[ad_1]

Introduction

In recent years, deep learning has significantly advanced the field of image classification. Convolutional neural networks (CNNs) have shown remarkable success in various image recognition tasks, such as object detection, face recognition, and scene understanding. However, the computational complexity of training deep CNNs on traditional digital computers remains a bottleneck for real-time image processing applications. Optical neural networks (ONNs) have emerged as a promising alternative to overcome these limitations by exploiting the inherent parallelism of light for accelerating neural network computations.

This thesis focuses on the development of ONNs for image classification tasks. The use of optical devices for neural network processing offers the potential for high-speed and energy-efficient computation, making them suitable for real-time applications such as autonomous vehicles, surveillance systems, and medical imaging. By integrating optics with deep learning techniques, we aim to achieve superior performance in image classification tasks compared to conventional digital approaches.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of deep learning and image classification
2.2 Evolution of optical computing and neural networks
2.3 Existing techniques for optical neural networks
2.4 Comparison between ONNs and traditional digital CNNs
2.5 Optoelectronic devices for ONNs
2.6 Applications of ONNs in image processing
2.7 Challenges and limitations of ONNs
2.8 Recent advancements in ONNs for image classification
2.9 Future research directions in ONNs
2.10 Summary of literature review

Chapter 3: System Design and Methodology
3.1 System architecture of ONNs for image classification
3.2 Optical components and devices used in ONNs
3.3 Data preprocessing and feature extraction
3.4 Training algorithms for ONNs
3.5 Performance evaluation metrics for image classification
3.6 Experimental setup and methodology
3.7 Validation and testing procedures
3.8 Optimization techniques for ONN performance

Chapter 4: System Implementation
4.1 Hardware implementation of ONNs
4.2 Software development for ONNs
4.3 Integration of optical and electronic components
4.4 Calibration and alignment of ONN system
4.5 Training and fine-tuning of ONNs
4.6 Performance evaluation and benchmarking
4.7 Testing on real-world image datasets
4.8 System optimization and scalability

Chapter 5: Conclusion and Summary
5.1 Recap of research objectives and contributions
5.2 Summary of key findings and results
5.3 Discussion of implications and significance of study
5.4 Limitations and future research directions
5.5 Conclusion and final remarks

Thesis Overview on Optical Neural Networks for Image Classification

Optical neural networks (ONNs) have attracted growing interest in recent years for their potential to revolutionize image classification tasks through the use of optical devices for accelerated computations. This thesis aims to explore the feasibility and performance of ONNs in image classification tasks, leveraging the parallel processing capabilities of light for high-speed and energy-efficient computation.

Chapter 1 provides an introduction to the research topic, outlining the background, problem statement, objectives, scope, significance, and structure of the thesis. Chapter 2 presents a comprehensive literature review on deep learning, image classification, optical computing, and ONNs, highlighting existing techniques, challenges, and recent advancements in the field.

In Chapter 3, the system design and methodology of ONNs for image classification are discussed, including the architecture, optical components, data preprocessing, training algorithms, performance evaluation metrics, and experimental setup. Chapter 4 delves into the system implementation of ONNs, covering hardware and software development, integration of optical and electronic components, training, testing, performance evaluation, and optimization techniques.

Finally, Chapter 5 concludes the thesis by summarizing the research findings, discussing implications and future research directions, and providing final remarks on the study. Overall, this thesis aims to contribute to the advancement of ONNs for image classification and inspire further research in this exciting and promising field.

[ad_2]

---

Purchase Detail

Download the complete project materials to this project with Abstract, Chapters 1 – 5, References and Appendix (Questionaire, Charts, etc), Click Here to place an order via whatsapp. Got question or enquiry; Click here to chat us up via Whatsapp.
You can also call 08111770269 or +2348059541956 to place an order or use the whatsapp button below to chat us up.
Bank details are stated below.

Bank: UBA
Account No: 1021412898
Account Name: Starnet Innovations Limited

The Blazingprojects Mobile App

Download and install the Blazingprojects Mobile App from Google Play to enjoy over 50,000 project topics and materials from 73 departments, completely offline (no internet needed) with monthly update to topics, click here to install.