Search

This study presents an automated methodology for evaluating micro-channels fabricated using a femtosecond laser on stainless steel substrates. We utilize 3D surface topography and metrological analyses to extract geometric features and detect fabrication defects. Standardized samples were analyzed using a light interferometer, and the resulting data were processed with Principal Component Analysis (PCA) and RANSAC algorithms to derive channel characteristics, such as depth, wall taper, and surface roughness. The proposed method identifies common defects, including bumps and V-defects, which can compromise the functionality of micro-channels. The effectiveness of the approach is validated by comparisons with commercial solutions. This automated procedure aims to enhance the reliability and precision of femtosecond laser micro-milling for industrial applications. The detected defects, combined with fabrication parameters, could be ingested in an AI-based process to optimize fabrication processes. [ABSTRACT FROM AUTHOR]

For the most diverse practical purposes and communication needs (from trade to conquests or exploratory voyages), man has always represented space through the production of maps. There are countless varieties of themes, styles and graphic languages, types of representation (pictorial maps, bird's eye views, cadastral and topographic maps) used throughout history to depict, in particular, the landscape and the urban fabric. The informative, historical, cultural and identity value of this cartographic heritage, now preserved in archives and institutions around the world, is inestimable. Due to the need to preserve its content from the ravages of time and to allow its "digital" use, an increasingly intense campaign of digitization of these sources has been launched in recent years (as has happened, for example, for other historical data, such as photographs and books). The growing availability of digital historical maps and, at the same time, the notable progress in data processing and analysis, today offers new perspectives for the use and valorization of these resources. In the case, in particular, of ancient cartography, there are increasingly broader scenarios for accurate studies on the evolution and transformation of urban structures, to support careful urban planning policies or reconstruct historical scenarios (Farella et al., 2021). [ABSTRACT FROM AUTHOR]

We present and discuss a runtime architecture that integrates sensorial data and classifiers with a logic-based decision-making system in the context of an e-Health system for the rehabilitation of children with neuromotor disorders. In this application, children perform a rehabilitation task in the form of games. The main aim of the system is to derive a set of parameters the child's current level of cognitive and behavioral performance (e.g., engagement, attention, task accuracy) from the available sensors and classifiers (e.g., eye trackers, motion sensors, emotion recognition techniques) and take decisions accordingly. These decisions are typically aimed at improving the child's performance by triggering appropriate re-engagement stimuli when their attention is low, by changing the game or making it more difficult when the child is losing interest in the task as it is too easy. Alongside state-of-the-art techniques for emotion recognition and head pose estimation, we use a runtime variant of a probabilistic and epistemic logic programming dialect of the Event Calculus, known as the Epistemic Probabilistic Event Calculus. In particular, the probabilistic component of this symbolic framework allows for a natural interface with the machine learning techniques. We overview the architecture and its components, and show some of its characteristics through a discussion of a running example and experiments. [ABSTRACT FROM AUTHOR]

Knowledge about the aboveground biomass (AGB) and the diameters at breast height (DBH) distribution can lead to a precise estimation of carbon density and forest structure which can be very important for ecology studies especially for those concerning climate change. In this study, we propose to predict DBH and AGB of individual trees using tree height (H) and crown diameter (CD), and other metrics extracted from airborne laser scanning (ALS) data as input. In the proposed approach, regression methods, such us support vector machine for regression (SVR) and random forests (RF), were used to find a transformation or a transfer function that links the input parameters (H, CD, and other ALS metrics) with the output (DBH and AGB). The developed approach was tested on two datasets collected in southern Norway comprising 3970 and 9467 recorded trees, respectively. The results demonstrate that the developed approach provides better results compared to a state-of-the-art work (based on a linear model with the standard least-squares method) with RMSE equal to 81.4 kg and 92.0 kg, respectively (compared to 94.2 kg and 110.0 kg) for the prediction of AGB, and 5.16 cm and 4.93 cm, respectively (compared to 5.49 cm and 5.30 cm) for DBH. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for driving chemometric analyses from spectroscopic data and based on a convolutional neural network (CNN) architecture. For such purpose, the well-known 2-D CNN is adapted to the monodimensional nature of spectroscopic data. In particular, filtering and pooling operations as well as equations for training are revisited. We also propose an alternative to train the resulting 1D-CNN by means of particle swarm optimization. The resulting trained CNN architecture is successively exploited to extract features from a given 1D spectral signature to feed any regression method. In this work, we resorted to 2 advanced and effective methods, which are support vector machine regression and Gaussian process regression. Experimental results conducted on 3 real spectroscopic datasets show the interesting capabilities of the proposed 1D-CNN methods. [ABSTRACT FROM AUTHOR]

In this letter, we propose to solve the change detection (CD) problem in multitemporal remote-sensing images using interactive segmentation methods. The user needs to input markers related to change and no-change classes in the difference image. Then, the pixels under these markers are used by the support vector machine classifier to generate a spectral-change map. To enhance further the result, we include the spatial contextual information in the decision process using two different solutions based on Markov random field and level-set methods. While the former is a region-driven method, the latter exploits both region and contour for performing the segmentation task. Experiments conducted on a set of four real remote-sensing images acquired by low as well as very high spatial resolution sensors and referring to different kinds of changes confirm the attractive capabilities of the proposed methods in generating accurate CD maps with simple and minimal interaction. [ABSTRACT FROM PUBLISHER]