Gain a new perspective on how the brain works and inspires new avenues for design in computer science and engineering This unique book is the first of its kind to introduce human memory and basic cognition in terms of physical circuits, beginning with the possibilities of ferroelectric behavior of neural membranes, moving to the logical properties of neural pulses recognized as solitons, and finally exploring the architecture of cognition itself. It encourages invention via the methodical study of brain theory, including electrically reversible neurons, neural networks, associative memory systems within the brain, neural state machines within associative memory, and reversible computers in general. These models use standard analog and digital circuits that, in contrast to models that include non-physical components, may be applied directly toward the goal of constructing a machine with artificial intelligence based on patterns of the brain. Writing from the circuits and systems perspective, the author reaches across specialized disciplines including neuroscience, psychology, and physics to achieve uncommon coverage of: Neural membranes Neural pulses and neural memory Circuits and systems for memorizing and recalling Dendritic processing and human learning Artificial learning in artificial neural networks The asset of reversibility in man and machine Electrically reversible nanoprocessors Reversible arithmetic Hamiltonian circuit finders Quantum versus classical Each chapter introduces and develops new material and ends with exercises for readers to put their skills into practice. Appendices are provided for non-experts who want a quick overview of brain anatomy, brain psychology, and brain scanning. The nature of this book, with its summaries of major bodies of knowledge, makes it a most valuable reference for professionals, researchers, and students with career goals in artificial intelligence, intelligent systems, neural networks, computer architecture, and neuroscience. A solutions manual is available for instructors; to obtain a copy please email the editorial department at [email protected].

This book helps readers identify if they have co-dependency traits that stem from wrong childhood programming. The book seeks to touch upon aspects of self-love, in the absence of which co-dependent people lose themselves in each situation to seek validation and approval from outsiders. The author courageously shares her childhood events, which, were not handled well by her primary caregivers, and shut her innocent brain encouraging her to tolerate exploitations by toxic people later on in her life as an adult. She gives a fresh perspective on the new-age therapy of healing through unblocking seven prominent chakras that play an important role in grounding us and developing our self-worth and self-love, through dedicated meditation. The author stresses the need to heal ourselves from childhood trauma to lead a stress-free, anxiety-free and healthy life.

The 2016 International Conference on Energy Science and Applied Technology (ESAT 2016) held on June 25-26 in Wuhan, China aimed to provide a platform for researchers, engineers, and academicians, as well as industrial professionals, to present their research results and development activities in energy science and engineering and its applied technology. The themes presented in Energy Science and Applied Technology ESAT 2016 are: Technologies in Geology, Mining, Oil and Gas; Renewable Energy, Bio-Energy and Cell Technologies; Energy Transfer and Conversion, Materials and Chemical Technologies; Environmental Engineering and Sustainable Development; Electrical and Electronic Technology, Power System Engineering; Mechanical, Manufacturing, Process Engineering; Control and Automation; Communications and Applied Information Technologies; Applied and Computational Mathematics; Methods and Algorithms Optimization; Network Technology and Application; System Test, Diagnosis, Detection and Monitoring; Recognition, Video and Image Processing.

Intelligent Learning Approaches for Renewable and Sustainable Energy provides a practical, systematic overview of the application of advanced intelligent control techniques, adaptive techniques, machine learning algorithms, and predictive control in renewable and sustainable energy.The book begins by introducing the intelligent learning approaches, and the roles of artificial intelligence and machine learning in terms of energy and sustainability, grid transformation, large-scale integration of renewable energy, and variability and flexibility of renewable sources. The second section of the book provides detailed coverage of intelligent learning techniques as applied to key areas of renewable and sustainable energy, including forecasting, supply and demand, integration, energy management, and optimization, supported by case studies, figures, schematics, and references.This is a useful resource for researchers, scientists, advanced students, energy engineers, R&D professionals, and other industrial personnel with an interest in sustainable energy and integration of renewable energy sources, energy systems, energy engineering, machine learning, and artificial intelligence. - Explores cutting-edge intelligent techniques and their implications for future energy systems development - Opens the door to a range of applications across forecasting, supply and demand, energy management, optimization, and more - Includes a range of case studies that provide insights into the challenges and solutions in real-world applications