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Machine-Learning-Based Microwave Sensing: A Case Study for the Food Industry

2021; Institute of Electrical and Electronics Engineers; Volume: 11; Issue: 3 Linguagem: Inglês

10.1109/jetcas.2021.3097699

2156-3365

M. Ricci, Bernardita Štitić, Luca Urbinati, Giuseppe Di Guglielmo, J. A. Tobon Vasquez, Luca P. Carloni, Francesca Vipiana, Mario R. Casu,

RFID technology advancements

Despite the meticulous attention of food industries to prevent hazards in packaged goods, some contaminants may still elude the controls. Indeed, standard methods, like X-rays, metal detectors and near-infrared imaging, cannot detect low-density materials. Microwave sensing is an alternative method that, combined with machine learning classifiers, can tackle these deficiencies. In this paper we present a design methodology applied to a case study in the food sector. Specifically, we offer a complete flow from microwave dataset acquisition to deployment of the classifiers on real-time hardware and we show the effectiveness of this method in terms of detection accuracy. In the case study, we apply the machine-learning based microwave sensing approach to the case of food jars flowing at high speed on a conveyor belt. First, we collected a dataset from hazelnut-cocoa spread jars which were uncontaminated or contaminated with various intrusions, including low-density plastics. Then, we performed a design space exploration to choose the best MLPs as binary classifiers, which resulted to be exceptionally accurate. Finally, we selected the two most light-weight models for implementation on both an ARM-based CPU and an FPGA SoC, to cover a wide range of possible latency requirements, from loose to strict, to detect contaminants in real-time. The proposed design flow facilitates the design of the FPGA accelerator that might be required to meet the timing requirements by using a high-level approach, which might be suited for the microwave domain experts without specific digital hardware skills.