Partial coherence: A nonstatistical approach, and some basic microwave experiments.

The basic concepts of second-order coherence theory are introduced, followed by a nonstatistical development of the coherence equations. The various possible generalizations of scalar second-order coherence are briefly described. Microwave experiments are reported in which incoherent as well as partially coherent radiating sources are simulated by suitable arrangements of noise tubes. Two effects in particular are examined: the factorization of the coherence function into space- and time-dependent factors under conditions of "spectral purity," and the spectral modulation of two superposed beams that have a large path delay between them (Alford and Gold effect).     

A rapidly converging algorithm for estimating respiratory mechanical parameters in a five-element model

A rapidly converging algorithm for computing values for respiratory mechanical parameters from forced random noise independance data was developed and verified. The algorithm, which was based on a five-element Mead-type model, minimized the sum of squared differences between the model's response and experimental data, while imposing a nonnegativity constraint on the parameter values. It yielded parameter values that showed excellent agreement with values obtained previously using standard nonlinear regression analysis, but required much less computer time, 10 s versus 1 h. When this algorithm is coupled with the forced random impedance data collection techniques, it provides a rapid noninvasive method for estimating respiratory inertance, central resistance, peripheral resistance, and airway compliance. The problem of estimating peripheral compliance was not solved by this algorithm.     

Simultaneous confidence intervals for all ratios to the best: theexponential distribution

Consider k independent exponential populations with different scale and location (possibly unknown) parameters. A set of simultaneous upper confidence intervals for all ratios to the largest scale parameter is derived. The data are assumed to be: (1) complete; or (2) incomplete with type-II censoring. The cases of known and unknown location parameters are treated separately     

Receiver operating characteristic analysis for intelligent medical systems--a new approach for finding confidence intervals

Intelligent systems are increasingly being deployed in medicine and healthcare, but there is a need for a robust and objective methodology for evaluating such systems. Potentially, receiver operating characteristic (ROC) analysis could form a basis for the objective evaluation of intelligent medical systems. However, it has several weaknesses when applied to the types of data used to evaluate intelligent medical systems. First, small data sets are often used, which are unsatisfactory with existing methods. Second, many existing ROC methods use parametric assumptions which may not always be valid for the test cases selected. Third, system evaluations are often more concerned with particular, clinically meaningful, points on the curve, rather than on global indexes such as the more commonly used area under the curve. A novel, robust and accurate method is proposed, derived from first principles, which calculates the probability density function (pdf) for each point on a ROC curve for any given sample size. Confidence intervals are produced as contours on the pdf. The theoretical work has been validated by Monte Carlo simulations. It has also been applied to two real-world examples of ROC analysis, taken from the literature (classification of mammograms and differential diagnosis of pancreatic diseases), to investigate the confidence surfaces produced for real cases, and to illustrate how analysis of system performance can be enhanced. We illustrate the impact of sample size on system performance from analysis of ROC pdf's and 95% confidence boundaries. This work establishes an important new method for generating pdf's, and provides an accurate and robust method of producing confidence intervals for ROC curves for the small sample sizes typical of intelligent medical systems. It is conjectured that, potentially, the method could be extended to determine risks associated with the deployment of intelligent medical systems in clinical practice.     

A phenomenological approach to estimating transit times in GaAsHBTs

A method is proposed for estimating transit times in heterojunction bipolar transistors (HBTs) which are fabricated from semiconductor materials in which the conduction band can be represented by a two-valley model. The transition times for exchange of electrons between the conduction band valleys are treated as phenomenological parameters and are shown to be linked by the electric field in the device. Incorporation of the transition rates into the continuity equations for upper and lower valley electrons yields a set of equations which can be solved under transient conditions to yield directly the transit times of carriers across the base and the base-collector space-charge region. Results from this approach are compared with Monte Carlo calculations and shown to exhibit good agreement     

A neural network approach to estimating rainfall from spacebornemicrowave data

Various techniques use microwave (MW) brightness temperature (BT) data, obtained from remote sensing orbiting platforms, to calculate rain rates. The most commonly used techniques are based on regressions or other statistical methods. An emerging tool in rainfall estimation using satellite data is artificial neural networks (NNs), NNs are mathematical models that are capable of learning complex relationships. They consist of highly interconnected, interactive data processing units. NNs are implemented in this study to estimate rainfall, and backpropagation is used as a learning scheme. The inputs for the training phase are BTs and the outputs are rainfall rates, all generated by three-dimensional (3D) simulations based on a 3D stochastic, space-time rainfall model, and a 3D radiative transfer model. Once training is complete the NNs are presented with multi-frequency and polarized (horizontal and vertical) BT data, obtained from the Special Sensor Microwave/Imager (SSM/I) instrument onboard the F10 and F11 polar-orbiting meteorological satellites. Hence, rainrates corresponding to real BT measurements are generated. The rainfall rates are also estimated using a log-linear regression model. Comparison of the two approaches, using simulated data, shows that the NN can represent more accurately the underlying relationship between BT and rainrate than the regression model, Comparison of the rates, estimated by both methods, with radar-estimated rainrates shows that NNs outperform the regression model. This study demonstrates the great potential of NNs in estimating rainfall from remotely sensed data     

Phase regression approach for estimating the parameters of a noisy multifrequency signal

A novel approach for estimating the parameters of a multifrequency signal from discrete samples corrupted by additive noise is presented. An established mathematical model indicates that noise influence on the discrete phase and amplitude spectra is equivalent to additive phase and amplitude noise, respectively. On this basis, a simple algorithm is proposed to estimate the frequency and phase of each sinusoid component by linear regression on the phase spectra of segmented signal blocks, while an amplitude estimator is directly derived from the spectrum of the window function. The circular nature of the phase spectrum is thoroughly explored. Also, an algorithmic scheme is presented. The derived variances of the estimators show that for a noisy signal this approach provides superior accuracy over the traditional approaches. Simulations and engineering application confirm the validity of the presented method.     

A rectilinear Gaussian model for estimating straight-line parameters

For characterizing straight lines in defocused images, a rectilinear Gaussian model (RGM) is proposed. Based on this model, a novel method for estimating the parameters of straight lines is presented. This method, called gray-scale least square (GLS) method, directly deals with gray-scale image data without requiring any preprocessing and hence no additional noise is introduced. Furthermore, the method is able to simultaneously estimate four parameters of straight lines by performing the algorithm only once, while two parameters can be typically estimated by traditional method. Besides this, all parameters are given in closed-form solution. In order to illustrate the effectiveness of RGM and the GLS method, the experiments are performed on a set of artificial images and natural images. The experimental results show that the GLS method outperforms the traditional method from the point of view of sensitivity to noise and accuracy of parameter estimation.     

Kalman filter with hypothesis testing: A tool for estimating uncertain parameters

In this paper we present a simple and practical algorithm for the estimation of uncertain parameters of linear systems. The uncertainty is twofold, involving random observation noise, and possible jumps in the parameter values. The jumps may occur at unknown points in time, and are of unknown magnitudes and directions. The algorithm is based on the Kalman filter, with a single-sample hypothesis test, which is used to employ a three-state decision rule (yes, no, maybe). The maybe choice invokes a fading memory Kalman filter. The overall algorithm contains the constant parameter filter, fading memory filter, and the set of tests and rules that enable it to switch back and forth between the two filters. Application examples are presented.     

A simple approach for estimating errors bars to accompanymoment-method and finite-element-method results

A simple method based on Richardson's extrapolation technique to approximately predict the error associated with numerical solutions is described. The accuracy of the proposed error-prediction method when applied to two-dimensional moment-method and finite-element solutions is demonstrated     

A new approach for estimating indoor radio propagationcharacteristics

Presents a new approach for estimating the propagation characteristics of indoor radio channels. The technique is based on the use of principal component analysis and the information theoretic criterion. It is shown, based on the simulation results, that the new technique can be used to overcome difficulties experienced by conventional methods and, as a result, is able to produce greater accuracy in its estimates of the channel parameters. The authors demonstrate the use of this technique by carrying out data analysis using measured indoor radio channel data     

An approach to estimating land mobile radio coverage

This paper reports some propagation and coverage prediction results using various models provided to the Federal Communications Commission, Spectrum Management Task Force.     

A pressure controller for estimating parameters for a nonlinear CSFmodel

Pressure feedback control of cerebrospinal fluid (CSF) infusion rate was used to estimate the parameters of a nonlinear model of the CSF system. The steady-state pressure and infusion rate were used to estimate the parameters of CSF formation and CSF absorption using the nonlinear least-squares method. The CSF compliance was then estimated using the transient portion of the pressure/infusion rate responses     

A method for estimating the parameters of the K distribution

A method that combines the maximum likelihood and the method of moments for estimating the parameters of the K distribution is proposed. The method results in the lowest variance of parameter estimates when compared with existing non-ML techniques     

A similarity learning approach to content-based image retrieval: application to digital mammography

In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.     

A service-oriented approach to facilitate WSAN application development

Due to the complex nature of developing Wireless Sensor and Actor Network (WSAN) applications it is obvious that new frameworks, tools, middleware and higher-level abstractions are needed to make the task of the developers easier. Depending on the WSAN system we want to develop, different characteristics must be taken into account but, perhaps, some of the most important are the capacity to add real-time constraints, the QoS and, of course energy saving. Our proposal USEME is a service-oriented and component-based framework which allows the easy combination of macro-programming and node-centric programming to develop real-time and efficient applications over WSANs. USEME allows the specification of real-time constraints between services, permits the use of groups to structure the network and is platform independent. Two prototypes (Imote2.Net and SunSPOT) have been implemented and several performance tests have been carried out.     

A Bayesian classification approach with application to speechrecognition

A Bayesian approach to classification of parametric information sources whose statistics are not explicitly given is studied and applied to recognition of speech signals based upon Markov modeling. A classifier based on generalized likelihood ratios, which depends only on the available training and testing data, is developed and shown to be optimal in the sense of achieving the highest asymptotic exponential rate of decay of the error probability. The proposed approach is compared to the standard classification approach used in speech recognition, in which the parameters for the sources are first estimated from the given training data, and then the maximum a posteriori decision rule is applied using the estimated statistics     

A maximum likelihood approach for estimating DS-CDMA multipathfading channels

We propose a maximum likelihood approach for near-far robust synchronization of asynchronous direct sequence code division multiple access (DS-CDMA) systems operating over multipath channels. The algorithm is suitable for use in, for instance, a slotted system where each user transmits a short data burst with an embedded training sequence. The algorithm is shown to outperform the standard sliding correlator estimator. The Cramer-Rao bound is derived and is used to indicate the best performance that can be achieved by an unbiased estimator     

A competitive minimax approach to robust estimation of random parameters

We consider the problem of estimating, in the presence of model uncertainties, a random vector x that is observed through a linear transformation H and corrupted by additive noise. We first assume that both the covariance matrix of x and the transformation H are not completely specified and develop the linear estimator that minimizes the worst-case mean-squared error (MSE) across all possible covariance matrices and transformations H in the region of uncertainty. Although the minimax approach has enjoyed widespread use in the design of robust methods, we show that its performance is often unsatisfactory. To improve the performance over the minimax MSE estimator, we develop a competitive minimax approach for the case where H is known but the covariance of x is subject to uncertainties and seek the linear estimator that minimizes the worst-case regret, namely, the worst-case difference between the MSE attainable using a linear estimator, ignorant of the signal covariance, and the optimal MSE attained using a linear estimator that knows the signal covariance. The linear minimax regret estimator is shown to be equal to a minimum MSE (MMSE) estimator corresponding to a certain choice of signal covariance that depends explicitly on the uncertainty region. We demonstrate, through examples, that the minimax regret approach can improve the performance over both the minimax MSE approach and a "plug in" approach, in which the estimator is chosen to be equal to the MMSE estimator with an estimated covariance matrix replacing the true unknown covariance. We then show that although the optimal minimax regret estimator in the case in which the signal and noise are jointly Gaussian is nonlinear, we often do not lose much by restricting attention to linear estimators.     

A robust method for estimating respiratory flow using tracheal sounds entropy

The relationship between respiratory sounds and flow is of great interest for researchers and physicians due to its diagnostic potentials. Due to difficulties and inaccuracy of most of the flow measurement techniques, several researchers have attempted to estimate flow from respiratory sounds. However, all of the proposed methods heavily depend on the availability of different rates of flow for calibrating the model, which makes their use limited by a large degree. In this paper, a robust and novel method for estimating flow using entropy of the band pass filtered tracheal sounds is proposed. The proposed method is novel in terms of being independent of the flow rate chosen for calibration; it requires only one breath for calibration and can estimate any flow rate even out of the range of calibration flow. After removing the effects of heart sounds (which distort the low-frequency components of tracheal sounds) on the calculated entropy of the tracheal sounds, the performance of the method at different frequency ranges were investigated. Also, the performance of the proposed method was tested using 6 different segment sizes for entropy calculation and the best segment sizes during inspiration and expiration were found. The method was tested on data of 10 healthy subjects at five different flow rates. The overall estimation error was found to be 8.3 +/- 2.8% and 9.6 +/- 2.8% for inspiration and expiration phases, respectively.