IAP 2014 Activities by Category - A.I. and Robotics

Julie Shah, Assistant Professor, Dept. of Aeronautics & Astronautics, Yaniv Turgeman, Head of Research, Senseable City Lab, Chris Green, Research Fellow, Senseable City Lab

Enrollment: Limited: Advance sign-up required
Sign-up by 12/13
Limited to 15 participants
Attendance: attendance thru Jan preferred, flexible
Prereq: Experience in one of the skillsets listed below

Do you have experience in quadcopters, environmental sensors, embedded development, digital fabrication, mechanical engineering, web development, any of the above?

Interested in working on new and unseen applications of UAV technology?

Through January we are developing autonomous flying vehicles to dynamically sense and map the invisible phenomena of the environment around us. As Unmanned Aerial Vehicle (UAV) technology begins to move into increasingly civic applications, this project will propose and demonstrate UAVs as a responsive infrastructure deployable across cities, that can help us deeper understand our surrounding environments, and solve real-world problems. We are starting with the Charles River Basin, facing a series of environmental challenges that are little understood, relatively unmapped and difficult to gather further information on... which is where our technology steps in.

Sensors, autonomous flight, fabrication methods and data visualisation techniques will be developed, combined and deployed to create an aerial, real-time, spatiotemporal sensing platform.

ps:  IAP UROP positions are available for this project - please indicate this in your reply.

Senseable City Lab:  http://senseable.mit.edu/

Interactive Robotics Group:  http://interactive.mit.edu/

 

Sponsor(s): Aeronautics and Astronautics
Contact: Chris Green, 9-209, 617 324-4474, CJGREEN@MIT.EDU

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Jan/06

Mon

09:30AM-05:30PM

9-209

Julie Shah - Assistant Professor, Dept. of Aeronautics & Astronautics, Yaniv Turgeman - Head of Research, Senseable City Lab, Chris Green - Research Fellow, Senseable City Lab

Maplesoft staff

Jan/23

Thu

03:00PM-04:00PM

E17-121

Enrollment: Limited: First come, first served (no advance sign-up)
Limited to 40 participants

MapleSim is a tool for modeling and analyzing mechatronic systems that relieves the burden typically associated with using traditional simulation tools to develop high-fidelity models.  This next-generation graphical tool will dramatically reduce your time and costs associated with up-front analysis, virtual prototyping, and parameter optimization of system designs.  With its intuitive, multi-domain modeling environment and powerful multibody modeling tools, MapleSim is uniquely suited to developing mechatronic systems, including applications such as robotics, guidance systems, active stabilizers, vibration attenuators, and "X-by-wire" systems found in road vehicles and aircraft.

In this presentation, a modeling and simulation expert from Maplesoft will demonstrate MapleSim's unique capabilities when designing systems where open-loop, closed-loop, kinematic, and dynamic behaviors need to be considered.  Tools for transferring work into an existing control development toolchain, and real-time simulation systems (for hardware-in-the-loop testing) will also be presented.

Sponsor(s): Information Services and Technology
Contact: Kim Koserski, 519-747-2373, kkoserski@maplesoft.com

James Cain, Manager - Experimental Learning Environments, OEIT

Jan/30

Thu

10:00AM-12:00PM

4-231

Enrollment: Register at link below:

Machine learning techniques are often used for data analysis and decision-making tasks such as forecasting, classification of risk, estimating probabilities of default, and data mining. However, implementing and comparing machine learning techniques to choose the best approach can be challenging. In this session, you will learn about several machine learning techniques available in MATLAB and how to quickly explore your data, evaluate machine learning algorithms, compare the results, and apply the best technique to your problem.

Highlights include unsupervised and supervised learning techniques such as:

-K-means and other clustering tools

-Neural networks

-Decision trees and ensemble learning

-Naïve Bayes classification

-Linear, logistic, and nonlinear regression

MathWorks at MIT IAP 2014

MathWorks is hosting six sessions during MIT's Independent Activities Period (IAP) 2014. Join us to learn how you can use MATLAB and Simulink for technical computing and application development in engineering, math, and science. Attend as many sessions as you like.

Please visit the following URL for more information and to register for this session:

http://www.mathworks.com/company/events/seminars/mit_iap14/index.html

Sponsor(s): Office of Educational Innovation and Technology, Electrical Engineering and Computer Science
Contact: Tim Mathieu, Tim.Mathieu@mathworks.com