Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/237222
Title: Optimization of Fiber Optic Biosensor for Strain Measurement in Ortho Applications Using Artificial Neural Networks
Researcher: Preeti
Guide(s): Shrivastava Abhishek, Rai H. M.
Keywords: Engineering and Technology,Engineering,Engineering Electrical and Electronic, biosensor, bones, mean square error, microbend, layer, strain, Artificial Neural Network, Epochs, Osteoporosis
University: Nims University Rajasthan
Completed Date: 2013
Abstract: newline A biosensor is a device for the detection of an analyte that combines a biological newlinecomponent with a physicochemical detector component. Biosensors are classified newlinedepending upon different criteria like bioreceptors, transducers and different types of newlinephysical and chemical interaction. The importance of bone quality has long been newlinerecognized by orthopedic clinicians and radiographers to account for damage newlineaccumulation and predict susceptibility to fractures. With the recent development of newlinebiosensors it is now possible to monitor the strain on the bones. Osteoporosis is a disease newlineof bones that leads to an increased risk of fracture that is affecting a large population in newlineIndia. In osteoporosis, the bone mineral density (BMD) is reduced, bone newlinemicroarchitecture is deteriorating, and the amount and variety of proteins in bone is newlinealtered. The photometric sensors have the advantage of being chemically inert and do not newlinecause thrombosis. Moreover, these are light flexible and EMI/RFI immune. Microbend newlineSensors are based on microbend induced excess transmission loss of an optical fiber to newlinedetect/measure displacement, pressure, strain, temperature etc. If a portion of fiber is newlinedeformed, the fiber would exhibit excess light loss. Such perturbation of fiber axis results newlinein redistribution of guided power between modes of the fiber and also coupling of the newlinefiber from one mode/mode group to another. Artificial neural networks are viewed here newlineas parallel computational models, with varying degrees of complexity, comprised of newlinedensely interconnected adaptive processing units. All the existing techniques for newlinebiosensing have used electrochemical type of biosensors and so far not much work has newlinebeen done using photometric biosensors. No one has so far worked in the area optimizing newlinethe fiber-optic biosensor for strain measurement in ortho applications using ANN. Thus newlinethere is a need for carrying out investigations in the field of optimization of fiber-optic newlinebiosensor for strain measurement in ortho applications using ANN. The
Pagination: 1-23, 1-172
URI: http://hdl.handle.net/10603/237222
Appears in Departments:Department of Electronics and Communication Engineering

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01 title page.pdfAttached File31.1 kBAdobe PDFView/Open
02 preface.pdf459.55 kBAdobe PDFView/Open
03 acknowledgement.pdf701.64 kBAdobe PDFView/Open
04 certificate guide.pdf632.77 kBAdobe PDFView/Open
05 certificate co guide.pdf520.79 kBAdobe PDFView/Open
06 abstract.pdf296.15 kBAdobe PDFView/Open
07 contents.pdf353.81 kBAdobe PDFView/Open
08 list of figures.pdf246.17 kBAdobe PDFView/Open
09 list of tables.pdf244.56 kBAdobe PDFView/Open
10 abbreviations and acronyms.pdf341.97 kBAdobe PDFView/Open
11 chapter 1.pdf793.4 kBAdobe PDFView/Open
12 chapter 2.pdf661.35 kBAdobe PDFView/Open
13 chapter 3.pdf313.54 kBAdobe PDFView/Open
14 chapter 4.pdf493.94 kBAdobe PDFView/Open
15 chapter 5.pdf650.68 kBAdobe PDFView/Open
16 chapter 6.pdf948.13 kBAdobe PDFView/Open
17 chapter 7.pdf768.08 kBAdobe PDFView/Open
18 chapter 8.pdf435.85 kBAdobe PDFView/Open
19 references.pdf226.44 kBAdobe PDFView/Open
20 published paper 1.pdf496.68 kBAdobe PDFView/Open
21 published paper 2.pdf339.1 kBAdobe PDFView/Open
22 published paper 3.pdf215.79 kBAdobe PDFView/Open
23 published paper 4.pdf276.28 kBAdobe PDFView/Open
24 published paper 5.pdf2.11 MBAdobe PDFView/Open


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