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Recent Trends in Peripheral Security Systems

為了解決Fiber-optic network的問題，作者Rawat, Abhishek,Deb, Dipankar,Upadhyay, Jatin 這樣論述：

Abhishek Rawat received his Bachelor of Engineering in Electronics and Communication Engineering (2001), from Rajiv Gandhi Technological University, Bhopal. He received his Master of Technology (2006) and Ph.D. (2012) from Maulana Azad National Institute of Technology Bhopal, India. He is currently

an Assistant Professor at the Institute of Infrastructure Technology Research and Management (IITRAM), Ahmedabad, Gujarat, India. He has 16 years of research, academic and professional experience in different premier institutions. Dr. Rawat is also senior member IEEE and has published more than 50 a

rticles in international journals, Book chapters, national and international conference proceedings. He received the Young Scientist Award from MPCOST Bhopal in 2007, involved in the field trails of the IRNSS receiver and published four Indian patents. His research interests include Navigation syste

ms, Satellite Communication and Peripheral security etc.Dipankar Deb completed his Ph.D. from the University of Virginia, Charlottesville, with Prof. Gang Tao, IEEE Fellow and Professor in the Department of ECE in 2007. He did his B.E. from NIT Karnataka, Surathkal (2000), and M.S. from the Universi

ty of Florida, Gainesville (2004). In 2017, he was elected to be IEEE Senior Member. He has served as Lead Engineer at GE Global Research Bengaluru (2012-2015) and as Assistant Professor in EE, IIT Guwahati 2010-2012. Presently, he is a Full Professor in Electrical Engineering at the Institute of In

frastructure Technology Research and Management (IITRAM), Ahmedabad, Gujarat, India. He is Associate Editor of IEEE Access journal and Book Series Editor for Control System Series/CRC Press/Taylor and Francis Group. He is also a book series editor of ＂Studies in Infrastructure and Control＂ with Spri

nger. He has published 36 SCI-indexed journals, 40 international conference papers, and 10 technical books with Springer and Elsevier. He holds 6 US Patents. His research interests include active flow control, adaptive control, cognitive robotics, and renewable energy systems (including wind, solar,

and fuel cells).Jatin Upadhyay received his M.E. in VLSI and Embedded Systems from Gujarat Technological University in 2015. He is currently a Ph.D. candidate at the Institute of Infrastructure Technology Research and Management, Ahmedabad, India. His current research interests include image proces

sing, neural network, and cognitive robotics. He has worked on high-speed data transmission over the FPGA development board using a fiber optic data link.

Fiber-optic network進入發燒排行的影片

Theoretical Study of DWDM Lightwave Transmission Accompany FBG Sensing

為了解決Fiber-optic network的問題，作者Lina Marlina 這樣論述：

The main subject of this thesis is to simulate the wavelength division multiplexing (WDM) using dispersion compensate fiber (DCF) placed in different positions in transmission links and the proposed model of combined with fiber Bragg grating (FBG). We developed the simulation by using the Optisyste

m15. The eight WDM data channels were successfully used to transmit the data at a 40 Gbps data rate with 0.8 nm spacing using different transmission modes. The data is transmitted using a variety of unidirectional and bidirectional transmission modes. The transmission system sent the signal at 200 k

m of single-mode fiber (SMF) with a dispersion value of 18 ps/nm/km and compensated with a 40 km length of DCF with dispersion -90 ps/nm/km. However, the consideration of long-haul communication has matters related to fiber loss and dispersion that can influence the system performance. Hence, the id

ea of using DCF is to become a solution and proposed three DCF different positions in the transmission link to observe the impact of dispersion control on the result. In the first proposed scheme, DCF is placed before the SMF in the transmission link to achieve the higher BER 2.16 × 10-8 and Q-facto

r 5.78. However, the second proposed scheme is post compensation, where DCF is put after SMF in transmission link can obtain higher BER of 3.42 × 10-10 with Q-factor of 6.49. Finally, the mix compensates fiber is used in the transmission to achieve BER 3.35 × 10-9 and a Q-factor value of 6.18. Resul

ts have demonstrated that the better performance among them is post compensate fiber. The WDM system is also analyzed using a different modulation type, including non-return zero (NRZ) and return zero (RZ) modulation format. It simulates several conditions with varying lengths of SMF. According to t

he combination model of 4 FBGs, an optical sensor network with eight data channel transmissions is successfully transmitting the data using NRZ modulation format at 10 Gbps data rate with channel spacing between two adjacent deployed FBG temperature sensors should be at least around 189.759 GHz.Usin

g the 40 km long transmission line, the BER can be achieved at 1.42 ×10-14. The expected temperature ranges of the FBG optical sensor from -20 up to 80°C with 20°C steps increment with the FBG reflected signal changes by 0.17 THz. The maximum length of fiber span for this system is 14 km.

Cciso Certified Chief Information Security Officer All-In-One Exam Guide

為了解決Fiber-optic network的問題，作者Bennett, Steve,Genung, Jordan 這樣論述：

Steve Bennett, CCISO, CISSP, CISA, has over 40 years of experience in information technology specializing in information security. He has served as an information security engineer and consultant for organizations in nearly every major business sector as well as federal, state, and local government

agencies. He has created courseware and taught classes covering diverse topics including information security, social engineering, auditing, systems administration, network monitoring and fiber optic communications.Jordan Genung, CCISO, CCISP, CISM, CISA serves as an Information Security Officer a

nd security advisor for public and private sector organizations. His experience includes security consulting for Fortune 100 companies and government agencies, building information security programs, and developing information security curriculum. Jordan holds a degree in Computer Science and Inform

ation Security from the University of Texas at San Antonio, an NSA and DHS National Center of Academic Excellence in Cyber Operations, Cyber Defense, and Research.

智慧瓊斯矩陣光學同調斷層掃描術之開發與應用

為了解決Fiber-optic network的問題，作者蕭天語 這樣論述：

中文摘要 ...................................................................... iAbstract ...................................................................... iiAcknowledgement ............................................................... iiiTable of Contents ....................................

......................... ivList of Figures ............................................................... viiList of Tables ................................................................ xList of Abbreviations ......................................................... xi1. Introduction ......

......................................................... 1　1.1 Brief Review of Optical Coherence Tomography (OCT) ...................... 1　1.2 Jones Matrix Optical Coherence Tomography (JM-OCT) ...................... 5　　1.2.1 Hardware Designs of Jones Matrix Measurement ......................

.. 5　　1.2.2 Algorithms for Jones Matrix Analysis ................................ 7　　1.2.3 Fields of Application ............................................... 10　1.3 Deep Learning ........................................................... 11　1.4 Motivation ..................................

............................ 13　1.5 Objective ............................................................... 13　1.6 Organization of this Dissertation ....................................... 142. Principle .................................................................. 16　2.1 Image Formation

of OCT .................................................. 16　　2.1.1 Imaging Principle and Selection of Interferometers .................. 16　　2.1.2 Chromatic Dispersion Compensation ................................... 19　　2.1.3 System and Image Attributes .........................................

20　2.2 Representation and Calculation of Polarization States ................... 21　　2.2.1 Overview of Methodologies ........................................... 21　　2.2.2 Jones Calculus ...................................................... 24　　2.2.3 Mueller Calculus ...........................

......................... 25　2.3 Deep Learning ........................................................... 27　　2.3.1 Artificial Neural Network ........................................... 27　　2.3.2 Evaluation Metrics .................................................. 29　　2.3.3 Model Visualization

Tools ........................................... 313. System Design .............................................................. 35　3.1 Minimalistic Fiber-Optic based JM-OCT ................................... 35　　3.1.1 Hardware Setup ...................................................... 35

　　3.1.2 Software Programming for the Instrument ............................. 43　　3.1.3 Optical Formalism ................................................... 46　3.2 Data Processing based on the TensorFlow Framework ....................... 51　　3.2.1 Dispersion Compensation ........................

..................... 51　　3.2.2 Diagonalization Method .............................................. 54　　3.2.3 Concurrent Decomposition Method ..................................... 57　3.3 Training Scheme for Deep Learning ....................................... 62　　3.3.1 OCT Data Preparation ..

.............................................. 62　　3.3.2 Imaging Preprocessing ............................................... 63　　3.3.3 Model Training ...................................................... 664. Clinical Applications ...................................................... 68　4.1

Subgingival Dental Calculus Identification .............................. 68　　4.1.1 Model Design and Disease Activation Maps ............................ 70　　4.1.2 Performance of Computer-Aided-Detection ............................. 72　　4.1.3 Preliminary Result of Dental Calculus using Polarizat

ion Imaging .... 77　4.2 Brain Tumor Classification and Peritumoral Nerve Fiber Visualization .... 80　　4.2.1 Classification based on Attention ResNet Model ...................... 81　　4.2.2 Brain Tumor Grading based on Transfer Learning ...................... 87　　4.2.3 Nerve Fiber Visualization at

Tumoral Boundary ....................... 895. Discussions ................................................................ 95　5.1 Instrument Performance .................................................. 95　5.2 Limitations of the Algorithm ............................................ 96　5.3 Int

egration of TensorFlow-based Frameworks .............................. 97　5.4 Clinical Applications ................................................... 996. Conclusions ................................................................ 101References ..................................................

.................. 104Appendix A Improvements of the Concurrent Decomposition Method ................ 121Appendix B Recruitment of the Brain Tumor Patients and the Data Preparation ... 126