ANSYS is a leader in simulation world.

ANSYS Twin Builder and Microsoft Azure Digital Twins teams came together to integrate physics-based simulations with IoT Data. In this video, Olivier and Sameer Kher (Senior Director at Ansys) discuss the benefits of this joint solution.

The ANSYS Twin Builder combines the power of physics-based simulations and analytics-driven digital twins to provide real-time data transfer, reusable components, ultrafast modeling, and other tools that enable teams to perform “what-if” analyses, and build, validate, and deploy complex systems more easily.

Azure Time Series Insights Gen2 is now GA.

In this video, learn how customers can create an Industrial IoT analytics platform using Azure Time Series Insights (TSI)

Learn more at https://aka.ms/iotshow/timeseriesinsightsAzure Time Series Insights Gen2 is now GA. In this episode we look at how customers can create an Industrial IoT analytics platform using Azure Time Series Insights (TSI)

Learn more at https://aka.ms/iotshow/timeseriesinsights

In this video, learn how selecting the right partition key can make a huge difference in cost and performance with Azure Cosmos DB.

Program Manager Deborah Chen discusses how data partitioning ensures scale, why partition keys are so important for performance and cost-management, and how to select the right partition key for read-heavy or write-heavy workloads.

For more information, visit: https://www.azurecosmosdb.com

Insight has developed a Detection & Prevention solution to improve health and safety in businesses and public spaces. Utilizing video, IoT Edge and Azure, this solution enables organizations to detect conditions indicative of contagious disease and help to assure social distancing practices are in place.

Learn more at https://aka.ms/iotshow/InsightConnectedPlatform

And if you want to dive into Ben’s guidance for IoT Edge development, check out his blog post:
https://www.linkedin.com/pulse/azure-iot-edge-development-strategies-ben-kotvis .

Here is a quick overview of the new capabilities for the Azure Digital Twins Preview, the IoT platform that enables the creation of next-generation IoT connected solutions that model the real world.

In this video, you will not only learn about the new features but you will also see them in action as we walk through how to get started with the service.

Learn more about Azure Digital Twins: https://aka.ms/iotshow/DigitalTwins

Azure digital twins combine traditional business data with a comprehensive model of many different aspects of reality in a single pane of glass driving operations, analytics and simulation.

This episode shows about how Bentley’s iTwin and iModel.js technologies are integrated with Azure Digital Twins in creating a solution for civil infrastructure design and operations that brings “live and time series sensor data” to the design and operations of the roads, bridges and tunnels in a roadway engineering project in Australia.

Learn more about iModel.JS at https://aka.ms/iotshow/iModelJS

In addition to powerful deep learning frameworks like TensorFlow for Arduino, there are also classical ML approaches suitable for smaller data sets on embedded devices that are useful and easy to understand — one of the simplest is KNN.

One advantage of KNN is once the Arduino has some example data it is instantly ready to classify! We’ve released a new Arduino library so you can include KNN in your sketches quickly and easily, with no off-device training or additional tools required.

In this article, we’ll take a look at KNN using the color classifier example. We’ve shown the same application with deep learning before — KNN is a faster and lighter weight approach by comparison, but won’t scale as well to larger more complex datasets.

With the adoption of IoT, connected applications and systems are moving to the cloud.

The number of end-devices and data generated on the cloud is also increasing. Edge devices like sensors, mobile devices, wearables, robots, and many other connected devices in IoT ecosystem generate a huge amount of decentralized data.

Due to lack of reliable connectivity, delays and difficulties in processing this huge data on cloud, there is a challenge in analyzing and extracting important insights from this data. To deal with this challenge, enterprises are leveraging edge analytics along with cloud computing.

This combination brings stability in the IoT network by bringing the computational power near to the source of data and reducing the delays in analytics, resulting in real-time insights and resolutions for the problems of various industries. In other terms, when data cannot be taken to the algorithm, edge analytics brings algorithms to the data and provide important insights.