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Alberto Sangiovanni-Vincentelli

Researcher at University of California, Berkeley

Publications -  946
Citations -  47259

Alberto Sangiovanni-Vincentelli is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Logic synthesis & Finite-state machine. The author has an hindex of 99, co-authored 934 publications receiving 45201 citations. Previous affiliations of Alberto Sangiovanni-Vincentelli include National University of Singapore & Lawrence Berkeley National Laboratory.

Papers
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Journal ArticleDOI

Automotive engine control and hybrid systems: challenges and opportunities

Andrea Balluchi, +4 more
TL;DR: In this article, the authors present a hybrid model of the engine in which both continuous and discrete time-domain as well as event-based phenomena are modeled in a separate but integrated manner.
Journal ArticleDOI

Multi-level logic minimization using implicit don't cares

K. Bartlett, +7 more
- 01 Jun 1988 - 
TL;DR: The authors introduce the concept of R-minimality, which implies minimality with respect to cube reshaping, and demonstrate the crucial role played by this concept in multilevel minimization.
Journal ArticleDOI

Secure State Estimation for Cyber-Physical Systems Under Sensor Attacks: A Satisfiability Modulo Theory Approach

Yasser Shoukry, +5 more
- 01 Mar 2017 - 
TL;DR: In this article, the authors present a secure state estimation algorithm that uses a satisfiability modulo theory approach to harness the complexity of the secure state estimator and provide guarantees on the soundness and completeness of the algorithm.
Book

Contracts for System Design

Albert Benveniste, +9 more
TL;DR: This paper intends to provide treatment where contracts are precisely defined and characterized so that they can be used in design methodologies such as the ones mentioned above with no ambiguity, and provides an important link between interfaces and contracts to show similarities and correspondences.
Proceedings ArticleDOI

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization Without Accessing Target Domain Data

Xiangyu Yue, +5 more
TL;DR: A new approach of domain randomization and pyramid consistency to learn a model with high generalizability for semantic segmentation of real-world self-driving scenes in a domain generalization fashion is proposed.