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Use Cases Fog Computing

Fog Computing

The concept of edge intelligence (EI) introduces a paradigm shift with regard to acquiring, storing, and processing data: the data processing is placed at the edge between the data source (e.g. a sensor) and the IoT core and storage services located in the cloud. As such, the literal definition of edge and intelligence is: the ability to acquire and apply knowledge and skills is shifted towards the outside of an area, here the core communication network or the cloud.

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Engaging Fans at one of the Largest Stadiums in the USA
Engaging Fans at one of the Largest Stadiums in the USA
Engaging and delighting fans has become the number one priority. However, the identity and behavior of fans within the stadium have been impossible to detect until Sirqul. Furthermore, standalone mobile apps historically have only seen a 5-30% penetration rate, thus leaving venues with poor data, insights and little interaction with the fan itself.This large stadium was looking for a set of recommendations to improve the fan experience, increase revenue and optimize operational efficiency based on this never seen before data.  
Employing Intel Deep Learning SDK Toward Bettering Image Recognition Models
Employing Intel Deep Learning SDK Toward Bettering Image Recognition Models
In this case study, the challenge explored involves LeNet*, one of the prominent image recognition topologies for handwritten digit recognition.   In the case study, we dive into how the training tool can be used to visually set up, tune, and train the Mixed National Institute of Standards and Technology (MNIST) dataset on Caffe* optimized for Intel® architecture. Data scientists are the intended audience.
Koito - Reality AI: Next-generation Adaptive Driving Beam (ADB) smart headlight
Koito - Reality AI: Next-generation Adaptive Driving Beam (ADB) smart headlight
The current versions of Adaptive Driving Beams available in Japan and Europe are using traditional machine vision techniques like template matching. Those machine learning techniques perform very well in constrained environments but are prone to many false positive when used in the dynamic, real world, with a lot more variations in targets and backgrounds.

The edge analytics market is estimated to grow from USD 1.94 billion in 2016 to USD 7.96 billion by 2021, at a Compound Annual Growth Rate (CAGR) of 32.6%.

Source: marketsandmarkets

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