Tothemoon24
Top 20
Apologies I can’t seem to shorten .
I’m a big fan of Tata / TCS some interesting patent information.
Generally speaking this is above my hourly rate
$2.34
I’m a big fan of Tata / TCS some interesting patent information.
Generally speaking this is above my hourly rate
$2.34
| 1 | EP3913534A4 | SYSTEM AND METHOD FOR REAL-TIME RADAR-BASED ACTION RECOGNITION USING SPIKING NEURAL NETWORK(SNN) Application | Publication/Patent Number: EP3913534A4 | Publication Date: 2021-11-24 | Application Number: EP20213433 | Filing Date: 2020-12-11 | Inventor: Rani, Smriti Dey, Sounak George, Arun Pal, Arpan Banerjee, Dighanchal Chakravarty, Tapas Chowdhury, Arijit Mukherjee, Arijit | Assignee: Tata Consultancy Services Limited | IPC: G06K9/00 | Abstract: This disclosure relates generally to action recognition and more particularly to system and method for real-time radar-based action recognition. The classical machine learning techniques used for learning and inferring human actions from radar images are compute intensive, and require volumes of training data, making them unsuitable for deployment on network edge. The disclosed system utilizes neuromorphic computing and Spiking Neural Networks (SNN) to learn human actions from radar data captured by radar sensor(s). In an embodiment, the disclosed system includes a SNNmodel having a data pre-processing layer, Convolutional SNN layers and a Classifier layer. The preprocessing layer receives radar data including doppler frequencies reflected from the target and determines a binarized matrix. The CSNN layers extracts features (spatial and temporal) associated with the target's actions based on the binarized matrix. The classifier layer identifies a type of the action performed by the target based on the features |
| 2 | EP3913534A1 | SYSTEM AND METHOD FOR REAL-TIME RADAR-BASED ACTION RECOGNITION USING SPIKING NEURAL NETWORK(SNN) Application | Publication/Patent Number: EP3913534A1 | Publication Date: 2021-11-24 | Application Number: EP20213433.4 | Filing Date: 2020-12-11 | Inventor: Dey, Sounak Mukherjee, Arijit Banerjee, Dighanchal Rani, Smriti George, Arun Chakravarty, Tapas Chowdhury, Arijit Pal, Arpan | Assignee: Tata Consultancy Services Limited | IPC: G06K9/46 | Abstract: This disclosure relates generally to action recognition and more particularly to system and method for real-time radar-based action recognition. The classical machine learning techniques used for learning and inferring human actions from radar images are compute intensive, and require volumes of training data, making them unsuitable for deployment on network edge. The disclosed system utilizes neuromorphic computing and Spiking Neural Networks (SNN) to learn human actions from radar data captured by radar sensor(s). In an embodiment, the disclosed system includes a SNNmodel having a data pre-processing layer, Convolutional SNN layers and a Classifier layer. The preprocessing layer receives radar data including doppler frequencies reflected from the target and determines a binarized matrix. The CSNN layers extracts features (spatial and temporal) associated with the target's actions based on the binarized matrix. The classifier layer identifies a type of the action performed by the target based on the features |
| 3 | US20210365778A1 | SYSTEM AND METHOD FOR REAL-TIME RADAR-BASED ACTION RECOGNITION USING SPIKING NEURAL NETWORK(SNN) Application | Publication/Patent Number: US20210365778A1 | Publication Date: 2021-11-25 | Application Number: US17/122,041 | Filing Date: 2020-12-15 | Inventor: Dey, Sounak Mukherjee, Arijit Banerjee, Dighanchal Rani, Smriti George, Arun Chakravarty, Tapas Chowdhury, Arijit Pal, | Assignee: Tata Consultancy Services Limited | IPC: G06N3/08 | Abstract: This disclosure relates generally to action recognition and more particularly to system and method for real-time radar-based action recognition. The classical machine learning techniques used for learning and inferring human actions from radar images are compute intensive, and require volumes of training data, making them unsuitable for deployment on network edge. The disclosed system utilizes neuromorphic computing and Spiking Neural Networks (SNN) to learn human actions from radar data captured by radar sensor(s). In an embodiment, the disclosed system includes a SNNmodel having a data pre-processing layer, Convolutional SNN layers and a Classifier layer. The preprocessing layer receives radar data including doppler frequencies reflected from the target and determines a binarized matrix. The CSNN layers extracts features (spatial and temporal) associated with the target's actions based on the binarized matrix. The classifier layer identifies a type of the action performed by the target based on the features. |