California Extremely Large Telescope: Conceptual Design for a Thirty-Meter Telescope

Following great success in the creation of the Keck Observatory, scientists at the California Institute of Technology and the University of California have begun to explore the scientific and technical prospects for a much larger telescope. The Keck telescopes will remain the largest telescopes in the world for a number of years, with many decades of forefront research ahead after that. Though these telescopes have produced dramatic discoveries, it is already clear that even larger telescopes must be built if we are to address some of the most profound questions about our universe. The time required to build a larger telescope is approximately ten years, and the California community is presently well-positioned to begin its design and construction. The same scientists who conceived, led the design, and guided the construction of the Keck Observatory have been intensely engaged in a study of the prospects for an extremely large telescope. Building on our experience with the Keck Observatory, we have concluded that the large telescope is feasible and is within the bounds set by present-day technology. Our reference telescope has a diameter of 30 meters, the largest size we believe can be built with acceptable risk. The project is currently designated the California Extremely Large Telescope (CELT)

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

ИНТЕЛЛЕКТУАЛЬНЫЙ числовым программным ДЛЯ MIMD-компьютер

For most scientific and engineering problems simulated on computers the solving of problems of the computational mathematics with approximately given initial data constitutes an intermediate or a final stage. Basic problems of the computational mathematics include the investigating and solving of linear algebraic systems, evaluating of eigenvalues and eigenvectors of matrices, the solving of systems of non-linear equations, numerical integration of initial- value problems for systems of ordinary differential equations.Для більшості наукових та інженерних задач моделювання на ЕОМ рішення задач обчислювальної математики з наближено заданими вихідними даними складає проміжний або остаточний етап. Основні проблеми обчислювальної математики відносяться дослідження і рішення лінійних алгебраїчних систем оцінки власних значень і власних векторів матриць, рішення систем нелінійних рівнянь, чисельного інтегрування початково задач для систем звичайних диференціальних рівнянь.Для большинства научных и инженерных задач моделирования на ЭВМ решение задач вычислительной математики с приближенно заданным исходным данным составляет промежуточный или окончательный этап. Основные проблемы вычислительной математики относятся исследования и решения линейных алгебраических систем оценки собственных значений и собственных векторов матриц, решение систем нелинейных уравнений, численного интегрирования начально задач для систем обыкновенных дифференциальных уравнений

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Template-based Monocular 3-D Shape Reconstruction And Tracking Using Laplacian Meshes

This thesis addresses the problem of recovering the 3-D shape of a deformable object in single images, or image sequences acquired by a monocular video camera, given that a 3-D template shape and a template image of the object are available. While being a very challenging problem in computer vision, being able to reconstruct and track 3-D deformable objects in videos allows us to develop many potential applications ranging from sports and entertainments to engineering and medical imaging. This thesis extends the scope of deformable object modeling to real-world applications of fully 3-D modeling of deformable objects from video streams with a number of contributions. We show that by extending the Laplacian formalism, which was first introduced in the Graphics community to regularize 3-D meshes, we can turn the monocular 3-D shape reconstruction of a deformable object given correspondences with a reference image into a much better-posed problem with far fewer degrees of freedom than the original one. This has proved key to achieving real-time performance while preserving both sufficient flexibility and robustness. Our real-time 3-D reconstruction and tracking system of deformable objects can very quickly reject outlier correspondences and accurately reconstruct the object shape in 3D. Frame-to-frame tracking is exploited to track the object under difficult settings such as large deformations, occlusions, illumination changes, and motion blur. We present an approach to solving the problem of dense image registration and 3-D shape reconstruction of deformable objects in the presence of occlusions and minimal texture. A main ingredient is the pixel-wise relevancy score that we use to weigh the influence of the image information from a pixel in the image energy cost function. A careful design of the framework is essential for obtaining state-of-the-art results in recovering 3-D deformations of both well- and poorly-textured objects in the presence of occlusions. We study the problem of reconstructing 3-D deformable objects interacting with rigid ones. Imposing real physical constraints allows us to model the interactions of objects in the real world more accurately and more realistically. In particular, we study the problem of a ball colliding with a bat observed by high speed cameras. We provide quantitative measurements of the impact that are compared with simulation-based methods to evaluate which simulation predictions most accurately describe a physical quantity of interest and to improve the models. Based on the diffuse property of the tracked deformable object, we propose a method to estimate the environment irradiance map represented by a set of low frequency spherical harmonics. The obtained irradiance map can be used to realistically illuminate 2-D and 3-D virtual contents in the context of augmented reality on deformable objects. The results compare favorably with baseline methods. In collaboration with Disney Research, we develop an augmented reality coloring book application that runs in real-time on mobile devices. The app allows the children to see the coloring work by showing animated characters with texture lifted from their colors on the drawing. Deformations of the book page are explicitly modeled by our 3-D tracking and reconstruction method. As a result, accurate color information is extracted to synthesize the character's texture

Of Priors and Particles: Structured and Distributed Approaches to Robot Perception and Control

Applications of robotic systems have expanded significantly in their scope, moving beyond the caged predictability of industrial automation and towards more open, unstructured environments. These agents must learn to reliably perceive their surroundings, efficiently integrate new information and quickly adapt to dynamic perturbations. To accomplish this, we require solutions which can effectively incorporate prior knowledge while maintaining the generality of learned representations. These systems must also contend with uncertainty in both their perception of the world and in predicting possible future outcomes. Efficient methods for probabilistic inference are then key to realizing robust, adaptive behavior. This thesis will first examine data-driven approaches for learning and combining perceptual models for both visual and tactile sensor modalities, common in robotics. Modern variational inference methods will then be examined in the context of online optimization and stochastic optimal control. Specifically, this thesis will contribute (1) data-driven visual and tactile perceptual models leveraging kinematic and dynamic priors, (2) a framework for joint inference with visuo-tactile sensing, (3) a family of particle-based, variational model predictive control and planning algorithms, and (4) a distributed inference scheme for online model adaptation.Ph.D