New blind source estimation methods based on constant modulus criterion

This thesis presents two novel algorithms for blind chancel equalization (BCE) and blind source separation (BSS). Beside these, a general framework for global convergent analysis is proposed. Finally, the open problem of equalising a non-irreducible system is answered by the algorithm proposed in this thesis.

Vision-based target tracking and surveillance with robust set-valued state estimation

Tracking a target from a video stream (or a sequence of image frames) involves nonlinear measurements in Cartesian coordinates. However, the target dynamics, modeled in Cartesian coordinates, result in a linear system. We present a robust linear filter based on an analytical nonlinear to linear measurement conversion algorithm. Using ideas from robust control theory, a rigorous theoretical analysis is given which guarantees that the state estimation error for the filter is bounded, i.e., a measure against filter divergence is obtained. In fact, an ellipsoidal set-valued estimate is obtained which is guaranteed to contain the true target location with an arbitrarily high probability. The algorithm is particularly suited to visual surveillance and tracking applications involving targets moving on a plane.

Uplink power control via adaptive hmm estimation

Transmit power control is an important consideration in the cellular system design since it increases system capacity, improves QoS and reduces multi-user interference. In this paper, an adaptive power control design based on the identification of the underlying pathloss of the fading channel is presented. Making power control decisions based on the measured received power allows to model the fading channel pathloss dynamics with a Hidden Markov Model. Applying the online HMM identification algorithm enables accurate estimation of the real pathloss which ensures efficient performance of the suggested power control scheme.

A robust set-valued state estimation approach to the problem of vision based SLAM for mobile robots

The problem of visual simultaneous localization and mapping (SLAM) is examined in this paper using ideas and algorithms from robust control and estimation theory. Using a stereo-vision based sensor, a nonlinear measurement model is derived which leads to nonlinear measurements of the landmark coordinates along with optical flow based measurements of the relative robot-landmark velocity. Using a novel analytical measurement transformation, the nonlinear SLAM problem is converted into the linear filter is guaranteed stable and the ALAM state estimation error is bounded within an ellipsoidal set. No similar results are available for the commonly employed extended Kalman filter which is known to exhibit divergent and inconsistency characteristics in practice.

Radiation exposure and the justification of computed tomography scanning in an Australian hospital emergency department

In an emergency department (ED), computed tomography (CT) is particularly beneficial in the investigation of high-speed trauma patients. With the advent of multidetector CT (MDCT) scanners, it is becoming faster and easier to conduct scans. In recent years, this has become evident with an increasing number of CT requests. Patients who have multiple CT scans during their hospital stay can receive radiation doses that have an increased theoretical risk of induction of cancer. It is essential that the clinical justification for each CT scan be considered on an individual basis and that due consideration is given to the radiation risk and possible diagnostic benefit. The current lack of a central State or Commonwealth data repository for medical images is a contributing factor to excessive radiation dosage to the population. The principles of justification and radiation risks are discussed in this study.

Robust ODF smoothing for accurate estimation of fiber orientation

Q-ball imaging was presented as a model free, linear and multimodal diffusion sensitive approach to reconstruct diffusion orientation distribution function (ODF) using diffusion weighted MRI data. The ODFs are widely used to estimate the fiber orientations. However, the smoothness constraint was proposed to achieve a balance between the angular resolution and noise stability for ODF constructs. Different regularization methods were proposed for this purpose. However, these methods are not robust and quite sensitive to the global regularization parameter. Although, numerical methods such as L-curve test are used to define a globally appropriate regularization parameter, it cannot serve as a universal value suitable for all regions of interest. This may result in over smoothing and potentially end up in neglecting an existing fiber population. In this paper, we propose to include an interpolation step prior to the spherical harmonic decomposition. This interpolation based approach is based on Delaunay triangulation provides a reliable, robust and accurate smoothing approach. This method is easy to implement and does not require other numerical methods to define the required parameters. Also, the fiber orientations estimated using this approach are more accurate compared to other common approaches.

Maximum likelihood estimation for rician distributed data in analytical q-ball imaging

Analytical q-ball imaging is widely used for reconstruction of orientation distribution function (ODF) using diffusion weighted MRI data. Estimating the spherical harmonic coefficients is a critical step in this method. Least squares (LS) is widely used for this purpose assuming the noise to be additive Gaussian. However, Rician noise is considered as a more appropriate model to describe noise in MR signal. Therefore, the current estimation techniques are valid only for high SNRs with Gaussian distribution approximating the Rician distribution. The aim of this study is to present an estimation approach considering the actual distribution of the data to provide reliable results particularly for the case of low SNR values. Maximum likelihood (ML) is investigated as a more effective estimation method. However, no closed form estimator is presented as the estimator becomes nonlinear for the noise assumption of the Rician distribution. Consequently, the results of LS estimator is used as an initial guess and the more refined answer is achieved using iterative numerical methods. According to the results, the ODFs reconstructed from low SNR data are in close agreement with ODFs reconstructed from high SNRs when Rician distribution is considered. Also, the error between the estimated and actual fiber orientations was compared using ML and LS estimator. In low SNRs, ML estimator achieves less error compared to the LS estimator.

Robust functional estimation and fault diagnosis for complex uncertain systems

System monitoring and fault diagnosis capabilities are the most important aspects in improving safety and reliability of automatic control systems. This research proposed new methodologies on fault diagnosis and estimation for complex uncertain systems. As a result of this research, complex industrial plants can now be more effectively controlled.

Estimation of nitrogen and phosphorus in effluent from the striped catfish farming sector in the Mekong Delta, Vietnam

In this study an attempt is made to estimate nitrogen and phosphorus discharged to the environment from the striped catfish (Pangasianodon hypophthalmus) farming sector in the Mekong Delta (8°33̕–10°55̕ N, 104°30̕–106°50̕ E), South Vietnam. The sector accounted for 687,000 t production in 2007 and 1,094,879 t in 2008, with over 95% of the produce destined for export to over 100 countries. Commercial and farm-made feeds are used in catfish farming, currently the former being more predominant. Nitrogen discharge levels were similar for commercial feeds (median 46.0 kg/t fish) and farm-made feeds (median 46.8 kg/t fish); whilst, phosphorus discharge levels for commercial feeds (median 14.4 kg/t fish) were considerably lower than for farm-made feeds (median 18.4 kg/t fish). Based on the median nutrient discharge levels for commercial feeds, striped catfish production in the Mekong Delta discharged 31,602 t N and 9,893 t P, and 50,364 t N and 15,766 t P in 2007 and 2008, respectively. However, the amount of nutrients returned directly to the Mekong River may be substantially less than this as a significant proportion of the water used for catfish farming as well as the sludge is diverted to other agricultural farming systems. Striped catfish farming in the Mekong Delta compared favourably with other cultured species, irrespective of the type of feed used, when the total amounts of N and P discharged in the production of a tonne of production was estimated.

Low complexity MMSE based channel estimation technique for LTE OFDMA systems

Long term evolution (LTE) is designed for high speed data rate, higher spectral efficiency, and lower latency as well as high-capacity voice support. LTE uses single carrierfrequency division multiple access (SC-FDMA) scheme for the uplink transmission and orthogonal frequency division multiple access (OFDMA) in downlink. The one of the most important challenges for a terminal implementation are channel estimation (CE) and equalization. In this paper, a minimum mean square error (MMSE) based channel estimator is proposed for an OFDMA systems that can avoid the ill-conditioned least square (LS) problem with lower computational complexity. This channel estimation technique uses knowledge of channel properties to estimate the unknown channel transfer function at non-pilot subcarriers.

Estimation of basis frequencies for time-varying SIMO channels : a second-order method

The complex exponential basis expansion model (CE-BEM) provides an accurate description for the time-varying (TV) channels encountered in mobile communications. Many blind channel identification and equalization approaches based on the CE-BEM require precise knowledge of the basis frequencies of TV channels. Existing methods for basis frequency estimation usually resort to the higher-order statistics of channel outputs and impose strict constraints on the source signal. In this paper, we propose a novel method to estimate the basis frequencies for blind identification and equalization of time-varying single-input multiple-output (SIMO) finite-impulse-response (FIR) channels. The proposed method exploits only the second-order statistics of channel outputs and does not require strong conditions on the source signal. As a result, it exhibits superior performance to the existing basis frequency estimation methods. The validity of our method is demonstrated by numerical simulations.

Demographic demand systems with application to equivalence scales estimation and inequality analysis : the Australian evidence

This paper proposes and applies an alternative demographic procedure for extending a demand system to allow for the effect of household size and composition changes, along with price changes, on expenditure allocation. The demographic procedure is applied to two recent demand functional forms to obtain their estimable demographic extensions. The estimation on pooled time series of Australian Household Expenditure Surveys yields sensible and robust estimates of the equivalence scale, and of its variation with relative prices. Further evidence on the usefulness of this procedure is provided by using it to evaluate the nature and magnitude of the inequality bias of relative price changes in Australia over a period from the late 1980s to the early part of the new millennium.