Decision-directed feedback equalisation for QAM signals

Proposed is a symbol-based decision-directed algorithm for blind equalisation of quadrature amplitude modulation (QAM) signals using a decision feedback scheme. Independently of QAM order, it presents: (i) an error equal to zero when the equaliser output coincides with the transmitted signal; (ii) simultaneous recovery of the modulus and phase of the signal; (iii) a misadjustment close to that of the normalised least-mean squares algorithm; (iv) fast convergence; and (v) the avoidance of degenerative solutions. Additionally, its stability is ensured when the step-size is properly chosen.

A regional multirnodulus algorithm for blind equalization of QAM signals: Introduction and steady-state analysis

It is well known that constant-modulus-based algorithms present a large mean-square error for high-order quadrature amplitude modulation (QAM) signals, which may damage the switching to decision-directed-based algorithms. In this paper, we introduce a regional multimodulus algorithm for blind equalization of QAM signals that performs similar to the supervised normalized least-mean-squares (NLMS) algorithm, independently of the QAM order. We find a theoretical relation between the coefficient vector of the proposed algorithm and the Wiener solution and also provide theoretical models for the steady-state excess mean-square error in a nonstationary environment. The proposed algorithm in conjunction with strategies to speed up its convergence and to avoid divergence can bypass the switching mechanism between the blind mode and the decision-directed mode. (c) 2012 Elsevier B.V. All rights reserved.

Accurate and robust reconstruction of proximal femur from sparse intraoperative data and dense point distribution model for surgical navigation

Constructing a 3D surface model from sparse-point data is a nontrivial task. Here, we report an accurate and robust approach for reconstructing a surface model of the proximal femur from sparse-point data and a dense-point distribution model (DPDM). The problem is formulated as a three-stage optimal estimation process. The first stage, affine registration, is to iteratively estimate a scale and a rigid transformation between the mean surface model of the DPDM and the sparse input points. The estimation results of the first stage are used to establish point correspondences for the second stage, statistical instantiation, which stably instantiates a surface model from the DPDM using a statistical approach. This surface model is then fed to the third stage, kernel-based deformation, which further refines the surface model. Handling outliers is achieved by consistently employing the least trimmed squares (LTS) approach with a roughly estimated outlier rate in all three stages. If an optimal value of the outlier rate is preferred, we propose a hypothesis testing procedure to automatically estimate it. We present here our validations using four experiments, which include 1 leave-one-out experiment, 2 experiment on evaluating the present approach for handling pathology, 3 experiment on evaluating the present approach for handling outliers, and 4 experiment on reconstructing surface models of seven dry cadaver femurs using clinically relevant data without noise and with noise added. Our validation results demonstrate the robust performance of the present approach in handling outliers, pathology, and noise. An average 95-percentile error of 1.7-2.3 mm was found when the present approach was used to reconstruct surface models of the cadaver femurs from sparse-point data with noise added.

ROBREG: Stata module providing robust regression estimators

robreg provides a number of robust estimators for linear regression models. Among them are the high breakdown-point and high efficiency MM-estimator, the Huber and bisquare M-estimator, and the S-estimator, each supporting classic or robust standard errors. Furthermore, basic versions of the LMS/LQS (least median of squares) and LTS (least trimmed squares) estimators are provided. Note that the moremata package, also available from SSC, is required.

Dynamic chanel model LMS updating for RSS-based localization

Received signal strength-based localization systems usually rely on a calibration process that aims at characterizing the propagation channel. However, due to the changing environmental dynamics, the behavior of the channel may change after some time, thus, recalibration processes are necessary to maintain the positioning accuracy. This paper proposes a dynamic calibration method to initially calibrate and subsequently update the parameters of the propagation channel model using a Least Mean Squares approach. The method assumes that each anchor node in the localization infrastructure is characterized by its own propagation channel model. In practice, a set of sniffers is used to collect RSS samples, which will be used to automatically calibrate each channel model by iteratively minimizing the positioning error. The proposed method is validated through numerical simulation, showing that the positioning error of the mobile nodes is effectively reduced. Furthermore, the method has a very low computational cost; therefore it can be used in real-time operation for wireless resource-constrained nodes.

Cancelación activa del ruido utilizando el KIT TMS320C5515 EZDSP

El control, o cancelación activa de ruido, consiste en la atenuación del ruido presente en un entorno acústico mediante la emisión de una señal igual y en oposición de fase al ruido que se desea atenuar. La suma de ambas señales en el medio acústico produce una cancelación mutua, de forma que el nivel de ruido resultante es mucho menor al inicial. El funcionamiento de estos sistemas se basa en los principios de comportamiento de los fenómenos ondulatorios descubiertos por Augustin-Jean Fresnel, Christiaan Huygens y Thomas Young entre otros. Desde la década de 1930, se han desarrollado prototipos de sistemas de control activo de ruido, aunque estas primeras ideas eran irrealizables en la práctica o requerían de ajustes manuales cada poco tiempo que hacían inviable su uso. En la década de 1970, el investigador estadounidense Bernard Widrow desarrolla la teoría de procesado adaptativo de señales y el algoritmo de mínimos cuadrados LMS. De este modo, es posible implementar filtros digitales cuya respuesta se adapte de forma dinámica a las condiciones variables del entorno. Con la aparición de los procesadores digitales de señal en la década de 1980 y su evolución posterior, se abre la puerta para el desarrollo de sistemas de cancelación activa de ruido basados en procesado de señal digital adaptativo. Hoy en día, existen sistemas de control activo de ruido implementados en automóviles, aviones, auriculares o racks de equipamiento profesional. El control activo de ruido se basa en el algoritmo fxlms, una versión modificada del algoritmo LMS de filtrado adaptativo que permite compensar la respuesta acústica del entorno. De este modo, se puede filtrar una señal de referencia de ruido de forma dinámica para emitir la señal adecuada que produzca la cancelación. Como el espacio de cancelación acústica está limitado a unas dimensiones de la décima parte de la longitud de onda, sólo es viable la reducción de ruido en baja frecuencia. Generalmente se acepta que el límite está en torno a 500 Hz. En frecuencias medias y altas deben emplearse métodos pasivos de acondicionamiento y aislamiento, que ofrecen muy buenos resultados. Este proyecto tiene como objetivo el desarrollo de un sistema de cancelación activa de ruidos de carácter periódico, empleando para ello electrónica de consumo y un kit de desarrollo DSP basado en un procesador de muy bajo coste. Se han desarrollado una serie de módulos de código para el DSP escritos en lenguaje C, que realizan el procesado de señal adecuado a la referencia de ruido. Esta señal procesada, una vez emitida, produce la cancelación acústica. Empleando el código implementado, se han realizado pruebas que generan la señal de ruido que se desea eliminar dentro del propio DSP. Esta señal se emite mediante un altavoz que simula la fuente de ruido a cancelar, y mediante otro altavoz se emite una versión filtrada de la misma empleando el algoritmo fxlms. Se han realizado pruebas con distintas versiones del algoritmo, y se han obtenido atenuaciones de entre 20 y 35 dB medidas en márgenes de frecuencia estrechos alrededor de la frecuencia del generador, y de entre 8 y 15 dB medidas en banda ancha. ABSTRACT. Active noise control consists on attenuating the noise in an acoustic environment by emitting a signal equal but phase opposed to the undesired noise. The sum of both signals results in mutual cancellation, so that the residual noise is much lower than the original. The operation of these systems is based on the behavior principles of wave phenomena discovered by Augustin-Jean Fresnel, Christiaan Huygens and Thomas Young. Since the 1930’s, active noise control system prototypes have been developed, though these first ideas were practically unrealizable or required manual adjustments very often, therefore they were unusable. In the 1970’s, American researcher Bernard Widrow develops the adaptive signal processing theory and the Least Mean Squares algorithm (LMS). Thereby, implementing digital filters whose response adapts dynamically to the variable environment conditions, becomes possible. With the emergence of digital signal processors in the 1980’s and their later evolution, active noise cancellation systems based on adaptive signal processing are attained. Nowadays active noise control systems have been successfully implemented on automobiles, planes, headphones or racks for professional equipment. Active noise control is based on the fxlms algorithm, which is actually a modified version of the LMS adaptive filtering algorithm that allows compensation for the acoustic response of the environment. Therefore it is possible to dynamically filter a noise reference signal to obtain the appropriate cancelling signal. As the noise cancellation space is limited to approximately one tenth of the wavelength, noise attenuation is only viable for low frequencies. It is commonly accepted the limit of 500 Hz. For mid and high frequencies, conditioning and isolating passive techniques must be used, as they produce very good results. The objective of this project is to develop a noise cancellation system for periodic noise, by using consumer electronics and a DSP development kit based on a very-low-cost processor. Several C coded modules have been developed for the DSP, implementing the appropriate signal processing to the noise reference. This processed signal, once emitted, results in noise cancellation. The developed code has been tested by generating the undesired noise signal in the DSP. This signal is emitted through a speaker simulating the noise source to be removed, and another speaker emits an fxlms filtered version of the same signal. Several versions of the algorithm have been tested, obtaining attenuation levels around 20 – 35 dB measured in a tight bandwidth around the generator frequency, or around 8 – 15 dB measured in broadband.

Information geometric measurements of generalisation

Neural networks can be regarded as statistical models, and can be analysed in a Bayesian framework. Generalisation is measured by the performance on independent test data drawn from the same distribution as the training data. Such performance can be quantified by the posterior average of the information divergence between the true and the model distributions. Averaging over the Bayesian posterior guarantees internal coherence; Using information divergence guarantees invariance with respect to representation. The theory generalises the least mean squares theory for linear Gaussian models to general problems of statistical estimation. The main results are: (1)~the ideal optimal estimate is always given by average over the posterior; (2)~the optimal estimate within a computational model is given by the projection of the ideal estimate to the model. This incidentally shows some currently popular methods dealing with hyperpriors are in general unnecessary and misleading. The extension of information divergence to positive normalisable measures reveals a remarkable relation between the dlt dual affine geometry of statistical manifolds and the geometry of the dual pair of Banach spaces Ld and Ldd. It therefore offers conceptual simplification to information geometry. The general conclusion on the issue of evaluating neural network learning rules and other statistical inference methods is that such evaluations are only meaningful under three assumptions: The prior P(p), describing the environment of all the problems; the divergence Dd, specifying the requirement of the task; and the model Q, specifying available computing resources.

Uncertainty analysis of pollutant build-up modelling based on a Bayesian Weighted Least Squares approach

Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality data sets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares Regression and Bayesian Weighted Least Squares Regression for the estimation of uncertainty associated with pollutant build-up prediction using limited data sets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in the prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling.

Weighted least-squares phase unwrapping algorithm based on derivative variance correlation map

Among different phase unwrapping approaches, the weighted least-squares minimization methods are gaining attention. In these algorithms, weighting coefficient is generated from a quality map. The intrinsic drawbacks of existing quality maps constrain the application of these algorithms. They often fail to handle wrapped phase data contains error sources, such as phase discontinuities, noise and undersampling. In order to deal with those intractable wrapped phase data, a new weighted least-squares phase unwrapping algorithm based on derivative variance correlation map is proposed. In the algorithm, derivative variance correlation map, a novel quality map, can truly reflect wrapped phase quality, ensuring a more reliable unwrapped result. The definition of the derivative variance correlation map and the principle of the proposed algorithm are present in detail. The performance of the new algorithm has been tested by use of a simulated spherical surface wrapped data and an experimental interferometric synthetic aperture radar (IFSAR) wrapped data. Computer simulation and experimental results have verified that the proposed algorithm can work effectively even when a wrapped phase map contains intractable error sources. (c) 2006 Elsevier GmbH. All rights reserved.

Weighted least squares with expectation-maximization algorithm for burst detection in U.K. water distribution systems

Flow measurement data at the district meter area (DMA) level has the potential for burst detection in the water distribution systems. This work investigates using a polynomial function fitted to the historic flow measurements based on a weighted least-squares method for automatic burst detection in the U.K. water distribution networks. This approach, when used in conjunction with an expectationmaximization (EM) algorithm, can automatically select useful data from the historic flow measurements, which may contain normal and abnormal operating conditions in the distribution network, e.g., water burst. Thus, the model can estimate the normal water flow (nonburst condition), and hence the burst size on the water distribution system can be calculated from the difference between the measured flow and the estimated flow. The distinguishing feature of this method is that the burst detection is fully unsupervised, and the burst events that have occurred in the historic data do not affect the procedure and bias the burst detection algorithm. Experimental validation of the method has been carried out using a series of flushing events that simulate burst conditions to confirm that the simulated burst sizes are capable of being estimated correctly. This method was also applied to eight DMAs with known real burst events, and the results of burst detections are shown to relate to the water company's records of pipeline reparation work. © 2014 American Society of Civil Engineers.

Weighted and two-stage least squares estimation of semiparametric truncated regression models

This paper provides a root-n consistent, asymptotically normal weighted least squares estimator of the coefficients in a truncated regression model. The distribution of the errors is unknown and permits general forms of unknown heteroskedasticity. Also provided is an instrumental variables based two-stage least squares estimator for this model, which can be used when some regressors are endogenous, mismeasured, or otherwise correlated with the errors. A simulation study indicates that the new estimators perform well in finite samples. Our limiting distribution theory includes a new asymptotic trimming result addressing the boundary bias in first-stage density estimation without knowledge of the support boundary. © 2007 Cambridge University Press.

Robust visual tracking using structurally random projection and weighted least squares

Sparse representation based visual tracking approaches have attracted increasing interests in the community in recent years. The main idea is to linearly represent each target candidate using a set of target and trivial templates while imposing a sparsity constraint onto the representation coefficients. After we obtain the coefficients using L1-norm minimization methods, the candidate with the lowest error, when it is reconstructed using only the target templates and the associated coefficients, is considered as the tracking result. In spite of promising system performance widely reported, it is unclear if the performance of these trackers can be maximised. In addition, computational complexity caused by the dimensionality of the feature space limits these algorithms in real-time applications. In this paper, we propose a real-time visual tracking method based on structurally random projection and weighted least squares techniques. In particular, to enhance the discriminative capability of the tracker, we introduce background templates to the linear representation framework. To handle appearance variations over time, we relax the sparsity constraint using a weighed least squares (WLS) method to obtain the representation coefficients. To further reduce the computational complexity, structurally random projection is used to reduce the dimensionality of the feature space while preserving the pairwise distances between the data points in the feature space. Experimental results show that the proposed approach outperforms several state-of-the-art tracking methods.