The first of NASA's high-level strategic goals is to extend and sustain human activities across the solar system. As the United States moves into the post-Shuttle era, meeting this goal is more challenging than ever. There are several desired outcomes for this goal, including development of an integrated architecture and capabilities for safe crewed and cargo missions beyond low Earth orbit. NASA's Flexible Path for the future human exploration of space provides the guidelines to achieve this outcome. Designing space system architectures to satisfy the Flexible Path starts early in design, when a downselection process works to reduce the broad spectrum of feasible system architectures into a more refined set that contains a handful of alternatives that are to be considered and studied further in the detailed design phases. This downselection process is supported by what is referred to as architecture space exploration (ASE). ASE is a systems engineering process which generates the design knowledge necessary to enable informed decision-making. The broad spectrum of potential system architectures can be impractical to evaluate. As the system architecture becomes more complex in its structure and decomposition, its space encounters a factorial growth in the number of alternatives to be considered. This effect is known in the literature as combinatorial explosion. For the Flexible Path, the development of new space system architectures can occur over the period of a decade or more. During this time, a variety of changes can occur which lead to new requirements that necessitate the development of new technologies, or changes in budget and schedule. Developing comprehensive and quantitative design knowledge early during design helps to address these challenges. Current methods focus on a small number of system architecture alternatives. From these alternatives, a series of 'one off' -type of trade studies are performed to refine and generate more design knowledge. These small-scale studies are unable to adequately capture the broad spectrum of possible architectures and typically use qualitative knowledge. The focus of this research is to develop a systems engineering method for system-level ASE during pre-phase A design that is rapid, exhaustive, flexible, traceable, and quantitative. Review of literature found a gap in currents methods that were able to achieve this research objective. This led to the development of the Set Theory-Influenced Architecture Space Exploration (STASE) methodology. The downselection process is modeled as a decision-making process with STASE serving as a supporting systems engineering method. STASE is comprised of two main phases: system decomposition and system synthesis. During system decomposition, the problem is broken down into three system spaces. The architecture space consists of the categorical parameters and decisions that uniquely define an architecture, such as the physical and functional aspects. The design space contains the design parameters that uniquely define individual point designs for a given architecture. The objective space holds the objectives that are used in comparing alternatives. The application of set theory across the system spaces enables an alternative form of representing system alternatives. This novel application of set theory allows the STASE method to mitigate the problem of combinatorial explosion. The fundamental definitions and theorems of set theory are used to form the mathematical basis for the STASE method. A series of hypotheses were formed to develop STASE in a scientific way. These hypotheses are confirmed by experiments using a proof of concept over a subset of the Flexible Path. The STASE method results are compared against baseline results found using the traditional process of representing individual architectures as the system alternatives. The comparisons highlight many advantages of the STASE method. The greatest advantage is that STASE comprehensively explores the architecture space more rapidly than the baseline. This is because the set theory-influenced representation of alternatives has a summation growth with system complexity in the architecture space. The resultant option subsets provide additional design knowledge that enables new ways of visualizing results and comparing alternatives during early design. The option subsets can also account for changes in some requirements and constraints so that new analysis of system alternatives is not required. An example decision-making process was performed for the proof of concept. This notional example starts from the entire architecture space with the goal of minimizing the total cost and the number of launches. Several decisions are made for different architecture parameters using the developed data visualization and manipulation techniques until a complete architecture was determined. The example serves as a use-case example that walks through the implementation of the STASE method, the techniques for analyzing the results, and the steps towards making meaningful architecture decisions.

<p> Designing self-regulating machines that can see and comprehend various real world objects around it are the main purpose of the AI domain. Recently, there has been marked advancements in the field of deep learning to create state-of-the-art DNNs for various CV applications. It is challenging to deploy these DNNs into resource-constrained micro-controller units as often they are quite memory intensive. Design Space Exploration is a technique which makes CNN/DNN memory efficient and more flexible to be deployed into resource-constrained hardware. MobileNet is small DNN architecture which was designed for embedded and mobile vision, but still researchers faced many challenges in deploying this model into resource limited real-time processors.</p><p> This thesis, proposes three new DNN architectures, which are developed using the Design Space Exploration technique. The state-of-the art MobileNet baseline architecture is used as foundation to propose these DNN architectures in this study. They are enhanced versions of the baseline MobileNet architecture. DSE techniques like data augmentation, architecture tuning, and architecture modification have been done to improve the baseline architecture. First, the Thin MobileNet architecture is proposed which uses more intricate block modules as compared to the baseline MobileNet. It is a compact, efficient and flexible architecture with good model accuracy. To get a more compact models, the KilobyteNet and the Ultra-thin MobileNet DNN architecture is proposed. Interesting techniques like channel depth alteration and hyperparameter tuning are introduced along-with some of the techniques used for designing the Thin MobileNet. All the models are trained and validated from scratch on the CIFAR-10 dataset. The experimental results (training and testing) can be visualized using the live accuracy and logloss graphs provided by the Liveloss package. The Ultra-thin MobileNet model is more balanced in terms of the model accuracy and model size out of the three and hence it is deployed into the NXP i.MX RT1060 embedded hardware unit for image classification application.</p>

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Indiana University-Purdue University Indianapolis (IUPUI) / Designing self-regulating machines that can see and comprehend various real world objects around it are the main purpose of the AI domain. Recently, there has been marked advancements in the field of deep learning to create state-of-the-art DNNs for various CV applications. It is challenging to deploy these DNNs into resource-constrained micro-controller units as often they are quite memory intensive. Design Space Exploration is a technique which makes CNN/DNN memory efficient and more flexible to be deployed into resource-constrained hardware. MobileNet is small DNN architecture which was designed for embedded and mobile vision, but still researchers faced many challenges in deploying this model into resource limited real-time processors. This thesis, proposes three new DNN architectures, which are developed using the Design Space Exploration technique. The state-of-the art MobileNet baseline architecture is used as foundation to propose these DNN architectures in this study. They are enhanced versions of the baseline MobileNet architecture. DSE techniques like data augmentation, architecture tuning, and architecture modification have been done to improve the baseline architecture. First, the Thin MobileNet architecture is proposed which uses more intricate block modules as compared to the baseline MobileNet. It is a compact, efficient and flexible architecture with good model accuracy. To get a more compact models, the KilobyteNet and the Ultra-thin MobileNet DNN architecture is proposed. Interesting techniques like channel depth alteration and hyperparameter tuning are introduced along-with some of the techniques used for designing the Thin MobileNet. All the models are trained and validated from scratch on the CIFAR-10 dataset. The experimental results (training and testing) can be visualized using the live accuracy and logloss graphs provided by the Liveloss package. The Ultra-thin MobileNet model is more balanced in terms of the model accuracy and model size out of the three and hence it is deployed into the NXP i.MX RT1060 embedded hardware unit for image classification application.

The purpose of this thesis has been to prepare a group interpretation script based on the National Aeronautics and Space Administration and its major manned programs. The script is designed to inform high school students and the general public of the space program. Available literature on oral interpretation and readers theatre have been investigated with particular attention given to the value of readers theatre as a means of instruction. Questionnaires were circulated among aerospace professors throughout the country and companies involved in the space industry. In their responses, aerospace company officials indicate strong support of this thesis and indicate a pressing need for such an informative script.

Formulation space exploration is a new strategy for multiobjective optimization that facilitates both divergent searching and convergent optimization during the early stages of design. The formulation space is the union of all variable and design objective spaces identified by the designer as being valid and pragmatic problem formulations. By extending a computational search into the formulation space, the solution to an optimization problem is no longer predefined by any single problem formulation, as it is with traditional optimization methods. Instead, a designer is free to change, modify, and update design objectives, variables, and constraints and explore design alternatives without requiring a concrete understanding of the design problem a priori. To facilitate this process, a new vector/matrix-based definition for multiobjective optimization problems is introduced, which is dynamic in nature and easily modified. Additionally, a set of exploration metrics is developed to help guide designers while exploring the formulation space. Finally, several examples are presented to illustrate the use of this new, dynamic approach to multiobjective optimization.

In 2018 ESA and NASA plan to send the ExoMars rover to the Martian surface. This rover is planned to have a suite of analytical equipment that includes a Raman spectrometer. In this context, an evaluation of Raman spectroscopy as an analytical tool for interplanetary studies is investigated. The preparation techniques for appropriate inorganic and organic mixtures are interrogated. Methods are investigated to optimize the homogeneity of over 50 samples involving mineral phases; calcite, gypsum and goethite and selected organic biomolecular systems; anthracene, naphthalene and beta-carotene. From mixtures produced of these organic and inorganic materials differences between homogeneity of the samples is observed. Different mixing techniques are investigated to reduce this, however all the samples display variation on a micron scale. To resolve this issue a grid system of 9 points is implemented on solid samples and solutions are used to produce standards. The standards are devised using a range of instrument validation parameters for comparison between commercially available spectrometers and the prototype instrument. From these standards a prototype instrument is optimized for data acquisition and an evaluation procedure for instrument performance is established. The prototype Raman spectrometer is evaluated to match the specifications of the spectrometer on board ExoMars rover. A range of astrobiological relevant samples are interrogated; geological samples, biomarkers, cellular systems and bio-geological inclusions. From these samples detection of organics is observed to be only possible, with Raman spectroscopy where organics are localised in high concentrations, upon grinding and mixing geological inclusions Raman spectroscopy is unable to detect the organic components. / Appendices 3 and 4 are full text of the articles which are referenced in the text, but the published copy is not allowed to be displayed under copyright restrictions and are not included with this online thesis.

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With ever increasing contents (safety, driver assistance, infotainment, etc.) in today's automotive systems that rely on electronics and software, the supporting architecture is integrated by a complex set of heterogeneous data networks. A modern automobile contains up to 100 ECUs and several heterogeneous communication buses (such as CAN, FlexRay, etc.), exchanging thousands of signals. The automotive Original Equipment Manufacturers (OEMs) and suppliers face a number of challenges such as reliability, safety and cost to incorporate the growing functionalities in vehicles. Additionally, reliability, safety and cost are major concerns for the industry. One of the important challenges in automotive design is the efficient and reliable transmission of signals over communication networks such as CAN and CAN-FD. With the growing features in automotives, the OEMs already face the challenge of saturation of bus bandwidth hindering the reliability of communication and the inclusion of additional features. In this dissertation, we study the problem of optimization of bandwidth utilization (BU) over CAN-FD networks. Signals are transmitted over the CAN/CAN-FD bus in entities called frames. The signal-to-frame-packing has been studied in the literature and it is compared to the bin packing problem which is known to be NP-hard. By carefully optimizing signal-to-frame packing, the CAN-FD BU can be reduced. In Chapter 3, we propose a method for offset assignment to signals and show its importance in improving BU. One of our contributions for an industrial setting is a modest improvement in BU of about 2.3%. Even with this modest improvement, the architecture's lifetime could potentially be extended by several product cycles, which may translate to saving millions of dollars for the OEM. Therefore, the optimization of signal-to-frame packing in CAN-FD is the major focus of this dissertation. Another challenge addressed in this dissertation is the reliable mapping of a task model onto a given architecture, such that the end-to-end latency requirements are satisfied. This avoids costly redesign and redevelopment due to system design errors. / Ph. D. / Automobiles today are equipped with a variety of advanced features, such as adaptive cruise control, lane departure warning systems, information and entertainment systems, etc. These advanced features rely on electronics and software. A modern automobile consists of up to 100 computer systems that are interconnected by several buses (in-vehicle communication networks), exchanging thousands of signals (which are data entities such as sensor data, control commands, etc.). The addition of new functionalities means additional complexity and more demand of existing resources such as bus bandwidth. The automotive companies face a number of challenges such as reliability, safety and cost to incorporate the growing features in vehicles with the limited resources. In this dissertation, we study the problem of optimization of bandwidth utilization (BU) over a communication bus used in automotives. In Chapter 3, we show that for an automobile company even a modest improvement in BU of about 2.3% could potentially extend the bus architecture’s lifetime by several product cycles. This may translate to saving millions of dollars for the company. Therefore, the optimization of bandwidth utilization over a communication bus is the major focus of this dissertation. Another problem addressed in this dissertation is the reliable mapping of a software model onto a given architecture (for an automotive system), such that the timing requirements are satisfied. This avoids costly redesign and redevelopment due to system design errors.

In 2018 ESA and NASA plan to send the ExoMars rover to the Martian surface. This rover is planned to have a suite of analytical equipment that includes a Raman spectrometer. In this context, an evaluation of Raman spectroscopy as an analytical tool for interplanetary studies is investigated. The preparation techniques for appropriate inorganic and organic mixtures are interrogated. Methods are investigated to optimize the homogeneity of over 50 samples involving mineral phases; calcite, gypsum and goethite and selected organic biomolecular systems; anthracene, naphthalene and beta-carotene. From mixtures produced of these organic and inorganic materials differences between homogeneity of the samples is observed. Different mixing techniques are investigated to reduce this, however all the samples display variation on a micron scale. To resolve this issue a grid system of 9 points is implemented on solid samples and solutions are used to produce standards. The standards are devised using a range of instrument validation parameters for comparison between commercially available spectrometers and the prototype instrument. From these standards a prototype instrument is optimized for data acquisition and an evaluation procedure for instrument performance is established. The prototype Raman spectrometer is evaluated to match the specifications of the spectrometer on board ExoMars rover. A range of astrobiological relevant samples are interrogated; geological samples, biomarkers, cellular systems and bio-geological inclusions. From these samples detection of organics is observed to be only possible, with Raman spectroscopy where organics are localised in high concentrations, upon grinding and mixing geological inclusions Raman spectroscopy is unable to detect the organic components.