Finding safe trajectories for multiagent autonomous systems can be difficult, especially as multiple robots and obstacles are added to the system. Control barrier functions (CBFs) have been effective in addressing this problem. Although the use of CBFs for guaranteeing safe operation is well established, there is no standard software implementation to simplify the integration of these techniques into robotic systems. We present a CBF Toolbox to fill this void. Although the CBF Toolbox can be used to ensure safety based on local control decisions, it may not be sufficient to guide a robots to their goals in certain environments. In these cases, path planning algorithms are required. We present one such algorithm, which is the multiagent extension of the CBF guided rapidly-exploring random trees (CBF-RRT) to demonstrate how the CBF Toolbox can be applied. This work addresses the theory behind the CBF Toolbox, as well as presenting examples of how it is applied to multiagent systems. Examples are shown for its use in both simulation and hardware experiments. Details are provided on CBF guided rapidly-exploring random trees (CBF-RRT), and its application to multiagent systems with multiagent CBF-RRT (MA-CBF-RRT) that streamlines safe path planning for teams of robots.

Age of information (AoI) captures the freshness of information and has been used broadly for scheduling data transmission in the Internet of Things (IoT). We consider a general scenario where a meaningful piece of information consists of multiple packets and the information would not be considered complete until all related packets have been correctly received. This general scenario, seemingly a trivial extension of exiting work where information update is in terms of single packet, is actually challenging in both scheduling algorithm design and theoretical analysis, because we need to track the history of received packets before a complete piece of information can be updated. We first analyse the necessary condition for optimal scheduling based on which we present an optimal scheduling method. The optimal solution, however, has high time complexity. To address the problem, we investigate the problem in the framework of restless multi-armed bandit (RMAB) and propose an index-based scheduling policy by applying Whittle index. We also propose a new transmission strategy based on erasure codes to improve the performance of scheduling policies in lossy networks. Performance evaluation results demonstrate that our solution outperforms other baseline policies such as greedy policy and naive Whittle index policy in both lossless and lossy networks. / Graduate

The present study was designed to investigate the efficiency of a visual and tactile presentation of a design as compared to just a visual presentation to determine if the two-sense method helped the subjects in drawing the designs accurately in less trials.

My work has always been about subjects I am very passionate about and I use them in a way to relate to others by creating a message. For my thesis, I delve deeper into my own life experience to create a piece more personal to me yet relatable to all. I am using creative storytelling and symbolism to create a narrative using not only the lens of a camera but also 3-D animation. “Dreaming Reality” is a statement of female empowerment and independence. Through this story, I hope to bring to light that young women can overcome their insecurities and become independent, driven, successful, and confident. Through the use of the dream process to overview life experiences, all audience members, not only women, can enjoy and connect with the story, as well as feel empowered to create their own life story through their creative lens.

A Multi-tenant architecture is a cost optimization for Software as a Service, where customers, also referred to as tenants are consolidated into the same server. The main idea of this architecture is to consolidate as many tenants as possible into the same server to reduce the total cost for the service provider. However, the contention of shared resources can lead to reduced performance for the tenants. One shared resource between the tenants is the data-layer.This thesis aims to analyze and to do a performance evaluation of different data-layer designs in a Multi-tenant architecture. The thesis compares three data-layer designs implemented in a RDBMS.An experiment was performed to evaluate the transactions per secondfor tenants when both the amount of data and the number of tenants increase. The evaluations were computed for the Create, Read, Update and Delete operations, also known as the CRUD-operations.Three data-layer designs were tested in the experiment; Shared table design, where tenants share the same database system as well as the tables, Separated table design, where tenants share the same database system but store and query data to their own private tables and Oracle Multi-tenant, where each tenant gets its own database.The experiment indicates that the Oracle Multi-tenant has the highest performance while the Shared table design suffers when both the number of tenants and the amount of data increase. / Multi-tenant arkitektur är en kostnadsoptimering för mjukvara som tillhandahålls över ett nätverk. Användarna, tenants är då betjänade av samma serverdator. Uppdraget för serverdatorn är att betjäna så många tenants som möjligt för att reducera kostnader för mjukvaruägaren. Men att dela resurser mellan tenants kan leda till försämrad prestanda. En delad resurs mellan tenants är dataskiktet.Målet med denna uppsats är att göra en analys och ett prestandatest av olika uppsättningar av dataskikt i en Multi-tenant arkitektur. Uppsatsen fokuserar endast på olika uppsättningar i ett RDBMS.Ett experiment utfördes där antalet tenants och data ökade för varje uppsättning. Experimentet evaluerade prestanda av Create, Read, Update och Delete operationer, även känt som CRUD-operationer. Tre uppsättningar testades; Shared table design, där tenants delar databas och tabeller, Separated table design, där tenants delar databas, men lagrar data i sina egna tabeller och Oracle Multi-tenant, där varje tenant får en egen databas.Experiment visar att Oracle Multi-tenant har högst prestanda medan Shared table design har lägst när antalet tenants och mängd data ökar.

Many multiple target tracking algorithms operate in the local frame of the sensor and have difficulty with track reallocation when targets move in and out of the sensor field of view. This poses a problem when an unmanned aerial vehicle (UAV) is tracking multiple ground targets on a road network larger than its field of view. We propose a Rao-Blackwellized Particle Filter (RBPF) to maintain individual target tracks and to perform probabilistic data association when the targets are constrained to a road network. This is particularly useful when a target leaves then re-enters the UAV’s field of view. The RBPF is structured as a particle filter of particle filters. The top level filter handles data association and each of its particles maintains a bank of particle filters to handle target tracking. The tracking particle filters incorporate both positive and negative information when a measurement is received. We implement two path planning controllers, exhaustive receding horizon control (ERHC) and a neural net trained with deep reinforcement learning (Deep-RL), and compare their ability to improve the certainty for multiple target location estimates. The controllers prioritize paths that reduce each target’s entropy. While the ERHC achieved optimal stead-state estimates the DeepRL controller identified more efficient sweeping search patterns when there is limited information regarding target locations. The neural net achieves O(1) computational complexity during decision making but must first be trained on a given map. In addition, we provide a theorem that calculates the lower-bound for the average-entropy of the RBPF. Particle Filter entropy is used as a unit of measurement as it gives a way of accurately comparing the precision of complex multi-modal estimates. This gives a reliable way of establishing the resources needed to accomplish mission objectives as well as providing a reliable method of determining the effectiveness of different multi-agent path planners. Finally we outline results both in simulation and hardware. In simulation we obtained the results for our different path planners over 2000 Monte Carlo runs and show how the different path planners compare and measure up to the lower-bound of average-entropy. The results from a hardware test provide evidence that the ideas presented in this thesis hold true in an end-to-end solution.

Many multiple target tracking algorithms operate in the local frame of the sensor and have difficulty with track reallocation when targets move in and out of the sensor field of view. This poses a problem when an unmanned aerial vehicle (UAV) is tracking multiple ground targets on a road network larger than its field of view. We propose a Rao-Blackwellized Particle Filter (RBPF) to maintain individual target tracks and to perform probabilistic data association when the targets are constrained to a road network. This is particularly useful when a target leaves then re-enters the UAV's field of view. The RBPF is structured as a particle filter of particle filters. The top level filter handles data association and each of its particles maintains a bank of particle filters to handle target tracking. The tracking particle filters incorporate both positive and negative information when a measurement is received. We implement two path planning controllers, exhaustive receding horizon control (ERHC) and a neural net trained with deep reinforcement learning (Deep-RL), and compare their ability to improve the certainty for multiple target location estimates. The controllers prioritize paths that reduce each target's entropy. While the ERHC achieved optimal stead-state estimates the Deep-RL controller identified more efficient sweeping search patterns when there is limited information regarding target locations. The neural net achieves O(1) computational complexity during decision making but must first be trained on a given map. In addition, we provide a theorem that calculates the lower-bound for the average-entropy of the RBPF. Particle Filter entropy is used as a unit of measurement as it gives a way of accurately comparing the precision of complex multi-modal estimates. This gives a reliable way of establishing the resources needed to accomplish mission objectives as well as providing a reliable method of determining the effectiveness of different multi-agent path planners. Finally we outline results both in simulation and hardware. In simulation we obtained the results for our different path planners over 2000 Monte Carlo runs and show how the different path planners compare and measure up to the lower-bound of average-entropy. The results from a hardware test provide evidence that the ideas presented in this thesis hold true in an end-to-end solution.

A place that mixes different kind of uses of space. Two main theatres inside and a lot of smaller rooms for different venues. A methaphor the tide have strong impact of shaping the building

This thesis aims to, from the perspective of architecture and urban place making, examine the potential of a church as a catalytic nodal public building and its corresponding potential to serve and uplift its community through both secular and religious functions. The church itself is nothing other than ‘the gathered congregation’ in a particular place at a particular time. (Moltman 1999:201) It is following this statement that the validity of merely embracing the typology of a building designed for given liturgy is questioned. This thesis hypothesises that all spaces that foster community meeting and ritual have the potential to become ‘church’, additionally that: in a Christian based spatial confi guration, spaces that facilitate this ‘church’ to occur are also sacred spaces. / Dissertation (MArch(Prof))--University of Pretoria, 2010. / Architecture / unrestricted

archives@tulane.edu / 1 / Yuntong Bai