الصفحة 1
الصفحة 1
Fundamentals of multimedia ; 3rd ed.
Addresses real issues commonly faced in the workplace. The essential concepts are explained in a practical way to enable students to apply their existing skills to address problems in multimedia. Fully revised and updated, this new edition now includes coverage of such topics as 3D TV, social networks, high-efficiency video compression and conferencing, wireless and mobile networks, and their attendant technologies.
Fundamentals of multimedia ; 2nd ed.
Addresses real issues commonly faced in the workplace. The essential concepts are explained in a practical way to enable students to apply their existing skills to address problems in multimedia. Fully revised and updated, this new edition now includes coverage of such topics as 3D TV, social networks, high-efficiency video compression and conferencing, wireless and mobile networks, and their attendant technologies.
Conceptual Modeling for Traditional and Spatio-Temporal Applications : The MADS Approach
This book shows that a conceptual design approach for spatio-temporal databases is both feasible and easy to apprehend. While providing a firm basis through extensive discussion of traditional data modeling concepts, the major focus of the book is on modeling spatial and temporal information. Parent, Spaccapietra and Zimányi provide a detailed and comprehensive description of an approach that fills the gap between application conceptual requirements and system capabilities, covering both data modeling and data manipulation features.
Concepts and Semantics of Programming Languages 1 : A Semantical Approach with OCaml and Python
Explores the syntactical constructs of the most common programming languages, and sheds a mathematical light on their semantics, while also providing an accurate presentation of the material aspects that interfere with coding. It is dedicated to functional and imperative features. Included is the formal study of the semantics of typing and execution; their acquisition is facilitated by implementation into OCaml and Python, as well as by worked examples. Data representation is considered in detail: endianness, pointers, memory management, union types and pattern-matching, etc., with examples in OCaml, C and C++. The second volume introduces a specific model for studying modular and object features and uses this model to present Ada and OCaml modules, and subsequently Java, C++, OCaml and Python classes and objects.
Machine learning approach for cloud data analytics in IoT
Covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications. Elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.
Knowledge Discovery from XML Documents ; 1st International Workshop, KDXD 2006, Singapore, April 9, 2006, Proceedings
The KDXD 2006 (Knowledge Discovery from XML Documents) workshop is the ?rst international workshop running this year in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2006. The workshop provided an important forum for the dissemination and exchange of new ideas and research related to XML data discovery and retrieval. The eXtensible Markup Language (XML) has become a standard language for data representation and exchange. With the continuous growth in XML data sources,the ability to manage collections of XML documents and discover knowledge from them for decision support becomes increasingly important. Due to the inherent ?exibility ofXML, in both structure and semantics, inferring important knowledge from XML data is faced with new challenges as well as bene?ts. The objective of the workshop was to bring together researchers and practitioners to discuss all aspects of the emerging XML data management challenges.
Artificial neural networks : Formal Models and Their Applications – ICANN 2005 ; 15th International Conference, Warsaw, Poland, September 11-15, 2005, Proceedings, Part II
The second volume contains 162 contributions related to Formal Models and their Applications and deals with new neural network models, supervised learning algorithms, ensemble-based learning, unsupervised learning, recurent neural networks, reinforcement learning, bayesian approaches to learning, learning theory, artificial neural networks for system modeling, decision making, optimalization and control, knowledge extraction from neural networks, temporal data analysis, prediction and forecasting, support vector machines and kernel-based methods, soft computing methods for data representation, analysis and processing, data fusion for industrial, medical and environmental applications, non-linear predictive models for speech processing, intelligent multimedia and semantics, applications to natural language processing, various applications, computational intelligence in games, and issues in hardware implementation.
Advances in spatial and temporal databases ; 7th International symposium, SSTD 2001, Redondo Beach, CA, USA, July 12-15, 2001 Proceedings
The Seventh International Symposium on Spatial and Temporal Databases (SSTD 2001), held in Redondo Beach, CA, USA, July 12{15, 2001, brought together leading researchers and developers in the area of spatial, temporal, and spatio-temporal databases to discuss the state of the art in spatial and temporal data management and applications, and to understand the challenges and - search directions in the advancing area of data management for moving objects. The symposium served as a forum for disseminating research in spatial and temporal data management, and for maximizing the interchange of knowledge among researchers from the established spatial and temporal database com- nities. The exchange of research ideas and results not only contributes to the academic arena, but also bene ts the user and commercial communities.
Adaptive Machine Learning Algorithms with Python : Solve Data Analytics and Machine Learing Problems on Edge Devices
Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use. You will: Apply adaptive algorithms to practical applications and examples / Understand the relevant data representation features and computational models for time-varying multi-dimensional data / Derive adaptive algorithms for mean, median, covariance, eigenvectors (PCA) and generalized eigenvectors with experiments on real data / Speed up your algorithms and put them to use on real-world stationary and non-stationary data / Master the applications of adaptive algorithms on critical edge device computation applications
