| 1. |
- Sengpiel, Verena, 1977, et al.
(författare)
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Confirmed reinfection with SARS-CoV-2 during a pregnancy: A case report.
- 2022
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Ingår i: Clinical case reports. - : Wiley. - 2050-0904. ; 10:2
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Tidskriftsartikel (refereegranskat)abstract
- Pregnancy might impact immunity after SARS-CoV-2 infection and/or vaccination. We describe the first case of reinfection with SARS-CoV-2 during a pregnancy. While the mother lacked detectable antibodies 2months after the first infection, both mother and baby had IgG antibodies at delivery. Infection did not cause any adverse pregnancy outcome.
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| 2. |
- Adamopoulou, Marianthi, et al.
(författare)
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Improving Cardiac Auscultation Signal Quality by using 4-Channel Stethoscope Array
- 2024
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Ingår i: Conference Record - IEEE Instrumentation and Measurement Technology Conference. - : IEEE conference proceedings. - 9798350380903
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Konferensbidrag (refereegranskat)abstract
- In cardiac auscultation, the ability to clearly hear any existing murmur sounds in heart sounds is crucial for proper diagnosis. This work aims to improve heart sound by the use of a stethoscope array and beamforming technique. The stethoscope array comprises four piezo elements for measurement, placed on the edges of a 40mm by 40mm rectangle. The directionality of the piezo elements reduces the effect of ambient noise in the measurement. The signal amelioration is achieved by isolating the systole and diastole sounds, and independently applying the delay-and-sum beamforming. This thereby makes any existing murmur sounds in the systole and/or diastole more audible and clearer to aid diagnosis. Finally, the designed stethoscope array and signal processing shows a gain of up to 33% for measured healthy heart samples, and up to 63% increase in murmur sound gain for measured sample with medically confirmed murmur.
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| 3. |
- Brugés, Javier Mauricio
(författare)
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Surface characterization methods for quality assessment of polyethylene-coated paperboard
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In manufacturing processes, the quality of a product often depends on its surface, and careful control of surface properties is critical to meet customer requirements. A thin layer of polyethylene (PE) is applied to paperboard to increase barrier functionality and high optical quality of the product. For PE-coated paperboard, product quality inspection is performed at the end of the manufacturing process by taking a portion of the reel to the laboratory for quality inspection. These associated offline characterization methods are destructive and time consuming and are representative of only a small portion of the product.The overall goal of this thesis is to provide new methods to characterize the Surface properties of PE-coated paperboard. Specifically, to determine imaging techniques for measuring surface parameters that affect its barrier functionality and surface roughness.In this thesis, two methods for surface characterization of PE-coated paperboard are presented to quantify the two most important product-related quality parameters, i.e. barrier functionality and optical quality, which are affected by the presence of defects in the coating and by the surface roughness of the product, respectively. First, a full-Stokes imaging polarimeter (FSIP) is used to detect the presence of PE-coated material and to distinguish between coated and uncoated samples at the pixel level. Second, a three-dimensional scanning electron microscope (3D SEM) is employed to calculate the Surface roughness of PE-coated paperboard. These surface characterization techniques offer an advantage over the industry standard due to the high speed and non-contact nature of the measurement, while increasing the throughput of the sample surface parameters studied.A classification accuracy of 99, 74% is achieved using a FSIP to distinguish between PE- and non-PE-coated paperboard at pixel level. Using the 3D SEM technique to measure the topography of PE-coated samples results in a faster method that is comparable in accuracy to a chromatic confocal microscope (CCM). The surface roughness measured with the 3D SEM differs from the standard method by up to 6% and good agreement with statistical parameters is found.In general, surface analysis of PE-coated is often a complex and difficult task for imaging techniques and suitable methods need to be evaluated for their sensitivity to measure the desired surface parameters. The presented characterization techniques inspect larger areas of PE-coated paperboard compared to current industry standards. These methods provide a quantitative solution for surface characterization to inspect the surface parameters necessary to assure the product’s quality.
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| 4. |
- Brugés Martelo, Javier Mauricio, et al.
(författare)
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Paperboard Coating Detection Based on Full-Stokes Imaging Polarimetry
- 2021
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:1
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Tidskriftsartikel (refereegranskat)abstract
- The manufacturing of high-quality extruded low-density polyethylene (PE) paperboard intended for the food packaging industry relies on manual, intrusive, and destructive off-line inspection by the process operators to assess the overall quality and functionality of the product. Defects such as cracks, pinholes, and local thickness variations in the coating can occur at any location in the reel, affecting the sealable property of the product. To detect these defects locally, imaging systems must discriminate between the substrate and the coating. We propose an active full-Stokes imaging polarimetry for the classification of the PE-coated paperboard and its substrate (before applying the PE coating) from industrially manufactured samples. The optical system is based on vertically polarized illumination and a novel full-Stokes imaging polarimetry camera system. From the various parameters obtained by polarimetry measurements, we propose implementing feature selection based on the distance correlation statistical method and, subsequently, the implementation of a support vector machine algorithm that uses a nonlinear Gaussian kernel function. Our implementation achieves 99.74% classification accuracy. An imaging polarimetry system with high spatial resolution and pixel-wise metrological characteristics to provide polarization information, capable of material classification, can be used for in-process control of manufacturing coated paperboard.
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| 5. |
- Brugés Martelo, Javier Mauricio, et al.
(författare)
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Surface topography characterization of high-quality PE coated paperboard using confocal chromatic microscope and 3D SEM stereo-photogrammetry technique
- 2017
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Konferensbidrag (refereegranskat)abstract
- Coating paperboard enhances printability and optical quality of the product as well as other important properties like packaging performance and shelf-life. To obtain high quality products a quality control of the manufacturing process requires identifying those manufacturing parameters that affect it. Roughness measurement and characterization of coating thickness are examples of these control parameters. Optical instruments measuring these quantities range from laboratory equipment to in-line and on-line sensors. However, the variety of instruments and sometimes misunderstanding of their limitations generate uncorrelated measurements, which are no longer valid to their comparison. The new ISO 25178 standard for surface texture provides guidelines to metrologists to address some this problem. Here, we report a case study for surface characterization of high quality printing polyethylene (PE) coated paperboard for high quality printing, where surface roughness is a key parameter. Two imaging methods to create topographic measurements will be compared, i.e. a confocal chromatic microscope and a scanning electron microscope (SEM). For the latter, stereo photogrammetry is used and 3D topographic profiles are obtained from Alicona MeX software. Leach and Haitjema [Leach, R., & Haitjema, H. (2010). Bandwidth characteristics and comparisons of surface texture measuring instruments. Measurement Science and Technology, 21(3), 032001] addressed the topic on how to design comparisons when using different instruments for areal texture measurement. We use their bandwidth matching concept, since it provides an extension to the ISO 25178 guidelines, ensuring that the instrumentation used to characterize the samples are within its measuring limits and further analysis of the results can be correlated. It is important to adopt a good metrology practice in order to translate these parameters into our future work. We expect to extend these findings into a real-time optical sensor, which later can be implemented in an industrial manufacturing environment for high optical quality paper and paperboard.
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| 6. |
- Carratu, M., et al.
(författare)
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A CNN-based approach to measure wood quality in timber bundle images
- 2021
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Ingår i: 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781728195391
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Konferensbidrag (refereegranskat)abstract
- At present, the Smart Industry is becoming a field of great interest for many worldwide researchers since it allows to experiment and research new advanced techniques. One of the most common explored approaches in operations where image processing has already been a milestone is the use of Convolutional Neural Networks (CNN). Those networks have enhanced the current image processing algorithms, achieving an improvement in decision processes usually based on human experience, where an analytical model is not always available. This paper proposes a novel approach for measuring the number of rotted logs in timber bundles using a CNN trained on thousands of timber log images extracted from bundles. Today, the Swedish forest industry bases the selling price of timber bundles on the evaluation of a visual inspection. This operation is based on human experience to evaluate and measure timber bundles' features, which is necessary to categorize them. The proposed approach has shown promising results compared to the actual visual inspection made by operators showing an F1 score with the best CNN architecture of 0.89.
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| 7. |
- Carratú, Marco, et al.
(författare)
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A novel IVS procedure for handling Big Data with Artificial Neural Networks
- 2020
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Ingår i: 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781728144603
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 8. |
- Carratù, M., et al.
(författare)
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A Sound Level Meter featured with automatic estimation of the measurement uncertainty
- 2022
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Ingår i: Measurement. - : Elsevier BV. - 0263-2241 .- 1873-412X. ; 188
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Tidskriftsartikel (refereegranskat)abstract
- In the area of measuring the environmental noise the equivalent sound pressure level LA,eq is adopted and compared with legal thresholds in order to characterize the site of interest. The paper describes an innovative Sound Level Meter (SLM) able to provide information about the measurand contribution to the measurement uncertainty estimation. This measurement technology is made possible thanks to an approach based on bootstrap method for selecting the suitable measurement episode for an estimation of LAeq. The firmware implementation of the developed SLM is disclosed with reference to a low-cost platform for real-time execution of the proposed methodology. Finally, a metrological characterization of the prototype performed in laboratory is reported as well as the performance comparison with a class 1 SLM in a real scenario. As a result, the smart features of the new SLM may be easily implemented by including commercial devices into the instrument schematics.
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| 9. |
- Carratu, M., et al.
(författare)
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An innovative method for log diameter measurements based on deep learning
- 2023
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Ingår i: 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781665453837
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Konferensbidrag (refereegranskat)abstract
- The widespread adoption of Deep Learning techniques for Computer Vision in recent years has brought major changes to the world of industry, contributing greatly to this sector's transition to Industry 4.0, also referred to as Smart Industry. This involves an increasingly predominant role of machines and automation within industrial processes. In this context, the Swedish forest industry is an excellent context for applying these techniques. In particular, this work will deal with automating the measurement of log diameters to date carried out manually by operators in the industry. The proposed methodology will use two object detection neural networks, one deputed to detect logs in the scene and the other for the calibrated target. The latter thus allows the camera calibration to be fully automated, enabling each diameter to be measured without any further operations by the operator. The results obtained are satisfactory and open the way for the industrial application of the proposed methodology.
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| 10. |
- Carratù, Marco, et al.
(författare)
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Cross-Correlation Estimation in Artificial Neural Network for Uncertainty Assessment
- 2024
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Ingår i: Conference Record - IEEE Instrumentation and Measurement Technology Conference. - : IEEE conference proceedings. - 9798350380903
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Konferensbidrag (refereegranskat)abstract
- One of the main challenges in Artificial Neural Networks (ANNs) is the development of reliable, valid, and reproducible systems. Prediction networks have had a disruptive impact, bringing numerous advantages in various fields, but for their common usage, it's necessary to quantify their quality. In particular, evaluating the uncertainty of the measurements obtained with these approaches allows their correct utilization. This work aims to analyze the covariances of the inputs of different neurons, particularly in those of the hidden layers of ANNs. Evaluating the covariance of the inputs of a single neuron finds primary use in the law of propagation of uncertainty, particularly for evaluating the correlation term in mathematical development, as defined by ISO GUM. Based on numerical evaluation, the proposed procedure aims to evaluate the PDFs of inputs to individual nodes and, therefore, the correlations among all inputs propagating within the network architecture.
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