Axial ventilation fans are used to improve the air quality, remove contaminants, and to control the temperature and humidity in occupied areas. Ventilation fans are one of the most harmful sources of noise due to their close proximity to occupied areas and widespread use. The prolonged exposure to hazardous noise levels can lead to noise-induced hearing loss. Consequently, there is a critical need to reduce noise levels from ventilation fans. Since fan noise scales with the 4-6th power of the fan tip speed, minimizing the fan tip speed and optimizing the duct geometry are effective methods to reduce fan noise. However, there is a tradeoff between reducing fan speed, noise and aerodynamic efficiency. To this end, a new innovative comprehensive optimum design methodology considering both aerodynamic efficiency and noise was formulated and implemented using a multi-objective genetic algorithm. The methodology incorporates a control vortex design approach that results in a spanwise chord and twist distribution of the blades that maximize the volumetric flow rate contribution of the outer radii, i.e. the axial flow velocity increases from the fan hub to the tip. The resulting blade geometry generates a given volumetric flow rate at the minimum fan tip speed. The fan design is complemented by the design of the optimum inlet duct geometry to maximize volumetric flow rate and minimize BL thickness for low noise generation. Good agreement with experimental results validates the design process. The present study also incorporates multi-element airfoils to further increase the aerodynamic characteristics of the fan blades and enable lower fan speeds and noise. Good agreement between experiments and predictions indicate that traditional blade element momentum methods can be implemented in conjunction with multi-element airfoil aerodynamic characteristics with good accuracy. A direct comparison of fans designed with single and multi-element airfoils has shown that fans designed with multi-element airfoils aerodynamically outperform single element fans. / Doctor of Philosophy / Axial ventilation fans are widely used to improve the air quality, remove contaminants, and to control the temperature and humidity in occupied areas. However, high noise levels from ventilations fans are a harmful source of noise that can lead to irreversible noise-induced hearing loss. Therefore, this work addresses a critical need for quiet and efficient ventilation fans. To this end, a new innovative comprehensive optimum design methodology considering both aerodynamic efficiency and noise was formulated, implemented, and tested. The methodology optimizes the fan geometry to maximize the volumetric flow rate and minimize noise. The fan design is complemented by the design of the optimum inlet duct geometry to increase the volumetric flow rate and minimize BL thickness for low noise generation. Good agreement with experimental results validates the design process. The present study also incorporates multi-element airfoils to further increase the aerodynamic characteristics of the fan blades. A direct comparison of fans designed with single and multi-element airfoils has shown that fans designed with multi-element airfoils aerodynamically outperform single element airfoil fans.

This work investigates the use of multi-degree of freedom (MDOF) passive and active vibration absorbers for the control of structural vibration as an improvement to conventional single degree of freedom (SDOF) vibration absorbers. An analytical model is first used to compare passive two degree of freedom (2DOF) absorbers to SDOF absorbers using point impedance as the performance criterion. The results show that one 2DOF absorber can provide the same impedance at two resonance frequencies as two SDOF absorbers for equal amounts of total mass. Experimental testing on a composite cylindrical shell supports the assertion that a 2DOF absorber can attenuate two resonance frequencies. Further modeling shows that MDOF absorbers can utilize the multiple mode shapes that correspond to their multiple resonance frequencies to couple into modes of a distributed primary system to improve the attenuation of structural resonance. By choosing the coupling positions of the MDOF absorber such that its mode shape mirrors that of the primary system, the mass of the absorber can be utilized at multiple resonance frequencies. For limited ranges of targeted resonance frequencies, this technique can result in MDOF absorbers providing attenuation equivalent to SDOF absorbers while using less mass. The advantage gained with the MDOF absorbers is dependent on the primary system. This work compares the advantage gained using the MDOF absorbers for three primary systems: MDOF lumped parameter systems, a pinned-pinned plate, and a cylindrical shell. The active vibration absorber study in this work is highly motivated by the desire to reduce structural vibration in a rocket payload fairing. Since the efficiency of acoustic foam is very poor at low frequencies, the target bandwidth was 50 to 200 Hz. A 2DOF active vibration absorber was desired to exhibit broad resonance characteristics over this frequency band. An analytical model was developed to facilitate the design of the mechanical and electrical properties of the 2DOF active vibration absorber, and is supported by experimental data. Eight active vibration absorbers were then constructed and used in a multiple-input multiple-output (MIMO) feed-forward control system on a mock payload fairing under high level acoustic excitation. The results show significant levels of global attenuation within the targeted frequency band. / Master of Science

No

GPU computing can significantly improve performance by taking advantage of massive parallelism of GPUs for data parallel applications. Computation in visualization applications is suitable for parallelization on the GPU, which can improve performance and interactivity in these applications. If used effectively, multiple GPUs can lead to a significant speedup over a single GPU. However, the use of multiple GPUs requires memory management, scheduling, and load balancing to ensure that a program takes full advantage of available processors. This work presents methods for data-driven and dynamic multi-GPU load balancing using a pipelined approach and a framework for use with different applications. Data-driven load balancing can improve utilization for applications by taking into account past performance for different combinations of input parameters. The dynamic load balancing method based on buffer fullness can adjust to workload changes at runtime to gain an additional performance improvement. This work provides a framework for load balancing to account for differing characteristics of applications. Implementation of a multi-GPU data structure allows for use of these load balancing methods in the framework. The effectiveness of the framework is demonstrated with performance results from interactive visualization that shows a significant speedup due to load balancing. / Master of Science

Many medical therapies, such as thermal tumor detection and hypothermia cancer treatments, utilize heat transfer mechanisms of the body. The focus of this work is the development and experimental validation of a method to simultaneously estimate thermal conductivity, volumetric specific heat, and perfusion of in-vivo tissue. The heat transfer through the tissue was modeled using a modified Pennes' equation. Using a least-squares parameter estimation method with regularization, the thermal properties could be estimated from the temperature response to the known applied heat flux. The method was tested experimentally using a new agar-water tissue phantom designed for this purpose. A total of 40 tests were performed. The results of the experiments show that conductivity can be successfully estimated for perfused tissue phantoms. The values returned for volumetric specific heat are lower than expected, while the estimated values of perfusion are far greater than expected. It is believed that the mathematical model is incorrectly accounting between these two terms. Both terms were treated as heat sinks, so it is conceivable that it is not discriminating between them correctly. Although the method can estimate all three parameters simultaneously, but it seems that the mathematical model is not accurately describing the system. In the future, improvements to the model could be made to allow the method to function accurately. / Master of Science

No / This intention of this chapter is to critically examine uses of the World Wide Web by fans of cult movies. It begins by outlining how cult movies are categorised, and notes the problems that this engenders. Then the relationship between technologies and subcultural practices is observed. Examples are presented to illustrate the question of whether, through remediation processes, such practices tell us anything new about forms of contemporary communication and consumption.

Boston University. University Professors Program Senior theses. / PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you. / 2999-01-02

The ovarian hormone 17β-estradiol (E2) is one of the central regulators of the female reproductive system. E2 is also a pleiotropic regulator since it can exert its non-reproductive role on other organ systems. E2 is neuroprotective, it maintains body's energy homeostasis, participates in various repair mechanism and is required for neural development. However, there is a substantial evidence suggesting that there might be a molecular reprogramming of E2's action when it is supplied exogenously after E2 deprivation. Though the length of E2 deprivation and age has been linked to this phenomenon, the molecular components and how they activate this reprogramming is still elusive. Our main goal was to perform global proteomics and metabolomics study to identify the molecular components and their interaction networks that are being altered in the brain and serum after a short-term E2 treatment following ovariectomy (OVX) in Sprague Dawley rats. One of the strength of our global study is that it gave us extensive information on the brain proteome itself by identification of a wide number of proteins in different brain sections. By analyzing the differentially expressed proteins, our proteomics study revealed 49 different networks to be altered in 7 sections of the brain. Most of the perturbed networks were involved in cell metabolism, neural development, protein synthesis, cellular trafficking and degradation, and several stress response signaling pathways. We assessed the neuroenergetic status of the brain based on E2's response to various energy generating pathways, including glycolysis, TCA cycle, and oxidative phosphorylation, and several signaling pathways. All energetics pathways were shown to be downregulated in E2 treatment, which suggests that E2 exerts its neuroprotective role by restoring energy homeostasis in OVX rat model by regulating complex signaling and metabolic networks. Our second focus was to determine the metabolite response (amino acids and lipids) after E2 treatment in the brain and serum by employing targeted metabolomics study. We have found that in rat brain cortex there was significant upregulation of a wide number of amino acids suggesting alternate route of metabolism. Another alternate explanation is that E2 replacement replenished the amino acid pool in the tissue. Pathway enrichment analysis revealed upregulation of several pathways, including amino sugar metabolism, purine metabolism, and glutathione metabolism. By combining proteomics and metabolomics in two different biological matrices we were able to gather a vast array of information on how E2 replacement after E2 deprivation can confer neuroprotection. Our findings will help to create a foundation of basic science to be used for developing potentially effective hormone therapies.

Effective multi-object tracking is crucial for autonomous vehicles to navigate safely and efficiently in dynamic environments. To make autonomous vehicles more affordable one area to address is the computational limitations of the sensors, therefore, cameras are often the first choice sensor. Three challenges in implementation of multi-object tracking in autonomous vehicles are: 1) In these vehicles, sensors like cameras are not static, which can cause motion blur in the frames and make tracking inefficient. 2) Traditional methods for motion compensation, such as those used in Kalman Filter-based Multi-Object Tracking, require extensive parameter tuning to match features between consecutive frames accurately. 3) Simple intersection over union (IoU) metric is insufficient for reliable identification in such environments. This thesis proposes a novel methodology for 2D multi-object tracking in autonomous vehicles using a camera-based Tracking-by-Detection (TBD) approach, emphasizing four key innovations: (1) A real-time deblurring module to mitigate motion blur, ensuring clearer frames for accurate detection; (2) deep learning-based motion compensation module that adapts dynamically to varying motion patterns, enhancing robustness; (3) adaptive cost function for association, incorporating object appearance and temporal consistency to improve upon traditional IoU metrics; (4) The integration of the Unscented Kalman Filter to effectively address non-linearities in the tracking process, enhancing state estimation accuracy. To maintain a Simple Online and Realtime (SORT) framework, we enhance detection by fine-tuning YOLOv8 and YOLOv9 models using autonomous driving datasets like BDD100K and KITTI, which are specifically tailored for these scenarios. Additionally, we incorporate a non-linear approach using the UKF to better capture the influence of various tracking dynamics, further improving tracking performance. Our evaluations show that the proposed methodology significantly outperforms existing state-of-the-art methods while maintaining the same inference rate as the baseline SORT model. These advancements not only improve the accuracy and reliability of multi-object tracking but also reduce the computational burden associated with parameter tuning and motion compensation. Consequently, this work presents a robust and efficient tracking solution for autonomous vehicles, making it viable for real-world deployment under both computational and cost constraints. / Master of Science / Tracking multiple objects is really important for self-driving cars to move safely in busy places. Cameras are often the best choice because they are cheaper and easier to use, but using cameras comes with three main challenges: (1) When cars move, cameras can make blurry images, which makes it harder to see and track things; (2) Traditional tracking methods, like Kalman Filters, need a lot of adjustments to work well; (3) Simple methods, like checking if objects overlap (called Intersection over Union), are not always good enough in crowded, complicated places. This thesis presents a new way to track lots of things using cameras, with four big improvements: (1) A real-time deblurring system that fixes blurry pictures so the camera can see things more clearly; (2) A smart system that uses deep learning to follow movement better; (3) A better way to match objects by using not just their positions but also how they look and move over time, which is better than old IoU methods; (4) A special tool called the Unscented Kalman Filter that helps track objects more accurately when their movements aren't simple or straight. To keep everything simple, fast, and real-time, we use object detectors to help find objects, and we train them with special self-driving datasets like BDD100K and KITTI. These datasets are great for showing the kinds of situations self-driving cars deal with. The Unscented Kalman Filter helps us track objects with more complicated movements, making everything more accurate. Our study show that this new way works much better than older methods, without making the system slower. These improvements make tracking more reliable and cut down on the time needed for tuning and adjusting. Overall, this work provides a strong and simple solution for tracking things in self-driving cars, even if the computer isn't super powerful or the budget is small.

Self-reconfigurable robotic systems are variable-morphology machines capable of changing their overall structure by rearranging the modules they are composed of. Individual modules are capable of connecting and disconnecting to and from one another, which allows the robot to adapt to changing environments. Optimally reconfiguring such systems is computationally prohibitive and thus in general self-reconfiguration approaches aim at approximating optimal solutions. Nonetheless, even for approximate solutions, centralized methods scale poorly in the number of modules. Therefore, the objective of this research is the development of decentralized self-reconfiguration methods for modular robotic systems. Building on completeness results of the centralized algorithms in this work, decentralized methods are developed that guarantee stochastic convergence to a given target shape. A game-theoretic approach lays the theoretical foundation of a novel potential game-based formulation of the self-reconfiguration problem. Furthermore, two extensions to the basic game-theoretic algorithm are proposed that enable agents to modify the algorithms' parameters during runtime and improve convergence times. The flexibility in the choice of utility functions together with runtime adaptability makes the presented approach and the underlying theory suitable for a range of problems that rely on decentralized local control to guarantee global, emerging properties. The experimental evaluation of the presented algorithms relies on a newly developed multi-robotic testbed called the "Robotarium" that is equipped with custom-designed miniature robots, the "GRITSBots". The Robotarium provides hardware validation of self-reconfiguration on robots but more importantly introduces a novel paradigm for remote accessibility of multi-agent testbeds with the goal of lowering the barrier to entrance into the field of multi-robot research and education.