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Quiet family man and hard-working snowplow driver Nels is the lifeblood of a glitzy resort town in the Rocky Mountains because he is the one who keeps the winter roads clear. He and his wife live in a comfortable cabin away from the tourists. The town has just awarded him “Citizen of the Year.” But Nels has to leave his quiet mountain life when his son is murdered by a powerful drug lord. As a man who has nothing to lose he is stoked by a drive for vengeance. This unlikely hero uses his hunting skills and transforms from an ordinary man into a skilled killer as he sets out to dismantle the cartel. Nels’ actions ignite a turf war between a manically unpredictable gangster known as Viking and a rival gang boss. Justice is served in one final spectacular confrontation that will leave (almost) no one unscathed.
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Robotic and mechatronic systems, autonomous vehicles, electric power systems and smart grids, as well as manufacturing and industrial production systems can exhibit complex nonlinear dynamics or spatio-temporal dynamics which need to be controlled to ensure good functioning and performance.
In this comprehensive reference, the authors present new and innovative control and estimation methods and techniques based on dynamical nonlinear and partial differential equation systems. Such results can be classified in five main domains for the control of complex nonlinear dynamical systems using respectively methods of approximate (local) linearization, methods of exact (global) linearization, Lyapunov stability approaches, control and estimation of distributed parameter systems and stochastic estimation and fault diagnosis methods.
Control and Estimation of Dynamical Nonlinear and Partial Differential Equation Systems: Theory and applications will be of interest to electrical engineering, physics, computer science, robotics and mechatronics researchers and professionals working on control problems, condition monitoring, estimation and fault diagnosis and isolation problems. It will also be useful to skilled technical personnel working on applications in robotics, energy conversion, transportation and manufacturing.
Aug
26
Increased use of artificial intelligence (AI) is being deployed in many hospitals and healthcare settings to help improve health care service delivery. Machine learning (ML) and deep learning (DL) tools can help guide physicians with tasks such as diagnosis and detection of diseases and assisting with medical decision making.
This edited book outlines novel applications of AI in e-healthcare. It includes various real-time/offline applications and case studies in the field of e-Healthcare, such as image recognition tools for assisting with tuberculosis diagnosis from x-ray data, ML tools for cancer disease prediction, and visualisation techniques for predicting the outbreak and spread of Covid-19.
Heterogenous recurrent convolution neural networks for risk prediction in electronic healthcare record datasets are also reviewed.
Suitable for an audience of computer scientists and healthcare engineers, the main objective of this book is to demonstrate effective use of AI in healthcare by describing and promoting innovative case studies and finding the scope for improvement across healthcare services.
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