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This thesis is missing page 155, the other copies of the thesis are also missing this page. -Digitization Centre / This thesis develops a two dimensional Viterbi Algorithm for the maximum likelihood sequence estimation over band limited baseband channels with intersymbol interference. Degradation, decision depth, 99% energy bandwidth and the channel cost are used as the performance measures for the comparisons of different channels. The four measures are extensively evaluated for channels with length up to four signalling intervals. The results of each measure are presented in contour form. Error events analysis shows that the degradation contours are governed by elliptical equations. Maximum degradation results from state path merge at a depth equal to the channel length plus one. By analysing periodic state sequences, we found that catastrophic error propagation contours are mainly governed by linear equations. Generally, channels with longer length have narrower minimum bandwidth but higher degradation. A channel cost similar to Shannon capacity equation is proposed to jointly minimize both degradation suffered and bandwidth required for signalling over a channel. According to the equation, the channel cost is influenced more by the bandwidth than by the degradation and thus the regions of low channel cost lie on the regions of narrow bandwidth. Also low channel cost regions are found to be on the regions of long decision depth and thus require higher complexity for maximum likelihood sequence estimation. In addition, it is found that minimum channel cost decreases with increasing channel length. / Thesis / Master of Engineering (ME)

Ecologists try to understand how changing habitats alter the populations of organisms living within them, and how, in turn, these changing populations alter the environment. By linking individual or cellular (physiological) processes to system level responses, mechanistic models can help describe the feedback loops between organisms and the environment. Aquatic systems have long used mechanistic models, but increasing model complexity over the last 50 years has led to difficulty in parameterization. In fact, it is often unclear how researchers are choosing parameters at all, even though small changes in parameters can change qualitative predictions. I explore the challenges in parameter estimation present in even an ideal situation. Specifically, I conduct individual experiments for all of the needed parameters to describe a simple lab-based, aquatic system; estimate those parameters using the results from these experiments supplemented with literature data; and run a large experiment designed to test how well the lab-estimated parameters predict actual zooplankton populations and nutrient changes over time. I document best practices for finding and reporting parameter choices and show whole ecosystem level consequences of a variety of decisions. To get the best predictions I find that a mix of parameter estimation methods are necessary. Trait-based approaches are another method to understand species-environment interactions. Trait-based methods aggregate species into functional traits, perhaps making qualitative predictions easier. Theory suggests that more functionally diverse systems will be more resilient. I test this prediction in a simple aquatic system but am unable to find consistent support for this hypothesis, and instead finding that results are highly dependent on what measures of ecosystem recovery are used. Overall, more species-specific information is critical to building better models for both mechanistic and trait-based approaches. I expand species-specific data by providing new information, and collating information from literature on a small, tropical Cladocera. / Thesis / Doctor of Philosophy (PhD) / Predicting what will happen to a habitat after a disturbance is critical for conservation and management. Species specific information is useful for building a mechanistic understanding of ecology. Predictions that include underlying processes (mechanisms) may be more robust to a changing environment than predictions based on correlations. Eutrophication, the addition of excess nutrients, is a common problem in freshwater habitats. Being able to predict the effects of nutrient addition is critical for ensuring the health of freshwater ecosystems. By using species-specific life history and morphological information and a simple lab system, I test different methods of predicting and understanding the consequences of eutrophication. I find that the ramifications of eutrophication are not easily predicted by species' categorizations or with a more detailed mechanistic model.

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Active sensors, such as radar, lidar, and sonar, emit signals into the environment and analyze the reflections to gather information such as distance, bearing, and, with more complex processing, shape and material. Conversely, passive sensors such as microphones and cameras, rely on signals produced by objects in the environment to collect data. This deprives the sensors of the ability to directly detect distance unless used in arrays, but affords them the benefits of being concealed and saving energy. In modern applications, we see active sensors filtering out any signals not originating from their transducers as if they were noise. However, contemporary research has shown that echolocating bats have the capability of taking advantage of both active and passive echolocation. By fusing the information a bat can gather from a conspecific's echoes with their own, it is suggested that more data may become available to the eavesdropping bat. Taking bioinspiration from these suggested abilities, we seek to explore the question of how fusing active and passive ultrasonic sensing may effect the information available to a robotic vehicle. Our first investigation was an experimental verification of the capabilities of a stereo sensor for passively tracking an ultrasonic sound source using limited a priori information about the target being tracked. Our results pos- itively supported a previous simulation study and showed that the Bayesian estimator was further able to recover from divergences due to hardware and software limitations. Break- ing from the limited assumptions of the previous work, we began a full investigation of the fusion of active and passive sensing with a numerical investigation of the effects of these sensing techniques on a robotic vehicle performing simultaneous localization and mapping (SLAM). The SLAM problem consists of robot that is placed in an unknown environment, which it proceeds to map and localize itself within. By ensonifying the environment with a stationary beacon, we compared the performance of the vehicle when using active, passive, and fused sensing strategies. Building upon previous numerical simulations, we found supporting evidence that, when information available through active sensing is limited, incorporating passive measurements improves the information available to the vehicle and may also improve the accuracy of its map and localization. Finally, we took the first step to fully realizing our initial goal by numerically investigating robotic eavesdropping on two dynamics vehicles. This work showed promising results for the continued investigation of fused sensing strategies and also highlighted the importance of formation control and landmark initialization. / Doctor of Philosophy / While the stereotype of bats being blind is a fallacy, it is true many species rely on their abilities of echolocation to navigate their surrounding environment. It has been observed that bats not only use the echoes of their own vocalizations to gather this information but also may eavesdrop on the echoes of other bats in their immediate area. This suggests that bats have the ability to effectively use two types of sensing at once, which are categorized as active and passive sensing. Active sensors are defined by their need to create signals that are sent out to the environment while passive sensors rely on signals they can collect from the environment to understand it. In this work we investigate the question: can robots that combine active and passive sensing capabilities into a single sensor gain more effective information about their environment? The first problem we investigate is an experimental proof of concept that it is possible to passively track an acoustic emitter without direct knowledge of how it is moving. Using a simple two microphone sensor we show support for previously tested numerical results that this form of tracking is possible. Moving away from this constrained system our later work uses modeling and simulation of the simultaneous localization and mapping (SLAM) problem in robotics to gain more understanding of the question above.