Trainable Sensor Network (TSN)

Built on a patent filed in 2005 and still top 3 in a Internet search for “trainable sensors network patent”. The multi-sensor platform is combined with built-in AI to provide a tool set that enables products to provide new functionality and continuous value even after being implemented to the field. The Trainable Edge has a diverse set of proven and tangible applications that include next generation Vendor Managed Inventory, Smart City Intersection Management, Connected Kitchen Products and Aerospace Production and a diverse set of Industry 4.0 applications.

Disruptive Features

Trainable Edge technology was built upon a patent first filed in 2005 by Brandt Innovative Technologies and still the top 3 results in Google search of “Trainable Sensor and Network”. Trainable Edge Technology utilizes multiple sensors along with Artificial Intelligence (AI) capabilities including Recognition and Deep Learning :

This AI capability allows our products to recognize targets with a much higher degree of accuracy and eliminating interference. Single mode sensors such as camera systems cannot provide this level of sensitivity, selectivity and disambiguation.

LinqThingz Trainable Edge is part of a complete IoT ecosystem that allows devices to work autonomously or connected to power cloud based computational and storage capacity. Trainable Sensor is way more than the typical IoT device that sends bits of data to a database. The Trainable Sensor is a complete closed loop solution to many real-world problems. Features include:

Heterogeneous sensor drivers to simultaneous coordinate and collect data from a plethora of sensors on he Trainable Sensor modules.
Multivariate Proportional Derivative Control (PID). PID is the quintessence of closed loop industrial control…typically from a statically defined single sensor…
Artificial Intelligence (AI) Engine. Our PID loop receives data from multiple sensors after being processed by our AI engine giving the about for our closed loop control react to the environment, improve over time and to leverage to power of deep learning insights