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CPU-OpenGL GPU-Based Real Time Beam Tracking by Mems Phase SLM.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
CPU-OpenGL GPU-Based Real Time Beam Tracking by Mems Phase SLM./
作者:
Tang, Chin-I.
面頁冊數:
1 online resource (93 pages)
附註:
Source: Masters Abstracts International, Volume: 84-03.
Contained By:
Masters Abstracts International84-03.
標題:
Optics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29322867click for full text (PQDT)
ISBN:
9798841726678
CPU-OpenGL GPU-Based Real Time Beam Tracking by Mems Phase SLM.
Tang, Chin-I.
CPU-OpenGL GPU-Based Real Time Beam Tracking by Mems Phase SLM.
- 1 online resource (93 pages)
Source: Masters Abstracts International, Volume: 84-03.
Thesis (M.S.)--The University of Arizona, 2022.
Includes bibliographical references
A GPU-accelerated real-time CGH computation and display framework for beam steering is reported in this dissertation. This approach utilizes CUDA for CGH computation and OpenGL API for 2D CGH rendering. The deterministic method for CGH beam steering is suitable for GPU computation because such method possesses low complexity, massive calculation, and independency of each pixel, which enables pixel-wise parallel processing with GPU. The performance of the presented approach for GPU-based CGH calculation and display on GTX 1650-Ti, results in ~10 times faster than CPU-based processing on i7-10750H 2.6GHz, which also achieves the maximum refresh rate (180 FPS) of the current generation of Texas Instrument Phase Light Modulator (PLM). In addition, we combine deep learning model (YOLOv4-tiny) for object detection and recognition, enable the system doing AI-based dynamic beam steering and trace the object of interest. Instead of single beam steering, multi-beam with variable beam ratio beam steering is also conducted on the same GPU framework, resulting in the beam steering speed more than 1k beams/second. Furthermore, the beam ratio simulation is introduced, which matches the measured beam ratio results. This simulation model allows us to predict and control the energy distribution. Thus, the AI-based dynamic multi-beam tracking with varying beam ratio is demonstrated in this dissertation.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023
Mode of access: World Wide Web
ISBN: 9798841726678Subjects--Topical Terms:
517925
Optics.
Subjects--Index Terms:
Beam steeringIndex Terms--Genre/Form:
542853
Electronic books.
CPU-OpenGL GPU-Based Real Time Beam Tracking by Mems Phase SLM.
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Source: Masters Abstracts International, Volume: 84-03.
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A GPU-accelerated real-time CGH computation and display framework for beam steering is reported in this dissertation. This approach utilizes CUDA for CGH computation and OpenGL API for 2D CGH rendering. The deterministic method for CGH beam steering is suitable for GPU computation because such method possesses low complexity, massive calculation, and independency of each pixel, which enables pixel-wise parallel processing with GPU. The performance of the presented approach for GPU-based CGH calculation and display on GTX 1650-Ti, results in ~10 times faster than CPU-based processing on i7-10750H 2.6GHz, which also achieves the maximum refresh rate (180 FPS) of the current generation of Texas Instrument Phase Light Modulator (PLM). In addition, we combine deep learning model (YOLOv4-tiny) for object detection and recognition, enable the system doing AI-based dynamic beam steering and trace the object of interest. Instead of single beam steering, multi-beam with variable beam ratio beam steering is also conducted on the same GPU framework, resulting in the beam steering speed more than 1k beams/second. Furthermore, the beam ratio simulation is introduced, which matches the measured beam ratio results. This simulation model allows us to predict and control the energy distribution. Thus, the AI-based dynamic multi-beam tracking with varying beam ratio is demonstrated in this dissertation.
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