His primary areas of study are Human–computer interaction, Wireless sensor network, Information retrieval, World Wide Web and Embedded system. His research in Human–computer interaction intersects with topics in User interface, Hidden Markov model and Handset. Many of his studies on Wireless sensor network apply to Real-time computing as well.
Mark Hansen combines subjects such as Granularity and Server with his study of Information retrieval. His World Wide Web research incorporates elements of Iterative search, Labeled data and Relevance. His Embedded system research is multidisciplinary, incorporating perspectives in Mobile phone, Decision tree, Accelerometer, Mobile search and Focus.
His scientific interests lie mostly in Artificial intelligence, Wireless sensor network, World Wide Web, Participatory sensing and Data mining. His work deals with themes such as Process, Radius of curvature, Computer vision and Pattern recognition, which intersect with Artificial intelligence. Mark Hansen works mostly in the field of Wireless sensor network, limiting it down to topics relating to Software deployment and, in certain cases, Wireless.
His work carried out in the field of World Wide Web brings together such families of science as Data stream mining and Information retrieval. Participatory sensing is intertwined with Human–computer interaction, Knowledge management, Variety, Mobile device and Data collection in his study. His work investigates the relationship between Human–computer interaction and topics such as Mobile search that intersect with problems in Mobile phone.
Mark Hansen has included themes like Mobile technology and Embedded system in his Human–computer interaction study. His Mobile technology study combines topics from a wide range of disciplines, such as User interface, Global Positioning System and Snapshot. The concepts of his Embedded system study are interwoven with issues in Wireless sensor network, Handset, Accelerometer, Focus and Hidden Markov model.
Mark Hansen spends much of his time researching Wireless sensor network, Embedded system, Human–computer interaction, Fault indicator and Data integrity. His Wireless sensor network study integrates concerns from other disciplines, such as Decision tree, Accelerometer, Mobile search, Focus and Hidden Markov model. His Embedded system research incorporates elements of Mobile technology, Global Positioning System, Handset and Mobile phone.
Mark Hansen combines subjects such as User interface and Snapshot with his study of Human–computer interaction. His Fault indicator study spans across into fields like Real-time computing, Fault, Feature set and Set.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Using mobile phones to determine transportation modes
Sasank Reddy;Min Mun;Jeff Burke;Deborah Estrin.
ACM Transactions on Sensor Networks (2010)
PEIR, the personal environmental impact report, as a platform for participatory sensing systems research
Min Mun;Sasank Reddy;Katie Shilton;Nathan Yau.
international conference on mobile systems, applications, and services (2009)
Model Selection and the Principle of Minimum Description Length
Mark H Hansen;Bin Yu.
Journal of the American Statistical Association (2001)
Method for organizing records of database search activity by topical relevance
Mark H. Hansen;Elizabeth A. Shriver.
System and method for providing interactive dialogue and iterative search functions to find information
Katherine G. August;Chin-Sheng Chuang;Michelle McNerney;Elizabeth A. Shriver.
Polynomial splines and their tensor products in extended linear modeling: 1994 Wald memorial lecture
Charles J. Stone;Mark H. Hansen;Charles Kooperberg;Young K. Truong.
Annals of Statistics (1997)
Sensor network data fault types
Kevin Ni;Nithya Ramanathan;Mohamed Nabil Hajj Chehade;Laura Balzano.
ACM Transactions on Sensor Networks (2009)
Urban sensing: out of the woods
Dana Cuff;Mark Hansen;Jerry Kang.
Communications of The ACM (2008)
Nonlinear estimation and classification
David D. Denison;Mark H. Hansen;Christopher C. Holmes;Bani Mallick.
Image browsing, processing, and clustering for participatory sensing: lessons from a DietSense prototype
Sasank Reddy;Andrew Parker;Josh Hyman;Jeff Burke.
ieee workshop on embedded networked sensors (2007)
Profile was last updated on December 6th, 2021.
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