T
Tomi Westerlund
Researcher at University of Turku
Publications - 165
Citations - 4150
Tomi Westerlund is an academic researcher from University of Turku. The author has contributed to research in topics: Computer science & Edge computing. The author has an hindex of 23, co-authored 134 publications receiving 2258 citations. Previous affiliations of Tomi Westerlund include Åbo Akademi University & Turku Centre for Computer Science.
Papers
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Journal ArticleDOI
Timepix3: a 65K channel hybrid pixel readout chip with simultaneous ToA/ToT and sparse readout
Tuomas Poikela,Juha Plosila,Tomi Westerlund,Michael Campbell,M. De Gaspari,X. Llopart,V. Gromov,R. Kluit,M. van Beuzekom,F Zappon,V. Zivkovic,C Brezina,Klaus Kurt Desch,Y Fu,Andre Kruth +14 more
TL;DR: A new architecture has been designed for sparse readout and can achieve a throughput of up to 40 Mhits/s/cm2 and the digital design uses a mixture of commercial and custom standard cell libraries and was verified using Open Verification Methodology (OVM) and commercial timing analysis tools.
Proceedings ArticleDOI
Fog Computing in Healthcare Internet of Things: A Case Study on ECG Feature Extraction
Tuan Nguyen Gia,Mingzhe Jiang,Amir-Mohammad Rahmani,Tomi Westerlund,Pasi Liljeberg,Hannu Tenhunen +5 more
TL;DR: Electrocardiogram feature extraction is chosen as the case study as it plays an important role in diagnosis of many cardiac diseases and fog computing helps achieving more than 90% bandwidth efficiency and offering low-latency real time response at the edge of the network.
Proceedings ArticleDOI
Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey.
TL;DR: The fundamental background behind sim-to-real transfer in deep reinforcement learning is covered and the main methods being utilized at the moment: domain randomization, domain adaptation, imitation learning, meta-learning and knowledge distillation are overviewed.
Journal ArticleDOI
Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision
Jorge Peña Queralta,Jussi Taipalmaa,Bilge Can Pullinen,Victor Kathan Sarker,Tuan Nguyen Gia,Hannu Tenhunen,Moncef Gabbouj,Jenni Raitoharju,Tomi Westerlund +8 more
TL;DR: The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area.
Proceedings ArticleDOI
Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey
TL;DR: In this article, the authors cover the fundamental background behind sim-to-real transfer in deep reinforcement learning and overview the main methods being utilized at the moment: domain randomization, domain adaptation, imitation learning, meta-learning and knowledge distillation.