Quality matters, but despite advances made in video streaming technology, delivering great quality live video over the
Internet is still not easy. There are challenges with predicting the scale and potential audience location; challenges with
managing complex encoder and origin technology; challenges of delivering video securely and reliably over the Internet;
and challenges of protecting live streams. Add to that the range of devices, the complexity of live event production, and
the demand for HD and 4K image quality and you get a scenario that is more complicated than ever before.
LOOK TO RELEVANT USE CASES FOR YOUR BUSINESS. While use cases vary across industries, the most common ones fall into these categories and are usually associated with the listed neural network:
• Image classification or object detection: convolutional neural network (CNN) • Time-series predictions: long short-term memory (LSTM) • Natural language processing: recurrent neural network (RNN) • Unlabeled data classification and data labeling: autoencoder (AE) • Anomaly detection: autoencoder (AE) • Recommender systems: multilayer perceptron (MLP)
Work with your deep learning talent or consultants to identify which use cases best match your organization and desired solutions. Then recreate a successful, already proven method.
As if the selection of manufacturer weren't enough, many encoders offered in today's marketplace are highly configurable. Resolution, shaft size, seal options and connector variations all confront the design engineer with serious choices. Output type is equally important. In this paper, we'll cover some of the more typical applications and common output types.
In most drilling applications encoders are used to provide position and speed feedback for proper control of equipment like top drives and pipe racking systems. However, these electronic devices require special attention when used in hazardous environments such as a drilling platform. In this paper, we discuss ways to make encoders suitable for hazardous duty.