Data Signal And Image Processing And Applications In Sensors Ii
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Data, Signal and Image Processing and Applications in Sensors II
Author: Manuel José Cabral Dos Santos Reis
language: en
Publisher:
Release Date: 2024-07-23
With the rapid advances in sensor technology, a vast and ever-growing amount of data in various domains and modalities is readily available. However, presenting raw signal data collected directly from sensors is sometimes inappropriate due to the presence of, for example, noise or distortion, among others. In order to obtain relevant and insightful metrics from sensor signals' data, further enhancement of the sensor signals acquired, such as noise reduction in one-dimensional electroencephalographic (EEG) signals or color correction in endoscopic images, and their analysis via computer-based medical systems, is needed. The processing of the data in themselves and the consequent extraction of useful information are also vital and included in the scope of this Special Issue. This SI of Sensors is aimed at highlighting advances in the development, testing, and application of data, signal, and image processing algorithms and techniques to all types of sensors and sensing methodologies. Experimental and theoretical results along with review papers, in as much detail as possible, are also considered. Some examples of the topics to be covered in this SI include the following: Ambient assisted living; Biomedical signal and image analysis; Machine learning in signal and image processing; Multimodal information processing for healthcare, monitoring, and surveillance; Real-time signal and image processing algorithms and architectures; Remote sensing processing; Sensors and smart sensors for IoT devices; Signal and image processing and understanding; Wearable sensor signal processing and its applications.
Signal Processing for Intelligent Sensor Systems with MATLAB®
Signal Processing for Intelligent Sensors with MATLAB®, Second Edition once again presents the key topics and salient information required for sensor design and application. Organized to make it accessible to engineers in school as well as those practicing in the field, this reference explores a broad array of subjects and is divided into sections: Fundamentals of Digital Signal Processing, Frequency Domain Processing, Adaptive System Identification and Filtering, Wavenumber Sensor Systems, and Signal Processing Applications. Taking an informal, application-based approach and using a tone that is more engineer-to-engineer than professor-to-student, this revamped second edition enhances many of the features that made the original so popular. This includes retention of key algorithms and development methodologies and applications, which are creatively grouped in a way that differs from most comparable texts, to optimize their use. New for the Second Edition: Inclusion of more solved problems Web access to a large collection of MATLAB® scripts used to support data graphs presented throughout the book Additional coverage of more audio engineering, transducers, and sensor networking technology A new chapter on Digital Audio processing reflects a growing interest in digital surround sound (5.1 audio) techniques for entertainment, home theaters, and virtual reality systems New sections on sensor networking, use of meta-data architectures using XML, and agent-based automated data mining and control Serving dual roles as both a learning resource and a field reference on sensor system networks, this book progressively reveals digestible nuggets of critical information to help readers quickly master presented algorithms and adapt them to meet their requirements. It illustrates the current trend toward agile development of web services for wide area sensor networking and intelligent processing in the sensor system networks that are employed in homeland security, business, and environmental and demographic information systems.
Multi-Sensor Image Fusion and Its Applications
Taking another lesson from nature, the latest advances in image processing technology seek to combine image data from several diverse types of sensors in order to obtain a more accurate view of the scene: very much the same as we rely on our five senses. Multi-Sensor Image Fusion and Its Applications is the first text dedicated to the theory and practice of the registration and fusion of image data, covering such approaches as statistical methods, color-related techniques, model-based methods, and visual information display strategies. After a review of state-of-the-art image fusion techniques, the book provides an overview of fusion algorithms and fusion performance evaluation. The following chapters explore recent progress and practical applications of the proposed techniques to solving problems in such areas as medical diagnosis, surveillance and biometric systems, remote sensing, nondestructive evaluation, blurred image restoration, and image quality assessment. Recognized leaders from industry and academia contribute the chapters, reflecting the latest research trends and providing useful algorithms to aid implementation. Supplying a 28-page full-color insert, Multi-Sensor Image Fusion and Its Applications clearly demonstrates the benefits and possibilities of this revolutionary development. It provides a solid knowledge base for applying these cutting-edge techniques to new challenges and creating future advances.