Chip design of MFCC extraction for speech recognition
Integration, the VLSI Journal
ViridiScope: design and implementation of a fine grained power monitoring system for homes
Proceedings of the 11th international conference on Ubiquitous computing
Challenges in resource monitoring for residential spaces
Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Using circuit-level power measurements in household energy management systems
Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Energy efficient building environment control strategies using real-time occupancy measurements
Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Home energy saving through a user profiling system based on wireless sensors
Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
The self-programming thermostat: optimizing setback schedules based on home occupancy patterns
Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
Demo: Snap: rapid sensornet deployment with a sensornet appstore
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
Tracking states of massive electrical appliances by lightweight metering and sequence decoding
Proceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data
MagnoTricorder: what you need to do before leaving home
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Real-time classification via sparse representation in acoustic sensor networks
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Monitoring massive appliances by a minimal number of smart meters
ACM Transactions on Embedded Computing Systems (TECS) - Special Section ESFH'12, ESTIMedia'11 and Regular Papers
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Fine-grained awareness on how and where energy is spent is being increasingly recognized as the key to conserve energy. While several solutions to monitor the energy consumption patterns for commercial and industrial users exist, energy reporting systems currently available to residential users require time-consuming and intrusive installation procedures, or are otherwise unable to provide device-level reports on energy consumption. To fill this gap, this paper discusses the design and performance evaluation of the Tiny Energy Accounting and Reporting System (TinyEARS), a fine-grained energy monitoring system that generates devicelevel power consumption reports primarily based on the acoustic signatures of household appliances. Experiments demonstrate that TinyEARS is able to report the power consumption of individual household appliances within a 10% error margin.