يوسف سامر عبد الامير المظفر

the output of this process is chaotic data that has been converted into binary data to be encrypted again through the colors of the cover image (red, green and blue) each separately and measuring the standard factors to know the strength of the system. The bit error rate was calculated after measuring the signal-to-noise ratio (SNR), system similarity (SSIM), and Peak Signal to Noise Ratio (PSNR) for these three hues. Using the logistic map, all three colors were found to be equally secure during encryption in the chaotic signal and due to unpredictability.

Extension of real data by synthetic data becomes more important aspect of the virtualization technics today. In this paper we demonstrate how synthetic data generated from real data can be used in the supervised classification process of three different recurrent neural networks: Long-Short Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU). Other aspect is presented concerning the influence of the noise to the classification of real and synthetic data series. The paper demonstrates that LSTM network has better classification performance than GRU, even the last one has higher accuracy during the training. Synthetic data can eternalize just part of the features of the original real data and extraction efficiency of these characteristics depend on the applied neural network.

Electronic learning (E-learning) has been a popular trend in education in recent years. As the cornerstone of E-Learning., the learning management system (LMS) must be available 24/7 with strong internet connectivity, enabling students to engage in paperless learning. However, not all universities, especially those in rural areas, have reliable internet connectivity. This has become an issue for universities that lack reliable internet connectivity and an adequate computer laboratory since they are unable to offer an E-learning environment that simulates a classroom setting. Even though the majority of students and teachers have smartphones, without reliable internet connectivity, it will be challenging to establish a connection to the learning management system's remote server. In this paper, a mobile learning management system based on Raspberry Pi and MoodleBox has been developed to address the aforementioned E-learning problem. This prototype can be used locally as a server even without an internet connection to provide the most fundamental LMS features. Students can view the information locally from their smartphone or laptop after teachers submit it to this portable system. This prototype has been successfully installed in our lab, and the LMS is simultaneously accessible through Wi-Fi,

The holiday season brings out the crowds at shopping centers in major cities. When there are substantial savings to be obtained, this becomes even more true. People today often fill their shopping carts with a wide selection of products. Once a customer has finished shopping, he or she should head to the counter to settle the payment. It takes time for the cashier to create the bill using a barcode scanner. Because of this, there are often substantial wait times at the cash registers. This project proposes a solution for retail centers to address the issues mentioned above. This might be accomplished by installing RFID tags on all mall merchandise and retrofitting all shopping carts with a radio-frequency identification reader and a liquid crystal display. Any item placed in the shopping cart will have its barcode scanned automatically, and the trolley's digital signboard will update to show the item's name and price. When an item is removed from a shopping cart, its cost is subtracted from the grand total, and the relevant data is transmitted to the central billing unit through radio-frequency (RF) modules. Customers may save a ton of time since billing can be done right on the tram. The researchers worked on developing a shopping cart, which is smart, and this system allows for faster checkout and payment of the bill in a secure manner, and the adoption of technology in the store increases the demand for it continuously, the computer will remember the barcode data you enter and compare it to its database, from which it will get the necessary data. The database and RF model will be connected through software. Small radio-frequency (RF) electrical modules called a “transmitter” and “receiver” can be used to transmit and receive signals between any two devices. The data is sent from the transmitter module to the receiver module, which then processes the data. The group believes that the intelligent shopping cart will result in a more dependable checkout procedure and less waiting time for customers.

Neural networks are more commonly used to identify systems for system diagnostics without fully implementing and building the system. The project aims to design and implement the neural network identification system and implement it based on a Field Programmable Gate Array (FPGA) by interfacing between MATLAB / Simulink and Xilinx programs. The work was done thanks to God through two methods: the first through the simulation method through the coupling between MATLAB and Xilinx. While the second method was practically done by loading the simulation designs onto the FPGA. Work performance is measured by subtracting the value resulting from the actual system operation of the identification signal system and the simplified system and the difference between the simulation result and practical result. The use of FPGA with system identification in this project gives us a big advantage that simulation results are equal to practical results (difference is zero).

According to the requirements of future technologies, underwater wireless optics (UWO) makes it possible to transmit data wirelessly using modulated optical beams in a broadband environment. The military, commercial, and academic communities benefit greatly from underwater wireless information transmission for purposes including tactical surveillance, pollution management, oil control and maintenance, marine exploration, and climate change monitoring. In visible light communications, data is transmitted using visible light. It is a more reliable system than wireless technology and other technologies that use the radio frequency spectrum. The most important problem encountered is the short distance for receiving information between the sender and the recipient. Therefore, a lens was used to obtain the best results and information sent within the longest possible distance between the transmitter and the receiving device. The data communication design is presented in this study. The concept uses the latest laser communication technology through eye-safe space technology. The separation distance between the devices is about eight metres. Data is transmitted using light waves in the visible spectrum (532 nm).