Information Technology & Software
Uses existing low cost building CMOS camera infrastructure to locate and track subjects, and an heirarchical programming architecture ‘Sensory Grammars’ to infer subject behavior; jointly developed with UMD.
Yale University investigators have developed a new MR imaging method that accelerates image acquisition beyond conventional and parallel imaging methods. Rather than using linear encoding gradients as employed by current parallel imaging methods, O-space imaging utilizes nonlinear fields as encoding gradients and eliminates phase encoding. Since the spatial encoding gradient shapes are tailored to an existing surface coil array, more efficient use is made of the spatial information in the coil profiles. As an added benefit, nonlinear gradients may be ramped faster than linear gradients, further reducing image acquisition times.
Yale University researchers have now invented a collection of practical codes which have rates near capacity and which are also supported by an elegant theory demonstrating that these codes scale to nearly optimal no matter what the computational level. In a breakthrough in the field of communications, this is the first coding method that is demonstrated (i) to have the communication rate close to channel capacity at all scales of code size and (ii) to have the error probability exponentially small as a function a function of the size of the code. This theory applies to the real-world case of additive white Gaussian noise. The predictable scaling of these sparse superposition codes makes them excellent candidates for use in future communications protocols.
The present invention is directed to a method and computer system for representing a dataset comprising N documents by computing a diffusion geometry of the dataset comprising at least a plurality of diffusion coordinates. The present method and system stores a number of diffusion coordinates, wherein the number is linear in proportion to N.