Positioning, 2011, 2, 65-77 doi:10.4236/pos.2011.22007 Published Online May 2011 (http://www.SciRP.org/journal/pos)

Copyright © 2011 SciRes.!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!POS

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Machine Perception Through Natural Intelligence Rostyslav Sklyar

Verchratskogo st. 15-1, Lviv 79010 Ukraine Email: sklyar@tsp.lviv.ua Received December 30th , 2010; revised March 1st , 2011; accepted April 20th, 2011. ABSTRACT The sensing organs are exponentially better than any of analogous artificial ones. That is why using them in full scale is a perspective trend to the efficient (advanced) machine perception. On the other hand, limitations of sensing organs could be replaced by the perfect artificial ones with the subsequent training the nervous system on their output signals. An attempt to lay down the foundations of biosensing by natural sensors and in addition to them by the artificial trans-ducers of physical quantities, also with their expansion into space arrays and external/implantable functioning in rela-tion to the nervous system is performed. The advances in nanotechnology are opening the way to achieving direct elec-trical contact of nanoelectronic structures with electrically and electrochemically active neurocellular structures. The transmission of the sensors’ signals to a processing unit has been maintaining by an electromagnetic transis-tor/memristor (externally) and superconducting transducer of ionic currents (implantable). The arrays of the advanced sensors give us information about the space and direction dynamics of the signals' spreading.The measuring method and necessary performance data of the sensor for the robot’s orientation in the ambient magnetic field with living be-ing-machine interaction in order to obtain input and output signals from brain and motor nerves to the measurement system and vice versa are introduced. The range of applied sensors differs from an induction sensor to superconducting induction magnetometer. The analytical expressions for arrangements of the head sensors in differential and vector (3D) relative positions are deduced. Sensitivity of the perception method makes it possible to recognize the linear translation of 10!2 m and disposal in space of 10!3 m3. Interaction between living beings and robotic equipment is given analytical treatment. Keywords: Magnetic Field, Induction Sensor, SuFET, Nerve Impulses, Interface, Gradiometer, Sensing Area

1. Introduction. Artificial Sensors with the Human Machine Interface

Electronic Nose is a smart instrument that is designed to detect and discriminate among complex odours using an array of sensors. The array of sensors consists of a num-ber of broadly tuned (non-specific) sensors that are treated with a variety of odour-sensitive biological or chemical materials [1].

This instrument provides a rapid, simple and non- invasive sampling technique, for the detection and iden-tification of a range of volatile compounds. The key function of an electronic nose is to mimic human olfac-tory system. Typically an electronic nose consists of three elements: a sensor array which is exposed to the volatiles, conversion of the sensor signals to a readable format and software analysis of the data to produce cha-racteristic outputs related to the odour encountered. The main parts of a typical biosensor are shown in Figure 1.

The artificial tactile sensor integrates a micro elec- tro-mechanical system (MEMS) array having a number

of sensing elements (16 channels in about 20 mm2) simi-lar to the innervation density of mechanoreceptors in the hand (about 1 unit/ mm2). The technological approach is based on a 3D MEMS core unit with a soft and com- pliant packaging. The microsensor can be integrated with a packaging architecture resulting in a robust and com-pliant tactile sensor for application in artificial hands, while sensitive enough to detect slip events, showing that silicon based tactile sensors can go beyond laboratory practice [2]. The tactile sensor array, depicted in Figure 2, had 16 channels as total tactile sensor outputs.

The measurement of magnetic fields (MFs) is an im-portant task for the majority of autonomous missions. The distribution of permanent and the value of periodical MFs give the data about placement of ferromagnetic ob-jects and sources of EM radiation respectively. On the other hand, these signals will be a reference point and guiding line for a walking robot (Figure 3).

Detection of some magnetic anomalies of the Earth’s MF and their variations is provided by fluxgate sensors

IOP PUBLISHING MEASUREMENT SCIENCE AND TECHNOLOGY

Meas. Sci. Technol. 23 (2012) 115101 (7pp) doi:10.1088/0957-0233/23/11/115101

Position and movement sensing at metrestandoff distances using ambient electricfieldH Prance, P Watson, R J Prance and S T Beardsmore-Rust

Sensor Technology Research Centre,Department of Engineering and Design, University of Sussex,Falmer, Brighton, BN1 9QT, UK

E-mail: r.j.prance@sussex.ac.uk

Received 14 June 2012, in final form 17 August 2012Published 8 October 2012Online at stacks.iop.org/MST/23/115101

AbstractWe describe a system for the measurement of changes in electric field which occur as a resultof the movement of people, or objects, in ambient electric fields with standoff distances ofseveral metres. A passive sensor system is used to measure the changes in electric field whichare due to several different mechanisms. From this we are able to extract presence, movementand position information with a positional accuracy of ∼10 cm. Furthermore, by examiningthe disturbances in ambient ac fields, such as those created by domestic electricity networks,we show that it is possible to recover static field information with a sensor that lacks dcsensitivity. In this way, we demonstrate that tracking of individuals within large room-scalespaces is possible. As a simple, passive, undetectable technique, with no line of sightrequirement, these measurements open up new possibilities in security, telehealth and humancomputer interfacing applications.

Keywords: sensors, movement, electrometer, security, telehealth

(Some figures may appear in colour only in the online journal)

1. Introduction

The applications for movement sensing and tracking systemsare wide ranging and include security, offender management[1], search and rescue, and the military, as well as the care of theelderly [2, 3]. In a number of these applications, it is sufficientto be aware when a given space is occupied by an individual orindividuals and to obtain information about their movementsaround, into and out of the space. In such cases, there is oftena requirement for long term, unattended, surveillance systemswhich do not generate excess data or false alarms [4, 5].

Passive techniques for the detection of subjects andmonitoring or tracking their movement, have severalclear advantages over active techniques. These includecompatibility with a need for covert surveillance, removal ofpotential hazards due to irradiation, light weight construction,lower power requirements and the capability for extended use.However, existing passive techniques tend to focus on eitheroptical or infrared sensors [6, 3]. Both types of sensor can be

obscured by objects, walls and poor visibility. Moreover, videoimaging systems generate extremely large data sets whichare complex to analyse and may contain significantly moreinformation than is required for the application, frequentlyleading to the additional burden of anonymizing the data.

In order to meet the requirement for movement sensingsystems capable of operating in environments where lineof sight is not available, such as in the presence of wallsor debris, substantial work has taken place [7, 8]. To date,however, much of this work has focused on the use of RF radartechniques for identifying movement through walls and debris[9, 10]. These techniques, while clearly effective, are active,which presents limitations when considering applicationsrequiring covert surveillance or extended use. By contrastin this paper we describe a new passive method whichcombines the use of ambient electric fields as the excitationsignal with a unique electric field sensing technology. Theelectric potential sensor (EPS), developed and patented atthe University of Sussex, can be described as a laboratory grade

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