Professor Tariq Durrani

Research Professor

Electronic and Electrical Engineering

Publications

Adversarial erasing attention for person re-identification in camera networks under complex environments
Liu Shuang, Hao Xiaolong, Zhang Ronghua, Zhang Zhong, Durrani Tariq S
IEEE Access Vol 8, pp. 56469-56479 (2020)
https://doi.org/10.1109/ACCESS.2020.2982032
Multi-evidence and multi-modal fusion network for ground-based cloud recognition
Liu Shuang, Li Mei, Zhang Zhong, Xiao Baihua, Durrani Tariq S
Remote Sensing Vol 12 (2020)
https://doi.org/10.3390/rs12030464
Infrared and visible image fusion with ResNet and zero-phase component analysis
Li Hui, Wu Xiao jun, Durrani Tariq S
Infrared Physics and Technology Vol 102 (2019)
https://doi.org/10.1016/j.infrared.2019.103039
Knowledge based fundamental and harmonic frequency detection in polyphonic music analysis
Li Xiaoquan, Yan Yijun, Ren Jinchang, Zhao Huimin, Zhao Sophia, Soraghan John, Durrani Tariq
Communications, Signal Processing, and Systems 6th International Conference on Communications, Signal Processing, and Systems, pp. 591-599 (2018)
https://doi.org/10.1007/978-981-10-6571-2_72
Copulas for statistical signal processing (Part I) : extensions and generalization
Zeng Xuexing, Ren Jinchang, Wang Zheng, Marshall Stephen, Durrani Tariq
Signal Processing Vol 94, pp. 691-702 (2014)
https://doi.org/10.1016/j.sigpro.2013.07.009
Copulas for statistical signal processing (part II) : simulation, optimal selection and practical applications
Zeng Xuexing, Ren Jinchang, Sun Meijun, Marshall Stephen, Durrani Tariq
Signal Processing Vol 94, pp. 681-690 (2014)
https://doi.org/10.1016/j.sigpro.2013.07.006

more publications

Research interests

Signal/Image processing and applications

Projects

Ocean colour remote sensing for wetland mapping and chlorophyll concentration monitoring with spontaneous fluorescence and stimulated emission
Ren, Jinchang (Principal Investigator) Durrani, Tariq (Co-investigator) McKee, David (Co-investigator)
01-Jan-2020 - 30-Jan-2022
Big Data-Driven Distributed Microseismic Monitoring Method for Hydrofracturing Oil Exploration
Yang, Erfu (Principal Investigator) Durrani, Tariq (Co-investigator) Li, David (Co-investigator) Post, Mark (Co-investigator)
01-Jan-2017 - 31-Jan-2019
Big Data-Driven Distributed Microseismic Monitoring Method for Hydrofracturing Oil Exploration
Yang, Erfu (Principal Investigator) Post, Mark (Co-investigator) Li, David (Co-investigator) Durrani, Tariq (Co-investigator)
This joint-project aims to address the fundamental research challenge related to the development of Big Data-driven distributed microseismic monitoring method for hydrofracturing oil exporation. This research challenge is related to microseismic image information processing and utilization, i.e., how to quickly and efficiently extract and analyze the information of interest for oil detection, tracking its distribution/storage from distributed data/images acquired by a huge number of sensors deployed across the oil field, through either fixed locations or autonomous vehicles platforms, such as unmanned ground vehicle (UGV). To address this challenge faced by the O&G industry, this joint project is focusing on novel Big Data-driven solutions which can integrate distributed data filtering, intelligent analytics and machine learning techniques to offer unique benefits in terms of its ability to bring in data from multiple and distributed sensing sources and provide a full suite of descriptive, predictive and prescriptive analytics.
31-Jan-2017 - 31-Jan-2019
Feature Fusion based on Abnormal Activity Detection in Crowded Scenes (2016 China NSFC Costshare)
Durrani, Tariq (Principal Investigator)
31-Jan-2017 - 31-Jan-2019
Geophysical Signal Processing, Oil and Gas Detection, Sensor Networks
Durrani, Tariq (Principal Investigator)
17-Jan-2017 - 01-Jan-2019
Cognitive Neuroscience-Inspired Information Integration in Heterogeneous Wireless Sensor Network
Durrani, Tariq (Principal Investigator)
01-Jan-2014 - 31-Jan-2018

more projects

Address

Electronic and Electrical Engineering
Royal College Building

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