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3 Results | Last Page

21 mins
ILP Video

Advanced Materials for Energy and Water Nexus

Gang Chen
Carl Richard Soderberg Professor of Power Engineering
MIT Department of Mechanical Engineering
This talk will present some of our recent work on advanced materials, including high thermal conductivity plastics and optically opaque and infrared transparent fabrics, and clean water technologies. Although polymers are usually thermal insulators, we show that they can be made as thermally conductive as metals by aligning molecular orientations. With properly chosen polymer fiber diameters, we design fabrics so that they are opaque to visible light and yet allow thermal radiation from human body to escape to environment for passively cooling of human body. We also demonstrate the ability of boiling water and even creating super-heated steam under unconcentrated sunlight using spectrally selective surfaces and thermal insulations.
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21 mins
ILP Video

Too small, too far, too dark, too foggy: on the use of Artificial Intelligence for imaging challenging objects

George Barbastathis
Professor of Optics and Mechanical Engineering
MIT Department of Mechanical Engineering
Computational Imaging systems consist of two parts: the physical part where light propagates through free space or optical elements such as lenses, prisms, etc. finally forming a raw intensity image on the digital camera; and the computational part, where algorithms try to restore the image quality or extract other type of information from the raw intensity image data. Computational Imaging promises to solve the challenge of imaging objects that are too small, i.e. of size at about the wavelength of illumination or smaller; too far, i.e. with extremely low numerical aperture; too dark, i.e. at very low photon counts; or too foggy, i.e. when the light has to propagate through a strongly scattering medium before reaching the detector. In this talk I will discuss the emerging trend in computational imaging to train deep neural networks (DNNs) to attack the quad of challenging objects. In several imaging experiments carried out by our group, objects rendered ?invisible? due to various adverse conditions such as extreme defocus, scatter, or very low photon counts were ?revealed? after processing of the raw images by DNNs. The DNNs were trained from examples consisting of pairs of known objects and their corresponding raw images. The objects were drawn from databases of faces and natural images, with the brightness converted to phase through a liquid-crystal spatial phase modulator. After training, the DNNs were capable of recovering unknown, i.e. hitherto not presented during training, objects from the raw images and recovery was robust to disturbances in the optical system, such as additional defocus or various misalignments. This suggests that DNNs may form robust internal models of the physics of light propagation and detection and generalize priors from the training set.
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31 mins
ILP Video

MIT Startup Exchange Lightning Talks

Cory Kidd, Catalia Health
Arjun Aggarwal, Desktop Metal
Benjamin Schreck, Feature Labs
Jason Tao Yu, Gradiant
Julia Somerdin, Labby
Catalia Health: Scalable chronic disease management using interactive robotics Desktop Metal: Metal 3D printing Feature Labs: Tools and APIs for data science automation Gradiant: Technology-driven water services company for water-critical industrial applications Labby: AI-powered Food Analytics Platform for Food, Beverage, Agro Testing
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