128 research outputs found

    Entropy in Image Analysis III

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    Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future

    Mathematics and Digital Signal Processing

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    Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems

    Deep Learning in Medical Image Analysis

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    The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

    Software model with verification of the imaging chamber in microwave tomography

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    Микроталасна томографија је метода за снимање објекта путем микроталаса. Након мерења објекта системом антена у комори решава се инверзни проблем нумеричком симулацијом и оптимизацијом. У овом раду се решава проблем верности моделовања антенског система у симулацији. Избегавањем апроксимација у моделовању антенског система, добија се вернији софтверски модел. Ради постизавања тог циља  жичана квадратна спирална антена је одабрана за коришћење у комори за снимање. Употреба предложене антене у микроталасној томографији је новитет. Реализован је софтверски симулациони модел коморе са предложеном антеном.  У симулацијама је извршено поређење са другим антенама често коришћеним у литератури за дату сврху. Израђен је прототип коморе са предложеним антенама. Извршена су мерења и поређења са предложеним моделом, како би се потврдила веродостојсност модела.Mikrotalasna tomografija je metoda za snimanje objekta putem mikrotalasa. Nakon merenja objekta sistemom antena u komori rešava se inverzni problem numeričkom simulacijom i optimizacijom. U ovom radu se rešava problem vernosti modelovanja antenskog sistema u simulaciji. Izbegavanjem aproksimacija u modelovanju antenskog sistema, dobija se verniji softverski model. Radi postizavanja tog cilja  žičana kvadratna spiralna antena je odabrana za korišćenje u komori za snimanje. Upotreba predložene antene u mikrotalasnoj tomografiji je novitet. Realizovan je softverski simulacioni model komore sa predloženom antenom.  U simulacijama je izvršeno poređenje sa drugim antenama često korišćenim u literaturi za datu svrhu. Izrađen je prototip komore sa predloženim antenama. Izvršena su merenja i poređenja sa predloženim modelom, kako bi se potvrdila verodostojsnost modela.Microwave tomography is method of object imaging by means of microwaves. After object measurement by system of antennas in chamber inverse problem is solved by numeric simulation and optimization. This thesis focuses on problem of trueness in modeling antenna system in simulation. Avoding approximations while modeling antenna system yield better trueness of software model. To achieve this target wire square spiral antenna is utilized in imaging chamber. Usage of proposed antenna in microwave tomography is novelty. Software simulation model of chamber with proposed antenna is designed and evaluated. Comparison with other antennas often used in literature for this purpose is done in simulation. Chamber with antennas is realized at the prototype level. Measurement and comparison with proposed model are done in order to verify its trueness

    Virginia Commonwealth University Courses

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    Listing of courses for the 2022-2023 year

    Advanced Computational Methods for Oncological Image Analysis

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    [Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

    Modelling, Simulation and Data Analysis in Acoustical Problems

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    Modelling and simulation in acoustics is currently gaining importance. In fact, with the development and improvement of innovative computational techniques and with the growing need for predictive models, an impressive boost has been observed in several research and application areas, such as noise control, indoor acoustics, and industrial applications. This led us to the proposal of a special issue about “Modelling, Simulation and Data Analysis in Acoustical Problems”, as we believe in the importance of these topics in modern acoustics’ studies. In total, 81 papers were submitted and 33 of them were published, with an acceptance rate of 37.5%. According to the number of papers submitted, it can be affirmed that this is a trending topic in the scientific and academic community and this special issue will try to provide a future reference for the research that will be developed in coming years

    Flame stability and burner condition monitoring through optical sensing and digital imaging

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    This thesis describes the design, implementation and experimental evaluation of a prototype instrumentation system for flame stability and burner condition monitoring on fossil-fuel-fired furnaces. A review of methodologies and technologies for the monitoring of flame stability and burner condition is given, together with the discussions of existing problems and technical requirements in their applications. A technical strategy, incorporating optical sensing, digital imaging, digital signal/image processing and soft computing techniques, is proposed. Based on this strategy, a prototype flame imaging system is developed. The system consists of a rigid optical probe, an optical-bearn-splitting unit, an embedded photodetector and signal-processing board, a digital camera, and a mini-motherboard with associated application software. Detailed system design, implementation, calibration and evaluation are reported. A number of flame characteristic parameters are extracted from flame images and radiation signals. Power spectral density, oscillation frequency, and a proposed universal flame stability index are used for the assessment of flame stability. Kernel-based soft computing techniques are employed for burner condition monitoring. Specifically, kernel principal components analysis is used for the detection of abnormal conditions in a combustion process, whilst support vector machines are used for the prediction of NO x emission and the identification of flame state. Extensive experimental work was conducted on a 9MW th heavy-oil-fired combustion test facility to evaluate the performance of the prototype system and developed algorithms. Further tests were carried out on a 660MWth heavy-oil-fired boiler to investigate the cause of the boiler vibration from a flame stability point of view. Results Obtained from the tests are presented and discussed

    Air Force Institute of Technology Research Report 2020

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    This Research Report presents the FY20 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document

    Pattern Recognition

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    Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition
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