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Optics and Spectroscopy for Fluid Characterization
This book is a printed edition of the Special Issue " Optics and Spectroscopy for Fluid Characterization" that was published in Applied Sciences
Combinatorial Testing in Cloud Computing
This book introduces readers to an advanced combinatorial testing approach and its application in the cloud environment. Based on test algebra and fault location analysis, the proposed combinatorial testing method can support experiments with 250 components (with 2 * (250) combinations), and can detect the fault location based on the testing results. This function can efficiently decrease the size of candidate testing sets and therefore increase testing efficiency. The proposed solution’s effectiveness in the cloud environment is demonstrated using a range of experiments.
Multi-Sensor Imaging and Fusion: Methods, Evaluations, and Applications, volume II
Multi-sensor image fusion focuses on processing images of the same object or scene acquired by multiple sensors, in which various sensors with multi-level and multi-spatial information are complemented and combined to ultimately yield a consistent interpretation of the observed environment. In recent years, multi-sensor image fusion has become a highly active topic, and various fusion methods have been proposed. Many effective processing methods, including multi-scale transformation, fuzzy inference, and deep learning, have been introduced to design fusion algorithms. Despite the great progress, there are still some noteworthy challenges in the field, such as the lack of unified fusion theories and methods for effective generalized fusion, the lack of fault tolerance and robustness, the lack of benchmarks for performance evaluation, the lack of work on specific applications of multi-sensor image fusion, and so on.
Soft Computing Principles and Integration for Real-Time Service-Oriented Computing
In recent years, soft computing techniques have emerged as a successful tool to understand and analyze the collective behavior of service- oriented computing software. Algorithms and mechanisms of self- organization of complex natural systems have been used to solve problems, particularly in complex systems, which are adaptive, ever- evolving, and distributed in nature across the globe. What fits more perfectly into this scenario other than the rapidly developing era of Fog, IoT, and Edge computing environment? Service- oriented computing can be enhanced with soft computing techniques embedded inside the Cloud, Fog, and IoT systems. Soft Computing Principles and Integration for Real-Time Ser...
Advances in Integrated Energy Systems Design, Control and Optimization
This book is a printed edition of the Special Issue "Advances in Integrated Energy Systems Design, Control and Optimization" that was published in Applied Sciences
China’s Global Vision and Actions
This insightful book examines the growing role of China on the global stage by gauging the varying reactions of international spectators to Beijing’s hugely significant Belt and Road Initiative. Laced with detailed empirical studies and an array of illustrative maps, Peter Rimmer assesses the domestic impact of the Initiative thus far and offers an astute appraisal of the imperial connotations of Beijing’s global logistical project.
Entropy in Image Analysis
Image analysis is a fundamental task for extracting information from images acquired across a range of different devices. Since reliable quantitative results are requested, image analysis requires highly sophisticated numerical and analytical methods—particularly for applications in medicine, security, and remote sensing, where the results of the processing may consist of vitally important data. The contributions to this book provide a good overview of the most important demands and solutions concerning this research area. In particular, the reader will find image analysis applied for feature extraction, encryption and decryption of data, color segmentation, and in the support new technologies. In all the contributions, entropy plays a pivotal role.
Dynamic Neural Networks for Robot Systems: Data-Driven and Model-Based Applications
Neural network control has been a research hotspot in academic fields due to the strong ability of computation. One of its wildly applied fields is robotics. In recent years, plenty of researchers have devised different types of dynamic neural network (DNN) to address complex control issues in robotics fields in reality. Redundant manipulators are no doubt indispensable devices in industrial production. There are various works on the redundancy resolution of redundant manipulators in performing a given task with the manipulator model information known. However, it becomes knotty for researchers to precisely control redundant manipulators with unknown model to complete a cyclic-motion generation CMG task, to some extent. It is worthwhile to investigate the data-driven scheme and the corresponding novel dynamic neural network (DNN), which exploits learning and control simultaneously. Therefore, it is of great significance to further research the special control features and solve challenging issues to improve control performance from several perspectives, such as accuracy, robustness, and solving speed.
