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Smart Fridge and Smart Shelf real-time monitoring for efficient inventory management can be used in different verticals such as convenience and grocery stores, laboratories, and healthcare facilities. This end-to-end IoT solution uses Bluetooth*, WiFi*, sensors, gateways and a cloud infrastructure to track, monitor, collect data, and alert end-users of inventory.
This session demonstrates both solutions and explains the technologies, tools and protocols used to create each:
• Smart Fridge uses temperature, humidity, weight, and other sensors to track refrigerator conditions, power usage, whether the doors are open or closed, and identify the number and types of products taken from the refrigerator.
• Smart Shelf uses load cells, proximity, temperature, humidity sensors and an e-paper display. This application can identify the weight on a shelf, temperature and humidity, and how many people have come within close proximity of the shelf. With the e-paper display, the color scheme, product, cost and other display attributes can be changed locally or remotely.
There are already hundreds of thousands of cameras in many smart cities, but how intelligent are their video cameras and video management systems? This session digs into the use of artificial intelligence to control cameras, and explains how sensor data can be used to analyze video stored at the edge.
CrowdOptic Intersect APIs expose developers to triangulation and cluster detection algorithms, guiding them through the basics of how we work with camera lines of sight to bring artificial intelligence to the smart city.
A quick demonstration will drive home the depth of these APIs, showing how smart phones leverage cameras in the smart city to effectively look through walls and around corners. Through real-world examples of this exciting technology, you’ll come away with a new understanding of what’s coming next.
The Context Sensing SDK for IOT is a Node.js, Go, C#, Java and Python-based framework supporting the collection, storage, sharing, analysis and use of sensor information. Designed to simplify the work of developers, system integrators, and prototyping teams, the framework provides an expandable plugin system for physical and virtual sensors, local and fog sync mechanisms, and general-purpose analysis modules. The code base is designed to scale from Intel Atom® processor-class devices to Intel® Xeon® processor-class devices.
In this session, two practical use cases are discussed: IoTAR: Combining data from IoT devices with the visualization capabilities of Augmented Reality unleashes new possibilities for developers in the Retail, Automotive and Transportation, Industrial Automation and Energy sectors. The IoTAR project shows how devices connected through the SDK can be visualized and controlled through leading AR devices in today's market, giving rich user interfaces in an augmented world. Adaptive Learning: Traditional education systems apply one-size-fits-all learning strategies and cannot be easily modified to meet individual student needs. Technology can help provide personalized learning experiences. Adaptive Learning detects students’ emotional states (satisfied, bored and confused) and behavioral engagement (on-task, off-task) and fuses them to determine each student's overall engagement. Teachers can react and modify their lessons based on real-time engagement data provided via a dashboard.
