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Full-Text Articles in Computer Engineering
2013 (Spring) Ensi Informer Magazine, Morehead State University. Engineering Sciences Department
2013 (Spring) Ensi Informer Magazine, Morehead State University. Engineering Sciences Department
ENSI Informer Magazine Archive
The ENSI Informer Magazine published in the spring of 2013.
Activity Intent Recognition Of The Torso Based On Surface Electromyography And Inertial Measurement Units, Zhe Zhang
Masters Theses 1911 - February 2014
This thesis presents an activity mode intent recognition approach for safe, robust and reliable control of powered backbone exoskeleton. The thesis presents the background and a concept for a powered backbone exoskeleton that would work in parallel with a user. The necessary prerequisites for the thesis are presented, including the collection and processing of surface electromyography signals and inertial sensor data to recognize the user’s activity. The development of activity mode intent recognizer was described based on decision tree classification in order to leverage its computational efficiency. The intent recognizer is a high-level supervisory controller that belongs to a three-level …
Dnad, A Simple Tool For Automatic Differentiation Of Fortran Codes Using Dual Numbers, Wenbin Yu, Maxwell Blair
Dnad, A Simple Tool For Automatic Differentiation Of Fortran Codes Using Dual Numbers, Wenbin Yu, Maxwell Blair
Mechanical and Aerospace Engineering Faculty Publications
DNAD (dual number automatic differentiation) is a simple, general-purpose tool to automatically differentiate Fortran codes written in modern Fortran (F90/95/2003) or legacy codes written in previous version of the Fortran language. It implements the forward mode of automatic differentiation using the arithmetic of dual numbers and the operator overloading feature of F90/95/2003. Very minimum changes of the source codes are needed to compute the first derivatives of Fortran programs. The advantages of DNAD in comparison to other existing similar computer codes are its programming simplicity, extensibility, and computational efficiency. Specifically, DNAD is more accurate and efficient than the popular complex-step …