J
Joao P. Hespanha
Researcher at University of California, Santa Barbara
Publications - 435
Citations - 41658
Joao P. Hespanha is an academic researcher from University of California, Santa Barbara. The author has contributed to research in topics: Control theory & Linear system. The author has an hindex of 72, co-authored 418 publications receiving 39004 citations. Previous affiliations of Joao P. Hespanha include University of California, Berkeley & University of California.
Papers
More filters
Journal ArticleDOI
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Journal ArticleDOI
A Survey of Recent Results in Networked Control Systems
TL;DR: This work reviews several recent results on estimation, analysis, and controller synthesis for NCSs, and addresses channel limitations in terms of packet-rates, sampling, network delay, and packet dropouts.
Book ChapterDOI
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
TL;DR: A face recognition algorithm which is insensitive to gross variation in lighting direction and facial expression is developed and the proposed “Fisherface” method has error rates that are significantly lower than those of the Eigenface technique when tested on the same database.
Proceedings ArticleDOI
Stability of switched systems with average dwell-time
Joao P. Hespanha,A.S. Morse +1 more
TL;DR: In this article, it was shown that scale-independent hysteresis can produce switching that is slow-on-the-average and therefore the results mentioned above can be used to study the stability of adaptive control systems.
Journal ArticleDOI
Trajectory-Tracking and Path-Following of Underactuated Autonomous Vehicles With Parametric Modeling Uncertainty
TL;DR: It is demonstrated how adaptive switching supervisory control can be combined with a nonlinear Lyapunov-based tracking control law to solve the problem of global boundedness and convergence of the position tracking error to a neighborhood of the origin that can be made arbitrarily small.