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DTSTART;TZID=America/New_York:20220817T110000
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CREATED:20221103T142252Z
LAST-MODIFIED:20221103T142252Z
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SUMMARY:Mithun Diddi's PhD Dissertation Defense
DESCRIPTION:“Multiple UAVs for Synchronous – Shared Tasks and Long-term Autonomy” \nAbstract: \nThis thesis focuses on the use of multiple unmanned aerial vehicles(UAVs) in a distributed framework from a systems perspective to synchronously perform shared tasks such as aerial beamforming and coordinated mapping and to enhance the reliability of performing periodic (mapping) tasks at remote locations for long-term autonomous(LTA) missions. We present an autonomy stack for multiple\, heterogeneous UAVs with a simulation framework. We implemented the end-to-end pipelines for perception and communication applications involving multiple UAVs. \nRepeated deployments in harsh-weather\, real-world locations are challenging and are limited by the need for human operators. These infrastructure-poor\, remote locations pose unique challenges to long-term autonomous missions. In these locations\, harvesting power onboard using solar panels may be a viable alternative for recharging batteries.\nIn the first part of the thesis\, we focus on hardware architecture for UAVs to enable reliable LTA missions with minimal human intervention. We developed a Size\, Weight\, and Power(SWaP) constrained Smart charging stack to minimize hotel loads seen during the recharging process and enable efficient charging of batteries. This leads to the design of a standalone\, solar rechargeable quadcopter.\nReal-world applications such as reconstructing a dynamic scene from multiple viewpoints and distributed aerial beamforming require multiple robots(agents) to coordinate and synchronously act to accomplish shared tasks. These tasks require spatially distant\, multiple UAVs to have time\, phase\, and frequency synchronization. We demonstrate a Synchronous UAV(S-UAV) architecture for wireless synchronization based on GPS disciplined oscillators and the associated software framework needed for temporal registration of data across multiple UAVs.\nWe have built four S-UAVs and demonstrate the ability to 3D reconstruct a dynamic scene from overlapping viewpoints. Dynamic baseline camera arrays formed using multiple S-UAVs are used to synchronously capture a dynamic environment with people moving around. A single-time instance of synchronously captured images of the scene is used to 3D reconstruct the dynamic environment while preserving static scene assumptions of Structure from Motion(SFM). \nIn the second part of the thesis\, we focus on multi UAV autonomy framework for real-world applications of UAVs in perception\, wireless communications\, and reliable LTA missions. We present ‘Simplenav\,’ a navigation stack for heterogeneous\, multiple UAVs\, and ‘OctoRosSim\,’ a computationally lightweight multi-UAV simulation framework for validating the multi-UAV planning and autonomy pipeline. We demonstrate this framework with novel applications of end-to-end autonomy pipelines developed for a coordinated swarm of UAVs. \nCommittee: \nProf. Hanumant Singh (Advisor) \nProf. Kaushik Chowdhury \nProf. Taskin Padir
URL:https://coe.northeastern.edu/event/mithun-diddis-phd-dissertation-defense/
LOCATION:432 ISEC\, 360 Huntington Ave\, Boston\, MA\, 02115\, United States
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DTSTAMP:20260511T105308
CREATED:20221103T142520Z
LAST-MODIFIED:20221103T142520Z
UID:34112-1660737600-1660741200@coe.northeastern.edu
SUMMARY:Mengshu Sun's PhD Dissertation Defense
DESCRIPTION:“Deep Learning Acceleration on Edge Devices with Algorithm/Hardware Co-Design” \nAbstract: \nAs deep learning has succeeded in a broad range of applications in recent years\, there is an increasing trend towards deploying deep neural networks (DNNs) on edge devices such as FPGAs and mobile phones. However\, there exists a significant gap between the extraordinary accuracy of state-of-the-art DNNs and efficient implementations on edge devices\, due to their limited resources for DNNs with high computation and memory intensity. With the target of simultaneously accelerating the inference and maintaining the accuracy of DNNs\, efficient implementations are investigated of deep learning on low-power and resource-constrained devices\, by presenting algorithm/hardware co-design frameworks that incorporate hardware-friendly DNN compression algorithms with hardware design optimizations.\nFirst\, the DNN compression algorithms are explored\, leveraging quantization and weight pruning techniques. As for quantization\, intra-layer mixed precision/scheme weight quantization is proposed to boost utilization of heterogeneous FPGA resources and therefore improving the FPGA throughput\, by assigning multiple precisions and/or multiple schemes at the filter level within each layer and maintaining the same ratio of filters across all the layers for each type of quantization assignment. As for weight pruning\, novel structured and fined-grained sparsity schemes are proposed and obtained with the reweighted regularization pruning algorithm\, and then incorporated into acceleration frameworks on FPGAs to make the acceleration rate of sparse models approach the pruning rate of the number of operations.\nSecond\, the hardware implementations are studied\, proposing an automatic DNN acceleration framework to generate DNN accelerators to satisfy a target frame rate (FPS). Unlike previous approaches that start from model compression and then optimizing the FPS for hardware implementations\, this automatic framework will provide an estimation of the FPS with the FPGA resource utilization analysis and performance analysis modules\, and the bit-width is reduced until the target FPS is met and the mixing ratio for quantization precisions/schemes is automatically determined to guide the quantization process and the accelerator implementation on hardware. A resource utilization model is developed to overcome the difficulty in estimating the LUT consumption\, and a novel computing engine for DNNs is designed with various optimization techniques in support of DNN compression to improve the computation parallelism and resource utilization efficiency. \nCommittee: \nProf. Xue Lin (Advisor)\nProf. Miriam Leeser\nProf. Xiaolin Xu
URL:https://coe.northeastern.edu/event/mengshu-suns-phd-dissertation-defense/
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