Speculative Thread Framework for Transient Management and Bumpless Transfer in Reconfigurable Digital Filters

There are many methods developed to mitigate transients induced when abruptly changing dynamic algorithms such as those found in digital filters or controllers. These "bumpless transfer" methods have a computational burden to them and take time to implement, causing a delay in the desired switching time. This paper develops a method that automatically reconfigures the computational resources in order to implement a transient management method without any delay in switching times. The method spawns a speculative thread when it predicts if a switch in algorithms is imminent so that the calculations are done prior to the switch being made. The software framework is described and experimental results are shown for a switching between filters in a filter bank.Comment: 6 pages, 7 figures, to be presented at American Controls Conference 201

A cohesive program of experimental modules distributed throughout the ECE curriculum

Issued as final reportNational Science Foundation (U.S.

Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSM

With the computational systems of even embedded devices becoming ever more powerful, there is a need for more effective and pro-active methods of dynamic power management. The work presented in this paper demonstrates the effectiveness of a reinforcement-learning based dynamic power manager placed in a software framework. This combination of Q-learning for determining policy and the software abstractions provide many of the benefits of co-design, namely, good performance, responsiveness and application guidance, with the flexibility of easily changing policies or platforms. The Q-learning based Quality of Service Manager (2QoSM) is implemented on an autonomous robot built on a complex, powerful embedded single-board computer (SBC) and a high-resolution path-planning algorithm. We find that the 2QoSM reduces power consumption up to 42% compared to the Linux on-demand governor and 10.2% over a state-of-the-art situation aware governor. Moreover, the performance as measured by path error is improved by up to 6.1%, all while saving power

Low-Overhead Reinforcement Learning-Based Power Management Using 2QoSM

With the computational systems of even embedded devices becoming ever more powerful, there is a need for more effective and pro-active methods of dynamic power management. The work presented in this paper demonstrates the effectiveness of a reinforcement-learning based dynamic power manager placed in a software framework. This combination of Q-learning for determining policy and the software abstractions provide many of the benefits of co-design, namely, good performance, responsiveness and application guidance, with the flexibility of easily changing policies or platforms. The Q-learning based Quality of Service Manager (2QoSM) is implemented on an autonomous robot built on a complex, powerful embedded single-board computer (SBC) and a high-resolution path-planning algorithm. We find that the 2QoSM reduces power consumption up to 42% compared to the Linux on-demand governor and 10.2% over a state-of-the-art situation aware governor. Moreover, the performance as measured by path error is improved by up to 6.1%, all while saving power

DEDICATION

To my family for their encouragement and unconditional support throughout the years. In the memory of my grandfather, teacher and mentor Nahi Ould-Mahmoud ii

Models of Mobile Hands-On STEM Education

Hands-on activities can improve student understanding of STEM topics dramatically, and laboratories are the most common implementation of hands-on learning. However, most experiments are performed in dedicated laboratories, which may be costly and inaccessible to students, and the labs may not be timely with respect to when students learn the associated theoretical concepts. Mobile hands-on labs are ones that use equipment that is affordable and portable, so that students can own the equipment and do the labs anywhere anytime. This paper presents three models of implementation of mobile hands-on education: a limited number of small, in-class labs given in lecture-based courses; full-scale labs done on student-owned equipment; and studio classes. These models were all implemented in Electrical and Computer Engineering programs, though the modules are also used in K-12 outreach activities

Demystifying Georgia Tech

Presented on January 22, 2019 at 11:00 a.m. in the Student Center, Room 343.Bonnie Ferri, Ph.D. is the vice provost for Graduate Education and Faculty Development at Georgia Tech, and she is a professor in the School of Electrical and Computer Engineering.Kim D. Harrington, Ph.D. is the associate vice president and chief human resources officer at Georgia Institute of Technology.John M. Stein currently serves within the role as vice president of the Division of Student Life.Runtime: 66:23 minutesWhile working towards their Ph.D. degree, students sometimes feel challenges in balancing the demands due to coursework, Teaching Assistant (TA) or Graduate Research Assistant (GRA) responsibilities, research, funding, preparation for the next steps in their career, etc. Similar challenges are faced by post-doctoral fellows as well. The challenges and the potential stress can be compounded if the student/post-doc also needs to manage family responsibilities, including the care of a small child (or children). In a dialogue event held during Fall 2018, students and post-doctoral fellows shared some of the challenges they face while managing research/work and family responsibilities. This event will focus on current and potential mechanisms and resources that could alleviate these challenges or provide support

Experimental Validation of Control Designs for Low-Loss Active Magnetic Bearings

This paper offers experimental validation of several recently developed nonlinear control laws, derived from the theory of integrator backstepping, control Lyapunov functions (CLF), and dissipativity, by implementing them on a spacecraft reaction wheel that is suspended by a low-loss active magnetic bearing (AMB). The electromagnets of the AMB are constrained by a generalized complementary flux constraint (GCFC). This constraint allows one to operate the AMB with a large bias flux, to obtain a desired bearing stiffness and force slew-rate, or with a very small (or even zero) bias flux for low-loss AMB operation. Experimental evidence is provided to illustrate the role of the flux bias in the control design and highlight the singularity issues associated with zero- and very low-bias AMB operation. Specifically, the tradeoff between bearing stiffness, power consumption, and power dissipation as a function of the bias is verified. Also, it is experimentally shown that the singularity issues present in the standard nonlinear backstepping control laws can be destabilizing in zero bias, and moreover, the newly developed CLF and passivity-based control laws effectively eliminate the zero-bias singularity issues. Nomenclature AMB active magnetic bearing FWB flywheel battery CMG control moment gyroscope ESCMG energy storage control moment gyroscope CFS constant flux sum CFC complementary flux constraint GCFC generalized complementary flux constraint ZB zero-bias LB low-bias PMSM permanent magnet synchronous motor IPACS Integrated Power and Attitude Control System I