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
Deep Learning Intelligent RF Spectral Monitoring In this blog, we highlight a number of different spectral monitoring use cases where deep learning can play a key role and show how intelligent spectral monitoring can be applied to a number of different use-cases in the RF domain.
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.
Deep Learning Training an FCN for Object Detection One of the many useful tasks that can be accomplished using deep learning is visual object detection. For example, a Deep Neural Network (DNN) can be trained to detect an object (such as
Signal Detection Using Deep Learning - Part II In the previous post, Signal Detection Using Deep Learning - Part I, we introduced basic methods for detecting signals corrupted by noise. The motivation for these posts is that there are many resources
OpenCV OpenCV 3.2.0 Setup on iOS Gone are the days of needing a dedicated desktop to be able to utilize the power of image processing software. OpenCV is one of the most popular image processing libraries out there, so
Deep Learning Training A CNN With The CIFAR-10 Dataset Using DIGITS 4.1 Recently, several friends and contacts have expressed an interest in learning about deep learning and how to train a neural network. Although there are many resources available, I usually point them towards the
Convolutional Neural Network Signal Detection Using Deep Learning There are many resources for learning how to use Deep Learning to process imagery. However, very few resources exist to demonstrate how to process data from other sensors such as acoustic, seismic, radio,
DIGITS Building a Digits Dev Machine on Ubuntu 16.04 Ubuntu 16.04 with CUDA 8.0, OpenCV 3.2.0, Caffe 0.15, and Digits 5.0 ==Notes: Updated: 6/22/2017 == Pre-Setup If you're like me, you like to have control