In this thesis, several computationally-efficient approximate soft demodulation schemes are developed for multiple-input multiple-output (MIMO) communication systems. These soft demodulators are designed to be deployed in the conventional iterative receiver ('turbo') architecture, and they are designed to provide good performance at substantially lower computational cost than that of the exact soft demodulator. The proposed demodulators are based on the principle of list demodulation and can be classified into two classes, according to the nature of the list-generation algorithm. One class is based on a tree-search algorithm and the other is based on insight generated from the analysis of semidefinite relaxation techniques for hard demodulation. The proposed tree-search demodulators are based on a multi-stack algorithm, developed herein, for efficiently traversing the tree structure that is inherent in the MIMO demodulation problem. The proposed scheme was inspired, in part, by the stack algorithm, which stores all the visited nodes in the tree in a single stack and chooses the next node to expand based on a 'best-first' selection scheme. The proposed algorithm partitions this global stack into a stack for each level of the tree. It examines the tree in the natural ordering of the levels and performs a best-first search in each of the stacks. By assigning appropriate priorities to the level at which the search for the next leaf node re-starts, the proposed demodulators can achieve performance-complexity trade-offs that dominate several existing soft demodulators, including those based on the stack algorithm and those based on 'sphere decoding' principles, especially in the low-complexity region. In the second part of this thesis it is shown that the randomization procedure that is inherent in the semidefinite relaxation (SDR) technique for hard demodulation can be exploited to generate the list members required for list-based soft demodulation. The direct application of this observation yields list-based soft demodulators that only require the solution of one SDP per demodulation-decoding iteration. By approximating the randomization procedure by a set of independent Bernoulli trials, this requirement can be reduced to just one semidefinite program (SDP) per channel use. An advantage of these demodulators over those based on optimal tree-search algorithms is that the computational cost of solving the SDP is a low-order polynomial in the problem size. The analysis and simulation experiments provided in the thesis show that the proposed SDR-based demodulators offer an attractive trade-off between performance and computational cost. The structure of the SDP in the proposed SDR-based demodulators depends on the signaling scheme, and the initial development focuses on the case of QPSK signaling. In the last chapter of this thesis, the extension to MIMO 16-QAM systems is developed, and some interesting observations regarding some existing SDR-based hard demodulation schemes for MIMO 16-QAM systems are derived. The simulation results reveal that the excellent performance-complexity trade-off of the proposed SDR-based schemes is preserved under the extension to 16-QAM signaling. / Thesis / Doctor of Philosophy (PhD)

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In this thesis, a computationally efficient method for detecting a whistle and capturing it using a 4 microphone array is proposed. Furthermore, methods are developed to efficiently process the data captured from all the microphones to estimate the direction of the sound source. The accuracy, the shortcoming and the constraints of the method proposed are also discussed. There is an emphasis placed on being computationally efficient so that the methods may be implemented on a low cost microcontroller and be used to provide a heading to an Unmanned Ground Vehicle. / MS / As humans, we rely on our sense of hearing to help us interact with the outside world. It helps us to listen not just to other people but also for sounds that maybe a warning for us. It can often be the first warning we get of an impending danger as we might hear a predator before we see it or we might hear a car brake and slip before we turn to look at it. However, it is not merely the ability to hear a sound that makes hearing so useful. It is the fact that we can tell which direction the sound is coming from that makes it so important. That is what allows us to know which direction to turn towards to respond to someone or from which direction the sound warning us of danger is coming. We may not be able to pinpoint the location of the source with complete accuracy but we can discern the general heading. It was this idea that inspired this research work. We wanted to be capable of estimating where a sound is coming from while being computationally efficient so that it may be implemented in real time with the help of a low cost microcontroller. This would then be used to provide a heading to an Unmanned Ground Vehicle while keeping the costs down.

<div>With increasing focus on large scale planning and allocation of resources for protection against future flood risk, it is necessary to analyze and improve the deficiencies in the conventional flood modeling approach through a better understanding of the interactions between river hydrodynamics and subsurface processes. Recent studies have shown that it is possible to improve the flood inundation modeling and mapping using physically-based integrated models that incorporate observable data through assimilation and simulate hydrologic fluxes using the fundamental laws of conservation of mass at multiple spatiotemporal scales. However, despite the significance of integrated modeling in hydrology, it has received relatively less attention within the context of flood hazard. The overall aim of this dissertation is to study the heterogeneity in complex physical processes that govern the watershed response during flooding and incorporate these effects in integrated models across large scales for improved flood risk estimation. Specifically, this dissertation addresses the following questions: (1) Can physical process incorporation using integrated models improve the characterization of antecedent conditions and increase the accuracy of the watershed response to flood events? (2) What factors need to be considered for characterizing scale-dependent physical processes in integrated models across large watersheds? (3) How can the computational efficiency and process representation be improved for modeling flood events at large scales? (4) Can the applicability of integrated models be improved for capturing the hydrodynamics of unprecedented flood events in complex urban systems?</div><div><br></div><div>To understand the combined effect of surface-subsurface hydrology and hydrodynamics on streamflow generation and subsequent inundation during floods, the first objective incorporates an integrated surface water-groundwater (SW-GW) modeling approach for simulating flood conditions. The results suggest that an integrated model provides a more realistic simulation of flood hydrodynamics for different antecedent soil conditions. Overall, the findings suggest that the current practice of simulating floods which assumes an impervious surface may not be providing realistic estimates of flood inundation, and that an integrated approach incorporating all the hydrologic and hydraulic processes in the river system must be adopted.</div><div><br></div><div>The second objective focuses on providing solutions to better characterize scale-dependent processes in integrated models by comparing two model structures across two spatial scales and analyzing the changes in flood responses. The results indicate that since the characteristic length scales of GW processes are larger than SW processes, the intrinsic scale (or resolution) of GW in integrated models should be coarser when compared to SW. The results also highlight the degradation of streamflow prediction using a single channel roughness when the stream length scales are increased. A distributed channel roughness variable along the stream length improves the modeled basin response. Further, the results highlight the ability of a dimensionless parameter 𝜂1, representing the ratio of the reach length in the study region to maximum length of the single stream draining at that point, for identifying which streams may require a distributed channel roughness.</div><div><br></div><div>The third objective presents a hybrid flood modeling approach that incorporates the advantages of both loosely-coupled (‘downward’) and integrated (‘upward’) modeling approaches by coupling empirically-based and physically-based approaches within a watershed. The computational efficiency and accuracy of the proposed hybrid modeling approach is tested across three watersheds in Indiana using multiple flood events and comparing the results with fully- integrated models. Overall, the hybrid modeling approach results in a performance comparable to a fully-integrated approach but at a much higher computational efficiency, while at the same time, providing objective-oriented flexibility to the modeler.</div><div><br></div><div>The fourth objective presents a physically-based but computationally-efficient approach for modeling unprecedented flood events at large scales in complex urban systems. The application of the proposed approach results in accurate simulation of large scale flood hydrodynamics which is shown using Hurricane Harvey as the test case. The results also suggest that the ability to control the mesh development using the proposed flexible model structure for incorporating important physical and hydraulic features is as important as integration of distributed hydrology and hydrodynamics.</div>