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In this modern era, it has become much simpler to replicate, sell, and copy the copyright owners' works without their permission as a result of the expansion of digitalization, and it is difficult to identify such violations, posing a threat to the creators' and copyright owners' rights. For many years, the internet has been regarded as one of the most serious threats to copyright, and the content available has varying levels of copyright protection. On the internet, there are numerous copyrighted works, including e-books, movies, news, and so on. Therefore, by using watermarking and steganography techniques, these issues can be solved, which are based on the author's signature information or logo. This paper concluded that the techniques of discrete cosine transform (DCT), discrete wavelet transform (DWT), one-time pad (OTP), and playfair are highly effective when used together to watermark an image or embed a secret message, our lab results validate that our algorithm scheme is robust against several sets of attacks, where the algorithm was assessed by computation of many evaluation metrics such as mean square error (MSE), signal-to-noise ratio (SNR), and peak signal-to-noise ratio (PSNR). [ABSTRACT FROM AUTHOR]

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision. In this article, these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data. The framework integrates Kalman filtering and Q-learning. Unlike smoothing Kalman filtering, our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error. Unlike traditional Q-learning, our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads. For evaluation, the model has been simulated on a single intersection consisting of four roads: east, west, north, and south. A comparison of the developed adaptive quantized Q-learning (AQQL) framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5% over the greedy approach and 340% over the state-of-the-art approach. Hence, AQQL provides an effective traffic control that can be applied in today's intelligent traffic system. [ABSTRACT FROM AUTHOR]

Fifth Generation (5G) signals using the millimeter wave (mmWave) spectrums are highly vulnerable to blockage due to rapid variations in channel link quality. This can cause the devices or User Equipment (UE) to suffer from connection failure. In a dual connectivity (DC) network, the channel's intermittency issues were partially solved by maintaining the UE's connectivity to primary (LTE advanced stations) and secondary (5G mmWave stations) simultaneously. Even though the dual-connected network performs excellently in maintaining connectivity, its performance drops significantly due to the inefficient handover from one 5G mmWave station to another. The situation worsens when UE travels a long distance in a highly dense obstacle environment, which requires multiple ineffective handovers that eventually lead to performance degradation. This research aimed to propose an Adaptive TTT Handover (ATH) mechanism that deals with unpredictable 5G mmWave wireless channel behaviors that are highly intermittent. An adaptive algorithm was developed to automatically adjust the handover control parameters, such as Time-to-Trigger (TTT), based on the current state of channel condition measured by the Signal-to-Interference-Noise Ratio (SINR). The developed algorithm was tested under a 5G mmWave statistical channel model to represent a time-varying channel matrix that includes fading and the Doppler effect. The performance of the proposed handover mechanism was analyzed and evaluated in terms of handover probability, latency, and throughput by using the Network Simulator 3 tool. The comparative simulation result shows that the proposed adaptive handover mechanism performs excellently compared to conventional handovers and other enhancement techniques. [ABSTRACT FROM AUTHOR]