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Dive into the research topics where Donald R. McGaughey is active.

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Featured researches published by Donald R. McGaughey.


Annals of Biomedical Engineering | 2003

Using the Fast Orthogonal Search with First Term Reselection to Find Subharmonic Terms in Spectral Analysis

Donald R. McGaughey; Michael J. Korenberg; Kathryn M. Adeney; Susan D. Collins; G. J. M. Aitken

AbstractThe fast orthogonal search (FOS) algorithm has been shown to accurately model various types of time series by implicitly creating a specialized orthogonal basis set to fit the desired time series. When the data contain periodic components, FOS can find frequencies with a resolution greater than the discrete Fourier transform (DFT) algorithm. Frequencies with less than one period in the record length, called subharmonic frequencies, and frequencies between the bins of a DFT, can be resolved. This paper considers the resolution of subharmonic frequencies using the FOS algorithm. A new criterion for determining the number of non-noise terms in the model is introduced. This new criterion does not assume the first model term fitted is a dc component as did the previous stopping criterion. An iterative FOS algorithm called FOS first-term reselection (FOS-FTR), is introduced. FOS-FTR reduces the mean-square error of the sinusoidal model and selects the subharmonic frequencies more accurately than does the unmodified FOS algorithm.


IEEE Transactions on Energy Conversion | 2006

Speed sensorless estimation of AC induction motors using the fast orthogonal search algorithm

Donald R. McGaughey; Mohammed Tarbouchi; Ken Nutt; Aziz Chikhani

This paper presents a method of estimating the speed of an induction motor using a measurement of the stator current. Speed-induced current harmonics are identified in the stator current using the fast orthogonal search (FOS) algorithm. The frequencies of these estimated harmonics are in turn used to estimate the speed of the motor given the number of rotor slots in the motor. Several optimizations of the FOS algorithm are presented to allow for real-time performance on an embedded digital signal processor. Experimental results of speed estimates on a 1/4 horsepower motor are presented to verify this approach.


Physica A-statistical Mechanics and Its Applications | 2002

Generating two-dimensional fractional Brownian motion using the fractional Gaussian process (FGp) algorithm

Donald R. McGaughey; George J. M. Aitken

Fractional Brownian motion (FBM) is a random fractal that has been used to model many one-, two- and multi-dimensional natural phenomena. The increments process of FBM has a Gaussian distribution and a stationary correlation function. The fractional Gaussian process (FGp) algorithm is an exact algorithm to simulate Gaussian processes that have stationary correlation functions. The approximate second partial derivative of two-dimensional FBM, called 2D fractional Gaussian noise, is found to be a stationary isotropic Gaussian process. In this paper, the expected correlation function for 2D fractional Gaussian noise is derived. The 2D FGp algorithm is used to simulate the approximate second partial derivative of 2D FBM (FBM2) which is then numerically integrated to generate 2D fractional Brownian motion (FBM2). Ensemble averages of surfaces simulated by the FGp2 algorithm show that the correlation function and power spectral density have the desired properties of 2D fractional Brownian motion.


Journal of Digital Imaging | 2013

Feature Selection in Computer-Aided Breast Cancer Diagnosis via Dynamic Contrast-Enhanced Magnetic Resonance Images

Megan Rakoczy; Donald R. McGaughey; Michael J. Korenberg; Jacob Levman; Anne L. Martel

The accuracy of computer-aided diagnosis (CAD) for early detection and classification of breast cancer in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is dependent upon the features used by the CAD classifier. Here, we show that fast orthogonal search (FOS), which provides a more efficient iterative manner of computing stepwise regression feature selection, can select features with predictive value from a set of kinetic and texture candidate features computed from dynamic contrast-enhanced magnetic resonance images. FOS can in minutes search candidate feature sets of millions of terms, which may include cross-products of features up to second-, third- or fourth-order. This method is tested on a set of 83 DCE-MRI images, of which 20 are for cancerous and 63 for benign cases, using a leave-one-out trial. The features selected by FOS were used in a FOS predictor and nearest-neighbour predictor and had an area under the receiver operating curve (AUC) of 0.889 and 0.791 respectively. The FOS predictor AUC is significantly improved over the signal enhancement ratio predictor with an AUC of 0.706 (p = 0.0035 for the difference in the AUCs). Moreover, using FOS-selected features in a support vector machine increased the AUC over that resulting when the features were manually selected.


Sensors | 2012

Accuracy Enhancement of Inertial Sensors Utilizing High Resolution Spectral Analysis

Aboelmagd Noureldin; Justin Armstrong; Ahmed El-Shafie; Tashfeen B. Karamat; Donald R. McGaughey; Michael J. Korenberg; Aini Hussain

In both military and civilian applications, the inertial navigation system (INS) and the global positioning system (GPS) are two complementary technologies that can be integrated to provide reliable positioning and navigation information for land vehicles. The accuracy enhancement of INS sensors and the integration of INS with GPS are the subjects of widespread research. Wavelet de-noising of INS sensors has had limited success in removing the long-term (low-frequency) inertial sensor errors. The primary objective of this research is to develop a novel inertial sensor accuracy enhancement technique that can remove both short-term and long-term error components from inertial sensor measurements prior to INS mechanization and INS/GPS integration. A high resolution spectral analysis technique called the fast orthogonal search (FOS) algorithm is used to accurately model the low frequency range of the spectrum, which includes the vehicle motion dynamics and inertial sensor errors. FOS models the spectral components with the most energy first and uses an adaptive threshold to stop adding frequency terms when fitting a term does not reduce the mean squared error more than fitting white noise. The proposed method was developed, tested and validated through road test experiments involving both low-end tactical grade and low cost MEMS-based inertial systems. The results demonstrate that in most cases the position accuracy during GPS outages using FOS de-noised data is superior to the position accuracy using wavelet de-noising.


Optics Letters | 2005

Real data results with wavelength-diverse blind deconvolution.

Harry R. Ingleby; Donald R. McGaughey

Multiframe blind deconvolution is extended to incorporate simultaneous image acquisition at multiple wavelengths (wavelength diversity). The assumption of common path-length errors across the diversity channels allows for a parallel deconvolution procedure that exploits this coupling. No assumptions about variations in the objects intensity distribution at different wavelengths are required. The method is described and initial results for real images collected with a bench-scale imaging system are presented.


IEEE Journal of Oceanic Engineering | 2009

Improved Active Sonar Performance Using Costas Waveforms

Sean Pecknold; Wayne M. Renaud; Donald R. McGaughey; James A. Theriault; R.F. Marsden

Costas waveforms are a class of waveforms having the form of frequency-hopped pulse trains. When used as a transmit waveform in an active sonar system they may provide superior performance to conventional waveforms such as continuous-wave (CW) and frequency-modulated (FM) pulses, as each Costas waveform provides both range and speed information for a target echo. Matched-filtering identifies individual Costas waveforms from among a set of Costas waveforms that are received. This characteristic allows for more frequent ensonification of a water mass for targets at moderate ranges, giving a higher rate of return echoes for improved target tracking. Alternatively, it offers the potential for overlapping bandwidths and waveform types to be used in a multisonar environment. In this work, the Costas wideband ambiguity, cross- and autocorrelation functions are derived. The range and speed resolutions of Costas waveforms are compared to CW and FM pulse resolutions both via a broadband pulse propagation model and using data obtained during the Defence Research And Development Canada-Atlantic (DRDC Atlantic) Towed Integrated Active-Passive Sonar (TIAPS) deep sea trials in September 2003. Costas waveforms are shown to provide simultaneous range and speed resolution in deep- and shallow-water environments, and Costas waveforms are shown to be individually identifiable at the receiver. The performance of Costas waveforms in reverberation is also briefly examined, and found to be similar to that of FM pulses.


photonics north | 2004

Experimental results of parallel multiframe blind deconvolution using wavelength diversity

Harry R. Ingleby; Donald R. McGaughey

We have developed a method to estimate both the original objects and the blurring function from a sequence of noisy blurred images, simultaneously collected at different wavelengths (wavelength diversity). The assumption of common path-length errors across the diversity channels allows for a parallel deconvolution procedure that exploits this coupling. In contrast with previous work, no a priori assumptions about the objects intensity distribution are required. The method is described, and preliminary results for both synthetic computer-generated images and real images collected with a bench-scale imaging system are presented, demonstrating the promise of the algorithm.


europe oceans | 2005

Time-series modeling using the waveform transmission through a channel program

Sean Pecknold; James A. Theriault; Donald R. McGaughey; Jeff Collins

DRDC Atlantic has developed a coherent transmission loss model that simulates the propagation of acoustic pulses through an ocean environment. Using a set of input eigenrays and an input waveform, the WATTCH (waveform transmission through a channel) model can generate a set of output time series. Each time series represents the expected signal corresponding to a given range and depth. Assuming the required eigenrays can be generated, WATTCH can simulate the effects of complex environments. This is illustrated by examples comparing measured data to modeled data in two environments. In adddition, an example is provided using WATTCH to model data for testing a target detection algorithm.


Optical Science and Technology, the SPIE 49th Annual Meeting | 2004

Parallel multiframe blind deconvolution using wavelength diversity

Harry R. Ingleby; Donald R. McGaughey

The resolution of images captured through ground-based telescopes is generally limited by blurring effects due to atmospheric turbulence. We have developed a method to estimate both the original objects and the blurring function from a sequence of noisy blurred images, simultaneously collected at different wavelengths (wavelength diversity). The assumption of common path-length errors across the diversity channels allows for a parallel deconvolution procedure that exploits this coupling. In contrast with previous work, no a priori assumptions about the object’s intensity distribution are required. The method is described, and preliminary results with real images collected with a bench-scale imaging system are presented, demonstrating the promise of the algorithm.

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Mohammed Tarbouchi

Royal Military College of Canada

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Harry R. Ingleby

Royal Military College of Canada

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Sean Pecknold

Defence Research and Development Canada

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James A. Theriault

Defence Research and Development Canada

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Sean Pecknold

Defence Research and Development Canada

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Aboelmagd Noureldin

Royal Military College of Canada

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R.F. Marsden

Royal Military College of Canada

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