Filipe / Ferrier / Cetto Informatics in Control, Automation and Robotics II
1. Auflage 2007
ISBN: 978-1-4020-5626-0
Verlag: Springer Netherland
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, 243 Seiten, eBook
ISBN: 978-1-4020-5626-0
Verlag: Springer Netherland
Format: PDF
Kopierschutz: 1 - PDF Watermark
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Weitere Infos & Material
Invited Speakers.- Combining Human & Machine Brains.- Redundancy: the Measurement Crossing Cutting-edge Technologies.- Hybrid Dynamic Systems.- Target Localization using Machine Learning.- Intelligent Control Systems and Optimization.- Model Predictive Control for Distributed Parameter Systems Using RBF Neural Networks.- Fuzzy Diagnosis Module Based on Interval Fuzzy Logic: Oil Analysis Application.- Deriving Behavior from Goal Structure for the Intelligent Control of Physical Systems.- Evolutionary Computation for Discrete and Continuous Time Optimal Control Problems.- Contributors to a Signal from an Artificial Contrast.- Real-Time Time-Optimal Control for a Nonlinear Container Crane Using a Neural Network.- Robotics and Automation.- Image-Based and Intrinsic-Free Visual Navigation of a Mobile Robot Defined as a Global Visual Servoing Task.- Synthesizing Deterministic Controllers in Supervisory Control.- An Uncalibrated Approach to Track Trajectories using Visual–Force Control.- A Strategy for Building Topological Maps through Scene Observation.- A Switching Algorithm for Tracking Extended Targets.- SFM for Planar Scenes: A Direct and Robust Approach.- Combining Two Methods to Accurately Estimate Dense Disparity Maps.- Precise Dead-Reckoning for Mobile Robots using Multiple Optical Mouse Sensors.- Image Binarisation using the Extended Kalman Filter.- Lower Limb Prosthesis: Final Prototype Release and Control Setting Methodologies.- Direct Gradient-Based Reinforcement Learning for Robot Behavior Learning.- Signal Processing, Systems Modeling and Control.- Performance Analysis of Timed Event Graphs with Multipliers using (Min, +) Algebra.- Modeling of Motor Neuronal Structures Via Transcranial Magnetic Stimulation.- Analysis and Synthesis of Digital Structure byMatrix Method.- Ann-Based Multiple Dimension Predictor for Ship Route Prediction.- A Parameterized Polyhedra Approach for the Explicit Robust Model Predictive Control.- A New Hierarchical Control Scheme for a Class of Cyclically Repeated Discrete-Event Systems.- Wavelet Transform Moments for Feature Extraction from Temporal Signals.
COMBINING HUMAN & MACHINE BRAINS (p. 3)
Practical Systems in Information & Control
Kevin Warwick
Department of Cybernetics, University of Reading, Reading, RG6 6AY, United Kingdom
Keywords:
Artificial intelligence, Biological systems, Implant technology, Feedback control.
Abstract:
In this paper a look is taken at how the use of implant technology can be used to either increase the range of the abilities of a human and/or diminish the effects of a neural illness, such as Parkinson’s Disease. The key element is the need for a clear interface linking the human brain directly with a computer.
The area of interest here is the use of implant technology, particularly where a connection is made between technology and the human brain and/or nervous system. Pilot tests and experimentation are invariably carried out apriori to investigate the eventual possibilities before human subjects are themselves involved. Some of the more pertinent animal studies are discussed here.
The paper goes on to describe human experimentation, in particular that carried out by the author himself, which led to him receiving a neural implant which linked his nervous system bi-directionally with the internet. With this in place neural signals were transmitted to various technological devices to directly control them. In particular, feedback to the brain was obtained from the fingertips of a robot hand and ultrasonic (extra) sensory input. A view is taken as to the prospects for the future, both in the near term as a therapeutic device and in the long term as a form of enhancement.
1 INTRODUCTION
Research is presently being carried out in which biological signals of some form are measured, are acted upon by some appropriate signal processing technique and are then employed either to control a device or as an input to some feedback mechanism (e.g. Penny et al., 2000).
In most cases the signals are measured externally to the body, thereby imposing errors into the situation due to problems in understanding intentions and removing noise – partly due to the compound nature of the signals being measured. Many problems also arise when attempting to translate electrical energy from the computer to the electronic signals necessary for stimulation within the human body.
For example, when only external stimulation is employed then it is extremely difficult, if not impossible, to select unique sensory receptor channels, due to the general nature of the stimulation.
Wearable computer and virtual reality techniques provide one route for creating a human-machine link. In the last few years items such as shoes and glasses have been augmented with microprocessors, but perhaps of most interest is research in which a miniature computer screen was fitted onto an otherwise standard pair of glasses in order to give the wearer a remote visual experience in which additional information about an external scene could be relayed (Mann, 1997).
In general though, despite being positioned adjacent to the human body, and even though indications such as stress and alertness can be witnessed, to an extent at least, wearable computers and virtual reality systems require significant signal conversion to take place in order to interface human sensory receptors with technology.
