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In this paper a universal steganalysis scheme is proposed for images. The scheme is based on the characteristic function moments of three-level wavelet subbands including the further decomposition coefficients of the first scale diagonal subband. The first three order statistical moments of each band are selected to form a feature vector for steganalysis. The Euclidean distance is used as the separability...
With the growing importance of data security a growing effort to find new approaches to the cryptographic algorithms designs appears. One of the trends is to research the use of other algebraic structures than the traditional, such as a quasigroup. Quasigroups are equivalent to the more familiar Latin squares. There are many characteristics that must quasigroups have from the cryptography point of...
Stacked Generalization algorithm aims to increase the individual classification performances of the classifiers by combining the information obtained from various classifiers in a multilayer architecture by either linear or nonlinear techniques. Performance of the algorithm varies depending on the application domains and the space analyses that affect the classification performances could not be applied...
The high quality of the information source can improve the accuracy of target recognition when data is fused via multi-information sources. In this paper, it proposes a new method to determine the reliability coefficient of the information source by information criterion. With calculating the reliabilities of information sources, the more reliable source would be used in the combination process to...
In this paper, we present a histogram-based two-phase calibration technique for capacitor mismatch and comparator offset of 1-bit/stage pipelined Analog-to-Digital Converters (ADCs). In the first phase, it calibrates the missing decision levels by capacitor resizing. Unlike previous works which require large capacitor arrays, only few switches are added to the circuit. The second phase performs missing...
The PeakFinder Algorithm is an unsupervised meAhod for discovering significant clustm (classes) in a noisy histogram whose underlying distribution estimates the probability density function of an n dimensional feature space for one symbolic category. The histogram is filtered using a suitable kernel function, whose strength (window size) is searched for the smallest value that yields the largest number...
Based on collective learning systems theory, ALISA (Adaptive Learning Image and Signal Analysis) is an adaptive image classification engine that has been designed and tested at the Research Institute for Applied Knowledge Processing (FAW) in Ulm, Germany, at Robeit Hosch GmbH in Stuttgart, Germany, at the University of the Americas in Puebla, Mexico, and at 'Me George Washington University in Washington...
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