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Signals Of Chaos Free Download ((HOT))



This pack is themed around security companies and bad video signals. Give your videos a techno-thriller style that combines text with footage in a fun, dynamic way. Simply download it and boot it up in Premiere Pro.




Signals of Chaos Free Download



Using phase space reconstruct technique from one-dimensional and multi-dimensional time series and the quantitative criterion rule of system chaos, and combining the neural network; analyses, computations and sort are conducted on electroencephalogram (EEG) signals of five kinds of human consciousness activities (relaxation, mental arithmetic of multiplication, mental composition of a letter, visualizing a 3-dimensional object being revolved about an axis, and visualizing numbers being written or erased on a blackboard). Through comparative studies on the determinacy, the phase graph, the power spectra, the approximate entropy, the correlation dimension and the Lyapunov exponent of EEG signals of 5 kinds of consciousness activities, the following conclusions are shown: (1) The statistic results of the deterministic computation indicate that chaos characteristic may lie in human consciousness activities, and central tendency measure (CTM) is consistent with phase graph, so it can be used as a division way of EEG attractor. (2) The analyses of power spectra show that ideology of single subject is almost identical but the frequency channels of different consciousness activities have slight difference. (3) The approximate entropy between different subjects exist discrepancy. Under the same conditions, the larger the approximate entropy of subject is, the better the subject's innovation is. (4) The results of the correlation dimension and the Lyapunov exponent indicate that activities of human brain exist in attractors with fractional dimensions. (5) Nonlinear quantitative criterion rule, which unites the neural network, can classify different kinds of consciousness activities well. In this paper, the results of classification indicate that the consciousness activity of arithmetic has better differentiation degree than that of abstract.


Extracting relevant properties of empirical signals generated by nonlinear, stochastic, and high-dimensional systems is a challenge of complex systems research. Open questions are how to differentiate chaotic signals from stochastic ones, and how to quantify nonlinear and/or high-order temporal correlations. Here we propose a new technique to reliably address both problems. Our approach follows two steps: first, we train an artificial neural network (ANN) with flicker (colored) noise to predict the value of the parameter, \(\alpha\), that determines the strength of the correlation of the noise. To predict \(\alpha\) the ANN input features are a set of probabilities that are extracted from the time series by using symbolic ordinal analysis. Then, we input to the trained ANN the probabilities extracted from the time series of interest, and analyze the ANN output. We find that the \(\alpha\) value returned by the ANN is informative of the temporal correlations present in the time series. To distinguish between stochastic and chaotic signals, we exploit the fact that the difference between the permutation entropy (PE) of a given time series and the PE of flicker noise with the same \(\alpha\) parameter is small when the time series is stochastic, but it is large when the time series is chaotic. We validate our technique by analysing synthetic and empirical time series whose nature is well established. We also demonstrate the robustness of our approach with respect to the length of the time series and to the level of noise. We expect that our algorithm, which is freely available, will be very useful to the community.


Dataset E-V: Five RR-interval time-series from healthy subjects. Each time series have \(\sim 100,000\) RR intervals (the signals were recorded using continuous ambulatory electrocardiograms during 24 h). It still a debate if the heart rate variability is chaotic or stochastic9. While some studies suggest that heart rate variability is a stochastic process9,60,61. Much chaos-detection analysis has been identified as a chaotic signal9,62. The dataset is open and freely available in63.


The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very tedious to interpret visually and highly difficult to extract the significant features from them. The linear and nonlinear methods are effective in identifying the changes in EEG signals for the detection of depression. Linear methods do not exhibit the complex dynamical variations in the EEG signals. Hence, chaos theory and nonlinear dynamic methods are widely used in extracting the EEG signal features for computer-aided diagnosis (CAD) of depression. Hence, this article presents the recent efforts on CAD of depression using EEG signals with a focus on using nonlinear methods. Such a CAD system is simple to use and may be used by the clinicians as a tool to confirm their diagnosis. It should be of a particular value to enable the early detection of depression.


The concept of transmitting information from one chaotic system to another derives from the observation of the synchronization of two chaotic systems. Having developed two chaotic systems that can be synchronized, scientists can modulate on one phase signal the information to be transmitted, and subtract (demodulate) the information from the corresponding phase signal of the coupled chaotic system.Chaos Applications in Telecommunications demonstrates this technique in various applications of communication systems. This book details methods of transmitting information at much higher levels of security than what is available by current techniques. Following a detailed introduction, the book demonstrates how chaotic signals are generated and transmitted. It then details the design of chaotic transmitters and receivers, and describes chaos-based modulation and demodulation techniques. The text describes how a chaos-based spreading sequence outperforms classical pseudorandom sequences in selective and nonselective channels. It also develops channel equalization techniques designed for chaotic communications systems by applying knowledge of systems dynamics, linear time-invariant representations of chaotic systems, and symbolic dynamics representations of chaotic systems. The final chapter explains a specific application for optical communications.This volume provides the essential information for those who want an integrated view of how an established concept such as chaos can open new roads in the communications and security fields.


The visualisation of chaos, and specifically of incidents on production, presents several challenges for industry and academia focused on observability. As we showed in this article, since chaos engineering, observability and visualisation involve the interaction of humans with machines, the bias in the interpretations is a constant risk. Through a study in which 28 engineers answered 12 questions related to classical dashboards versus visual metaphors, it was possible to conclude that observability is not only limited by the quantity and quality of those signals, but the way in which those signals are visualised and interpreted. The conclusion was that the visual metaphors could perform better than classical dashboards, however, since both involve humans, none are a guarantee that operators are interpreting the data in an incident in a proper way. 041b061a72


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