Artificial Intelligence Radio - Transceiver (AIR-T)

by Deepwave Digital

A high-performance SDR seamlessly integrated with state-of-the-art deep learning hardware

View all updates Jul 24, 2018

FUNDED!

Deepwave is excited to announce that we are funded!

First off, we would like to thank everyone who supported the launch of the Artificial Intelligence Radio - Transceiver. We are very excited about the momentum the AIR-T has generated. We want to truly thank everyone who has helped us achieve this goal, weeks ahead of schedule.

Over the past month, we have received emails and inquiries from individuals, researchers, and companies from around the world working on creative projects far beyond what we had imagined the AIR-T would be used for. For those people, we just want to let you know that we hear you too.

Thank You!

The Deepwave Team


$193,813 raised

of $20,000 goal

969% Funded! Order Below

Product Choices

$5,899

Air-T With Enclosure

The AIR-T is the first platform that enables out-of-the-box machine learning wireless systems. Simply port current GNURadio or code directly in Python to access the radio frequency spectrum. Includes, AIR-T board, four MCX-to-SMA cables, getting started tutorial, and power supply. The Air-T enclosure is expertly constructed from aluminum to produce a polished, elegant, and sleek metallic silver finish. It measures 192 x 182 x 79 mm (7.5 x 7.2 3.1 inches) and the power button illuminates blue when they system is on. All RF ports are brought to the front of the enclosure for ease of use and all computer peripherals connections are brought to the rear.

Credits

Deepwave Digital

We are a dedicated team of radio frequency and wireless industry experts with over fifty years of combined experience providing digital signal processing solutions to the commercial and defense industries. We understand both the technical capabilities as well as the limitations of these technologies, leading us to develop novel hardware and software solutions to combine the fields of artificial intelligence and signal processing.


John Ferguson

Steven Kline

Peter Witkowski

Steve Lisi

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