H
Henry Tirri
Researcher at Nokia
Publications - 107
Citations - 3980
Henry Tirri is an academic researcher from Nokia. The author has contributed to research in topics: Bayesian network & Bayesian probability. The author has an hindex of 24, co-authored 107 publications receiving 3926 citations. Previous affiliations of Henry Tirri include University of Helsinki & Helsinki University of Technology.
Papers
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Journal ArticleDOI
A Probabilistic Approach to WLAN User Location Estimation
TL;DR: The feasibility of this approach is demonstrated by reporting results of field tests in which a probabilistic location estimation method is validated in a real-world indoor environment.
Journal ArticleDOI
A statistical modeling approach to location estimation
TL;DR: In this article, a location estimation method based on a statistical signal power model is proposed. But this method requires nonstandard features either in the mobile terminal or the network, such as the cell-ID method in GSM/GPRS cellular networks, which is usually problematic due to their inadequate location estimation accuracy.
Patent
Location estimation in wireless telecommunication networks
Petri Myllymäki,Henry Tirri,Petri Kontkanen,Jussi Lahtinen,Tomi Silander,Teemu Roos,Antti Tuominen,Kimmo Valtonen,Hannes Wettig +8 more
TL;DR: In this article, a method for estimating a receiver's location (X) in a wireless communication environment (RN) having several channels is proposed, where a set of calibration data (CD) is determined for each calibration point, each set comprising the location and at least one measured signal parameter (V) for each of several channels.
Proceedings ArticleDOI
Developing GIS-supported location-based services
Kirsi Virrantaus,Jouni Markkula,A. Garmash,Vagan Terziyan,Jari Veijalainen,A. Katanosov,Henry Tirri +6 more
TL;DR: The main topic of the paper is the question, how Geographic Information Systems (GIS) and the data hosted currently by them could be used in the context of LBS.
Journal ArticleDOI
B-course: a web-based tool for bayesian and causal data analysis
TL;DR: With the restrictions stated in the support material, B-Course is a powerful analysis tool exploiting several theoretically elaborate results developed recently in the fields of Bayesian and causal modeling.