Intelligent Connectivity

With recent outbreaks of multiple large-scale financial crises, amplified by interconnected risk sources, a new paradigm of fund management has emerged. This new paradigm leverages “embedded” quantitative processes and methods to provide more transparent, adaptive, reliable and easily implemented “risk assessment-based” practices. This book surveys the most widely used factor models employed within the field of financial asset pricing. Through the concrete application of evaluating risks in the hedge fund industry, the authors demonstrate that signal processing techniques are an interesting alternative to the selection of factors (both fundamentals and statistical factors) and can provide more efficient estimation procedures, based on lq regularized Kalman filtering for instance. With numerous illustrative examples from stock markets, this book meets the needs of both finance practitioners and graduate students in science, econometrics and finance. Contents Foreword, Rama Cont. 1. Factor Models and General Definition. 2. Factor Selection. 3. Least Squares Estimation (LSE) and Kalman Filtering (KF) for Factor Modeling: A Geometrical Perspective. 4. A Regularized Kalman Filter (rgKF) for Spiky Data. Appendix: Some Probability Densities. About the Authors Serge Darolles is Professor of Finance at Paris-Dauphine University, Vice-President of QuantValley, co-founder of QAMLab SAS, and member of the Quantitative Management Initiative (QMI) scientific committee. His research interests include financial econometrics, liquidity and hedge fund analysis. He has written numerous articles, which have been published in academic journals. Patrick Duvaut is currently the Research Director of Telecom ParisTech, France. He is co-founder of QAMLab SAS, and member of the Quantitative Management Initiative (QMI) scientific committee. His fields of expertise encompass statistical signal processing, digital communications, embedded systems and QUANT finance. Emmanuelle Jay is co-founder and President of QAMLab SAS. She has worked at Aequam Capital as co-head of R&D since April 2011 and is member of the Quantitative Management Initiative (QMI) scientific committee. Her research interests include SP for finance, quantitative and statistical finance, and hedge fund analysis.

Based on their own experiences of in-depth case studies of software projects in international corporations, in this book the authors present detailed practical guidelines on the preparation, conduct, design and reporting of case studies of software engineering. This is the first software engineering specific book on the case study research method.

This collective work identifies the latest developments in the field of the automatic processing and analysis of digital color images. For researchers and students, it represents a critical state of the art on the scientific issues raised by the various steps constituting the chain of color image processing. It covers a wide range of topics related to computational color imaging, including color filtering and segmentation, color texture characterization, color invariant for object recognition, color and motion analysis, as well as color image and video indexing and retrieval. Contents 1. Color Representation and Processing in Polar Color Spaces, Jes?s Angulo, S?bastien Lef?vre and Olivier Lezoray. 2. Adaptive Median Color Filtering, Fr?d?rique Robert-Inacio and Eric Dinet. 3. Anisotropic Diffusion PDEs for Regularization of Multichannel Images: Formalisms and Applications, David Tschumperl?. 4. Linear Prediction in Spaces with Separate Achromatic and Chromatic Information,Olivier Alata, Imtnan Qazi, Jean-Christophe Burie and Christine Fernandez-Maloigne. 5. Region Segmentation, Alain Cl?ment, Laurent Busin, Olivier Lezoray and Ludovic Macaire. 6. Color Texture Attributes, Nicolas Vandenbroucke, Olivier Alata, Christ?le Lecomte, Alice Porebski and Imtnan Qazi. 7. Photometric Color Invariants for Object Recognition, Damien Muselet. 8. Color Key Point Detectors and Local Color Descriptors, Damien Muselet and Xiaohu Song. 9. Motion Estimation in Color Image Sequences, Bertrand Augereau and Jenny Benois-Pineau.

Learning Dreamweaver is a dream with this instructional book-and-video training package! Dreamweaver CS5 Digital Classroom covers Dreamweaver CS5 and Dreamweaver CS5.5. Adobe Dreamweaver allows you to easily create robust Web sites without needing extensive programming knowledge or skills. The latest version of Dreamweaver boasts enhanced capabilities and this exciting book-and-downloadable video training package makes learning the new features of Dreamweaver less intimidating. Sixteen self-paced lessons explain how to design, develop, and maintain a fully functioning site. The complementary lessons featured in the video tutorials are each approximately five minutes long and demonstrate the concepts and features covered in the lesson. Features full-color, step-by-step tutorials that complement the topics covered in each lesson Walks you through the basics of the newest release of Dreamweaver, such as applying style sheets, using dynamic HTML, adding style with images and multimedia, and publishing and maintaining a Web site Addresses how to use hyperlinks to navigate throughout a Website or link to other sites on the Internet and use databases to create dynamic Web sites It's not a dream—Dreamweaver CS5 Digital Classroom really is just like having your own personal instructor guiding you through each lesson. Note: DVD and other supplementary materials are not included as part of eBook file. These materials are available for download upon purchase.

A unified view of the use of computer vision technology for different types of vehicles Computer Vision in Vehicle Technology focuses on computer vision as on-board technology, bringing together fields of research where computer vision is progressively penetrating: the automotive sector, unmanned aerial and underwater vehicles. It also serves as a reference for researchers of current developments and challenges in areas of the application of computer vision, involving vehicles such as advanced driver assistance (pedestrian detection, lane departure warning, traffic sign recognition), autonomous driving and robot navigation (with visual simultaneous localization and mapping) or unmanned aerial vehicles (obstacle avoidance, landscape classification and mapping, fire risk assessment). The overall role of computer vision for the navigation of different vehicles, as well as technology to address on-board applications, is analysed. Key features: Presents the latest advances in the field of computer vision and vehicle technologies in a highly informative and understandable way, including the basic mathematics for each problem. Provides a comprehensive summary of the state of the art computer vision techniques in vehicles from the navigation and the addressable applications points of view. Offers a detailed description of the open challenges and business opportunities for the immediate future in the field of vision based vehicle technologies. This is essential reading for computer vision researchers, as well as engineers working in vehicle technologies, and students of computer vision.