Resources

Reality AI Wins 2020 Future Mobility Award in “AI for Safety” Category

GOTHENBURG, SWEDEN (October 6, 2020) – In a ceremony held both virtually and in-person, Reality AI was recognized with the 2020 Future Mobility Award in the

Reality AI CEO, Stuart Feffer, Featured on The Global Safety Podcast from Lloyd’s Register Foundation

Each year, an estimated 2.8mil people die from accidents in the workplace. Families are torn apart, reputations ruined, share prices crash and consumer confidence tumbles.

Sensing solutions for automotive applications – Q&A with Infineon

Sensing solutions for automotive applications – Q&A with Infineon 07 Jan 2021 – Originally appeared on JustAuto Three megatrends are shaping the auto industry: electro-mobility,

Upcoming Webinar: “How We Built It: Cars Can See Around Corners with Sound” featuring Reality AI x Infineon Technologies

Join us for our first Reality AI webinar of the year, featuring Infineon Technologies, as we show you how we built our latest demo, using

Reality AI Partners with Infineon to Let Cars See Around Corners with Sound

Working with Infineon Technologies, a world leader in semiconductor solutions, and other partners, Reality AI has developed a system architecture, sample hardware, and firmware, and a product development program for Automotive Tier 1 and Tier 2 suppliers to create their own versions of this system.

Upcoming Webinar: “How We Built It: Cars Can See Around Corners with Sound” featuring Reality AI x Infineon Technologies

Join us for our first Reality AI webinar of the year as we show you how we built our latest demo, using all the features of Reality AI Tools.

REALITY AI ANNOUNCES “REALITY AI FOR MATLAB”

Reality AI today announced the beta program for “Reality AI for MATLAB”, an add-on to its Reality AI Tools® software that enables users to develop optimized feature computations and machine learning models for advanced sensing automatically.

Webinar Replay: Reality AI x ARM Virtual Tech Talk

In case you missed our virtual tech talk with January 2021 virtual tech talk with ARM, you can now watch it on demand.

Reality AI CEO Featured on Latest Mobility Now! Podcast

Stuart explains why sound detection is a critical part of ADAS and autonomous driving. He shares the science behind Reality AI’s technology, and how it finds unique features in sounds to accurately identify them.

REALITY AI LETS CARS “SEE AROUND CORNERS WITH SOUND”

Working with its partner DENSO Corporation, Reality AI has developed a new software solution that uses external microphones on cars to detect targets that are not in the direct line of sight.

Sensing Solutions for Automotive Applications – Q&A with Infineon

“a completely new sensory input for cars… will likely play an important role in future sensor suites”
— Lars Ulrich, VP of Automotive at Infineon Technologies Americas

Reality AI to Host ARM AI Virtual Tech Talk on January 26

Date: January 26, 2021
Time: 8am PST / 4pm BST / 11pm CST
Stuart Feffer, CEO & Co-Founder Reality AI

EMBEDDED AI AND MACHINE LEARNING ADDING NEW ADVANCEMENTS IN TECH SPACE

With current tools, integrating new options for machine learning for signals (like Reality AI) it is getting simpler.

Reality AI CEO, Stuart Feffer, Featured on The Global Safety Podcast from Lloyd’s Register Foundation

In this episode of The Global Safety Podcast, Tom Heap explores the Lloyd’s Register Safety Accelerator and the world of Safetytech, the sector tackling workplace fatalities with a potential market value of over $800bil.

Mobility Xlab Interviews Future Mobility Award Winners

Nalin Balan is the head of business development at Reality AI and he took some time to talk to us about their work and winning the Future Mobility Award.

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

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

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.

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.

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.

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

Predictive Maintenance and Condition Monitoring Demo - Built with Reality AI ToolsTM (Japanese Translation)

Date: June 22- 24 | 11:00 am – 3:30 ET

Sensor Innovation will bring together engineers and technology influencers hungry to learn, understand, and conquer design challenges. It will feature engaging keynotes, panels, tech talks, and roundtable breakout sessions covering today’s applications and future trends across Sensors, Embedded and IoT & Wireless.

Don’t Miss Tech Talk: Think Like an Engineer, and Not a Data Scientist

Most ML project processes begin with the data. Start there, and find an algorithm that fits the data best. But when you’re working with sensors, you need to start with the instrumentation — because even small changes to sensor configuration can have a huge impact on project success, for better or worse. In this session, we’ll talk about the right process for ML projects with sensor data, and demonstrate how software tools like Reality AI help with instrumentation design and optimization, increasing the likelihood of machine learning success.

Resources

Reality AI Wins 2020 Future Mobility Award in “AI for Safety” Category

Reality AI CEO, Stuart Feffer, Featured on The Global Safety Podcast from Lloyd’s Register Foundation

Sensing solutions for automotive applications – Q&A with Infineon

Upcoming Webinar: “How We Built It: Cars Can See Around Corners with Sound” featuring Reality AI x Infineon Technologies

Reality AI Partners with Infineon to Let Cars See Around Corners with Sound

Upcoming Webinar: “How We Built It: Cars Can See Around Corners with Sound” featuring Reality AI x Infineon Technologies

REALITY AI ANNOUNCES “REALITY AI FOR MATLAB”

Webinar Replay: Reality AI x ARM Virtual Tech Talk

Reality AI CEO Featured on Latest Mobility Now! Podcast

REALITY AI LETS CARS “SEE AROUND CORNERS WITH SOUND”

Sensing Solutions for Automotive Applications – Q&A with Infineon

Reality AI to Host ARM AI Virtual Tech Talk on January 26

EMBEDDED AI AND MACHINE LEARNING ADDING NEW ADVANCEMENTS IN TECH SPACE

Reality AI CEO, Stuart Feffer, Featured on The Global Safety Podcast from Lloyd’s Register Foundation

Mobility Xlab Interviews Future Mobility Award Winners

Reality AI Technical Whitepaper

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

Reality AI Data Collection Whitepaper

The Complete Guide to Machine Learning for Sensors and Signal Data

The 2020 Ultimate Guide to Machine Learning for Embedded Systems

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

Predictive Maintenance and Condition Monitoring Demo - Built with Reality AI ToolsTM (Japanese Translation)

Sensors Innovation Week
June 2021

Sensors Converge
September 2021

Autotech Council Science Fair
October 2021

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Sensors Innovation Week June 2021

Sensors Converge September 2021

Autotech Council Science Fair October 2021

Contact

Links

Services

Follow

About

Sensors Innovation Week
June 2021

Sensors Converge
September 2021

Autotech Council Science Fair
October 2021