The future of Internet of Things (IOT) will be driven by the applications we choose, and this will create new engineering challenges for the future.

With the advent of 5G or LoRaWAN communications, the problem of connectivity seems like it is about to be solved, and the vision of an IOT spanning all industrial and consumer applications seems to be almost a reality. Simultaneously, the availability of low-cost, low-power sensing and computing platforms means that it is feasible for nearly anyone to create an IoT device. However, market acceptance depends on creating something that enhances the quality of life for those using the system and can be used sustainably. To achieve this, there is a need for new approaches to the design, engineering, and maintenance of IoT systems, which, in turn, must be driven by a deep understanding of the real-world context of the application.

iHomeLab has been applying IoT technology for smart energy management and active and assisted living for over 10 years. In this talk, Andrew Paice highlights the key factors for the acceptance of technology, and reflects on the resulting implications for the design, development, and implementation of IoT systems. This leads to a vision of human- or application- centered IoT design for the future.

Insight. Implementation. Integration.

AI, or artificial intelligence, is transforming the products we build and the way we do business. It also presents new challenges for those who need to build AI into their systems. Creating an “AI-driven” system requires more than developing intelligent algorithms. It also requires:

• Insights from domain experts to generate the tests, models, and scenarios required to build confidence in the overall system
• Implementation details including data preparation, compute-platform selection, modeling and simulation, and automatic code generation
• Integration into the final engineered system

Join us as Jason Ghidella demonstrates how engineers and scientists are using MATLAB^® and Simulink^® to successfully design and incorporate AI into the next generation of smart, connected systems.

Machine tools remain the major vehicle driving technological progress today. This industry segment is highly influenced by rapid evolution in Industry 4.0 technologies, including machine connectivity, Internet of Things (IoT), manufacturing process simulation, production flow modelling, and cloud-based analytics for predictive maintenance.

Artificial intelligence and machine learning technologies have a special place in the industrial IoT. Driven by increasing manufacturing performance needs together with growing technology complexity on one side and the reducing base of skilled operators combined with shrinking product cycle times and time to market on the other, machine tool makers have to respond in a smart way to meet expectations in productivity gains. This expected change applies both to end users, such as machine operators and workshop managers, and to product development teams of machine tool builders.

As a major industrial player, GF Machining Solutions is at the forefront of integrating machine learning technologies into its products. In this presentation, Dr. Sergei Schurov discusses several applications of such algorithms to the GF Solutions portfolio and will explore the benefits and challenges of bringing them into new system designs while remaining competitive in the market.