MARC details
| 000 -LEADER |
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| fixed length control field |
07722nam a22002657a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
201210b2024 a|||f bm|| 00| 0 eng d |
| 024 7# - Author Identifier |
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| Source of number or code |
ORCID |
| Standard number or code |
0000-0002-7567-6203 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
EG-CaNU |
| Transcribing agency |
EG-CaNU |
| 041 0# - Language Code |
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| Language code of text |
eng |
| Language code of abstract |
eng |
| -- |
ara |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
621 |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Alaa AbdAlrhman Mohammed |
| 245 1# - TITLE STATEMENT |
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| Title |
Fractional Calculus Based Image Simplification Algorithm For Medical applications |
| Statement of responsibility, etc. |
/Alaa AbdAlrhman Mohammed |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Date of publication, distribution, etc. |
2024 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
p. |
| Other physical details |
ill. |
| Dimensions |
21 cm. |
| 500 ## - GENERAL NOTE |
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| Materials specified |
Supervisor: Ahmed G. Radwan |
| 502 ## - Dissertation Note |
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| Dissertation type |
Thesis (MS.c)—Nile University, Egypt, 2024. |
| 504 ## - Bibliography |
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| Bibliography |
"Includes bibliographical references" |
| 505 0# - Contents |
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| Formatted contents note |
Contents: Contents Page<br/>V<br/>List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XI<br/>List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XVII<br/>List of Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XX<br/>Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IX<br/>Acknowledgment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VIII<br/>Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XXII<br/>Chapters:<br/>1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1<br/>1.1 Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1<br/>1.2 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . 3<br/>2. Thesis Motivation and Concepts of Review . . . . . . . . . . . . . . . . 5<br/>2.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5<br/>2.2 Fractional Calculus . . . . . . . . . . . . . . . . . . . . . . . . . . . 7<br/>2.2.1 Fractional Operator based on GL definition . . . . . . . . . 8<br/>2.2.2 Fractional Operator based on RL Definition . . . . . . . . . 10<br/>2.2.3 Fractional Operator Based on Deformable Definition . . . . 13<br/>2.3 Image Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . 15<br/>2.3.1 Fractional Kernels . . . . . . . . . . . . . . . . . . . . . . . 17<br/>2.4 Anisotropic Diffusion Filters . . . . . . . . . . . . . . . . . . . . . . 21<br/>2.4.1 Hardware Implementation of Anisotropic Filters on FPGA<br/>Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25<br/>2.4.2 Pixel buffer . . . . . . . . . . . . . . . . . . . . . . . . . . . 26<br/>2.4.3 Kernel convolution . . . . . . . . . . . . . . . . . . . . . . . 28<br/>2.4.4 G function . . . . . . . . . . . . . . . . . . . . . . . . . . . 29<br/><br/>3. New Implementation of GL-based Fractional Operator. 28<br/>3.1 Optimization Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 28<br/>3.1.1 Average fixed window optimization method . . . . . . . . . . 28<br/>3.1.2 Optimum α method . . . . . . . . . . . . . . . . . . . . . . . 29<br/>3.1.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . 30<br/>3.1.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32<br/>3.2 An Improved Approximation of GL Fractional Integral . . . . . . . . 33<br/>3.2.1 Mathematical Analysis . . . . . . . . . . . . . . . . . . . . . . 33<br/>3.2.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 34<br/>3.2.3 Fixed window method . . . . . . . . . . . . . . . . . . . . . . 35<br/>3.2.4 Quadratic approximation method . . . . . . . . . . . . . . . . 35<br/>3.2.5 PWL approximation method . . . . . . . . . . . . . . . . . . . 36<br/>3.2.6 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . 38<br/>3.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40<br/>4. Hardware Accelerator of Fractional-order Derivative/Integral Operator based on Phase Optimized Filters with Applications 41<br/>4.1 Filters Approximations . . . . . . . . . . . . . . . . . . . . . . . . . . 41<br/>4.1.1 Proposed Phase Optimized FIR . . . . . . . . . . . . . . . . . 41<br/>VI<br/>4.1.2 Proposed Phase Optimized IIR . . . . . . . . . . . . . . . . . 44<br/>4.2 FPGA Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 46<br/>4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48<br/><br/>4.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53<br/>4.4.1 Heaviside . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54<br/>4.4.2 Edge Detection . . . . . . . . . . . . . . . . . . . . . . . . . . 54<br/>4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55<br/>5. Application: Fractional based Image Processing 56<br/>5.1 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 56<br/>5.2 Fractional Image Enhancement . . . . . . . . . . . . . . . . . . . . . 57<br/>5.2.1 Double Fractional Kernel System . . . . . . . . . . . . . . . . 57<br/>5.2.2 Fractional Kernels Optimization . . . . . . . . . . . . . . . . . 58<br/>5.2.3 Single fractional kernels optimization . . . . . . . . . . . . . . 59<br/>5.2.4 Double fractional kernels system optimization . . . . . . . . . 61<br/>5.2.5 Constrained Double Optimization . . . . . . . . . . . . . . . . 62<br/>5.3 Fractional Anisotropic Diffusion Filter . . . . . . . . . . . . . . . . . 66<br/>5.3.1 Fractional Gradient Operator . . . . . . . . . . . . . . . . . . 67<br/>5.3.2 Anisotropic Filter Design . . . . . . . . . . . . . . . . . . . . . 68<br/>5.3.3 Optimizing Fractional Anisotropic Filter . . . . . . . . . . . . 72<br/>5.3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72<br/>5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78<br/>6. Conclusion and Future Work 79<br/>6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79<br/>VII<br/>Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81<br/>Appendices:<br/>A. Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 |
| 520 3# - Abstract |
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| Abstract |
Abstract: Fractional calculus is a powerful mathematical tool used to describe complex systems in physics, engineering, and finance. However, implementing fractional order<br/>systems in hardware can be resource-intensive, requiring large amounts of memory<br/>and processing power. Field programmable gate arrays (FPGAs) provide a platform<br/>for implementing fractional order systems that can conduct logic in parallel, resulting<br/>in faster and more efficient processing.<br/>This thesis proposes three new fractional order implementation methods that improve accuracy and reduce resource requirements. The results show that the proposed<br/>methods offer significant improvements in accuracy, power, latency, memory-units usage and DSPs compared to traditional methods. This thesis also proposes a soft IP<br/>library for different fractional operators implementation methods that can be included<br/>in FPGA different applications. In addition to exploring new implementation methods, this thesis investigates the use of fractional calculus in image enhancement and<br/>image simplification. The proposed methods are evaluated using metrics such as peak<br/>signal-to-noise ratio (PSNR) and the average gradient metric (AG) to determine their<br/>effectiveness.<br/>Overall, this thesis makes significant contributions to the field of fractional calculus, that can be of value in many fields. The proposed implementation methods and<br/><br/>X<br/>image processing techniques can be used in a wide range of applications, including<br/>control systems, signal processing, and image processing.<br/>Keywords<br/>Fractional Calculus, FPGA, Matlab, Image Processing, Portable Devices, Anisotropic<br/>Filters, Grünwald–Letnikov, GA, FPA |
| 546 ## - Language Note |
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| Language Note |
Text in English, abstracts in English and Arabic |
| 650 #4 - Subject |
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| Subject |
MSD |
| 655 #7 - Index Term-Genre/Form |
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| Source of term |
NULIB |
| focus term |
Dissertation, Academic |
| 690 ## - Subject |
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| School |
MSD |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
| 650 #4 - Subject |
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| -- |
317 |
| 655 #7 - Index Term-Genre/Form |
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| -- |
187 |
| 690 ## - Subject |
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| -- |
317 |