Informatics Publishing Limited

B.K. Harsha ¹, B. Srinivasa Rao ², S. Tamijeselvan ³, M. Ganesha ⁴, Nihar Ranjan Behera ⁵

Affiliations
1 School of Computing and Information Technology, Reva University, IN
2 Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, IN
3 Department of Radiography, Mother Theresa PG and Research Institute of Health Sciences, IN
4 Department of Computer Science and Engineering, A J Institute of Engineering and Technology, IN
5 Department of Business and Management, Swiss School of Business and Management Geneva, CH

Abstract

Image representation and rendering have become critical in numerous applications such as virtual reality, medical imaging, and computer graphics. Traditional rendering techniques often face challenges in efficiently handling complex scenes and achieving photorealistic results while maintaining low computational costs. The problem lies in the high-dimensional nature of image data, leading to slow processing times and reduced scalability. This research presents an AI-enhanced technique called Linear RepRender, which leverages deep learning to transform high-dimensional image representations into simplified linear forms for faster rendering. The proposed method employs a combination of convolutional neural networks (CNNs) and linear regression models to reduce image complexity. Specifically, the CNN extracts low-level and high-level features from the image, while the linear regression step approximates the scene’s core visual elements. This hybrid approach significantly improves rendering speed without sacrificing image quality. Furthermore, the method incorporates a loss function optimized for minimizing discrepancies between the rendered and ground truth images. Experimental results demonstrate that Linear RepRender outperforms traditional rendering algorithms, such as ray tracing and rasterization, in terms of computational efficiency and visual accuracy. On a dataset of complex 3D scenes, the proposed method achieved a 35% reduction in rendering time and a 22% improvement in peak signal-to-noise ratio (PSNR) compared to stateof-the-art methods. Additionally, Linear RepRender was able to handle up to 1.5 million polygons per scene with minimal visual artifacts, making it suitable for real-time applications.

Keywords

AI-Enhanced Rendering, Image Representation, Linear Regression, Convolutional Neural Networks, Real-Time Rendering

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K. Prabhu Chandran ¹, P. T. Kalaivaani ², Venkatesh Kavididevi ³, M. Ganesha ⁴

Affiliations
1 Department of Electronics and Communication Engineering, Prathyusha Engineering College, IN
2 Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, IN
3 Department of Information Technology, Vardhaman College of Engineering, IN
4 Department of Computer Science and Engineering, A J Institute of Engineering and Technology, IN

Abstract

The escalating demand for efficient wireless communication systems has prompted researchers to explore innovative solutions to optimize traffic flow and routing. The existing wireless communication infrastructure faces challenges such as congestion, latency, and suboptimal routing, impeding the seamless transmission of data. Traditional optimization approaches fall short in adapting to dynamic network conditions, necessitating the exploration of advanced methodologies. Despite recent advancements in optimization techniques, a notable research gap exists in the integration of bio-inspired algorithms like the Emperor Penguin Optimizer with machine learning models such as Conditional Generative Adversarial Nets for the purpose of wireless traffic and routing enhancement. Bridging this gap is crucial for achieving adaptive and robust wireless communication systems. This study addresses the challenges posed by the dynamic nature of wireless networks, aiming to enhance their performance through the synergistic application of the Emperor Penguin Optimizer (EPO) and Conditional Generative Adversarial Nets (CGANs). This research leverages the inherent strengths of the EPO, inspired by the collective foraging behavior of emperor penguins, to dynamically optimize the wireless network parameters. Concurrently, CGAN are employed to intelligently learn and adapt routing strategies based on real-time network conditions. The symbiotic integration of these two methodologies creates a powerful framework for adaptive wireless traffic and routing. The results indicate a significant improvement in traffic flow, reduced latency, and optimized routing paths in comparison to conventional methods. The EPO-CGAN framework demonstrates adaptability to varying network conditions, showcasing its potential to revolutionize wireless communication systems.

Keywords

Wireless Communication, Emperor Penguin Optimizer, Conditional Generative Adversarial Nets, Traffic Optimization, Routing Enhancement.

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