عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
شماره کتابشناسی ملی
شماره
TL52623
زبان اثر
زبان متن نوشتاري يا گفتاري و مانند آن
انگلیسی
عنوان و نام پديدآور
عنوان اصلي
Analysis of Optical Fiber Speckle Patterns for Detection of IVUS Catheter Tip in 3D Space: An Intelligent Sensor Research
نام عام مواد
[Thesis]
نام نخستين پديدآور
Razmyar, Soroush
نام ساير پديدآوران
Mostafavi, M. Taghi
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
The University of North Carolina at Charlotte
تاریخ نشرو بخش و غیره
2020
يادداشت کلی
متن يادداشت
106 p.
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
Ph.D.
کسي که مدرک را اعطا کرده
The University of North Carolina at Charlotte
امتياز متن
2020
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
This research study presents the architectural design and computational framework for an intelligent tracking sensor constructed from a multimode fiber optic. As laser light travels through an inhomogeneous medium, such as multimode fiber, the random interactions between light rays generate a circular output pattern commonly referred to as speckle patterns. Speckle patterns are highly responsive to the variation in the physical status of a multimode fiber. As a multimode fiber deforms, analysis of speckle pattern variations provides information about the external perturbations causing the deformation. This study presents a novel algorithm for calculating 3D transformations from a series of speckle patterns, which is modeled in three tiers. In the first tier, we have performed a series of experiments to demonstrate, in a deforming multimode fiber, the structural variation of speckle patterns contains deterministic information. That also provides a systematic approach for measuring the deformation parameters of a multimode fiber using a convolutional neural network. Second, we have studied the oscillating behavior of multimode fiber as a function of its length to find the relationship between the sensor's heading direction and the deformation of its sensing fiber tip. By utilizing a Long Short-Term Memory model, we have demonstrated that long-term dependencies between the deformation parameters provide a stable and reliable indication of the intelligent sensor's direction. In the end, we have utilized these findings to develop a novel computational framework for the intelligent sensor. This computational framework includes a pipeline of deep learning models to extract features from a sequence of speckle patterns, and a motion model to estimate the trajectory of the sensor from the extracted features.
اصطلاحهای موضوعی کنترل نشده
اصطلاح موضوعی
Biomedical engineering
اصطلاح موضوعی
Computer science
اصطلاح موضوعی
Optics
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )
مستند نام اشخاص تاييد نشده
Razmyar, Soroush
نام شخص - ( مسئولیت معنوی درجه دوم )
مستند نام اشخاص تاييد نشده
Mostafavi, M. Taghi
شناسه افزوده (تنالگان)
مستند نام تنالگان تاييد نشده
The University of North Carolina at Charlotte
دسترسی و محل الکترونیکی
نام الکترونيکي
مطالعه متن کتاب وضعیت انتشار
فرمت انتشار
p
اطلاعات رکورد کتابشناسی
نوع ماده
[Thesis]
کد کاربرگه
276903
اطلاعات دسترسی رکورد
سطح دسترسي
a
تكميل شده
Y
