the new DCA algorithm enables neighboring cells to adopt different rates of asymmetry without a significant capacity loss.

When comparing UTRA-TDD with UTRA-FDD it can be found that in the UTRA-TDD mode additional interference scenarios exist. Mobile stations (MSs) can interfere with each other and so can base stations (BSs). Since the source and sink of this type of interference are the same we call it same-entity interference. It is shown by a novel interference resolving algorithm that same-entity interference can be constructively exploited to enable asynchronous overlaps in a UTRA-TDD network. Asynchronous overlaps exist when any cell A is transmitting while the neighbour cell B is receiving. It was found that in a network where an asynchronous overlap exists the algorithm proposed reduces outage from 14% to 6%. In contrast, the outage of an ideally synchronised network was found to be 3.5%. In this 'ideal' network asynchronous overlaps are disallowed resulting in a significant drawback. This is that the flexibility of a TDD system to easily adopt different channel asymmetries is significantly limited.

This paper investigates the performance of a compact space-time diversity receiver for mobile communication systems. Expressions for the bit error rate and outage mean SNR are derived as a function of the channel covariance matrix. These results define the sensitivity of the performance of such a receiver to the environment (fading characteristics and angular spread) and the antenna array (geometry and coupling).

In this paper, we describe a new pervasive conversational system that provides access to multiple desktop applications, from multiple client devices, using multiple input modalities. Client devices currently supported include desktop and telephone, and the applications incorporated include email, calendarand address book. When the access is from a desktop, both conversational natural language and graphical inputs are supported. The paper describes the overall architecture to support such a pervasive conversational system, along with innovations in continuous speech recognition, statistical natural language understanding, and dialog management that were developed to build the system. The paper also describes the partially unsupervised Wizard-of-Oz style setup to collect real-use data, and the performance of the statistical models constructed using this data for speech recognition and natural language understanding.

This paper addresses suggestions in the literature that the generation of speech is a nonlinear process. This has sparked great interest in the area of nonlinear analysis of speech with a number of studies being conducted to investigate whether low dimensional chaotic attractors exist for speech. This paper examines a corpus of sustained vowel sounds which were recorded for this study to ensure dynamical invariance. The sounds are assessed by a range of the invariant geometric features developed for the analysis of chaotic systems such as correlation dimension, Lyapunov exponents, and short-term predictability. The results presented suggest that although voiced speech is well characterized by a small number of dimensions, it is not necessarily chaotic. Finally, a synthesis technique for voiced sounds is developed inspired by the technique for estimating the Lyapunov exponents.

In this paper the effective bandwidths of stable distributions are studied. Effective bandwidths are being heavily promoted as the most appropriate method for call admission control (CAC) and resource allocation within ATM networks. Previous work in teletraffic modelling has suggested that models based on stable distributions provide an efficient mechanism for capturing the long range dependence and infinite variance associated with teletraffic data (the Joseph and Noah effects; see Willinger et al. 1997). This has potentially serious implications for effective bandwidths and we show how the effective bandwidth of such data is theoretically infinite. We then present two approximate methods for estimating the effective bandwidth of data based on stable distribution.

An important family of blind equalization algorithms identify a communication channel model based on fitting higher order cumulants, which poses a nonlinear optimization problem. Since higher order cumulant-based criteria are multimodal, conventional gradient search techniques require a good initial estimate to avoid converging to local minima. We present a novel scheme which uses genetic algorithms to optimize the cumulant fitting cost function. A microgenetic algorithm implementation is adopted to further enhance computational efficiency. As is demonstrated in computer simulation, this scheme is robust and accurate and has a fast convergence performance.

Thrombosis of coronary arteries is a condition responsible for many acute coronary syndromes. The ability to categorise thrombus belonging to distinct pathological groups, would contribute to the understanding of the pathophysiologic structure of individual lesions, as well as making a significant contribution to treatment choice. Here, the authors investigate the use of statistical texture analysis techniques to assess the ability of 30 MHz intravascular ultrasound (IVUS) data, in raw and scan-converted form, to characterise intracoronary thrombus. Three clot types were assessed in the study, these were, red (R), white (W) and plasma (P). Histopathological analysis, the de facto standard in identifying tissue composition, was used to form a Gold Standard based upon clot composition, from which the results were verified. The results show the ability of the texture analysis techniques used to discriminate clot lesions, and highlights the advantage of using the raw data over the scan-converted data in assessing thrombus composition in vitro.

In this study, statistical and fractal texture analysis was used to assess the ability of 30 MHz intravascular ultrasound (IVUS) data, in raw and scan-converted form, to characterise atherosclerotic plaque. Data from 3 different plaque groups was assessed in the study: 1) loose fibrotic tissue; 2) dense fibrotic tissue; and 3) calcium. The composition of each group was verified from histo-pathological analysis, providing a Gold Standard from which results were verified. Scan-converted images were used to locate 33 regions of interest (ROI) within areas of known tissue composition. Fractal and statistical textural features were computed on ROI data in raw and scan-converted form. The results show the ability of the method to discriminate between groups, in particular they highlight the advantage of using the raw data over the scan-converted data to assess coronary artery disease.

Speech synthesis can be produced using many v aried techniques from formant/parametric synthesis to concatenation approaches.This paper present s a n o v el technique 1 , based on the nonlinear dynamics of speech rather than than the time or frequency domain representations.It is demonstrated that the technique can be implemented eectively and used to produce high quality synthesised speech 2 .

The paper investigates adaptive equalization of time-dispersive mobile radio fading channels and develops a robust high performance Bayesian decision feedback equalizer (DFE). The characteristics and implementation aspects of this Bayesian DFE are analyzed, and its performance is compared with those of the conventional symbol or fractional spaced DFE and the maximum likelihood sequence estimator (MLSE). In terms of computational complexity, the adaptive Bayesian DFE is slightly more complex than the conventional DFE but is much simpler than the adaptive MLSE. In terms of error rate in symbol detection, the adaptive Bayesian DFE outperforms the conventional DFE dramatically. Moreover, for severely fading multipath channels, the adaptive MLSE exhibits significant degradation from the theoretical optimal performance and becomes inferior to the adaptive Bayesian DFE.>

A multi-stage blind clustering algorithm is proposed for equalisation of multi-level quadrature amplitude modulation (M-QAM) channels. A hierarchical decomposition divides the task of equalising a high-order QAM channel into much simpler sub-tasks. Each sub-task can be accomplished fast and reliably using a blind clustering algorithm derived originally for 4-QAM signals. The constant modulus algorithm (CMA) is used as a benchmark to assess this multi-stage blind equaliser. It is demonstrated that the new blind algorithm achieves much faster convergence and is very robust when input symbols are not sufficiently white. This multi-stage clustering equaliser only requires slightly more computations than the CMA and, like the latter, its computational complexity does not increase as the levels of digital symbols increase.>

A set of ordinary differential equations (ODEs) has been constructed to represent a continuous sigma delta modulator (C/spl Sigma//spl Delta/M) [1], i.e. a continuous equivalent of a conventional sigma delta modulator (/spl Sigma//spl Delta/M). This paper is concerned with a stability analysis of these equations with particular reference being made to values representing the gains of the feedback loop and the integrator leakage in the circuit. Conditions for the stability of the equations representing second and third order modulation are derived. The results from this process are used on a conventional simulation of sigma-delta modulation, and the applicability of this method is discussed. The criteria for stable operation are extended to n/sup th/ order sigma delta modulation and to a generalised architecture for sigma delta modulation. The results are compared to the conditions for stability derived from the work of various authors.>

Narrowband indoor communication channels can be characterised by statistical distributions. The Rayleigh and Ricean distributions are commonly used to describe measured channels, but in some instances, other distributions may be found to be more appropriate for the task. Determining the reason for one particular distribution being more appropriate for one environment over another may be a non-trivial task, In order to assist in this process, a channel simulation based on the physical structure of the environment is presented, along with narrowband results, obtained both from measurement and simulation experiments. Using these results, the basis for a Nakagami distributed channel, observed in some indoor communication environments, is be shown.

The authors present three third- and three fourth-order cumulant based algorithms for blind deconvolution and identification of the nonminimum phase (NMP) systems. In the algorithms, based on a noncausal AR (autoregressive) model and a theorem relating to the inverse filter coefficients, the problem of blind deconvolution and identification of a NMP system is reduced to that of solving the corresponding set of linear equations. Thus, the uniqueness of the solution can normally be guaranteed. Furthermore, only the diagonal slices of cumulants are employed in the algorithms, which results in the algorithms being simpler and more accurate. A simulation example is presented for the case of unskewed continuous input, and the feasibility and efficiency of the algorithm are confirmed.>

The performance of a Kalman decision-feedback equalizer (DFE) that uses a channel estimator based on a least-mean-squares (LMS) algorithm is studied for a variety of stationary and nonstationary communications channels. This structure provides a means of model order reduction by using the residuals of the LMS to provide information on the unmodeled paths in the communication channel, which is then incorporated into the Kalman DFE structure as observation noise. The structure is compared with a conventional DFE that is trained by a Godard-Kalman algorithm with exponential windowing and adaptive Kalman structure previously reported (B. Mulgrew and C.F.N. Cowan, 1987). The results indicate that the best performance, in terms of final MSE (mean square error), is offered by the adaptive Kalman DFE structure, the final MSE being lower than that achieved by the conventional DFE by some 5-10 dB.>

In this paper a comparison is made between the convergence and tracking properties of Least Squares (LS) and Least Mean Squares (LMS) algorithms as high frequency (HF) channel estimators. Theoretical results are derived for the asymptotic error achieved by the LS algorithms under white-input conditions in the HF channel. This result is more accurate than previous analyses of LS algorithms in a nonstationary enviroment [5,8,9]. Utilising a state space definition of the channel model a minimum variance Kalman estimator is derived using the a-priori knowledge of the parameters which define the Markov process.