This work examines the design of a Kalman filter based navigation algorithm for the Canadian Scientific Submersible Facility's (CSSF) ROPOS ROV. The 5000m ROV is typically hired by scientists to deploy and recover small scientific instrumentation packages on the sea floor, and collect subsea biological and geological samples. To efficiently complete these tasks a navigation system that can provide a global positioning accuracy of +/-2.5m is required. However. the ROPOS navigation system presently relies on noisy USBL acoustic positioning measurements (+/- 15m at 2500m). To overcome the limitations of the USBL signal and increase the navigation system accuracy. it is proposed that a depth sensor, Doppler velocity log and OCTANS gyrocompass be used in conjunction with a model-based extended Kalman filter (EKF) algorithm to provide a single navigation data stream. To examine the efficacy of the proposed solution. non-linear models of the ROPOS ROV and its tether are presented. Parameters are identified for both the ROPOS and tether models, and the models are coupled. permitting realistic dynamic simulation of the ROPOS system. A virtual pilot, based on a PID automatic control scheme. is created to fly the virtual ROPOS vehicle between waypoints in the simulation. An instrument simulator is developed that is capable of producing asynchronous measurement data from virtual instruments. Using this simulation facility, realistic ROPOS maneuvers are executed. During the simulations, ROPOS' virtual instruments (depth sensor, DVL, USBL and OCTANS) produce pseudo-measurements that are typical of the real ROPOS sensor suite. These measurements are fed to the EKF navigation algorithm. This work successfully showed that the EKF filter framework can be used to blend ROPOS's asynchronous sensor data, such that a navigation accuracy of ≈2.5m RMS is produced. It is found that without the OCTANS instrument. the advanced ROV process model permits robust filter operation. even in cases of USBL and/or DVL drop-out. In the case where the OCTANS instrument is providing velocity data, the filter does not require an advanced ROV process model within the EKF in order to maintain filter accuracy during USBL and DVL dropout. Rather. accuracy is sufficiently maintained with a simple constant velocity model of the vehicle motion. However, it was also shown that the ROPOS velocity signal estimation can be greatly enhanced by the advanced ROPOS process model. It was also found that that the tether effects are paramount in the advanced ROPOS process model. When the tether disturbances are not sensed. the advanced model position-estimation performance is equivalent to a constant velocity process model.

The phenotype inference from genotype in RNA viruses maps the viral genome/protein sequences to the molecular functions in order to understand the underlying molecular mechanisms that are responsible for the function changes. The inference is currently done through a laborious experimental process which is arguably inefficient, incomplete, and unreliable. The wealth of RNA virus sequence data in the presence of different phenotypes promotes the rise of computational approaches to aid the inference. Key residue identification and genotype-phenotype mapping function learning are two approaches to identify the critical positions out of hitchhikers and elucidate the relations among them. The existing computational approaches in this area focus on prediction accuracy, yet a number of fundamental problems have not been considered: the scalability of the data, the capability to suggest informative biological experiments, and the interpretability of the inferences. A common scenario of inference done by biologists with mutagenesis experiments usually involves a small number of available sequences, which is very likely to be inadequate for the inference in most setups. Accordingly biologists desire models that are capable of inferring from such limited data, and algorithms that are capable of suggesting new experiments when more data is needed. Another important but always been neglected property of the models is the interpretability of the mapping, since most existing models behave as ’black boxes’. To address these issues, in the thesis I design a supervised combinatorial filtering algorithm that systematically and efficiently infers the correct set of key residue positions from available labeled data. For cases where more data is needed to fully converge to an answer, I introduce an active learning algorithm to help choose the most informative experiment from a set of unlabeled candidate strains or mutagenesis experiments to minimize the expected total laboratory time or financial cost. I also propose Disjunctive Normal Form (DNF) as an appropriate assumption over the hypothesis space to learn interpretable genotype-phenotype functions. The challenges of these approaches are the computational efficiency due to the combinatorial nature of our algorithms. The solution is to explore biological plausible assumptions to constrain the solution space and efficiently find the optimal solutions under the assumptions. The algorithms were validated in two ways: 1) prediction quality in a cross-validation manner, and 2) consistency with the domain experts’ conclusions. The algorithms also suggested new discoveries that have not been discussed yet. I applied these approaches to a variety of RNA virus datasets covering the majority of interesting RNA phenotypes, including drug resistance, Antigenicity shift, Antibody neutralization and so on to demonstrate the prediction power, and suggest new discoveries of Influenza drug resistance and Antigenicity. I also prove the extension of the approaches in the area of severe acute community disease.

This thesis presents a new power circuit configuration to be used in shunt active power filters. A new control algorithm based on the linear voltage control suitable for the proposed circuit is introduced. The system is analysed both in time and frequency domains. The practical implementation of the system proves its suitability for the proposed task. The switching frequency of the proposed circuit is much lower than that in other active filters. The switching losses are then considerably reduced, in addition to the fact that the switching devices can withstand larger values of currents being switched on and off at lower frequencies which is an advantage to this circuit. The component sizes (capacitors and inductors) in the proposed circuit are also much smaller than those in other filter configurations. In addition, the thesis presents a new method for categorising the active filter systems proposed in the surveyed literature. The survey includes a comparison of these techniques showing their respective merits and drawbacks. The thesis also includes an implementation of a reference current generator that is suitable for single-phase applications without the need for excessive computations. The technique involves a modified Fourier analysis, which is suitable for active filtering applications.

In this paper I will give a brief and general overview of the characteristics of spatial data, why it is useful to use such data and how to use the information included in spatial data. The first question to be answered is: how to detect spatial dependency and spatial autocorrelation in data? Such effects can for instance be found by calculating Moran's I, which is a measure for spatial autocorrelation. The Moran's I is also the basis for a test for spatial autocorrelation (Moran's test). Once we found some spatial structure we can use special models and estimation techniques. There are two famous spatial processes, the SAR- (spatial autoregressive) and the SMA- (spatial moving average process) process, which are used to model spatial effects. For estimation of spatial regression models there are mainly two different possibilities, the first one is called spatial filtering, where the spatial effect is filtered out and standard techniques are used, the second one is spatial two stage least square estimation. Finally there are some results of a spatial analysis of R&D spillovers data (for a panel dataset with 22 countries and 20 years) shown. (author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics

There is currently a wide variety of six degree-of-freedom (6-DOF) motion capture technologies available. However, these systems tend to be very expensive and thus cost prohibitive. A software system was developed to provide 6-DOF motion capture using the Nintendo Wii remote’s (wiimote) sensors, an infrared beacon, and a novel hierarchical linear-quaternion Kalman filter. The software is made freely available, and the hardware costs less than one hundred dollars. Using this motion capture software, a robotic control system was developed to teleoperate a 6-DOF robotic manipulator via the operator’s natural hand movements. The teleoperation system requires calibration of the wiimote’s infrared cameras to obtain an estimate of the wiimote’s 6-DOF pose. However, since the raw images from the wiimote’s infrared camera are not available, a novel camera-calibration method was developed to obtain the camera’s intrinsic parameters, which are used to obtain a low-accuracy estimate of the 6-DOF pose. By fusing the low-accuracy estimate of 6-DOF pose with accelerometer and gyroscope measurements, an accurate estimation of 6-DOF pose is obtained for teleoperation. Preliminary testing suggests that the motion capture system has an accuracy of less than a millimetre in position and less than one degree in attitude. Furthermore, whole-system tests demonstrate that the teleoperation system is capable of controlling the end effector of a robotic manipulator to match the pose of the wiimote. Since this system can provide 6-DOF motion capture at a fraction of the cost of traditional methods, it has wide applicability in the field of robotics and as a 6-DOF human input device to control 3D virtual computer environments.

A Cyber-Physical System integrates computations and dynamics of physical processes. It is an engineering discipline focused on technology with a strong foundation in mathematical abstractions. It shares many of these abstractions with engineering and computer science, but still requires adaptation to suit the dynamics of the physical world. In such a dynamic system, mobility management is one of the key issues against developing a new service. For example, in the study of a new mobile network, it is necessary to simulate and evaluate a protocol before deployment in the system. Mobility models characterize mobile agent movement patterns. On the other hand, they describe the conditions of the mobile services. The focus of this thesis is on mobility modeling in cyber-physical systems. A macroscopic model that captures the mobility of individuals (people and vehicles) can facilitate an unlimited number of applications. One fundamental and obvious example is traffic profiling. Mobility in most systems is a dynamic process and small non-linearities can lead to substantial errors in the model. Extensive research activities on statistical inference and filtering methods for data modeling in cyber-physical systems exist. In this thesis, several methods are employed for multimodal data fusion, localization and traffic modeling. A novel energy-aware sparse signal processing method is presented to process massive sensory data. At baseline, this research examines the application of statistical filters for mobility modeling and assessing the difficulties faced in fusing massive multi-modal sensory data. A statistical framework is developed to apply proposed methods on available measurements in cyber-physical systems. The proposed methods have employed various statistical filtering schemes (i.e., compressive sensing, particle filtering and kernel-based optimization) and applied them to multimodal data sets, acquired from intelligent transportation systems, wireless local area networks, cellular networks and air quality monitoring systems. Experimental results show the capability of these proposed methods in processing multimodal sensory data. It provides a macroscopic mobility model of mobile agents in an energy efficient way using inconsistent measurements.

An algorithm for tracking single visual targets is developed in this study. Feature detection is the necessary and appropriate image processing technique for this algorithm. The main point of this approach is to use the data supplied by the feature detection as the observation from a group of targets having similar motion dynamics. Therefore a single visual target is regarded as a group of multiple targets. Accurate data association and state estimation under clutter are desired for this application similar to other multi-target tracking applications. The group tracking approach is used with the well-known probabilistic data association technique to cope with data association and estimation problems. The applicability of this method particularly for visual tracking and for other cases is also discussed.

In this thesis, symbol timing recovery in MSK signals is investigated making use of matched filtering. A decision-directed symbol synchronizer cascaded with an MLSE receiver is proposed for fine timing. Correlation (matched filter) method is used to recover the timing epoch from the tentative decisions obtained from the Viterbi algorithm. The fractional delays are acquired using interpolation and an iterative maximum search process. In order to investigate the tracking performance of the proposed symbol synchronizer, a study is carried out on three possible optimum timing phase criteria: (i) Mazo criterion, (ii) the minimum squared ISI criterion (msISI), and (iii) the minimum BER criterion. Moreover, a discussion is given about the timing sensitivity of the MLSE receiver. The performance of the symbol synchronizer is assessed by computer simulations. It is observed that the proposed synchronizer tracks the variations of the channels almost the same as the msISI criterion. The proposed method eliminates the cycle slips very succesfully and is robust to frequency-selective multipath fading channel conditions even in moderate signal-to-noise ratios.

Due to its unique features such as controllable power factor, controllable bi-directional power flow, and rapid dynamic response, Voltage Source Converters (VSCs) have been widely used in various industrial applications such as distributed generation systems, power distribution systems, uninterruptible power supplies (UPS), AC motor drives, etc. To optimize the performance of the VSC, many control algorithms have been proposed. This thesis investigates development of the nonlinear control for the VSC in two applications: power factor control and active power filtering. A detailed description of the dynamic model of the VSC system is presented in different reference frames. A linearization-based control scheme is introduced for power factor regulation and verified by switched simulation and real-time experiment on a test stand which has been constructed at the Applied Nonlinear Control Lab (ANCL), University of Alberta. In addition, an internal model-based control scheme is introduced to perform active power filtering. This algorithm is verified by simulation. / Controls

In the Oueme River, a lowland river in Benin, Africa, artificial ponds constructed in the floodplain (whedos) are colonized during the high-water period by a presumably random sample of fishes from the river channel. As water slowly recedes from the floodplain, fishes are isolated in whedos until they are harvested near the end of the dry season. I surveyed fishes in whedos and adjacent main-channel and floodplain habitats during two low-water (2008 and 2009) and one falling-water (2010-2011) periods, and measured a suite of physicochemical variables including dissolved oxygen, temperature, specific conductivity, and percent cover of aquatic vegetation in the falling-water period to investigate if fish assemblage structure of whedos resulted from stochastic or deterministic processes. I also investigated food web structure of whedos by analyzing carbon (delta13C) and nitrogen (delta15N) stable isotope ratios of fish and primary producer tissue samples, and samples of net primary production, soluble reactive phosphorus (SRP), NH4+, NO2-, and NO3- collected during the falling-water period. Whedos were covered with dense growth of aquatic vegetation, and dissolved oxygen concentrations were lower in whedos compared to a natural floodplain depression and the main channel. Multivariate analyses revealed that habitat types were distinct with regard to fish assemblage structure and abiotic conditions. Assemblages in whedos and natural floodplain depressions were differentiated from those of the river channel, with the floodplain habitats being dominated by piscivorous fishes that tolerate aquatic hypoxia. These results indicate that fish assemblage structure of whedos was influenced by deterministic processes during the falling- and low-water periods when these water bodies were isolated. Floodplain habitats were more nutrient-rich than the river channel, and whedos were net heterotrophic. Microphytobenthos and C3 macrophytes accounted for a large fraction of fish biomass in whedos, compared with the river channel, which was mainly supported by seston. Whedo food webs had fewer trophic transfers compared to the food web of the river channel.