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Machine-learning-assisted intelligent processing and optimization of complex systems
Focuses on the most recent developments in intelligent optimization methods and their applications in various test cases. The reprint covers various topics, including distributed multiagent modeling, metaheuristic algorithms, multisource data fusion, mobile computing and mobile sensing, machine learning-based intelligent processing for modeling complex manufacturing systems, and data-driven intelligent modeling
Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks
Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.
Learn BlackBerry 10 App Development : A Cascades-Driven Approach
Learn how to leverage the BlackBerry 10 Cascades framework to create rich native applications. Learn BlackBerry 10 App Development gives you a solid foundation for creating BlackBerry 10 apps efficiently. Along the way, you will learn how to use QML and JavaScript for designing your app’s UI, and C++/Qt for the application logic. No prior knowledge of C++ is assumed and the book covers the fundamental aspects of the language for writing BlackBerry 10 apps. Also a particular emphasis is put on how to create a visually enticing user experience with the Cascades framework, which is based on Qt and QML.
Android Essentials
Android Essentials is a no–frills, no–nonsense, code–centric run through the guts of application development on Google's Mobile OS. This book uses the development of a sample application to work through topics, focusing on giving developers the essential tools and examples required to make viable commercial applications work. Covering the entirety of the Android catalog in less than 150 pages is simply impossible. Instead, this book focuses on just four main topics: the application life cycle and OS integration, user interface, location–based services, and networking.
Android Application Development for the Intel® Platform
The number of Android devices running on Intel processors has increased since Intel and Google announced, in late 2011, that they would be working together to optimize future versions of Android for Intel Atom processors. Today, Intel processors can be found in Android smartphones and tablets made by some of the top manufacturers of Android devices, such as Samsung, Lenovo, and Asus.
Algorithms – ESA 2005 ; 13th Annual European Symposium, Palma de Mallorca, Spain, October 3-6, 2005, Proceedings
This volume contains the 75 contributed papers and the abstracts of the threeinvited lectures presented at the 13th Annual European Symposium on Algo-rithms (ESA 2005), held in Spain, 2005. respectively.Papers were solicited in all areas of algorithmic research, including but notlimited to algorithmic aspects of networks, approximation and on-line algo-rithms, computational biology, computational geometry, computational financeand algorithmic game theory, data structures, database and information re-trieval, external memory algorithms, graph algorithms, graph drawing, machinelearning, mobile computing, pattern matching and data compression, quantumcomputing, and randomized algorithms. The algorithms could be sequential,distributed, or parallel. Submissions were especially encouraged in the area ofmathematical programming and operations research, including combinatorialoptimization, integer programming, polyhedral combinatorics, and semidefiniteprogramming.Each extended abstract was submitted to one of the two tracks.
Active Conceptual Modeling of Learning : Next Generation Learning-Base System Development
This volume contains a collection of the papers presented during the 1st International ACM-L Workshop, which was held on November 8, 2006 during the 25th International Conference on Conceptual Modeling, ER 2006, held November 6–9,2006, in Tucson, Arizona, plus several invited papers.These papers plus the invited papers represent the current thinking in conceptual modeling research, The active model can only be realized through technology integration (e.g., AI, software engineering, information technology,cognitive science, art and sciences, philosophy, etc.)
