Neurons pit and stuff olives accurately

Neurons pit and stuff olives accurately

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.

NeuroMem in a Factory 4.0 system in a Russian steel furnace

NeuroMem in a Factory 4.0 system in a Russian steel furnace

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.

 

Favoring Radial Basis Function over Deep Learning for industrial IOT

Favoring Radial Basis Function over Deep Learning for industrial IOT

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.

You can read more on this topic in an article by written Philippe Lambinet and published in the IIOT World magazine. Mr. Lambinet is the president of Cogito Instruments  and has chosen to integrate NeuroMem RBF neural network chips in Cogito’s CompactRIO® cartridge compatible with the National Instruments® platform.

Accurate person identification based on face and voice

Accurate person identification based on face and voice

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.

Read his interview with Asia Tech News

Read his complete paper published at the IEEE Symposium Series on Computational Intelligence

 

Neurons grading the ripeness of coffee cherries in the field

Neurons grading the ripeness of coffee cherries in the field

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.

 

 

Neurons inspect fishes in the North Sea

Neurons inspect fishes in the North Sea

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.

Read the complete white paper. (Award from the Association for the Advancement of Artificial Intelligence in the category “Practical Use of AI”)