What Are The Key Concepts Driving Efficiency In Connected Automation Systems Case Study
“Software Engineers Are Making The Decisions On What Hardware To Choose”
As the Internet of Things (IoT) picks up pace to properly disrupt the market, changes behind the scenes affect it in various ways. Let’s take a look at what these are and the role they play.
Colm Prendergast, director of IoT Technology, Analog Devices speaks with Dilin Anand from EFY.
What are the key concepts driving efficiency in connected automation systems?
It is the services space that enables efficiency in the system. If it is a proactive maintenance system that you are setting up, you need to be able to gain information from the data collected and use that to change the behaviour in the real world. This gives rise to the idea of feedback data, where you need …show more content…
How does data analytics work for information collected as video – isn’t the data too complex?
Information extraction happens at the node itself in the case of media. We take video and images at video graphics array (VGA) resolution, and use that as the source data on which the information extraction is done. Once this information is extracted only those pieces would be sent forth for the next set of analytics. This helps to ensure that the network is not overstressed with data.
Can you tell us about any exciting imaging product coming up in the IoT space?
One of the exciting products coming up are cameras that have the entire analytics engine built in to it, coming with occupancy detection and open application program interfaces (APIs) to enable to developers to build on top of it. Sensor integration is a trend, but what is rising up now is that of plug and play sensors that are easier to use and allow an integration of security. This makes the development pipeline easier, and also lets a piece of hardware in the field to be upgraded over time. Overall, it signifies a shift of firms from being based on capital expenditure towards being based on operating expenditure. This lets you switch to your selected services as the need comes …show more content…
One example use-case would be a security alarm that can detect whether the object is a human or animal. We have a family of processors known as the Blackfin line, which are signal processors with an architecture designed for low-power digital signal processing. It’s a lot about of contextual analysis of the images.
Tell us about an interesting engineering trend in the IoT space?
A big trend is that the nature of our customer base is changing; software engineers are making the decisions on what hardware to chose. Hardware systems are integrated to single chips and they are outsourcing hardware development to ADI. This shows that the current Internet of Things decision makers are more software centred due to the data science elements involved.
What challenges do you face while building tools for software engineers?
Challenges with data science are due to the fact that it tends to be a very specialised field. So when we build platforms for it, the questions being asked are on how do we democratise data science, and on how can we enable sophisticated data science tools for software developers.
What is getting engineers to bring more devices to the IoT
As the Internet of Things (IoT) picks up pace to properly disrupt the market, changes behind the scenes affect it in various ways. Let’s take a look at what these are and the role they play.
Colm Prendergast, director of IoT Technology, Analog Devices speaks with Dilin Anand from EFY.
What are the key concepts driving efficiency in connected automation systems?
It is the services space that enables efficiency in the system. If it is a proactive maintenance system that you are setting up, you need to be able to gain information from the data collected and use that to change the behaviour in the real world. This gives rise to the idea of feedback data, where you need …show more content…
How does data analytics work for information collected as video – isn’t the data too complex?
Information extraction happens at the node itself in the case of media. We take video and images at video graphics array (VGA) resolution, and use that as the source data on which the information extraction is done. Once this information is extracted only those pieces would be sent forth for the next set of analytics. This helps to ensure that the network is not overstressed with data.
Can you tell us about any exciting imaging product coming up in the IoT space?
One of the exciting products coming up are cameras that have the entire analytics engine built in to it, coming with occupancy detection and open application program interfaces (APIs) to enable to developers to build on top of it. Sensor integration is a trend, but what is rising up now is that of plug and play sensors that are easier to use and allow an integration of security. This makes the development pipeline easier, and also lets a piece of hardware in the field to be upgraded over time. Overall, it signifies a shift of firms from being based on capital expenditure towards being based on operating expenditure. This lets you switch to your selected services as the need comes …show more content…
One example use-case would be a security alarm that can detect whether the object is a human or animal. We have a family of processors known as the Blackfin line, which are signal processors with an architecture designed for low-power digital signal processing. It’s a lot about of contextual analysis of the images.
Tell us about an interesting engineering trend in the IoT space?
A big trend is that the nature of our customer base is changing; software engineers are making the decisions on what hardware to chose. Hardware systems are integrated to single chips and they are outsourcing hardware development to ADI. This shows that the current Internet of Things decision makers are more software centred due to the data science elements involved.
What challenges do you face while building tools for software engineers?
Challenges with data science are due to the fact that it tends to be a very specialised field. So when we build platforms for it, the questions being asked are on how do we democratise data science, and on how can we enable sophisticated data science tools for software developers.
What is getting engineers to bring more devices to the IoT