Latest Technologies - Exploration

Deep Learning is a type of machine learning in which a model learns to perform classification tasks directly from images, text or sound. Its usually implemented using a neural network architecture. Here deep refers to the "Number of layers in the network" i.e the more layers, the deeper the network. Traditional neural network contain only 2-3 layers, but in deep network contains hundreds of layers. Deep learning example is  A self-driving vehicle slows down as it approaches a pedestrian network . Deep learning is especially well-suited to identification applications such as face recognition, text translation, voice recognition and advanced driver assistance systems and traffic sign recognition etc. Deep learning is growing faster because it really works.  What is the Difference Between Deep Learning  and Machine Learning? Deep learning is a sub-type of machine learning. For example with machine  learning, you manually extract the relevant features of...

EDGE Essentials in IIoT  Architecting an IIoT system begins at the edge where industrial "things" are instrumented with sensors, actuators and edge nodes.  Key attributes:  On board data processing reduces the latency for time critical control decisions.   Synchronized data can be acquired. Edge ready hardware is available. To deploy IoT systems at the edge, below mentioned are the considerations.  Data Acquisition  Data processing Control Communication Security Deployment Data Acquisition:  Data is collected using edge nodes such as sensors, actuators at the machinery. Data can be temperature, pressure, position, vibration, rpm, motor/pump speed etc. When we talk about Data Acquisition, we have to consider sample rate of the data (i.e. at what time/moment need to collect each sample/data), resolution, triggering, signal conditioning and Isolation.  Let me explain each term: Sample rate is number of samples per second ...