A framework for analog circuit optimization

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 49-50).This thesis presents a system for optimization of analog circuit topologies and component values. The topology is optimized using simulated annealing, while the component values are optimized using gradient descent. Local minima are avoided and constraints are kept through the use of coordinate transformations, as well as the use of default starting points for component values. The system is targeted for use in 3D integrated circuit design. The architecture is extendable, and is designed to eventually include capabilities for automated layout and mixed-signal design.by Piotr Mitros.M.Eng

Constraint satisfaction modules : a methodology for analog circuit design

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 119-122).This dissertation describes a methodology for solving convex constraint problems using analog circuits. It demonstrates how this methodology can be used to design circuits that solve function-fitting problems through iterated gradient descent. In particular, it shows how to build a small circuit that can model a nonlinearity by observation, and predistort to compensate for this nonlinearity. The system fits into a broader effort to investigate non-traditional approaches to circuit design. First, it breaks the traditional input-output abstraction barrier; all ports are bidirectional. Second, it uses a different methodology for proving system stability with local rather than global properties. Such stability arguments can be scaled to much more complex systems than traditional stability criteria.by Piotr Mitros.Ph.D

Big data for monitoring educational systems

This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education

Understanding in-video dropouts and interaction peaks in online lecture videos

With thousands of learners watching the same online lecture videos, analyzing video watching patterns provides a unique opportunity to understand how students learn with videos. This paper reports a large-scale analysis of in-video dropout and peaks in viewership and student activity, using second-by-second user interaction data from 862 videos in four Massive Open Online Courses (MOOCs) on edX. We find higher dropout rates in longer videos, re-watching sessions (vs first-time), and tutorials (vs lectures). Peaks in re-watching sessions and play events indicate points of interest and confusion. Results show that tutorials (vs lectures) and re-watching sessions (vs first-time) lead to more frequent and sharper peaks. In attempting to reason why peaks occur by sampling 80 videos, we observe that 61% of the peaks accompany visual transitions in the video, e.g., a slide view to a classroom view. Based on this observation, we identify five student activity patterns that can explain peaks: starting from the beginning of a new material, returning to missed content, following a tutorial step, replaying a brief segment, and repeating a non-visual explanation. Our analysis has design implications for video authoring, editing, and interface design, providing a richer understanding of video learning on MOOCs

Understanding in-video dropouts and interaction peaks in online lecture videos

Engineering and Applied Science

Constraint Modules: An Introduction

We describe a methodology for solving convex constraint problems using analog circuits. We demonstrate how this methodology can be used to design circuits that solve function-fitting problems through iterated gradient descent. In particular, we show how to build a small circuit that can model a nonlinearity by observation, and predistort to compensate for this nonlinearity. The system fits into a broader effort to investigate non-traditional approaches to circuit design. First, it breaks the traditional input-output abstraction barrier; all ports are bidirectional. Second, it uses primarily local properties of the circuit to show stability. Such stability arguments can be scaled to much more complex systems than traditional stability criteria

A Methodology for Analog Circuit Design

requirements for the degree o

Big Data Analysis in Higher Education: Promises and Pitfalls

The grand challenge in data-intensive research and analysis in higher education is to find the means to extract knowledge from the extremely rich data sets being generated today and to distill this into usable information for students, instructors, and the public

Who does what in a massive open online course?

Massive open online courses (MOOCs) collect valuable data onstudent learning behavior: essentially complete records of all student interactions in a self-contained learning environment,with the benefit of large sample sizes. We present an overview of how the 108,000 participants behaved in 6.002x - Circuits and Electronics, the first course in MITx (now edX). We divide participants into tranches based on the extent of their assessment activities, ranging from browsers (who constituted ~ 76% of the participants but accounted for only 8% of the total time spent in the course) to certificate-earners (7% of theparticipants who accounted for 60% of the total time). We examine how the certificate earners allocated their time amongst the various course components and study what fraction of each they accessed. We analyze transitions between course components, showing, how student behavior differs when solving homework vs. exam problems. This work lays the foundation for future studies of how use of various course components, and transitions among them, influence learning in MOOCs.National Science Foundation (U.S.) (DUE-1044294