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ML
1998
ACM
153views Machine Learning» more  ML 1998»
13 years 9 months ago
Bayesian Landmark Learning for Mobile Robot Localization
To operate successfully in indoor environments, mobile robots must be able to localize themselves. Most current localization algorithms lack flexibility, autonomy, and often optim...
Sebastian Thrun
ETAI
1998
99views more  ETAI 1998»
13 years 9 months ago
A Logical Account of the Common Sense Informatic Situation for a Mobile Robot
Any model of the world a robot constructs on the basis of its sensor data is necessarily both incomplete, due to the robot’s limited window on the world, and uncertain, due to s...
Murray Shanahan
IJRR
2007
186views more  IJRR 2007»
13 years 9 months ago
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract a person’s activities and significant plac...
Lin Liao, Dieter Fox, Henry A. Kautz
SIGKDD
2008
149views more  SIGKDD 2008»
13 years 9 months ago
Knowledge discovery from sensor data (SensorKDD)
Wide-area sensor infrastructures, remote sensors, RFIDs, and wireless sensor networks yield massive volumes of disparate, dynamic, and geographically distributed data. As such sen...
Ranga Raju Vatsavai, Olufemi A. Omitaomu, Joao Gam...
PERVASIVE
2006
Springer
13 years 9 months ago
Declarative Support for Sensor Data Cleaning
Pervasive applications rely on data captured from the physical world through sensor devices. Data provided by these devices, however, tend to be unreliable. The data must, therefor...
Shawn R. Jeffery, Gustavo Alonso, Michael J. Frank...
JFR
2006
88views more  JFR 2006»
13 years 9 months ago
Discovering natural kinds of robot sensory experiences in unstructured environments
We derive categories directly from robot sensor data to address the symbol grounding problem. Unlike model-based approaches where human intuitive correspondences are sought betwee...
Daniel H. Grollman, Odest Chadwicke Jenkins, Frank...
IJWMC
2006
85views more  IJWMC 2006»
13 years 9 months ago
A distributed clustering method for energy-efficient data gathering in sensor networks
: Since sensor nodes operate on batteries, energy-efficient mechanisms for gathering sensor data are indispensable in prolonging the lifetime of a sensor network as long as possibl...
Junpei Kamimura, Naoki Wakamiya, Masayuki Murata
ECOI
2010
117views more  ECOI 2010»
13 years 9 months ago
Ensemble extraction for classification and detection of bird species
Advances in technology have enabled new approaches for sensing the environment and collecting data about the world. Once collected, sensor readings can be assembled into data stre...
Eric P. Kasten, Philip K. McKinley, Stuart H. Gage
MOBIDE
2010
ACM
13 years 10 months ago
Using data mining to handle missing data in multi-hop sensor network applications
A sensor's data loss or corruption, aka sensor data missing, is a common phenomenon in modern wireless sensor networks. It is more severe for multi-hop sensor network (MSN) a...
Le Gruenwald, Hanqing Yang, Md. Shiblee Sadik, Rah...
DMSN
2010
ACM
13 years 11 months ago
DEMS: a data mining based technique to handle missing data in mobile sensor network applications
In Mobile Sensor Network (MSN) applications, sensors move to increase the area of coverage and/or to compensate for the failure of other sensors. In such applications, loss or cor...
Le Gruenwald, Md. Shiblee Sadik, Rahul Shukla, Han...