The images transmission become more and more widely used in everyday life and even have been known to be vulnerable to interception and unauthorized access. The security of their transmission became necessary. In this paper an improved version of the Achterbahn-128 for image encryption and decryption have been proposed. The proposed design is based on seventeen binary primitive nonlinear feedback shift registers (NLFSRs) whose polynomials are primitive and a nonlinear Boolean function. The outputs of seventeen registers are combined by the nonlinear Boolean function to produce keysteam sequence. The proposed scheme is compared to a Achterbahn-128. The results of several experimental, statistical analysis and sensitivity analysis show that the proposed image encryption scheme is better than Achterbahn-128 and provides an efficient and secure way for image encryption and transmission.
In this study, the authors present a modelling method based on the adaptive linear combiner to denoise single-trial event-related potentials. The orthonormal Hermite basis functions act as inputs of the adaptive linear combiner. To estimate and to adjust the parameters of the adaptive filter, the authors use the variable step-size least mean square algorithm which is well suited to track rapid changes of non-stationary signals. The performance of the method is tested with simulated evoked potentials and with real visual event-related potentials. For simulated data, the adaptive Hermite model gave significant enhancement in latency and amplitude estimation as well as in the observation of single-trial event-related potentials, in comparison with wavelet techniques and with other models of adaptive filters. For the real data, the proposed method filters the ongoing electroencephalogram activity, thus allowing a better identification of single-trial visual event-related potentials. The results confirm that the Hermite adaptive linear combiner model provides a simple and fast tool that helps to study single-trial event-related potential responses.
Abstract-In this work a new algorithm for encryption image is introduced. This algorithm makes it possible to cipher and decipher images by guaranteeing a maximum security. The algorithm introduced is based on stream cipher with nonlinear filtering function. The Boolean function used in this algorithm is resilient function satisfying all the cryptographic criteria necessary carrying out the best possible compromises. In order to evaluate performance, the proposed algorithm was measured through a series of tests. Experimental results illustrate that the scheme is highly key sensitive, highly resistance to the noises and shows a good resistance against brute-force, Berlekamp-Massey Attack and algebraic attack.
The greenhouse climate is a non-linear system that contains multiple inputs (predictors) and multiple outputs (responses). This project aimed to provide a solution, aided by artificial intelligence, to the issue of variations in time, input and output factors in a greenhouse internal climate that can adversely affect tomato seedlings. Machine learning Methodologies such as fuzzy inference and neural networks have been applied to mimic idealistic behavior. This paper proposes an adaptive system based on artificial neural networks technique embedded with fuzzy logic technique calls Adaptive Neuro Fuzzy Inference System (ANFIS) to predict air humidity, air temperature, internal radiation, and CO2 concentration while the seeds grow, in order to produce favorable greenhouse climate conditions. The input parameters include ten meteorological and control actuators that majorly influence tomato plants during their growth process in the greenhouse climate. This discussion revolves around a linguistic ANFIS model that will operate during the 48 days that it takes for the seedlings to grow. It will provide estimates of the greenhouse climate using meteorological data along with control actuators rooted in trained neural networks with back propagation optimization algorithm, and 500 iterations of the least square algorithm. Simulations have revealed the efficiency of this model.
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