Freescale Semiconductor Releases Intelligent Sensing Framework 2.1

Freescale Semiconductor today introduced its Intelligent Sensing Framework (ISF) 2.1, which now includes integration with Freescale’s Processor Expert tool to help create, configure, and generate embedded sensor-based applications for Freescale microcontrollers (MCUs).

This integration, together with the framework’s sensor fusion functionality and expanded support for additional sensor types, speeds and simplifies the development of sensor-based solutions for the Internet of Tomorrow – from the connected home and wearables to new medical and connected industrial applications.

A large portion of new, sensor-based Internet of Things (IoT) solutions originate from startup organizations often unfamiliar with the complex process of abstracting, combining and using sensor data. As sensors become increasingly critical to the success of IoT applications, embedded developers need to find ways to quickly and easily integrate multiple streams of sensor data with the MCUs powering their applications.

To address this dynamic, the integration of Freescale’s ISF with Processor Expert development technology provides an easy-to-use tool that streamlines and simplifies the process of abstracting and incorporating multiple sensor data streams into a device or application based on Freescale’s popular families of Kinetis MCUs. Developers can then focus on how the sensor data is used, rather than how it is abstracted and combined.

“The growth of the IoT is enabling services to be created around products that were previously unconnected and unaware of the environments within which they operate,” said James Bates, SVP and GM for Freescale’s Analog and Sensors group. “At the heart of this transformation are intelligent sensor clusters that can deliver information wirelessly and securely. ISF, combined with Processor Expert technology, allows developers to create embedded applications using sensor clusters much more quickly. By removing the burden of abstracting and gathering sensor data, developers can focus on adding their own functionality and IP, driving new innovation for the IoT faster than ever before.”

Leveraging Freescale’s Kinetis MCUs, connectivity solutions, IoT protocols, and security solutions, ISF 2.1 can dramatically reduce time-to-market for sensor-based IoT applications. The framework can be deployed across a wide range of Freescale Kinetis MCUs, provides out-of-box support for the majority of Freescale’s intelligent sensors, and complements Freescale’s broad MCU enablement ecosystem to provide developers a seamless, integrated design environment for incorporating Freescale MCUs and sensors into customers’ embedded system designs.

ISF 2.1 is designed to grow with the Freescale sensor portfolio, and now integrates Freescale’s Sensor Fusion library as a drop-in Processor Expert orientation sensor component. This component enables full configuration of the underlying sensors and fusion algorithms. The framework can deliver sensor data in common engineering units which allow developers to switch between sensors without additional coding to support different device-specific formats. ISF is a key aspect for easily obtaining data to be used in Freescale’s Sensor Data Analytics workflow as demonstrated by the company’s embedded data logger for analytics.

Additional Features:

  • Register-level interface to sensors
  • User-defined serial data streaming
  • Support for a broad set of Freescale accelerometers, magnetometers, gyroscopes and pressure sensors
  • Support for the CodeWarrior and Kinetis Design Studio IDEs
  • Comprehensive documentation: Software Reference Manual, API document, release notes
  • Available training videos
  • Example projects targeting Freescale’s Freedom development platforms and sensor shield boards

ISF will be on display at the upcoming Freescale Technology Forum next month in Austin, Texas. In addition to demonstrations planned for the event, Freescale representatives will present detailed information on ISF and its data logger technology during tracks entitled: “Sensor Data Collection and Processing for Sensor Data Analytics.”



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