TY - JOUR
AU - Razzak, Imran
AB - sensors Editorial Sensor Data Fusion Based on Deep Learning for Computer Vision Applications and Medical Applications 1 , 2 , 3 4 4 Rizwan Ali Naqvi * , Muhammad Arsalan * , Talha Qaiser , Tariq Mahmood Khan and Imran Razzak School of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK School of Computer Science and Engineering, University of New South Wales, Sydney 1466, Australia * Correspondence: rizwanali@sejong.ac.kr (R.A.N.); arsal@dongguk.edu (M.A.) Sensor fusion is the process of merging data from many sources, such as radar, lidar and camera sensors, to provide less uncertain information compared to the information collected from single source. Data fusion, on the other hand, is a process in which multiple data sources increase the measurement reliability, range, and accuracy. Different measuring principles are also used to confirm detected objects. The combined term sensor data fusion is defined as the gathering of data that individual sensors functioning independently cannot provide. It combines the advantages of many sensors and measurement techniques in an efficient manner. A wide range of emerging applications 
TI - Sensor Data Fusion Based on Deep Learning for Computer Vision Applications and Medical Applications
JF - Sensors (Basel, Switzerland)
DO - 10.3390/s22208058
DA - 2022-10-21
UR - https://www.deepdyve.com/lp/pubmed-central/sensor-data-fusion-based-on-deep-learning-for-computer-vision-exrNl0NQIg
VL - 22
IS - 20
DP - DeepDyve
ER -