Image encryption for Offshore wind power based on 2D-LCLM and Zhou Yi Eight Trigrams

Offshore wind power is an important part of the new power system, due to the complex and changing situation at ocean, its normal operation and maintenance cannot be done without information such as images, therefore, it is especially important to transmit the correct image in the process of information transmission. In this paper, we propose a new encryption algorithm for offshore wind power based on two-dimensional lagged complex logistic mapping (2D-LCLM) and Zhou Yi Eight Trigrams. Firstly, the initial value of the 2D-LCLM is constructed by the Sha-256 to associate the 2D-LCLM with the plaintext. Secondly, a new encryption rule is proposed from the Zhou Yi Eight Trigrams to obfuscate the pixel values and generate the round key. Then, 2D-LCLM is combined with the Zigzag to form an S-box. Finally, the simulation experiment of the algorithm is accomplished. The experimental results demonstrate that the algorithm can resistant common attacks and has prefect encryption performance.Comment: accepted by Int. J. of Bio-Inspired Computatio

Analyzing the Efficiency of a New Image Encryption Method Based on Aboodh Transformations

الهدف من هذا البحث هو تطوير طريقة تشفير فريدة من نوعها تستخدم طريقة Aboodh وتحويلها العكسي مع طريقة S-Box. تشير نتائج التقييمات إلى أن هذا العمل مناسب للاستخدام في تطبيقات التشفير الآمنة، ويوفر أدلة فيما يتعلق ببناء نظام تشفير للصور بناءً على السلوكيات المعقدة التي يوضحها. بعد تطبيق المنهجيات التي تم تقديمها لتصوير البيانات المأخوذة من مواقف الحياة الواقعية، تم تقييم النتائج باستخدام مجموعة واسعة من المعايير الإحصائية ومعايير الأداء. نتائج هذا التحقيق تؤدي إلى تحسين موثوقية نظام التشفير.The goal of this research is to develop a unique cryptographic method that makes use of Aboodh and its inverse transform in combination with the S-Box approach. The results of evaluations indicate that this work is appropriate for use in safe cryptographic applications, and it provides clues regarding the building of an image cryptosystem based on the complicated behaviors that it demonstrates. After applying the methodologies that have been provided to depict data taken from real-life situations, the results have been evaluated using a wide variety of statistical and performance criteria. The findings of this investigation result in an improvement to the reliability of the cryptosystem

A multiple beta wavelet-based locally regularized ultraorthogonal forward regression algorithm for time-varying system identification with applications to EEG

Time-varying (TV) nonlinear systems widely exist in various fields of engineering and science. Effective identification and modeling of TV systems is a challenging problem due to the nonstationarity and nonlinearity of the associated processes. In this paper, a novel parametric modeling algorithm is proposed to deal with this problem based on a TV nonlinear autoregressive with exogenous input (TV-NARX) model. A new class of multiple beta wavelet (MBW) basis functions is introduced to represent the TV coefficients of the TV-NARX model to enable the tracking of both smooth trends and sharp changes of the system behavior. To produce a parsimonious model structure, a locally regularized ultraorthogonal forward regression (LRUOFR) algorithm aided by the adjustable prediction error sum of squares (APRESS) criterion is investigated for sparse model term selection and parameter estimation. Simulation studies and a real application to EEG data show that the proposed MBW-LRUOFR algorithm can effectively capture the global and local features of nonstationary systems and obtain an optimal model, even for signals contaminated with severe colored noise

Proceeding seminar ICABS di Malaysia

proceeding seminar internasiona