Company Filing History:
Years Active: 2022-2025
Title: Innovations by Dighanchal Banerjee
Introduction
Dighanchal Banerjee is a notable inventor based in Kolkata, India. He has made significant contributions to the field of technology, particularly in the areas of time-series forecasting and action recognition. With a total of four patents to his name, Banerjee is recognized for his innovative approaches that leverage advanced computational techniques.
Latest Patents
One of his latest patents is titled "System and method for online time-series forecasting using spiking reservoir." This invention addresses the limitations of existing systems in efficiently analyzing and forecasting time series data. By converting time series values into an encoded multivariate spike train, the system extracts temporal features and predicts future values using a linear combination of these features. The method employs FORCE learning to optimize the forecasting process, enhancing the system's memory capabilities.
Another significant patent is "System and method for real-time radar-based action recognition using spiking neural network (SNN)." This invention focuses on recognizing human actions through radar data, utilizing neuromorphic computing and SNNs. The system includes a preprocessing layer that analyzes radar data, followed by convolutional SNN layers that extract relevant features. The classifier layer then identifies the type of action performed, making it suitable for deployment on network edges.
Career Highlights
Dighanchal Banerjee is currently employed at Tata Consultancy Services Limited, where he continues to innovate and develop cutting-edge technologies. His work has positioned him as a key figure in the field of machine learning and artificial intelligence.
Collaborations
Some of his notable coworkers include Arun George and Sounak Dey, who contribute to the collaborative environment that fosters innovation within their projects.
Conclusion
Dighanchal Banerjee's contributions to technology through his patents demonstrate his commitment to advancing the fields of time-series forecasting and action recognition. His work not only addresses current challenges but also paves the way for future innovations.