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Sergey Edward Lyshevski

Researcher at Rochester Institute of Technology

Publications -  289
Citations -  2892

Sergey Edward Lyshevski is an academic researcher from Rochester Institute of Technology. The author has contributed to research in topics: Nonlinear system & Motion control. The author has an hindex of 25, co-authored 287 publications receiving 2746 citations. Previous affiliations of Sergey Edward Lyshevski include Purdue University & University of South Carolina.

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Book

Handbook of Nanoscience, Engineering, and Technology

TL;DR: The Handbook of Nanoscience, Engineering, and Technology as discussed by the authors provides an up-to-date account of the most promising technologies and developments in the nano field, including nanomagnet logic, quantum transport at the nanoscale, terahertz emission from Bloch oscillator systems, molecular logic, electronic optics in graphene, and electromagnetic metamaterials.
Journal ArticleDOI

MEMS and NEMS - systems, devices, and structures

TL;DR: In this paper, the authors present a review of the latest trends in engineering and science: Micro- and Nanoscale Systems Introduction to Design of MEMS and NEMS Biological and Bio-Biosystems Analogies Overview of Nano- and Microelectromechanical Systems Applications of micro- and nanoelectric systems, Devices, and Structures Introduction to Modeling, Analysis, and Simulation Electromagnetics and its Application for MEMS, Induction Micromachines Synchronous Reluctance Micromotors Microfabrication Magnetization Dynamics
Book

Electromechanical Systems, Electric Machines, And Applied Mechatronics

TL;DR: In this paper, the authors used MATLAB for the analysis and modelling of dynamic systems using MATLAB, including two-phase and three-phase Induction Machines with different parameters.
Proceedings ArticleDOI

Optimal control of nonlinear continuous-time systems: design of bounded controllers via generalized nonquadratic functionals

TL;DR: By using the Hamilton-Jacobi framework and sufficiency theory, a solution of the constrained optimization problem for nonlinear systems with soft and hard bounds imposed on control is presented in this paper.