Fundamentals of speech recognition
Fundamentals of speech recognition
A survey of design techniques for system-level dynamic power management
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special section on low-power electronics and design
Layered Representations for Human Activity Recognition
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Thwarting the power-hungry disk
WTEC'94 Proceedings of the USENIX Winter 1994 Technical Conference on USENIX Winter 1994 Technical Conference
S-SEER: selective perception in a multimodal office activity recognition system
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Getting to green: understanding resource consumption in the home
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
The potential for location-aware power management
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
It's not easy being green: understanding home computer power management
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Profiling energy use in households and office spaces
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
Locomotion@location: when the rubber hits the road
Proceedings of the 9th international conference on Autonomic computing
Saving energy at work: the design of a pervasive game for office spaces
Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
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Context-aware power management (CAPM) uses context (e.g., user location) likely to be available in future ubiquitous computing environments, to effectively power manage a building's energy consuming devices. The objective of CAPM is to minimise overall energy consumption while maintaining user-perceived device performance. The principal context required by CAPM is when the user is NOT USING and when the user is about to use a device. Accurately inferring this user context is challenging and there is a balance between how much energy additional context can save and how much it will cost energy wise. This paper presents results from a detailed user study that investigated the potential of such CAPM. The results show that CAPM is a hard problem. It is possible to get within 6% of the optimal policy, but policy performance is very dependent on user behaviour. Furthermore, adding more sensors to improve context inference can actually increase overall energy consumption.