International Business Machines Corporation

Hitachi Global Storage Technologies Netherlands B.v.

Globalfoundries Inc.

Hendrik F Hamann

Levente Klein

Siyuan Lu

Sergio Adolfo Bermudez Rodriguez

Theodore Gerard Van Kessel

Fernando Jimenez Marianno

Madhusudan K Iyengar

Michael Alan Schappert

Yves Corfield Martin

Hemantha Kumar Wickramasinghe

James Andrew Lacey

Roger R Schmidt

Conrad M Albrecht

Josephine B Chang

Supratik Guha

Marcus Oliver Freitag

Robert J Von Gutfeld

S Jay Chey

Martin Patrick O'Boyle

Ramachandran Muralidhar

Sharathchandra UmapathiRao Pankanti

Alejandro Gabriel Schrott

Hans-Dieter Wehle

Ali Afzali-Ardakani

Jonathan Lenchner

Prabjit Singh

Matthew R Archibald

Vanessa Lopez-Marrero

Sergio A Bermudez

Richard C Chu

Yurii A Vlasov

Rajarshi Das

Dennis G Manzer

Emanuel Tutuc

Youngdeok Hwang

Andriy Stepanchuk

Johannes Walter Schmude

Guy M Cohen

Oki Gunawan

Praveen Chaudhari

Chie Ching Poon

Alan Weger

Gerald James Tesauro

Wang Zhou

Ildar Khabibrakhmanov

Herschel M Marchman

Alan Claassen

Roland Nagy

Carlo Siebenschuh

Jeffrey Donald Gelorme

Murthy V Devarakonda

Jeffrey Owen Kephart

Jan-Ulrich Thiele

Xiaoyan Shao

Jun Song Wang

Hongfei Li

Bret W Lehman

Shouheng Sun

Christopher L Molloy

Sudhir Muniswamy Gowda

Jamil A Wakil

Sharee Jane McNab

Akihisa Sakurai

Snorri Ingvarsson

Chung H Lam

Pradip Bose

Hubertus Franke

Eren Kursun

Jeffrey Scott Lille

Tymon Barwicz

Sufi Zafar

Neil Leslie Robertson

Talia S Gershon

Younghun Kim

Hon-Sum Philip Wong

David Robert Medeiros

Timothy Carl Strand

Chen-Yong Cher

Bruce Gordon Elmegreen

Kevin Robert Coffey

Fengnian Xia

Kota V R M Murali

Nancy C LaBianca

Brad L Brech

Prakash Kasiraj

Michelle Leigh Steen

Walter Hirt

Michael Paul Salo

James Vichiconti

Wanli Min

Aveek N Chatterjee

Shankar Km

Raymond Lloyd

Yasuo Amemiya

Jiaxing Liu

Michael T Prikas

Ronald F Ridgeway, Jr

John Wamburu

Nigel C P Hinds

William Michael John Green

From each of a plurality of cameras, a visual input of a location is received over a network. For each visual input from the plurality of cameras, a coupling correction is performed between a shaking of the camera with respect to the visual input by subtracting velocity vectors of the plurality of cameras from velocity vectors of pixels defining the visual input to provide a processed input. It is determined whether a shaking identified in the processed input is above a predetermined threshold based on the processed input, thereby detecting one or more anomalies. From the one or more anomalies, at least one of a location, magnitude, or depth of an earthquake are inferred based on the shaking identified in the processed input of each of the plurality of cameras.

Earthquake detection and response via distributed visual input

A computer implemented method rasterizes point cloud data. A number of processor units rasterizes the point cloud data into rasterized layers based on classes in which each rasterized layer in the rasterized layers corresponds to a class in the classes. The number of processor units creates key value pairs from the rasterized layers. The number of processor units store the key value pairs in a key value store. According to other illustrative embodiments, a computer system and a computer program product for rasterizing point cloud data are provided.

Point cloud data management using key value pairs for class based rasterized layers

A device includes a housing with a reference electrode having a metal plug extending through the housing. One or more sensing surfaces are disposed on an exterior of the housing, where the one or more sensing surfaces are configured to perform a nutrient measurement of a soil. A moisture sensor is disposed on an exterior of the housing for detecting a moisture level of the soil.

Soil nutrient sensing platform

A method includes: mapping a boundary of a volume of flowing fluid; partitioning the volume by a computational mesh; finding a contaminant location at a first sensor that is disposed within the volume; obtaining a measured velocity of the flowing fluid within the volume; generating a reversed velocity vector field within the mesh, in response to the measured velocity; time stepping the contaminant location from the first sensor along the reversed velocity vector field until the contaminant location intersects the boundary of the volume; and finding a contaminant source at the intersection of the time stepped contaminant location with the boundary of the volume.

Finding a contaminant source in a volume of flowing fluid

In some examples, a method of vector-raster data fusion includes receiving vector data for a geographical location, and statistically analyzing the vector data to obtain vector statistics. In some examples the method further includes rasterizing the vector statistics, and storing at least one of the vector data and the rasterized vector statistics together in a key-value store together with previously stored raster data for the geographical location. In some examples, the vector data further includes metadata, and the method further includes storing the metadata in at least one of the key-value store or a separate vector database.

Distributed vector-raster fusion

Methods and systems for data center management include collecting sensor data from one or more sensors in a rack; determining a location and identifying information for each asset in the rack using a set of asset tags associated with respective assets; communicating the sensor and asset location to a communication module; receiving an instruction from the communication module; and executing the received instruction to change a property of the rack.

Server rack for improved data center management

A computer produces predictions throughout a raster field in response to point data, by obtaining a partially empty matrix of point data, filling a matrix of extrapolated raster data by dilating the point data in a first convolutional neural network, and generating a matrix of aggregate raster data by combining the extrapolated raster data with organic raster data in a second convolutional neural network.

Combining point observations with raster data for machine learning

A computer-implemented method for determining field boundaries and crop forecasts in each field is provided. The method includes deriving vegetation indices for each geo-spatial pixel of each image of multi-spectral imagery at a plurality of points in time, constructing minimum bounding boxes for each image according to the vegetation indices, and generating, based on a neural network analysis of each image and the minimum bounding boxes, a geo-spatial plot of crops including a predicted plot of future crop usage for an area including each field in the multi-spectral imagery.

Method and system for crop recognition and boundary delineation

Methods and systems for generating a composite image in remote sensing applications are described. In an example, a device can receive an image having a plurality of points specified in an image space. The device can extract a portion of the image and transform points among the extracted portion from the image space to a parameter space defined by a distance parameter and an orientation parameter. The device can identify a set of intersection points in the parameter space that indicate at least one occurrence of a geometry feature in the extracted portion of the image. The device can augment the portion of the image with a plurality of new pixels based on the identified set of intersection points. The device can generate a composite image using the augmented image, where the composite image can include a plurality of augmented images corresponding to other portions of the image.

Scalable visual analytics for remote sensing applications

Methods and systems for managing vegetation include training a machine learning model based on an image of a training data region before a weather event, an image of the training data region after the weather event, and information regarding the weather event. A risk score is generated for a second region using the trained machine learning model based on an image of the second region and predicted weather information for the second region. The risk score is determined to indicate high-risk vegetation in the second region. A corrective action is performed to reduce the risk of vegetation in the second region.

Predicting and correcting vegetation state

A method of generating an aggregate forecast includes obtaining historical forecasts for a number of time steps and at least one location, obtaining historical conditions for the time steps and the at least one location, training a machine learning algorithm to produce an aggregate historical forecast in response to the historical conditions and the historical forecasts, and producing an aggregate current forecast by running the trained machine learning algorithm on current forecasts. The historical forecasts and the current forecasts vary in at least one of spatial resolution or temporal resolution, and include a first forecast that is valid for a first time step and a second forecast that is valid for a second time step.

Combining forecasts of varying spatial and temporal resolution

A computer-implemented method includes accessing, by a processing unit, an existing layer representing geospatial-temporal data at a selected timestamp. A first overview layer of the existing layer is generated by iteratively aggregating each cluster of cells of the existing layer into a corresponding lower-resolution cell of the first overview layer. The first overview layer therefore has a lower resolution than the existing layer. A query is received related to the geospatial-temporal data in the existing layer, and the query is processed with reference to the first overview layer.

Efficient querying using overview layers of geospatial-temporal data in a data analytics platform

Methods and systems for predicting irradiance include learning a classification model using unsupervised learning based on historical irradiance data. The classification model is updated using supervised learning based on an association between known cloudiness states and historical weather data. A cloudiness state is predicted based on forecasted weather data. An irradiance is predicted using a regression model associated with the cloudiness state.

Solar forecasting using machine learned cloudiness classification

Embodiments of the present invention are directed to a computer-implemented method for detecting vegetation near a power line. A non-limiting example of the computer-implemented method includes detecting the spectral characteristics of a target location. The spectral characteristics can be used to detect a tree at the target location. The location of the power lines that are within a buffer distance of the tree can be detected. Given that the tree is within a buffer distance, a risk value can be calculated for the tree. The risk value can convey the likelihood that the tree will contact the power line. If the risk is above a threshold amount, a work order can be issued to direct a crew to trim or cut the tree to prevent it from contacting the power line.

Automated management of potentially hazardous objects near power lines

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Yorktown Heights, NY, United States of America

Hendrik F Hamann