@article {14328,
	title = {Disaggregated water sensing from a single, pressure-based sensor: An extended analysis of HydroSense using staged experiments},
	journal = {Pervasive and Mobile Computing},
	volume = {8},
	year = {2012},
	month = {2012/02//},
	pages = {82 - 102},
	abstract = {We present an extended analysis of our previous work on the HydroSense technology, which is a low-cost and easily installed single-point sensor of pressure for automatically disaggregating water usage activities in the home (Froehlich et~al., 2009~[53]). We expand upon this work by providing a survey of existing and emerging water disaggregation techniques, a more comprehensive description of the theory of operation behind our approach, and an expanded analysis section that includes hot versus cold water valve usage classification and a comparison between two classification approaches: the template-based matching scheme used in Froehlich et~al. (2009)~[53] and a new stochastic approach using a Hidden Markov Model. We show that both are successful in identifying valve- and fixture-level water events with greater than 90\% accuracies. We conclude with a discussion of the limitations in our experimental methodology and open problems going forward.},
	keywords = {Activity sensing, H1.2, H5.2, Infrastructure-mediated sensing, Sensors, Water usage},
	isbn = {1574-1192},
	doi = {10.1016/j.pmcj.2010.08.008},
	url = {http://www.sciencedirect.com/science/article/pii/S1574119210000842},
	author = {Larson,Eric and Jon Froehlich and Campbell,Tim and Haggerty,Conor and Atlas,Les and Fogarty,James and Patel,Shwetak N.}
}
@article {14330,
	title = {Fast track article: Disaggregated water sensing from a single, pressure-based sensor: An extended analysis of HydroSense using staged experiments},
	journal = {Pervasive Mob. Comput.},
	volume = {8},
	year = {2012},
	month = {2012/02//},
	pages = {82 - 102},
	abstract = {We present an extended analysis of our previous work on the HydroSense technology, which is a low-cost and easily installed single-point sensor of pressure for automatically disaggregating water usage activities in the home (Froehlich et al., 2009 [53]). We expand upon this work by providing a survey of existing and emerging water disaggregation techniques, a more comprehensive description of the theory of operation behind our approach, and an expanded analysis section that includes hot versus cold water valve usage classification and a comparison between two classification approaches: the template-based matching scheme used in Froehlich et al. (2009) [53] and a new stochastic approach using a Hidden Markov Model. We show that both are successful in identifying valve- and fixture-level water events with greater than 90\% accuracies. We conclude with a discussion of the limitations in our experimental methodology and open problems going forward.},
	keywords = {Activity sensing, H1.2, H5.2, Infrastructure-mediated sensing, Sensors, Water usage},
	isbn = {1574-1192},
	doi = {10.1016/j.pmcj.2010.08.008},
	url = {http://dx.doi.org/10.1016/j.pmcj.2010.08.008},
	author = {Larson,Eric and Jon Froehlich and Campbell,Tim and Haggerty,Conor and Atlas,Les and Fogarty,James and Patel,Shwetak N.}
}
@article {14317,
	title = {Disaggregated End-Use Energy Sensing for the Smart Grid},
	journal = {IEEE Pervasive Computing},
	volume = {10},
	year = {2011},
	month = {2011/03//Jan},
	pages = {28 - 39},
	abstract = {This article surveys existing and emerging disaggregation techniques for energy-consumption data and highlights signal features that might be used to sense disaggregated data in an easily installed and cost-effective manner.},
	keywords = {Calibration, disaggregated end-use energy sensing, Disaggregated energy sensing, disaggregation data techniques, Electricity, Energy consumption, Energy efficiency, energy-consumption data, Gas, Home appliances, Sensors, Smart grid, Smart grids, smart power grids, Sustainability, Water},
	isbn = {1536-1268},
	doi = {10.1109/MPRV.2010.74},
	author = {Jon Froehlich and Larson,E. and Gupta,S. and Cohn,G. and Reynolds,M. and Patel,S.}
}
@article {12454,
	title = {A Fast Bilinear Structure from Motion Algorithm Using a Video Sequence and Inertial Sensors},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {33},
	year = {2011},
	month = {2011/01//},
	pages = {186 - 193},
	abstract = {In this paper, we study the benefits of the availability of a specific form of additional information-the vertical direction (gravity) and the height of the camera, both of which can be conveniently measured using inertial sensors and a monocular video sequence for 3D urban modeling. We show that in the presence of this information, the SfM equations can be rewritten in a bilinear form. This allows us to derive a fast, robust, and scalable SfM algorithm for large scale applications. The SfM algorithm developed in this paper is experimentally demonstrated to have favorable properties compared to the sparse bundle adjustment algorithm. We provide experimental evidence indicating that the proposed algorithm converges in many cases to solutions with lower error than state-of-art implementations of bundle adjustment. We also demonstrate that for the case of large reconstruction problems, the proposed algorithm takes lesser time to reach its solution compared to bundle adjustment. We also present SfM results using our algorithm on the Google StreetView research data set.},
	keywords = {3D urban modeling, algorithms, Artificial intelligence, CAMERAS, computer vision., Convergence, fast bilinear structure, Google StreetView research data set, Image Interpretation, Computer-Assisted, Image reconstruction, Image sensors, Image sequences, Imaging, Three-Dimensional, inertial sensors, Information Storage and Retrieval, Linear systems, minimization, MOTION, motion algorithm, Motion estimation, multiple view geometry, Pattern Recognition, Automated, Sensors, SfM equations, sparse bundle adjustment algorithm, structure from motion, Three dimensional displays, vertical direction, Video Recording, video sequence, video signal processing},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2010.163},
	author = {Ramachandran, M. and Veeraraghavan,A. and Chellapa, Rama}
}
@conference {16429,
	title = {Participatory, embodied, multi-agent simulation},
	booktitle = {Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems},
	series = {AAMAS {\textquoteright}06},
	year = {2006},
	month = {2006///},
	pages = {1457 - 1458},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {We will demonstrate the integration of a software-based multi-agent modeling platform with a participatory simulation environment and real-time control over a physical agent (robot). Both real and virtual participants will be able to act as agents in a simulation that will control a physical agent. The backbone of this demonstration is a widely used, freely available, mature modeling platform known as NetLogo.},
	keywords = {embedded agents, multi-agent simulation, participatory simulation, ROBOTICS, Sensors},
	isbn = {1-59593-303-4},
	doi = {10.1145/1160633.1160913},
	url = {http://doi.acm.org/10.1145/1160633.1160913},
	author = {Blikstein,Paulo and Rand, William and Wilensky,Uri}
}
