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This chapter proposes a watermarking technique using Ridgelet and Discrete Wavelet Transform (DWT) techniques. A wavelet transform is the wavelet function representation. A wavelet is a mathematical function which divides a continuous time signal into different scale components, where each scale components is assigned with a frequency range. Wavelets represent objects with point singularities, while ridgelets represents objects with line singularities. The Ridgelet transform Technique is a multi-scale representation for functions on continuous spaces that are smooth away from discontinuities along lines. The proposed technique applies Ridgelet transform on the cover image to obtain ridgelet coefficients. These coefficients are transformed by using 2-level DWT to get low frequency sub-bands – LL1 and LL2. The mutual similarities between LL1 and LL2 sub-bands are considered for embedding watermark. The obtained watermarked image has better quality when compared to a few exiting methods.

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REPORT DOCUMENTATION PAGEForm ENCY USE ONLY (Leave blank)2. REPORT DATE May 7, 2000 REPORT TYPE AND DATES COVEREDFinal en A-firQk-~&1 Mpur OQ LE AND SUBTITLEShape and Image Analysis using Neural Networks, Fractals and Wavelets. THOR(S)C.R. Rao S.R.T. Kumara FUNDING NUMBERS DAAH04-96-1-0082 ^FORMING ORGANIZATION NAMES(S) AND ADDRESS(ES)The e views, opinions and/or findings contained in this report are those of the author(s) and should not be construed as official Department of the Army position, policy or decision, unless so designated by other documentation.'ISTRIBUTION / AVAILABILITY STATEMENT >proved for public release; distribution unlimited. b. DISTRIBUTION CODE ISTRACT (Maximum 200 words)A general theory and appropriate statistical analysis are developed for discrimination of objects by shape, i.e., by using features which are invariant to location, scale and orientation, in particular to reflection also. In the case of landmark data, features such as Euclidean distances between landmarks or angles of triangles after suitable triangulation are considered. When the objects do not have recognizable landmarks, as in the case of closed boundaries, we can use topological properties of points on the boundary at intervals of constant length as features. To deal with such cases, a new geometry of circular vectors with a suitably defined metric is developed. This enables the use of distance methods such as k-NN rule in pattern recognition. We have also concentrated on the extraction of features for representing shapes. As a generalization we have considered the signals from machining process and studied characterization using chaos and fractal analysis. We extended this work to represent shapes using wavelets, Fourier descriptors, fractal image compression and iterated functional systems. We have conducted a comparative analysis. In the contemporary internet world search engines need sophisticated techniques to search for images of interest based on shapes. We proposed a preliminary model and web bot based upon our shape analysis study to develop an image search engine for the www. IBJECT TERMS Statement of the Problems Studied Technical Objectives and MotivationDiscriminant analysis based on multiple measurements is a fundamental tool in several practical applications ranging from disputed paternity and authorship of a manuscript to medical diagnosis character recognition, and detection and identification of signals. In many situations, the measurements made on an object depend on its location, scale and orientation. The technical problem involved is to extract features of objects which are invariant to the above transformations, which are called shape measurements and develop the statistical methodology for discrimination by shape.Multifractal image analysis dealing with image representation and image compression is a problem of paramount importance in activities, and will assume tremendous importance in the future due to proliferation of new techniques such as remote and virtual collaboration in the internet. T...

Industrial Internet of Things (IIoT) is changing many driving enterprises like transportation, mining, horticulture, energy and medical care. Machine Learning calculations are utilized for getting stages for IT frameworks. The IoT network unit hubs typically asset in a strange manner by making them more responsible to digital assaults. IIoT frameworks requests various situations in genuine one among them is giving security and the causes that encompass them in true viewpoints. It incorporates a system called PriModChain causes security and reliability on IIoT information by joining differential protection, Ethereum block chain and unified Machine learning. Consequently, security will be compromised and we use PriMod chain for giving protection and different compliances and created utilizing Python with attachment programming on essential PC.