A Two-Stage Queuing Approach to Support Real-Time QoS Guarantee for Multimedia Services in TDMA Wireless Networks

2004 ◽  
pp. 33-40 ◽  
Author(s):  
Ren-Hao Cheng ◽  
Po-Cheng Huang ◽  
Mong-Fong Horng ◽  
Jiang-Shiung Ker ◽  
Yau-Hwang Kuo
Keyword(s):  
Wireless Networks ◽  
Real Time ◽  
Multimedia Services ◽  
Two Stage ◽  
Qos Guarantee

scholarly journals A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network

2020 ◽  
Vol 53 (2) ◽  
pp. 15374-15379
Author(s):  
Hu He ◽  
Xiaoyong Zhang ◽  
Fu Jiang ◽  
Chenglong Wang ◽  
Yingze Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection Method ◽  
Driver Fatigue ◽  
Two Stage ◽  
Fatigue Detection

scholarly journals A Real-Time, Distributed, Directional TDMA MAC Protocol for QoS-aware Communication in Multi-hop Wireless Networks

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Shivam Garg ◽  
Venu Sri Sushma Kuchipudi ◽  
Elizabeth Serena Bentley ◽  
Sunil Kumar
Keyword(s):  
Wireless Networks ◽  
Real Time ◽  
Mac Protocol ◽  
Tdma Mac

scholarly journals On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data

2020 ◽  
Vol 13 (1) ◽  
pp. 89
Author(s):  
Manuel Carranza-García ◽  
Jesús Torres-Mateo ◽  
Pedro Lara-Benítez ◽  
Jorge García-Gutiérrez
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Remote Sensing Data ◽  
Autonomous Driving ◽  
Two Stage ◽  
Detection Systems ◽  
One Stage ◽  
Time Requirements ◽  
Speed Accuracy

Object detection using remote sensing data is a key task of the perception systems of self-driving vehicles. While many generic deep learning architectures have been proposed for this problem, there is little guidance on their suitability when using them in a particular scenario such as autonomous driving. In this work, we aim to assess the performance of existing 2D detection systems on a multi-class problem (vehicles, pedestrians, and cyclists) with images obtained from the on-board camera sensors of a car. We evaluate several one-stage (RetinaNet, FCOS, and YOLOv3) and two-stage (Faster R-CNN) deep learning meta-architectures under different image resolutions and feature extractors (ResNet, ResNeXt, Res2Net, DarkNet, and MobileNet). These models are trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context. For the experimental study, we use the Waymo Open Dataset, which is the largest existing benchmark. Despite the rising popularity of one-stage detectors, our findings show that two-stage detectors still provide the most robust performance. Faster R-CNN models outperform one-stage detectors in accuracy, being also more reliable in the detection of minority classes. Faster R-CNN Res2Net-101 achieves the best speed/accuracy tradeoff but needs lower resolution images to reach real-time speed. Furthermore, the anchor-free FCOS detector is a slightly faster alternative to RetinaNet, with similar precision and lower memory usage.

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