Rob Purser
Development Manager for IoT and Hardware Interfacing for MATLAB at MathWorks. Leads the creation of software that connects MATLAB with the real world
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Targets, ThingSpeak, and MATLAB: analytics throughout your IoT system backup
In this presentation, we will discuss three kinds of analytics common in IoT systems: sensor analytics at the edge node for data reduction, cloud-based online analytics for situational awareness, and historical data analytics for developing predictive and classification algorithms. You’ll see practical techniques for integrating all these types of analytics into your IoT system. We will demonstrate these techniques using various environmental data sources, including data from weather stations, noise monitors, tide gauges, and traffic sensors. These examples utilize embedded programming, image and signal processing, and machine learning techniques to demonstrate how analytics can be integrated at all phases in your IoT system.
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October 2016 LXI IoT Presentation
The Internet of Things is all the rage, but what is it, really? Is it relevant to instrumentation manufacturers and their customers. Should standards like LXI rethink their role in light of it?
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Prototyping IoT Analytics: Hands on with ThingSpeak™ and MATLAB®
The Internet of Things typically involves a discussion of smart devices and the cloud, with much less attention paid to the data collection, pre-processing of acquired data, and development of real-time analytics algorithms. A successful data analytics strategy involves embedded sensor analytics, historical data analysis, and online analytics. In this hands-on session, each participant will work with devices and try out the various types of analytics in action.
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Targets, ThingSpeak, and MATLAB: analytics throughout your IoT system
In this presentation, we will discuss three kinds of analytics common in IoT systems: sensor analytics at the edge node for data reduction, cloud-based online analytics for situational awareness, and historical data analytics for developing predictive and classification algorithms. You’ll see practical techniques for integrating all these types of analytics into your IoT system. We will demonstrate these techniques using various environmental data sources, including data from weather stations, noise monitors, tide gauges, and traffic sensors. These examples utilize embedded programming, image and signal processing, and machine learning techniques to demonstrate how analytics can be integrated at all phases in your IoT system.
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Integrating Analytics and Algorithms into your IoT System
In this presentation, we will discuss three kinds of analytics common in IoT systems: sensor analytics at the edge node for data reduction, cloud-based online analytics for situational awareness, and historical data analytics for developing predictive and classification algorithms. You’ll see practical techniques for integrating all these types of analytics into your IoT system. We will demonstrate these techniques using various environmental data sources, including data from weather stations, noise monitors, tide gauges, and traffic sensors. These examples utilize embedded programming, image and signal processing, and machine learning techniques to demonstrate how analytics can be integrated at all phases in your IoT system.
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Greater Boston CMG: Analysis and Visualization Techniques for Environmental IoT Systems
Data from our environment surrounds us: sensors and inexpensive edge nodes make it easier than ever to collect lots of data on our surroundings, both indoors & outdoors. From weather data and noise levels to counting cars on the highway outside our window, we can correlate this data with external sources of data to gain deeper insights into our surroundings. Working with live environmental time series data brings unique challenges that span traditional instrumentation, embedded programming, signal processing, and statistics. We’ll discuss some of the environmental Internet of Things data collection and analysis that we’ve done, and three kinds of analytics that we’ve applied: sensor analytics at the edge node, historical data analytics, and online analytics at the data aggregator as the data comes in. You’ll see practical techniques for working with this data, tools, and visualizations.
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Analysis and Visualization Techniques for Environmental IoT Systems
Data from our environment surrounds us: sensors and inexpensive edge nodes make it easier than ever to collect lots of data on our surroundings, both indoors & outdoors. From weather data and noise levels to counting cars on the highway outside our window, we can correlate this data with external sources of data to gain deeper insights into our surroundings. Working with live environmental time series data brings unique challenges that span traditional instrumentation, embedded programming, signal processing, and statistics. We’ll discuss some of the environmental Internet of Things data collection and analysis that we’ve done, and three kinds of analytics that we’ve applied: sensor analytics at the edge node, historical data analytics, and online analytics at the data aggregator as the data comes in. You’ll see practical techniques for working with this data, tools, and visualizations.