Mittuniversitetet

This commentary highlights the importance of uncertainty and estimation associated with the measurement of noise levels in the industrial field. As example evaluating noise workers’ exposure in a workplace has to take into account levels of uncertainty. The authors investigated the effect of the variability of the measurand with respect to the uncertainty of the measurement and proposed a novel data-driven sampling approach which consists of three phases. Firstly, because the reliability of the noise indicators assessment depends significantly on the temporal variability of the noise, a statistic procedure is proposed to locate a corresponding minimum number of sound pressure levels necessary for having statistical significance of the initial data set. Next, a numerical algorithm finds and deletes the anomalous values (outliers) from a population of real data. Finally, the inherent variability of uncertainty of acoustic noise is calculated using the normal bootstrap method. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The paper focuses on the analysis of biometric measurements in dynamic acquisition conditions and their impact on the reliability of the recognition judgments. To this aim, a suitable simulator of stereoscopic systems has been designed and realized. It relies on a fully simulated procedure based on the following steps: (i) generation of a set of realistic 3D face models through a proper face simulator software; (ii) definition of an arbitrary trajectory for the face models and stereo images to simulate a set of images acquired in different poses (positions and orientations) of the subject during the movement; (iii) addition of selectable levels of motion blur in a controlled environment, to simulate critical acquisition conditions. This procedure allows ensuring that the recognition results are not due to the natural change of expression of real faces or an imperfect image acquisition device. Moreover, every face model is moved exactly with the same trajectory in front of the stereoscopic system, allowing compare the recognition performances all along the trajectory, also in controlled and under repeatable blur levels. A face biometrics procedure, based on a popular recognition algorithm, is then run on the generated images and the recognition performances are analyzed in detail. The achieved results demonstrated how the motion blur and also the slight differences between the acquired images and the reference ones significantly affect the performance in the recognition of such kinds of systems, thus confirming the usefulness of the proposed simulator. © 2021 IEEE.

Aerospace and automotive industries make more and more use of carbon fiber laminates. They offer high advantages in terms of mechanical performance but are difficult to work on and need precise and expensive tools. Drilling is the most common operation; drill bits must be accurately verified before use to avoid damages during the process. This paper proposes an optical inspection system for drill bits wearing estimation based on a custom-designed illumination device and a processing algorithm based on computational imaging. The elaboration of the computational image is used to measure the drill bit cutting profile. Drill bits have been worn on carbon fiber laminates, and a tool wear model has been extracted using the proposed system. The proposed method has been tested on both used and new drill bits, used in the aerospace industry. © 2021 IEEE.

Whatever the type of surgery related to inner organs, traditional or robotic, the contact with them during surgery is a key moment for pursuing the intervention. Contacts by means of surgery instruments namely scalpels, staples, clamps, graspers, etc. are decisive moments. False, and erroneous touching and manoeuvring of organs operated on can cause irreversible damage as regard morphological aspects (outer impact) and physiological aspects (inner impact). The topic is a great challenge in the effort to measure and characterize damages. In general, electrical instruments for surgery employ the following technologies: Ultrasound, radiofrequency (monopolar, and bipolar), and laser. They all result in thermal damages difficult to evaluate. The article proposes a method for a pre-screening of organ features during robotic surgery sessions by pointing out mechanical and thermal stresses. A dedicated modelling has been developed based on experimental activities during surgery session. The idea is to model tissue behaviour from real images to help surgeons to be aware of handling during surgery. This is the first step for generalization by considering the type of organ. The measurement acquisitions have been performed by means of an advanced external camera located over the surgery quadrant. The modelling and testing have been carried out on kidneys. The modelling, carried out through Comsol Multiphysics, is based on the bioheat approach. A further comparative technique has been implemented. It is based on computer vision for robotics. The findings of human tissue behavior exhibit reliable results. © 2021. The Author(s).

Accurate measurement of exposure to noise in the workplace is important for employee health prevention as well as cost implications for employers. Standard ISO 9612 employs an engineering methodology for estimating noise exposure levels including levels of uncertainty. In this procedure some aspects are left to the discretion of the operator. Beginning with preliminary studies on the determination of the measurement intervals for evaluating workers' exposure to noise, this paper proposes an innovative approach to estimating work conditions for bus drivers. Measurement results are analysed and compared to the estimations based on both the ISO 9612 and the continuous acquisition analysis showing the feasibility of the proposal for accurately measuring the exposure to acoustic noise in a typical work situation. 

In food packaging, low-density polyethylene (PE) coating is applied to paperboards to act as a functional barrier and to provide the smoothness required to enhance printability. These characteristics are related to the material’s surface roughness, the parameter monitored during the manufacturing process. Measurement of surface roughness using optical profilometry has gained importance in the paper industry. The optical instruments used to measure surface roughness are limited spatially by the relationship with the light wavelength at which they operate. A scanning electron microscope (SEM) is an alternative for overcoming the spatial resolution limitation, and the use of stereo-photogrammetry on SEM images can be seen as an alternative profilometry technique to measure surface roughness. In this investigation, the surface topography of industrially manufactured high-quality PE-coated paperboard was studied, comparing the SEM stereo-photogrammetry technique with a reference profilometry method, i. e., chromatic confocal microscopy (CCM). We found close agreement between the calculated surface roughness and the results of the techniques used and compared them according to the new ISO 25178 Geometric Product Specifications. We concluded that SEM stereo-photogrammetry provides comparable accurate alternative profilometry method for characterizing the surface roughness of PE-coated paperboard in the micrometer scale.

Citizen quality of life can be improved through facilities and services that must be thought to ease citizen interaction with municipal authorities, offices, and structures. Advanced metering infrastructures (AMIs) can be proposed as the backbone of smart city projects. The chapter deals with this topic by describing devices and results of a pilot project designed and carried out by the authors for experiencing the RF 169 MHz wM-Bus in AMI. The AMI was installed in Salerno, an Italian middle city of about 1,40,000 inhabitants and covering a land area of 58.96 km2. Five public services have been loaded on the AMI to help find the affordability of necessary investments: gas and water metering, car parking management, elder tele-assistance, and pollution measurements. The pilot project has involved the 1.5% of the citizens in 11 city districts. Results provided a great amount of data and information about reliability and efficiency of devices and networks and have been held into account by the authors of the national standard on the shared management of the 169 MHz frequency band (UNI CEI TS 11762:2019). These results let understand that in the next future solutions like those described in the chapter can become products and services available for all citizens. © 2021, Springer Nature Switzerland AG.

Software systems have been long introduced as support to the early detection of melanoma through the automatic analysis of suspicious skin lesions. Nevertheless, their behavior is not yet similar to the performance exhibited by expert dermatologists in terms of diagnostic accuracy. Instead, a software system should be adopted by non-experienced dermatologists in order to improve the measurement and detection results for skin atypical patterns and the accuracy of the corresponding second opinion. This paper describes an image-based measurement and classification system able to score pigmented skin lesions according to the Seven-Point Check-list diagnostic method. Focus is devoted to the measurement procedure of biological structures more closely related to the atypical character of the nevus. Moreover, the performances of the measurement system are evaluated by considering the support to dermatologists with different experiences during the clinical activity. © 2020 by the authors.

In recent times, thanks to the availability of a large quantity of data coming from the industrial process, several techniques based on a data-driven approach could be developed. Between all the data-driven techniques, as Principle Component Regression, Support Vector Machines, Artificial Neural Networks, Neuro-Fuzzy Systems, and many others, the data on which they rely should be analyzed to find correlations and dependencies that could improve their design. For this reason, the Input variable Selection (IVS) process has become of great interest in the recent period. The classical IVS relies on classical statistics, as Pearson coefficients, able to discover linear dependencies among data; today, due to the significant amount of data available, the challenge of also discovering non-linear dependencies appears to be a necessary skill, mainly for the design and development of a neural network. This paper proposes the use of a novel statistical tool named Maximal Information Coefficient (MIC) for developing an IVS procedure able to discover dependencies in a considerable dataset and guide the IVS designer to the selection of input variables in a data-driven application. As a case study, the procedure will be applied to a real application developed in the context of the Swedish forest industry, in order to choose the input variables of a neural network able to estimate the timber bundles volume, which represents an expensive parameter to measure in this context.