Reality AI Resource Library

EBOOKS

Complete Guide(smaller)

The Complete Guide to Machine Learning for Sensors and Signal Data

Machine learning for sensors and signal data is becoming easier than ever:  hardware is becoming smaller and sensors are getting cheaper, making IoT devices widely available for a variety of applications ranging from predictive maintenance to user behavior monitoring.

Whether you are using sounds, vibrations, images, electrical signals or accelerometer or other kinds of sensor data, you can build richer analytics by teaching a machine to detect and classify events happening in real-time, at the edge, using an inexpensive microcontroller for processing – even with noisy, high variation data.

Go beyond the Fast Fourier Transform (FFT).  This definitive guide to machine learning for high sample-rate sensor data is packed with tips from our signal processing and machine learning experts.

 

Download the full version of the e-book to read it at your own pace.

Ultimate-guide(Smaller)

The 2020 Ultimate Guide to Machine Learning for Embedded Systems

Machine learning is a powerful method for building models that use data to make predictions.  In embedded systems — typically running microcontrollers and constrained by processing cycles, memory, size, weight, power consumption, and cost — machine learning can be difficult to implement, as these environments cannot usually make use of the same tools that work in cloud server environments.

This Ultimate Guide to Machine Learning for Embedded Systems includes information on how to make machine learning work in microcontroller and other constrained environments when the data being monitored comes from sensors.

So now you know a little more about what we mean by “machine learning for embedded systems”, but maybe you’re still unsure about where or how to start?

 

Download this entire guide to read it at your leisure.

Whitepapers

Technical whitepaper(Smaller)

Reality AI Technical Whitepaper

Explore the technical details behind the Reality AI approach to machine learning with signals:

  • Why signals require a different approach than other machine learning problems

  • The importance of “features” to effective machine learning

  • Why the FFT probably isn’t good enough, and what other options are better

  • The difference between Reality AI and Deep Learning

TP JAP(smaller)

Reality AI Technical Whitepaper (Japanese Translation) / テクニカルホワイトペーパー

Explore the technical details behind the Reality AI approach to machine learning with signals:

  • Why signals require a different approach than other machine learning problems

  • The importance of “features” to effective machine learning

  • Why the FFT probably isn’t good enough, and what other options are better

  • The difference between Reality AI and Deep Learning

Data collection(Smaller)

Reality AI Data Collection Whitepaper

Reality AI provides software for R&D engineers who build products and internal solutions using sensors.   Working with accelerometers, vibration, sound, electrical (current/voltage/capacitance), radar, RF, proprietary sensors, and other types of sensor data, Reality AI software identifies signatures of events and conditions, correlates changes in signatures to target variables, and detects anomalies.

Since data collection and preparation is both the most costly part of any machine learning project, and also the place where most failed projects go wrong, Reality AI software contains functionality to keep data collection on track, to assist with its pre-ML processing, and to get the most out of it using synthetic augmentation techniques.

This whitepaper covers the approach we recommend for data collection planning, execution, and post-collection processing.

Video Library

Automotive Sound Recognition and Localization - See Around Corners with Sound - Winner of 2020 Future Mobility Award

RealityCheckTM Voice Anti-Spoofing for Wakeword and Voice UI

Predictive Maintenance and Condition Monitoring Demo - Built with Reality AI ToolsTM