Parkinson's disease is notoriously difficult to diagnose as it relies primarily on the appearance of motor symptoms such as tremors, stiffness, and slowness, but these symptoms often appear several years after the disease onset. Now, Dina Katabi, the Thuan (1990) and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT and principal investigator at MIT Jameel Clinic, and her team have developed an artificial intelligence model that can detect Parkinson's just from reading a person's breathing patterns. The tool in question is a neural network, a series of connected algorithms that mimic the way a human brain works, capable of assessing whether someone has Parkinson's from their nocturnal breathing -- i.e., breathing patterns that occur while sleeping. The neural network, which was trained by MIT PhD student Yuzhe Yang and postdoc Yuan Yuan, is also able to discern the severity of someone's Parkinson's disease and track the progression of their disease over time. Yang and Yuan are co-first authors on a new paper describing the work, published today in Nature Medicine.

Street lamps constitute the densest electrically operated public infrastructure in urban areas. Their changeover to energy-friendly LED light quickly amortizes and is increasingly leveraged for smart city projects, where LED street lamps double, for example, as wireless networking or sensor infrastructure. We make the case for a new paradigm called SLaaP--street lamps as a platform. SLaaP is proposed as an open, enabling platform, fostering innovative citywide services for the full range of stakeholders and end users--seamlessly extending from everyday use to emergency response. In this article, we first describe the role and potential of street lamps and introduce one novel base service as a running example. We then discuss citywide infrastructure design and operation, followed by addressing the major layers of a SLaaP infrastructure: hardware, distributed software platform, base services, value-added services and applications for users and'things.' Finally, we discuss the crucial roles and participation of major stakeholders: citizens, city, government, and economy. Recent years have seen the emergence of smart street lamps, with very different meanings of'smart'--sometimes related to the original purpose as with usage-dependent lighting, but mostly as add-on capabilities like urban sensing, monitoring, digital signage, WiFi access, or e-vehicle charging.a The future holds even more use cases: for example, after a first wave of 5G mobile network rollouts from 2020 onward, a second wave shall apply mm-wave frequencies for which densely deployed light poles can be appropriate'cell towers.'

Vivek Goyal isn't a professional photographer, but he and his colleagues have developed an intriguing party trick: they can capture the image of an object completely out of sight. They demonstrated the trick in a windowless room on the Boston University campus, where Goyal works as an electrical engineering professor. In the room, a flat-screen monitor displayed a series of crude drawings created by Goyal's graduate student, Charles Saunders. Among them were several masterpieces: A mushroom that resembles Toad from Mario Kart, a Simpsons-yellow dude wearing a sideways red baseball cap, the red letters "BU" for school pride. These are the images that Goyal and his team wanted to capture while pointing the camera lens in a completely different direction.