Kim, J.; Seon, J.; Kim, S.; Lee, S.; Kim, J.; Hwang, B.; Sun, Y.; Kim, J. End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms. Appl. Sci. 2024, 14, 10832. https://doi.org/10.3390/app142310832

AMA Style

Kim J, Seon J, Kim S, Lee S, Kim J, Hwang B, Sun Y, Kim J. End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms. Applied Sciences. 2024; 14(23):10832. https://doi.org/10.3390/app142310832

Chicago/Turabian Style

Kim, Jeongho, Joonho Seon, Soohyun Kim, Seongwoo Lee, Jinwook Kim, Byungsun Hwang, Youngghyu Sun, and Jinyoung Kim. 2024. "End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms" Applied Sciences 14, no. 23: 10832. https://doi.org/10.3390/app142310832

APA Style

Kim, J., Seon, J., Kim, S., Lee, S., Kim, J., Hwang, B., Sun, Y., & Kim, J. (2024). End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms. Applied Sciences, 14(23), 10832. https://doi.org/10.3390/app142310832

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

For more information on the journal statistics, click here.

Multiple requests from the same IP address are counted as one view.

Article Menu

Article Versions Notes

Further Information

Guidelines

MDPI Initiatives

Follow MDPI