In this paper, we propose a novel approach of vector quantization using a merge-based hierarchical neural network. Vector quantization¡]VQ¡^is known as a very useful technique for lossy data compression. Recently, Neural network¡]NN¡^algorithms have been used for VQ. Vlajic and Card proposed a modified adaptive resonance theory (modified ART2¡^[1] which is a constructing tree structure clustering method. However, modified ART2 has disadvantages of slow construction rate and constructing many redundant levels. Therefore, we propose a more efficient approach for constructing the tree in this paper. Our method establishes only those required levels without losing the fidelity of a compressed image.

p, li { white-space: pre-wrap; } p, li { white-space: pre-wrap; } This thesis deals with text ciphering. The presented paper describes at first basic theoretical background of cryptology and basic distribution of cryptographic algorithms. Then it describes a brief history of encryption from beginning to present. Theoretical description of ciphering methods and its implementation details are discussed here. All basic types of conventional encryption algorithms and also some modern ciphering methods are included; these are substitution, transposition, steganographic or combinations encryption systems. The result of this thesis is the library of algorithms for text ciphering in Java with a sample application, which demonstrates its funcionality.

Telemetry system, Optimisation, Sensoric networks, Smart Grid, Internet of Things, Sensors, Information security, Cryptography, Cryptography algorithms, Cryptosystem, Confidentiality, Integrity, Authentication, Data freshness, Non-Repudiation.