Description
SPRINGER Device Applications Of Nonlinear Dynamics (Understanding Complex Systems) by BAGLIO SALVATORE ET.AL
This book is devoted to applications of complex nonlinear dynamic phenomena to real systems and device applications. In recent decades there has been significant progress in the theory of nonlinear phenomena, but there are comparatively few devices that actually take this rich behavior into account. The text applies and exploits this knowledge to propose devices which operate more efficiently and cheaply, while affording the promise of much better performance. Opening Plenary Talk.- Use of Chaos to Improve Equipments.- Nonlinear Dynamics, Materials and Sensing Devices.- Noise Induced Switching Between Oscillation States in a Nonlinear Micromechanical Oscillator.- Nonadiabaticity in Modulated Optical Traps.- Signal Processing and Control in Nonlinear Nanomechanical Systems.- Signal Modulation by Martensitic Control of Shape Memory Alloy Thin Film Actuator Architectures.- Exploiting Dynamic Cooperative Behavior in a Coupled-Core Fluxgate Magnetometer.- Motion Sensors and Actuators Based on Ionic Polymer-Metal Composites.- Pattern Formation Stability and Collapse in 2D Driven Particle Systems.- Uncertainty Sources in RTD-Fluxgate.- Modeling and Design of Ferrofluidic Sensors.- Thermocromic Materials for Temperature Sensors in New Applications.- A SQUID Ring-Resonator Finate State Machine.- Signal Processing and Applications.- Suprathreshold Stochastic Resonance Mediated by Multiplicative Noise.- Noise for Health: Phage-Based Rapid Bacterial Identification Method.- Parametric Resonance Near Hopf-Turing Instability Boundary.- Recurrent Neural Networks in Rainfall-Runoff Modeling at Daily Scale.- Distributed Data Acquisition System for Environment Monitoring Nonlinear Processes.- Automatic Safety Control in Food Processing.- Using a TI C6701 DSP Rapid Prototyping System for Nonlinear Adaptive Filtering to Mitigate Interference.- Gunn Oscillations Described by the MEP Hydrodynamical Model of Semiconductors.- Dynamic Test Data Generation for the Nonlinear Models with Genetic Algorithms.- Neuro-Fuzzy Based Nonlinear Models.- Recon.gurable Pattern Generators Using Nonlinear Electronic Circuits.- Condfiguring A Non-Linear Process Control System Using Virtual Instrumentation.