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www.sbir.gov
KrishCorp
1201 Connecticut Ave NW Suite 600
Washington, DC 20036-0282
United States
Hubzone Owned:
Yes
Socially and Economically Disadvantaged:
Yes
Woman Owned:
No
Duns:
079295508
Principal Investigator
Name: Shivkumar Krishnamoorthy
Phone: (301) 213-6850
Email: raja@krishcorp.com
Business Contact
Name: Shivkumar Krishnamoorthy
Phone: (301) 213-6850
Email: raja@krishcorp.com
Research Institution
N/A
Abstract
KrishCorp is proposing a solution that simulates cognitive data capture, mimicking the way a human brain detects, contextualizes, and classifies data, through an image classification engine. This engine will identify and classify different objects and artifacts in an image or video feed and present an output of tagged metadata as well as a descriptive text transcript of the objects, artifacts and/or pertinent relational data. The engine will consist of two models; one which will be trained by using conventional image classification machine learning, and a second which will be a complementary contextual layer relating the objects to one another in a nuanced and innovative way. The engine will be able to identify patterns and make associations while processing multisensory inputs in the same manner as neuro-biological systems. To this end, the contextual layer of the engine, trained by a second model, can be extended to incorporate, analyze, and correlate multiple types of data from various sensors like air pressure, temperature, and other complex telemetry available in space-based applications. Using our solution, NASA systems will be able to ultimately perform autonomous decision making based on the multi-dimensional cognitive awareness our system will provide. The engine we will build will not only classify and tag objects contained in the data sources fed to it but also will build the context around it to give a more robust and nuanced representation. The model will employ a unique combination of a Spiking Neural Network (SNN) for image recognition and Long Short-term Memory (LSTM) to aid in unsupervised learning. The processed data will then be fed to the context building model where it will interpret the relational elements of the classified objects to provide a cognitive This model will in turn provide an accurate textual description of the original data source.
January 2022 | SBIR.gov

KrishCorp
1201 Connecticut Ave NW Suite 600
Washington, DC 20036-0282
United States
Hubzone Owned:
Yes
Socially and Economically Disadvantaged:
Yes
Woman Owned:
No
Duns:
079295508
Principal Investigator
Name: Shivkumar Krishnamoorthy
Phone: (301) 213-6850
Email: raja@krishcorp.com
Business Contact
Name: Shivkumar Krishnamoorthy
Phone: (301) 213-6850
Email: raja@krishcorp.com
Research Institution
N/A
Abstract
KrishCorp is proposing a solution that simulates cognitive data capture, mimicking the way a human brain detects, contextualizes, and classifies data, through an image classification engine. This engine will identify and classify different objects and artifacts in an image or video feed and present an output of tagged metadata as well as a descriptive text transcript of the objects, artifacts and/or pertinent relational data. The engine will consist of two models; one which will be trained by using conventional image classification machine learning, and a second which will be a complementary contextual layer relating the objects to one another in a nuanced and innovative way. The engine will be able to identify patterns and make associations while processing multisensory inputs in the same manner as neuro-biological systems. To this end, the contextual layer of the engine, trained by a second model, can be extended to incorporate, analyze, and correlate multiple types of data from various sensors like air pressure, temperature, and other complex telemetry available in space-based applications. Using our solution, NASA systems will be able to ultimately perform autonomous decision making based on the multi-dimensional cognitive awareness our system will provide. The engine we will build will not only classify and tag objects contained in the data sources fed to it but also will build the context around it to give a more robust and nuanced representation. The model will employ a unique combination of a Spiking Neural Network (SNN) for image recognition and Long Short-term Memory (LSTM) to aid in unsupervised learning. The processed data will then be fed to the context building model where it will interpret the relational elements of the classified objects to provide a cognitive This model will in turn provide an accurate textual description of the original data source.