Showing 3 results for Hakim
S. Mohammadi, S. Talebi, A. Hakimi,
Volume 8, Issue 2 (June 2012)
Abstract
In this paper we introduce two innovative image and video watermarking
algorithms. The paper’s main emphasis is on the use of chaotic maps to boost the
algorithms’ security and resistance against attacks. By encrypting the watermark
information in a one dimensional chaotic map, we make the extraction of watermark for
potential attackers very hard. In another approach, we select embedding positions by a two
dimensional chaotic map which enables us to satisfactorily distribute watermark
information throughout the host signal. This prevents concentration of watermark data in a
corner of the host signal which effectively saves it from being a target for attacks that
include cropping of the signal. The simulation results demonstrate that the proposed
schemes are quite resistant to many kinds of attacks which commonly threaten
watermarking algorithms.
M. Ehsani, A. Oraee, B. Abdi, V. Behnamgol, S. M. Hakimi,
Volume 19, Issue 1 (March 2023)
Abstract
A novel nonlinear controller is proposed to track active and reactive power for a Brushless Doubly-Fed Induction Generator (BDFIG) wind turbine. Due to nonlinear dynamics and the presence of parametric uncertainties and perturbations in this system, sliding mode control is employed. To generate a smooth control signal, dynamic sliding mode method is used. Uncertainties bound is not required in the suggested algorithm, since the adaptive gain in the controller relation is used in this study. Convergence of the sliding variable to zero and adaptive gain to the uncertainty bound are verified using Lyapunov stability theorem. The proposed controller is evaluated in a comprehensive simulation on the BDFIG model. Moreover, output performance of the proposed control algorithm is compared to the conventional and second-order sliding mode and proportional-integral-derivative (PID) controllers.
Duaa A. Kareem, Zaineb M. Alhakeem, Nawar Hayder Tawfeeq, Batool Dahham Al-Ali, Heba Hakim,
Volume 22, Issue 2 (June 2026)
Abstract
Signal forecasting in the medical field has many applications, such as signal correction and anomaly detection. According to this application, robust forecasting is required to obtain a signal identical to the original signal. This study proposes a forecasting technique that obtains a robust signal that can be used in different applications. A long short-term memory neural network (LSTM-NN) was used to predict future samples from present and past samples. An Electroencephalography (EEG) dataset was used to test this technique. Four channels were used as input examples, one of which was the predicted output. All four channel samples were fed into the four networks to predict the future samples. To decrease complexity, only one hidden layer is used for this purpose. The statistical results are promising for applications that require an almost perfectly predicted signal. The number of hidden cells is first very low (five cells only), which gives a Root Mean Square Error of less than 20, whereas when the number of hidden cells is increased to 100, the Root Mean Square Error (RMSE) is approximately 7.5 for all four channels.
