OmniSIG™ SDK

With the OmniSIG™ SDK tool suite, customers can curate RF datasets, train state-of-the-art deep learning inference models for custom wireless sensing applications, and deploy them to edge-sensing devices. OmniSIG SDK contains DeepSig’s industry-leading baseline RF dataset for machine learning, including many consumer wireless signals. Customers can also incorporate their own custom data, signals, and signatures to train the AI sensor.

THE SDK FOR MACHINE LEARNING AND WIRELESS DATA

The OmniSIG SDK contains tools for:

1. Sorting, labeling, and curating RF data;
2. Training the OmniSIG Sensor model with labeled data;
3. Evaluating its performance; and then
4. Deploying the trained deep learning model into an OmniSIG runtime.

This tool suite is a market-first, enabling customers to custom-tune DeepSig’s deep learning models for signal detection and classification for their specific RF signatures and applications.

OmniSIG SDK was designed by engineers with decades of industry experience to enable signal processing on complex-valued RF sample data. It contains specialized features to assist in working with large RF datasets that don’t exist elsewhere.

DATA MANAGEMENT, LABELING & CURATION

AI workflows are driven by data. DeepSig has built a comprehensive labeling tool designed for working with signal recordings. The OmniSIG SDK provides an easy way to visualize signal captures and label them for use in AI systems, using automated, semi-automated, and hand-tuning methods.

The OmniSIG Sensor and OmniSIG SDK combine to enable a data engine that can improve signal detection and classification accuracy by capturing spurious data. Labeling the spurious data, retraining the network, and redeploying automatically improves the sensor’s ability to detect signals and signatures of interest. Leveraging the OmniSIG SDK lets your team focus identifying and triaging the rare cases with anomalies or interference to improve system performance without dealing with the complexities of deep learning techniques.

TRAINING AND VALIDATING A MODEL

The world of artificial intelligence is moving fast, and DeepSig’s machine learning research scientists are at the top of the field. A primary goal of the OmniSIG SDK is making the latest advances in AI available to customers without requiring machine learning expertise. We accomplish this by incorporating these advances into the OmniSIG Sensor and providing a simple-to-use web interface to configure model training.

Using either datasets provided by DeepSig (included with an OmniSIG SDK license) or custom signal captures, customers train the OmniSIG deep learning model until it reaches the desired level of performance.

Using a single desktop-class GPU, training an OmniSIG model takes only a few hours, depending on the required level of performance. Models can be exported from a live SDK training process at any point for testing and validation.

DEPLOYING WITH OMNISIG

Once trained, the custom model deploys with the OmniSIG™ Sensor for immediate use in wireless sensing systems. The output of the model is a metadata stream used to help downstream systems and operators build advanced AI-based systems.