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Kenneth P. Camilleri
Also published under:K. P. Camilleri, Kenneth Camilleri
Affiliation
Department of Systems & Control Engineering, University of Malta, Msida, Malta
Topic
Convolutional Neural Network,Data Acquisition Protocol,Force Level,Surface Electromyography,Training Data,sEMG Signals,Active Feedback,Active Force,Actuator,Adaptive Learning,Additional Constraints,Additional Input,Amount Of Training Data,Amplitude Data,Amplitude Information,Amputation,Appearance Variations,Attention Vector,Auditory Feedback,B-mode Images,Baseline Drift,Battery Model,Biceps Brachii,Bicubic,Biopac Systems,Blur Kernel,Brain-computer Interface Applications,Brain-computer Interface System,Brain-computer Interfaces Performance,Canonical Correlation Analysis,Carotid Stenosis,Center Of The Screen,Changes In Facial Expressions,Channel Attention,Classification Performance,Cognitive Load,Compression Level,Constant Length,Contraction Type,Convolutional Layers,Convolutional Neural Networks Network,Cost Function,Daily Life Tasks,Data Sources,Dataset Size,Deep Network,Deep Neural Network,Degradation Parameters,Diagonal Matrix,Dice Similarity Coefficient,
Biography
Kenneth P. Camilleri (M'91) received the B.Elec.Eng. (Honors) degree in electrical engineering from the University of Malta, Msida, and the M.Sc. degree in signal processing and machine intelligence and the Ph.D. degree in image processing and pattern recognition from the University of Surrey, Guildford, U.K., in 1994 and 1999, respectively.
His research interests include texture analysis and segmentation, sketch and scribble image understanding and interpretation, computer vision, statistical pattern recognition and computational intelligence, and EEG signal analysis applied to the diagnosis of brain diseases and to brain–computer interfacing.
His research interests include texture analysis and segmentation, sketch and scribble image understanding and interpretation, computer vision, statistical pattern recognition and computational intelligence, and EEG signal analysis applied to the diagnosis of brain diseases and to brain–computer interfacing.