Surface Texture-Audio Correlation for MultiModal Recognition Enhancement Shyam Maheshwari, Hemant Makwana Procedia Computer Science, 2025 In this research, we study the correlation between surface textures and audio signals produced during the movement using advanced metrics such as Structural Similarity Index Measure (SSIM), Peak Signal to Noise Ratio (PSNR) and Histogram of Oriented Gradients (HOG). Results show that rough textures achieve higher SSIM values up to 0.9914, and result in greater preserved structure than the smoother textures. Rough textures reached a PSNR at 29.85 dB, indicating less noise and higher fidelity. In addition, we found the HOG similarity to be as high as 0.9517 for rough textures exhibiting consistent gradient patterns. These results show that rough textures have an advantage when stability of structure and signal quality is desired, contradicting the commonplace preference of smoother textures in multi-modal recognition systems. The results of the study highlight the robustness of integrating visual and auditory modalities for improved recognition accuracy, and have applications in texture generation, biometric authentication, multimedia processing and virtual reality. Further work will be directed towards real time system integration as well as analysis of the broader texture range.
Texture-Auditory Analysis Through Machine Learning Techniques Shyam Maheshwari, Hemant Makwana 2024 15th International Conference on Computing Communication and Networking Technologies Icccnt 2024, 2024 In order to investigate the complex link between surface texture and auditory signals, this research integrates machine learning approaches. Texture perception, influencing quality, safety, and comfort in various environments, has traditionally focused on visual and tactile cues, but the role of auditory stimuli is gaining traction. Leveraging sound analysis, the study aims to understand and enhance sensory experiences. A comprehensive analysis of how well different machine learning (ML) models perform in two distinct tasks: image classification and sound feature extraction. The accuracy and loss metrics are examined for each model to provide insights into their effectiveness in capturing patterns and features from the respective data types. Models like Mobilenet for image classification and Cross Gradient Booster for sound feature extraction demonstrate strong generalization capabilities, indicating their potential suitability for real-world applications and diverse datasets.
Efficient Multi-Resolution Haptic Rendering for Real-Time Applications S Maheshwari, H Makwana International Conference on Recent Advancements and Modernisations in … , 2025 2025
Surface Texture-Audio Correlation for MultiModal Recognition Enhancement S Maheshwari, H Makwana Procedia Computer Science 260, 360-372 , 2025 2025
Exploring Audio-Visual Correlation for Real-Time Texture Analysis S Maheshwari, DH Makwana, DDK Lal Library Progress International 44 (03), 11997-12011 , 2024 2024
Texture-Auditory Analysis Through Machine Learning Techniques S Maheshwari, H Makwana 2024 15th International Conference on Computing Communication and Networking … , 2024 2024
Real-Time Haptic Rendering: Perception, Optimization, and Multi-Modal Integration S Maheshwari, H Makwana, D Lal Library Progress International 44 (3), 18893-18912 , 2024 2024 Citations: 1
Review of software defined networking: applications, challenges and advantages U Singh, V Vankhede, S Maheshwari, D Kumar, N Solanki International Conference on Inventive Computation Technologies, 815-826 , 2019 2019 Citations: 6
MOST CITED SCHOLAR PUBLICATIONS
Review of software defined networking: applications, challenges and advantages U Singh, V Vankhede, S Maheshwari, D Kumar, N Solanki International Conference on Inventive Computation Technologies, 815-826 , 2019 2019 Citations: 6
Real-Time Haptic Rendering: Perception, Optimization, and Multi-Modal Integration S Maheshwari, H Makwana, D Lal Library Progress International 44 (3), 18893-18912 , 2024 2024 Citations: 1
Efficient Multi-Resolution Haptic Rendering for Real-Time Applications S Maheshwari, H Makwana International Conference on Recent Advancements and Modernisations in … , 2025 2025
Surface Texture-Audio Correlation for MultiModal Recognition Enhancement S Maheshwari, H Makwana Procedia Computer Science 260, 360-372 , 2025 2025
Exploring Audio-Visual Correlation for Real-Time Texture Analysis S Maheshwari, DH Makwana, DDK Lal Library Progress International 44 (03), 11997-12011 , 2024 2024
Texture-Auditory Analysis Through Machine Learning Techniques S Maheshwari, H Makwana 2024 15th International Conference on Computing Communication and Networking … , 2024 2024
