S
Stefano Scanzio
Researcher at National Research Council
Publications - 79
Citations - 1276
Stefano Scanzio is an academic researcher from National Research Council. The author has contributed to research in topics: Redundancy (engineering) & Wireless network. The author has an hindex of 17, co-authored 68 publications receiving 1096 citations. Previous affiliations of Stefano Scanzio include Polytechnic University of Turin.
Papers
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Journal ArticleDOI
Linear hidden transformations for adaptation of hybrid ANN/HMM models
TL;DR: The results show that the proposed approach always outperforms the use of transformations in the feature space and yields even better results when combined with linear input transformations.
Journal ArticleDOI
Evaluation of EtherCAT Distributed Clock Performance
TL;DR: The performance of the DC mechanism is evaluated by means of a thorough campaign of experimental measurements carried out on a real network setup, and a number of factors have been taken into account that can affect accuracy and precision.
Proceedings ArticleDOI
On the Use of a Multilingual Neural Network Front-End
TL;DR: This paper presents a front-end consisting of an Artificial Neural Network architecture trained with multilingual corpora that produces discriminant features that can be used as observation vectors for language or task dependent recognizers.
Journal ArticleDOI
Implementation and Evaluation of the Reference Broadcast Infrastructure Synchronization Protocol
TL;DR: This paper describes reference broadcast infrastructure synchronization (RBIS), a clock synchronization protocol for IEEE 802.11 infrastructure wireless networks, especially tailored for industrial and home automation networks, and in many application contexts, it offers several advantages compared with other solutions targeted at similar purposes.
Proceedings ArticleDOI
Adaptation of Hybrid ANN/HMM Models Using Linear Hidden Transformations and Conservative Training
TL;DR: A new solution, called conservative training, is proposed that compensates for the lack of adaptation samples in certain classes that outperforms the use of transformations in the feature space and yields even better results when combined with linear input transformations.