Affective computing
Affective interactions
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
An Affective Module for an Intelligent Tutoring System
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
To feel or not to feel: the role of affect in human-computer interaction
International Journal of Human-Computer Studies - Application of affective computing in humanComputer interaction
Affective computing: challenges
International Journal of Human-Computer Studies - Application of affective computing in humanComputer interaction
Robot expressionism through cartooning
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Survey on Parallel Programming Model
NPC '08 Proceedings of the IFIP International Conference on Network and Parallel Computing
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
3rd international workshop on affective interaction in natural environments (AFFINE)
Proceedings of the international conference on Multimedia
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
MultiGPU computing using MPI or OpenMP
ICCP '10 Proceedings of the Proceedings of the 2010 IEEE 6th International Conference on Intelligent Computer Communication and Processing
Auto-generation of Parallel Finite-Differencing Code for MPI, TBB and CUDA
IPDPSW '11 Proceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum
Computer Methods and Programs in Biomedicine
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In this paper the feasibility of adopting Graphic Processor Units towards real-time emotion aware computing is investigated for boosting the time consuming computations employed in such applications. The proposed methodology was employed in analysis of encephalographic and electrodermal data gathered when participants passively viewed emotional evocative stimuli. The GPU effectiveness when processing electroencephalographic and electrodermal recordings is demonstrated by comparing the execution time of chaos/complexity analysis through nonlinear dynamics (multi-channel correlation dimension/D2) and signal processing algorithms (computation of skin conductance level/SCL) into various popular programming environments. Apart from the beneficial role of parallel programming, the adoption of special design techniques regarding memory management may further enhance the time minimization which approximates a factor of 30 in comparison with ANSI C language (single-core sequential execution). Therefore, the use of GPU parallel capabilities offers a reliable and robust solution for real-time sensing the user's affective state.