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XLVI Mexican Conference on Biomedical Engineering
This book reports on cutting-edge research and best practices in the broad fiel of biomedical engineering. Based on the XLVI Mexican Congress on Biomedical Engineering, CNIB 2023, held on November 2-4, 2023 in Villahermosa Tabasco, Mexico, this first volume of the proceedings covers research topics in biomedical signals and image processing, artificial intelligence, biosensors, and wearable systems, with applications ranging from disease classification and diagnosis, to health monitoring and medical therapy. All in all, this book provides a timely snapshot on state-of-the-art achievements in biomedical engineering and current challenges in the field. It addresses both researchers and professionals, and it is expect to foster future collaborations between the two groups, as well as international collaborations. .
XLV Mexican Conference on Biomedical Engineering
This book reports on fundamental research, cutting-edge technologies and industrially-relevant applications in biomedical engineering. It covers methods for analysis, modeling and simulation of biological systems, reporting on the development and design of advanced biosensors, nanoparticles and wearable devices. It covers applications in disease monitoring and therapy, tissue engineering, sport and rehabilitation, and telehealth. It also reports on engineering methods for improving and monitoring medical service, and on advanced robotic applications. Gathering the proceedings of the XLV Congreso Nacional de Ingeniería Biomédica (CNIB2022), organised by the Mexican Society of Biomedical Engineering, this book offers a timely snapshot on technologies and methods in bioengineering, and on challenges related to their practical implementation in the health sector.
Catalog
None
Handbook of Multibiometrics
Details multimodal biometrics and its exceptional utility for increasingly reliable human recognition systems. Reveals the substantial advantages of multimodal systems over conventional identification methods.
Mazzatti Gardiol
None
Systemic Injustice
Judicial Reforms in Mexico
Neural Fuzzy Control Systems With Structure And Parameter Learning
A general neural-network-based connectionist model, called Fuzzy Neural Network (FNN), is proposed in this book for the realization of a fuzzy logic control and decision system. The FNN is a feedforward multi-layered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities.In order to set up this proposed FNN, the author recommends two complementary structure/parameter learning algorithms: a two-phase hybrid learning algorithm and an on-line supervised structure/parameter learning algorithm.Both of these learning algorithms require exact supervised training data for learning. In some real-time applications, exact training data may be expensive or even impossible to get. To solve this reinforcement learning problem for real-world applications, a Reinforcement Fuzzy Neural Network (RFNN) is further proposed. Computer simulation examples are presented to illustrate the performance and applicability of the proposed FNN, RFNN and their associated learning algorithms for various applications.
