M
Michael D. Todd
Researcher at University of California, San Diego
Publications - 291
Citations - 6238
Michael D. Todd is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Structural health monitoring & Computer science. The author has an hindex of 42, co-authored 270 publications receiving 5375 citations. Previous affiliations of Michael D. Todd include University of California & WellDynamics.
Papers
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Journal ArticleDOI
Energy Harvesting for Structural Health Monitoring Sensor Networks
TL;DR: Some future research directions that are aimed at transitioning the concept of energy harvesting for embedded SHM sensing systems from laboratory research to field-deployed engineering prototypes are defined.
Journal ArticleDOI
A Bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing
Eric B. Flynn,Michael D. Todd +1 more
TL;DR: A novel approach for optimal sensor and/or actuator placement for structural health monitoring (SHM) applications by implementing an appropriate statistical model of the wave propagation and feature extraction process within a detection theory framework.
Journal ArticleDOI
Experimental studies of using wireless energy transmission for powering embedded sensor nodes
David Mascareñas,Eric B. Flynn,Michael D. Todd,Timothy G. Overly,Kevin M. Farinholt,Gyuhae Park,Charles R. Farrar +6 more
TL;DR: In this article, a mobile-host based wireless energy transmission system is proposed to provide both power and data interrogation commands to sensor nodes for structural health monitoring (SHM) applications.
Journal ArticleDOI
Development of an impedance-based wireless sensor node for structural health monitoring
TL;DR: This paper developed a wireless impedance sensor node equipped with a low-cost integrated circuit chip that can measure and record the electrical impedance of a piezoelectric transducer, a microcontroller that performs local computing and a wireless telemetry module that transmits the structural information to a base station.
Journal ArticleDOI
A review of nonlinear dynamics applications to structural health monitoring
TL;DR: In this article, the authors provide a review of examples from nonlinear dynamical systems theory and nonlinear system identification techniques that are used for the feature extraction portion of the damage detection process.