Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Qualitative Case Study Methodology: Automatic Design and Correction of Ceramic Colors

This paper is focused on two major problems within the ceramics industry. The reproduction of a desired color from pigments (which is time-intensive), and the correction of colors on the production line (which is costly) are processes which still rely heavily on numerous experiments carried out by human operators. This study presents the key aspects of these two processes and suggests some mathematical and computer sciences tools, aimed at automatizing the current procedures. Data was provided by an industrial partner, where the proposed models and solutions will be tested and validated

The computerization of archaeology: survey on AI techniques

This paper analyses the application of artificial intelligence techniques to various areas of archaeology and more specifically: a) The use of software tools as a creative stimulus for the organization of exhibitions; the use of humanoid robots and holographic displays as guides that interact and involve museum visitors; b) The analysis of methods for the classification of fragments found in archaeological excavations and for the reconstruction of ceramics, with the recomposition of the parts of text missing from historical documents and epigraphs; c) The cataloguing and study of human remains to understand the social and historical context of belonging with the demonstration of the effectiveness of the AI techniques used; d) The detection of particularly difficult terrestrial archaeological sites with the analysis of the architectures of the Artificial Neural Networks most suitable for solving the problems presented by the site; the design of a study for the exploration of marine archaeological sites, located at depths that cannot be reached by man, through the construction of a freely explorable 3D version

Syväoppivat neuroverkot karkaistun lasin laadun arviointiin

In this work, automated methods for counting the number of shards in a picture of broken glass are reviewed. The minimum number of shards in a specific observation field is a standardized requirement for tempered glass. The operator of the tempering machine typically counts the shards manually. Manual counting is laboursome, time consuming and prone to human errors. Several image processing and machine learning methods for automating the counting task are experimented with in this work. The best performing method proved to be a deep learning convolutional neural network combined with a simple postprocessing scheme. The neural network segments each shard in an image and with postprocessing the shard count can be obtained robustly. Architecture of the framework is presented in detail in this work and its performance is evaluated extensively. The framework reached below 5 % mean absolute counting error on the used validation data.Tässä työssä tarkastellaan automaattisia menetelmiä murtuneen lasin sirulukumäärän laskemiseen kameralla otetusta kuvasta. Sirujen minimilukumäärä tietyllä tarkastelualueella on standardoitu vaatimus karkaistulle lasille. Laskennan suorittaa tyypillisesti karkaisukoneen operaattori manuaalisesti. Manuaalinen laskenta on työlästä, aikaa vievää ja altis inhimillisille virheille. Työssä tutkitaan erilaisia kuvankäsittelymenetelmiä ja koneoppimiseen perustuvia menetelmiä sirulaskennan automatisoimiseksi. Parhaaksi menetelmäksi valikoitui syväoppiva konvolutiivinen neuroverkko yhdistettynä yksinkertaiseen jälkikäsittelyyn. Neuroverkko erittelee sirut kuvasta ja jälkikäsittelyllä saadaan laskettua luotettavasti lukumäärä siruille. Systeemin rakenne esitetään työssä seikkaperäisesti ja sen suorituskykyä arvioidaan kattavasti. Menetelmällä saavutettiin alle 5 % keskimääräinen laskentavirhe käytetyllä validointidatalla

Similarity reasoning for local surface analysis and recognition

This thesis addresses the similarity assessment of digital shapes, contributing to the analysis of surface characteristics that are independent of the global shape but are crucial to identify a model as belonging to the same manufacture, the same origin/culture or the same typology (color, common decorations, common feature elements, compatible style elements, etc.). To face this problem, the interpretation of the local surface properties is crucial. We go beyond the retrieval of models or surface patches in a collection of models, facing the recognition of geometric patterns across digital models with different overall shape. To address this challenging problem, the use of both engineered and learning-based descriptions are investigated, building one of the first contributions towards the localization and identification of geometric patterns on digital surfaces. Finally, the recognition of patterns adds a further perspective in the exploration of (large) 3D data collections, especially in the cultural heritage domain. Our work contributes to the definition of methods able to locally characterize the geometric and colorimetric surface decorations. Moreover, we showcase our benchmarking activity carried out in recent years on the identification of geometric features and the retrieval of digital models completely characterized by geometric or colorimetric patterns

Audio-Material Modeling and Reconstruction for Multimodal Interaction

Interactive virtual environments enable the creation of training simulations, games, and social applications. These virtual environments can create a sense of presence in the environment: a sensation that its user is truly in another location. To maintain presence, interactions with virtual objects should engage multiple senses. Furthermore, multisensory input should be consistent, e.g. a virtual bowl that visually appears plastic should also sound like plastic when dropped on the floor. In this dissertation, I propose methods to improve the perceptual realism of virtual object impact sounds and ensure consistency between those sounds and the input from other senses. Recreating the impact sound of a real-world object requires an accurate estimate of that object's material parameters. The material parameters that affect impact sound---collectively forming the audio-material---include the material damping parameters for a damping model. I propose and evaluate damping models and use them to estimate material damping parameters for real-world objects. I also consider how interaction with virtual objects can be made more consistent between the senses of sight, hearing, and touch. First, I present a method for modeling the damping behavior of impact sounds, using generalized proportional damping to both estimate more expressive material damping parameters from recorded impact sounds and perform impact sound synthesis. Next, I present a method for estimating material damping parameters in the presence of confounding factors and with no knowledge of the object's shape. To accomplish this, a probabilistic damping model captures various external effects to produce robust damping parameter estimates. Next, I present a method for consistent multimodal interaction with textured surfaces. Texture maps serve as a single unified representation of mesoscopic detail for the purposes of visual rendering, sound synthesis, and rigid-body simulation. Finally, I present a method for geometry and material classification using multimodal audio-visual input. Using this method, a real-world scene can be scanned and virtually reconstructed while accurately modeling both the visual appearances and audio-material parameters of each object.Doctor of Philosoph

Addressing subjectivity in the classification of palaeoenvironmental remains with supervised deep learning convolutional neural networks

Archaeological object identifications have been traditionally undertaken through a comparative methodology where each artefact is identified through a subjective, interpretative act by a professional. Regarding palaeoenvironmental remains, this comparative methodology is given boundaries by using reference materials and codified sets of rules, but subjectivity is nevertheless present. The problem with this traditional archaeological methodology is that higher level of subjectivity in the identification of artefacts leads to inaccuracies, which then increases the potential for Type I and Type II errors in the testing of hypotheses. Reducing the subjectivity of archaeological identifications would improve the statistical power of archaeological analyses, which would subsequently lead to more impactful research. In this thesis, it is shown that the level of subjectivity in palaeoenvironmental research can be reduced by applying deep learning convolutional neural networks within an image recognition framework. The primary aim of the presented research is therefore to further the on-going paradigm shift in archaeology towards model-based object identifications, particularly within the realm of palaeoenvironmental remains. Although this thesis focuses on the identification of pollen grains and animal bones, with the latter being restricted to the astragalus of sheep and goats, there are wider implications for archaeology as these methods can easily be extended beyond pollen and animal remains. The previously published POLEN23E dataset is used as the pilot study of applying deep learning in pollen grain classification. In contrast, an image dataset of modern bones was compiled for the classification of sheep and goat astragali due to a complete lack of available bone image datasets and a double blind study with inexperienced and experienced zooarchaeologists was performed to have a benchmark to which image recognition models can be compared. In both classification tasks, the presented models outperform all previous formal modelling methods and only the best human analysts match the performance of the deep learning model in the sheep and goat astragalus separation task. Throughout the thesis, there is a specific focus on increasing trust in the models through the visualization of the models’ decision making and avenues of improvements to Grad-CAM are explored. This thesis makes an explicit case for the phasing out of the comparative methods in favour of a formal modelling framework within archaeology, especially in palaeoenvironmental object identification

New Global Perspectives on Archaeological Prospection

This volume is a product of the 13th International Conference on Archaeological Prospection 2019, which was hosted by the Department of Environmental Science in the Faculty of Science at the Institute of Technology Sligo. The conference is held every two years under the banner of the International Society for Archaeological Prospection and this was the first time that the conference was held in Ireland. New Global Perspectives on Archaeological Prospection draws together over 90 papers addressing archaeological prospection techniques, methodologies and case studies from 33 countries across Africa, Asia, Australasia, Europe and North America, reflecting current and global trends in archaeological prospection. At this particular ICAP meeting, specific consideration was given to the development and use of archaeological prospection in Ireland, archaeological feedback for the prospector, applications of prospection technology in the urban environment and the use of legacy data. Papers include novel research areas such as magnetometry near the equator, drone-mounted radar, microgravity assessment of tombs, marine electrical resistivity tomography, convolutional neural networks, data processing, automated interpretive workflows and modelling as well as recent improvements in remote sensing, multispectral imaging and visualisation

Extending BIM for air quality monitoring

As we spend more than 90% of our time inside buildings, indoor environmental quality is a major concern for healthy living. Recent studies show that almost 80% of people in European countries and the United States suffer from SBS (Sick Building Syndrome), which affects physical health, productivity and psychological well-being. In this context, environmental quality monitoring provides stakeholders with crucial information about indoor living conditions, thus facilitating building management along its lifecycle, from design, construction and commissioning to usage, maintenance and end-of-life. However, currently available modelling tools for building management remain limited to static models and lack integration capacities to efficiently exploit environmental quality monitoring data. In order to overcome these limitations, we designed and implemented a generic software architecture that relies on accessible Building Information Model (BIM) attributes to add a dynamic layer that integrates environmental quality data coming from deployed sensors. Merging sensor data with BIM allows creation of a digital twin for the monitored building where live information about environmental quality enables evaluation through numerical simulation. Our solution allows accessing and displaying live sensor data, thus providing advanced functionality to the end-user and other systems in the building. In order to preserve genericity and separation of concerns, our solution stores sensor data in a separate database available through an application programming interface (API), which decouples BIM models from sensor data. Our proof-of-concept experiments were conducted with a cultural heritage building located in Bled, Slovenia. We demonstrated that it is possible to display live information regarding environmental quality (temperature, relative humidity, CO2, particle matter, light) using Revit as an example, thus enabling end-users to follow the conditions of their living environment and take appropriate measures to improve its quality.Pages 244-250