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A New Breed of Hybrid Connectors for IoT and Machine Learning Systems
Bob Stanton, Director of Technology, Omnetics Connector Corporation
Connectors are supporting a dynamic era of technological advancements that center on the need to see, measure, store, process, and transmit ever-increasing amounts of data. IoT and machine learning systems combine, compare, and respond to multiple streams of information simultaneously. The latest sensors collect a wide range of information that was undetectable a few years ago and connect high-speed, low-voltage digital signals to central logic processing centers.
» An automatic eye data system
Many of these new information networks can no longer follow the long-established standard for wiring and connections. Their complexity has increased ten-fold while they must also become smaller and more flexible. Today’s data acquisition methods from sensors, transducers, MEMs, and other membrane-controlled field effect transistors require their own custom interconnection and power sources.
Connectors and cables must do more than ever
To meet the multiple signal demands of these new networks, hybridized connectors and matching cables combine power and multiple signal types. Starting with experience and test standards for performance and reliability, miniaturized cable and connector designs are customized for the form, fit, and functions needed for each network. Some of the conduit systems must provide power to multiple sensors and collect information from various detection methods including mechanical, analog, digital, and RF.
  » This modified Micro-D connector offers over 5 A of power to a network of sensors. Its 24 pins are spread out to collect data from multiple sensors. A specially designed cable contains separate sections shielded from each other to handle both digital and analog data.

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