The field of digital watermarking has recently seen vast interests covering theoretical studies, novel techniques, attacks and analysis. This is due to the fact that over the last 15 years, the watermarking community has focused on developing and introducing new techniques for watermark embedding and detection. Analysis of these techniques leads to methods for attack and development of countermeasures which then used to discover faults and limitations in applications, encouraging the development of better ones. In this paper, comprehensive overview of digital watermarking are discussed. This includes the general model, types, applications and future trends of current implementations. The proposed technique is described and analyzed. The paper concludes with future plans of the chosen method in digital watermarking.Index Terms -Balance multiwavelets (BMW), digital watermarking, human visual system (HVS), wavelet transform.

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There are quite a number of researches in proposing digital image watermarking using Discrete Wavelet Transform (DWT). However, it is clearly observed that each of them is individually distinctive in terms of its scopes and applications. In this paper, a proposed system was thoroughly explained. It includes general work flow, proposed algorithms for both original method and improved method, which were named as subband matching and selective subband matching, respectively and various attacks performed for evaluation. Modification was made to the algorithms where only selected matching subbands were used in embedding and extracting the watermark in this improved method. These methods were compared and tested against attacks using standard benchmark, Stirmark. Experimental results of the proposed methods' performance were analyzed using Peak Signal to Noise Ratio (PSNR) calculations and Structural SIMilarity (SSIM) index for watermark imperceptibility and robustness, respectively. The improvement could be seen in quality of the watermarked image (imperceptibility) and of extracted watermark (robustness).

The integration of neural networks into computational fluid dynamics (CFD) has led to significant advancements in fluid dynamics simulations, enhancing accuracy and effectiveness. This convergence presents a promising avenue for studying fluid systems and has the potential to revolutionize our understanding of their complex behaviours. To foster collaboration and drive research in this field, a comprehensive bibliometric review was conducted, examining the intersection of fluid dynamics education and neural networks. From a pool of 999 articles gathered from the Google Scholar database, 779 articles published between 2018 and 2023 were selected using the Publishing or Perish (PoP) software. These articles were analysed and categorized using VOSviewer, revealing three distinct clusters representing the primary research topics in computational fluid dynamics and neural networks. The findings of this study provide valuable insights into the current state of research in this emerging field, offering a comprehensive overview of key themes and their relationships. These insights can guide researchers and practitioners in identifying gaps, exploring collaborations, and advancing interdisciplinary approaches. Moving forward, further exploration of the identified clusters and associated keywords can deepen understanding of specific research topics, while investigations into methodologies and techniques employed in the articles can contribute to the development of advanced computational tools and frameworks for fluid dynamics simulations. Collaborative efforts and interdisciplinary approaches hold great potential for advancing our knowledge of fluid systems and driving progress in this exciting field.