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A Self-Learning Multi-Sensing Selection Process: Measuring Objects One by One

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3 Author(s)
Alessandro Golfarelli ; ARCES-LYRAS LAB, University of Bologna, Campus of Forlì, Italy. agolfarelli@arces.unibo.it ; Rossano Codeluppi ; Marco Tartagni

The paper presents a smart approach for a real time inspection and selection of granular objects in continuous flow. In the proposed approach, parallel channels are carved on a planar substrate to contain object flow. Each channel operates independently by processing and selecting grains one by one in real-time using multiple sensing units. A 3D conformational characterization of single objects is achieved by means of simultaneous cross-combined optical and impedimetric sensing technique. The sorting process is based on a 2 phase operative methodology defined by software control: 1) a self-learning step where the apparatus "learns" to identify objects by inputting a-priori selected classes of objects so that decision thresholds are adjusted accordingly; 2) an operative selection process where objects are detected, classified using a decisional algorithm and selected in real time by electromechanical actuators. As working example, cereal grain selection is presented.

Published in:

Sensors, 2007 IEEE

Date of Conference:

28-31 Oct. 2007