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Entropy, Volume 16, Issue 12 (December 2014) – 30 articles , Pages 6195-6738

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263 KiB  
Article
A Large Deviation Principle and an Expression of the Rate Function for a Discrete Stationary Gaussian Process
by Olivier Faugeras and James MacLaurin
Entropy 2014, 16(12), 6722-6738; https://doi.org/10.3390/e16126722 - 22 Dec 2014
Viewed by 4731
Abstract
We prove a large deviation principle for a stationary Gaussian process over Rb,indexed by Ζd (for some positive integers d and b), with positive definite spectral density, andprovide an expression of the corresponding rate function in terms of the [...] Read more.
We prove a large deviation principle for a stationary Gaussian process over Rb,indexed by Ζd (for some positive integers d and b), with positive definite spectral density, andprovide an expression of the corresponding rate function in terms of the mean of the processand its spectral density. This result is useful in applications where such an expression isneeded. Full article
(This article belongs to the Section Statistical Physics)
304 KiB  
Article
A Representation of the Relative Entropy with Respect to a Diffusion Process in Terms of Its Infinitesimal Generator
by Oliver Faugeras and James MacLaurin
Entropy 2014, 16(12), 6705-6721; https://doi.org/10.3390/e16126705 - 22 Dec 2014
Cited by 1 | Viewed by 5044
Abstract
In this paper we derive an integral (with respect to time) representation of the relative entropy (or Kullback–Leibler Divergence) R(μ||P), where μ and P are measures on C([0,T];Rd). The underlying measure P is a weak solution to a martingale problem with [...] Read more.
In this paper we derive an integral (with respect to time) representation of the relative entropy (or Kullback–Leibler Divergence) R(μ||P), where μ and P are measures on C([0,T];Rd). The underlying measure P is a weak solution to a martingale problem with continuous coefficients. Our representation is in the form of an integral with respect to its infinitesimal generator. This representation is of use in statistical inference (particularly involving medical imaging). Since R(μ||P) governs the exponential rate of convergence of the empirical measure (according to Sanov’s theorem), this representation is also of use in the numerical and analytical investigation of finite-size effects in systems of interacting diffusions. Full article
(This article belongs to the Section Information Theory, Probability and Statistics)
1699 KiB  
Article
Effect of the Postural Challenge on the Dependence of the Cardiovascular Control Complexity on Age
by Aparecida M. Catai, Anielle C.M. Takahashi, Natália M. Perseguini, Juliana C. Milan, Vinicius Minatel, Patrícia Rehder-Santos, Andrea Marchi, Vlasta Bari and Alberto Porta
Entropy 2014, 16(12), 6686-6704; https://doi.org/10.3390/e16126686 - 22 Dec 2014
Cited by 44 | Viewed by 6477
Abstract
Short-term complexity of heart period (HP) and systolic arterial pressure (SAP) was computed to detect age and gender influences over cardiovascular control in resting supine condition (REST) and during standing (STAND). Healthy subjects (n = 110, men = 55) were equally divided [...] Read more.
Short-term complexity of heart period (HP) and systolic arterial pressure (SAP) was computed to detect age and gender influences over cardiovascular control in resting supine condition (REST) and during standing (STAND). Healthy subjects (n = 110, men = 55) were equally divided into five groups (21–30; 31–40; 41–50; 51–60; and 61–70 years of age). HP and SAP series were recorded for 15 min at REST and during STAND. A normalized complexity index (NCI) based on conditional entropy was assessed. At REST we found that both NCIHP and NCISAP decreased with age in the overall population, but only women were responsible for this trend. During STAND we observed that both NCIHP and NCISAP were unrelated to age in the overall population, even when divided by gender. When the variation of NCI in response to STAND (ΔNCI = NCI at REST-NCI during STAND) was computed individually, we found that ΔNCIHP progressively decreased with age in the overall population, and women were again responsible for this trend. Conversely, ΔNCISAP was unrelated to age and gender. This study stresses that the complexity of cardiovascular control and its ability to respond to stressors are more importantly lost with age in women than in men. Full article
(This article belongs to the Special Issue Entropy and Cardiac Physics)
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<p>Box-and-whisker plots of NCI<sub>HP</sub> at REST (upper panels) and linear regression of NCI<sub>HP</sub> on age (lower panels). The linear regression over all values (<span class="html-italic">i.e.</span>, solid circles: age 21–30; open circles: age 31–40; solid triangles pointing down: age 41–50; open triangles pointing up: age 51–60; solid squares: age 61–70) and its 95% confidence interval are plotted when a significant change from 21–30 to 61–70 is detected according to one-way analysis of variance. The symbol * indicates <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Box-and-whisker plots of NCI<sub>SAP</sub> at REST (upper panels) and linear regression of NCI<sub>SAP</sub> on age (lower panels). The linear regression over all values (<span class="html-italic">i.e.</span>, solid circles: age 21–30; open circles: age 31–40; solid triangles pointing down: age 41–50; open triangles pointing up: age 51–60; solid squares: age 61–70) and its 95% confidence interval are plotted when a significant change from 21–30 to 61–70 is detected according to one-way analysis of variance. The symbol * indicates <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Box-and-whisker plots of NCI<sub>HP</sub> during STAND (upper panels) and linear regression of NCI<sub>HP</sub> on age (lower panels). The linear regression over all values (<span class="html-italic">i.e.</span>, solid circles: age 21–30; open circles: age 31–40; solid triangles pointing down: age 41–50; open triangles pointing up: age 51–60; solid squares: age 61–70) and its 95% confidence interval are plotted when a significant change from 21–30 to 61–70 is detected according to one-way analysis of variance. The symbol * indicates <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Box-and-whisker plots of NCI<sub>SAP</sub> during STAND (upper panels) and linear regression of NCI<sub>SAP</sub> on age (lower panels). The linear regression over all values (<span class="html-italic">i.e.</span>, solid circles: age 21–30; open circles: age 31–40; solid triangles pointing down: age 41–50; open triangles pointing up: age 51–60; solid squares: age 61–70) and its 95% confidence interval are plotted when a significant change from 21–30 to 61–70 is detected according to one-way analysis of variance. The symbol * indicates <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Linear regression of ΔNCI<sub>HP</sub> (upper panels) and ΔNCI<sub>SAP</sub> (lower panels) on age. The linear regression over all values (<span class="html-italic">i.e.</span>, solid circles: age 21–30; open circles: age 31–40; solid triangles pointing down: age 41–50; open triangles pointing up: age 51–60; solid squares: age 61–70) and its 95% confidence interval are plotted when a significant change from 21–30 to 61–70 is detected according to one-way analysis of variance. ΔNCI is assessed as NCI at REST minus NCI during STAND.</p>
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830 KiB  
Article
Fast Rate Estimation for RDO Mode Decision in HEVC
by Maxim P. Sharabayko and Oleg G. Ponomarev
Entropy 2014, 16(12), 6667-6685; https://doi.org/10.3390/e16126667 - 19 Dec 2014
Cited by 12 | Viewed by 8139
Abstract
The latter-day H.265/HEVC video compression standard is able to provide two-times higher compression efficiency compared to the current industrial standard, H.264/AVC. However, coding complexity also increased. The main bottleneck of the compression process is the rate-distortion optimization (RDO) stage, as it involves numerous [...] Read more.
The latter-day H.265/HEVC video compression standard is able to provide two-times higher compression efficiency compared to the current industrial standard, H.264/AVC. However, coding complexity also increased. The main bottleneck of the compression process is the rate-distortion optimization (RDO) stage, as it involves numerous sequential syntax-based binary arithmetic coding (SBAC) loops. In this paper, we present an entropy-based RDO estimation technique for H.265/HEVC compression, instead of the common approach based on the SBAC. Our RDO implementation reduces RDO complexity, providing an average bit rate overhead of 1.54%. At the same time, elimination of the SBAC from the RDO estimation reduces block interdependencies, thus providing an opportunity for the development of the compression system with parallel processing of multiple blocks of a video frame. Full article
(This article belongs to the Section Information Theory, Probability and Statistics)
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<p>Rate-distortion optimization (RDO) block estimation data-flow.</p>
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<p>SBAC abstraction as a Mealy machine.</p>
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<p>Coding unit (CU) partitioning on prediction units (PUs) in the case of intra-prediction.</p>
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<p>Illustration of the neighbor PUs, whose intra-prediction modes are the MPM (most probable mode) for the current PU.</p>
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<p>Scan patterns for an 8 ×8 transform block.</p>
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<p>Residual data block example with (<b>A</b>) coefficients and (<b>B</b>) coefficient groups.</p>
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<p>Arithmetic coding of coefficient Group “B”.</p>
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<p>Average percentage of bins by context groups for the input of the SBAC on the BasketballDrill video sequence for (<b>A</b>) 4 ×4 transform unit (TU), (<b>B</b>) 8 ×8 TU, (<b>C</b>) 16 ×16 TU and (<b>D</b>) 32 ×32 TU.</p>
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<p>Scatter plot of the rate estimation on the (<b>A</b>) BasketballDrill and (<b>B</b>) PeopleOnStreet test sequences.</p>
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312 KiB  
Article
The Effects of Spontaneous Random Activity on Information Transmission in an Auditory Brain Stem Neuron Model
by Hiroyuki Mino
Entropy 2014, 16(12), 6654-6666; https://doi.org/10.3390/e16126654 - 19 Dec 2014
Cited by 8 | Viewed by 6028
Abstract
This paper presents the effects of spontaneous random activity on information transmission in an auditory brain stem neuron model. In computer simulations, the supra-threshold synaptic current stimuli ascending from auditory nerve fibers (ANFs) were modeled by a filtered inhomogeneous Poisson process modulated by [...] Read more.
This paper presents the effects of spontaneous random activity on information transmission in an auditory brain stem neuron model. In computer simulations, the supra-threshold synaptic current stimuli ascending from auditory nerve fibers (ANFs) were modeled by a filtered inhomogeneous Poisson process modulated by sinusoidal functions at a frequency of 220–3520 Hz with regard to the human speech spectrum. The stochastic sodium and stochastic high- and low-threshold potassium channels were incorporated into a single compartment model of the soma in spherical bushy neurons, so as to realize threshold fluctuations or a variation of spike firing times. The results show that the information rates estimated from the entropy of inter-spike intervals of spike trains tend toward a convex function of the spontaneous rates when the intensity of sinusoidal functions decreases. Furthermore, the results show that a convex function of the spontaneous rates tends to disappear as the frequency of the sinusoidal function increases, such that the phase-locked response can be unobserved. It is concluded that this sort of stochastic resonance (SR) phenomenon, which depends on the spontaneous rates with supra-threshold stimuli, can better enhance information transmission in a smaller intensity of sinusoidal functions within the human speech spectrum, like the situation in which the regular SR can enhance weak signals. Full article
(This article belongs to the Special Issue Entropy in Human Brain Networks)
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<p>Electrical equivalent circuit of a spherical bushy neuron model with four kinds of stochastic ion channels.</p>
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<p>An illustrative example of a membrane potential (<b>top</b>), an excitatory synaptic conductance with an alpha function (<b>middle</b>) and an intensity function of the inhomogeneous Poisson process modulated by a sinusoidal function (<b>bottom</b>) as a function of time.</p>
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<p>An intensity function (<b>top</b>), raster plots (<b>middle</b>) and the estimated spike firing rates (bottom) as a function of time at <span class="html-italic">λ<sub>spon</sub></span> = 5 (<b>left</b>), 25 (<b>middle</b>) and 100 (<b>right</b>) s<sup>−1</sup> at <span class="html-italic">λ<sub>c</sub></span> = 200 s<sup>−1</sup>, <span class="html-italic">f</span> = 220 Hz. The post-stimulus time histograms (PSTHs) were generated with a bin width of 0.1 ms in the bottom traces.</p>
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<p>Inter-spike interval histogram (ISIH) at <span class="html-italic">λ<sub>spon</sub></span> = 5 (<b>left</b>), 25 (<b>middle</b>) and 100 (<b>right</b>) s<sup>−1</sup> at <span class="html-italic">λ<sub>c</sub></span> = 200 s<sup>−1</sup>, <span class="html-italic">f</span> = 220 Hz. The ISIHs were generated with a bin width of 0.5 ms.</p>
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<p>Information rate as a function of spontaneous spike rate in which <span class="html-italic">λ<sub>c</sub></span> is set to 100 s<sup>−1</sup> (blue), 200 s<sup>−1</sup> (cyan), 400 s<sup>−1</sup> (magenta) and 800 s<sup>−1</sup> (red) at <span class="html-italic">f</span> = 220 Hz.</p>
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<p>Information rate as a function of spontaneous spike rate in which <span class="html-italic">f</span> is set to 220 Hz (blue), 880 Hz (cyan), 1760 Hz (magenta) and 3520 Hz (red) at <span class="html-italic">λ<sub>c</sub></span> = 200 s<sup>−1</sup>.</p>
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<p>Information rate as a function of spontaneous spike rate in which the number of sodium channels is set to 8312 (blue), 27,708 (cyan), 83,126 (magenta) and 166,253 (red) at <span class="html-italic">f</span> = 220 Hz and <span class="html-italic">λ<sub>c</sub></span> = 200 s<sup>−1</sup>.</p>
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328 KiB  
Article
The McMillan Theorem for Colored Branching Processes and Dimensions of Random Fractals
by Victor Bakhtin
Entropy 2014, 16(12), 6624-6653; https://doi.org/10.3390/e16126624 - 19 Dec 2014
Cited by 2 | Viewed by 4810
Abstract
For the simplest colored branching process, we prove an analog to the McMillan theorem and calculate the Hausdorff dimensions of random fractals defined in terms of the limit behavior of empirical measures generated by finite genetic lines. In this setting, the role of [...] Read more.
For the simplest colored branching process, we prove an analog to the McMillan theorem and calculate the Hausdorff dimensions of random fractals defined in terms of the limit behavior of empirical measures generated by finite genetic lines. In this setting, the role of Shannon’s entropy is played by the Kullback–Leibler divergence, and the Hausdorff dimensions are computed by means of the so-called Billingsley–Kullback entropy, defined in the paper. Full article
(This article belongs to the Section Information Theory, Probability and Statistics)
1300 KiB  
Article
Detection and Modeling of Cyber Attacks with Petri Nets
by Bartosz Jasiul, Marcin Szpyrka and Joanna Śliwa
Entropy 2014, 16(12), 6602-6623; https://doi.org/10.3390/e16126602 - 19 Dec 2014
Cited by 38 | Viewed by 10670
Abstract
The aim of this article is to present an approach to develop and verify a method of formal modeling of cyber threats directed at computer systems. Moreover, the goal is to prove that the method enables one to create models resembling the behavior [...] Read more.
The aim of this article is to present an approach to develop and verify a method of formal modeling of cyber threats directed at computer systems. Moreover, the goal is to prove that the method enables one to create models resembling the behavior of malware that support the detection process of selected cyber attacks and facilitate the application of countermeasures. The most common cyber threats targeting end users and terminals are caused by malicious software, called malware. The malware detection process can be performed either by matching their digital signatures or analyzing their behavioral models. As the obfuscation techniques make the malware almost undetectable, the classic signature-based anti-virus tools must be supported with behavioral analysis. The proposed approach to modeling of malware behavior is based on colored Petri nets. This article is addressed to cyber defense researchers, security architects and developers solving up-to-date problems regarding the detection and prevention of advanced persistent threats. Full article
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<p>Colored Petri net (CP-net) model of PRONTOnet : Page hierarchy graph.</p>
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<p>CP-net model of PRONTOnet: Primary module.</p>
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<p>CP-net: Acquisition module.</p>
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<p>CP-net: verification module.</p>
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<p>CP-net: Virut module (classifier).</p>
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<p>CP-net malware modeling tool: First window.</p>
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<p>Malware editor window of the CP-net malware modeling tool (CPN MM).</p>
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<p>Editor for malware symptoms.</p>
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<p>Result of the detection of the Virut malware.</p>
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1435 KiB  
Article
Depth Image Coding Using Entropy-Based Adaptive Measurement Allocation
by Huihui Bai, Mengmeng Zhang, Meiqin Liu, Anhong Wang and Yao Zhao
Entropy 2014, 16(12), 6590-6601; https://doi.org/10.3390/e16126590 - 17 Dec 2014
Cited by 5 | Viewed by 5590
Abstract
Differently from traditional two-dimensional texture images, the depth images of three-dimensional (3D) video systems have significant sparse characteristics under the certain transform basis, which make it possible for compressive sensing to represent depth information efficiently. Therefore, in this paper, a novel depth image [...] Read more.
Differently from traditional two-dimensional texture images, the depth images of three-dimensional (3D) video systems have significant sparse characteristics under the certain transform basis, which make it possible for compressive sensing to represent depth information efficiently. Therefore, in this paper, a novel depth image coding scheme is proposed based on a block compressive sensing method. At the encoder, in view of the characteristics of depth images, the entropy of pixels in each block is employed to represent the sparsity of depth signals. Then according to the different sparsity in the pixel domain, the measurements can be adaptively allocated to each block for higher compression efficiency. At the decoder, the sparse transform can be combined to achieve the compressive sensing reconstruction. Experimental results have shown that at the same sampling rate, the proposed scheme can obtain higher PSNR values and better subjective quality of the rendered virtual views, compared with the method using a uniform sampling rate. Full article
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<p>Basic framework of image compression based on CS.</p>
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<p>Block diagram of the proposed scheme.</p>
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<p>Example of the anti-ground noise filter.</p>
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<p>Flowchart of measurement allocation.</p>
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<p>A typical example of measurement allocation.</p>
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<p>A typical example of threshold determination.</p>
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<p>Objective quality comparison for Balloons, Kendo and Pantomime. (<b>a</b>), (<b>c</b>) and (<b>e</b>): for depth map; (<b>b</b>), (<b>d</b>) and (<b>f</b>): for synthesized virtual viewpoint.</p>
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<p>Subjective quality comparison of synthesized virtual viewpoint for Balloons and Kendo. (<b>a</b>), (<b>c</b>), (<b>e</b>) and (<b>g</b>): uniform sampling; (<b>b</b>), (<b>d</b>), (<b>f</b>) and (<b>h</b>): proposed scheme.</p>
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1157 KiB  
Article
Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques
by Jose Luis Rodríguez-Sotelo, Alejandro Osorio-Forero, Alejandro Jiménez-Rodríguez, David Cuesta-Frau, Eva Cirugeda-Roldán and Diego Peluffo
Entropy 2014, 16(12), 6573-6589; https://doi.org/10.3390/e16126573 - 17 Dec 2014
Cited by 101 | Viewed by 11484
Abstract
Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, [...] Read more.
Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures. This scheme extracts entropy metrics from EEG records to obtain a feature vector. Then, these features are optimized in terms of relevance using the Q-α algorithm. Finally, the resulting set of features is entered into a clustering procedure to obtain a final segmentation of the sleep stages. The proposed method reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low. Full article
(This article belongs to the Special Issue Entropy and Electroencephalography)
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<p>Flow chart of the method proposed. The EEG records are first processed to extract features that are then selected to optimize the information density. Finally, a clustering algorithm creates the partition of records into sleep stages.</p>
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<p>Results obtained for each patient separately.</p>
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<p>Performance of the NN and the proposed clustering-based classification method in terms of the set of features used (standard PCA or Q-α) for (<b>a</b>) accuracy and (<b>b</b>) Kappa coefficient. The results of the method proposed are highlighted (Average accuracy: 0.81).</p>
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<p>Example of result comparison using manual and automatic scoring. The hypnograms represent the class obtained using the method proposed in contrast to manual labels for a set of epochs from a single subject.</p>
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<p>Results using an additional experimental database from subjects aged 18 to 79 and taking temazepam. The performance decreases but it also applies to other methods such as that based on NN.</p>
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18016 KiB  
Article
Enhanced Automatic Wavelet Independent Component Analysis for Electroencephalographic Artifact Removal
by Nadia Mammone and Francesco C. Morabito
Entropy 2014, 16(12), 6553-6572; https://doi.org/10.3390/e16126553 - 17 Dec 2014
Cited by 54 | Viewed by 12933
Abstract
Electroencephalography (EEG) is a fundamental diagnostic instrument for many neurological disorders, and it is the main tool for the investigation of the cognitive or pathological activity of the brain through the bioelectromagnetic fields that it generates. The correct interpretation of the EEG is [...] Read more.
Electroencephalography (EEG) is a fundamental diagnostic instrument for many neurological disorders, and it is the main tool for the investigation of the cognitive or pathological activity of the brain through the bioelectromagnetic fields that it generates. The correct interpretation of the EEG is misleading, both for clinicians’ visual evaluation and for automated procedures, because of artifacts. As a consequence, artifact rejection in EEG is a key preprocessing step, and the quest for reliable automatic processors has been quickly growing in the last few years. Recently, a promising automatic methodology, known as automatic wavelet-independent component analysis (AWICA), has been proposed. In this paper, a more efficient and sensitive version, called enhanced-AWICA (EAWICA), is proposed, and an extensive performance comparison is carried out by a step of tuning the different parameters that are involved in artifact detection. EAWICA is shown to minimize information loss and to outperform AWICA in artifact removal, both on simulated and real experimental EEG recordings. Full article
(This article belongs to the Special Issue Entropy and Electroencephalography)
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<p>Description of the block diagram of the enhanced-wavelet-ICA (EAWICA) processing system for EEG artifact rejection. The EEG recording is first partitioned into the four major EEG brain waves, and the wavelet components (WCs) are extracted. The subset of artifactual WCs is then selected, passed through ICA, and the independent components (WICs) are extracted. The WICs affected by artifacts are detected by entropy and kurtosis and then passed through a further step: the automatic rejection of the artifactual epochs. Inverse ICA and wavelet reconstruction are then performed in order to recover an artifact-free EEG dataset. The blocks are numerically labeled according to the corresponding subsections of Section 2.</p>
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<p>The independent components wavelet independent components (WICs) extracted during the processing of the dataset affected by eye blink artifacts.</p>
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<p>The WICs shown in <a href="#f2-entropy-16-06553" class="html-fig">Figure 2</a> after they have been processed and cleaned by AWICA.</p>
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<p>The WICs shown in <a href="#f2-entropy-16-06553" class="html-fig">Figure 2</a> after they have been processed and cleaned by EAWICA.</p>
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<p>Wavelet decomposition tree used for the simulated artifactual EEG dataset. The legend illustrates which details are used to reconstruct the brain waves. Since the frequency band was 0–50 Hz, a small approximation was used to reconstruct delta, theta and alpha bands.</p>
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<p>Wavelet decomposition tree used for the real EEG dataset. The legend illustrates which details are used to reconstruct the brain waves.</p>
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<p>AWICA <span class="html-italic">vs.</span> EAWICA performance comparison for different <span class="html-italic">Th1-Th2-α</span> settings and different kinds of artifacts: eye blink (<b>top-left</b>); muscular activity (subplot top-right); electrical shift (<b>bottom-left</b>); linear trend (<b>bottom-right</b>). The original artifact-free EEG and the final reconstructed EEG are compared. The parameters used to compare the two signals are the peak-SNR and the RMSE (the settings corresponding to the largest PSNR and the smallest RMSE ensures the best performance and is defined as <span class="html-italic">optimal</span>). The x-axis accounts for percentual RMSE (referring to the overall largest RMSE, when the results of either AWICA and EAWICA are considered), and the y-axis accounts for percentual PSNR (referring to the overall largest PSNR, when the results of either AWICA and EAWICA are considered). The results yielded by AWICA are represented by a red (o), whereas the results yielded by EAWICA are represented by a blue (+).</p>
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<p>Visual comparison between the performance of AWICA and EAWICA in the artifact rejection. Once the optimal <span class="html-italic">Th1-Th2-α</span> configuration was selected for either AWICA and EAWICA, the two techniques were tested against each other over the four semi-simulated artifactual EEG dataset (EEG with eye blink, muscle activity, electrical shift and linear trend). Each subplot shows the original artifact-free EEG, the EEG with the simulated artifact and the EEG reconstructed after artifact rejection. For eye blink removal, the channels involved are: Fp1 (Ch1) and Fp2 (Ch2). For the other artifacts, the channels involved are C3 (Ch1) and C4 (Ch2). The left column shows the results of AWICA artifact removal, whereas the right column shows the results of EAWICA artifact removal. EAWICA removed eye blink artifact and muscular activity better than AWICA; furthermore, it removed electrical shift and linear trend artifacts with a lesser distortion and attenuation of the EEG, especially in the artifact-free segments.</p>
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<p>Comparison among the power spectral density (PSD) of the artifact-free EEG and the PSD of the corresponding EEG reconstructed by AWICA and by EAWICA. The two techniques were tested against each other over the four semi-simulated artifactual EEG dataset (EEG with eye blink, muscle activity, electrical shift and linear trend). Each subplot corresponds to a different artifact removal and shows the three PSDs (original artifact-free EEG, EEG reconstructed by AWICA and EEG reconstructed by EAWICA).</p>
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306 KiB  
Article
Chaos Control and Synchronization of a Hyperchaotic Zhou System by Integral Sliding Mode control
by Yashar Toopchi and Jidong Wang
Entropy 2014, 16(12), 6539-6552; https://doi.org/10.3390/e16126539 - 12 Dec 2014
Cited by 25 | Viewed by 6772
Abstract
In this paper, an adaptive integral sliding mode control scheme is proposed for synchronization of hyperchaotic Zhou systems. In the proposed scheme, an integral sliding mode control is designed to stabilize a hyperchaotic Zhou system with known parameters to its unstable equilibrium at [...] Read more.
In this paper, an adaptive integral sliding mode control scheme is proposed for synchronization of hyperchaotic Zhou systems. In the proposed scheme, an integral sliding mode control is designed to stabilize a hyperchaotic Zhou system with known parameters to its unstable equilibrium at the origin. The control is then applied to the synchronization of two identical systems, i.e., a slave and a master hyperchaotic Zhou system with unknown parameters. The adaptive control mechanism introduced synchronizes the systems by estimating the unknown parameters. Simulation results have shown that the proposed method has an excellent convergence from both speed and accuracy points of view, and it outperforms Vaidyanathan’s scheme, which is a well-recognized scheme in this area. Full article
(This article belongs to the Special Issue Complex Systems and Nonlinear Dynamics)
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<p>(<b>a</b>) Phase portrait of the hyperchaotic Zhou system; (<b>b</b>) time-history of states <span class="html-italic">x</span><sub>1</sub>, <span class="html-italic">x</span><sub>2</sub>, <span class="html-italic">x</span><sub>3</sub> and <span class="html-italic">x</span><sub>4</sub>.</p>
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<p>Time responses of the controlled hyperchaotic Zhou system.</p>
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<p>Trajectories of the synchronization states <span class="html-italic">x</span><sub>1</sub> (<b>a</b>), <span class="html-italic">x</span><sub>2</sub> (<b>b</b>), <span class="html-italic">x</span><sub>3</sub> (<b>c</b>) and <span class="html-italic">x</span><sub>4</sub> (<b>d</b>) (the controller is off for <span class="html-italic">t</span> &lt; 2 and is on for <span class="html-italic">t</span> ≥ 2).</p>
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<p>Trajectories of the synchronization errors of <span class="html-italic">e</span><sub>1</sub>, <span class="html-italic">e</span><sub>2</sub>, <span class="html-italic">e</span><sub>3</sub> and <span class="html-italic">e</span><sub>4</sub> (the controller is off for <span class="html-italic">t</span> &lt; 2 and is on for <span class="html-italic">t</span> ≥ 2).</p>
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<p>Comparison between simulations results of the proposed method and the previously known method: <span class="html-italic">x</span><sub>1</sub> (<b>a</b>), <span class="html-italic">x</span><sub>2</sub> (<b>b</b>), <span class="html-italic">x</span><sub>3</sub> (<b>c</b>) and <span class="html-italic">x</span><sub>4</sub> (<b>d</b>).</p>
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<p>Comparison between the simulation errors of the proposed method and the previously known method <span class="html-italic">e</span><sub>1</sub>, <span class="html-italic">e</span><sub>2</sub>, <span class="html-italic">e</span><sub>3</sub> and <span class="html-italic">e</span><sub>4</sub>.</p>
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<p>Comparison results of parameter convergence between two schemes: time history of parameters <span class="html-italic">a</span>, <span class="html-italic">b</span>, <span class="html-italic">c</span> and <span class="html-italic">d</span> in the proposed scheme (<b>a</b>) and Vaidyanathan’s scheme (<b>b</b>).</p>
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209 KiB  
Article
Consensus of Discrete Multiagent System with Various Time Delays and Environmental Disturbances
by Zheping Yan, Di Wu and Yibo Liu
Entropy 2014, 16(12), 6524-6538; https://doi.org/10.3390/e16126524 - 11 Dec 2014
Cited by 6 | Viewed by 5414
Abstract
In this paper, the consensus problem of discrete multiagent systems with time varying sampling periods is studied. Firstly, with thorough analysis of various delays among agents, the control input of each agent is designed with consideration of sending delay and receiving delay. With [...] Read more.
In this paper, the consensus problem of discrete multiagent systems with time varying sampling periods is studied. Firstly, with thorough analysis of various delays among agents, the control input of each agent is designed with consideration of sending delay and receiving delay. With construction of discrete dynamics of state error vector, it is proved by applying Halanay inequality that consensus of the system can be reached. Further, the definition of bounded consensus is proposed in the situation where environmental disturbances exist. In order to handle this problem, the Halanay inequality is extended into a more general one with boundedness property. Based on the new Halanay inequality obtained, the boundedness of consensus error is guaranteed. At last, simulation examples are presented to demonstrate the theoretical conclusions. Full article
(This article belongs to the Special Issue Complex Systems and Nonlinear Dynamics)
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<p>Communication graph of multiagent system.</p>
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<p>Consensus error of multiagent system without disturbances.</p>
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<p>Consensus error of multiagent system with disturbances.</p>
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199 KiB  
Article
Geometric Thermodynamics: Black Holes and the Meaning of the Scalar Curvature
by Miguel Ángel García-Ariza, Merced Montesinos and Gerardo F. Torres del Castillo
Entropy 2014, 16(12), 6515-6523; https://doi.org/10.3390/e16126515 - 11 Dec 2014
Cited by 16 | Viewed by 6851
Abstract
In this paper we show that the vanishing of the scalar curvature of Ruppeiner-like metrics does not characterize the ideal gas. Furthermore, we claim through an example that flatness is not a sufficient condition to establish the absence of interactions in the underlying [...] Read more.
In this paper we show that the vanishing of the scalar curvature of Ruppeiner-like metrics does not characterize the ideal gas. Furthermore, we claim through an example that flatness is not a sufficient condition to establish the absence of interactions in the underlying microscopic model of a thermodynamic system, which poses a limitation on the usefulness of Ruppeiner’s metric and conjecture. Finally, we address the problem of the choice of coordinates in black hole thermodynamics. We propose an alternative energy representation for Kerr-Newman black holes that mimics fully Weinhold’s approach. The corresponding Ruppeiner’s metrics become degenerate only at absolute zero and have non-vanishing scalar curvatures. Full article
(This article belongs to the Special Issue Entropy and Spacetime)
224 KiB  
Article
Statistical Power Law due to Reservoir Fluctuations and the Universal Thermostat Independence Principle
by Tamás Sándor Biró, Péter Ván, Gergely Gábor Barnaföldi and Károly Ürmössy
Entropy 2014, 16(12), 6497-6514; https://doi.org/10.3390/e16126497 - 9 Dec 2014
Cited by 36 | Viewed by 6696
Abstract
Certain fluctuations in particle number, \(n\), at fixed total energy, \(E\), lead exactly to a cut-power law distribution in the one-particle energy, \(\omega\), via the induced fluctuations in the phase-space volume ratio, \(\Omega_n(E-\omega)/\Omega_n(E)=(1-\omega/E)^n\). The only parameters are \(1/T=\langle \beta \rangle=\langle n \rangle/E\) and [...] Read more.
Certain fluctuations in particle number, \(n\), at fixed total energy, \(E\), lead exactly to a cut-power law distribution in the one-particle energy, \(\omega\), via the induced fluctuations in the phase-space volume ratio, \(\Omega_n(E-\omega)/\Omega_n(E)=(1-\omega/E)^n\). The only parameters are \(1/T=\langle \beta \rangle=\langle n \rangle/E\) and \(q=1-1/\langle n \rangle + \Delta n^2/\langle n \rangle^2\). For the binomial distribution of \(n\) one obtains \(q=1-1/k\), for the negative binomial \(q=1+1/(k+1)\). These results also represent an approximation for general particle number distributions in the reservoir up to second order in the canonical expansion \(\omega \ll E\). For general systems the average phase-space volume ratio \(\langle e^{S(E-\omega)}/e^{S(E)}\rangle\) to second order delivers \(q=1-1/C+\Delta \beta^2/\langle \beta \rangle^2\) with \(\beta=S^{\prime}(E)\) and \(C=dE/dT\) heat capacity. However, \(q \ne 1\) leads to non-additivity of the Boltzmann–Gibbs entropy, \(S\). We demonstrate that a deformed entropy, \(K(S)\), can be constructed and used for demanding additivity, i.e., \(q_K=1\). This requirement leads to a second order differential equation for \(K(S)\). Finally, the generalized \(q\)-entropy formula, \(K(S)=\sum p_i K(-\ln p_i)\), contains the Tsallis, Rényi and Boltzmann–Gibbs–Shannon expressions as particular cases. For diverging variance, \(\Delta\beta^2\) we obtain a novel entropy formula. Full article
(This article belongs to the Special Issue Entropic Aspects in Statistical Physics of Complex Systems)
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<p>The general entropy <span class="html-italic">K</span>(<span class="html-italic">S</span>) (full line) and <span class="html-italic">S</span> (dashed line) are plotted for λ = ∞, meaning divergently large fluctuations with respect to the Gaussian model. For comparison the λ = 1 case, the traditional Boltzmann–Gibbs formula is indicated by the dotted line.</p>
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<p>The second derivatives of the general entropy K(S) (full line) and <span class="html-italic">S</span> (dashed line) are plotted for λ = ∞. For comparison the same derivative for the λ = 1 (Boltzmann) case is indicated by the dotted line.</p>
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1551 KiB  
Article
An Evolutionary Algorithm for the Texture Analysis of Cubic System Materials Derived by the Maximum Entropy Principle
by Dapeng Wang, Dazhi Wang, Baolin Wu, Fu Wang and Zhide Liang
Entropy 2014, 16(12), 6477-6496; https://doi.org/10.3390/e16126477 - 9 Dec 2014
Cited by 1 | Viewed by 5566
Abstract
Based on the principle of maximum entropy method (MEM) for quantitative texture analysis, the differential evolution (DE) algorithm was effectively introduced. Using a DE-optimized algorithm with a faster but more stable convergence rate of iteration reliable complete orientation distributions (C-ODF) have [...] Read more.
Based on the principle of maximum entropy method (MEM) for quantitative texture analysis, the differential evolution (DE) algorithm was effectively introduced. Using a DE-optimized algorithm with a faster but more stable convergence rate of iteration reliable complete orientation distributions (C-ODF) have been obtained for deep-drawn IF steel sheets and the recrystallized aluminum foils after cold-rolling, which are designated as showing a macroscopic cubic-orthogonal symmetry. With special reference to the data processing, no more other assumptions are required for DE-optimized MEM except that the system entropy approach the maximum. Full article
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<p>Constant-<span class="html-italic">φ</span> section of ODF representation for a Aluminum foil in comparison derived by different algorithms. <b>(a)</b> <span class="html-italic">R</span>-ODF derived by Two-step Method; <b>(b)</b> <span class="html-italic">C</span>-ODF derived by MEM using the damped Newton algorithm, <span class="html-italic">G</span> = 4000 iterations; <b>(c)</b> <span class="html-italic">C</span>-ODF derived by MEM using DE algorithm, <span class="html-italic">G</span> = 600.</p>
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<p>Constant-<span class="html-italic">φ</span> section of ODF representation for a Aluminum foil in comparison derived by different algorithms. <b>(a)</b> <span class="html-italic">R</span>-ODF derived by Two-step Method; <b>(b)</b> <span class="html-italic">C</span>-ODF derived by MEM using the damped Newton algorithm, <span class="html-italic">G</span> = 4000 iterations; <b>(c)</b> <span class="html-italic">C</span>-ODF derived by MEM using DE algorithm, <span class="html-italic">G</span> = 600.</p>
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<p>Constant-<span class="html-italic">φ</span> section of ODF representation for IF steel sheet in comparison derived by different algorithms. <b>(a)</b> <span class="html-italic">R</span>-ODF derived by Two-step Method; <b>(b)</b> <span class="html-italic">C</span>-ODF derived by MEM using the damped Newton algorithm, <span class="html-italic">G</span><sub>max</sub> = 5000 iterations; <b>(c)</b> <span class="html-italic">C</span>-ODF derived by MEM using DE algorithm, <span class="html-italic">G</span><sub>max</sub> = 500.</p>
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<p>Constant-<span class="html-italic">φ</span> section of ODF representation for IF steel sheet in comparison derived by different algorithms. <b>(a)</b> <span class="html-italic">R</span>-ODF derived by Two-step Method; <b>(b)</b> <span class="html-italic">C</span>-ODF derived by MEM using the damped Newton algorithm, <span class="html-italic">G</span><sub>max</sub> = 5000 iterations; <b>(c)</b> <span class="html-italic">C</span>-ODF derived by MEM using DE algorithm, <span class="html-italic">G</span><sub>max</sub> = 500.</p>
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<p>The rectangular coordinate system of a specimen in combination with a crystalline one.</p>
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<p>The division of 72 equivalent orientation units.</p>
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1505 KiB  
Article
A Memristive Diode Bridge-Based Canonical Chua’s Circuit
by Mo Chen, Jingjing Yu, Qing Yu, Changdi Li and Bocheng Bao
Entropy 2014, 16(12), 6464-6476; https://doi.org/10.3390/e16126464 - 8 Dec 2014
Cited by 50 | Viewed by 7988
Abstract
A novel memristor circuit is presented, which is generated from the canonical Chua’s circuit by replacing the Chua’s diode with a first order memristive diode bridge. The circuit dynamical characteristics with the variations of circuit parameters are investigated both theoretically and numerically. It [...] Read more.
A novel memristor circuit is presented, which is generated from the canonical Chua’s circuit by replacing the Chua’s diode with a first order memristive diode bridge. The circuit dynamical characteristics with the variations of circuit parameters are investigated both theoretically and numerically. It can be found that the circuit has three determined equilibrium points, including a zero saddle point and two nonzero saddle-foci with index 2. Specially, the circuit is non-dissipative in the neighborhood of the zero saddle point, and there exists complex nonlinear phenomena of coexisting bifurcation modes and coexisting chaotic attractors. Experimental observations are performed to verify the numerical simulation results. Full article
(This article belongs to the Special Issue Complex Systems and Nonlinear Dynamics)
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<p>Generalized memristor realized by memristive diode bridge with parallel RC filter [<a href="#b16-entropy-16-06464" class="html-bibr">16</a>].</p>
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<p>Generalized memristor-based canonical Chua’s circuit.</p>
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<p>Dynamics of generalized memristor based canonical Chua’s circuit. (<b>a</b>) Phase portrait in <span class="html-italic">v</span><sub>1</sub>(<span class="html-italic">t</span>)-<span class="html-italic">v</span><sub>2</sub>(<span class="html-italic">t</span>) plane; (<b>b</b>) Phase portrait in <span class="html-italic">v</span><sub>1</sub>(<span class="html-italic">t</span>)-<span class="html-italic">i</span>(<span class="html-italic">t</span>) plane and (<b>c</b>) Poincaré mapping in <span class="html-italic">v</span><sub>1</sub>(<span class="html-italic">t</span>)-<span class="html-italic">v</span><sub>2</sub>(<span class="html-italic">t</span>) plane.</p>
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<p>Two function curves and their intersection points.</p>
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<p>Dynamics with the value of <span class="html-italic">L</span> increasing. (<b>a</b>) Bifurcation diagram of <span class="html-italic">v</span><sub>2</sub>(<span class="html-italic">t</span>) and (<b>b</b>) Lyapunov exponent spectra.</p>
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<p>Chaotic and periodic orbits in <span class="html-italic">v</span><sub>1</sub>(<span class="html-italic">t</span>)-<span class="html-italic">v</span><sub>2</sub>(<span class="html-italic">t</span>) plane. (<b>a</b>) Coexisting limit cycles with period-1 (<span class="html-italic">L</span> = 55 mH); (<b>b</b>) coexisting limit cycles with period-2 (<span class="html-italic">L</span> = 85 mH); (<b>c</b>) coexisting spiral chaotic attractors (<span class="html-italic">L</span> = 160 mH) and (<b>d</b>) double-scroll chaotic attractor (<span class="html-italic">L</span> = 220 mH).</p>
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<p>Time-domain waveforms of <span class="html-italic">v</span><sub>2</sub>(<span class="html-italic">t</span>). (<b>a</b>) <span class="html-italic">L</span> = 85 mH and (<b>b</b>) <span class="html-italic">L</span> = 185 mH.</p>
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<p>Experimental results of phase portraits in <span class="html-italic">v</span><sub>1</sub>(<span class="html-italic">t</span>)-<span class="html-italic">v</span><sub>2</sub>(<span class="html-italic">t</span>) plane. (<b>a</b>) Coexisting limit cycles with period-1 (<span class="html-italic">L</span> = 55 mH); (<b>b</b>) coexisting limit cycles with period-2 (<span class="html-italic">L</span> = 85 mH); (<b>c</b>) coexisting spiral chaotic attractors (<span class="html-italic">L</span> = 160 mH) and (<b>d</b>) double-scroll chaotic attractor (<span class="html-italic">L</span> = 220 mH).</p>
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966 KiB  
Article
An Entropy-Based Damage Characterization
by Mehdi Amiri and Mohammad Modarres
Entropy 2014, 16(12), 6434-6463; https://doi.org/10.3390/e16126434 - 5 Dec 2014
Cited by 60 | Viewed by 8070
Abstract
This paper presents a scientific basis for the description of the causes of damage within an irreversible thermodynamic framework and the effects of damage as observable variables that signify degradation of structural integrity. The approach relies on the fundamentals of irreversible thermodynamics and [...] Read more.
This paper presents a scientific basis for the description of the causes of damage within an irreversible thermodynamic framework and the effects of damage as observable variables that signify degradation of structural integrity. The approach relies on the fundamentals of irreversible thermodynamics and specifically the notion of entropy generation as a measure of degradation and damage. We first review the state-of-the-art advances in entropic treatment of damage followed by a discussion on generalization of the entropic concept to damage characterization that may offers a better definition of damage metric commonly used for structural integrity assessment. In general, this approach provides the opportunity to described reliability and risk of structures in terms of fundamental science concepts. Over the years, many studies have focused on materials damage assessment by determining physics-based cause and affect relationships, the goal of this paper is to put this work in perspective and encourage future work of materials damage based on the entropy concept. Full article
(This article belongs to the Section Thermodynamics)
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<p>(<b>a</b>) Evolution of observable field variable and (<b>b</b>) evolution of damage accumulation.</p>
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<p>Entropy balance for a system.</p>
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<p>Entropy generation and entropy flow for a system.</p>
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<p>Electrochemical reaction occurring on the surface of iron in a basic solution (Reproduced from McCafferty [<a href="#b89-entropy-16-06434" class="html-bibr">89</a>]).</p>
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<p>Schematic of a tribosystem (<b>a</b>) including wearing body and counter body, (<b>b</b>) control volume enclosing interface of dissipative processes, thermodynamic model.</p>
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1714 KiB  
Article
Detecting Chaos from Agricultural Product Price Time Series
by Xin Su, Yi Wang, Shengsen Duan and Junhai Ma
Entropy 2014, 16(12), 6415-6433; https://doi.org/10.3390/e16126415 - 5 Dec 2014
Cited by 26 | Viewed by 6946
Abstract
Analysis of the characteristics of agricultural product price volatility and trend forecasting are necessary to formulate and implement agricultural price control policies. Taking wholesale cabbage prices as an example, a multiple test methodology has been adopted to identify the nonlinearity, fractality, and chaos [...] Read more.
Analysis of the characteristics of agricultural product price volatility and trend forecasting are necessary to formulate and implement agricultural price control policies. Taking wholesale cabbage prices as an example, a multiple test methodology has been adopted to identify the nonlinearity, fractality, and chaos of the data. The approaches used include the R/S analysis, the BDS test, the power spectra, the recurrence plot, the largest Lyapunov exponent, the Kolmogorov entropy, and the correlation dimension. The results show that there is chaos in agricultural wholesale price data, which provides a good theoretical basis for selecting reasonable forecasting models as prediction techniques based on chaos theory can be applied to forecasting agricultural prices. Full article
(This article belongs to the Special Issue Complex Systems and Nonlinear Dynamics)
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<p>The log<span class="html-italic">n~</span>log(<span class="html-italic">R</span>/<span class="html-italic">S</span>)<span class="html-italic"><sub>n</sub></span> curves of <span class="html-italic">X<sub>Nl</sub></span> and <span class="html-italic">X<sub>LLD</sub></span>. The red curve is for <span class="html-italic">X<sub>Nl</sub></span> and the blue curve is for <span class="html-italic">X<sub>LLD</sub></span>.</p>
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<p>Power spectrum for <span class="html-italic">X<sub>Nl</sub></span> and <span class="html-italic">X<sub>LLD</sub></span>. The red curve is for <span class="html-italic">X<sub>Nl</sub></span>. The blue curve is for <span class="html-italic">X<sub>LLD</sub></span>. The <span class="html-italic">x</span> axis represents the values of frequency, and the <span class="html-italic">y</span> axis represents the values of spectrum.</p>
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<p>The <span class="html-italic">m~E</span><sub>1</sub> and <span class="html-italic">E</span><sub>2</sub> plot of <span class="html-italic">X<sub>Nl</sub></span>.</p>
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<p>The <span class="html-italic">m</span>~<span class="html-italic">E</span><sub>1</sub> and <span class="html-italic">E</span><sub>2</sub> plot of <span class="html-italic">X<sub>LLD</sub></span>.</p>
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<p>The RP plot of <span class="html-italic">X<sub>Nl</sub></span>.</p>
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<p>The RP plot of <span class="html-italic">X<sub>LLD</sub></span>.</p>
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<p>%DET curves of <span class="html-italic">X<sub>Nl</sub></span> and <span class="html-italic">X<sub>LLD</sub></span>.</p>
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<p>The <span class="html-italic">X<sub>Nl</sub></span> plot of ln<span class="html-italic">r~</span>ln<span class="html-italic">C</span>(<span class="html-italic">m</span>, <span class="html-italic">N</span>, <span class="html-italic">r</span>, τ).</p>
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<p>The <span class="html-italic">X<sub>Nl</sub></span> plots of ln<span class="html-italic">r~</span>ln<span class="html-italic">C′</span>(<span class="html-italic">r</span>) and ln<span class="html-italic">r</span>~ln<span class="html-italic">C</span>″(<span class="html-italic">r</span>)).</p>
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870 KiB  
Article
Determining Common Weights in Data Envelopment Analysis with Shannon’s Entropy
by Xiao-Guang Qi and Bo Guo
Entropy 2014, 16(12), 6394-6414; https://doi.org/10.3390/e16126394 - 4 Dec 2014
Cited by 27 | Viewed by 7708
Abstract
Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. In the traditional DEA models, the DMU is allowed to use its most favorable multiplier weights to maximize its efficiency. There [...] Read more.
Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. In the traditional DEA models, the DMU is allowed to use its most favorable multiplier weights to maximize its efficiency. There is usually more than one efficient DMU which cannot be further discriminated. Evaluating DMUs with different multiplier weights would also be somewhat irrational in practice. The common weights DEA model is an effective method for solving these problems. In this paper, we propose a methodology combining the common weights DEA with Shannon’s entropy. In our methodology, we propose a modified weight restricted DEA model for calculating non-zero optimal weights. Then these non-zero optimal weights would be aggregated to be the common weights using Shannon’s entropy. Compared with the traditional models, our proposed method is more powerful in discriminating DMUs, especially when the inputs and outputs are numerous. Our proposed method also keeps in accordance with the basic DEA method considering the evaluation of the most efficient and inefficient DMUs. Numerical examples are provided to examine the validity and effectiveness of our proposed methodology. Full article
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<p>(<b>a</b>) CCR efficiency under different weight restrictions; (<b>b</b>) Distances between DMUs’ optimal weights and the common weights.</p>
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<p>(<b>a</b>) Common weights under different weight restrictions; (<b>b</b>) Normalized common weights under different weight restrictions.</p>
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1055 KiB  
Article
Entropy Analysis of RR and QT Interval Variability during Orthostatic and Mental Stress in Healthy Subjects
by Mathias Baumert, Barbora Czippelova, Anand Ganesan, Martin Schmidt, Sebastian Zaunseder and Michal Javorka
Entropy 2014, 16(12), 6384-6393; https://doi.org/10.3390/e16126384 - 3 Dec 2014
Cited by 21 | Viewed by 7454
Abstract
Autonomic activity affects beat-to-beat variability of heart rate and QT interval. The aim of this study was to explore whether entropy measures are suitable to detect changes in neural outflow to the heart elicited by two different stress paradigms. We recorded short-term ECG [...] Read more.
Autonomic activity affects beat-to-beat variability of heart rate and QT interval. The aim of this study was to explore whether entropy measures are suitable to detect changes in neural outflow to the heart elicited by two different stress paradigms. We recorded short-term ECG in 11 normal subjects during an experimental protocol that involved head-up tilt and mental arithmetic stress and computed sample entropy, cross-sample entropy and causal interactions based on conditional entropy from RR and QT interval time series. Head-up tilt resulted in a significant reduction in sample entropy of RR intervals and cross-sample entropy, while mental arithmetic stress resulted in a significant reduction in coupling directed from RR to QT. In conclusion, measures of entropy are suitable to detect changes in neural outflow to the heart and decoupling of repolarisation variability from heart rate variability elicited by orthostatic or mental arithmetic stress. Full article
(This article belongs to the Special Issue Entropy and Cardiac Physics)
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<p>Illustration of the two-dimensional warping algorithm. The template beat (blue) is mapped to a warping grid (blue), which is adapted to the incoming beat (black) by moving the warping points such that the distance between template and incoming beat is minimized. The optimally fitted template and the corresponding warping points are shown in red.</p>
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<p>Mean and standard deviations of beat-to-beat RR and QT interval time series during baseline (bl), head-up tilt (tilt), recovery (rec), mental arithmetic task (ma) and recovery (rec) phases. Data are expressed as group mean values and standard deviations. Asterisks indicate significant changes with respect to baseline measurement. (****<span class="html-italic">p</span> &lt; 0.0001, ***<span class="html-italic">p</span> &lt; 0.001, **<span class="html-italic">p</span> &lt; 0.01, *<span class="html-italic">p</span> &lt; 0.05)</p>
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<p>Sample entropy and cross-sample entropy of beat-to-beat RR and QT interval time series during baseline (bl), head-up tilt (tilt), recovery (rec), mental arithmetic task (ma) and recovery (rec) phases. Data are expressed as group mean values and standard deviations. Asterisks indicate significant changes with respect to baseline measurement. (*<span class="html-italic">p</span> &lt; 0.05)</p>
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<p>Conditional coupling from RR to QT and from QT to RR, respectively, with lags shown below during baseline (bl), head-up tilt (tilt), recovery (rec), mental arithmetic task (ma) and recovery (rec) phases. Data are expressed as group mean values and standard deviations. Asterisks indicate significant changes with respect to baseline measurement. (*<span class="html-italic">p</span> &lt; 0.05)</p>
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619 KiB  
Correction
Correction on Iliyasu, A.M. et al. Hybrid Quantum-Classical Protocol for Storage and Retrieval of Discrete-Valued Information. Entropy, 2014, 16, 3537-3551
by Abdullah M. Iliyasu, Salvador E. Venegas-Andraca, Fei Yan and Ahmed S. Salama
Entropy 2014, 16(12), 6382-6383; https://doi.org/10.3390/e16126382 - 2 Dec 2014
Viewed by 5033
Abstract
The authors wish to make the following corrections to this paper [1]: The correct name of the fourth author is: Ahmed S. Salama. In the Acknowledgment Section, we added the research Project No. 2014/01/2079. Below is the corrected version of the section.[...] [...] Read more.
The authors wish to make the following corrections to this paper [1]: The correct name of the fourth author is: Ahmed S. Salama. In the Acknowledgment Section, we added the research Project No. 2014/01/2079. Below is the corrected version of the section.[...] Full article
438 KiB  
Article
The Information Geometry of Bregman Divergences and Some Applications in Multi-Expert Reasoning
by Martin Adamčík
Entropy 2014, 16(12), 6338-6381; https://doi.org/10.3390/e16126338 - 1 Dec 2014
Cited by 18 | Viewed by 10050
Abstract
The aim of this paper is to develop a comprehensive study of the geometry involved in combining Bregman divergences with pooling operators over closed convex sets in a discrete probabilistic space. A particular connection we develop leads to an iterative procedure, which is [...] Read more.
The aim of this paper is to develop a comprehensive study of the geometry involved in combining Bregman divergences with pooling operators over closed convex sets in a discrete probabilistic space. A particular connection we develop leads to an iterative procedure, which is similar to the alternating projection procedure by Csiszár and Tusnády. Although such iterative procedures are well studied over much more general spaces than the one we consider, only a few authors have investigated combining projections with pooling operators. We aspire to achieve here a comprehensive study of such a combination. Besides, pooling operators combining the opinions of several rational experts allows us to discuss possible applications in multi-expert reasoning. Full article
(This article belongs to the Special Issue Maximum Entropy Applied to Inductive Logic and Reasoning)
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<p>An illustration of a divergence.</p>
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<p>A Bregman divergence.</p>
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<p>The extended Pythagorean property.</p>
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<p>An illustration of an averaging projective procedure <span class="html-italic">F</span>.</p>
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<p>The situation in the proof of Theorem 5.</p>
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<p>The situation in the proof of Theorem 7 for <span class="html-italic">n</span> = 2.</p>
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<p>The illustration of the four-point property.</p>
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<p>The illustration of Example 4.</p>
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<p>The situation in the proof of Theorem 10 for <span class="html-italic">n</span> = 2.</p>
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6384 KiB  
Article
Landscape Analysis of Geographical Names in Hubei Province, China
by Xixi Chen, Tao Hu, Fu Ren, Deng Chen, Lan Li and Nan Gao
Entropy 2014, 16(12), 6313-6337; https://doi.org/10.3390/e16126313 - 1 Dec 2014
Cited by 14 | Viewed by 8630
Abstract
Hubei Province is the hub of communications in central China, which directly determines its strategic position in the country’s development. Additionally, Hubei Province is well-known for its diverse landforms, including mountains, hills, mounds and plains. This area is called “The Province of Thousand [...] Read more.
Hubei Province is the hub of communications in central China, which directly determines its strategic position in the country’s development. Additionally, Hubei Province is well-known for its diverse landforms, including mountains, hills, mounds and plains. This area is called “The Province of Thousand Lakes” due to the abundance of water resources. Geographical names are exclusive names given to physical or anthropogenic geographic entities at specific spatial locations and are important signs by which humans understand natural and human activities. In this study, geographic information systems (GIS) technology is adopted to establish a geodatabase of geographical names with particular characteristics in Hubei Province and extract certain geomorphologic and environmental factors. We carry out landscape analysis of mountain-related geographical names and water-related geographical names respectively. In the end, we calculate the information entropy of geographical names of each county to describe the diversity and inhomogeneity of place names in Hubei province. Our study demonstrates that geographical names represent responses to the cultural landscape and physical environment. The geographical names are more interesting in specific landscapes, such as mountains and rivers. Full article
(This article belongs to the Special Issue Entropy and Space-Time Analysis in Environment and Health)
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<p>Study Area: Hubei Province, Central China.</p>
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<p>The DEM of Hubei Province with a spatial resolution of 30 m.</p>
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<p>The spatial arrangement of rivers greater than 4th order in Hubei Province.</p>
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<p>The flowchart for landscape analysis of geographical names.</p>
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<p>Absolute numbers of landscape-related toponyms.</p>
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<p>Relative numbers of landscape-related toponyms.</p>
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<p>The hillshade image of Hubei Province.</p>
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<p>The distribution probability of the landscape-related words of toponyms in Hubei Province.</p>
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<p>The area on river systems for regression analysis.</p>
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1044 KiB  
Article
Ab intio Investigation of the Thermochemistry and Kinetics of the SO2 + O3 → SO3 + O2 Reaction in Aircraft Engines and the Environment
by Xuechao Guo, Alexey B. Nadykto, Yisheng Xu, Qingzhu Zhang and Jingtian Hu
Entropy 2014, 16(12), 6300-6312; https://doi.org/10.3390/e16126300 - 1 Dec 2014
Cited by 3 | Viewed by 7672
Abstract
In the present work, the mechanisms, thermochemistry and kinetics of the reaction of SO2 with O3 have been studied using the CCSD(T)/6-31G(d) + CF method. It has been shown that there exist two possible pathways A and B of the [...] Read more.
In the present work, the mechanisms, thermochemistry and kinetics of the reaction of SO2 with O3 have been studied using the CCSD(T)/6-31G(d) + CF method. It has been shown that there exist two possible pathways A and B of the SO2 + O3 → SO3 + O2 reaction. The two pathways’ A and B barrier heights are 0.61 kcal mol−1 and 3.40 kcal mol−1, respectively, while the energy of the SO2 + O3 → SO3 + O2 reaction is −25.25 kcal mol−1. The canonical variational transition state theory with small-curvature tunneling (CVT/SCT) has been applied to study the reaction kinetics. The CVT/SCT study shows that the rate constants K for pathways A and B, KA = 1.11 × 10−12exp(−2526.13/T) and KB = 2.7 × 10−14exp(−1029.25/T), respectively, grow as the temperature increases and are much larger than those of the SO2 + O3 → SO3 + O2 reaction over the entire temperature range of 200–1500 K. This indicates that ionization of O3 and high temperatures are favorable for the SO2 oxidation via the reaction with ozone. The new data obtained in the present study can be utilized directly for the evaluation of experiments and model predictions concerning SO2 oxidation and kinetic modeling of gas-phase chemistry of pollutants/nucleation precursors formed in aircraft engines and the Earth’s atmosphere. Full article
(This article belongs to the Section Thermodynamics)
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<p>Geometries of stationary points associated with the SO<sub>2</sub> + O<sub>3</sub><sup>−</sup> → SO<sub>3</sub><sup>−</sup> + O<sub>2</sub> reaction obtained at B3LYP/aug-cc-pvdz level of theory. Bond distances are given in Å. TS1 and TS2 denote transition states of pathway A and B, respectively.</p>
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<p>The potential energy surface for the SO<sub>2</sub> + O<sub>3</sub> → SO<sub>3</sub><sup>−</sup> + O<sub>2</sub> reaction.</p>
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<p>Comparison of the rate constants K<sub>A</sub> and K<sub>B</sub> for SO<sub>2</sub> + O<sub>3</sub><sup>−</sup> → SO<sub>3</sub><sup>−</sup> + O<sub>2</sub> reaction at 200−1500 K with the experimental and theoretical data for the SO<sub>2</sub> + O<sub>3</sub> → SO<sub>3</sub> + O<sub>2</sub> reaction [<a href="#b32-entropy-16-06300" class="html-bibr">32</a>,<a href="#b43-entropy-16-06300" class="html-bibr">43</a>,<a href="#b58-entropy-16-06300" class="html-bibr">58</a>]. Symbols A, B, exp*, theor*, NIST* refer to pathway A (present study), pathway B (present study), ref. [<a href="#b32-entropy-16-06300" class="html-bibr">32</a>], ref. [<a href="#b43-entropy-16-06300" class="html-bibr">43</a>] and NIST fitted data ref. [<a href="#b58-entropy-16-06300" class="html-bibr">58</a>], respectively.</p>
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<p>The possible reaction pathways of the SO<sub>2</sub>+O<sub>3</sub><sup>−</sup>→SO<sub>3</sub><sup>−</sup>+O<sub>2</sub> reaction with the potential barriers E* (kcal mol<sup>−1</sup>) and reaction heats (kcal mol<sup>−1</sup>).</p>
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1391 KiB  
Article
Adaptive Synchronization of Fractional Neural Networks with Unknown Parameters and Time Delays
by Weiyuan Ma, Changpin Li, Yujiang Wu and Yongqing Wu
Entropy 2014, 16(12), 6286-6299; https://doi.org/10.3390/e16126286 - 1 Dec 2014
Cited by 45 | Viewed by 6762
Abstract
In this paper, the parameters identification and synchronization problem of fractional-order neural networks with time delays are investigated. Based on some analytical techniques and an adaptive control method, a simple adaptive synchronization controller and parameter update laws are designed to synchronize two uncertain [...] Read more.
In this paper, the parameters identification and synchronization problem of fractional-order neural networks with time delays are investigated. Based on some analytical techniques and an adaptive control method, a simple adaptive synchronization controller and parameter update laws are designed to synchronize two uncertain complex networks with time delays. Besides, the system parameters in the uncertain network can be identified in the process of synchronization. To demonstrate the validity of the proposed method, several illustrative examples are presented. Full article
(This article belongs to the Special Issue Complex Systems and Nonlinear Dynamics)
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<p>Chaotic attractor of the system (<a href="#fd37-entropy-16-06286" class="html-disp-formula">19</a>).</p>
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<p>Asymptotically stable equilibrium of the system (<a href="#fd37-entropy-16-06286" class="html-disp-formula">19</a>).</p>
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<p>Asymptotically stable periodic solution of the system (<a href="#fd37-entropy-16-06286" class="html-disp-formula">19</a>).</p>
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<p>Identification of uncertain parameters <span class="html-italic">Â</span>.</p>
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<p>Identification of uncertain parameters <math display="inline"> <mover accent="true"> <mi>B</mi> <mo>^</mo></mover></math>.</p>
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<p>The time evolution of synchronization errors <span class="html-italic">e</span><sub>1</sub>(<span class="html-italic">t</span>), <span class="html-italic">e</span><sub>2</sub>(<span class="html-italic">t</span>).</p>
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<p>Time evolution of the controlling strength <span class="html-italic">k</span><sub>1</sub>(<span class="html-italic">t</span>), <span class="html-italic">k</span><sub>2</sub>(<span class="html-italic">t</span>).</p>
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<p>The system errors of different fractional.</p>
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<p>The error norms <math display="inline"> <mrow> <mrow> <mo>‖</mo> <mrow> <mi>A</mi> <mo>−</mo> <mover accent="true"> <mi>A</mi> <mo>^</mo></mover></mrow> <mo>‖</mo></mrow></mrow></math> of different fractional.</p>
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266 KiB  
Article
A Thermodynamical Selection-Based Discrete Differential Evolution for the 0-1 Knapsack Problem
by Zhaolu Guo, Xuezhi Yue, Kejun Zhang, Shenwen Wang and Zhijian Wu
Entropy 2014, 16(12), 6263-6285; https://doi.org/10.3390/e16126263 - 28 Nov 2014
Cited by 13 | Viewed by 6484
Abstract
Many problems in business and engineering can be modeled as 0-1 knapsack problems. However, the 0-1 knapsack problem is one of the classical NP-hard problems. Therefore, it is valuable to develop effective and efficient algorithms for solving 0-1 knapsack problems. Aiming at the [...] Read more.
Many problems in business and engineering can be modeled as 0-1 knapsack problems. However, the 0-1 knapsack problem is one of the classical NP-hard problems. Therefore, it is valuable to develop effective and efficient algorithms for solving 0-1 knapsack problems. Aiming at the drawbacks of the selection operator in the traditional differential evolution (DE), we present a novel discrete differential evolution (TDDE) for solving 0-1 knapsack problem. In TDDE, an enhanced selection operator inspired by the principle of the minimal free energy in thermodynamics is employed, trying to balance the conflict between the selective pressure and the diversity of population to some degree. An experimental study is conducted on twenty 0-1 knapsack test instances. The comparison results show that TDDE can gain competitive performance on the majority of the test instances. Full article
(This article belongs to the Special Issue Entropy in Bioinspired Intelligence)
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206 KiB  
Article
On a Local Fractional Wave Equation under Fixed Entropy Arising in Fractal Hydrodynamics
by Yu Zhang, Dumitru Baleanu and Xiaojun Yang
Entropy 2014, 16(12), 6254-6262; https://doi.org/10.3390/e16126254 - 28 Nov 2014
Cited by 22 | Viewed by 5406
Abstract
In this paper, based on fixed entropy, the adiabatic equation of state in fractal flow is discussed. The local fractional wave equation for the velocity potential is also obtained by using the non-differential perturbations for the pressure and density of fractal hydrodynamics. Full article
(This article belongs to the Section Complexity)
14220 KiB  
Article
Generation and Nonlinear Dynamical Analyses of Fractional-Order Memristor-Based Lorenz Systems
by Huiling Xi, Yuxia Li and Xia Huang
Entropy 2014, 16(12), 6240-6253; https://doi.org/10.3390/e16126240 - 28 Nov 2014
Cited by 35 | Viewed by 9448
Abstract
In this paper, four fractional-order memristor-based Lorenz systems with the flux-controlled memristor characterized by a monotone-increasing piecewise linear function, a quadratic nonlinearity, a smooth continuous cubic nonlinearity and a quartic nonlinearity are presented, respectively. The nonlinear dynamics are analyzed by using numerical simulation [...] Read more.
In this paper, four fractional-order memristor-based Lorenz systems with the flux-controlled memristor characterized by a monotone-increasing piecewise linear function, a quadratic nonlinearity, a smooth continuous cubic nonlinearity and a quartic nonlinearity are presented, respectively. The nonlinear dynamics are analyzed by using numerical simulation methods, including phase portraits, bifurcation diagrams, the largest Lyapunov exponent and power spectrum diagrams. Some interesting phenomena, such as inverse period-doubling bifurcation and intermittent chaos, are found to exist in the proposed systems. Full article
(This article belongs to the Special Issue Recent Advances in Chaos Theory and Complex Networks)
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<p>Bifurcation diagram of System (7) with respect to parameter <span class="html-italic">c</span><sub>1</sub>.</p>
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<p>Time history, phase portrait and power spectrum diagram of the chaotic attractor when <span class="html-italic">c</span><sub>1</sub> = 35.</p>
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<p>Bifurcation diagram of System <a href="#fd10-entropy-16-06240" class="html-disp-formula">(8)</a> with respect to parameter <span class="html-italic">c</span><sub>2</sub>.</p>
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<p>Time histories, phase portraits and power spectrum diagrams of the chaotic attractor, period-5 orbit, period-3 orbit and period-1 orbit.</p>
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<p>Bifurcation diagram of System <a href="#fd13-entropy-16-06240" class="html-disp-formula">(11)</a> with respect to parameter <span class="html-italic">c</span><sub>3</sub>.</p>
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<p>Time histories, phase portraits and power spectrum diagrams of the chaotic attractor, period-5 orbit, period-3 orbit and period-1 orbit.</p>
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<p>Bifurcation diagram of System <a href="#fd16-entropy-16-06240" class="html-disp-formula">(14)</a> with respect to parameter <span class="html-italic">c</span><sub>4</sub>.</p>
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<p>Time histories, phase portraits and power spectrum diagrams of the chaotic attractor, period-3 orbit, quasi-period orbit and period-1 orbit.</p>
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882 KiB  
Article
Ordinal Patterns, Entropy, and EEG
by Karsten Keller, Anton M. Unakafov and Valentina A. Unakafova
Entropy 2014, 16(12), 6212-6239; https://doi.org/10.3390/e16126212 - 27 Nov 2014
Cited by 67 | Viewed by 11877
Abstract
In this paper we illustrate the potential of ordinal-patterns-based methods for analysis of real-world data and, especially, of electroencephalogram (EEG) data. We apply already known (empirical permutation entropy, ordinal pattern distributions) and new (empirical conditional entropy of ordinal patterns, robust to noise empirical [...] Read more.
In this paper we illustrate the potential of ordinal-patterns-based methods for analysis of real-world data and, especially, of electroencephalogram (EEG) data. We apply already known (empirical permutation entropy, ordinal pattern distributions) and new (empirical conditional entropy of ordinal patterns, robust to noise empirical permutation entropy) methods for measuring complexity, segmentation and classification of time series. Full article
(This article belongs to the Special Issue Entropy and Electroencephalography)
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<p>The ordinal patterns of order <span class="html-italic">d</span> = 2.</p>
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<p>Empirical permutation entropy of EEG data with epileptic seizure.</p>
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<p>Empirical permutation entropy and 0.1-robust empirical permutation entropy of a time series generated by the logistic map; <span class="html-italic">σ</span> stands for the standard deviation of added centered Gaussian white noise.</p>
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<p>Empirical permutation entropy and 0.1-robust empirical permutation entropy of a time series generated by the tent map; <span class="html-italic">σ</span> stands for the standard deviation of added centered Gaussian white noise.</p>
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<p>Comparison of the computational times, measured by the MATLAB function “cputime”, of the approximate entropy, sample entropy, empirical permutation entropy and empirical conditional entropy of ordinal patterns computed with the MATLAB scripts from [<a href="#b51-entropy-16-06212" class="html-bibr">51</a>,<a href="#b52-entropy-16-06212" class="html-bibr">52</a>], [<a href="#b50-entropy-16-06212" class="html-bibr">50</a>] (“PE.m”) and [<a href="#b37-entropy-16-06212" class="html-bibr">37</a>] (“CE.m”), correspondingly.</p>
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<p>Approximate entropy, sample entropy, empirical permutation entropy and empirical conditional entropy of ordinal patterns of a time series generated by the <span class="html-italic">β</span>-transformation for <span class="html-italic">β</span> = 11 with dependence on the length <span class="html-italic">N</span> and order <span class="html-italic">d</span>.</p>
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<p>The values of approximate entropy, sample entropy, empirical permutation entropy and empirical conditional entropy of ordinal patterns of a time series generated by the beta-transformation with dependence on the parameter <span class="html-italic">β</span>.</p>
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<p>The values of the entropies of the recordings from the groups A and B, C and D, and E; <span class="html-italic">N</span> = 4097.</p>
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<p>The values of empirical permutation entropy and empirical conditional entropy of ordinal patterns versus the values of approximate entropy and sample entropy; <span class="html-italic">N</span> = 4097.</p>
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2785 KiB  
Article
Complex Modified Hybrid Projective Synchronization of Different Dimensional Fractional-Order Complex Chaos and Real Hyper-Chaos
by Jian Liu
Entropy 2014, 16(12), 6195-6211; https://doi.org/10.3390/e16126195 - 27 Nov 2014
Cited by 26 | Viewed by 5964
Abstract
This paper introduces a type of modified hybrid projective synchronization with complex transformationmatrix (CMHPS) for different dimensional fractional-order complex chaos and fractional-order real hyper-chaos. The transformationmatrix in this type of chaotic synchronization is a non-square matrix, and its elements are complex numbers. Based [...] Read more.
This paper introduces a type of modified hybrid projective synchronization with complex transformationmatrix (CMHPS) for different dimensional fractional-order complex chaos and fractional-order real hyper-chaos. The transformationmatrix in this type of chaotic synchronization is a non-square matrix, and its elements are complex numbers. Based on the stability theory of fractional-order systems, by employing the feedback control technique, necessary and sufficient criteria on CMHPS are derived. Furthermore, CMHPS between fractional-order real hyper-chaotic Rössler system and other two different dimensional fractional-order complex Lorenz-like chaotic systems is provided as two examples to discuss reduced order and increased order synchronization, respectively. Full article
(This article belongs to the Special Issue Complex Systems and Nonlinear Dynamics)
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<p>The hyper-chaotic attractor of the fractional-order real Rössler system <a href="#fd27-entropy-16-06195" class="html-disp-formula">(25)</a> for <span class="html-italic">c</span><sub>1</sub> = 0.32, <span class="html-italic">c</span><sub>2</sub> = 3, <span class="html-italic">c</span><sub>3</sub> = 0.5, <span class="html-italic">c</span><sub>4</sub> = 0.05, <span class="html-italic">α</span> = 0.95.</p>
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<p>Chaotic attractor projections of fractional-order complex Chen system <a href="#fd29-entropy-16-06195" class="html-disp-formula">(26)</a> for <span class="html-italic">p</span><sub>1</sub> = 35, <span class="html-italic">p</span><sub>2</sub> = 28, <span class="html-italic">p</span><sub>3</sub> = 3, <span class="html-italic">α</span> = 0.95.</p>
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<p>Reduced order synchronization-CMHPS between four-dimensional fractional-order real hyper-chaotic Rössler drive system <a href="#fd27-entropy-16-06195" class="html-disp-formula">(25)</a> and three-dimensional fractional-order complex chaotic Chen response system <a href="#fd29-entropy-16-06195" class="html-disp-formula">(26)</a> with the controller <a href="#fd34-entropy-16-06195" class="html-disp-formula">(28)</a>. (<b>a</b>) <math display="inline"> <mrow> <msubsup> <mi>z</mi> <mn>1</mn> <mi>r</mi></msubsup></mrow></math> synchronizes <span class="html-italic">y</span><sub>1</sub>; (<b>b</b>) <math display="inline"> <mrow> <msubsup> <mi>z</mi> <mn>1</mn> <mi>i</mi></msubsup></mrow></math> anti-synchronizes <span class="html-italic">y</span><sub>1</sub>; (<b>c</b>) <math display="inline"> <mrow> <msubsup> <mi>z</mi> <mn>2</mn> <mi>r</mi></msubsup></mrow></math>anti-synchronizes <span class="html-italic">y</span><sub>1</sub>; (<b>d</b>) <math display="inline"> <mrow> <msubsup> <mi>z</mi> <mn>1</mn> <mi>i</mi></msubsup></mrow></math>synchronizes 2<span class="html-italic">y</span><sub>2</sub>; (<b>e</b>) <span class="html-italic">z</span><sub>3</sub> synchronizes <span class="html-italic">y</span><sub>3</sub> − <span class="html-italic">y</span><sub>4</sub>.</p>
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<p>The CMHPS error dynamic of fractional-order real hyper-chaotic Rössler drive system <a href="#fd27-entropy-16-06195" class="html-disp-formula">(25)</a> and fractional-order complex chaotic Chen response system <a href="#fd29-entropy-16-06195" class="html-disp-formula">(26)</a> with the controller <a href="#fd34-entropy-16-06195" class="html-disp-formula">(28)</a>.</p>
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<p>The chaotic attractor projections of fractional-order complex Lorenz system <a href="#fd35-entropy-16-06195" class="html-disp-formula">(29)</a> for <span class="html-italic">q</span><sub>1</sub> = 10, <span class="html-italic">q</span><sub>2</sub> = 180, <math display="inline"> <mrow> <msub> <mi>q</mi> <mn>3</mn></msub> <mo>=</mo> <mfrac> <mn>8</mn> <mn>3</mn></mfrac></mrow></math>, <span class="html-italic">α</span> = 0.95.</p>
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<p>Increased order synchronization-CMHPS between three-dimensional fractional-order complex chaotic Lorenz drive system <a href="#fd35-entropy-16-06195" class="html-disp-formula">(29)</a> and four-dimensional fractional-order real hyper-chaotic Rössler response system <a href="#fd37-entropy-16-06195" class="html-disp-formula">(30)</a> with the controller <a href="#fd42-entropy-16-06195" class="html-disp-formula">(32)</a>. (<b>a</b>) <span class="html-italic">x</span><sub>1</sub> synchronizes <math display="inline"> <mrow> <msubsup> <mi>w</mi> <mn>1</mn> <mi>i</mi></msubsup></mrow></math>; (<b>b</b>) <span class="html-italic">x</span><sub>2</sub> anti-synchronizes <math display="inline"> <mrow> <msubsup> <mi>w</mi> <mn>2</mn> <mi>i</mi></msubsup></mrow></math>; (<b>c</b>) <span class="html-italic">x</span><sub>3</sub> synchronizes 2<span class="html-italic">w</span><sub>3</sub>; (<b>d</b>) <span class="html-italic">x</span><sub>4</sub> anti-synchronizes <span class="html-italic">w</span><sub>3</sub>.</p>
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<p>The CMHPS error dynamic of fractional-order complex chaotic Lorenz drive system <a href="#fd35-entropy-16-06195" class="html-disp-formula">(29)</a> and fractional-order real hyper-chaotic Rössler response system <a href="#fd37-entropy-16-06195" class="html-disp-formula">(30)</a> with the controller <a href="#fd42-entropy-16-06195" class="html-disp-formula">(32)</a>.</p>
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