Patents by Inventor Nilotpal Sinha

Nilotpal Sinha has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 12695527
    Abstract: A computer-implemented method and an apparatus for automatic modulation recognition that enables the detection and identification of modulation schemes in received raw signals with a signal receiving unit without prior information about the raw signal detail, comprising a computing unit configured to transform received raw signals from time domain to frequency domain including the noise in the signal with segmenting the signal and computing its modulation in multiple image with the spectrogram extraction process in order to capture temporal dependencies and sequential information by treating the raw signals as images; augmentation of the data for increasing the dataset size for increasing the accuracy with the limited data; training the data for enabling the network to learn spatiotemporal relationships based on a signal-to-noise ratio level; applying the data to one algorithm of two, which are convolutional neural network and convolutional neural network long short-term memory network hybrid algorithm.
    Type: Grant
    Filed: October 31, 2024
    Date of Patent: July 28, 2026
    Assignee: MULTIVERSE COMPUTING S.L.
    Inventors: Alessandro Genuardi, Nilotpal Sinha, Luc Andrea, Samuel Mugel, Roman Orus
  • Publication number: 20260095268
    Abstract: A computer-implemented method and an apparatus for automatic modulation recognition that enables the detection and identification of modulation schemes in received raw signals with a signal receiving unit without prior information about the raw signal detail, comprising a computing unit configured to transform received raw signals from time domain to frequency domain including the noise in the signal with segmenting the signal and computing its modulation in multiple image with the spectrogram extraction process in order to capture temporal dependencies and sequential information by treating the raw signals as images; augmentation of the data for increasing the dataset size for increasing the accuracy with the limited data; training the data for enabling the network to learn spatiotemporal relationships based on a signal-to-noise ratio level; applying the data to one algorithm of two, which are convolutional neural network and convolutional neural network long short-term memory network hybrid algorithm.
    Type: Application
    Filed: October 31, 2024
    Publication date: April 2, 2026
    Inventors: Alessandro Genuardi, Nilotpal Sinha, Luc Andrea, Samuel Mugel, Roman Orus