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This paper identifies and analyses some drawbacks of the logistic map which is still one of the most used chaotic maps in image encryption algorithms. As some of the disadvantages are caused by inappropriate implementations of the logistic map, this paper proposes a set of rules which should lead to enhancement of the desired chaotic behavior. Probably the most important rule introduces alternating value of parameter utilized by the logistic map. With careful choice of values and an adapted quantization technique, some of the issues should be fixed and theoretically also the values of numerical parameters should be improved. These assumptions are verified by applying the proposed set of rules on an algorithm from our prior work. Effects of the proposed rules on the used algorithm are investigated and all necessary modifications are thoroughly discussed. The paper also compares obtained values of commonly used numerical parameters and computational complexity with some other image encryption algorithms based on more complex chaotic systems.

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This study deals with the problem of fiber-free optical communication systems—known as free space optics—using received signal strength identifier (RSSI) prediction analysis for hard switching of optical fiber-free link to base radio-frequency (RF) link and back. Adverse influences affecting the atmospheric transmission channel significantly impair optical communications, therefore attention was paid to the practical design, as well as to the implementation of the monitoring device that is used to record and process weather information along a transmission path. The article contains an analysis and methodology of the solution of the high availability of the optical link. Attention was paid to the technique of hard free space optics (FSO)/RF-switching with regard to the amount of received optical power detected and its relation to the quantities influencing the optical communication line. For this purpose, selected methods of machine learning were used, which serve to predict the received optical power. The process of analysis of prediction of received optical power is realized by regression models. The study presents the design of the optimal data input matrix model, which forms the basis for the training of the prediction models for estimating the received optical power.