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The generation of computer-generated holograms (CGHs) requires a significant amount of computational power. To accelerate the process, highly parallel field-programmable gate arrays (FPGAs) are deemed to be a promising computing platform to implement non-iterative hologram generation algorithms. In this paper, we present a cost-optimized heterogeneous FPGA architecture based on a one-step phase retrieval algorithm for CGH generation. The results indicate that our hardware implementation is 2.5× faster than the equivalent software implementation on a personal computer with a high-end multi-core CPU. Trade-offs between cost and performance are demonstrated, and we show that the proposed heterogeneous architecture can be used in a compact display system that is cost and size optimized.
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http://dx.doi.org/10.1364/AO.398904 | DOI Listing |
Environ Sci Technol
September 2025
Mechanical Engineering, Colorado State University, Fort Collins, Colorado 80523, United States.
This study assesses the potential for purpose-grown bioenergy feedstocks to meet the United States Sustainable Aviation Fuel (SAF) Grand Challenge targets. A combined life cycle assessment, techno-economic analysis, geospatial modeling, and evolutionary optimization framework was applied to evaluate the county-level deployment of nine feedstocks across seven land classifications. The findings underscore critical trade-offs between land use, fuel production costs, and emissions reductions in achieving national SAF targets.
View Article and Find Full Text PDFSensors (Basel)
March 2024
Department of Machine Learning, Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data. Integrating radiological scans, histopathology images, and molecular information with clinical data is essential for developing a holistic understanding of the disease and optimizing treatment. The need for integrating data from multiple sources is further pronounced in complex diseases such as cancer for enabling precision medicine and personalized treatments.
View Article and Find Full Text PDFThe generation of computer-generated holograms (CGHs) requires a significant amount of computational power. To accelerate the process, highly parallel field-programmable gate arrays (FPGAs) are deemed to be a promising computing platform to implement non-iterative hologram generation algorithms. In this paper, we present a cost-optimized heterogeneous FPGA architecture based on a one-step phase retrieval algorithm for CGH generation.
View Article and Find Full Text PDF