As coronavirus cases in Israel surge past 1,200, researchers in the country are predicting where it will spread next analyzing responses to questionnaires with AI.
After members of the public report their condition, algorithms evaluate their answers to connect symptoms to locations.
Today, designers are converting the image feeds from these sensors into GigE Vision to use traditional machine vision processing for analysis. Looking ahead, there will be obvious value in fully integrating the output from all of the sensors within an application to provide a complete data set for analysis and eventually AI.
Software techniques enable the design of virtual GigE Vision sensors that can be networked to share data with other devices and local or cloud-based processing.
Embedded smart devices enable more sophisticated processing at the sensor level. Key to this has been the introduction of lower-cost, compact embedded boards with processing power required for
Embedded smart devices integrate off-the-shelf sensors and processing platforms to enable compact, lower-power devices that can be more easily networked in IoT applications
Traditionally, inspection has relied on a camera or sensor transmitting data back to a central processor for analysis.
The introduction of the GigE Vision standard in 2006 brought new levels of product interoperability and networking connectivity for machine vision system designers, paving the way for the emergence of IoT.
One of the most hyped technologies in recent years has been the Internet of Things (IoT), a trend that has entered our consumer lives via home monitoring systems, wearable devices, connected cars, and remote health care.