All-optical real-time tracking of high-speed phenomena (Conference Presentation)

2019 ◽  
Author(s):  
Thomas Godin ◽  
Pierre-Henry Hanzard ◽  
Ammar Hideur
Keyword(s):  
Real Time ◽  
High Speed ◽  
All Optical ◽  
Real Time Tracking

scholarly journals Multitarget Real-Time Tracking Algorithm for UAV IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Tao Hong ◽  
Qiye Yang ◽  
Peng Wang ◽  
Jinmeng Zhang ◽  
Wenbo Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Target Tracking ◽  
Real Time ◽  
High Speed ◽  
Rapid Development ◽  
Machine Learning Algorithms ◽  
Urban Life ◽  
Tracking Accuracy ◽  
Tracking Model ◽  
Real Time Tracking

Unmanned aerial vehicles (UAVs) have increased the convenience of urban life. Representing the recent rapid development of drone technology, UAVs have been widely used in fifth-generation (5G) cellular networks and the Internet of Things (IoT), such as drone aerial photography, express drone delivery, and drone traffic supervision. However, owing to low altitude and low speed, drones can only limitedly monitor and detect small target objects, resulting in frequent intrusion and collision. Traditional methods of monitoring the safety of drones are mostly expensive and difficult to implement. In smart city construction, a large number of smart IoT cameras connected to 5G networks are installed in the city. Captured drone images are transmitted to the cloud via a high-speed and low-latency 5G network, and machine learning algorithms are used for target detection and tracking. In this study, we propose a method for real-time tracking of drone targets by using the existing monitoring network to obtain drone images in real time and employing deep learning methods by which drones in urban environments can be guided. To achieve real-time tracking of UAV targets, we employed the tracking-by-detection mode in machine learning, with the network-modified YOLOv3 (you only look once v3) as the target detector and Deep SORT as the target tracking correlation algorithm. We established a drone tracking dataset that contains four types of drones and 2800 pictures in different environments. The tracking model we trained achieved 94.4% tracking accuracy in real-time UAV target tracking and a tracking speed of 54 FPS. These results comprehensively demonstrate that our tracking model achieves high-precision real-time UAV target tracking at a reduced cost.

scholarly journals A Low-Power High-Speed Spintronics-Based Neuromorphic Computing System Using Real-Time Tracking Method

2018 ◽  
Vol 8 (3) ◽  
pp. 627-638 ◽  
Author(s):  
Hooman Farkhani ◽  
Mohammad Tohidi ◽  
Sadaf Farkhani ◽  
Jens Kargaard Madsen ◽  
Farshad Moradi
Keyword(s):  
Low Power ◽  
Real Time ◽  
High Speed ◽  
Computing System ◽  
Neuromorphic Computing ◽  
Tracking Method ◽  
Real Time Tracking

Real-Time and High-Speed All-Optical Quantization by Slicing Supercontinuum Spectrum

2018 ◽  
Vol 55 (10) ◽  
pp. 100701 ◽  
Author(s):  
郭亚 Guo Ya ◽  
李璞 Li Pu ◽  
郭龑强 Guo Yanqiang ◽  
郭园园 Guo Yuanyuan ◽  
郭晓敏 Guo Xiaomin ◽  
...  
Keyword(s):  
Real Time ◽  
High Speed ◽  
All Optical ◽  
Supercontinuum Spectrum ◽  
Optical Quantization

scholarly journals High-Sensitivity Real-Time Tracking System for High-Speed Pipeline Inspection Gauge

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 731 ◽  
Author(s):  
Guanyu Piao ◽  
Jingbo Guo ◽  
Tiehua Hu ◽  
Yiming Deng
Keyword(s):  
Real Time ◽  
High Speed ◽  
Tracking System ◽  
High Sensitivity ◽  
Decay Rates ◽  
Least Square ◽  
Physical Parameters ◽  
Burial Depth ◽  
Pipeline Inspection ◽  
Real Time Tracking

Real-time tracking of pipeline inspection gauges (PIGs) is an important aspect of ensuring the safety of oil and gas pipeline inline inspections (ILIs). Transmitting and receiving extremely low frequency (ELF) magnetic signals is one of the preferred methods of tracking. Due to the increase in physical parameters of the pipeline including transportation speed, wall thickness and burial depth, the ELF magnetic signals received are short transient (1-second duration) and very weak (10 pT), making the existing above-ground-marker (AGM) systems difficult to operate correctly. Based on the short transient very weak characteristics of ELF signals studied with a 2-D finite-element method (FEM) simulation, a data fusion model was derived to fuse the envelope decay rates of ELF signals by a least square (LS) criterion. Then, a fast-decision-tree (FDT) method is proposed to estimate the fused envelope decay rate to output the maximized orthogonal signal power for the signal detection through a determined topology and a fast calculation process, which was demonstrated to have excellent real-time detection performance. We show that simulation and experimental results validated the effectiveness of the proposed FDT method, and describe the high-sensitivity detection and real-time implementation of a high-speed PIG tracking system, including a transmitter, a receiver, and a pair of orthogonal search coil sensors.

scholarly journals High-speed all-optical Haar wavelet transform for real-time image compression

2017 ◽  
Vol 25 (9) ◽  
pp. 9802 ◽  
Author(s):  
Milad Alemohammad ◽  
Jasper R. Stroud ◽  
Bryan T. Bosworth ◽  
Mark A. Foster
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Real Time ◽  
High Speed ◽  
Haar Wavelet ◽  
Haar Wavelet Transform ◽  
All Optical ◽  
Real Time Image ◽  
Time Image
Sign in / Sign up

Export Citation Format

Share Document