Nowadays, the use of big data analysis and IoT (Internet of Things) technologies is growing tremendously within companies and organizations in several different application fields. In this scenario, smart farming refers to monitoring of environmental conditions and soil parameter to improve farm productivity, to optimize soil conservation, to save water and to limit plant diseases. During the design of such innovative IoT technologies it is fundamental to carry out a reliability and failure analysis of the device. This could allow to introduce adequate diagnostic solutions to improve the system’s availability. In this work, a failure analysis using FMEA (Failure Modes and Effects Analysis) approach of a smart sensor node for precision farming has been developed. The results of the analysis allow to improve the design of the device introducing a diagnostic-oriented prototype able to solve the major criticalities arisen during the FMEA.

In this article, a coffee beans fraud detection based on a deep learning approach is proposed, which has been achieved after classifying the two coffee varieties to distinguish them in a real-time industrial scenario. The coffee bean quality is typically defined by visual inspection, which is subjective, needing significant effort and time, and susceptible to fault detection. For these reasons, a different method is required to be objective and precise. Therefore, object detection techniques were employed to automatically classify the coffee bean samples according to their specie using an own dataset consisting of over 2500 coffee beans. Furthermore, a convolutional neural network (CNN) based on the YOLO algorithm was employed to categorize the coffee beans automatically. The result of this study has revealed that the object detection technique could be used as an effective method to classify coffee bean species and discover food fraud.

This paper focuses on the process of establishing the Laboratory for Pressure at the Montenegrin Bureau of Metrology (MBM), the National Metrology Institute of Montenegro, which disseminates the unit of pressure in the field of calibration and conformity assessment in a small, centralized metrology system. Establishment of a traceability chain, development and the continual improvement of measurement methods, participation in interlaboratory comparisons and PT schemes, accreditation according to ISO/IEC 17025, registration of calibration and measurement capabilities (CMC) in BIPM KCDB for pressure, are stages in the development of the Laboratory for Pressure and also in proving its competencies at the international level.

The plastic recycling industry necessitates fast and reliable methods to recognize the different polymer types to improve the recycling capacity. In this contribution, the coupling of a miniaturized Near-Infrared (NIR) spectroscopy technique with a robust data analysis is presented. Comparison of multiple machine learning techniques, such as Support-Vector Machines (SVM), Fine Tree, Bagged Tree, and Ensemble Learning, and chemometric approaches, such as Principal Component Analysis (PCA) and Partial Least Squares – Discriminant Analysis (PLS-DA), were combined to provide a broad overview and a rational means for selecting the approach in analysing NIR data for plastic waste sorting.

Personal Dosimetry Laboratory of the Public Health Institute of Republic of Srpska (PHIRS) is estimating doses to approximately 1500 occupationally exposed workers to ionizing radiation on annual basis which represents 1/3 of occupationally exposed workers of Bosnia and Herzegovina. It is well known fact that the impact of ionizing radiation to human body and the risk of severe adverts effects are correlated with the magnitude of exposure. Therefore, delivering precise and accurate measurement result is essential in control of occupational exposure. Inter-comparison exercise plays an important role in testing laboratories to demonstrate technical competence. Participation in globally acknowledged and recognized EURADOS inter-comparison exercise for whole body dosemeters in photon fields is being conducted in 2 – years cycle by the laboratory. This paper is focused on the analysis of the EURADOS inter-comparison results. From 2014 to 2020, in total 4 inter-comparison exercises have been conducted, each requesting circa 30 dosemeters to be labelled and dispatched for irradiations. Entire number of the dosemeters is divided into groups for irradiations to a reference values with different irradiation qualities, dose ranges, angle of incidence and one group is kept to monitor the background. Statistical analysis of measured quantity Hp(10) has been reported with response values (arithmetic mean, median, minimum, maximum and coefficient of variation). All response values are inside the trumpet curve defined in ISO 14146, ranging in mean response from 0.85 to 0.92. In addition, results are compared to En value and estimated uncertainty budget. This demonstrates that measured results are in compliance with recommendations on accuracy for radiation protection purposes.