Summary of binary detection with vector ob­ servation in iid Gaussian noise. Reviews of forex. : First remove center point from signal and its effect on observation. In this paper, quantizer design for weak-signal detection under arbitrary binary channel in generalized Gaussian noise is studied. Since the performances of the generalized likelihood ratio test (GLRT) and Rao test are asymptotically characterized by the noncentral chi-squared probability density function (PDF), the threshold design problem can be formulated as a noncentrality parameter.

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Absfrucf- We present a Markov random field model which allows realistic edge modeling while providing stable maximum a posteriori MAP solutions. The proposed model, which we refer to as a generalized Gaussian Markov random field (GGMRF), is named for its similarity to the generalized Gaussian distribution used in robust detection and estimation. The model satisifies several desirable analytical and computational properties for MAP estimation, including continuous dependence of the estimate on the data, invariance of the character of solutions to scaling of data, and a solution which lies at the unique global mini-mum of the U posteriori log-likeihood function. The GGMRF is demonstrated to be useful for image reconstruction in low-dosage transmission tomography.

In this paper we propose a new watermarking scheme for digital images that allows watermark recovery even if the image has been subjected to generalized geometrical transforms. The watermark is given by a binary number and every watermark bit is represented by a two dimensional function. The functions are weighted, using a mask that is proportional to the luminance, and then modulated onto the blue component of the image. To recover an embedded bit, the embedded watermark is estimated using a prediction filter. The sign of the correlation between the estimated watermark and the original function determines the embedded bit. In order to allow recovery even after affine transforms the function of each bit is embedded several times at horizontally and vertically shifted locations. In the watermark recovery process we first compute a prediction of the embedded watermark.

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Then the autocorrelation function is computed for this prediction. The multiple embedding of the watermark results in ad. Digital fingerprinting is a technique for identifying users who might try to use multimedia content for unintended purposes, such as redistribution. These fingerprints are typically embedded into the content using watermarking techniques that are designed to be robust to a variety of attacks. Binary options trading etrade. A cost-e#ective attack against such digital fingerprints is collusion, where several di#erently marked copies of the same content are combined to disrupt the underlying fingerprints. In this paper, we investigate the problem of designing fingerprints that can withstand collusion and allow for the identification of colluders.

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We begin by introducing the collusion problem for additive embedding. We then study the e#ect that averaging collusion has upon orthogonal modulation. We introduce an e#cient detection algorithm for identifying the fingerprints associated with K colluders that requires log(n/K)) correlations for a group of n users. We next develop a fingerprinting scheme based upon code modulation that does not require as many basis signals as orthogonal modulation. We propose a new class of codes, called anti-collusion codes (ACC), which have the property that the composition of any subset of K or fewer codevectors is unique. Using this property, we can therefore identify groups of K or fewer colluders.