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What we shall eat to-morrow, I haven't the slightest idea! said Widow Wang to her eldest son, as he started out one morning in search of work. Oh, the gods will provide. I'll find a few coppers somewhere, replied the boy, trying to speak cheerfully, although in his heart he also had not the slightest idea in which direction to turn.

Nonfiction. --- Geography. --- Science.

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This edited book will serve as a source of reference for technologies and applications for multimodality data analytics in big data environments. After an introduction, the editors organize the book into four main parts on sentiment, affect and emotion analytics for big multimodal data; unsupervised learning strategies for big multimodal data; supervised learning strategies for big multimodal data; and multimodal big data processing and applications. The book will be of value to researchers, professionals and students in engineering and computer science, particularly those engaged with image and speech processing, multimodal information processing, data science, and artificial intelligence.

Big data. --- Artificial intelligence. --- Artificial Intelligence. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers

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Traditional wireless sensor networks (WSNs) capture scalar data such as temperature, vibration, pressure, or humidity. Motivated by the success of WSNs and also with the emergence of new technology in the form of low-cost image sensors, researchers have proposed combining image and audio sensors with WSNs to form wireless multimedia sensor networks (WMSNs). This introduces practical and research challenges, because multimedia sensors, particularly image sensors, generate huge amounts of data to be processed and distributed within the network, while sensor nodes have restricted battery power and hardware resources.   This book describes how reconfigurable hardware technologies such as field-programmable gate arrays (FPGAs) offer cost-effective, flexible platforms for implementing WMSNs, with a main focus on developing efficient algorithms and architectures for information reduction, including event detection, event compression, and multicamera processing for hardware implementations. The authors include a comprehensive review of wireless multimedia sensor networks, a complete specification of a very low-complexity, low-memory FPGA WMSN node processor, and several case studies that illustrate information reduction algorithms for visual event compression, detection, and fusion.   The book will be of interest to academic researchers, R&D engineers, and computer science and engineering graduate students engaged with signal and video processing, computer vision, embedded systems, and sensor networks.

Electrical engineering --- Applied physical engineering --- Computer science --- Computer architecture. Operating systems --- Computer. Automation --- computervisie --- beeldverwerking --- draadloze computernetwerken --- computers --- informatica --- landbouw --- multimedia --- computerbesturingssystemen --- OS (operating system) --- computernetwerken --- computerkunde --- elektrotechniek --- Wireless sensor networks. --- Computer science. --- Computer network architectures. --- Multimedia systems. --- Computer vision. --- Computer engineering. --- Electrical engineering.