The ability of traditional active sonar processing methods to detect targets is often limited by clutter and reverberation from ocean environments. Similarly, multipath arrivals from radiating sources such as ships and submarines are received at sensors in passive sonar systems. Reverberation and multipath signals introduce constructive and destructive interference patterns in received spectrograms in both active and passive sonar applications that vary with target range and frequency. The characterization and use of interference phenomena can provide insights into environmental parameters and target movement in conjunction with standard processing methods including spectrograms and array beamforming. This thesis focuses on utilizing the time-frequency interference structure of moving targets captured on sonar arrays to enhance the resolution and abilities of conventional sonar methods to detect and localize targets. Physics-based methods for interference-based beamforming and target depth separation are presented with application of these methods shown using broadband simulated array data.

This thesis develops a model of the Cal Poly Wind Turbine that is used to determine if there is an imbalance in the turbine rotor. A theoretical model is derived to estimate the expected vibrations when there is an imbalance in the rotor. Vibration and acceleration data are collected from the turbine tower during operation to confirm the model is useful and accurate for determining imbalances in the turbine. Digital signal processing techniques for analyzing the vibration data are explored and tested with simulation data. This includes frequency shifts, lock-in amplifiers, phase-locked loops, discrete Fourier transforms, and decimation filters. The processed data is fed into an algorithm that determines if there is an imbalance. The detection algorithm consists of a machine learning classification model that uses experimental data to train and increase the success rate of the imbalance detection. Various models are explored, including the K-Nearest Neighbors algorithm, logistic regression, and neural networks. These models have trade-offs between mathematical complexity, required computing power, scalability, and accuracy. With proper implementations of these detection models, the imbalance detection accuracy was measured to be about 90%.

Graph Signal Processing (GSP) has been used in the analysis of functional Magnetic Resonance Imaging(fMRI). As a holistic view of brain function and the connections between and within brain regions, by structuring data as node points within the brain and modelling the edge connections between nodes. Many studies have used GSP with Blood Oxygenation Level Dependent (BOLD) imaging of the brain and brain activation. Meanwhile, the methodology has seen little use in muscle imaging. Similar to brain BOLD, muscle BOLD (mBOLD) also aims to demonstrate muscle activation. Muscle BOLD depends on oxygenation, vascularization, fibre type, blood flow, and haemoglobin count. Nevertheless the mBOLD signal still follows muscle activation closely. Electromyography (EMG) is another modality for measuring muscle activation. Both mBOLD and EMG can be represented and analyzed with GSP. In order to better understand muscle activation during contraction the proposed method focused on using GSP to model mBOLD data both alone and jointly with EMG. Simultaneous mBOLD imaging and EMG recording of the calf muscles was performed, creating a multimodal dataset. A generalized filtering methodology was developed for the removal of the MRI gradient artifact in EMG sensors within the MR bore. The filtered data was then used to generate a GSP model of the muscle, focusing on gastrocnemius, soleus, and tibialis anterior muscles. The graph signals were constructed along two edge connection dimensions; coherence and fractility. For the standalone mBOLD graph signal models, the models’ goodness of fits were 1.3245 × 10-05 and 0.06466 for coherence and fractility respectively. The multimodal models showed values of 2.3109 × -06 and 0.0014799. These results demonstrate the promise of modelling muscle activation with GSP and its ability to incorporate multimodal data into a singular model. These results set the stage for future investigations into using GSP to represent muscle with mBOLD, EMG, and other biosignal modalities. / Thesis / Master of Applied Science (MASc) / Magnetic Resonance Imaging(MRI) and electromyography (EMG) are techniques used in the analysis of muscle, for detecting injury or deepening the understanding of muscle function. Graph Signal Processing (GSP) is a methodology used to represent data and the information flow between positions. While GSP has been used in modelling the brain, applications to muscle are scarce. This work aimed to model muscle activation using GSP methods, using both MRI and EMG data. To do so, a method for being able to simultaneously record MRI and EMG data was developed through hardware construction and the software implementation of EMG signal filtering. The collected data were then used to construct multiple GSP models based on the coherence and complexity of the signals, the goodness of fit for each of the constructed models were then compared. In conclusion, it is feasible to use GSP to model muscle activity with multimodal MRI and EMG data. This shows promise for future investigations into the applications of GSP to muscle research.

Performing non-destructive testing (NDT) on materials is a helpful tool for maintenance and quality control because the materials are not destroyed or disturbed; ultrasound imaging is one type of NDT. Ultrasound imaging of heterogeneous materials contains many echoes from the material itself. These echoes come from changes in the acoustic impedance, i.e. changes in the relation between the density and the sound speed of the material. However, these echoes will show speckle characteristics in images, making it hard to detect any defects in the imaged material. In this work, a method of suppressing this speckle noise is proposed. The proposed method is a 2D Wiener filter, which with the help of an image of the healthy material models changes in the material when a new image is taken later. The filter models the changes of the speckle noise between images of a defected- and healty material and then supresses the speckle from the image with defects. The filter works well on the artificial images used in this work but have yet to be tested on actual data. A version of a weighted moving average filter was also looked into, but this filter did not produce usable results.

<p>We examine the robust downlink beamforming design from the point of outage probability constraint. We further reason that since the estimated downlink channel correlation (DCC) matrices form a manifold in the signal space, the estimation error should be measured in terms of Riemannian distance (RD) instead of the commonly used Euclidean distance (ED). Applying this concept of measure to our design constraint, we establish approximated outage probability constraints using multidimensional ball set and multidimensional cube set. We transform the design problem into a convex optimization problem which can be solved efficiently by standard methods. Our proposed methods apply to both Gaussian distribution assumption and uniform distribution assumption. Simulation results show that the performance of our design is superior to those of other robust beamformers recently developed.</p> / Master of Applied Science (MASc)

<p>We consider the problem of power allocation for the single-cell multiple-input single- output (MISO) downlink in a time division duplex (TDD) system. In such systems, the base station (BS) acquires information about the channel state during the training component of the uplink phase. The resulting estimation errors are modeled prob- abilistically, and the receivers specify quality-of-service (QoS) constraints in terms of a target signal-to-interference-and-noise ratio that is to be achieved with a given outage probability. For a fixed beamforming structure, we seek a power allocation that minimizes the transmission power required to satisfy the users’ QoS requests.</p> <p>The proposed approach to that problem begins with the observation that for TDD systems the channel estimation error at the base station can be modeled as being additive and Gaussian. Under that model, we obtain a precise deterministic characterization of the outage probability, and mildly conservative approximations thereof. Although the resulting deterministic optimization problems are not convex, we have been able to obtain good solutions using straightforward coordinate update algorithms. In fact, these solutions provide significantly better performance than the existing approaches, which are based on convex restrictions, because the proposed approximations are less conservative. By developing some approximations of the precise deterministic characterization of the outage probability, we develop algorithms that have good performance and much lower computational cost.</p> / Master of Applied Science (MASc)

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published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

<p>In recent years, deploying multiple antennas at both transmitter and receiver has appeared as a very promising technology. By exploiting the spatial domain, multiple-input multiple-output (MIMO) systems can support extremely high data rates as long as the environments can provide sufficiently rich scattering. To design high performance MIMO wireless systems and predict system performance under various circumstances, it is of great interest to have accurate MIMO wireless channel models for different scenarios. In this thesis, we characterize and model MIMO radio propagation channels based on indoor MIMO channel measurements.</p><p>The recent development on MIMO radio channel modeling is briefly reviewed in this thesis. The models are categorized into non-physical and physical models, and discussed respectively. The non-physical models primarily rely on the statistical characteristics of MIMO channels obtained from the measured data, while the physical models describe the MIMO channel (or its distribution) via some physical parameters. We also briefly mention the MIMO channel modeling work within the IEEE 802.11n and 3GPP/3GPP2 standardization work.</p><p>For the narrowband case, a non line-of-sight (NLOS) indoor MIMO channel model is presented. The model is based on a Kronecker structure of the channel covariance matrix and the fact that the channel is complex Gaussian. It is extended to line-of-sight (LOS) scenario by estimating and modeling the dominant component separately. For the wideband case, two NLOS MIMO channel models are proposed. The first model uses the average power delay profile and the Kronecker structure of the second order moments of each channel tap to model the wideband MIMO channel, while the second model combines a simple single-input single-output (SISO) model with the same Kronecker structure of the second order moments. Monte-Carlo simulations are used to generate indoor MIMO channel realizations according to the above models. The results are compared with the measured data and good agreement has been observed.</p><p>Under the assumption of spatial wide sense stationary, a lower bound of the maximum Kronecker model errors is obtained by employing a combination of grid search and semidefinite programming to explore the feasible region. Numerical examples show that the bound is tight for moderate number of grid points. By comparing the worst case model errors with the model errors obtained from the measured channels, we find that the channel correlation matrix in these measurements can, indeed, be well approximated by the Kronecker product of the correlation matrix at the transmitter and the receiver.</p><p>To model wideband MIMO channels, it is important to investigate the angular statistics on both the tap and cluster levels. Based on 5~GHz indoor wireless channel measurements, a frequency domain space alternating generalized expectation maximization (FD-SAGE) algorithm is employed to estimate the multipath components from the measured data. We then manually identify the clusters of the multipaths and calculate the tap and cluster angular spreads (ASs) for each identified cluster. It is found that for the 100 MHz channels, the average tap AS is just a few degrees less than the cluster AS and the difference diminishes for small channel bandwidth.</p>

No description available.