Blogs
Accelerated modeling of anomalies and novel events for predictive maintenance
Smart sensors are driving the deployment of monitoring systems in our everyday lives from wearables tracking our mobility to complex sensor hubs ensuring the quality of a production line and the proper operation of its machinery. Their “smartness” comes from software...
Democratizing neural networks to inspect fuzzy, foamy and fatty products
Production and Quality Control managers are always interested in Machine Vision systems that can improve manufacturing processes, control the quality of products, and predict machinery maintenance before failure. They will have more faith in a system tested on their...
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...
Tracking targets on the ground, in the air, under a microscope…
Target Tracking with NeuroMem networks Stereoscopy with a NeuroMem network Target Tracking (Wendall Deck) IR Target-Flare Discrimination (Royal Military College, Kingston, Ontario, Canada) RBF network for tracking a ground moving target
Face detection and identification with NeuroMem
Face detection with a NeuroMem network Face and voice recognition with NeuroMem (IIT Delhi) Part1: Face recognition with NeuroMem CM1K chip Part2: Face recognition with NeuroMem IP Identification of a person based on voice and face recognition (Auckland University)...
NeuroMem at work with objects, surfaces, waveforms, text and data
Glass surface defect detection Handwritten character recognition Cancer cell classification (courtesy of Dr. Suri) Lidar image classification Function Approximation for Adaptive Optics (Institut St Louis, France ) Image noise reduction (IBM) Network Intrusion...