Massachusetts Institute of Technology

At+t Corp.

The Charles Stark Draper Laboratory, Inc.

AT&T Intellectual Property I, LP

Vanu, Inc.

Piotr Indyk

Shanmugavelayutham Muthukrishnan

Nikolaos Koudas

Christopher C Yu

Martin Joel Strauss

Yaron Rachlin

Ioannis Kotidis

Anna C Gilbert

Brian Gardiner

Rishi Gupta

Eric Price

Dina Katabi

Sudipto Guha

Haitham Zuhair Al-Hassanieh

Fadel Adib

David R Karger

Jon Feldman

William W Whitacre

Omid Salehi-Abari

Lei Hamilton

Timothy Galvin

Albert M Chan

Raghavendra Madyastha

Sudipto Cuha

Michael Rodriguez

An approach to acquisition of a propagation direction using a phased antenna array avoids a need to scan space. Given all possible directions for setting the antenna beam, the approach provably finds the optimal direction in logarithmic number of measurements. Further, the approach can be applied within the existing 802.11ad standard for mmWave LAN, and can support both clients and access points.

Hash based beam alignment

Systems and methods may identify at least a first set of right singular vectors and a first set of singular values of a subset of the matrix, reduce the subset by an amount of energy of a selected data entry of the subset based on the first set of right singular vectors and the first set of singular values, incorporate a new data entry from the matrix into the subset, update the first set of right singular vectors and the first set of singular values of the subset based on the new data entry by a singular value decomposition (SVD) update, and generate the low-rank approximation of the matrix based on the updated first set of right singular vectors and the updated first set of singular values.

Systems and methods for low-rank matrix approximation

Methods and apparatuses for compressive sensing that enable efficient recovery of features in an input signal based on acquiring a few measurements corresponding to the input signal. One method of compressive sensing includes folding an image to generate first and second folds, and recovering a feature of the image based on the first and second folds without reconstructing the image. One example of a compressive sensing apparatus includes a lens, a focal plane array coupled to the lens and configured to generate first and second folds based on the image, and a decoder configured to receive the first and second folds and to recover a feature of the image without reconstructing the image. The feature may be a local geometric feature or a corner. Compressive sensing methods and apparatuses for determining translation and rotation between two images are also disclosed.

Compressive sensing with local geometric features

An offset estimator (e.g., a time delay, a spatial image offset, etc.) makes use of a transform approach (e.g., using Fast Fourier Transforms). The sparse nature of a cross-correlation is exploited by limiting the computation required in either or both of the forward and inverse transforms. For example, only a subset of the transform values (e.g., a regular subsampling of the values) is used. In some examples, an inverse transform yields a time aliased version of the cross-correlation. Further processing then identifies the most likely offset of the original signals by considering offsets that are consistent with the aliased output.

Fast transform based offset determination

A system and method are provided for summarizing dynamic data from distributed sources through the use of histograms. In particular, the method comprises receiving a first data signal at a first location, determining a first array sketch of the first data signal, and constructing a first output histogram from the first array sketch and a first robust histogram via a first hybrid histogram. Array sketches of a number of data signals may be calculated, and added to yield a single vector sum. The histogram is constructed from the vector sum. In that way, the vector sum may be analyzed without revealing the individual data signals that form the basis of the sum.

Method and apparatus for using histograms to produce data summaries

A system, method and computer-readable medium are disclosed for identifying representative data using sketches. The method embodiment comprises generating a plurality of vectors from a data set, modifying each of the vectors of the plurality of vectors and selecting one of the plurality of generated vectors according to a comparison of a summed distance between a modified vector associated with the selected generated vector and remaining modified vectors. Modifying the generated vectors may involve reduced each generated vector to a lower dimensional vector. The summed distance then represents a summed distance between the lower dimensional vector and remaining lower dimensional vectors.

System, method and computer-readable medium for providing pattern matching

Methods, systems, devices, and computer program products for decoding of Walsh codewords are disclosed.

Decoding of Walsh codes

Method and apparatus for using histograms to product data summaries

A system and method are provided for monitoring dynamic data from distributed sources through the use of histograms. In the method, an array sketch of the digital signal is determined, a robust histogram is constructed from the array sketch, and an output histogram is constructed from the array sketch and the robust histogram via a hybrid histogram. Dyadic intervals of a representation of the array sketch are used in constructing the robust histogram.

A method and system for identifying representative data trends using sketches. A sketch is a lower dimensional vector used to represent higher dimensional data. The properties of sketches include data dimensionality reduction, sketches synthesized from other sketches, and the distance between sketches comparable to the distance between the data the sketches represent. Exemplary embodiments include identifying relaxed periods and average trends.

Method and system for identifying representative trends using sketches

Growing community of inventors

Cambridge, MA, United States of America

Piotr Indyk