Conventional vision systems are mostly based on imaging sensors consisting of arrays of light-sensitive pixels, each of which measures the intensity of light falling upon it. Such pixels cannot directly acquire other important multimodal information of light, such as its incident angle, wavelength, and phase. While the intensity information is sufficient for conventional applications such as photography, it has significant limitations in advanced vision tasks. We will show a new class of photodetectors enabled by nanophotonics that can measure multimodal information of light waves.

In the last two decades, automation has had a significant impact on software testing and analysis.  Automated testing techniques, such as symbolic execution, concolic testing, and feedback-directed fuzzing, have found numerous critical faults, security vulnerabilities, and performance bottlenecks in mature and well-tested software systems. The key strength of automated techniques is their ability to quickly search state spaces by performing repetitive and expensive computational tasks at a rate far beyond the human attention span and computation speed.

Positioning has been the Holy Grail of wireless sensing research with a wide range of applications from tracking virtual reality devices to in-body implants. However, despite two decades of active research, a widely deployable system with high accuracy has always been elusive. Wireless signals reflected from objects in the environment interfere with and distort the signal from the intended target device, corrupting the position estimates.

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