
Principles and Applications of Ubiquitous Sensing
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"This book provides a concise review of sensing methods and many sensor types, with a focus on medical applications"...."Readers interested in learning about many types of sensing methods will find this book extremley interesting and well worth reading" IEEE, Oct 2017More details
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Professor Dargie holds a PhD in Computer Engineering from the Technical University of Dresden, Germany (2006). His educational background in electrical engineering, electronics, and computer engineering inspired him to consolidate these subject areas in a single book. He has rich teaching and research experience at various universities, having taught wireless sensor networks, computer networks, and stochastic processes for more than eight years. These courses have enabled him to collect a vast amount of material used in this book. In 2010 he co-authored a book on wireless sensor networks, which is now being used as a text book in many universities around the world (Fundamentals of Wireless Sensor Networks: Theory and Practice, Wiley).
Content
Preface xiii
About the Companion Website xv
List of Abbreviations xvii
1 Introduction 1
1.1 System Overview 2
1.1.1 Sensing System 2
1.1.2 Conditioning System 3
1.1.3 Analogue-to-digital Signal Conversion 3
1.1.4 Processor 4
1.2 Example: AWireless Electrocardiogram 4
1.3 Organisation of the Book 7
2 Applications 9
2.1 Civil Infrastructure Monitoring 9
2.1.1 Bridges and Buildings 10
2.1.2 Water Pipelines 17
2.2 Medical Diagnosis and Monitoring 21
2.2.1 Parkinson's Disease 21
2.2.2 Alzheimer's Disease 25
2.2.3 Sleep Apnea and Medical Journalling 26
2.2.4 Asthma 28
2.2.5 Gastroparesis 31
2.3 Water-quality Monitoring 34
References 39
3 Conditioning Circuits 44
3.1 Voltage and Current Sources 44
3.2 Transfer Function 45
3.3 Impedance Matching 51
3.4 Filters 56
3.5 Amplification 61
3.5.1 Closed-loop Amplifiers 63
3.5.2 Difference Amplifier 65
References 70
4 Electrical Sensing 72
4.1 Resistive Sensing 73
4.2 Capacitive Sensing 78
4.3 Inductive Sensing 84
4.4 Thermoelectric Effect 91
References 94
5 Ultrasonic Sensing 96
5.1 UltrasonicWave Propagation 100
5.2 Wave Equation 106
5.3 Factors Affecting UltrasonicWave Propagation 108
References 111
6 Optical Sensing 114
6.1 Photoelectric Effect 116
6.2 Compton Effect 120
6.3 Pair Production 126
6.4 Raman Scattering 127
6.5 Surface Plasmon Resonance 131
References 133
7 Magnetic Sensing 136
7.1 Superconducting Quantum Interference Devices 136
7.1.1 DC-SQUID 139
7.1.2 RF-SQUID 141
7.2 Anisotropic Magnetoresistive Sensing 142
7.3 Giant Magnetoresistance 148
7.4 Tunnelling Magnetoresistance 151
7.5 Hall-effect Sensing 155
References 157
8 Medical Sensing 160
8.1 Excitable Cells and Biopotentials 161
8.1.1 Resting Potential 162
8.1.2 Channel Current 166
8.1.3 Action Potentials 166
8.1.4 Propagation of Action Potentials 167
8.1.5 Measuring Action Potentials 171
8.2 Cardiac Action Potentials 175
8.2.1 Propagation of Cardiac Action Potentials 177
8.2.2 The Electrocardiogram 180
8.2.2.1 Re-entry 181
8.2.2.2 Loss of Membrane Potential 182
8.2.2.3 Afterdepolarisations 183
8.3 Brain Action Potentials 185
8.3.1 Electroencephalography 188
8.3.2 Volume Conduction 193
8.3.3 Electrode Placement 195
References 198
9 Microelectromechanical Systems 202
9.1 Miniaturisation and Scaling 202
9.1.1 Physical Properties 203
9.1.2 Mechanical Properties 203
9.1.3 Thermal Properties 204
9.1.4 Electrical and Magnetic Properties 205
9.1.5 Fluid Properties 205
9.1.6 Chemical Properties 206
9.1.7 Optical Properties 206
9.2 Technology 206
9.2.1 Growth and Deposition 207
9.2.2 Photolithography 207
9.2.3 Etching 209
9.3 Micromachining 209
9.3.1 Surface Micromachining 210
9.3.2 Bulk Micromachining 211
9.3.2.1 Reactive Ion Etching 212
9.3.2.2 Micromolding 215
9.3.2.3 Non-silicon Micromolding 216
9.3.2.4 Plastic Micromolding 217
9.4 System Integration 218
9.5 Micromechanical Sensors 220
9.5.1 Pressure and Force Sensors 220
9.5.1.1 Piezoelectric Effect 222
9.5.1.2 Piezoresistance 226
9.5.1.3 Fabrication of a Piezoresistive Sensor 227
9.5.2 Flow Sensors 227
9.5.2.1 Floating Plate 228
9.5.2.2 Artificial Hair Cell 231
9.5.3 Accelerometers 234
9.5.3.1 Fabrication of an Accelerometer 235
9.5.4 Gyroscopes 236
9.5.4.1 Fabrication of a Gyroscope 246
References 249
10 Energy Harvesting 253
10.1 Factors Affecting the Choice of an Energy Source 253
10.1.1 Sensing Lifetime 254
10.1.2 Sensor Load 254
10.1.3 Energy Source 255
10.1.4 Storage 256
10.1.5 Regulation 257
10.2 Architecture 263
10.3 Prototypes 265
10.3.1 Microsolar Panel 265
10.3.2 Microgenerator 269
10.3.3 Piezoelectricity 272
References 275
11 Sensor Selection and Integration 278
11.1 Sensor Selection 278
11.1.1 Accuracy 278
11.1.2 Sensitivity 280
11.1.3 Zero-offset 280
11.1.4 Reproducibility 280
11.1.5 Span 281
11.1.6 Stability 281
11.1.7 Resolution 282
11.1.8 Selectivity 282
11.1.9 Response Time 282
11.1.10 Self-heating 282
11.1.11 Hysteresis 283
11.1.12 Ambient Condition 283
11.1.13 Overload Characteristics 283
11.1.14 Operating Life 284
11.1.15 Cost, Size, andWeight 284
11.2 Example: Temperature Sensor Selection 284
11.2.1 Resistance Temperature Detectors 284
11.2.2 Thermistors 285
11.2.3 Thermocouples 286
11.2.4 Infrared 286
11.3 Sensor Integration 287
11.3.1 Dead Volume 287
11.3.2 Self-heating 287
11.3.3 Internal Heat Sources 294
11.3.3.1 External Heat and Radiation Sources 296
References 296
12 Estimation 298
12.1 Sensor Error as a Random Variable 299
12.2 Zero-offset Error 303
12.3 Conversion Error 305
12.4 Accumulation of Error 309
12.4.1 The Central LimitTheorem 313
12.5 Combining Evidence 315
12.5.1 Weighted Sum 316
12.5.2 Maximum-likelihood Estimation 322
12.5.3 Minimum Mean Square Error Estimation 325
12.5.4 Kalman Filter 328
12.5.5 The Kalman Filter Formalism 334
References 335
Index 337
Chapter 1
Introduction
Most advanced biological and man-made physical systems require reliable sensing to function properly. The more sensors they integrate, the more complete and comprehensive is the information they can gather from their surroundings. For a long time, practical challenges have set limits to the number of sensors that can be embedded into physical systems, processes, or environments. Among these challenges are limited space, the difficulty and obtrusiveness of wiring, heat dissipation, and power supply. Miniaturisation of sensors has fundamentally changed the way we deal with these challenges. Furthermore, integrating processing and wireless communication capabilities into sensing systems has enabled not only dynamic programmability but also networking, so that data can be processed (aggregated, filtered, compressed) in a distributed manner or can be packed in packets and transferred to a different location where they can be processed by employing advanced signal-processing algorithms.
The past decade has witnessed an explosion of interest in wireless sensors and wireless sensor networks, for which there are a variety of applications. In civil engineering, these sensors and networks can be employed to monitor the integrity of infrastructure, such as pipelines, bridges, and buildings. In the medicine and healthcare domain, they have already proved to be indispensable, but they are also finding new applications in augmenting existing diagnosis and monitoring infrastructure and in enabling more independent and flexible lifestyles for patients. In agriculture and environmental science, wireless sensors and wireless sensor networks are useful for precision agriculture, for monitoring the quality, amount, and flow of water, and for studying wildlife without the need to interfere with it.
However, the usefulness of the applications that employ sensors depends on the depth of understanding pertaining to the design and operation of the sensors as well as the quality of the data-processing algorithms employed. The faith an application developer puts in a sensor should be based on a quantitative understanding of its reliability, accuracy, precision, sensing range, sensitivity, and lifetime as well as on the strength of the assumptions supporting the data-processing schemes. Otherwise, the relevance of the application will necessarily be limited to laboratory settings, or prototypes. On the other hand, a fundamental understanding of sensors and their design leads to innovative ideas and the identification of totally new application domains.
Interestingly, the basic concepts of sensors are straightforward to grasp and the electrical circuits required to realise a sensing system are relatively simple and comprehensible, for example, compared to the design of high-frequency communication systems. This is because, in most practical situations, sensors have to deal with low-frequency signals that can be detected and processed by relativelysimple electrical components. The purpose of this book is to acquaint the reader with:
- the fundamental principles of electrical, ultrasonic, optical, and magnetic sensing
- the broad range of issues pertaining to the design and manufacturing of microelectromechanical sensors (MEMS)
- the principles of energy harvesting and sensor integration
- the fundamental assumptions and methodologies pertaining to the processing of sensed data.
I have tried to make the book self-contained by discussing the necessary prerequisites within the book itself, so that the reader is not obliged to refer to other materials in order to understand the text.
1.1 System Overview
Figure 1.1 displays the most essential building blocks of a self-contained sensing system. These are the sensing system, the conditioning system, the processor, and the wireless transceiver. The figure is intended to give a complete picture, but we shall not be dealing with the wireless transceiver here. Whether or not these blocks are distinct from each other depends on many factors, such as the quality of the signal that can be sensed by the sensor, the targeted energy and space efficiency, and the ease with which the wireless sensor should be integrated into and interact with other systems. The processor and the wireless transceiver are usually connected with the rest of the system using standard buses that use standard or quasi-standard protocols. Hence, the main design issue is how to integrate the remaining building blocks. I shall briefly summarise the function of each building block and highlight the different trade-offs that influence its integration with the other blocks.
Figure 1.1 The main building blocks of a wireless sensor.
1.1.1 Sensing System
The process we wish to observe or monitor is called the measurand. It either releases some form of energy that describes its condition in some way, or external energy in the form of an electrical (radio-frequency), optical, or acoustic signal is applied to it, so that from the way it modifies some of the characteristics of the signal (magnitude, phase, frequency or a combination of these), its condition can be determined or inferred. The human body is a typical example of a measurand, because it is a remarkable signal generator. The human brain generates electromagnetic signals that can be sensed by electroencephalogram or magnetoencephalogram. Likewise, the human cardiovascular and muscular systems generate electric potentials that can be sensed by employing an electrocardiogram or electromyogram. In contrast, ultrasound systems release ultrasonic waves into a human organ and the spectrum of the reflected signal is analysed to determine the organ's condition. If a measurand's condition is determined from the signal it releases, then the sensing method is called "passive sensing". Otherwise, it is called active sensing. Passive sensing introduces less intrusion into the measurand compared to active sensing, but the amount of energy that can be collected through passive sensing is normally small.
1.1.2 Conditioning System
Regardless of the sensing mechanism employed, there are important conditions the sensor and the signal it produces should fulfil before useful features can be extracted from the signal. One of them is appropriate interfacing. When a sensor is employed to a measurand, apparently the measurand "perceives" the presence of the sensor, because the sensor draws some amount of power from it. This power must not affect the measurand's proper operation. A related issue to interfacing is impedancematching. If the impedance of the measurand as seen by the sensor is not matched by the sensor's own input impedance, maximum power does not transfer from the measurand to the sensor. Instead, power dissipation in the form of heat may be experienced at the interface, disturbing the measurand and reducing the efficiency of the sensor. Therefore, the impedance of the measurand (human body, water, air) should be taken into account when the sensor circuits are designed.
Even with the interfacing problem solved, the signal produced by the sensor may not accurately reflect the measurand's true condition for a number of reasons. Noise may be added to the sensed signal from the surrounding environment or from the internal circuits of the sensor itself. Likewise, some portion of the signal may be removed, suppressed, or distorted, because the sensor circuits act as filters. Therefore, the bandwidth of the desired signal and the bandwidth of the sensing circuits should be matched. It must be noted that in most real-world cases, the signal produced by a measurand contains a range of frequency components. The purpose of the conditioning system is to deal with all these issues. A conditioning component typically consists of a filter circuit and a differential amplifier, the order in which they appear usually depending on the nature of the measurand as well as the strength of the signal produced by the sensing system.
1.1.3 Analogue-to-digital Signal Conversion
This component is not directly shown in Figure 1.1 because it may be a part of the conditioning system or the processor or it may be a distinct entity. Regardless of its specific position, the analogue signal the sensor produces and the conditioning system pre-processes should first be converted to a digital bit stream before it can be further processed by a microcontroller or a digital signal processor (DSP). In some sensors, the analogue-to-digital converter (ADC) is an integral part of the conditioning system, while in others it is a separate block. Modern microcontrollers also integrate multiple general-purpose ADCs, to one of which the analogue signal coming from a conditioning circuit can directly be fed. Next to the transceiver and the processor, the ADC is the largest power consumer and hence care must be taken in choosing a suitable ADC. Several factors determine the choice of an ADC. For example, if the sensor signal is noisy, it is better not to use a powerful pre-amplifier lest the noise is amplified together with the useful signal. In this case it is better to use a high-resolution ADC, so that an efficient DSP algorithm can eliminate the noise. But a high-resolution ADC consumes a large amount of power and generates a large amount of data, which require a sizeable resource to process, store, and communicate. If, on the other hand, there is a small noise component in the signal, then it is better first to amplify the signal and use a low-resolution ADC. If the ADC is not an integral component of the sensor or conditioning system, then it is possible to use the sensor for...
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