IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA

Modern cryptography techniques are virtually unbreakable. As the Internet and other forms of electronic communication become more prevalent, electronic security is becoming increasingly important. Cryptography is used to protect e-mail messages, credit card information, and corporate data. The design of the cryptography system is a conventional cryptography that uses one key for encryption and decryption process. The chosen cryptography algorithm is stream cipher algorithm that encrypt one bit at a time. The central problem in the stream-cipher cryptography is the difficulty of generating a long unpredictable sequence of binary signals from short and random key. Pseudo random number generators (PRNG) have been widely used to construct this key sequence. The pseudo random number generator was designed using the Artificial Neural Networks (ANN). The Artificial Neural Networks (ANN) providing the required nonlinearity properties that increases the randomness statistical properties of the pseudo random generator. The learning algorithm of this neural network is backpropagation learning algorithm. The learning process was done by software program in Matlab (software implementation) to get the efficient weights. Then, the learned neural network was implemented using field programmable gate array (FPGA)

Real-time Arabic Video Captioning Using CNN and Transformer Networks Based on Parallel Implementation

Video captioning techniques have practical applications in fields like video surveillance and robotic vision, particularly in real-time scenarios. However, most of the current approaches still exhibit certain limitations when applied to live video, and research has predominantly focused on English language captioning. In this paper, we introduced a novel approach for live real-time Arabic video captioning using deep neural networks with a parallel architecture implementation. The proposed model primarily relied on the encoder-decoder architecture trained end-to-end on Arabic text. Video Swin Transformer and deep convolutional network are employed for video understanding, while the standard Transformer architecture is utilized for both video feature encoding and caption decoding. Results from experiments conducted on the translated MSVD and MSR-VTT datasets demonstrate that utilizing an end-to-end Arabic model yielded better performance than methods involving the translation of generated English captions to Arabic. Our approach demonstrates notable advancements over compared methods, yielding a CIDEr score of 78.3 and 36.3 for the MSVD and MSRVTT datasets, respectively. In the context of inference speed, our model achieved a latency of approximately 95 ms using an RTX 3090 GPU for a temporal video segment with 16 frames captured online from a camera device