Understanding Smart Sensors - Nomads.usp

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158 Understanding Smart SensorsTemperaturesensorGassensorSmokesensorHumiditysensorPassiveIR scanRadarscanTasktableData acquisition taskBlackboard readings and buffersTasktableCertainty engine taskBlackboard threat scores and alarmsTarget acquisition and tracingFigure 7.6 A variety of sensors providing input to the fuzzy logic certainty engine of amobile robot. (After: [13].)manner roughly analogous to neurons in the human brain [11]. Neural networksare useful in systems that are difficult to define. They have the additionaladvantage of being able to operate in a high-noise environment. Complex ornumerous input patterns are among the problems that neural networks areaddressing [14].Figure 7.7(a) shows the structure of a neuron [14]. The neuron learnsfrom a set of training data. Each input to the neuron is multiplied by the synapseweight. The neuron sums the results of all the weighted inputs andprocesses the results with a typically nonlinear transfer function to determinethe output. A local memory stores previous computations and modifies theweights as it learns.A neural network consists of a number of neurons connected in somemanner, as indicated in Figure 7.7(b). In that model, a hidden layer exists thatprevents direct interaction between the input and the output. However, thehidden layer is not required, and more than one may exist. Also, the numberof neurons does not have to be the same in each layer. To train or conditionthe neuron for proper response, several sets of inputs with known results areapplied to the neural network. The output is compared for each set of inputs tothe known results, and the weights are adjusted to compensate for errors.
Control Techniques 159InputX1SynapseweightsInputX2W2W1Summing nodeΣSemilinear functionNeuronoutputYInputX3W3WNInputXN(a)X0Y0X1Y1X2Y2X3Y3X4Y4Input layerHidden layerOutput layer(b)Figure 7.7 (a) Neuron and (b) neural network. (After: [14].)
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Understanding Smart SensorsSecond E
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Library of Congress Cataloging-in-P
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ContentsPrefacexix1 Smart Sensor Ba
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Contentsix3.3.3 Hall Effect 603.3.4
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Contentsxi5.8 Summary 116References
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Contentsxiii8.3.1 Surface Acoustica
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Contentsxv11.1.1 Integration and Me
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Contentsxvii14.4.4 Electrostatic Me
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PrefaceThe number one challenge fac
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Prefacexxifor well over 6 years. A
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2 Understanding Smart Sensorsobserv
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4 Understanding Smart Sensorssmart
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6 Understanding Smart Sensorsto pro
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8 Understanding Smart Sensorsinclud
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10 Understanding Smart SensorsLevel
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12 Understanding Smart Sensorsuser
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14 Understanding Smart Sensorstechn
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16 Understanding Smart Sensorschang
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18 Understanding Smart Sensorstechn
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20 Understanding Smart Sensors Surf
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22 Understanding Smart Sensorssecon
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24 Understanding Smart SensorsSilic
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26 Understanding Smart SensorsSilic
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28 Understanding Smart SensorsSacri
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30 Understanding Smart Sensors2.4.3
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32 Understanding Smart Sensorsmeasu
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34 Understanding Smart SensorsIon+S
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36 Understanding Smart Sensorssidew
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38 Understanding Smart SensorsTable
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40 Understanding Smart Sensorsoptim
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42 Understanding Smart SensorsAlumi
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44 Understanding Smart SensorsThe f
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46 Understanding Smart Sensors[21]
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The Nature of Semiconductor Sensor
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4Getting Sensor Information Into th
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Getting Sensor Information Into the
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5Using MCUs/DSPs to Increase Sensor
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Using MCUs/DSPs to Increase Sensor
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Page 193 and 194: Control Techniques 171[19] De Silva
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MEMS Beyond Sensors 209InletThermop
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MEMS Beyond Sensors 211Figure 9.8 A
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MEMS Beyond Sensors 2139.3.2 Microo
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MEMS Beyond Sensors 215Over-range p
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MEMS Beyond Sensors 217+ 10°− 10
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MEMS Beyond Sensors 219achieved wit
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MEMS Beyond Sensors 221Methods for
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MEMS Beyond Sensors 223Figure 9.19
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MEMS Beyond Sensors 225[21] Tortone
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10Packaging, Testing, and Reliabili
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Packaging, Testing, and Reliability
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11Mechatronics and Sensing SystemsP
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Mechatronics and Sensing Systems 25
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12Standards for Smart SensingThe la
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Standards for Smart Sensing 275Netw
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Standards for Smart Sensing 277NCAP
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Standards for Smart Sensing 279Proc
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Standards for Smart Sensing 281PID
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Standards for Smart Sensing 283•
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Standards for Smart Sensing 285•
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Standards for Smart Sensing 287Tabl
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Standards for Smart Sensing 289perf
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Standards for Smart Sensing 2911 0
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Standards for Smart Sensing 293obje
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Standards for Smart Sensing 295Mult
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13The Implications of Smart SensorS
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The Implications of Smart Sensor St
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14The Next Phase of Sensing Systems
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The Next Phase of Sensing Systems 3
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List of Acronyms and Abbreviations3
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List of Acronyms and Abbreviations
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Glossaryablationvaporization of mat
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Glossary 353buscalibrationconnectio
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Glossary 355end point straight line
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Glossary 357data transmission and p
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Glossary 359multiplexer device that
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Glossary 361repeatability the maxim
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Glossary 363spread spectrum techniq
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Glossary 365transducer a device con
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Selected BibliographyBooks and Jour
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Selected Bibliography 369Northeaste
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Selected Bibliography 371Miscellane
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About the AuthorRandy Frank is a te
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Index4-to 20-mA signal transmitter,
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Index 377definitions, 119-20introdu
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Index 379HVAC, 318-19MCU with integ
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Index 381Linearization, 108Linear p
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Index 383in chemical measurements,
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Index 385Ratiometricity, 56Reactive
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Index 387IC fabrication, 19micromec
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Index 389Transducer-independent int
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