Encryption security and encryption speed are two important aspects of image encryption algorithms. Due to their increasingly large size, video images present a great challenge to currently available cryptographic algorithms; the processes of encryption and decryption of images are so computationally intensive that they introduce delays beyond acceptable real-time application limits. [1] In this paper we introduce a new algorithm that uses Dynamic Total Shuffling as both encryption keys and the control stream to verify which key will be used for each block.The goal is to provide a highly secure encryption algorithm with a wide space for encryption speed.
Encryption security and encryption speed are two important aspects of image encryption algorithms. Due to their increasingly large size, video images present a great challenge to currently available cryptographic algorithms; the processes of encryption and decryption of images are so computationally intensive that they introduce delays beyond acceptable real-time application limits. [1] In this paper we introduce a new algorithm that uses dynamic square matrices as both encryption keys and the control stream to verify which key will be used for each block. The study case showed in this paper works on GF(7) and for encryption key sizes varying from 3X3 to 12X12 The goal is to provide a highly secure encryption algorithm with a wide space for encryption speed.
There are several advantages of Phoneme recognition. identification. It is easier to use speech for data entrance spoken communication for data ingress than other tools. It allows writing user-friendly data entrance exploiter-friendly data ingress programs. There are several difficulties in speech voice communication recognition. One of these difficulties is noise. Variability in speech is another problem. Even the speech of same speaker varies. The ability of artificial neural networks to generalize and optimize more quickly than some conventional algorithms algorithmic rule has been observed in different areas of research inquiry such as speech and pattern convention recognition, financial forecasting prognostication, image data compression and noise reduction simplification in signal processing. Neural networks take advantage of the redundancy incorporated in their distributed processing structures the proposed system depends on Artificial Neural Networks Network as decision making qualification algorithm to find the best match peer for the tested phonemes. Phoneme. The data used in this project are Arabic phonemes language phoneme stored as 8-bit mono infectious mononucleosis 8000Hz PCM WAVE Sound Auditory sensation file. The results showed that the accuracy of the proposed system is 98% recognizes the phonemes efficiently.
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