This work presents a method based on information-theoretic analysis of iris biometric that aims to extract homogeneous regions of high entropy. Successful extraction of these regions facilitates the development of effective systems for generation of cryptographic keys of lengths up to 400 bits per iris. At the same time, this approach allows for the application of simpler error correction codes with equal false accept rate levels, which reduces the overall complexity of this class of systems.

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This paper analyzed WMN routing protocols, modeling, and implementation of software environment for emulation of nodes in WMN network in education area. Use of emulators would enable students and teachers to research performance of the WMN (Wireless Mesh Network) protocol. This concept would contribute to an easier understanding of the WMN protocol related to different algorithms for transfer of routing traffic between nodes. ß

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In this article we examine automated language‐independent authorship verification using text examples in several representative Indo‐European languages, in cases when the examined texts belong to an open set of authors, that is, the author is unknown. We showcase the set of developed language‐dependent and language‐independent features, the model of training examples, consisting of pairs of equal features for known and unknown texts, and the appropriate method of authorship verification. An authorship verification accuracy greater than 90% was accomplished via the application of stylometric methods on four different languages (English, Greek, Spanish, and Dutch, while the verification for Dutch is slightly lower). For the multilingual case, the highest authorship verification accuracy using basic machine‐learning methods, over 90%, was achieved by the application of the kNN and SVM‐SMO methods, using the feature selection method SVM‐RFE. The improvement in authorship verification accuracy in multilingual cases, over 94%, was accomplished via ensemble learning methods, with the MultiboostAB method being a bit more accurate, but Random Forest is generally more appropriate.

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For the last two decades a large number of different automatic modulation classification (AMC) algorithms were developed, and many improvements in classification performance are reported. This was commonly achieved by engaging complex structures of neural networks, or other adaptable mechanisms for achieving better precision, when it comes to decisionmaking. Still, from practical implementation point of view, low algorithm complexity, economical usage of resources and fast execution remain to represent very desirable properties of an AMC algorithm. These properties are recognized in AMC algorithms based on higher-order cumulants as classification features, so their further improvement is of interest. Previous performance analysis of an algorithm based on sixthorder cumulants, in scenarios with complex valued signals' classification, showed that improvements are possible in the context of resources engaged and speed of execution. In this paper a novel approach is presented, for improving the correctness of classification process with sixthorder cumulants and simple twostep feature extraction structure, by engaging a new method for reduction of observed signal's modulation order which directly improves the classification performance. While tested with sixthorder cumulants, proposed method preserves good statistical properties of signal's higher-order cumulants in general, so it can be adopted in other AMC algorithms as well. Proposed modulation order reduction method is described in details, tested through computer simulations within the sixthorder cumulant AMC algorithm, and achieved improvements in performance are presented and explained.

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