Implementazione ed ottimizzazione di algoritmi per l'analisi di Biomedical Big Data

Big Data Analytics poses many challenges to the research community who has to handle several computational problems related to the vast amount of data. An increasing interest involves Biomedical data, aiming to get the so-called personalized medicine, where therapy plans are designed on the specific genotype and phenotype of an individual patient and algorithm optimization plays a key role to this purpose. In this work we discuss about several topics related to Biomedical Big Data Analytics, with a special attention to numerical issues and algorithmic solutions related to them. We introduce a novel feature selection algorithm tailored on omics datasets, proving its efficiency on synthetic and real high-throughput genomic datasets. We tested our algorithm against other state-of-art methods obtaining better or comparable results. We also implemented and optimized different types of deep learning models, testing their efficiency on biomedical image processing tasks. Three novel frameworks for deep learning neural network models development are discussed and used to describe the numerical improvements proposed on various topics. In the first implementation we optimize two Super Resolution models showing their results on NMR images and proving their efficiency in generalization tasks without a retraining. The second optimization involves a state-of-art Object Detection neural network architecture, obtaining a significant speedup in computational performance. In the third application we discuss about femur head segmentation problem on CT images using deep learning algorithms. The last section of this work involves the implementation of a novel biomedical database obtained by the harmonization of multiple data sources, that provides network-like relationships between biomedical entities. Data related to diseases and other biological relates were mined using web-scraping methods and a novel natural language processing pipeline was designed to maximize the overlap between the different data sources involved in this project

Optimized decomposition basis using Lanczos filters for lossless compression of biomedical images

International audienceThis paper proposes to introduce Lanczos interpolation filters as wavelet atoms in an optimized decomposition for embedded lossy to lossless compression of biomedical images. The decomposition and the Lanczos parameter are jointly optimized in a generic packet structure in order to take into account the various contents of biomedical imaging modalities. Lossless experimental results are given on a large scale database. They show that in comparison with a well known basis using 5/3 biorthogonal wavelets and a dyadic decomposition, the proposed approach allows to improve the compression by more than 10\% on less noisy images and up to 30% on 3D-MRI while providing similar results on noisy datasets

Innovation Of Petrophysical And Geomechanical Experiment Methodologies: The Application Of 3D Printing Technology

The petrophysical and geomechanical properties of rocks link the geology origin with engineering practice, which serves as the fundamental of various disciplinaries associated with subsurface porous media, including civil engineering, underground water, geological exploration, and petroleum engineering. The research methodologies can be mainly divided into three aspects: theoretical modelling, numerical simulation, and experiments, in which the last approach plays a critical role that can support, validate, calibrate, or even refute a hypothesis. Only replying on repeatable trials and consolidate analysis of precise results can the experiments be successful and convincing, though uncertainties, due to multiple factors, need to be scrutinized and controlled. The challenges also existed in the characterization and measurements of rock properties as a result of heterogeneity and anisotropy as well as the inevitable impact of experimental operation. 3D printing, a cutting-edge technology, was introduced and utilized in the study that is supposed to be capable of controlling the mineralogy, microstructure, physical properties of physical rock replicas and further benefit the petrophysical and geomechanical experimental methodologies. My PhD research project attempted to answer the questions from the standpoint of petrophysicisits and geomechanics scientist: Can 3D printed rocks replicate natural rocks in terms of microstructure, petrophysical and geomechanical properties? If not, by any means can we improve the quality of replicas to mimic the common rock types? Which 3D printing method is best suitable for our research purposes? How could it be applied in the conventional experiments and integrated with theoretical calculation or numerical simulation? Three main types of printing materials and techniques (gypsum, silica sand, resin) were characterized first individually, which demonstrated varying microstructure, anisotropy, petrophysical and geomechanical properties. Post-processing effect was examined on the 3D printed gypsum rocks that show impact differences on nanoscale and microscale pore structures. Through comparison, resin, the material used in stereolithography technology, best suits the reconstruction of intricate pore network that aims to complement digital rock physics and ultimately be applied in petrophysical research. Gypsum material, however, has been proved as the best candidate for geomechanical research spanning from reference samples to upscaling methods validation. Currently, a practical approach of utilizing 3D printing in petroleum geoscience is taking advantages of the characteristics we focus on the research while disregarding the other properties, by which a suitable 3D printing material and technique can emerge

Software for Exascale Computing - SPPEXA 2016-2019

This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest

WOFEX 2021 : 19th annual workshop, Ostrava, 1th September 2021 : proceedings of papers

The workshop WOFEX 2021 (PhD workshop of Faculty of Electrical Engineer-ing and Computer Science) was held on September 1st September 2021 at the VSB – Technical University of Ostrava. The workshop offers an opportunity for students to meet and share their research experiences, to discover commonalities in research and studentship, and to foster a collaborative environment for joint problem solving. PhD students are encouraged to attend in order to ensure a broad, unconfined discussion. In that view, this workshop is intended for students and researchers of this faculty offering opportunities to meet new colleagues.Ostrav

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Modeling EMI Resulting from a Signal Via Transition Through Power/Ground Layers

Signal transitioning through layers on vias are very common in multi-layer printed circuit board (PCB) design. For a signal via transitioning through the internal power and ground planes, the return current must switch from one reference plane to another reference plane. The discontinuity of the return current at the via excites the power and ground planes, and results in noise on the power bus that can lead to signal integrity, as well as EMI problems. Numerical methods, such as the finite-difference time-domain (FDTD), Moment of Methods (MoM), and partial element equivalent circuit (PEEC) method, were employed herein to study this problem. The modeled results are supported by measurements. In addition, a common EMI mitigation approach of adding a decoupling capacitor was investigated with the FDTD method