Elastography is a non-invasive medical imaging modality that is used as a diagnostic tool for the early detection of several pathological changes in soft tissues. Elastography techniques provide the local strain distributions experienced by soft tissues due to compression. The resulting strain images are called “elastograms”. In elastography, the local tissue strains are usually estimated as the gradient of local tissue displacement. The local tissue displacements are estimated from the time delays between gated pre- and post-compression echo signals. The quality of the resulting elastograms is highly dependent on the accuracy of these local displacement estimates. While several time delay estimation (TDE) techniques have been proposed for elastography applications, there is a lack of systematic study that statistically compares the performance of these techniques. This information could prove to be of great importance to improve currently employed elastographic clinical methods. This study investigates the performance of selected time delay estimators for elastography applications. Time delay estimators based on Generalized Cross Correlation (GCC), Sum of Squared Differences (SSD) and Sum of Absolute Differences (SAD) are proposed and implemented. Within the class of GCC algorithms, we further consider: an FFT-based cross correlation algorithm (GCC-FFT), a hybrid time-domain and frequency domain cross correlation algorithm with prior estimates (GCC-PE) and an algorithm based on the use of fractional Fourier transform to compute the cross correlation (GCC -FRFT) . Image quality factors of the elastograms obtained using the different TDE techniques are analyzed and the results are compared using standard statistical tools. The results of this research suggests that correlation based techniques outperform SSD and SAD techniques in terms of SNRe, CNRe, dynamic range and robustness. The sensitivity of GCC-FFT and SSD were statistically similar and statistically higher than those of all other methods. Within the class of GCC methods, there is no statistically significant difference between SNRe of GCC-FFT, GCC-PE and GCC –FRFT for most of the strain values considered in this study. However, in terms of CNRe, GCC-FFT and GCC-FRFT were significantly better than other TDE algorithms. Based on these results, it is concluded that correlation-based algorithms are the most effective in obtaining high quality elastograms.

Dans cette thèse, on s'intéresse au développement de nouvelles méthodes d'auscultation GPR pour déterminer la géométrie et la structure des chaussées. Cette thèse a deux objectifs principaux. Tout d'abord, elle a pour but d'améliorer la compréhension des mécanismes de diffusion à très large bande dans un milieu stratifié composé d'interfaces rugueuses. Avec l'augmentation des fréquences d'utilisation de différents systèmes, les interfaces de chaussée ne peuvent plus être considérées comme planes. Ainsi, la rugosité des interfaces doit être prise en compte dans la modélisation de la propagation. Donc, une analyse de l'influence de cette rugosité sur l'onde rétrodiffusée a été réalisée. Elle a permis de montrer que la rugosité induit une décroissance en fréquence de l'amplitude des échos. Cette décroissance a ensuite été introduite dans le modèle du signal. Dans un second temps, plusieurs méthodes de traitement de signal ont été proposées pour estimer conjointement les paramètres de rugosité et d'épaisseur. D'abord, des méthodes multidimensionnelles ont été proposées en prenant en compte l'influence de la rugosité. Ensuite, afin de réduire la charge de calcul, des méthodes monodimensionnelles ont été proposées. Ces méthodes ont été évaluées à partir de signaux simulés. Les résultats ont montré de bonnes performances pour l'estimation des temps de retard et des paramètres de rugosité des interfaces. Enfin, les méthodes de traitement proposées dans ce manuscrit ont été testées sur des données expérimentales, qui permettent de valider les résultats théoriques et de montrer la faisabilité de la mesure de couches minces de chaussée et du paramètre de rugosité. / In this thesis, we focus on the development of new GPR methods to estimate the pavement structure. This thesis has two main objectives. First, it aims to improve the understanding of the scattering mechanisms for large-band radars in a stratified medium composed of rough interfaces. With increasing frequencies, pavement interfaces can no longer be considered as flat. The interface roughness must be taken into account in the propagation modelling. Thus, the influence of the roughness has been analysed. It has been shown that the interface roughness provides a continuous frequency decay of the magnitude of the echoes. This continuous frequency decay has then been introduced into the signal model. Secondly, several signal processing methods have been proposed to jointly estimate the roughness and thickness of pavement. Thus, multidimensional methods have been proposed by taking into account the roughness.Then, in order to reduce the computational burden, one-dimensional methods have also been proposed. From simulations, it can be seen that the proposed algorithms provide a good performance in parameter estimations (time delay, permittivity, roughness and thickness). Finally, the proposed signal processing methods are tested on experimental data. The results confirm the theoretical prediction. They show the feasibility to estimate both the thickness of thin pavements and roughness parameter.

ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / Traditional angle of arrival algorithms operate with uniform receiver arrays. Non-uniform arrays typically introduce significant elevation of computation complexity. This paper utilizes the double-integration method for the accurate estimation of the angle of arrival with non-uniform receiver arrays, while maintaining high computation efficiency. Because of the simplicity, the double-integration method is not significantly affected by the increase of the number of receivers or the non-uniform configuration. This approach allows us to perform high-speed high-accuracy estimation of the two-dimensional bearing angle without the constraints of structured receiver arrays, which is important to the realization of real-time tracking of mobile acoustic sources.

<p>The demand for reliable high rate and efficient communication is ever increasing. In this thesis we look at two different problems in such systems, and their possible solutions.</p><p>In recent years orthogonal frequency division multiplexing (OFDM) has gone from a promising data transmission technique to become a mainstream technique used in several current and future standards. The main attractive property of OFDM is that it is inherently resilient to multipath reflections because of its long symbol time. However, this comes at the cost of a relatively high sensitivity to carrier frequency offsets (CFOs).</p><p>In this thesis we present a technique for CFO estimation in OFDM systems that is based on locating the spectral minimas within so-called null or virtual subcarriers embedded in the spectrum.~The spectral minimas are found iteratively over a number of symbols and is therefore mainly useful for frequency offset tracking or in systems where an estimate is not immediately required, such as in TV or radio broadcasting systems. However, complexity wise the estimator is relatively easy to implement and it does not need any extra redundancy beside a nonmodulated subcarrier. The estimator performance is studied both in a channel with additive white Gaussian noise and in a frequency selective channel environment.</p><p>A goal for many years has been to be able to implement as much as possible of a radio system in the digital domain, the ultimate goal being so called software defined radio (SDR). One important part of an SDR receiver is the high speed analog-to-digital converter(ADC) and one path to reach this goal is to use a number of parallel, time-interleaved, ADCs. Such ADCs are, however, sensitive to sampling instant offsets, DC offset and gain offset.</p><p>This thesis also discusses iterative time-delay estimators (TDEs) utilizing adjustable fractional-delay filters. The TDEs could for example be used to estimate and calibrate the relative delay between the ADCs comprising the time interleaved ADC. TDEs using a direct correlator and an average squared difference function are compared. Furthermore, an analysis of the effects of the batch length dependence is presented.</p> / Report code: LiU-Tek-Lic-2006:33.

The demand for reliable high rate and efficient communication is ever increasing. In this thesis we look at two different problems in such systems, and their possible solutions. In recent years orthogonal frequency division multiplexing (OFDM) has gone from a promising data transmission technique to become a mainstream technique used in several current and future standards. The main attractive property of OFDM is that it is inherently resilient to multipath reflections because of its long symbol time. However, this comes at the cost of a relatively high sensitivity to carrier frequency offsets (CFOs). In this thesis we present a technique for CFO estimation in OFDM systems that is based on locating the spectral minimas within so-called null or virtual subcarriers embedded in the spectrum.~The spectral minimas are found iteratively over a number of symbols and is therefore mainly useful for frequency offset tracking or in systems where an estimate is not immediately required, such as in TV or radio broadcasting systems. However, complexity wise the estimator is relatively easy to implement and it does not need any extra redundancy beside a nonmodulated subcarrier. The estimator performance is studied both in a channel with additive white Gaussian noise and in a frequency selective channel environment. A goal for many years has been to be able to implement as much as possible of a radio system in the digital domain, the ultimate goal being so called software defined radio (SDR). One important part of an SDR receiver is the high speed analog-to-digital converter(ADC) and one path to reach this goal is to use a number of parallel, time-interleaved, ADCs. Such ADCs are, however, sensitive to sampling instant offsets, DC offset and gain offset. This thesis also discusses iterative time-delay estimators (TDEs) utilizing adjustable fractional-delay filters. The TDEs could for example be used to estimate and calibrate the relative delay between the ADCs comprising the time interleaved ADC. TDEs using a direct correlator and an average squared difference function are compared. Furthermore, an analysis of the effects of the batch length dependence is presented. / Report code: LiU-Tek-Lic-2006:33.

In this thesis, an algorithm based on frequency-space domain MUSIC method is presented for estimating the propagation delay of a wireless multipath channel.For indoor geolocation systems, the time-of-arrival (TOA) is the most popular technique for accurate positioning system. The basic idea in TOA-based techniques is to accurately estimate the propagation delay of the radio signal arriving from the direct line-of-sight (DLOS) path. However, dense multipath environments may cause unresolved paths, and yield an error in the estimation of the DLOS path. UWB (Ultra-wideband) technology provides an excellent means for wireless positioning due to its high resolution capability in the time domain. Its ability to resolving multipath components makes it possible to obtain accurate location estimates. In this thesis, we investigate the use of UWB signals in positioning and combine frequency-domain MUSIC algorithm. At the same time, the structure of time-space-time method is studied. In addition, we propose a frequency-space domain MUSIC algorithm, called FSF-MUSIC algorithm, and use the spatial smoothing technique to improve the performance of the algorithm. For a two-multipath case, analysis and simulation results of multipath resolvability and the variance of estimation errors of signal arrival time are discussed.

Fault detection and diagnosis presents a big challenge within the petrochemical industry. The annual economic impact of unexpected shutdowns is estimated to be $20 billion. Assistive technologies will help with the effective detection and classification of the faults causing these shutdowns. Clustering analysis presents a form of unsupervised learning which identifies data with similar properties. Various algorithms were used and included hard-partitioning algorithms (K-means and K-medoid) and fuzzy algorithms (Fuzzy C-means, Gustafson-Kessel and Gath-Geva). A novel approach to the clustering problem of time-series data is proposed. It exploits the time dependency of variables (time delays) within a process engineering environment. Before clustering, process lags are identified via signal cross-correlations. From this, a least-squares optimal signal time shift is calculated. Dimensional reduction techniques are used to visualise the data. Various nonlinear dimensional reduction techniques have been proposed in recent years. These techniques have been shown to outperform their linear counterparts on various artificial data sets including the Swiss roll and helix data sets but have not been widely implemented in a process engineering environment. The algorithms that were used included linear PCA and standard Sammon and fuzzy Sammon mappings. Time shifting resulted in better clustering accuracy on a synthetic data set based on than traditional clustering techniques based on quantitative criteria (including Partition Coefficient, Classification Entropy, Partition Index, Separation Index, Dunn’s Index and Alternative Dunn Index). However, the time shifted clustering results of the Tennessee Eastman process were not as good as the non-shifted data. Copyright / Dissertation (MEng)--University of Pretoria, 2009. / Chemical Engineering / unrestricted

Currently there are several methods to measure the performance of surface streets, but their capabilities in dynamically estimating vehicle delay are limited. The objective of this research is to develop a method to automate traffic delay estimation in real-time using existing field traffic data collection technologies. This research has focused on method and algorithm development that can be applied to existing technologies. Two algorithms were developed to run automatically using Microsoft Excel and Visual Basic to calculate traffic delay from data collected from existing vehicle detection. The algorithms were developed using computer modeling software to simulate different lane configurations. The lane configurations tested were through-only lanes, through lanes with a midblock driveway, and through lanes with a turning bay. Different levels of volumes were simulated for each of the lane configurations. Results were promising for each lane configuration. The through-only configuration showed excellent results with maximum errors less than 3 seconds per vehicle for each test. The through lanes with the driveways test was evaluated using added detection at the driveway locations and no detection at the driveways. Results using the driveway sensors had 93 percent of the calculated average delays with less than 5 seconds per vehicle of error. Results without the driveway sensors had 84 percent of the calculated average delays with less than 5 seconds of error. Results for the turning bay configuration had 94 percent of the calculated turning bay results with less than 5 seconds per vehicle of error. It is recommended to conduct a hardware-in-loop analysis to make certain the algorithms developed in this study perform as expected in a dynamic operation.

Nowadays, computer networks have always been complicated deployment for both the scientific and industry groups as they attempt to comprehend and analyze network performance as well as design efficient procedures for their operation. In software-defined networking (SDN), predicting latency (delay) is essential for enhancing performance, power consumption and resource utilization in meeting its significant latency requirements. In this thesis, we present a graph-based formulation of Abilene Network and other topologies and apply a Graph Neural Network (GNN)-based model, Spatial-Temporal Graph Convolutional Network (STGCN), to predict end-to-end packet delay on this formulation. The evaluation uses STGCN to compare with other machine learning methods: Multiple Linear Regression (MLR), Extreme Gradient Boosting (XGBOOST), Random Forest (RF), and Neural Network (NN). Datasets in use include Abilene, 15-node scale-free, 24-node GEANT2, and 50-node networks. Notably, our GNN-based methodology can achieve 97.0%, 95.9%, 96.1%, and 63.1% less root mean square error (RMSE) in the most complex network situation than the baseline predictor, MLR, XGBOOST and RF, respectively. All the experiments show that STGCN has good prediction performance with small and stable prediction errors. This thesis illustrates the feasibility and benefits of a GNN approach in predicting end-to-end delay in software-defined networks.

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