Smart Sensors and Systems (eBook)

Chong-Min Kyung received B.S. in EE from Seoul National University in 1975, M.S. and Ph.D. in EE from KAIST in 1977 and 1981, respectively. From 1981 to 1983, he worked at Bell Telephone Laboratories, Murray Hill. Since he joined KAIST in 1983, he has been working on CAD algorithms, 3-D graphics, and System-on-a-Chip design and verification methodology, development of various RISC/CISC microprocessors, VLIW and reconfigurable DSP cores. His current research includes system-level low-power design, electrical/thermal co-design in 3D IC, architectures of H.264 video CODEC and energy-rate-distortion-power optimization. He served as General Chair in the Korean Semiconductor Conference 2002, ISOCC 2004, A-SSCC 2007, and ASP_DAC 2008. He is founding Director of the IDEC(Integrated Circuit Design Education Center), and currently leads Center for Integrated Smart Sensors, funded as Global Frontier Project by Korean Government. He received the Most Excellent Design Award, and Special Feature Award in the University Design Contest in the ASP-DAC 1997 and 1998, respectively. He received the Best Paper Awards in the 36th DAC held in New Orleans, LA, the 10th ICSPAT, Orlando, FL, in September 1999, and the 1999 ICCD Austin, TX. In 2000, he received National Medal from Korean government for his contribution to research and education in IC design. He is a member of National Academy of Engineering Korea, and Korean Academy of Science and Technology. He is IEEE fellow and Hynix Chair Professor at KAIST. Youn-Long Lin is  a Tsing Hua Chair Professor of Computer Science of  National Tsing Hua University. He was born in Yun-Lin, Taiwan. He received his B.S. degree in electronics engineering from National Taiwan University of Science and Technology (formerly, National Taiwan Institute of Technology), Taipei, Taiwan, in 1982, and his Ph.D. in computer science from the University of Illinois, Urbana-Champaign, IL, U.S.A. in 1987.  Upon his graduation, he joined National Tsing Hua University, Hsin-Chu, Taiwan, where he established the THEDA Group (Tsing Hua EDA), served as Director of the University Computer and Communication Center, Chairman of the Department of Computer Science, Secretariat General of the University, Chief Librarian of the University Library, and the Dean of Research & Development.  He is also an adjunct professor of Peking University, Beijing and a guest professor of Waseda University, Japan.

Preface 5
Contents 7
Part I: Materials and Structural Platform for Smart Sensors 9
Chapter 1: Biomimetic Materials and Structures for Sensor Applications 10
1.1 Introduction 10
1.2 Structures for the Detection of Mechanical Stimuli 11
1.2.1 Hair-Like Structures for Flow Detection 12
1.2.2 Structures for Tactile Sensing 13
1.2.3 Structures for Light Detection 18
1.3 Biomimetic Structures for Biochemical Sensing 18
1.3.1 Structures for Biomimetic Olfactory Senses 19
1.3.2 Structures for Biomimetic Tasting Senses 20
1.4 Optical Sensors 21
1.5 Self-Assembled Structures and Materials for Sensors 23
1.5.1 Cellulose-Based Structures for Sensors 24
1.5.2 Collagen-Based Structures for Sensors 25
1.5.3 Virus-Based Structures for Sensors 26
1.6 Conclusions 29
References 29
Chapter 2: A Multi-Modal CMOS Sensor Platform Towards Personalized DNA Sequencing 33
2.1 Introduction 33
2.2 High-Sensitivity CMOS pH-TVC ISFET Sensor 34
2.2.1 CMOS ISFET Model 35
2.2.2 pH-TVC Readout Scheme 37
2.2.3 Top Architecture and Operation 40
2.2.4 Results and Discussions 40
2.3 High Accuracy CMOS Dual-Mode Sensor 43
2.3.1 Contact Imaging and pH-Based Sensing 44
2.3.2 Top Architecture and Operation 47
2.3.3 Results and Discussions 48
2.4 CMOS THz Metamaterial Sensor 51
2.4.1 Metamaterial-Based Source and Detector 52
2.4.2 Top Architecture and Operation 56
2.4.3 Results and Discussions 56
2.5 Conclusion 58
References 59
Part II: Circuit Platforms for Smart Sensors 61
Chapter 3: Circuit Design in mm-Scale Sensor Platform for Future IoT Applications 62
3.1 Introduction: Bell´s Law and Size of Computing Systems 63
3.2 Challenges for Enabling mm-Scale IoT Systems 65
3.3 Low-Power Circuit Design Technologies for mm-Scale Systems 67
3.3.1 Low-Power Sensing Modalities 67
3.3.2 Low-Power Microprocessor 70
3.3.3 Low-Power Energy Harvesters and Power Converter 72
3.3.4 Low-Power Wireless Receiver and Transmitter 75
3.3.5 Low-Power Timers 78
3.3.6 Low-Power Voltage/Current References 80
3.4 Conclusions 84
References 84
Chapter 4: Smart Sensor Microsystems: Application-Dependent Design and Integration Approaches 87
4.1 Introduction 87
4.2 Microsystem 89
4.2.1 Device Size (Form Factor) 89
4.2.2 Power Source (or Device) Lifetime 90
4.2.3 Physical Interface with Surroundings 91
4.2.4 Example: Implantable Blood Flow Sensor 91
4.3 Sensing 95
4.3.1 Physical Parameters to Sense 95
4.3.2 Minimum Detection Limit, Bandwidth, and Dynamic Range 95
4.3.3 Sensing Duty Cycle 97
4.3.4 Example: Neural Recording 98
4.3.4.1 Dual-Supply Ultra-Low-Power Neural Recording IC with Dynamic Range Folding 99
4.3.4.2 Ultra-Low-Power Neural Recording IC with Delta-Modulation-Based Spike Detection 100
4.4 Communication 103
4.4.1 Communication Medium 103
4.4.2 Example: Body-Channel Communication 103
4.4.3 Distance, Symmetry, and Protocol 106
4.4.4 Example: Wireless Capsule Endoscopy 107
4.4.5 Data Rate 108
4.4.6 Communication Duty Cycle 109
4.5 Conclusion 110
References 110
Chapter 5: Energy Efficient RRAM Crossbar-Based Approximate Computing for Smart Cameras 112
5.1 Introduction 112
5.2 Preliminaries 114
5.2.1 RRAM Characteristics and Device Model 114
5.2.2 Neural Approximator 116
5.3 RRAM-Based Analog Approximate Computing 117
5.3.1 RRAM-Based Approximate Computing Unit 117
5.3.2 RRAM-Based Approximate Computing Framework 119
5.4 Configuration Flow for RRAM-ACU 120
5.4.1 Training Phase: Neural Approximator Training Algorithm 121
5.4.2 Mapping Phase: Mapping Neural Approximator Weights to RRAM Conductance States 122
5.4.3 Tuning Phase: Tuning RRAM Devices to Target States 123
5.5 Evaluation 127
5.5.1 Experiment Setup 127
5.5.2 Benchmark Evaluation 127
5.5.3 System Level Evaluation: HMAX 130
5.6 Conclusion and Discussion 133
References 134
Chapter 6: NVRAM-Assisted Optimization Techniques for Flash Memory Management in Embedded Sensor Nodes 137
6.1 Introduction 138
6.2 Background and Motivation 139
6.2.1 NVRAM-Based Sensor Node 139
6.2.2 A Representative FTL Scheme 140
6.2.3 Motivation 141
6.3 NV-FTL: Write-Activity-Aware FTL 143
6.3.1 Overview 143
6.3.2 NV-FTL Description 144
6.4 Evaluation 148
6.4.1 Experimental Setup 149
6.4.2 Results and Discussion 149
6.4.2.1 NVRAM Endurance 150
6.4.2.2 PCM Wear-Leveling 151
6.5 Conclusion 153
References 153
Part III: Sensors for Image Capture and Vision Processing 156
Chapter 7: Artificially Engineered Compound Eye Sensing Systems 157
7.1 Introduction 157
7.2 Artificial Compound Eyes 161
7.2.1 Planar-Type Compound Eye Imaging Systems 161
7.2.2 Hemispherical Compound Eye Lens Arrays and Optic Components 163
7.2.3 Curved Image Sensors and Hemispherical Compound Eye Imaging Systems 167
7.3 Summary 173
References 174
Chapter 8: Intelligent Vision Processing Technology for Advanced Driver Assistance Systems 175
8.1 Introduction 176
8.2 Existing ADAS Systems 177
8.2.1 Pedestrian and Motorcyclist Detection System 177
8.2.2 Lane Departure Warning System 178
8.2.3 Forward Collision Warning System 179
8.2.4 Speed Limit Detection System 180
8.2.4.1 Speed Limit Signs Detection 180
8.2.4.2 Speed Limit Signs Verification 180
8.2.4.3 Speed Limit Signs Recognition 181
8.2.5 Inclement Weather Processing Technology (DLCE) 181
8.3 Advanced ADAS System 182
8.3.1 Pedestrian and Motorcyclist Detection System 182
8.3.2 Lane Departure Warning System and Forward Collision Warning System 184
8.3.3 Speed Limit Detection System 185
8.3.4 Inclement Weather Processing Technology (DLCE) 185
8.4 Implementation Issues 185
8.4.1 Pedestrian and Motorcyclist Detection System 186
8.4.2 Lane Departure Warning System 188
8.4.3 Forward Collision Warning System 190
8.4.4 Speed Limit Detection System 192
8.4.4.1 Shape Detection 192
8.4.4.2 Achromatic Decomposition 194
8.4.4.3 Binarization and Digit Segmentation 194
8.4.4.4 Digit Recognition 195
8.4.5 Inclement Weather Processing Technology (DLCE) 197
8.5 Conclusion 201
References 202
Pedestrian, Motorcyclist, and Vehicle Detection System (PMD) 202
Lane Departure Warning System 204
Forward Collision Warning System 204
Speed Limit Detection System 205
Inclement Weather Processing Technology (DLCE) 205
Part IV: Smart Sensors for Biomedical and Health Monitoring 207
Chapter 9: Implantable Optical Neural Interface 208
9.1 Introduction 209
9.2 Optical Monitoring of Neural Activity 212
9.2.1 Optical Probes for Neural Recording 212
9.2.2 Optical Systems for Neural Recording 214
9.2.2.1 Miniaturized Microscopy 215
9.2.2.2 Confocal Microscopy 215
9.2.2.3 Two-Photon Microscopy 217
9.2.2.4 Microendoscopy 220
9.3 Optical Neural Modulation of Nerve Systems 223
9.3.1 Light-Sensitive Proteins 223
9.3.2 Optical Neuromodulation System 225
9.4 Challenges and Perspectives 227
9.4.1 Miniaturization of Optical Hardware 227
9.4.2 Power Consumption 227
9.4.3 Heating Problem 228
9.4.4 Single-Cell Manipulation 228
9.4.5 Clinical Application 228
9.5 Conclusions 229
References 230
Chapter 10: Real-Time Programmable Closed-Loop Stimulation/Recording Platforms for Deep Brain Study 236
10.1 Introduction 237
10.2 Considerations for Closed-Loop System Design 238
10.3 General Closed-Loop Deep Brain Stimulation and Recording System Design 239
10.3.1 Neural Recording System 240
10.3.1.1 Amplifier 241
10.3.1.2 A/D Converter 242
10.3.2 Neural Stimulation System 242
10.3.3 Digital Integrated Circuits 243
10.4 Closed-Loop Control Policy and Digital Signal Processing 245
10.4.1 Digital Filtering 245
10.4.2 Time Domain Signal Processing Techniques 246
10.4.3 Frequency Domain Signal Processing Techniques 247
10.5 A Design Case: A Real-Time Closed-Loop Neurostimulation System Based on Neural Phase Synchrony Detection 247
10.5.1 Introduction to Closed-Loop Neurostimulation System 247
10.5.2 System Architecture 249
10.5.2.1 Recording and Functional Electrical Stimulation 249
10.5.2.2 Phase Analysis 250
10.5.2.3 Closed-Loop Stimulator Control Unit 252
10.5.3 Real-Time Digital Processing Platform 253
10.5.4 Implementation Results 254
10.5.4.1 Data Collection 256
10.5.4.2 Statistical Results 256
10.5.4.3 Phase Synchronization Detection in the EEG Dataset 257
10.5.4.4 Comparison with Other Studies 258
10.6 Conclusions 259
References 261
Chapter 11: Internet of Medical Things: The Next PC (Personal Care) Era 264
11.1 Telehealth for the Next Personal Care (PC) Era 267
11.1.1 The Effectiveness of Telehealth Care on Caregiver Burden, Mastery of Stress, and Family Function Among Family Caregivers 268
11.1.1.1 Experimental Method 269
11.1.1.2 Experimental Results 269
11.1.2 Assessment of the Clinical Outcomes and Cost-Effectiveness of a Fourth-Generation Synchronous Telehealth Program 274
11.1.2.1 First Study: Experimental Method 274
11.1.2.2 First Study: Experimental Results 275
11.1.2.3 Second Study: Experimental Method 275
11.1.2.4 Second Study: Experimental Results 278
11.2 Biomedical SoC Solutions for the Next Personal Care (PC) Era 283
11.2.1 Biomedical SoC Outside the Body: CMOS Assay SoC for Rapid Blood Screening 284
11.2.1.1 System Architecture 284
11.2.1.2 Circuit Implementation 285
11.2.1.3 Experimental Results 288
11.2.2 Biomedical SoC Outside the Body: Portable Gas-Chromatography Microsystem for Volatile Compound Detection 290
11.2.2.1 System Architecture 292
11.2.2.2 Circuit Implementation 295
11.2.2.3 Experimental Results 296
11.2.3 Communication Through the Body: Biomedical SoC for Intra-Body Communication System 297
11.2.3.1 System Architecture 299
11.2.3.2 Circuit Implementation 299
11.2.3.3 Experimental Results 301
11.2.4 Biomedical SoC Inside the Body: Implantable Release-on-Demand Wireless CMOS Drug Delivery SoC 303
11.2.4.1 System Architecture 304
11.2.4.2 Drug Reservoir Implementation 304
11.2.4.3 SoC Circuit Implementation 305
11.2.4.4 Implementation Results 307
11.2.5 Biomedical SoC Outside the Body: Implantable Pain-Control-on-Demand Batteryless Wireless CMOS SoC 309
11.2.5.1 System Architecture 309
11.2.5.2 Circuit Implementation 310
11.2.5.3 Experimental Results 312
11.2.6 Biomedical SoC Inside the Body: Implantable Batteryless Remotely Controlled Locomotive SoC 314
11.2.6.1 System Architecture 314
11.2.6.2 Circuit Implementation 316
11.2.6.3 Experimental Results 317
11.2.7 Energy-Efficient Biomedical-Signal-Processing SoC 319
11.2.7.1 On-Chip ECG Signal Processing 320
11.2.7.2 On-Chip EEG/ECoG Signal Processing 322
11.2.7.3 On-Chip Neural Spike Sorting 323
References 328
Chapter 12: Functional Nanofibers for Flexible Electronics 333
12.1 Introduction 333
12.2 Electrospinning 335
12.3 Flexible Components and Devices Based on Electrospun Nanofibers 337
12.3.1 Components for a Typical Electrical System: Field-Effect Transistors 338
12.3.2 Transparent Electrodes: A Component of Cutting-Edge Technology 340
12.3.3 Devices for Interaction: Sensors 342
12.3.4 Devices for Energy Generation and Storage: Solar Cells, Batteries, and Supercapacitors 345
12.4 Conclusions and Outlook 351
References 352
Chapter 13: Urine Microchip Sensing System 357
13.1 Biosensing: Creatinine and Albumin 359
13.1.1 Urinary Creatinine 359
13.1.2 Measurement of Creatinine Concentration 360
13.1.3 Urinary Albumin 363
13.2 Urine Detection System 364
13.2.1 Front-End Readout Circuit 365
13.2.2 Analog-to-Digital Converter 370
13.2.3 Micro-Control Unit 373
13.2.4 Mixed-Signal IC Design 375
13.3 Clinical Validation 377
13.3.1 Chronic Kidney Disease 377
13.3.2 Urine Microchip Sensing System 378
References 379
Part V: Big Data as Sensor Applications 383
Chapter 14: Building a Practical Global Indoor Positioning System 384
14.1 Introduction 385
14.2 Location Technology Stack 386
14.3 Methods and Tools to Construct a GIPS 388
14.3.1 Deployment Process of Indoor Positioning System 388
14.3.2 Indoor Map Registration and Modeling 388
14.3.2.1 Indoor Map Drawing and Registration 388
14.3.2.2 Modeling of Indoor Areas 389
14.3.3 Radio Map Construction 389
14.3.4 Mapping of Radio Maps and Positioning Algorithms 391
14.3.4.1 Positioning Algorithms 392
14.3.4.2 Mapping Radio Maps into Positioning Algorithms 393
14.3.5 Probabilistic Positioning Algorithm for Radio Maps with Crowdsourced Fingerprints 394
14.3.5.1 Emission Probability 395
14.3.5.2 Dynamic Transition Probability 395
14.3.5.3 Extension of Viterbi Algorithm 396
14.3.6 Testing and Evaluation 396
14.4 KAIST Indoor Locating System 397
14.4.1 KAI-Map 397
14.4.1.1 KAI-Radio Map 397
14.4.1.2 KAI-Indoor Map 398
14.4.2 KAI-Pos 399
14.4.3 KAI-Navi 400
14.5 Evaluation and Examples 401
14.5.1 Performance Evaluation of Radio Map Construction Methods 401
14.5.2 Performance Evaluation of Proposed Probabilistic Tracking Algorithm 402
14.5.3 Examples of Using KAILOS 403
14.6 Conclusion 406
References 406
Chapter 15: Proximity-Based Federation of Smart Objects and Their Application Framework 408
15.1 Introduction 408
15.2 Smart Object and Its Three Levels of Formal Modeling 411
15.2.1 Smart Object and Its Port Matching (The First-Level Formal Modeling) 411
15.2.2 Semantics of Federation 413
15.2.3 Software Smart Object 414
15.2.4 Graph Rewriting System (The Second-Level Formal Modeling) 415
15.2.5 Catalytic Reaction Network (The Third-Level Modeling) 418
15.3 Middleware Framework for implementing Catalytic Reaction Networks 425
15.4 Concluding Remarks 433
References 435
Chapter 16: Edge Computing for Cooperative Real-Time Controls Using Geospatial Big Data 437
16.1 Introduction 438
16.2 Edge Computing 440
16.2.1 Real-Time Control of Geospatial Data 441
16.2.2 History and Research Challenge of Edge Computing 441
16.3 Safety Management in Urban Districts 442
16.3.1 Urban Sensing from Inaccurate Sensing Information 443
16.3.2 Crowd Sensing Using Heterogeneous Sensors 445
16.3.2.1 Laser Range Scanner-Based Crowd Sensing 445
16.3.2.2 Smartphone-Based Crowd Detection 447
16.3.3 Pedestrian Flow Estimation Using Heterogeneous Sensors 450
16.3.3.1 Urban Pedestrian Flows Mobility 450
16.3.3.2 Sensor Allocation for Human Mobility Detection 452
16.3.4 Emergency Planning Based on Crowd Sensing 454
16.3.5 Edge Computing Paradigm for Safety Management in Urban Districts 456
16.4 Prediction of Vehicle Speeds in Snowy Urban Roads 457
16.5 Conclusion 461
References 461
Chapter 17: Challenges of Application of ICT in Cattle Management: Remote Management System for Cattle Grazing in Mountainous ... 463
17.1 Background and Introduction 464
17.1.1 The Current Situation of Agriculture in Japan 464
17.1.2 Current Situation of Beef Production in Japan 464
17.1.3 Our Aim to the Future: A New Management System of Cattle by ICT 466
17.2 Systems of ICT Remote Management of Cattle 467
17.2.1 Construction of System to Lead Cattle to Feed Using a Smartphone 467
17.2.1.1 Actuator Included a Sound System, Automatic Feeder, and Motorized Stanchion 467
17.2.1.2 Wireless Node with Wi-Fi Connectivity 468
17.2.1.3 IP Camera (Panasonic Co., Ltd. SW-174W) 468
17.2.1.4 Wireless Access Point (Gonet MBW3100) 468
17.2.1.5 Web Server 470
17.2.1.6 Smartphone 470
17.2.2 Practical Effects of System to Call Cattle to Feed 470
17.3 System to Monitor the Location of Cattle in Pastures 471
17.3.1 Construction of System to Monitor the Location of Cattle in Pastures 471
17.3.1.1 Localization System for Cattle in Pastures 471
17.3.1.2 Path-Loss Model for RSS-Based Trilateration 473
17.3.1.3 Considering Topographical Feature Using Particle Filter 473
17.3.2 Practical Effects of System to Monitor Location of Cattle 475
17.3.2.1 Measurement Equipment 475
17.3.2.2 Channel Measurement 476
17.3.2.3 Localization Experiment 476
17.4 Conclusion 478
References 479
Chapter 18: Health Sensor Data Analysis for a Hospital and Developing Countries 481
18.1 Introduction 481
18.2 Sensor Data Analysis in Hospital 482
18.2.1 Sensor Data Collection for Nursing Activities 484
18.2.1.1 Protocol 484
Labeled Data Collection 484
Unlabeled Data Collection 485
Formatting the Dataset 485
18.2.1.2 Overview of the Dataset 485
18.2.2 Activity Recognition Method 487
18.2.2.1 Approach 487
18.2.2.2 Method 488
18.2.2.3 Evaluation 490
Preprocessing 490
Result 490
18.2.3 Activity Recognition for a Whole Day 491
18.2.4 Correlation with the Nurses´ Profile 491
18.2.5 Correlation with Patients´ Discharge Delays 494
18.2.6 Related Work 495
18.2.7 Conclusion 496
18.3 Sensor Data Analysis in Developing Countries 496
18.3.1 Methods 497
18.3.1.1 Overview 497
18.3.1.2 The Portable Health Clinic 498
18.3.1.3 Stratification Algorithm 498
18.3.1.4 Questionnaires on First and Second Visits 499
18.3.1.5 System Operation 500
18.3.1.6 Teleconsultation and Teleprescription 501
18.3.1.7 Booklet for Health Guidance 501
18.3.1.8 Ethical Considerations 501
18.3.2 Results 502
18.3.2.1 Overview 502
18.3.2.2 Risk Factors Associated with Overall Health Condition 503
18.3.2.3 Comparison with Results of Health Checkups in Japan 505
18.3.2.4 The Second Health Checkup 506
18.3.2.5 Predicting Blood Glucose Test Results 508
18.3.3 Discussion 509
18.3.3.1 Comparison with Prior Work 509
18.3.3.2 Results of the Health Checkup 509
18.3.3.3 Cost Evaluation 510
18.3.3.4 Limitations 511
18.3.4 Conclusions 511
18.4 Future Directions 511
References 511
Index 515