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How to Use Analytics-Driven Embedded Systems to Drive Smart Technology Development

Analytics-driven embedded systems are here. The ability to create analytics that process massive amounts of business and engineering data is enabling design engineers in many industries to develop smarter products and services. Engineers today can use analytics to describe and predict a system’s behavior, and further combine analytics with embedded control systems and sensors to automate actions and decisions. In some implementations, the analytics are performed in the cloud to improve embedded-system performance. In other cases, the analytics run directly in the embedded systems.
While many engineers may be familiar with pre-processing data and developing analytics algorithms, this article will focus on running analytics in real time and integrating analytics with sensors and embedded systems, as well as IT systems and cloud applications.

Real-time analytics

Developing analytics for embedded systems starts with a workflow in which data are accessed and preprocessed, and algorithms are selected. In the training workflow in Figure 1, one uses stored data to develop pre-processing code and machine learning to develop a trained model. In the prediction workflow in Figure 1, the same pre-processing code and the trained model are applied to live data to perform real-time analytics.

Figure 1 – An overview of the machine learning workflow for both the training path and prediction path used for real-time analytics. Copyright: © 1984–2016 The MathWorks, Inc.

Figure 1 – An overview of the machine learning workflow for both the training path and prediction path used for real-time analytics. Copyright: © 1984–2016 The MathWorks, Inc.

This workflow can be used to combine engineering, scientific, and field data with business and transactional data (see Figure 2 for examples of data sources for each type). It allows the creation of sophisticated analytics to develop smarter systems. Combining sensor-generated data with other real-time sources such as historical data, is the power behind the Internet of Things (IoT), the machine-to-machine coordination of Industry 4.0, and the automotive trend towards a connected and autonomous vehicle.

Figure 2 – Real-time analytics can be used for sophisticated systems by combining sensor data with data sources from engineering, scientific, and field data as well as business and transactional data. Copyright: © 1984–2016 The MathWorks, Inc.

Figure 2 – Real-time analytics can be used for sophisticated systems by combining sensor data with data sources from engineering, scientific, and field data as well as business and transactional data. Copyright: © 1984–2016 The MathWorks, Inc.

Integrating analytics in IT systems and the cloud

In some system implementations, analytics are performed in enterprise IT systems to improve embedded-system performance. The analytics can be automatically generated as deployable components compatible with IT development environments such as Java, Microsoft .NET, Excel, and C/C++, enabling them to be integrated – without recoding – into web, database, desktop, and scalable enterprise applications running on-premise or in a private or public cloud.

Figure 3 – Analytics can be integrated into Business Systems (top), smart connected systems (left), or a combination of both. Copyright: © 1984–2016 The MathWorks, Inc.

Figure 3 – Analytics can be integrated into Business Systems (top), smart connected systems (left), or a combination of both. Copyright: © 1984–2016 The MathWorks, Inc.

Integrating analytics with sensors and embedded devices

Data reduction, sensor fusion, or predictive analytics can be integrated to run directly on embedded systems in smart connected systems, also shown in Figure 3. The accelerating IoT trend towards smarter and more connected sensors adds increased pressure to move more processing and analytics as close to the sensors as possible. This has the benefit of shrinking the amount of data that is transferred over the network, reducing the cost of transmission and the power consumption of wireless devices. For embedded system designers, it’s important to consider not only algorithm performance, but also the overall system robustness, reliability, and cost in the architecture and design. To do this, a Model-Based Design approach can be used to simulate the system, automatically generate embedded code, and continuously test and verify the analytics being integrated into the embedded system.

For example, in the case of Advanced Driver Assistance Systems (ADAS), by combining data analytics and Model-Based Design workflows, ADAS engineers are able to refine algorithms using large test sets with ground truth labeling, perform rigorous simulation and validation, and then automatically generate code of the validated algorithms for automotive embedded systems.

We’re just beginning to imagine the possibilities of analytics-driven embedded systems. Innovations in sensors, big data, and machine learning now make it possible for engineering teams to develop smarter and more autonomous systems that have the potential to dramatically improve designs and create new categories of products and services previously unimaginable.

Paul Pilotte headshotContributed by: Paul Pilotte, Technical Marketing Manager, MathWorks. Paul Pilotte has more than 20 years of experience in technical marketing and development in technical computing, security software, data communications, and test-equipment markets. He currently is technical marketing manager at MathWorks focusing on MATLAB toolboxes for statistics, optimization, symbolic math, and computational finance. He holds a Bachelor’s and Master’s degrees in electrical engineering from MIT and an MBA from Babson College.

 

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