Block-Parallel Chaotic Algorithms for Image Reconstruction

The paper is devoted to the elaboration and implementation of block-parallel asynchronous algorithms for computer tomography. The numerical reconstruction algorithms and numerical simulation results for a number of modeling objects and some particular systems of reconstruction are presented.Разработаны и выполнены блочно-параллельные алгоритмы компьютерной томографии.Представлены численные алгоритмы восстановления и результаты численного моделирования для ряда тестовых задач и некоторых частных случаев систем реконструкции сбора данных.Розроблено та виконано блочно-паралельні алгоритми комп’ютерної томографії. Наведено чисельні алгоритми відновлення та результати чисельного моделювання для тестових задач і деяких окремих випадків систем реконструкції збирання даних

Chaotic Iterative Algorithms for Image Reconstruction from Incomplete Projection Data

The problem of computer tomography is considered from incomplete projection data with chaotic algorithms for some particular systems of reconstruction. The numerical reconstruction algorithms and numerical simulation results are presented for a number of modeling objects which can be described by means of discrete functions.Рассмотрена задача компьютерной томографии по неполным проекционным данным с использованием хаотических алгоритмов для некоторых специальных систем восстановления. Представлены численные алгоритмы восстановления и результаты численного моделирования для ряда моделируемых объектов, которые могут быть описаны посредством дискретных функций.Розглянуто задачу комп’ютерної томографії по неповним проективним даним з використанням хаотичних алгоритмів для деяких спеціальних систем відновлення. Наведено числові алгоритми відновлення та результати числового моделювання для об’єктів, які можуть бути описані дискретними функціями

Implementation of medical imaging with telemedicine for the early detection and diagnoses of breast cancer to women in remote areas

Nowadays, the cancer topic has become a global concern. Furthermore, breast cancer persists to be the top leading cause of death to women population and the second cause of cancer death after the lung cancer globally. Various technologies and techniques have been searched, developed and studied over the years to detect the disease at the early stage; the early diagnosis saves many lives in both developed and developing countries. The detection of cancer through a screening process before its symptoms emerge increases the survival rate dramatically (Li, Meaney and Paulsen). Moreover, sufficient knowledge of the disease, qualified staff, accurate, appropriate treatment and diagnosis contribute to the successful cure of the disease; however, the cancer treatment is not affordable by many and sometimes not available to the very needy, and more precisely in developing countries. In this research, we aimed to explore the early detection of breast cancer using the new image compression algorithm: DYNAMAC, a compression tool that finds its basis in nonlinear dynamical systems theory; we implemented this algorithm through the D-transform, a digital sequence used to compress the digital media (Wang and Huang) & (Antoine, Murenzi and Vandergheynst). The goal is to use this method to analyze the average profile of diseased and healthy breast images obtained from a digital mammography to detect diseased tissues. After the detection of cancerous tumors, we worked to establish a remote care to women victims of breast cancer using the Telecommunication infrastructure through primarily Teleradiology and the Next Generation Internet (NGI) technology. Over the methods and techniques previously used in the area of medical imaging techniques, DYNAMAC algorithm is the most easily implemented along with its features that include cost saving in addition to best meeting the requirements of the breast imaging technology

Doctor of Philosophy

dissertationConfocal microscopy has become a popular imaging technique in biology research in recent years. It is often used to study three-dimensional (3D) structures of biological samples. Confocal data are commonly multichannel, with each channel resulting from a different fluorescent staining. This technique also results in finely detailed structures in 3D, such as neuron fibers. Despite the plethora of volume rendering techniques that have been available for many years, there is a demand from biologists for a flexible tool that allows interactive visualization and analysis of multichannel confocal data. Together with biologists, we have designed and developed FluoRender. It incorporates volume rendering techniques such as a two-dimensional (2D) transfer function and multichannel intermixing. Rendering results can be enhanced through tone-mappings and overlays. To facilitate analyses of confocal data, FluoRender provides interactive operations for extracting complex structures. Furthermore, we developed the Synthetic Brainbow technique, which takes advantage of the asynchronous behavior in Graphics Processing Unit (GPU) framebuffer loops and generates random colorizations for different structures in single-channel confocal data. The results from our Synthetic Brainbows, when applied to a sequence of developing cells, can then be used for tracking the movements of these cells. Finally, we present an application of FluoRender in the workflow of constructing anatomical atlases

Prospects of brain–machine interfaces for space system control

The dream of controlling and guiding computer-based systems using human brain signals has slowly but steadily become a reality. The available technology allows real-time implementation of systems that measure neuronal activity, convert their signals, and translate their output for the purpose of controlling mechanical and electronic systems. This paper describes the state of the art of non-invasive brain-machine interfaces (BMIs) and critically investigates both the current technological limits and the future potential that BMIs have for space applications. We present an assessment of the advantages that BMIs can provide and justify the preferred candidate concepts for space applications together with a vision of future directions for their implementation. © 2008 Elsevier Ltd. All rights reserved

Synthetic presentation of iterative asynchronous parallel algorithms.

Iterative asynchronous parallel methods are nowadays gaining renewed interest in the community of researchers interested in High Performance Computing (HPC), in the specific case of massive parallelism. This is because these methods avoid the deadlock phenomena and that moreover a rigorous load balancing is not necessary, which is not the case with synchronous methods. Such iterative asynchronous parallel methods are of great interest when there are many synchronizations between processors, which in the case of iterative methods is the case when convergence is slow. Indeed in iterative synchronous parallel methods, to respect the task sequence graph that defines in fact the logic of the algorithm used, processors must wait for the results they need and calculated by other processors; such expectations of the results emitted by concurrent processors therefore cause idle times for standby processors. It is to overcome this drawback that asynchronous parallel iterative methods have been introduced first for the resolution of large scale linear systems and then for the resolution of highly nonlinear algebraic systems of large size as well, where the solution may be subject to constraints. This kind of method has been widely studied worldwide by many authors. The purpose of this presentation is to present as broadly and pedagogically as possible the asynchronous parallel iterative methods as well as the issues related to their implementation and application in solving many problems arising from High Performance Computing. We will therefore try as much as possible to present the underlying concepts that allow a good understanding of these methods by avoiding as much as possible an overly rigorous mathematical formalism; references to the main pioneering work will also be made. After a general introduction we will present the basic concepts that allow to model asynchronous parallel iterative methods including as a particular case synchronous methods. We will then present the algorithmic extensions of these methods consisting of asynchronous sub-domain methods, asynchronous multisplitting methods as well as asynchronous parallel methods with flexible communications. In each case an analysis of the behavior of these methods will be presented. Note that the first kind of analysis allows to obtain an estimate of the asymptotic rate of convergence. The difficult problem of the stopping test of asynchronous parallel iterations will be also studied, both by computer sciences considerations and also by numerical aspects related to the mathematical analysis of the behavior of theses iterative parallel methods. The parallel asynchronous methods have been implemented on various architectures and we will present the main principles that made it possible to code them. These parallel asynchronous methods have been used for the resolution of several kind of mathematical problems and we will list the main applications processed. Finally we will try to specify in which cases and on which type of architecture these methods are efficient and interesting to use

A roadmap to integrate astrocytes into Systems Neuroscience.

Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms