Once again the NeuroMem neurons help solve an industrial application with simplicity and accuracy. This time they are trained to recognize the acceptable aspect of olives and their proper alignment in a pocket to be pitted and stuffed, thus improving the quality of the goods and reducing waste. This collaborative study involves four major universities in Spain and is published in the Special Issue “IoT Technologies and the Agricultural Value Chain” of the Sensors journal.
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.
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.
The Advanced Numerical Research and Analysis Group of the Defense R&D Organization of India demonstrates that the NeuroMem neurons outperform other hardware solutions to recognize faces. Their research project and conclusions are supported by two white papers and a board development featuring the NeuroMem IP installed on a Xilinx FPGA.