F
Frank P.-W. Lo
Researcher at Imperial College London
Publications - 32
Citations - 587
Frank P.-W. Lo is an academic researcher from Imperial College London. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 9, co-authored 23 publications receiving 251 citations. Previous affiliations of Frank P.-W. Lo include The Chinese University of Hong Kong.
Papers
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Security and Privacy for the Internet of Medical Things Enabled Healthcare Systems: A Survey
TL;DR: The security and privacy challenges, requirements, threats, and future research directions in the domain of IoMT are reviewed providing a general overview of the state-of-the-art approaches.
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EEG-based user identification system using 1D-convolutional long short-term memory neural networks
TL;DR: A novel approach based on 1D Convolutional Long Short-term Memory Neural Network (1D-Convolutional LSTM) for EEG-based user identification system can greatly improve the accuracy of user identification systems by utilizing the spatiotemporal features of the EEG signals with L STM, and lowering cost of the systems by reducing the number of EEG electrodes used in the systems.
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Food Volume Estimation Based on Deep Learning View Synthesis from a Single Depth Map
TL;DR: A view synthesis approach based on deep learning is proposed to reconstruct 3D point clouds of food items and estimate the volume from a single depth image and is evaluated by comparing the volume estimated by the synthesized3D point cloud with the ground truth volume of the object items.
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Image-Based Food Classification and Volume Estimation for Dietary Assessment: A Review
TL;DR: After a comprehensive exploration, it is found that integrated dietary assessment systems combining with different approaches could be the potential solution to tackling the challenges in accurate dietary intake assessment.
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Point2Volume: A Vision-Based Dietary Assessment Approach Using View Synthesis
TL;DR: Compared to previous methods, this method has addressed several major challenges in vision-based dietary assessment, such as view occlusion and scale ambiguity, and it outperforms previous approaches in accurate portion size estimation.