Leland Stanford Junior University

California Institute of Technology

University of California

Emmanuel J Candes

David Leigh Donoho

Terence Tao

Peter D Olcott

Craig Steven Levin

Ewout Van Den Berg

Garry Chinn

Carlos Alberto Sing-Long Collao

Laurent Demanet

A method for providing an image from a device with a plurality of sensors and a plurality of time to digital converters (TDC) is provided. Data signals are generated by some of the plurality of sensors, wherein each sensor of the plurality of sensors provides output in parallel to more than one TDC of the plurality of TDCs and wherein each TDC of the plurality of TDCs receives in parallel input from more than one sensor of the plurality of sensors and where a binary matrix indicates which sensors are connected to which TDC. The data signals are transmitted from the sensors to the TDCs. TDC signals are generated from the data signals. Group testing is used to decode the TDC signals based on the binary matrix.

Digital multiplexing readout for sparse signals on imaging arrays

A computer-implemented method for correcting transmission errors. According to the method, a transmitted vector corrupted by error can be recovered solving a linear program. The method has applications in the transmission of Internet media, Internet telephony, and speech transmission. In addition, error correction is embedded as a key building block in numerous algorithms, and data-structures, where corruption is possible; corruption of digital data stored on a hard-drive, CD, DVD or similar media is a good example. In short, progress in error correction has potential to impact several storage and communication systems.

Computer-implemented method for correcting transmission errors using linear programming

Fast digital implementations of the second generation curvelet transform for use in data processing are disclosed. One such digital transformation is based on unequally-spaced fast Fourier transforms (USFFT) while another is based on the wrapping of specially selected Fourier samples. Both digital transformations return a table of digital curvelet coefficients indexed by a scale parameter, an orientation parameter, and a spatial location parameter. Both implementations are fast in the sense that they run in about O(nlog n) flops for n by n Cartesian arrays or about O(N log N) flops for Cartesian arrays of size N=n; in addition, they are also invertible, with rapid inversion algorithms of about the same complexity.

Methods for performing fast discrete curvelet transforms of data

A data processing technique called a Ridgelet transform is disclosed for more efficiently representing information. Original data samples (e.g., in the time domain) are received and transformed into frequency domain values provided in Cartesian coordinates. The frequency domain values provided in Cartesian coordinates are then transformed to digital polar coordinates (provided in a digital polar grid). Because the polar grid is non-uniform, the polar coordinate values can be weighted or normalized. A Wavelet transform is performed on data derived the frequency domain values provided in digital polar coordinates to generate Wavelet coefficients (or Ridgelet coefficients). Next a thresholding or filtering process can be performed on the Ridgelet coefficients to select a group of larger Wavelet coefficients and to discard the remaining Wavelet coefficients.

Digital ridgelet transform via digital polar coordinate transform

Growing community of inventors

Stanford, CA, United States of America

Emmanuel J Candes