Deep Learning Deep Learning Meets DSP: OFDM Signal Detection In this blog post, we'll focus specifically on detection of RF signals modulated using Orthogonal Frequency Division Multiplexing (OFDM) using deep learning.

Deep Learning Choosing a Convolutional Neural Network Architecture for Real-Time Object Tracking (Part 2) This is part 2 of 3 in a series about selecting appropriate network architectures for real-time object tracking. In part 1 we compared the inference speed of various existing object detection networks. Now

Deep Learning Choosing a Convolutional Neural Network Architecture for Real-Time Object Tracking (Part 1) In a previous blog post we talked about how to train a convolutional neural network(CNN) for object detection in images. Object detection combines classification and localization. One use for object detection is

DGX Getting Python 3 running on a DGX We recieved a DGX station a few weeks ago and it is a powerhouse. We will not dive into the specs of the machine since it was covered in a previous post, where

DGX The DGX has arrived! If you've ever had the opportunity to train a neural network, you've probably realized that it's a major time saver to start with weights from a pre-trained model. Training a model from scratch

Signals Real-World RF Processing: Theory, Challenges & Machine Learning Understanding the theory and processing techniques from the digital communications world is critical in order to apply any sort of machine learning to the RF domain.

background subtraction Background Subtraction We'll show how to actually generate an estimate of the background, and then use it to perform detection on moving objects.

Deep Learning Modulation Recognition Using Deep Learning In this blog post, we'll talk about how to apply deep learning to modulation recognition, a challenging problem that has many applications in the digital communications world.