If you are a web programmer, you need to know modern PHP. This book presents with many new areas in which PHP plays a large role. If you want to write a mobile application using geo-location data, Pro PHP Programming will show you how. Additionally, if you need to make sure that you can write a multilingual indexing application using Sphinx, this book will help you avoid the pitfalls. Of course, Pro PHP Programming gives a thorough survey of PHP post-5.3. You'll begin by working throughan informative survey and clear guide to object-oriented PHP. Then, you'll be set for the core of the book on modern PHP applications. Now, you'll be able to start with the chapter on PHP for mobile programming and move on to sampling social media applications. You'll also be guided through new PHP programming language features like closures and namespaces. Pro PHP Programming deals with filtering data from users and databases next, so you'll bewell prepared for relational and NoSQL databases. Of course, you can alsolearn about data retrieval from other sources, like OCR libraries or websites. Then the question of how to format and present data arises, andinPro PHP Programming, you'llfind solutions via JSON, AJAX and XML. What you'll learn PHP 5.3 object-oriented programming and new PHP features Writing programs talking to SQL-based and NoSQL-based databases alike Using modern PHP programmer's tools Writing social media applications Investigating NoSQL datastores Writing PHP programs for a cloud ecology Who this book is for

This book is for intermediate and advanced PHP programmers, as well as programmers coming from other languages such asPython, Perl, C/C++. It can also be utilized by programmers with knowledge of a previous PHP version who want to return to PHP. Table of Contents Object Orientation Exceptions and References Mobile PHP Social Media Cutting Edge Form Design and Management Database Interaction I Database Interaction II Database Interaction III Libraries Security Agile Development with Zend Studio for Eclipse, Bugzilla, Mylyn, and Subversion Refactoring, Unit Testing, and Continuous Intergration XML JSONand AJAX Conclusions Regular Expressions

Coherent imaging systems offer unique benefits to system operators in terms of resolving power, range gating, selective illumination and utility for applications where passively illuminated targets have limited emissivity or reflectivity. In contrast to incoherent imaging systems, partially coherent illumination causes difficulty during image processing due to high levels of image speckle caused by constructive and de- structive interference effects unique to the highly coherent illumination source. Image speckle is caused by the random phase delays that occur due to target roughness and the turbulent atmosphere between the remote target and optical system. To combat such effects, a number of short-exposure images are combined by incoherent averaging to arrive at an image that has greatly decreased levels of speckle. Unfortunately, such average images suffer from decreased spatial resolution due to blur resulting from atmospheric distortion. Effective image restoration may be realized by inverse filtering the recovered average image with an optical transfer function that describes the overall optical system and atmospheric turbulence. In cases where it is inconvenient or impossible to measure the parameters of this evolving function, blind deconvolution algorithms may be applied to estimate both the unknown remote scene reflectance, as well as the unknown system transfer function. This research proposes a novel blind deconvolution algorithm that is based on a maximum a posteriori Bayesian estimator constructed upon a physically-based statistical model for the intensity of the partially coherent light at the imaging detector. The estimator is initially constructed using a shift- invariant system model, and is later extended to the case of a shift-variant optical system by the addition of a transfer function term that quantifies optical blur for given field-of-views and atmospheric conditions. The estimators are evaluated using both synthetically generated imagery,