International Business Machines Corporation

Charles Daniel Wolfson

Sushain Pandit

David Alec Selby

Brent P Gross

Ivan Matthew Milman

Martin Oberhofer

Albert Maier

Brian Patrick Byrne

Guenter Anton Sauter

Mike W Grasselt

Dan Jeffrey Mandelstein

Oliver Suhre

Matthias Kloppmann

Cynthia Maro Saracco

Chung-Sheng Li

Harald Clyde Smith

Bernhard Mitschang

Otis Smart

Harikumar Venkatesan

Su Liu

Fang Wang

Heather Maria Hinton

John Hans Handy-Bosma

Frank Leymann

Mei Yang Selvage

Berthold Reinwald

James C Kleewein

Peter Martin Schwarz

Kevin Christopher Brown

Matthias Schunter

Constance Jane Nelin

Jacques Labrie

Larry Justin Brown

Matthias Tschaffler

Michael R Self

Michael P Waidner

Holger Kache

Frederick L White

Alan P Plante

Stefan Dessloch

Veera Maddipati

Charles K Shank

Paulo R R Pereira

Kuntal Dey

Jian Ding

Florian Pinel

Mukesh Kumar Mohania

Curt Lee Cotner

Stephen James Todd

Serge Bourbonnais

Debbie A Anglin

Hamish C Hunt

L Venkata Subramaniam

Amanda Elizabeth Chessell

Patrick Dantressangle

Andrew T Penrose

Guoqiang Hu

Mary Ann Roth

Tanveer Afzal Faruquie

Jitendra Singh

Marc-Thomas Schmidt

Timothy Nicholas Holloway

Shilpi Ahuja

Rong Qu

Valer-Alin Crisan

Philip L Schwartz

Corey A Stubbs

Bruce A Benfield

Jeffrey Blight

Thomas Kemp

Rory O Donnell

Berhard Mitschang

Vander R Alves

Morgan Y Tong

In an approach for determining the date of planting of a crop growing in an agricultural field, a processor receives an aerial image of one or more agricultural fields in a pre-determined geographical region. A processor selects a plurality of points from the aerial image. A processor calculates a Vegetation Index of one or more crops growing at the plurality of points selected. A processor compares the Vegetation Index calculated for the one or more crops growing at the plurality of points selected to the Vegetation Index known for a plurality of historical reference signatures. A processor generates an actual signature. A processor cross-correlates the actual signature against the plurality of historical reference signatures to measure a degree of similarity. A processor identifies the one or more crops growing in the one or more agricultural fields in the pre-determined geographical region from the cross-correlation.

Agricultural crop estimated date of planting

In an approach for routing a user analytic request to a registry of analytics models and data sources and operationalizing the user analytic request, a processor receives a user analytic request. A processor applies natural language processing to the user analytic request. A processor associates a first set of vectors and a second set of vectors from the user analytic request to one or more analytics models and data sources by utilizing a plurality of domain-specific ontologies and a plurality of knowledge bases. A processor performs a semantic search for one or more concepts in the one or more analytics models and data sources. A processor receives the one or more concepts found in the one or more analytics models and data sources. A processor selects an analytics model to process the user analytic request. A processor executes the analytics model. A processor outputs a result to the user.

Context-driven analytics selection, routing, and management

In an approach for identifying a crop using satellite observation data and crop rotation data, a processor receives an aerial image of one or more agricultural fields in a pre-determined geographical region from an optical satellite. A processor selects a plurality of points from the aerial image. A processor clips the aerial image into one or more smaller images to generate one or more observed signatures. A processor determines there is a change in a spatial distribution of one or more crops. A processor generates an actual signature from the one or more observed signatures. A processor cross-correlates the actual signature against a plurality of historical reference signatures to determine a degree of similarity. A processor ranks one or more results of the cross-correlation. A processor identifies the one or more crops. A processor calculates a first estimate of an amount of the one or more crops.

Agricultural crop identification using satellite and crop rotation

A system and method are provided for inferring location attributes from data entries. The method comprises for data entries in a structured data set format, a computer system selecting a sample of rows. The computer system then identifies columns containing geospatial and temporal information based on the column headings. The computer system next identifies location information within the structured data set. The computer system determines implied location information based on the identified location information. The computer system derives location values based on the identified and implied location information using consolidation rules, resulting in a final set of location attributes for the data entries. The computer system then associates the final set of location attributes with the data entries.

Inferring location attributes from data entries

A method of predicting crop yield includes generating, via a processor, a plurality of vectors representative of growing conditions for a current time period and a plurality of vectors representative of growing conditions for a previous time period. The processor compares the plurality of vectors for the current time to the vectors of the previous time periods for corresponding growing conditions and determines which previous vectors are closest to the current vectors. The plurality of previous time periods are each associated with crop yields. Thus, the processor can determine a crop yield for the current time period for a selected crop producing field and crop type based on crop yields for the closest previous time periods.

Crop monitoring to determine and control crop yield

Historical device positioning data captured from one or more devices over a period of time is received. The historical device positioning data includes historical latitude, longitude, and elevation data of the one or more devices. Building boundaries for a give building are identified based upon the historical latitude and longitude data. The historical device positioning data corresponding to locations within the building boundaries of the building is clustered using a machine learning-based clustering algorithm, resulting in clusters with corresponding cluster centroids. The cluster centroids are associated with respective floors within the building. A current floor of the building on which a specific device is located is determined by mapping current device positioning data of the specific device to the closest cluster centroid.

Spatial device clustering-based emergency response floor identification

Computer-implemented event detection includes obtaining, at one or more processors, multimedia data including multiple frames of video data and corresponding audio data. The processor(s) process the multiple frames to detect at least one object and to track the object(s) between two or more of the frames. The processor(s) generate sonification audio data representing a position of the object(s) in the two or more frames, movement of the object(s), or both the position and the movement of object(s). The processor(s) generate combined audio data including the audio data and the sonification audio data. The processor(s) generate one or more feature vectors representing the combined audio data and provide the feature vector(s) as input to a trained event classifier to detect an event represented in the multimedia data.

Computer-implemented event detection using sonification

Embodiments of the present invention disclose a method, a computer program product, and a computer system for generating a wind map based on multimedia analysis. A computer receives a multimedia source configuration and builds a wind scale reference database. In addition, the computer extracts and processes both wind speed data and contextual data from the multimedia. Moreover, the computer analyses temporal and spatial features, as well as generates a wind map based on the extracted context, extracted wind speed, and analysed temporal and spatial features. Lastly, the wind map generator validates and modifies the wind scale reference database.

Multimedia derived wind map

According to one embodiment, a method, computer system, and computer program product for detecting and managing anomalies in one or more sensors is provided. The present invention may include simulating, by one or more environmental models, an expected output of the one or more sensors; responsive to identifying that an actual output of the one or more sensors differs from the expected output by a threshold value, detecting one or more anomalous sensors; and performing one or more actions to manage the one or more anomalous sensors based on the presence of the one or more sensors within a plurality of anomaly reaction zones.

Detecting and managing anomalies in underground sensors for agricultural applications

A system and method for managing and controlling data licenses for information assets. An information asset is stored in a repository, and registered in a catalog for the repository. A data license is associated with the information asset in the catalog, wherein the data license is stored as part of the information asset's metadata in the catalog. Access to the information asset stored in the repository is controlled based on the data license. A new data license is automatically created for a new information asset derived from an existing information asset, wherein the new data license is derived from an existing data license for the existing information asset. A lineage graph of the information asset is automatically created for audit purposes, to show that the information asset has been accessed in compliance with the data license.

Data license manager

Known geospatial device coordinates are clustered using a clustering algorithm into device clusters with cluster centroids. Each device cluster corresponds to a geographical location. Each cluster centroid is annotated with a regional floor-height value of the respective geographical location. Current device data of a device, including geographic location and elevation, are received. An approximate current floor upon which the first device is located is determined using the elevation of the first device and the annotated regional floor-height value of a closest cluster centroid, the closest cluster centroid determined based, at least in part, on the geographic location of the first device. An individual is directed to the device's geographic location and approximate current floor. The device clusters are re-computed based upon feedback from the individual regarding the device's actual floor.

First responder feedback-based emergency response floor identification

Using a location sensor, a location including a latitude and a longitude of a device is determined. Using an air pressure sensor, a first elevation of a device is determined. Using the location, a corresponding street address is determined. Using the street address and the first elevation, a floor number is determined. The street address and the floor number are reported to an emergency response system.

Enhanced elevation detection for emergency response systems

At an emergency response system, a first location including a latitude and a longitude at which a first device is located is received. At an emergency response system, an air pressure reading measured by the first device is received. Using the air pressure reading and a mean sea-level barometric measurement, a first elevation of a device is computed. Using the first location, a corresponding street address is computed. Using the street address and the first elevation, a floor number is computed. The street address and the floor number are transmitted to a second device.

Known geospatial device coordinates are clustered using a machine learning-based clustering algorithm into device clusters with cluster centroids. Each device cluster corresponds to a geographical location. Each cluster centroid is annotated with a regional floor-height value of the respective geographical location. Current device data of a device, including latitude, longitude, and elevation, are received, and a geographic location of the device is determined using the latitude and longitude. An approximate current floor upon which the first device is located is determined by mapping the current device data of the first device to a closest cluster centroid, and calculating the approximate current floor using the elevation of the first device and the annotated regional floor-height value of the closest cluster centroid. An individual is directed to the device's geographic location and approximate current floor. The device clusters are re-computed based upon feedback from the individual regarding the device's actual floor.

An initiation of an emergency communication with an emergency service associated with an emergency telephone number is detected by a mobile communications device. A completion of the emergency communication using a first communication path associated with a mobile communications service provider is attempted. The mobile communication device determines that the emergency communication using the first communication path has failed. An identity of a destination of the failed emergency communication is determined using location and telemetry data from the mobile communication device. A connection of the mobile communications device to the identified destination is facilitated using a second communication path. The mobile communications device is identified to the determined destination as an originator of the connection.

Emergency call service backup using device user plane communications

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Austin, TX, United States of America

Charles Daniel Wolfson