Saeideh Nabipour

Saeideh Nabipour, Javad Javidan, Majid Khorrami et al.

Watermarking is a technique for hiding of data in a medium coverage so that its presence is not detectable by a human eye and is recoverable only by the authorized recipient. Two of the most important features of watermarked image are transparency and robustness which are largely related to the security of watermarking algorithms. In this paper, an image watermarking scheme based on BCH error correction code in Discrete Cosine Transformation (DCT) domain is considered. Before embedding process, the watermark is encoded through BCH coding. Then it is embedded into the Discrete Cosine Transformation (DCT) coefficients of cover image. In order to decrease embedding complexity and speed up the process of finding the best position to insert a watermark signal, lookup table method is utilized. The key features of proposed method include the reduction of time required in the process embedding of information, security and ability to correct the error caused by variety of attacks and destructions as well. Watermarked image robustness has been investigated against different kinds attacks and the simulation results indicate that the proposed algorithm outperforms the existing methods in terms of imperceptibility, robustness and security.

Saeideh Nabipour, Masoume Gholizade, Nima Nabipour

Euclids algorithm is widely used in calculating of GCD (Greatest Common Divisor) of two positive numbers. There are various fields where this division is used such as channel coding, cryptography, and error correction codes. This makes the GCD a fundamental algorithm in number theory, so a number of methods have been discovered to efficiently compute it. The main contribution of this paper is to investigate a method that computes the GCD of two 32-bit numbers based on Euclidean algorithm which targets six different Xilinx chips. The complexity of this method that we call Optimized_GCDSAD is achieved by utilizing Sum of Absolute Difference (SAD) block which is based on a fast carry-out generation function. The efficiency of the proposed architecture is evaluated based on criteria such as time (latency), area delay product (ADP) and space (slice number) complexity. The VHDL codes of these architectures have been implemented and synthesized through ISE 14.7. A detailed comparative analysis indicates that the proposed Optimized_GCDSAD method based on SAD block outperforms previously known results.

Aidin Zehtab-Salmasi, Ali-Reza Feizi-Derakhshi, Narjes Nikzad-Khasmakhi et al.

Price prediction is one of the examples related to forecasting tasks and is a project based on data science. Price prediction analyzes data and predicts the cost of new products. The goal of this research is to achieve an arrangement to predict the price of a cellphone based on its specifications. So, five deep learning models are proposed to predict the price range of a cellphone, one unimodal and four multimodal approaches. The multimodal methods predict the prices based on the graphical and non-graphical features of cellphones that have an important effect on their valorizations. Also, to evaluate the efficiency of the proposed methods, a cellphone dataset has been gathered from GSMArena. The experimental results show 88.3% F1-score, which confirms that multimodal learning leads to more accurate predictions than state-of-the-art techniques.