We explain how to apply statistical techniques to solve several language-recognition problems that arise in cryptanalysis and other domains. Language recognition is important in cryptanalysis because, among other applications, an exhaustive key search of any cryptosystem from ciphertext alone requires a test that recognizes valid plaintext. Written for cryptanalysts, this guide should also be helpful to others as an introduction to statistical inference on Markov chains. Modeling language as a finite stationary Markov process, we adapt a statistical model of pattern recognition to language recognition. Within this framework we consider four welldefined language-recognition problems: 1) recognizing a known language, 2) distinguishing a known language from uniform noise, 3) distinguishing unknown 0th-order noise from unknown 1st-order language, and 4) detecting non-uniform unknown language. For the second problem we give a most powerful test based on the Neyman-Pearson Lemma. For the oth...

This paper presents the principle of a perception system meant for delivering alarms when dreaded activities are suspected. It includes a Symbolic Layer (SL) that deals with activities recognition and alarm decision, and a Resource Management System (RML) that, upon request, provides SL with information. The domain to be watched over includes several areas that cannot be checked at the same time because of the limited capabilities of the resources, and the crucial problem is to optimize the use of the resources. It is shown how sensors are dynamically allocated to limited parts of the scene and how it is decided between immediate alarm or additional information request without explicit situation assessment. The principle of an implementation within a real time multi-task architecture is also presented.