Accuracy-Constrained Efficiency Optimization and GPU Profiling of CNN Inference for Detecting Drainage Crossing Locations (open access)

Accuracy-Constrained Efficiency Optimization and GPU Profiling of CNN Inference for Detecting Drainage Crossing Locations

Article describes how the accurate and efficient determination of hydrologic connectivity has garnered significant attention from both academic and industrial sectors due to its critical implications for environment management. To address these challenges, the focus of the author's study is on detecting drainage crossings through the application of advanced convolutional neural networks.
Date: November 12, 2023
Creator: Zhang, Yicheng; Pandey, Dhroov; Wu, Di; Kundu, Turja; Li, Ruopu & Shu, Tong
System: The UNT Digital Library
Air Corridors: Concept, Design, Simulation, and Rules of Engagement (open access)

Air Corridors: Concept, Design, Simulation, and Rules of Engagement

Article presenting fundamental insights into the design of air corridors with high operational efficiency as well as zero collisions. It begins with the definitions of air cube, skylane or track, intersection, vertiport, gate, and air corridor. Then a multi-layered air corridor model is proposed. Traffic at intersections is analyzed in detail with examples of vehicles turning in different directions. The concept of capacity of an air corridor is introduced along with the nature of distribution of locations of vehicles in the air corridor and collision probability inside the corridor are discussed. Finally, results of traffic flow simulations are presented.
Date: November 12, 2021
Creator: Muna, Sabrina Islam; Mukherjee, Srijita; Namuduri, Kamesh; Compere, Marc; Akbas, Mustafa Ilhan; Molnár, Péter et al.
System: The UNT Digital Library
Pareto Optimization of CNN Models via Hardware-Aware Neural Architecture Search for Drainage Crossing Classification on Resource-Limited Devices (open access)

Pareto Optimization of CNN Models via Hardware-Aware Neural Architecture Search for Drainage Crossing Classification on Resource-Limited Devices

Article describes how embedded devices, constrained by limited memory and processors, require deep learning models to be tailored to their specifications. This research explores customized model architectures for classifying drainage crossing images.
Date: November 12, 2023
Creator: Li, Yuke; Baik, Jiwon; Rahman, Md Marufi; Anagnostopoulos, Iraklis; Li, Ruopu & Shu, Tong
System: The UNT Digital Library