Mechanical Design of Piezoelectric Energy Harvesters: Generating Electricity from Human Walking provides the state-of-the-art, recent mechanical designs of piezoelectric energy harvesters based on piezoelectric stacks. The book discusses innovative mechanism designs for energy harvesting from multidimensional force excitation, such as human walking, which offers higher energy density. Coverage includes analytical modeling, optimal design, simulation study, prototype fabrication, and experimental investigation. Detailed examples of their analyses and implementations are provided. The book's authors provide a unique perspective on this field, primarily focusing on novel designs for PZT Energy harvesting in biomedical engineering as well as in integrated multi-stage force amplification frame. This book presents force-amplification compliant mechanism design and force direction-transmission mechanism design. It explores new mechanism design approaches using piezoelectric materials and permanent magnets. Readers can expect to learn how to design new mechanisms to realize multidimensional energy harvesting systems.

This book provides an overview of the noteworthy developments in the field of micromachining, with a specific focus on microinjection systems used for biological micromanipulation. The author also explores the design, development, and fabrication of new mechanical designs for micromachines, with plenty of examples that elucidate their modeling and control. The design and fabrication of a piezoelectric microinjector, constant force microinjector, constant force microgripper, PDVF microforce sensor, and a piezoelectric microsyringe are presented as examples of new technology for microinjection systems. This book is appropriate for both researchers and advanced students in bioengineering.

Support vector machines (SVMs) are used in a range of applications, including drug design, food quality control, metabolic fingerprint analysis, and microarray data-based cancer classification. While most mathematicians are well-versed in the distinctive features and empirical performance of SVMs, many chemists and biologists are not as familiar with what they are and how they work. Presenting a clear bridge between theory and application, Support Vector Machines and Their Application in Chemistry and Biotechnology provides a thorough description of the mechanism of SVMs from the point of view of chemists and biologists, enabling them to solve difficult problems with the help of these powerful tools. Topics discussed include: Background and key elements of support vector machines and applications in chemistry and biotechnology Elements and algorithms of support vector classification (SVC) and support vector regression (SVR) machines, along with discussion of simulated datasets The kernel function for solving nonlinear problems by using a simple linear transformation method Ensemble learning of support vector machines Applications of support vector machines to near-infrared data Support vector machines and quantitative structure-activity/property relationship (QSAR/QSPR) Quality control of traditional Chinese medicine by means of the chromatography fingerprint technique The use of support vector machines in exploring the biological data produced in OMICS study Beneficial for chemical data analysis and the modeling of complex physic-chemical and biological systems, support vector machines show promise in a myriad of areas. This book enables non-mathematicians to understand the potential of SVMs and utilize them in a host of applications.

This book provides an overview of the noteworthy developments in the field of micromachining, with a specific focus on microinjection systems used for biological micromanipulation. The author also explores the design, development, and fabrication of new mechanical designs for micromachines, with plenty of examples that elucidate their modeling and control. The design and fabrication of a piezoelectric microinjector, constant force microinjector, constant force microgripper, PDVF microforce sensor, and a piezoelectric microsyringe are presented as examples of new technology for microinjection systems. This book is appropriate for both researchers and advanced students in bioengineering.

This book explores emerging methods and algorithms that enable precise control of micro-/nano-positioning systems. The text describes three control strategies: hysteresis-model-based feedforward control and hysteresis-model-free feedback control based on and free from state observation. Each paradigm receives dedicated attention within a particular part of the text. Readers are shown how to design, validate and apply a variety of new control approaches in micromanipulation: hysteresis modelling, discrete-time sliding-mode control and model-reference adaptive control. Experimental results are provided throughout and build up to a detailed treatment of practical applications in the fourth part of the book. The applications focus on control of piezoelectric grippers. Advanced Control of Piezoelectric Micro-/Nano-Positioning Systems will assist academic researchers and practising control and mechatronics engineers interested in suppressing sources of nonlinearity such as hysteresis and drift when combining position and force control of precision systems with piezoelectric actuation.

Support vector machines (SVMs) are used in a range of applications, including drug design, food quality control, metabolic fingerprint analysis, and microarray data-based cancer classification. While most mathematicians are well-versed in the distinctive features and empirical performance of SVMs, many chemists and biologists are not as familiar with what they are and how they work. Presenting a clear bridge between theory and application, Support Vector Machines and Their Application in Chemistry and Biotechnology provides a thorough description of the mechanism of SVMs from the point of view of chemists and biologists, enabling them to solve difficult problems with the help of these powerful tools. Topics discussed include: Background and key elements of support vector machines and applications in chemistry and biotechnology Elements and algorithms of support vector classification (SVC) and support vector regression (SVR) machines, along with discussion of simulated datasets The kernel function for solving nonlinear problems by using a simple linear transformation method Ensemble learning of support vector machines Applications of support vector machines to near-infrared data Support vector machines and quantitative structure-activity/property relationship (QSAR/QSPR) Quality control of traditional Chinese medicine by means of the chromatography fingerprint technique The use of support vector machines in exploring the biological data produced in OMICS study Beneficial for chemical data analysis and the modeling of complex physic-chemical and biological systems, support vector machines show promise in a myriad of areas. This book enables non-mathematicians to understand the potential of SVMs and utilize them in a host of applications.