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E-raamat: Recent Advances in Information, Communications and Signal Processing

Edited by (Nanyang Technological University, Singapore), Edited by (Nanyang Technological University, Singapore)
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Research in information, communications and signal processing has brought about new services, applications and functions in a large number of fields which include consumer electronics, biomedical devices and defense. These applications play an important role in advancing technologies to enhance human life in general.

Recent Advances in Information, Communications and Signal Processing provides students, researchers, and engineers information pertaining to recent advances in these fields. In terms of research in signal processing topics, the two chapters included in this book have an emphasis on advances in algorithmic development in the biomedical, and human-computer interfaces domain areas. More specifically, the use of deep learning for placental maturity staging is discussed as well as the use of vibration analysis for localizing impacts on surfaces for human-computer applications. In terms of communications signal processing, advances in new wireless communication such as NOMA (non-orthogonal multiple access) and millimeter-wave antenna design for 5G cellular mobile radio, as well as innovations in LDPC (low density parity check code) decoding and networking coding, are featured.

Topics include: signal processing, deep neural networks, source localization, mechanical vibration analysis, non-orthogonal multiple access, millimeter-wave communication, low density parity check code, and network coding.

Preface xi
Acknowledgements xiii
List of Contributors xv
List of Figures xix
List of Tables xxv
List of Notations xxvii
List of Abbreviations xxix
1 Non-orthogonal Multiple Access: Recent Developments and Future Trends 1(34)
A. Anwar
B.C. Seet
X.J. Li
1.1 Introduction
2(2)
1.2 Classification of NOMA Schemes
4(2)
1.2.1 NOMA via Code Domain Multiplexing
4(1)
1.2.1.1 Low density spreading CDMA
4(1)
1.2.1.2 Low density spreading OFDM
4(1)
1.2.1.3 Sparse code multiple access
4(1)
1.2.1.4 Multi-user shared access
5(1)
1.2.1.5 Interleave-division multiple access
5(1)
1.2.2 NOMA via Power Domain Multiplexing
5(1)
1.3 NOMA
6(2)
1.3.1 Basic Principle
6(1)
1.3.2 NOMA Transmitter and Receiver Architectures
7(1)
1.3.3 Motivations to Adopt NOMA as MA Scheme for 5G
8(1)
1.4 Review of Some Recent Developments for NOMA
8(12)
1.4.1 Throughput and Outage Analysis
8(2)
1.4.2 Power Allocation and User Grouping
10(2)
1.4.3 Fairness in NOMA
12(1)
1.4.4 MIMO NOMA
12(3)
1.4.5 Massive MIMO NOMA
15(1)
1.4.6 Cooperation in NOMA
16(1)
1.4.7 NOMA for Relaying Networks
17(1)
1.4.8 NOMA and Simultaneous Wireless Information and Power Transfer
18(2)
1.5 Performance-limiting Factors for Existing NOMA
20(6)
1.5.1 Proposed PIC-based Receiver Structure
21(5)
1.5.2 Performance Comparison
23(3)
1.6 Future Research Direction
26(3)
1.6.1 Modulation and Coding Scheme
26(1)
1.6.2 Hybrid MA
27(1)
1.6.3 Imperfect CSI
27(1)
1.6.4 Cross Layer Optimization
27(1)
1.6.5 HARQ Design for NOMA
27(1)
1.6.6 Massive MIMO NOMA
28(1)
1.6.7 Full Duplex NOMA
28(1)
1.7 Conclusion
29(1)
References
29(6)
2 Beam Steering MIMO Antenna for Mobile Phone of 5G Cellular Communications Operating at MM-Wave Frequencies: Design 35(30)
T. Thomas
Peter Gardner
Alexandros Feresidis
K. Veeraswamy
2.1 Introduction
36(1)
2.2 The 5th-Generation Cellular Mobile Communications
37(6)
2.2.1 Design Issues at Base Station for 5G Cellular Mobile Communication System
37(1)
2.2.2 Design Issues at User Equipment for 5G Cellular Mobile Communication System
38(1)
2.2.3 Applications and Techniques Supported by 5G Technology
39(4)
2.3 Proposed Antenna: Design and Analysis
43(16)
2.3.1 Proposed MIMO Antenna Model #1
43(7)
2.3.2 Proposed MIMO Antenna Model #2
50(5)
2.3.3 Proposed MIMO Antenna Model #3
55(4)
2.4 Conclusion
59(3)
References
62(3)
3 Random Linear Network Coding with Source Precoding for Multi-session Networks 65(40)
Xiaoli Xu
Yong Zeng
Yong L. Guan
3.1 Introduction
65(2)
3.2 Network Model with RLNC
67(2)
3.3 Precoder Design and Achievable Rate Region for Double-Unicast Networks
69(18)
3.3.1 An Optimal Achievable Rate Region with RLNC
70(1)
3.3.2 A Linear Capacity-achieving Scheme
71(12)
3.3.3 An Achievable Region in Terms of Min-cuts
83(2)
3.3.4 Joint Routing and RLNC
85(2)
3.4 Asymptotic Capacity-achieving for Multi-source Erasure Networks
87(11)
3.4.1 The Capacity Region
88(2)
3.4.2 Asymptotical Capacity-achieving with RLNC
90(4)
3.4.2.1 Time-extended networks
90(1)
3.4.2.2 Linear finite-field MAC
91(3)
3.4.3 Multi-source Erasure Network with Broadcast Channels
94(3)
3.4.4 General Model for Wireless Erasure Networks
97(1)
3.5 Notes and Further Reading
98(1)
Appendix
98(2)
References
100(5)
4 Decoding Scheduling for Low-Density Parity-Check Codes 105(32)
Huang-Chang Lee
Yen-Ming Chen
Yeong-Luh Ueng
4.1 Introduction
105(2)
4.2 Belief Propagation Decoding for LDPC Codes
107(1)
4.3 Fixed Schedules
107(15)
4.3.1 The Flooding Schedule
108(1)
4.3.2 Standard Sequential Schedules
108(1)
4.3.3 Decoding Schedules for Faster Convergence
108(1)
4.3.4 Protograph-based LDPC Codes
109(1)
4.3.5 Protograph-based Edge-wise Schedule
109(2)
4.3.6 The M2I2-based Algorithm
111(3)
4.3.7 High-order Prediction for the M2I2-based Algorithm
114(4)
4.3.8 Performance Evaluation
118(4)
4.4 A Reduction of the Complexity for Scheduling Arrangement
122(6)
4.4.1 Performance Evaluation for the LM2I2-based Algorithm
124(4)
4.5 Lower Error Floor via Schedule Diversity
128(6)
4.5.1 Decoding Scheme Combined with Schedule Diversity
129(3)
4.5.2 Comparison with Other Error Floor Lowering Techniques
132(2)
4.6 Remarks
134(1)
References
134(3)
5 Location Template Matching on Rigid Surfaces for Human-Computer Touch Interface Applications 137(28)
Nguyen Q. Hanh
V.G. Reju
Andy W.H. Khong
5.1 Introduction
137(3)
5.2 LTM for Impact Localization on Solids
140(2)
5.3 Time-reversal Theory-based LTM
142(1)
5.4 Classical Plate Theory-based LTM
143(7)
5.4.1 All-pole Filter Model-based LTM (AP-LTM)
146(2)
5.4.2 Zak Transform for Time-frequency-based LTM (Z-LTM)
148(2)
5.5 Noise-robust LTM Using Band-limited Components
150(10)
5.5.1 Band-limited Components as Location-dependent Features
150(1)
5.5.2 The BLC-LTM Algorithm
151(6)
5.5.3 Experiment Results
157(3)
5.6 Concluding Remarks
160(1)
References
161(4)
6 Automatic Placental Maturity Grading via Deep Convolutional Networks 165(20)
Baiying Lei
Feng Jiang
Yuan Yao
Wanjun Li
Siping Chen
Dong Ni
Tianfu Wang
6.1 Introduction
166(1)
6.2 Related Work
167(3)
6.3 Methodology
170(5)
6.3.1 Convolutional Neural Network
170(3)
6.3.1.1 Convolution
170(1)
6.3.1.2 Down-sampling
171(1)
6.3.1.3 Activation functions
172(1)
6.3.2 Automatic Grading Algorithm Based on CNN
173(2)
6.3.2.1 Framework
173(1)
6.3.2.2 Data augmentation
173(2)
6.3.2.3 Transfer learning
175(1)
6.3.2.4 Feature visualization
175(1)
6.4 Experiments
175(5)
6.4.1 Experimental Settings
175(2)
6.4.2 Experimental Results
177(1)
6.4.3 Result Analysis
178(2)
6.5 Conclusion
180(1)
References
180(5)
Index 185(2)
About the Editors 187
Andy W. H. Khong, Yong Liang Guan
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
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