On-board gimbal systems for camera stabilization in helicopters are typically based on linear models. Such models, however, are inaccurate due to system nonlinearities and complexities. As an alternative approach, artificial neural networks can provide a more accurate model of the gimbal system based on their non-linear mapping and generalization capabilities. This thesis investigates the applications of artificial neural networks to model the inertial characteristics (on the azimuth axis) of the inner gimbal in a gyro-stabilized multi-gimbal system. The neural network is trained with time-domain data obtained from gyro rate sensors of an actual camera system. The network performance is evaluated and compared with measured data and a traditional linear model. Computer simulation results show the neural network model fits well with the measured data and significantly outperforms a traditional model.

Applying reinforcement learning to control systems enables the use of machine learning to develop elegant and efficient control laws. Coupled with the representational power of neural networks, reinforcement learning algorithms can learn complex policies that can be difficult to emulate using traditional control system design approaches. In this thesis, three different model-free reinforcement learning algorithms, including Monte Carlo Control, REINFORCE with baseline, and Guided Policy Search are compared in simulated, continuous action-space environments. The results show that the Guided Policy Search algorithm is able to learn a desired control policy much faster than the other algorithms. In the inverted pendulum system, it learns an effective policy up to three times faster than the other algorithms. In the cartpole system, it learns an effective policy up to nearly fifteen times faster than the other algorithms.

The United States trucking industry is immense. Employing over three million drivers and traveling to every city in the country. Semi-Trucks travel millions of miles each week and encompass roads that civilians travel on. These vehicles should be safe and allow efficient travel for all. Autonomous vehicles have been discussed in controls for many decades. Now fleets of autonomous vehicles are beginning their integration into society. The ability to create an autonomous system requires domain and system specific knowledge. Approaches to implement a fully autonomous vehicle have been developed using different techniques in control systems such as Kalman Filters, Neural Networks, Model Predictive Control, and Adaptive Control. However some of these control techniques require superb models, immense computing power, and terabytes of storage. One way to circumvent these issues is by the use of an adaptive control scheme. Adaptive control systems allow for an existing control system to self-tune its performance for unknown variables i.e. when an environment changes. In this thesis a LQR error state control system is derived and shown to maintain a magnitude of 15 cm of steady state error from the center-line of the road. In addition a proposed LQR-MRAC controller is used to test the robustness of a lane-keeping control system. The LQR-MRAC controller was able to improve its transient response peak error from the center-line of the road of the tractor and the trailer by 9.47 [cm] and 7.27 [cm]. The LQR-MRAC controller increased tractor steady state error by 0.4 [cm] and decreased trailer steady state error by 1 [cm]. The LQR-MRAC controller was able to outperform modern control techniques and can be used to improve the response of the tractor-trailer system to handle mass changes in its environment.

Robot path planning is a critical feature of autonomous systems. Rapidly-exploring Random Trees (RRT) is a path planning technique that randomly samples the robot configuration space to find a path between the start and end point. This thesis studies and compares the performance of four important RRT algorithms, namely, the original RRT, the optimal RRT (also termed RRT*), RRT*-Smart, and Informed RRT* for six different environments. The performance measures include the final path length (which is also the shortest path length found by each algorithm), time to find the first path, run time (of 1000 iterations) for each algorithm, total number of sampling nodes, and success rate (out of 100 runs). It is found that both RRT*-Smart and Informed RRT* algorithm result in shorter path lengths than the original RRT and RRT*. Typically, RRT*-Smart can find a suboptimal path in less number of iterations while the Informed RRT* is able to find the shortest path with increased number of iterations. On the other hand, the original RRT and RRT* are better suited for real-time applications as the Informed RRT* and RRT*-Smart have longer run time due to the additional steps in their processes.

The Covid-19 pandemic outbreak has caused social destruction all across the world. In this thesis, we focus on the impact of Covid-19 on online consumer behavior. Understanding whatis generating these behavioral changes among online shoppers is essential, but it is even moreimportant to investigate and assess if these behavioral changes will persist among onlineshoppers beyond the pandemic. In this study, the primary objective is to examine changes inthe online consumer behavior of the Swedish ready-made clothing industry in response toCovid-19 and determine if these changes will affect future online shopping intentions using thetheory of planned behavior. We thus pay attention to three key components: subjective norms,attitudes, and behavioral controls. We conducted a qualitative study and gathered rich insightsthrough 10 semi-structured interviews with online clothing shoppers in Sweden. The findingsdemonstrate that participants' intentions to shop online in the future are predominantly impactedby factors such as perceived behavioral control, attitude toward behavior, and media, rather thansubjective norms factors like family and friends. Furthermore, in the post-pandemic era,behavioral patterns related to online clothes purchases have changed significantly due to issuesassociated with Covid-19.

There are approximately 30 trillion cells in the human body, and nearly every cell has the same genomic sequence. Yet, due to differential gene expression, we have around 200 distinct cell types each with varying functionalities. The cell type specific states are maintained via the binding of multiple regulatory proteins to different locations along the genome in a process known as transcriptional regulation. Additionally, disruptions to the transcriptional regulation process may lead to the development of disease. Hence, uncovering the complex interplay of protein-DNA interactions along the genome is of critical importance. The advent of technologies probing the genomic sequence, as well as the development of powerful computational modeling techniques to relate DNA sequences to molecular phenotype, has enabled the understanding of many molecular processes genome wide. However, these computational methods require significant adaptation to biological systems - to accurately and fully account for the biology behind the molecular processes, as well as the biases associated with the data generating systems and processes. In this thesis, we address three main issues that arise from the use of omics data, more specifically ChIP-seq data, when identifying regulatory proteins along the genome. The first part of the thesis involves the study of the biases and noise associated with ChIP-seq experiments. Each experiment is prone to noise and bias, and as such we propose the use of a customized set of weighted controls, instead of equally weighted controls, for each ChIP-seq experiment in the peak calling process to mitigate the noise and bias. To do this, we implement a peak calling algorithm, called Weighted Analysis of ChIP-seq (WACS), which is an extension of the well-known peak caller MACS2, to incorporate the weighted controls in the peak calling process. We show that our approach assists in a better approximation of the noise distribution in controls, and fundamentally improves our understanding of ChIP-seq signals and their biases. Another aspect we explore in this thesis is the ability to uncover cell type specificity of transcription factor binding from the ChIP-seq data. A transcription factor may bind to various parts of the genome in different cell types, due to modifications in the DNA-binding preferences of the transcription factor, or other mechanisms, such as chromatin accessibility or cooperative binding, thus leading to a "DNA signature" of differential binding. We develop a deep learning approach, called SigTFB (Signatures of TF Binding) and conduct a wide scale analysis of hundreds of transcription factors to identify and quantify the varying degrees of cell type specific DNA signatures of various transcription factors across cell types. We also assess the consistency of cell type specificity for a specific transcription factor when assayed by different antibodies. We show that many transcription factors are indeed cell type specific, while others are more general with lower cell type specificity. Finally, to further explain the biology behind a transcription factor's cell type specificity, or lack that of, we conduct a wide scale motif enrichment analysis of all transcription factors in question. We show that cell type specific transcription factors are typically associated with corresponding differences in motif enrichment and gene expression. Together, these contributions deepen our knowledge of transcription factor binding, and how experimental and cell type specific variations can be uncovered.

The Marchamalo salt flats, built in the 18th century, are located near Cabo de Palos, Murcia (Spain), in a hot spot area with constant pressures of tourism and urban speculation. The salt flats are currently abandoned, but its landscape and ecological value still subsist. This Thesis aims to develop a low impact lighting Master plan for Marchamalo salt flats in which a comprehensive analysis is relevant for the design project. A holistic analysis method is employed which considers three agents: “Space”, “Humans” and “Light” to which the factor “Flora & Fauna” was added since the original method was developed for an urban context while the salt flats are located in a natural protected area. Each agent is studied in depth, separately and the interrelations between them. Applying the interpretation of facts and data developed in the analysis and by means of alternative urban lighting solutions like phosphorescence, portable lamps and smart controls; a customised urban lighting design is proposed based on the salt flats identity, preservation of darkness, multi-sensorial experience of the realm and the reconnection with the sky and nature. The lighting proposal will also be the guiding thread to raise awareness about the importance of protecting the heritage in the Region of Murcia in general, taking as a particular example the case of Marchamalo salt flat.

Yes

The economic prosperity of the late 1990s was characterized by a perceived expansive growth that increased the expectations of a company's performance. WorldCom, a telecommunications company, was a victim of these expectations that led to the evolution of a fraud designed to deceive the public until the economic outlook improved. Through understanding what led to the fraud, how the fraud grew, and what its effects were, lessons can be derived to gain a better understanding of the reasons behind a fraud and to prevent future frauds from occurring or growing as big as the WorldCom fraud did.

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