Spatial information transmission using orthogonal mutual coherence coding

We use the coherence of a light beam to encode spatial information. We apply this principle to obtain spatial superresolution in a limited aperture system. The method is based on shaping the mutual intensity function of the illumination beam in a set of orthogonal distributions, each one carrying the information for a different frequency bandpass or spatial region of the input object. The coherence coding is analogous to time multiplexing but with multiplexing time slots that are given by the coherence time of the illumination beam. Most images are static during times much longer than this coherence time, and thus the increase of resolution in our system is obtained without any noticeable cost.     

Estimating the errors on measured entropy and mutual information

Information entropy and the related quantity mutual information are used extensively as measures of complexity and to identify nonlinearity in dynamical systems. Expressions for the probability distribution of entropies and mutual informations calculated from finite amounts of data exist in the literature but the expressions have seldom been used in the field of nonlinear dynamics. In this paper formulae for estimating the errors on observed information entropies and mutual informations are derived using the standard error analysis familiar to physicists. Their validity is demonstrated by numerical experiment. For illustration the formulae are then used to evaluate the errors on the time-lagged mutual information of the logistic map.     

Entanglement entropy and mutual information production rates in acoustic black holes

A method to investigate acoustic Hawking radiation is proposed, where entanglement entropy and mutual information are measured from the fluctuations of the number of particles. The rate of entropy radiated per one-dimensional (1D) channel is given by S=κ/12, where κ is the sound acceleration on the sonic horizon. This entropy production is accompanied by a corresponding formation of mutual information to ensure the overall conservation of information. The predictions are confirmed using an ab initio analytical approach in transonic flows of 1D degenerate ideal Fermi fluids.     

Robust visual correspondence computation using monogenic curvature phase based mutual information

Visual correspondence has been a major research topic in the fields of image registration, 3D reconstruction, and object tracking for some decades. However, due to the radiometric variations of images, conventional approaches fail to produce robust matching results. The traditional method of intensity-based mutual information performs very good for global variations between images, however, its performance degrades in the case of local radiometric variations. Monogenic curvature phase information, as an important local feature of the image, has the advantage of being robust against brightness variation. Hence, in this Letter, we propose an approach to compute the visual correspondence by coupling the advantages of mutual information and monogenic curvature phase. Experimental results demonstrate that the proposed approach can work robustly under radiometric variations.     

Reduction of entropy uncertainty for qutrit system under non-Markov noisy environment

We explore the entropy uncertainty for qutrit system under non-Markov noisy environment and discuss the effects of the quantum memory system and the spontaneously generated interference(SGI)on the entropy uncertainty in detail.The results show that,the entropy uncertainty can be reduced by using the methods of quantum memory system and adjusting of SGI.Particularly,the entropy uncertainty can be decreased obviously when both the quantum memory system and the SGI are simultaneously applied.     

Human eye iris recognition using the mutual information

This article presents the new biometric electro-optical measuring method supported by PC for identification of a person by its eye iris image recognition. The aim of this approach is to show the ability of mutual information to such recognition. Couples of the comparative human iris images were geometrically aligned by maximization of their mutual information and subsequently recognized. The mutual information was estimated using the efficient algorithm proposed by G. A. Darbellay and I. Vajda. The decision whether two compared iris images belong to the same eye depends on the chosen threshold of the mutual information. Only a simple preprocessing of the iris images is needed to acquire their characteristic geometric parameters before the recognition is performed. The investigations carried out showed the correct (successful) personal identification better than 99%.     

Tsallis entropy measure of noise-aided information transmission in a binary channel

Noise-aided information transmission via stochastic resonance is shown and analyzed in a binary channel by means of information measures based on the Tsallis entropy. The analysis extends the classic reference of binary information transmission based on the Shannon entropy, and also parallels a recent study based on the Rényi entropy. The conditions for a maximally pronounced stochastic resonance identify optimal Tsallis measures. The study involves a correspondence between Tsallis and Rényi information measures, specially relevant to the characterization of stochastic resonance, and establishing that for such effects identical properties are shared in common by both Tsallis and Rényi measures.     

利用暂态过程测量互感系数

介绍了在电源输出为方波时,分别利用RL电路的暂态过程和RLC电路的暂态过程测定互感器互感系数的方法.     

Registering multiple medical images using the shared chain mutual information

A new approach to the simultaneous registration of multiple medical images is proposed using shared chain mutual information (SCMI) as the matching measure. The presented method applies SCMI to measure the shared information between the multiple images. Registration is achieved by adjusting the relative position of the floating image until the SCMI between all the images is maximized. Using this measure, we registered three and four simulated magnetic resonance imaging (MRI) images using downhill simplex optimization to search for the optimal transformation parameters. Accuracy and validity of the proposed method for multiple-image registration are testified by comparing the results with that of twoimage registration. Furthermore, the performance of the proposed method is validated by registering the real ultrasonic image sequence.     

Reduction of PDF uncertainty in the measurement of the weak mixing angle at the ATLAS experiment

We investigate the parton distribution function(PDF) uncertainty in the measurement of the effective weak mixing angle sin ~2θ_(eff)~l at the CERN Large Hadron Collider(LHC).The PDF-induced uncertainty is large in proton-proton collisions at the LHC due to the dilution effect.The measurement of the Drell-Yan forward-backward asymmetry(AFB) at the LHC can be used to reduce the PDF uncertainty in the sin~2θ_(eff)~l measurement.However,when including the full mass range of lepton pairs in the A_(FB) data analysis,the correlation between the PDF updating procedure and the sin~2θ_(eff)~l extraction leads to a sizable bias in the obtained sin ~2θ_(eff)~l value.From our studies,we find that the bias can be significantly reduced by removing Drell-Yan events with invariant mass around the Z-pole region,while most of the sensitivity in reducing the PDF uncertainty remains.Furthermore,the lepton charge asymmetry in the Wboson events as a function of the rapidity of the charged leptons,A_±(η_l),is known to be another observable which can be used to reduce the PDF uncertainty in the sin~2θ_(eff)~l measurement.The constraint from A_±(η_l)is complementary to that from A_(FB),and thus no bias affects the sin~2θ_(eff)~l extraction.The studies are performed using the error PDF Updating Method Package(ePump),which is based on Hessian updating methods.In this article,the CT14 HERA2 PDF set is used as an example.     

Image registration by maximization of mutual information based on edge width matching using particle swarm optimization

Mutual information (MI) based image registration has been found to be quite effective in many medical image applications. However, standard MI hampers the convergence of registration transformation parameters since it contains local maxima. In this paper, a novel registration method is proposed. At first,MI based on edge width matching is computed to avoid great change of joint probability distribution and get less local maxima. Particle swarm optimization (PSO), which combines local search methods with global ones balancing exploration and exploitation, is done to search the optimal registration parameter.PSO has less computational complexity as its complex behavior follows only a few simple rules. It could avoid local maxima and reach global optimal results. This method is applicable to a variety of multimodal images, and suitable to different interpolation methods. Theoretical analysis and experiments show that this method is effective and accurate to register multimodal medical images.     

Robustness measurement of scale-free networks based on motif entropy

As a classical complex network model, scale-free network is widely used and studied. And motifs, as a high-order subgraph structure, frequently appear in scale-free networks, and have a great influence on the structural integrity, functional integrity and dynamics of the networks. In order to overcome the shortcomings in the existing work on the robustness of complex networks, only nodes or edges are considered, while the defects of high-order structure in the network are ignored. From the perspective of network motif, we propose an entropy of node degree distribution based on motif to measure the robustness of scale-free networks under random attacks. The effectiveness and superiority of our method are verified and analyzed in the BA scale-free networks.     

Analysis of systems based on entropy and information characteristics

An approach is proposed for studying various systems based on the concept of entropy potentials of their parameters. In this approach, any state of a system can be described quantitatively using the values of entropy potentials of corresponding parameters. With such an approach, the states and the evolution processes in the systems being analyzed can be mapped onto the coordinate space of the “information field” of the system, which makes it possible to simplify the solution of a number of applied problems.     

Characteristics analysis of acupuncture electroencephalograph based on mutual information Lempelben-Ziv complexity

As a convenient approach to the characterization of cerebral cortex electrical information, electroencephalograph (EEG) has potential clinical application in monitoring the acupuncture effects. In this paper, a method composed of the mutual information method and Lempel-Ziv complexity method (MILZC) is proposed to investigate the effects of acupuncture on the complexity of information exchanges between different brain regions based on EEGs. In the experiments, eight subjects are manually acupunctured at ‘Zusanli’acupuncture point (ST-36) with different frequencies (i.e., 50, 100, 150, and 200 times/min) and the EEGs are recorded simultaneously. First, MILZC values are compared in general. Then average brain connections are used to quantify the effectiveness of acupuncture under the above four frequencies. Finally, significance index P values are used to study the spatiality of the acupuncture effect on the brain. Three main findings are obtained: (ⅰ) MILZC values increase during the acupuncture; (ⅱ) manual acupunctures (MAs) with 100 times/min and 150 times/min are more effective than with 50 times/min and 200 times/min; (ⅲ) contralateral hemisphere activation is more prominent than ipsilateral hemisphere's. All these findings suggest that acupuncture contributes to the increase of brain information exchange complexity and the MILZC method can successfully describe these changes.     

基于符号化部分互信息熵的多参数生物电信号的耦合分析

提出了一种新的时间序列耦合信息分析方法–-基于部分互信息符号化部分互信息熵. 研究表明, 多参量的生物电信号各参量间具有耦合关系, 使用符号化的部分互信息能够很好地对生物电信号时间序列进行分析, 从而获得其耦合程度.应用该算法对生物电信号计算并进行假设检验, 结果表明清醒期的生物电信号耦合程度显著高于睡眠期, 证明符号化部分互信息可以用来分析时间序列间的耦合信息, 而且生物电信号的耦合程度可以作为度量一个物理过程是否处于活跃状态的参数, 未来可以应用于临床医学以及生物电传感器等领域. 关键词： 符号化 部分互信息熵 生物电信号 耦合     

Characteristics analysis of acupuncture electroencephalograph based on mutual information Lempel-Ziv complexity

As a convenient approach to the characterization of cerebral cortex electrical information,electroencephalograph (EEG) has potential clinical application in monitoring the acupuncture effects.In this paper,a method composed of the mutual information method and Lempel-Ziv complexity method (MILZC) is proposed to investigate the effects of acupuncture on the complexity of information exchanges between different brain regions based on EEGs.In the experiments,eight subjects are manually acupunctured at ’Zusanli’ acupuncture point (ST-36) with different frequencies (i.e.,50,100,150,and 200 times/min) and the EEGs are recorded simultaneously.First,MILZC values are compared in general.Then average brain connections are used to quantify the effectiveness of acupuncture under the above four frequencies.Finally,significance index P values are used to study the spatiality of the acupuncture effect on the brain.Three main findings are obtained:(i) MILZC values increase during the acupuncture;(ii) manual acupunctures (MAs) with 100 times/min and 150 times/min are more effective than with 50 times/min and 200 times/min;(iii) contralateral hemisphere activation is more prominent than ipsilateral hemisphere’s.All these findings suggest that acupuncture contributes to the increase of brain information exchange complexity and the MILZC method can successfully describe these changes.     

基于信息熵的变星光谱快速识别方法

变星对人类研究宇宙的起源与发展具有重要意义,对于变星研究的困难首先源于对变星的筛选和识别,即如何从海量恒星光谱数据中有效识别变星光谱。传统的异常数据定义试图通过不同的方式寻找异常数据与一般模式之间的偏差,进而予以定量分析和筛选。然而,这种方法的时间复杂度过大,且结果存在不可理解和无法解释的问题。文章利用熵可以反映系统有序程度与稳定程度的特性,引入信息熵作为衡量数据集一般模式的标准,提出了基于信息熵的变星光谱快速识别方法。该方法显著降低了算法的时间复杂度,有效地消除了人为主观因素对识别结果的影响。采用国家天文台提供的Sloan数字巡天数据实验验证了该方法的可行性和有效性。     

基于信息熵的社交网络观点演化模型

随着网络服务的发展,社交网络逐渐成为信息传播的新媒介.因此,研究网络舆情演化具有重要意义和实用价值.为了更好地研究网络舆论,在信息熵的基础上,提出了一个社交网络观点演化模型.此模型存在以下两个特点:一是可以反映个体面对正负两种观点趋向做出抉择时的心理过程;二是可以反映个体形成新观点时主观因素和客观因素的影响.在仿真实验中,讨论了舆论环境对个体观点演化的影响,初始观点和自信度对观点演化的影响,以及意见领袖对群体观点演化的影响.实验结果表明,该模型可以反映真实社交网络中个体的心理学特征,比如个体的观点形成会受到舆论环境的影响,自信的个体不愿意接受他人的观点,当意见领袖存在时群体的观点会受到影响等.     

Estimation of pressure-particle velocity impedance measurement uncertainty using the Monte Carlo method

The pressure-particle velocity (PU) impedance measurement technique is an experimental method used to measure the surface impedance and the absorption coefficient of acoustic samples in situ or under free-field conditions. In this paper, the measurement uncertainty of the the absorption coefficient determined using the PU technique is explored applying the Monte Carlo method. It is shown that because of the uncertainty, it is particularly difficult to measure samples with low absorption and that difficulties associated with the localization of the acoustic centers of the sound source and the PU sensor affect the quality of the measurement roughly to the same extent as the errors in the transfer function between pressure and particle velocity do.