Jaakko Suutala
Jaakko Suutala
Associate Professor of Artificial Intelligence, University of Oulu
Verified email at - Homepage
Cited by
Cited by
Convergent communication, sensing and localization in 6G systems: An overview of technologies, opportunities and challenges
C De Lima, D Belot, R Berkvens, A Bourdoux, D Dardari, M Guillaud, ...
IEEE Access 9, 26902-26925, 2021
Spectrum occupancy measurements: A survey and use of interference maps
M Höyhtyä, A Mämmelä, M Eskola, M Matinmikko, J Kalliovaara, ...
IEEE Communications Surveys & Tutorials 18 (4), 2386-2414, 2016
6G white paper on machine learning in wireless communication networks
S Ali, W Saad, N Rajatheva, K Chang, D Steinbach, B Sliwa, C Wietfeld, ...
arXiv preprint arXiv:2004.13875, 2020
6G white paper on localization and sensing
A Bourdoux, AN Barreto, B van Liempd, C de Lima, D Dardari, D Belot, ...
arXiv preprint arXiv:2006.01779, 2020
Methods for person identification on a pressure-sensitive floor: Experiments with multiple classifiers and reject option
J Suutala, J Röning
Information Fusion 9 (1), 21-40, 2008
Towards the adaptive identification of walkers: Automated feature selection of footsteps using distinction sensitive LVQ
J Suutala, J Röning
Int. Workshop on Processing Sensory Information for Proactive Systems (PSIPS …, 2004
Discriminative temporal smoothing for activity recognition from wearable sensors
J Suutala, S Pirttikangas, J Röning
Ubiquitous Computing Systems: 4th International Symposium, UCS 2007, Tokyo …, 2007
Gaussian process person identifier based on simple floor sensors
J Suutala, K Fujinami, J Röning
Smart Sensing and Context: Third European Conference, EuroSSC 2008, Zurich …, 2008
White Paper on Machine Learning in 6G Wireless Communication Networks: 6G Research Visions, No. 7, 2020
A Samad, W Saad, R Nandana, C Kapseok, D Steinbach, B Sliwa, ...
University of Oulu, 2020
Combining classifiers with different footstep feature sets and multiple samples for person identification
J Suutala, J Roning
Proceedings.(ICASSP'05). IEEE International Conference on Acoustics, Speech …, 2005
Footstep identification from pressure signals using Hidden Markov Models
S Pirttikangas, J Suutala, J Riekki, J Röning
Proc. Finnish Signal Processing Symposium (FINSIG’03), 124-128, 2003
Global spectrum observatory network setup and initial findings
T Taher, R Attard, A Riaz, D Roberson, J Taylor, K Zdunek, J Hallio, ...
2014 9th International Conference on Cognitive Radio Oriented Wireless …, 2014
Reject-optional LVQ-based two-level classifier to improve reliability in footstep identification
J Suutala, S Pirttikangas, J Riekki, J Röning
Pervasive Computing: Second International Conference, PERVASIVE 2004, Linz …, 2004
Learning vector quantization in footstep identification
S Pirttikangas, J Suutala, J Riekki, J Röning
Proc. 3rd IASTED International Conference on Artificial Intelligence and …, 2003
Feature selection and activity recognition to detect water waste from water tap usage
TT Vu, A Sokan, H Nakajo, K Fujinami, J Suutala, P Siirtola, T Alasalmi, ...
2011 IEEE 17th International Conference on Embedded and Real-Time Computing …, 2011
Footstep pattern matching from pressure signals using segmental semi-markov models
K Koho, J Suutala, T Seppänen, J Röning
2004 12th European Signal Processing Conference, 1609-1612, 2004
Classification uncertainty of multiple imputed data
T Alasalmi, H Koskimäki, J Suutala, J Röning
2015 IEEE Symposium Series on Computational Intelligence, 151-158, 2015
Better classifier calibration for small datasets
A Tuomo, J Suutala, J Röning, H Koskimäki
ACM Transactions on Knowledge Discovery from Data (TKDD) 14 (3), 1-19, 2020
Real-time non-intrusive appliance load monitor
T Alasalmi, J Suutala, J Röning
International conference on Smart grids and Green IT Systems, 203-208, 2012
Persons tracking with Gaussian process joint particle filtering
J Suutala, K Fujinami, J Röning
2010 IEEE International Workshop on Machine Learning for Signal Processing …, 2010
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