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.
Layer 2 is currently used as an umbrella term for all operations that are performed “off chain” and use blockchains to settle transactions. This is based on the work of Tadge Dryja, who is one of the authors of the Lightning Network paper, and he continues to lead the DCI’s research in this area. The Lightning Network is one of the first applications of payment channels, and we’re confident we’ll see more. Another application we’ve been working on involves smart contracts. In order to create useful smart contracts, we need oracles, data feeds that verify real-world occurrences and submit this information in a format that can be used in a blockchain.
With the proliferation of commercial wearable devices, we are now able to obtain unprecedented insight into the ever-changing physical state of our bodies. These devices allow real-time monitoring of biosignals that can generate actionable information to enable optimized interventions to avoid injury and enhance performance. Combat and medical planners across all military services are keenly interested in harnessing wearable sensor advances to diagnose, predict, and improve warfighter health and performance. However, moving from civilian promise to military reality is complex, with unique requirements of hardware design, real-time networking, data management, cybersecurity, predictive model building, and decision science. Emerging technologies for military on-the-move monitoring will be highlighted, along with a discussion of an integrated open systems architecture approach for functional evolution.
Digital fabrication and computational materials are enabling the design and manufacturing of objects that are mass-customizable, interconnected, and can fundamentally adapt to users’ needs and requirements. This talk will present a series of research projects and technologies that push the boundaries of how materials and computers can be intertwined to create new products and experiences — from the nanoscale to a stadium, from a single person to a crowd — and that redefine how we perceive and interact with physical world.
The MIT Trust Data Consortium aims to provide people, organizations, and computers the ability to manage access to their data more securely, efficiently, and equitably, while protecting personal data from incursion and corruption. As we have moved from the analog world to the digital world, our data, security, and governance systems have not kept pace. This has created numerous issues ranging from data insecurity (such as the large-scale government and private sector data losses of recent years) to a widening digital divide between rich and poor, including the global disenfranchisement of over 1.5 billion people who lack legal identity.
Electrochemical energy storage is emerging as a critical technology to enable sustainable electricity generation by alleviating intermittency from renewable sources, reducing transmission congestion, enhancing grid resiliency, and decoupling generation from demand. While several different rechargeable batteries have been proposed for and demonstrated in these applications, further cost reductions are needed for ubiquitous adoption. As such, recent research has focused on the discovery and development of new chemistries. Though exciting, most of these emerging concepts only consider new materials in isolation rather than as part of a battery system. Understanding the critical relationships between materials properties and overall battery price is key to enabling systematic improvements. In this presentation, I will discuss an approach to mapping feasible design spaces for incipient energy storage systems through techno-economic modeling and to using this knowledge to identify critical pathways at an early stage in the research and development process. While redox flow batteries will be used as an exemplar technology, the methods to be described here are applicable to a wide range of electrochemical systems and envisioned applications.
The rapid, stable cycling of rechargeable batteries requires well-controlled phase transformations of the redox active materials in each electrode, between the charged and discharged states. In Li-ion batteries, common intercalation materials, such as graphite and iron phosphate, undergo phase separation (into Li-rich and Li-poor phases), which limits the power density and causes degradation. A general mathematical theory, supported by recent x-ray imaging experiments, will be presented that shows how phase separation can be controlled by electro-autocatalytic reactions. For Li-metal batteries, theoretical and experimental results will be presented for the stability of lithium electrodeposition, controlled by electrokinetic phenomena in charged porous separators.
Our laboratory focuses on the science and applications of nanocrystals, especially semiconductor nanocrystal (aka quantum dots). Our research ranges from the very fundamental to applications in electro-optics and biology. There is an ongoing effort to address the challenges of making new compositions and morphologies of nanocrystals and nanocrystal heterostructures, and new ligands so that the nanocrystals can be incorporated into hybrid organic/inorganic devices, or biological systems. We are collaborating with a number of biology and medical groups to design nanocrystal probes that meet specific challenges.
Accelerated penetration of distributed energy resources (DER) for power generation and demand response (DR), the notion of just-in-time flexible consumption, are enabling the transformation from the current power grid structure to a modernized, cyber-enabled grid. In order to carry out an efficient design of Transactive Systems, a tightly integrated design of wholesale and retail markets and pricing policies is needed that incentivizes end-users' participation, accommodates physical constraints, and enables global objectives through local and distributed decision-making.
This talk will outline how this integrated design of wholesale and retail markets can be carried out. The two streams of research investigations from our lab will be featured: one is a dynamic wholesale market mechanism with the ability to make decisions at multiple time-scales, and the other is a hierarchical architecture capable of achieving volt-var control in the presence of large penetration of DERs and DRs. We will discuss how the results from these can be combined to result in an overall hierarchical Transactive architecture for smart distribution grids.