A Spanish team selects a NeuroMem network to help pit and stuff olives accurately, improving quality and reducing waste. This collaborative study involves four major universities in Spain and proves again the power and simplicity of our neurons to solve industrial IOT applications. The system features a PC, camera and PLC, and a NeuroMem network which handles the difficult task of classifying the shiny olives as they roll on a belt. Read the entire publication.
Note that the CM1K chip mentioned in the paper has reached its end of life, but its successor, the NM500, is composed of the same neurons and controlled with the library of registers. A firmware version of the neurons is also available for FPGA.
NeuroTechnologijos installs a NeuroMem-powered monitoring system in a steel blaster furnace in Magnitogorsk – Russia. Its solution is composed of a bank of NT Adaptive Controllers designed and manufactured by NeuroTechnologijos and mounted in an industrial enclosure. Each NT Adaptive Controller receives signals from the machinery equipment and uses a NeuroMem neural network to verify that the signals stay within normal waveforms in amplitude, frequency and envelope. If novelties are detected, a second neural network can automatically learn the new waveforms for later review by a human supervisor.
Industrial IOT is favoring RBF-type classification over Deep Learning for several essential reasons. First is the convenience to run tasks locally, without dependency to a remote server. Indeed an RBF classifier is a lifelong learner which can be taught incrementally and in real-time. Adapting to changes of production, new materials and other environmental conditions can be done immediately. This eliminates the need to send the new annotated examples to the cloud and wait for the generation of a new inference engine. Secondly, an RBF classifier does not report probabilities but rather prefer to enumerate multiple categories when the neurons do not reach a consensus. This behavior is much preferable when the application carries a cost of the mistake, whether it is for processing, quality control or predictive maintenance.
Dr. Manan Suri, professor at the Indian Institute of Technology, Delhi and his team from the Department of Electrical Engineering have conceptualized and qualified a system combining two NeuroMem neural networks to accurately authenticate persons based on their voice and face. The hardware platform includes a NeuroMem CM1K chip so its 1024 neurons can perform the learning and classification of patterns in real-time and near sensor.
Oceanit teamed up with Kauai Coffee Company and Kamehameha Schools to develop an innovation that can ‘see’ the ripeness of coffee cherries utilizing a NeuroMem neural network and win Hawaii’s first annual AGathon. By using a portable prediction system to determine ripeness, Kauai Coffee’s harvest values could be improved by more than a quarter million dollars per harvest. Read the complete report.
The Oceanit team developed a system around the Raspberry PI equipped with RaspiCam vision module and a shield board populated with two NeuroMem NM500 chips.
Pisces Fish Machinery Inc. has developed and sold over 50 smart cameras powered by NeuroMem neurons to inspect fishes directly on the fileting lines on-board of fishing vessels. At the beginning of a new expedition, the fishermen perform the training of the neurons through a simple touch screen interface. The camera inspects 6 fishes per second with 98% accuracy and as a result the crew can be reduced leaving more storage space for the catch.
