Qoherent develops the RIA Hub, an end-to-end development suite that enables the creation and deployment of intelligent radio applications using software-defined radio technology. The platform provides tools for synthesizing radio signals, curating datasets, and training machine learning models, addressing the need for efficient radio signal processing in complex environments.
Funding
$230K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Developing intelligent radio applications using software-defined radio (SDR) technology is challenging due to the complexities of radio signal processing, the need for curated datasets, and the difficulty of training machine learning models for radio environments. Existing solutions lack integrated tools for signal synthesis, data curation, and model training, hindering efficient development and deployment.
Solution
Qoherent's RIA Hub is a comprehensive development suite designed to streamline the creation and deployment of intelligent radio applications on software-defined radio platforms. Built upon the open-source RIA project, RIA Hub provides tools for synthesizing radio signals, curating datasets from SigMF recordings, and training machine learning models tailored for radio signal processing. The platform offers experiment, training, and deployment automation features, interoperability with various DSP and deep learning technologies, and streamlined deployment with leading SDR hardware solutions. By providing a library of functions, pre-trained models, model implementations, workflows, and datasets, RIA Hub accelerates prototyping and reduces the barriers to entry for intelligent radio development.
Target Audience
The primary target audience includes researchers, engineers, and developers in government labs, corporate research, and universities working on intelligent radio technology and software-defined radio applications.
Features
- Tools for generating radio signals using Python, GNU Radio, and MATLAB.
- Utilities for curating radio datasets from SigMF recordings and saving them as HDF5 files.
- Pre-built workflows for training high-performance machine learning models optimized for radio signals.
- Experiment, training, and deployment automation features.
- Interoperability with a selection of DSP and deep learning technologies.
- Streamlined deployment with leading SDR hardware solutions.
- Library of functions, pre-trained models, model implementations, workflows, and datasets.