Entropy

20 pages, 2833 KiB

Open AccessArticle

A Novel Drinking Category Detection Method Based on Wireless Signals and Artificial Neural Network

by Jie Zhang, Zhongmin Wang, Kexin Zhou and Ruohan Bai

Entropy 2022, 24(11), 1700; https://doi.org/10.3390/e24111700 - 21 Nov 2022

Viewed by 1518

With the continuous improvement of people’s health awareness and the continuous progress of scientific research, consumers have higher requirements for the quality of drinking. Compared with high-sugar-concentrated juice, consumers are more willing to accept healthy and original Not From Concentrated (NFC) juice and [...] Read more.

With the continuous improvement of people’s health awareness and the continuous progress of scientific research, consumers have higher requirements for the quality of drinking. Compared with high-sugar-concentrated juice, consumers are more willing to accept healthy and original Not From Concentrated (NFC) juice and packaged drinking water. At the same time, drinking category detection can be used for vending machine self-checkout. However, the current drinking category systems rely on special equipment, which require professional operation, and also rely on signals that are not widely used, such as radar. This paper introduces a novel drinking category detection method based on wireless signals and artificial neural network (ANN). Unlike past work, our design relies on WiFi signals that are widely used in life. The intuition is that when the wireless signals propagate through the detected target, the signals arrive at the receiver through multiple paths and different drinking categories will result in distinct multipath propagation, which can be leveraged to detect the drinking category. We capture the WiFi signals of detected drinking using wireless devices; then, we calculate channel state information (CSI), perform noise removal and feature extraction, and apply ANN for drinking category detection. Results demonstrate that our design has high accuracy in detecting drinking category. Full article

(This article belongs to the Topic Machine and Deep Learning)

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18 pages, 4528 KiB

Open AccessArticle

Scale Enhancement Pyramid Network for Small Object Detection from UAV Images

by Jian Sun, Hongwei Gao, Xuna Wang and Jiahui Yu

Entropy 2022, 24(11), 1699; https://doi.org/10.3390/e24111699 - 21 Nov 2022

Cited by 5 | Viewed by 1784

Abstract

Object detection is challenging in large-scale images captured by unmanned aerial vehicles (UAVs), especially when detecting small objects with significant scale variation. Most solutions employ the fusion of different scale features by building multi-scale feature pyramids to ensure that the detail and semantic [...] Read more.

Object detection is challenging in large-scale images captured by unmanned aerial vehicles (UAVs), especially when detecting small objects with significant scale variation. Most solutions employ the fusion of different scale features by building multi-scale feature pyramids to ensure that the detail and semantic information are abundant. Although feature fusion benefits object detection, it still requires the long-range dependencies information necessary for small objects with significant scale variation detection. We propose a simple yet effective scale enhancement pyramid network (SEPNet) to address these problems. A SEPNet consists of a context enhancement module (CEM) and feature alignment module (FAM). Technically, the CEM combines multi-scale atrous convolution and multi-branch grouped convolution to model global relationships. Additionally, it enhances object feature representation, preventing features with lost spatial information from flowing into the feature pyramid network (FPN). The FAM adaptively learns offsets of pixels to preserve feature consistency. The FAM aims to adjust the location of sampling points in the convolutional kernel, effectively alleviating information conflict caused by the fusion of adjacent features. Results indicate that the SEPNet achieves an AP score of 18.9% on VisDrone, which is 7.1% higher than the AP score of state-of-the-art detectors RetinaNet achieves an AP score of 81.5% on PASCAL VOC. Full article

(This article belongs to the Special Issue Application of Information Theory to Computer Vision and Image Processing)

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21 pages, 628 KiB

Open AccessArticle

Transversality Conditions for Geodesics on the Statistical Manifold of Multivariate Gaussian Distributions

by Trevor Herntier and Adrian M. Peter

Entropy 2022, 24(11), 1698; https://doi.org/10.3390/e24111698 - 21 Nov 2022

Cited by 2 | Viewed by 1422

Abstract

We consider the problem of finding the closest multivariate Gaussian distribution on a constraint surface of all Gaussian distributions to a given distribution. Previous research regarding geodesics on the multivariate Gaussian manifold has focused on finding closed-form, shortest-path distances between two fixed distributions [...] Read more.

We consider the problem of finding the closest multivariate Gaussian distribution on a constraint surface of all Gaussian distributions to a given distribution. Previous research regarding geodesics on the multivariate Gaussian manifold has focused on finding closed-form, shortest-path distances between two fixed distributions on the manifold, often restricting the parameters to obtain the desired solution. We demonstrate how to employ the techniques of the calculus of variations with a variable endpoint to search for the closest distribution from a family of distributions generated via a constraint set on the parameter manifold. Furthermore, we examine the intermediate distributions along the learned geodesics which provide insight into uncertainty evolution along the paths. Empirical results elucidate our formulations, with visual illustrations concretely exhibiting dynamics of 1D and 2D Gaussian distributions. Full article

(This article belongs to the Special Issue Information and Divergence Measures)

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2 pages, 443 KiB

Open AccessCorrection

Correction: Kudo, K. Localization Detection Based on Quantum Dynamics. Entropy 2022, 24, 1085

by Kazue Kudo

Entropy 2022, 24(11), 1697; https://doi.org/10.3390/e24111697 - 21 Nov 2022

Viewed by 888

Abstract

In the original publication [...] Full article

(This article belongs to the Special Issue Quantum Chaos—Dedicated to Professor Giulio Casati on the Occasion of His 80th Birthday)

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21 pages, 5315 KiB

Open AccessArticle

Bearing Fault Diagnosis Method Based on RCMFDE-SPLR and Ocean Predator Algorithm Optimizing Support Vector Machine

by Mingxiu Yi, Chengjiang Zhou, Limiao Yang, Jintao Yang, Tong Tang, Yunhua Jia and Xuyi Yuan

Entropy 2022, 24(11), 1696; https://doi.org/10.3390/e24111696 - 20 Nov 2022

Cited by 4 | Viewed by 1297

Abstract

For the problem that rolling bearing fault characteristics are difficult to extract accurately and the fault diagnosis accuracy is not high, an unsupervised characteristic selection method of refined composite multiscale fluctuation-based dispersion entropy (RCMFDE) combined with self-paced learning and low-redundant regularization (SPLR) is [...] Read more.

For the problem that rolling bearing fault characteristics are difficult to extract accurately and the fault diagnosis accuracy is not high, an unsupervised characteristic selection method of refined composite multiscale fluctuation-based dispersion entropy (RCMFDE) combined with self-paced learning and low-redundant regularization (SPLR) is proposed, for which the fault diagnosis is carried out by support vector machine (SVM) optimized by the marine predator algorithm (MPA). First, we extract the entropy characteristics of the bearings under different fault states by RCMFDE and the introduction of the fine composite multiscale coarse-grained method and fluctuation strategy improves the stability and estimation accuracy of the bearing characteristics; then, a novel dimensionality-reduction method, SPLR, is used to select better entropy characteristics, and the local flow structure of the fault characteristics is preserved and the redundancy is constrained by two regularization terms; finally, using the MPA-optimized SVM classifier by combining Levy motion and Eddy motion strategies, the preferred RCMFDE is fed into the MPA–SVM model for fault diagnosis, for which the obtained bearing fault diagnosis accuracy is 97.67%. The results show that the RCMFDE can effectively improve the stability and accuracy of the bearing characteristics, the SPLR-based low-dimensional characteristics can suppress the redundancy characteristics and improve the effectiveness of the characteristics, and the MPA-based adaptive SVM model solves the parameter randomness problem and, therefore, the proposed method has outstanding superiority. Full article

(This article belongs to the Special Issue Fault Diagnosis Methods Based on Information Theory or Machine Learning: From Theory to Application)

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23 pages, 382 KiB

Open AccessArticle

Are Guessing, Source Coding and Tasks Partitioning Birds of A Feather?

by M. Ashok Kumar, Albert Sunny, Ashish Thakre, Ashisha Kumar and G. Dinesh Manohar

Entropy 2022, 24(11), 1695; https://doi.org/10.3390/e24111695 - 19 Nov 2022

Cited by 1 | Viewed by 1539

Abstract

This paper establishes a close relationship among the four information theoretic problems, namely Campbell source coding, Arikan guessing, Huleihel et al. memoryless guessing and Bunte and Lapidoth tasks’ partitioning problems in the IID-lossless case. We first show that the aforementioned problems are mathematically [...] Read more.

This paper establishes a close relationship among the four information theoretic problems, namely Campbell source coding, Arikan guessing, Huleihel et al. memoryless guessing and Bunte and Lapidoth tasks’ partitioning problems in the IID-lossless case. We first show that the aforementioned problems are mathematically related via a general moment minimization problem whose optimum solution is given in terms of Renyi entropy. We then propose a general framework for the mismatched version of these problems and establish all the asymptotic results using this framework. The unified framework further enables us to study a variant of Bunte–Lapidoth’s tasks partitioning problem which is practically more appealing. In addition, this variant turns out to be a generalization of Arıkan’s guessing problem. Finally, with the help of this general framework, we establish an equivalence among all these problems, in the sense that, knowing an asymptotically optimal solution in one problem helps us find the same in all other problems. Full article

(This article belongs to the Special Issue Types of Entropies and Divergences with Their Applications)

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14 pages, 454 KiB

Open AccessFeature PaperArticle

Fast Driving of a Particle in Two Dimensions without Final Excitation

by Xiao-Jing Lu, Mikel Palmero, Ion Lizuain and Juan Gonzalo Muga

Entropy 2022, 24(11), 1694; https://doi.org/10.3390/e24111694 - 19 Nov 2022

Viewed by 1096

Abstract

Controlling the motional state of a particle in a multidimensional space is key for fundamental science and quantum technologies. Applying a recently found two-dimensional invariant combined with linear invariants, we propose protocols to drive a particle in two dimensions so that the final [...] Read more.

Controlling the motional state of a particle in a multidimensional space is key for fundamental science and quantum technologies. Applying a recently found two-dimensional invariant combined with linear invariants, we propose protocols to drive a particle in two dimensions so that the final harmonic trap is translated and rotated with respect to the initial one. These protocols realize a one-to-one mapping between initial and final eigenstates at some predetermined time and avoid any final excitations. Full article

(This article belongs to the Special Issue Shortcuts to Adiabaticity II)

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15 pages, 2778 KiB

Open AccessArticle

Bifurcation and Entropy Analysis of a Chaotic Spike Oscillator Circuit Based on the S-Switch

by Petr Boriskov, Andrei Velichko, Nikolay Shilovsky and Maksim Belyaev

Entropy 2022, 24(11), 1693; https://doi.org/10.3390/e24111693 - 19 Nov 2022

Cited by 3 | Viewed by 1632

Abstract

This paper presents a model and experimental study of a chaotic spike oscillator based on a leaky integrate-and-fire (LIF) neuron, which has a switching element with an S-type current-voltage characteristic (S-switch). The oscillator generates spikes of the S-switch in the form of chaotic [...] Read more.

This paper presents a model and experimental study of a chaotic spike oscillator based on a leaky integrate-and-fire (LIF) neuron, which has a switching element with an S-type current-voltage characteristic (S-switch). The oscillator generates spikes of the S-switch in the form of chaotic pulse position modulation driven by the feedback with rate coding instability of LIF neuron. The oscillator model with piecewise function of the S-switch has resistive feedback using a second order filter. The oscillator circuit is built on four operational amplifiers and two field-effect transistors (MOSFETs) that form an S-switch based on a Schmitt trigger, an active RC filter and a matching amplifier. We investigate the bifurcation diagrams of the model and the circuit and calculate the entropy of oscillations. For the analog circuit, the “regular oscillation-chaos” transition is analysed in a series of tests initiated by a step voltage in the matching amplifier. Entropy values are used to estimate the average time for the transition of oscillations to chaos and the degree of signal correlation of the transition mode of different tests. Study results can be applied in various reservoir computing applications, for example, in choosing and configuring the LogNNet network reservoir circuits. Full article

(This article belongs to the Section Complexity)

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19 pages, 920 KiB

Open AccessArticle

A Hybrid Opinion Formation and Polarization Model

by Baizhong Yang, Quan Yu and Yi Fan

Entropy 2022, 24(11), 1692; https://doi.org/10.3390/e24111692 - 19 Nov 2022

Viewed by 1295

Abstract

The last decade has witnessed a great number of opinion formation models that depict the evolution of opinions within a social group and make predictions about the evolution process. In the traditional formulation of opinion evolution such as the DeGroot model, an agent’s [...] Read more.

The last decade has witnessed a great number of opinion formation models that depict the evolution of opinions within a social group and make predictions about the evolution process. In the traditional formulation of opinion evolution such as the DeGroot model, an agent’s opinion is represented as a real number and updated by taking a weighted average of its neighbour’s opinions. In this paper, we adopt a hybrid representation of opinions that integrate both the discrete and continuous nature of an opinion. Basically, an agent has a ‘Yes’, ‘Neutral’ or ‘No’ opinion on some issues of interest and associates with its Yes opinion a support degree which captures how strongly it supports the opinion. With such a rich representation, not only can we study the evolution of opinion but also that of support degree. After all, an agent’s opinion can stay the same but become more or less supportive of it. Changes in the support degree are progressive in nature and only a sufficient accumulation of such a progressive change will result in a change of opinion say from Yes to No. Hence, in our formulation, after an agent interacts with another, its support degree is either strengthened or weakened by a predefined amount and a change of opinion may occur as a consequence of such progressive changes. We carry out simulations to evaluate the impacts of key model parameters including (1) the number of agents, (2) the distribution of initial support degrees and (3) the amount of change of support degree changes in a single interaction. Last but not least, we present several extensions to the hybrid and progressive model which lead to opinion polarization. Full article

(This article belongs to the Special Issue Statistical Physics and Its Applications in Economics and Social Sciences)

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13 pages, 2210 KiB

Open AccessArticle

BHGAttN: A Feature-Enhanced Hierarchical Graph Attention Network for Sentiment Analysis

by Junjun Zhang, Zhengyan Cui, Hyun Jun Park and Giseop Noh

Entropy 2022, 24(11), 1691; https://doi.org/10.3390/e24111691 - 18 Nov 2022

Cited by 1 | Viewed by 1724

Abstract

Recently, with the rise of deep learning, text classification techniques have developed rapidly. However, the existing work usually takes the entire text as the modeling object and pays less attention to the hierarchical structure within the text, ignoring the internal connection between the [...] Read more.

Recently, with the rise of deep learning, text classification techniques have developed rapidly. However, the existing work usually takes the entire text as the modeling object and pays less attention to the hierarchical structure within the text, ignoring the internal connection between the upper and lower sentences. To address these issues, this paper proposes a Bert-based hierarchical graph attention network model (BHGAttN) based on a large-scale pretrained model and graph attention network to model the hierarchical relationship of texts. During modeling, the semantic features are enhanced by the output of the intermediate layer of BERT, and the multilevel hierarchical graph network corresponding to each layer of BERT is constructed by using the dependencies between the whole sentence and the subsentence. This model pays attention to the layer-by-layer semantic information and the hierarchical relationship within the text. The experimental results show that the BHGAttN model exhibits significant competitive advantages compared with the current state-of-the-art baseline models. Full article

(This article belongs to the Special Issue Machine and Deep Learning for Affective Computing)

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11 pages, 3187 KiB

Open AccessEditor’s ChoiceArticle

Quantum Coherence in Loopless Superconductive Networks

by Massimiliano Lucci, Valerio Campanari, Davide Cassi, Vittorio Merlo, Francesco Romeo, Gaetano Salina and Matteo Cirillo

Entropy 2022, 24(11), 1690; https://doi.org/10.3390/e24111690 - 18 Nov 2022

Cited by 2 | Viewed by 1097

Abstract

Measurements indicating that planar networks of superconductive islands connected by Josephson junctions display long-range quantum coherence are reported. The networks consist of superconducting islands connected by Josephson junctions and have a tree-like topological structure containing no loops. Enhancements of superconductive gaps over specific [...] Read more.

Measurements indicating that planar networks of superconductive islands connected by Josephson junctions display long-range quantum coherence are reported. The networks consist of superconducting islands connected by Josephson junctions and have a tree-like topological structure containing no loops. Enhancements of superconductive gaps over specific branches of the networks and sharp increases in pair currents are the main signatures of the coherent states. In order to unambiguously attribute the observed effects to branches being embedded in the networks, comparisons with geometrically equivalent—but isolated—counterparts are reported. Tuning the Josephson coupling energy by an external magnetic field generates increases in the Josephson currents, along the above-mentioned specific branches, which follow a functional dependence typical of phase transitions. Results are presented for double comb and star geometry networks, and in both cases, the observed effects provide positive quantitative evidence of the predictions of existing theoretical models. Full article

(This article belongs to the Special Issue Quantum Information and Quantum Optics)

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18 pages, 13397 KiB

Open AccessArticle

Spike Spectra for Recurrences

by K. Hauke Kraemer, Frank Hellmann, Mehrnaz Anvari, Jürgen Kurths and Norbert Marwan

Entropy 2022, 24(11), 1689; https://doi.org/10.3390/e24111689 - 18 Nov 2022

Cited by 2 | Viewed by 1663

Abstract

In recurrence analysis, the

τ

-recurrence rate encodes the periods of the cycles of the underlying high-dimensional time series. It, thus, plays a similar role to the autocorrelation for scalar time-series in encoding temporal correlations. However, its Fourier decomposition does not have a [...] Read more.

In recurrence analysis, the

τ

-recurrence rate encodes the periods of the cycles of the underlying high-dimensional time series. It, thus, plays a similar role to the autocorrelation for scalar time-series in encoding temporal correlations. However, its Fourier decomposition does not have a clean interpretation. Thus, there is no satisfactory analogue to the power spectrum in recurrence analysis. We introduce a novel method to decompose the

τ

-recurrence rate using an over-complete basis of Dirac combs together with sparsity regularization. We show that this decomposition, the inter-spike spectrum, naturally provides an analogue to the power spectrum for recurrence analysis in the sense that it reveals the dominant periodicities of the underlying time series. We show that the inter-spike spectrum correctly identifies patterns and transitions in the underlying system in a wide variety of examples and is robust to measurement noise. Full article

(This article belongs to the Section Signal and Data Analysis)

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15 pages, 2334 KiB

Open AccessArticle

Lack of Habituation in Migraine Patients Based on High-Density EEG Analysis Using the Steady State of Visual Evoked Potential

by Msallam Abbas Abdulhussein, Zaid Abdi Alkareem Alyasseri, Husam Jasim Mohammed and Xingwei An

Entropy 2022, 24(11), 1688; https://doi.org/10.3390/e24111688 - 18 Nov 2022

Cited by 2 | Viewed by 1688

Abstract

Migraine is a periodic disorder in which a patient experiences changes in the morphological and functional brain, leading to the abnormal processing of repeated external stimuli in the inter-ictal phase, known as the habituation deficit. This is a significant feature clinically of migraine [...] Read more.

Migraine is a periodic disorder in which a patient experiences changes in the morphological and functional brain, leading to the abnormal processing of repeated external stimuli in the inter-ictal phase, known as the habituation deficit. This is a significant feature clinically of migraine in both two types with aura or without aura and plays an essential role in studying pathophysiological differences between these two groups. Several studies indicated that the reason for migraine aura is cortical spreading depression (CSD) but did not clarify its impact on migraine without aura and lack of habituation. In this study, 22 migraine patients (MWA, N = 13), (MWoA, N = 9), and healthy controls (HC, N = 19) were the participants. Participants were exposed to the steady state of visual evoked potentials also known as (SSVEP), which are the signals for a natural response to the visual motivation at four Hz or six Hz for 2 s followed by the inter-stimulus interval that varies between 1 and 1.5 s. The order of the temporal frequencies was randomized, and each temporal frequency was shown 100 times. We recorded from 128 customized electrode locations using high-density electroencephalography (HD-EEG) and measured amplitude and habituation for the N1–P1 and P1–N2 from the first to the sixth blocks of 100 sweep features in patients and healthy controls. Using the entropy, a decrease in amplitude and SSVEP N1-P1 habituation between the first and the sixth block appeared in both MWA and MWoA (p = 0.0001, Slope = −0.4643), (p = 0.065, Slope = 0.1483), respectively, compared to HC. For SSVEP P1–N2 between the first and sixth block, it is varied in both MWA (p = 0.0029, Slope = −0.3597) and MWoA (p = 0.027, Slope = 0.2010) compared to HC. Therefore, migraine patients appear amplitude decrease and habituation deficit but with different rates between MWA, and MWoA compared to HCs. Our findings suggest this disparity between MWoA and MWA in the lack of habituation and amplitude decrease in the inter-ictal phase has a close relationship with CSD. In light of the fact that CSD manifests during the inter-ictal phase of migraine with aura, which is when migraine seizures are most likely to occur, multiple researchers have lately reached this conclusion. This investigation led us to the conclusion that CSD during the inter-ictal phase and migraine without aura are associated. In other words, even if previous research has not demonstrated it, CSD is the main contributor to both types of migraine (those with and without aura). Full article

(This article belongs to the Special Issue Entropy for the Brain and Applied Computation)

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18 pages, 409 KiB

Open AccessArticle

Hybrid Beamforming Design for Self-Interference Cancellation in Full-Duplex Millimeter-Wave MIMO Systems with Dynamic Subarrays

by Gengshan Wang, Zhijia Yang and Tierui Gong

Entropy 2022, 24(11), 1687; https://doi.org/10.3390/e24111687 - 18 Nov 2022

Cited by 1 | Viewed by 1930

Abstract

Full-duplex (FD) millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communication is a promising solution for the extremely high-throughput requirements in future cellular systems. The hybrid beamforming structure is preferable for its low hardware complexity and low power consumption with acceptable performance. In this paper, we [...] Read more.

Full-duplex (FD) millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communication is a promising solution for the extremely high-throughput requirements in future cellular systems. The hybrid beamforming structure is preferable for its low hardware complexity and low power consumption with acceptable performance. In this paper, we introduce the hardware efficient dynamic subarrays to the FD mmWave MIMO systems and propose an effective hybrid beamforming design to cancel the self-interference (SI) in the considered system. First, assuming no SI, we obtain the optimal fully digital beamformers and combiners via the singular value decomposition of the uplink and downlink channels and the water-filling power allocation. Then, based on the obtained fully digital solutions, we get the dynamic analog solutions and digital solutions using the Kuhn–Munkres algorithm-aided dynamic hybrid beamforming design. Finally, we resort to the null space projection method to cancel the SI by projecting the obtained digital beamformer at the base station onto the null space of the equivalent SI channel. We further analyze the computational complexity of the proposed method. Numerical results demonstrate the superiority of the FD mmWave MIMO systems with the dynamic subarrays using the proposed method compared to the systems with the fixed subarrays and the half-duplex mmWave communications. When the number of RF chains is 6 and the signal-to-noise ratio is 10 dB, the proposed design outperforms the FD mmWave MIMO systems with fixed subarrays and the half-duplex mmWave communications, respectively, by 22.4% and 47.9% in spectral efficiency and 19.9% and 101% in energy efficiency. Full article

(This article belongs to the Section Signal and Data Analysis)

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14 pages, 289 KiB

Open AccessFeature PaperArticle

Entropy of Quantum Measurements

by Stanley Gudder

Entropy 2022, 24(11), 1686; https://doi.org/10.3390/e24111686 - 18 Nov 2022

Cited by 1 | Viewed by 1129

Abstract

If a is a quantum effect and

ρ

is a state, we define the

ρ

-entropy

S_{a} (ρ)

which gives the amount of uncertainty that a measurement of a provides about

ρ

. The smaller

S_{a} (ρ)

[...] Read more.

If a is a quantum effect and

ρ

is a state, we define the

ρ

-entropy

S_{a} (ρ)

which gives the amount of uncertainty that a measurement of a provides about

ρ

. The smaller

S_{a} (ρ)

is, the more information a measurement of a gives about

ρ

. In Entropy for Effects, we provide bounds on

S_{a} (ρ)

and show that if

a + b

is an effect, then

S_{a + b} (ρ) \geq S_{a} (ρ) + S_{b} (ρ)

. We then prove a result concerning convex mixtures of effects. We also consider sequential products of effects and their

ρ

-entropies. In Entropy of Observables and Instruments, we employ

S_{a} (ρ)

to define the

ρ

-entropy

S_{A} (ρ)

for an observable A. We show that

S_{A} (ρ)

directly provides the

ρ

-entropy

S_{I} (ρ)

for an instrument

I

. We establish bounds for

S_{A} (ρ)

and prove characterizations for when these bounds are obtained. These give simplified proofs of results given in the literature. We also consider

ρ

-entropies for measurement models, sequential products of observables and coarse-graining of observables. Various examples that illustrate the theory are provided. Full article

(This article belongs to the Special Issue Entropy in Quantum Systems and Quantum Field Theory (QFT) II)

19 pages, 3607 KiB

Open AccessArticle

Variational Quantum Algorithm Applied to Collision Avoidance of Unmanned Aerial Vehicles

by Zhaolong Huang, Qiting Li, Junling Zhao and Meimei Song

Entropy 2022, 24(11), 1685; https://doi.org/10.3390/e24111685 - 18 Nov 2022

Cited by 3 | Viewed by 1946

Abstract

Mission planning for multiple unmanned aerial vehicles (UAVs) is a complex problem that is expected to be solved by quantum computing. With the increasing application of UAVs, the demand for efficient conflict management strategies to ensure airspace safety continues to increase. In the [...] Read more.

Mission planning for multiple unmanned aerial vehicles (UAVs) is a complex problem that is expected to be solved by quantum computing. With the increasing application of UAVs, the demand for efficient conflict management strategies to ensure airspace safety continues to increase. In the era of noisy intermediate-scale quantum (NISQ) devices, variational quantum algorithms (VQA) for optimizing parameterized quantum circuits with the help of classical optimizers are currently one of the most promising strategies to gain quantum advantage. In this paper, we propose a mathematical model for the UAV collision avoidance problem that maps the collision avoidance problem to a quadratic unconstrained binary optimization (QUBO) problem. The problem is formulated as an Ising Hamiltonian, then the ground state is solved using two kinds of VQAs: the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA). We select conditional value-at-risk (CVaR) to further promote the performance of our model. Four examples are given to validate that with our method the probability of obtaining a feasible solution can exceed 90% based on appropriate parameters, and our method can enhance the efficiency of a UAVs’ collision avoidance model. Full article

(This article belongs to the Special Issue Quantum Computing for Complex Dynamics)

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17 pages, 5032 KiB

Open AccessArticle

Diffusion and Velocity Correlations of the Phase Transitions in a System of Macroscopic Rolling Spheres

by Francisco Vega Reyes, Álvaro Rodríguez-Rivas, Juan F. González-Saavedra and Miguel A. López-Castaño

Entropy 2022, 24(11), 1684; https://doi.org/10.3390/e24111684 - 18 Nov 2022

Viewed by 1578

Abstract

We study an air-fluidized granular monolayer composed of plastic spheres which roll on a metallic grid. The air current is adjusted so that the spheres never lose contact with the grid and so that the dynamics may be regarded as pseudo two dimensional [...] Read more.

We study an air-fluidized granular monolayer composed of plastic spheres which roll on a metallic grid. The air current is adjusted so that the spheres never lose contact with the grid and so that the dynamics may be regarded as pseudo two dimensional (or two dimensional, if the effects of the sphere rolling are not taken into account). We find two surprising continuous transitions, both of them displaying two coexisting phases. Moreover, in all the cases, we found the coexisting phases display a strong energy non-equipartition. In the first transition, at a weak fluidization, a glass phase coexists with a disordered fluid-like phase. In the second transition, a hexagonal crystal coexists with the fluid phase. We analyze, for these two-phase systems, the specific diffusive properties of each phase, as well as the velocity correlations. Surprisingly, we find a glass phase at a very low packing fraction and for a wide range of granular temperatures. Both phases are also characterized by strong anticorrelated velocities upon a collision. Thus, the dynamics observed for this quasi two-dimensional system unveil phase transitions with peculiar properties, very different from the predicted behavior in well-know theories for their equilibrium counterparts. Full article

(This article belongs to the Section Statistical Physics)

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24 pages, 1395 KiB

Open AccessFeature PaperArticle

Thermodynamically Consistent Models for Coupled Bulk and Surface Dynamics

by Xiaobo Jing and Qi Wang

Entropy 2022, 24(11), 1683; https://doi.org/10.3390/e24111683 - 17 Nov 2022

Viewed by 1364

Abstract

We present a constructive paradigm to derive thermodynamically consistent models coupling the bulk and surface dynamics hierarchically following the generalized Onsager principle. In the model, the bulk and surface thermodynamical variables are allowed to be different and the free energy of the model [...] Read more.

We present a constructive paradigm to derive thermodynamically consistent models coupling the bulk and surface dynamics hierarchically following the generalized Onsager principle. In the model, the bulk and surface thermodynamical variables are allowed to be different and the free energy of the model comprises the bulk, surface, and coupling energy, which can be weakly or strongly non-local. We illustrate the paradigm using a phase field model for binary materials and show that the model includes the existing thermodynamically consistent ones for the binary material system in the literature as special cases. In addition, we present a set of such phase field models for a few selected mobility operators and free energies to show how boundary dynamics impart changes to bulk dynamics and vice verse. As an example, we show numerically how reactive transport on the boundary impacts the dynamics in the bulk using a reactive transport model for binary reactive fluids by adopting a structure-preserving algorithm to solve the model equations in a rectangular domain. Full article

(This article belongs to the Special Issue Thermodynamics of Matter in Wide Range of Entropies)

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17 pages, 3668 KiB

Open AccessArticle

Numerical Simulation of Heat and Mass Transfer in Sludge Low-Temperature Drying Process

by Zhenyu Wang, Qiang Wang, Ju Lai, Dong Liu, Anjie Hu, Lin Xu and Yongcan Chen

Entropy 2022, 24(11), 1682; https://doi.org/10.3390/e24111682 - 17 Nov 2022

Cited by 2 | Viewed by 1862

Abstract

Based on the sludge mass transfer flux model, this paper conducts a simulation study on the drying characteristics of sludge under low-temperature environment and compares it with the previous experimental results. It is found that when the sludge moisture content is low, the [...] Read more.

Based on the sludge mass transfer flux model, this paper conducts a simulation study on the drying characteristics of sludge under low-temperature environment and compares it with the previous experimental results. It is found that when the sludge moisture content is low, the change of its drying curve is basically consistent with the experimental results, but there is a large error when the sludge moisture content is 0.4–0.6. In order to better simulate sludge drying characteristics, a model of cracking and shrinkage coefficients based on sludge moisture content is proposed, and the effective diffusion coefficient and mass transfer coefficient are modified. The maximum error between simulation and experiment is reduced to 23.78%. Based on this model, the sludge drying mechanism was studied. It was found that heat transfer and diffusion played a major role in the initial stage of sludge drying, and diffusion played a major role in sludge drying 30 min later. Full article

(This article belongs to the Special Issue Applied Thermodynamics and Heat Transfer)

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17 pages, 771 KiB

Open AccessArticle

Fractional-Order Active Disturbance Rejection Control with Fuzzy Self-Tuning for Precision Stabilized Platform

by Jianjian Zhao, Tao Zhao and Nian Liu

Entropy 2022, 24(11), 1681; https://doi.org/10.3390/e24111681 - 17 Nov 2022

Cited by 3 | Viewed by 1490

Abstract

In this paper, a novel fractional-order active disturbance rejection control with fuzzy self-tuning method (FSFOADRC) is proposed for photoelectric tracking system (PTS). Firstly, aiming at the internal uncertainty of PTS and external disturbance, a fraction-order extended state observer (FOESO) is designed, and the [...] Read more.

In this paper, a novel fractional-order active disturbance rejection control with fuzzy self-tuning method (FSFOADRC) is proposed for photoelectric tracking system (PTS). Firstly, aiming at the internal uncertainty of PTS and external disturbance, a fraction-order extended state observer (FOESO) is designed, and the FOESO can transform the plant into a simple form, which greatly simplifies the mathematical model. Secondly, a fuzzy regulator is applied to the proportion–differentiation controller (PD), increasing the flexibility and adaptivity of the controller. In addition, the stability of the whole control system can be guaranteed. Eventually, numerical comparative simulations are implemented to verify the feasibility and superiority of the proposed method. Compared with the integral-order active disturbance rejection control (IOADRC), fractional-order active disturbance rejection control (FOADRC) without the fuzzy regulator and proportion–integration–differentiation (PID) controller, the proposed method performs better with faster response, smaller overshoot, and stronger disturbance suppression capability. Full article

(This article belongs to the Section Complexity)

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24 pages, 798 KiB

Open AccessArticle

Genetic Algorithm Based on a New Similarity for Probabilistic Transformation of Belief Functions

by Yilin Dong, Lei Cao and Kezhu Zuo

Entropy 2022, 24(11), 1680; https://doi.org/10.3390/e24111680 - 17 Nov 2022

Cited by 2 | Viewed by 1460

Abstract

Recent studies of alternative probabilistic transformation (PT) in Dempster–Shafer (DS) theory have mainly focused on investigating various schemes for assigning the mass of compound focal elements to each singleton in order to obtain a Bayesian belief function for decision-making problems. In the process [...] Read more.

Recent studies of alternative probabilistic transformation (PT) in Dempster–Shafer (DS) theory have mainly focused on investigating various schemes for assigning the mass of compound focal elements to each singleton in order to obtain a Bayesian belief function for decision-making problems. In the process of such a transformation, how to precisely evaluate the closeness between the original basic belief assignments (BBAs) and transformed BBAs is important. In this paper, a new aggregation measure is proposed by comprehensively considering the interval distance between BBAs and also the sequence inside the BBAs. Relying on this new measure, we propose a novel multi-objective evolutionary-based probabilistic transformation (MOEPT) thanks to global optimizing capabilities inspired by a genetic algorithm (GA). From the perspective of mathematical theory, convergence analysis of EPT is employed to prove the rationality of the GA used here. Finally, various scenarios in evidence reasoning are presented to evaluate the robustness of EPT. Full article

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30 pages, 556 KiB

Open AccessFeature PaperArticle

Better Heisenberg Limits, Coherence Bounds, and Energy-Time Tradeoffs via Quantum Rényi Information

by Michael J. W. Hall

Entropy 2022, 24(11), 1679; https://doi.org/10.3390/e24111679 - 17 Nov 2022

Cited by 1 | Viewed by 1706

Abstract

An uncertainty relation for the Rényi entropies of conjugate quantum observables is used to obtain a strong Heisenberg limit of the form

RMSE \geq f (α) / (⟨ N ⟩ + \frac{1}{2})

, bounding the root mean square [...] Read more.

An uncertainty relation for the Rényi entropies of conjugate quantum observables is used to obtain a strong Heisenberg limit of the form

RMSE \geq f (α) / (⟨ N ⟩ + \frac{1}{2})

, bounding the root mean square error of any estimate of a random optical phase shift in terms of average photon number, where

f (α)

is maximised for non-Shannon entropies. Related simple yet strong uncertainty relations linking phase uncertainty to the photon number distribution, such as

Δ Φ \geq {max}_{n} p_{n}

, are also obtained. These results are significantly strengthened via upper and lower bounds on the Rényi mutual information of quantum communication channels, related to asymmetry and convolution, and applied to the estimation (with prior information) of unitary shift parameters such as rotation angle and time, and to obtain strong bounds on measures of coherence. Sharper Rényi entropic uncertainty relations are also obtained, including time-energy uncertainty relations for Hamiltonians with discrete spectra. In the latter case almost-periodic Rényi entropies are introduced for nonperiodic systems. Full article

(This article belongs to the Special Issue Quantum Mechanics and Its Foundations III)

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29 pages, 4549 KiB

Open AccessArticle

Brain Inspired Cortical Coding Method for Fast Clustering and Codebook Generation

by Meric Yucel, Serdar Bagis, Ahmet Sertbas, Mehmet Sarikaya and Burak Berk Ustundag

Entropy 2022, 24(11), 1678; https://doi.org/10.3390/e24111678 - 17 Nov 2022

Cited by 2 | Viewed by 1887

Abstract

A major archetype of artificial intelligence is developing algorithms facilitating temporal efficiency and accuracy while boosting the generalization performance. Even with the latest developments in machine learning, a key limitation has been the inefficient feature extraction from the initial data, which is essential [...] Read more.

A major archetype of artificial intelligence is developing algorithms facilitating temporal efficiency and accuracy while boosting the generalization performance. Even with the latest developments in machine learning, a key limitation has been the inefficient feature extraction from the initial data, which is essential in performance optimization. Here, we introduce a feature extraction method inspired by energy–entropy relations of sensory cortical networks in the brain. Dubbed the brain-inspired cortex, the algorithm provides convergence to orthogonal features from streaming signals with superior computational efficiency while processing data in a compressed form. We demonstrate the performance of the new algorithm using artificially created complex data by comparing it with the commonly used traditional clustering algorithms, such as Birch, GMM, and K-means. While the data processing time is significantly reduced—seconds versus hours—encoding distortions remain essentially the same in the new algorithm, providing a basis for better generalization. Although we show herein the superior performance of the cortical coding model in clustering and vector quantization, it also provides potent implementation opportunities for machine learning fundamental components, such as reasoning, anomaly detection and classification in large scope applications, e.g., finance, cybersecurity, and healthcare. Full article

(This article belongs to the Section Information Theory, Probability and Statistics)

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14 pages, 3148 KiB

Open AccessArticle

Point Cloud Geometry Compression Based on Multi-Layer Residual Structure

by Jiawen Yu, Jin Wang, Longhua Sun, Mu-En Wu and Qing Zhu

Entropy 2022, 24(11), 1677; https://doi.org/10.3390/e24111677 - 17 Nov 2022

Cited by 2 | Viewed by 1810

Abstract

Point cloud data are extensively used in various applications, such as autonomous driving and augmented reality since it can provide both detailed and realistic depictions of 3D scenes or objects. Meanwhile, 3D point clouds generally occupy a large amount of storage space that [...] Read more.

Point cloud data are extensively used in various applications, such as autonomous driving and augmented reality since it can provide both detailed and realistic depictions of 3D scenes or objects. Meanwhile, 3D point clouds generally occupy a large amount of storage space that is a big burden for efficient communication. However, it is difficult to efficiently compress such sparse, disordered, non-uniform and high dimensional data. Therefore, this work proposes a novel deep-learning framework for point cloud geometric compression based on an autoencoder architecture. Specifically, a multi-layer residual module is designed on a sparse convolution-based autoencoders that progressively down-samples the input point clouds and reconstructs the point clouds in a hierarchically way. It effectively constrains the accuracy of the sampling process at the encoder side, which significantly preserves the feature information with a decrease in the data volume. Compared with the state-of-the-art geometry-based point cloud compression (G-PCC) schemes, our approach obtains more than 70–90% BD-Rate gain on an object point cloud dataset and achieves a better point cloud reconstruction quality. Additionally, compared to the state-of-the-art PCGCv2, we achieve an average gain of about 10% in BD-Rate. Full article

(This article belongs to the Special Issue Information Theory-Based Deep Learning Tools for Computer Vision)

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10 pages, 279 KiB

Open AccessArticle

Quantum Mutual Information, Fragile Systems and Emergence

by Yasmín Navarrete and Sergio Davis

Entropy 2022, 24(11), 1676; https://doi.org/10.3390/e24111676 - 17 Nov 2022

Cited by 1 | Viewed by 1097

Abstract

In this paper, we present an analytical description of emergence from the density matrix framework as a state of knowledge of the system, and its generalized probability formulation. This description is based on the idea of fragile systems, wherein the observer modifies the [...] Read more.

In this paper, we present an analytical description of emergence from the density matrix framework as a state of knowledge of the system, and its generalized probability formulation. This description is based on the idea of fragile systems, wherein the observer modifies the system by the measurement (i.e., the observer effect) in order to detect possible emergent behavior. We propose the use of a descriptor, based on quantum mutual information, to calculate if subsystems of systems have inner correlations. This may contribute to a definition of emergent systems in terms of emergent information. Full article

(This article belongs to the Section Information Theory, Probability and Statistics)

15 pages, 894 KiB

Open AccessArticle

Inference, Prediction, & Entropy-Rate Estimation of Continuous-Time, Discrete-Event Processes

by Sarah E. Marzen and James P. Crutchfield

Entropy 2022, 24(11), 1675; https://doi.org/10.3390/e24111675 - 17 Nov 2022

Viewed by 1627

Abstract

Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground. However, a much broader class of discrete-event processes operates in continuous-time. Here, we provide new methods for inferring, predicting, and estimating them. The methods rely on [...] Read more.

Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground. However, a much broader class of discrete-event processes operates in continuous-time. Here, we provide new methods for inferring, predicting, and estimating them. The methods rely on an extension of Bayesian structural inference that takes advantage of neural network’s universal approximation power. Based on experiments with complex synthetic data, the methods are competitive with the state-of-the-art for prediction and entropy-rate estimation. Full article

(This article belongs to the Section Information Theory, Probability and Statistics)

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14 pages, 4802 KiB

Open AccessArticle

Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting

by Mohammed A. A. Al-qaness, Ahmed A. Ewees, Laith Abualigah, Ayman Mutahar AlRassas, Hung Vo Thanh and Mohamed Abd Elaziz

Entropy 2022, 24(11), 1674; https://doi.org/10.3390/e24111674 - 17 Nov 2022

Cited by 18 | Viewed by 1913

Abstract

The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is [...] Read more.

The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is an ANNs that has showed promising performance in time-series prediction. The DNR has the capability to deal with the nonlinear characteristics of historical data for time-series forecasting applications. However, it faces certain limitations in training and configuring its parameters. To this end, we utilized the power of metaheuristic optimization algorithms to boost the training process and optimize its parameters. A comprehensive evaluation is presented in this study with six MH optimization algorithms used for this purpose: whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), genetic algorithm (GA), sine–cosine algorithm (SCA), differential evolution (DE), and harmony search algorithm (HS). We used oil-production datasets for historical records of crude oil production from seven real-world oilfields (from Tahe oilfields, in China), provided by a local partner. Extensive evaluation experiments were carried out using several performance measures to study the validity of the DNR with MH optimization methods in time-series applications. The findings of this study have confirmed the applicability of MH with DNR. The applications of MH methods improved the performance of the original DNR. We also concluded that the PSO and WOA achieved the best performance compared with other methods. Full article

(This article belongs to the Special Issue Fractal and Multifractal Analysis of Complex Networks II)

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16 pages, 302 KiB

Open AccessArticle

The κ-Deformed Calogero–Leyvraz Lagrangians and Applications to Integrable Dynamical Systems

by Partha Guha

Entropy 2022, 24(11), 1673; https://doi.org/10.3390/e24111673 - 17 Nov 2022

Viewed by 1053

Abstract

The Calogero–Leyvraz Lagrangian framework, associated with the dynamics of a charged particle moving in a plane under the combined influence of a magnetic field as well as a frictional force, proposed by Calogero and Leyvraz, has some special features. It is endowed with [...] Read more.

The Calogero–Leyvraz Lagrangian framework, associated with the dynamics of a charged particle moving in a plane under the combined influence of a magnetic field as well as a frictional force, proposed by Calogero and Leyvraz, has some special features. It is endowed with a Shannon “entropic” type kinetic energy term. In this paper, we carry out the constructions of the 2D Lotka–Volterra replicator equations and the

N = 2

Relativistic Toda lattice systems using this class of Lagrangians. We take advantage of the special structure of the kinetic term and deform the kinetic energy term of the Calogero–Leyvraz Lagrangians using the

κ

-deformed logarithm as proposed by Kaniadakis and Tsallis. This method yields the new construction of the

κ

-deformed Lotka–Volterra replicator and relativistic Toda lattice equations. Full article

(This article belongs to the Special Issue Twenty Years of Kaniadakis Entropy: Current Trends and Future Perspectives)

22 pages, 4089 KiB

Open AccessArticle

A New Exergy Disaggregation Approach for Complexity Reduction and Dissipative Equipment Isolation in Thermoeconomics

by Rodrigo Guedes dos Santos, Atilio Barbosa Lourenço, Pedro Rosseto de Faria, Marcelo Aiolfi Barone and José Joaquim Conceição Soares Santos

Entropy 2022, 24(11), 1672; https://doi.org/10.3390/e24111672 - 17 Nov 2022

Cited by 1 | Viewed by 1552

Abstract

Thermoeconomics connects thermodynamic and economic concepts in order to provide information not available in conventional energy and economic analysis. Most thermoeconomicists agree that exergy is the most appropriate thermodynamic magnitude to associate with cost. In some applications, exergy disaggregation is required. Despite the [...] Read more.

Thermoeconomics connects thermodynamic and economic concepts in order to provide information not available in conventional energy and economic analysis. Most thermoeconomicists agree that exergy is the most appropriate thermodynamic magnitude to associate with cost. In some applications, exergy disaggregation is required. Despite the improvement in result accuracy, the modeling complexity increases. In recent years, different exergy disaggregation approaches have been proposed, mostly to deal with dissipative components and residues, despite all of them also increasing the complexity of thermoeconomics. This study aims to present a new thermoeconomic approach based on exergy disaggregation, which is able to isolate dissipative components with less modeling complexity. This approach, called the A&F Model, splits the physical exergy into two terms, namely, Helmholtz energy and flow work. These terms were evaluated from a thermoeconomic point of view, through a cost allocation in an ideal Carnot cycle, and they were also applied and compared with the UFS Model, through a cost allocation analysis, in a case study with an organic Rankine cycle-powered vapor compression refrigeration system. The complexity and computational effort reduction in the A&F are significantly less than in the UFS Model. This alternative approach yields consistent results. Full article

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11 pages, 3462 KiB

Open AccessFeature PaperArticle

Information Shift Dynamics Described by Tsallis q = 3 Entropy on a Compact Phase Space

by Jin Yan and Christian Beck

Entropy 2022, 24(11), 1671; https://doi.org/10.3390/e24111671 - 17 Nov 2022

Viewed by 1543

Abstract

Recent mathematical investigations have shown that under very general conditions, exponential mixing implies the Bernoulli property. As a concrete example of statistical mechanics that are exponentially mixing we consider the Bernoulli shift dynamics by Chebyshev maps of arbitrary order

N \geq 2

, [...] Read more.

Recent mathematical investigations have shown that under very general conditions, exponential mixing implies the Bernoulli property. As a concrete example of statistical mechanics that are exponentially mixing we consider the Bernoulli shift dynamics by Chebyshev maps of arbitrary order

N \geq 2

, which maximizes Tsallis

q = 3

entropy rather than the ordinary

q = 1

Boltzmann-Gibbs entropy. Such an information shift dynamics may be relevant in a pre-universe before ordinary space-time is created. We discuss symmetry properties of the coupled Chebyshev systems, which are different for even and odd N. We show that the value of the fine structure constant

α_{e l} = 1 / 137

is distinguished as a coupling constant in this context, leading to uncorrelated behaviour in the spatial direction of the corresponding coupled map lattice for

N = 3

. Full article

(This article belongs to the Special Issue Non-additive Entropy Formulas: Motivation and Derivations)

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