Improved ECG watermarking technique using curvelet transform

Hiding data in electrocardiogram signals are a big challenge due to the embedded information that can hamper the accuracy of disease detection. On the other hand, hiding data into ECG signals provides more security for, and authenticity of, the patient\u27s data. Some recent studies used non-blind watermarking techniques to embed patient information and data of a patient into ECG signals. However, these techniques are not robust against attacks with noise and show a low performance in terms of parameters such as peak signal to noise ratio (PSNR), normalized correlation (NC), mean square error (MSE), percentage residual difference (PRD), bit error rate (BER), structure similarity index measure (SSIM). In this study, an improved blind ECG-watermarking technique is proposed to embed the information of the patient\u27s data into the ECG signals using curvelet transform. The Euclidean distance between every two curvelet coefficients was computed to cluster the curvelet coefficients and after this, data were embedded into the selected clusters. This was an improvement not only in terms of extracting a hidden message from the watermarked ECG signals, but also robust against image-processing attacks. Performance metrics of SSIM, NC, PSNR and BER were used to measure the superiority of presented work. KL divergence and PRD were also used to reveal data hiding in curvelet coefficients of ECG without disturbing the original signal. The simulation results also demonstrated that the clustering method in the curvelet domain provided the best performance-even when the hidden messages were large size

A STANDALONE HYBRID POWER SYSTEM FOR THE OUTBACK COMMUNITY OF OODNADATTA IN SOUTH AUSTRALIA

Abstract: Hybrid electrical power systems consisting of a combination fossil fuels powered generators and renewable energy sources in standalone grid-off configurations are gaining prominence in remote communities where grid extension are difficult and often very costly. This paper focuses on the design of a hybrid power system in an outback community of South Australia -Oodnadatta. An optimal standalone hybrid power system comprising photovoltaics, diesel and propane is proposed with the lowest levelised Cost of Electricity (COE) with at least 10% solar contribution. The proposed hybrid system is designed with the HOMER software utilising average daily solar radiation values for the years 2007-2009 and estimated power consumption data. The existing conventional power system in Oodnadatta is also modelled to compare and contrast with the proposed hybrid system to assess its impact on COE and greenhouse gas emissions

Data-Driven Decision Support Systems in E-Governance:Leveraging AI for Policymaking

Data-driven decision support systems have been used more and more in e-governance as a result of the digital revolution. In order to improve the efficacy and efficiency of policymaking, this research article investigates the integration of artificial intelligence (AI) approaches into e-governance systems. Governments can access enormous volumes of data, and AI algorithms are used to analyze and extract insightful data that enables decision-making based on facts. The article emphasizes the advantages of using AI in the e-governance space while formulating policy. Decision support systems can analyze and understand complicated information by utilizing cutting-edge machine learning and data analytics approaches, revealing trends, patterns, and correlations that would be challenging for human analysts to manually find. As a result, decision-makers in government may make well-informed choices based on impartial research and data. The paper also examines the difficulties and factors to be considered when implementing AI in decision support systems for e-governance. With an emphasis on the significance of responsible AI governance frameworks, ethical issues, including algorithmic bias, transparency, and accountability are addressed. The article also explores the effects of incorporating AI into decision-making processes, including potential sociopolitical effects and the requirement for stakeholder participation and public confidence. The results of this study show how data-driven decision support systems may revolutionize e-governance policies when equipped with AI technology. Governments may enhance their decision-making processes and the outcomes of governance by using the power of big data and sophisticated analytics. This will lead to better public service delivery.</p

Image Watermarking in Curvelet Domain Using Edge Surface Blocks

Digital image watermarking aims to protect the information in an image without significantly affecting visual quality. In this paper, a new image watermarking technique has been proposed that uses Gaussian filters and first-order partial differential matrix to extort the edge surface of a host image. This paper influence on the edge surface curvelet coefficients as human eyes are not equally sensitive to a smooth and an edged surface. To preserve the quality of the artwork and to increase the resistance against attacks, the author utilizes the edge surface area of an image, coarse levels of curvelet transform, and strength parameters. The selection of host coefficients are conforming to the human visual system (HVS) is the uniqueness of the research. The exploitation of the Gaussian filters and first-order partial differential coarse curvelet coefficients and the watermark strength parameter offers robustness against image processing attacks. The standard visual quality perception of HVS evaluation metrics are used to measure the superiority of the presented work