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OmniSIG Studio

Train the OmniSIG Neural Network

Discover and classify signals – Train and deploy new OmniSIG models

OmniSIG Studio provides a simple, yet powerful tool for editing, labeling, and curating waveforms to train DeepSig’s unique deep learning architecture for detection and classification.  Enable rapid, no-code development of deep learning models for complex RF waveforms and train and deploy models in minutes or hours!  

Features

Web-based Interface

Packaged with an easy-to-use front end, RF signal captures of interest may be loaded, labeled and annotated with the integrated spectrogram. Studio’s training page is used to start, monitor, and quickly evaluate new OmniSIG neural network models.

Customized Signal Detection

Creation of custom AI-enabled RF detection and classification is as easy as capturing signals of interest and labeling them. In an easy-to-use drag-and-drop environment, with no coding or prior AI experience required, annotated files can be used to evolve a new custom neural network.

Fast and Easy Development

Provides a customizable environment for rapid development of RF classification models. OmniSIG software may be provided with compact new Studio models to begin detecting and classifying new and unknown signals at the edge within minutes or hours!

Advanced AI Made Easy

Hundreds of layers with thousands of parameters adapt to the annotated signals of interest provided to Studio. The convolutional neural network adapts and evolves to the information provided to it for an optimized RF signal identification solution.

Label and Annotate Data with Ease

Creating and managing RF datasets using OmniSIG Studio makes a complex task simple and easy. Whether using DeepSig’s comprehensive library of pre-qualified waveforms or adding newly captured RF datasets, Studio’s labeling tools provide an intuitive interface for annotating and labeling signals. This data is saved in an easy-to-read JSON format, following SigMF specifications.

No-code Capability

Intuitive Graphical User Interface (GUI) alleviates the need to write code when developing new neural network models. Quickly create new RF signal detection models, reduces development time, and lowers the cost.

Architecture

OmniSIG Studio works with captured RF data to build or add to pre-existing annotated datasets to train DeepSig’s convolutional neural network.  Trained models are deployed to an edge instance of OmniSIG  for real-time detection and classification of RF signals.

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