Ricardo A. Ramírez-Mendoza – författare

Biometrics provide quantitative representations of human features, physiological and behavioral. This book is a compilation of biometric technologies developed by various research groups in Tecnologico de Monterrey, Mexico. It provides a summary of biometric systems as a whole, explaining the principles behind physiological and behavioral biometrics and exploring different types of commercial and experimental technologies and current and future applications in the fields of security, military, criminology, healthcare education, business, and marketing.

Examples of biometric systems using brain signals or electroencephalography (EEG) are given. Mobile and home EEG use in children’s natural environments is covered. At the same time, some examples focus on the relevance of such technology in monitoring epileptic encephalopathies in children.

Using reliable physiological signal acquisition techniques, functional Human Machine Interfaces (HMI) and Brain-Computer Interfaces (BCI) become possible. This is the case of an HMI used for assistive navigation systems, controlled via voice commands, head, and eye movements. A detailed description of the BCI framework is presented, and applications of user-centered BCIs, oriented towards rehabilitation, human performance, and treatment monitoring are explored.

Massive data acquisition also plays an essential role in the evolution of biometric systems. Machine learning, deep learning, and Artificial Intelligence (AI) are crucial allies here. They allow the construction of models that can aid in early diagnosis, seizure detection, and data-centered medical decisions. Such techniques will eventually lead to a more concise understanding of humans.

Biometrics provide quantitative representations of human features, physiological and behavioral. This book is a compilation of biometric technologies developed by various research groups in Tecnologico de Monterrey, Mexico. It provides a summary of biometric systems as a whole, explaining the principles behind physiological and behavioral biometrics and exploring different types of commercial and experimental technologies and current and future applications in the fields of security, military, criminology, healthcare education, business, and marketing.

Examples of biometric systems using brain signals or electroencephalography (EEG) are given. Mobile and home EEG use in children’s natural environments is covered. At the same time, some examples focus on the relevance of such technology in monitoring epileptic encephalopathies in children.

Using reliable physiological signal acquisition techniques, functional Human Machine Interfaces (HMI) and Brain-Computer Interfaces (BCI) become possible. This is the case of an HMI used for assistive navigation systems, controlled via voice commands, head, and eye movements. A detailed description of the BCI framework is presented, and applications of user-centered BCIs, oriented towards rehabilitation, human performance, and treatment monitoring are explored.

Massive data acquisition also plays an essential role in the evolution of biometric systems. Machine learning, deep learning, and Artificial Intelligence (AI) are crucial allies here. They allow the construction of models that can aid in early diagnosis, seizure detection, and data-centered medical decisions. Such techniques will eventually lead to a more concise understanding of humans.

A key challenge in science and engineering is to provide a quantitative description of the systems under investigation, leveraging the noisy data collected. Such a description may be a complete mathematical model or a mechanism to return controllers corresponding to new, unseen inputs. Recent advances in the theories are described in detail, along with their applications in engineering. The book aims to develop model-free system analysis and control strategies, i.e., data-driven control from theoretical analysis and engineering applications based only on measured data. The study aims to develop system identification, and combination in advanced control theory, i.e., data-driven control strategy as system and controller are generated from measured data directly. The book reviews the development of system identification and its combination in advanced control theory, i.e., data-driven control strategy, as they all depend on measured data. Firstly, data-driven identification is developed for the closed-loop, nonlinear system and model validation, i.e., obtaining model descriptions from measured data. Secondly, the data-driven idea is combined with some control strategies to be considered data-driven control strategies, such as data-driven model predictive control, data-driven iterative tuning control, and data-driven subspace predictive control. Thirdly data-driven identification and data-driven control strategies are applied to interested engineering. In this context, the book provides algorithms to perform state estimation of dynamical systems from noisy data and some convex optimization algorithms through identification and control problems.

A key challenge in science and engineering is to provide a quantitative description of the systems under investigation, leveraging the noisy data collected. Such a description may be a complete mathematical model or a mechanism to return controllers corresponding to new, unseen inputs. Recent advances in the theories are described in detail, along with their applications in engineering. The book aims to develop model-free system analysis and control strategies, i.e., data-driven control from theoretical analysis and engineering applications based only on measured data. The study aims to develop system identification, and combination in advanced control theory, i.e., data-driven control strategy as system and controller are generated from measured data directly. The book reviews the development of system identification and its combination in advanced control theory, i.e., data-driven control strategy, as they all depend on measured data. Firstly, data-driven identification is developed for the closed-loop, nonlinear system and model validation, i.e., obtaining model descriptions from measured data. Secondly, the data-driven idea is combined with some control strategies to be considered data-driven control strategies, such as data-driven model predictive control, data-driven iterative tuning control, and data-driven subspace predictive control. Thirdly data-driven identification and data-driven control strategies are applied to interested engineering. In this context, the book provides algorithms to perform state estimation of dynamical systems from noisy data and some convex optimization algorithms through identification and control problems.