A Covert Communication Method Using Special Bitcoin Addresses Generated by Vanitygen

As an extension of the traditional encryption technology, information hiding has been increasingly used in the fields of communication and network media, and the covert communication technology has gradually developed. The blockchain technology that has emerged in recent years has the characteristics of decentralization and tamper resistance, which can effectively alleviate the disadvantages and problems of traditional covert communication. However, its combination with covert communication thus far has been mostly at the theoretical level. The BLOCCE method, as an early result of the combination of blockchain and covert communication technology, has the problems of low information embedding efficiency, the use of too many Bitcoin addresses, low communication efficiency, and high costs. The present research improved on this method, designed the V-BLOCCE which uses base58 to encrypt the plaintext and reuses the addresses generated by Vanitygen multiple times to embed information. This greatly improves the efficiency of information embedding and decreases the number of Bitcoin addresses used. Under the premise of ensuring the order, the Bitcoin transaction OP_RETURN field is used to store the information required to restore the plaintext and the transactions are issued at the same time to improve the information transmission efficiency. Thus, a more efficient and feasible method for the application of covert communication on the blockchain is proposed. In addition, this paper also provides a more feasible scheme and theoretical support for covert communication in blockchain.     

The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy

What is the role of social interactions in the creation of price bubbles? Answering this question requires obtaining collective behavioural traces generated by the activity of a large number of actors. Digital currencies offer a unique possibility to measure socio-economic signals from such digital traces. Here, we focus on Bitcoin, the most popular cryptocurrency. Bitcoin has experienced periods of rapid increase in exchange rates (price) followed by sharp decline; we hypothesize that these fluctuations are largely driven by the interplay between different social phenomena. We thus quantify four socio-economic signals about Bitcoin from large datasets: price on online exchanges, volume of word-of-mouth communication in online social media, volume of information search and user base growth. By using vector autoregression, we identify two positive feedback loops that lead to price bubbles in the absence of exogenous stimuli: one driven by word of mouth, and the other by new Bitcoin adopters. We also observe that spikes in information search, presumably linked to external events, precede drastic price declines. Understanding the interplay between the socio-economic signals we measured can lead to applications beyond cryptocurrencies to other phenomena that leave digital footprints, such as online social network usage.     

An EWMA chart for monitoring the covariance matrix of a multivariate process based on dissimilarity index

In this article, we propose an exponentially weighted moving average (EWMA) control chart for monitoring the covariance matrix of a multivariate process based on the dissimilarity index of 2 matrices. The proposed control chart essentially monitors the covariance matrix by comparing the individual eigenvalues of the estimated EWMA covariance matrix with those of the estimated covariance matrix from the in‐control (IC) phase I data. It is different from the conventional EWMA charts for monitoring the covariance matrix, which are either based on comparing the sum or product or both of the eigenvalues of the estimated EWMA covariance matrix with those of the IC covariance matrix. We compare the performance of the proposed chart with that of the best existing chart under the multivariate normal process. Furthermore, to prevent the control limit of the proposed EWMA chart developed using the limited IC phase I data from having extensively excessive false alarms, we use a bootstrap resampling method to adjust the control limit to guarantee that the proposed chart has the actual IC ARL(average run length) not less than the nominal level with a certain probability. Finally, we use an example to demonstrate the applicability and implementation of the proposed EWMA chart.     

Estimation of Growth Curve Models with Structured Error Covariances by Generalized Estimating Equations

The growth curve model is useful for the analysis of longitudinal data. It helps investigate an overall pattern of change in repeated measurements over time and the effects of time-invariant explanatory variables on the temporal pattern. The traditional growth curve model assumes that the matrix of covariances between repeated measurements is unconstrained. This unconstrained covariance matrix often appears unattractive. In this paper, the generalized estimating equation method is adopted to estimate parameters of the growth curve model. As a result, the proposed method allows a more variety of constrained covariance structures than the traditional growth curve model. An empirical application is provided so as to illustrate the proposed method.

     

Unleashing Empirical Equations with “Nonlinear Fitting” and “GUM Tree Calculator”

Empirical equations having large numbers of fitted parameters, such as the international standard reference equations published by the International Association for the Properties of Water and Steam (IAPWS), which form the basis of the “Thermodynamic Equation of Seawater—2010” (TEOS-10), provide the means to calculate many quantities very accurately. The parameters of these equations are found by least-squares fitting to large bodies of measurement data. However, the usefulness of these equations is limited since uncertainties are not readily available for most of the quantities able to be calculated, the covariance of the measurement data is not considered, and further propagation of the uncertainty in the calculated result is restricted since the covariance of calculated quantities is unknown. In this paper, we present two tools developed at MSL that are particularly useful in unleashing the full power of such empirical equations. “Nonlinear Fitting” enables propagation of the covariance of the measurement data into the parameters using generalized least-squares methods. The parameter covariance then may be published along with the equations. Then, when using these large, complex equations, “GUM Tree Calculator” enables the simultaneous calculation of any derived quantity and its uncertainty, by automatic propagation of the parameter covariance into the calculated quantity. We demonstrate these tools in exploratory work to determine and propagate uncertainties associated with the IAPWS-95 parameters.     

基于协方差矩阵拟合的宽带无网格波达角估计

宽带波达角(Direction of Arrival,DOA)估计是声呐系统阵列信号处理中一个重要的研究方向。文章提出了一种基于相干子空间的改进稀疏与参数方法(Coherent Signal-subspace based Modified Sparse and Parameter Approach,C-MSPA),以实现高精度和高空间方位分辨能力的宽带DOA估计。算法利用聚焦矩阵将各子带上的采样协方差矩阵投影至聚焦频率上。完成聚焦后,文章基于频率选择的范德蒙分解理论对协方差矩阵拟合准则进行改进,使重构的协方差矩阵中包含的DOA信息严格限制在聚焦区域内,最终对重构的协方差矩阵进行范德蒙分解,得到DOA估计值。所提出的算法无需选取正则参数,同时避免了基不匹配问题。仿真和湖上实测数据分析结果表明,所提出的方法实现了高空间方位分辨能力且提高了DOA估计精度。     

Performance of the adaptive generalised matched subspace constant false alarm rate detector in non-Gaussian noise: an experimental analysis

The authors deal with the performance analysis of an adaptive version of the generalised matched subspace detector (GMSD) in compound-Gaussian clutter with unknown covariance matrix. The original GMSD was proposed to detect subspace signals in compound-Gaussian noise with known covariance matrix and ensures the constant false alarm rate (CFAR) property. In real situations, this assumption is unrealistic, which means that the covariance matrix must be estimated from training data. The authors use a robust estimate of the covariance matrix called the fixed-point estimate, recently proposed in the literature. The performance of the obtained adaptive detector, in terms of CFAR behaviour and probability of detection, is evaluated in the presence of real sea clutter data, collected by the McMaster IPIX radar.     

Stereoselective Release Behaviors of Imprinted Bead Matrices

ABSTRACT

In this work, the stereoselective release behaviors of “low”-swelling molecularly imprinted polymer (MIP) bead matrices in pressed-coat tablet type were studied. Either R-propranolol selective MIP or S-propranolol selective MIP was combined with excipients and racemic propranolol and fabricated into the matrix. Subsequently, the release of different propranolol enantiomers from the matrices was examined. Also, the microscopic structure of the hydrated “low”-swelling MIP matrix was determined using a cryogenic scanning electron microscope in order to compare with that of the hydrated “high”-swelling MIP matrix. In vitro release profiles of the “low”-swelling matrices showed a difference in the release of enantiomers, in that the non-template isomer was released faster than the template isomer. However, in the last phase of dissolution this difference reduced and later reversed, resulting at last in the type of specificity being similar to that obtained previously with “high”-swelling MIP matrices.

n summary, MIP beads can be fashioned into matrices and incorporated into different formulations to regulate the resultant stereoselectivity. From the behaviors of stereoselective release observed in MIP matrices, we can conclude that the enantioselective-controlled delivery mechanism of MIPs via formulations depends on the relative affinity of the enantiomer for the template sites, as well as the nature of the polymer, such as hydrophobicity and swellability.     

Supply chain modelling and managing,using timed coloured Petri nets: a case study

This paper aims at developing a new methodology for designing and managing a supply chain (SC) and, at the same time, for evaluating the performance of every stakeholder involved in a production chain. The methodology proposed has been applied to a footwear supply chain and is based on coloured Petri nets (CPNs). The supply chain analysed in this paper is a complex production system consisting of a network of manufacturers and service suppliers related to logistics systems that provide transportation and storage. The model developed uses coloured, timed Petri nets to represent a supply chain and it is such that resources are the Petri Net (PN) places, the tokens are jobs, orders and/or products, while the colours represent job attributes. These colours are used to encode different data types and values that are attached to tokens. A “coloured token” represents a specific production order or a certain amount of a particular material supplied. Thus, it can be processed in different ways and it can be easily localised within the CPN model. The use of coloured Petri nets allows companies to create a compact representation of states, actions and events of the modelled system. The particular structure of this network allows the designers the easy realisation of a simulator using an “object-oriented”, dedicated programming, which is a useful tool for developing what-if analyses.     

Least-squares approximation of a space distribution for a given covariance and latent sub-space

In this paper, a new method to approximate a data set by another data set with constrained covariance matrix is proposed. The method is termed Approximation of a DIstribution for a given COVariance (ADICOV). The approximation is solved in any projection subspace, including that of Principal Component Analysis (PCA) and Partial Least Squares (PLS). Given the direct relationship between covariance matrices and projection models, ADICOV is useful to test whether a data set satisfies the covariance structure in a projection model. This idea is broadly applicable in chemometrics. Also, ADICOV can be used to simulate data with a specific covariance structure and data distribution. Some applications are illustrated in an industrial case of study.     

Computer Simulation of Product Size Distribution of a Laboratory Ball Mill

In this study, a computer simulation of a suitable matrix notation of the Broadbent and Callcott grinding model was written. First, a chromite sample was ground in the laboratory type batch ball mill at different grinding times, with different particle size distributions and different capacities. Second, elements of the matrix for computer simulation were found and a “milling matrix” was formed. Third, particle size distributions were predicted by computer simulation for the whole test. Finally, a relationship between the computer simulation predicted by dates and experimental results was established, with a good correlation within the limits of mean error values.     

A spatial rank–based multivariate EWMA chart for monitoring process shape matrices

In this article, we propose a nonparametric EWMA control chart for monitoring the shape matrix of a multivariate process based on a spatial rank test and the exponentially weighted moving average scheme. The proposed control chart is essentially developed using an estimated spatial rank covariance matrix to test the shape matrix of the covariance matrix of multivariate distributions with heavy tails. Based on our simulation studies, the proposed control chart outperforms the only existing nonparametric control chart in many practical out‐of‐control scenarios for monitoring the shape matrix of the covariance matrix of many multivariate processes. Further, we point out the weaknesses of both the nonparametric EWMA control charts for monitoring the shape matrix of multivariate processes in real applications and propose one possible method to overcome these weaknesses. We also use an example from a white wine production process to demonstrate the applicability and implementation of the proposed control chart.     

Approximating multivariate Markov chains for bootstrapping through contiguous partitions

This paper extends Markov chain bootstrapping to the case of multivariate continuous-valued stochastic processes. To this purpose, we follow the approach of searching an optimal partition of the state space of an observed (multivariate) time series. The optimization problem is based on a distance indicator calculated on the transition probabilities of the Markov chain. Such criterion aims at grouping those states exhibiting similar transition probabilities. A second methodological contribution is represented by the addition of a contiguity constraint, which is introduced to force the states to group only if they have “near” values (in the state space). This requirement meets two important aspects: first, it allows a more intuitive interpretation of the results; second, it contributes to control the complexity of the problem, which explodes with the cardinality of the states. The computational complexity of the optimization problem is also addressed through the introduction of a novel Tabu Search algorithm, which improves both the quality of the solution found and the computing times with respect to a similar heuristic previously advanced in the literature. The bootstrap method is applied to two empirical cases: the bivariate process of prices and volumes of electricity in the Spanish market; the trivariate process composed of prices and volumes of a US company stock (McDonald’s) and prices of the Dow Jones Industrial Average index. In addition, the method is compared with two other well-established bootstrap methods. The results show the good distributional properties of the present proposal, as well as a clear superiority in reproducing the dependence among the data.     

Process identification using a new component analysis model and particle swarm optimization

A novel component analysis model is proposed to identify the mixed process signals which are frequently encountered in the statistical process control (SPC) and engineering process control (EPC) practice. Based upon one of existing state-of-the-art evolutionary algorithms, called particle swarm optimization (PSO), the proposed model provides a solution (i.e., demixing matrix) by maximizing the determinant of the corresponding second-order moment (variance–covariance) matrix of the reconstructed signals. Then, the estimated demixing matrix is used to separate mixed signals arising from several original process signals. The process signals considered in this paper include inconsistent variance series, autoregressive (AR) series, step change, and Gaussian noises in the process data. In practice, most of industrial manufacturing processes can be well characterized by a mixture of these four types of data. By following the proposed model, the blind signal separation framework can be cast into a nonlinear constrained optimization problem, where only the demixing matrix appears as unknown. Several illustrative examples involving linear mixtures of the process signals with different statistical characteristics are demonstrated to justify the new component analysis model.     

A modification of UniGrow 2‐parameter driving force model for short fatigue crack growth

In this paper, a modification of the UniGrow model is proposed to predict total fatigue life with the presence of a short fatigue crack by incorporating short crack propagation into the UniGrow crack growth model. The UniGrow model is modified by 2 different methods, namely the “short crack stress intensity correction method” and the “short crack data‐fitting method” to estimate the total fatigue life including both short and long fatigue crack propagations. Predicted fatigue lives obtained from these 2 methods were compared with experimental data sets of 2024‐T3, 7075‐T56 aluminium alloys, and Ti‐6Al‐4V titanium alloy. Two proposed methods have shown good fatigue life predictions at relatively high maximum stresses; however, they provide conservative fatigue life predictions at lower stresses corresponding high cycle fatigue lives where short crack behaviour dominates total fatigue life at lower stress levels.     

Effect of the Nanodiamond Host on a Nitrogen‐Vacancy Color‐Centre Emission State

Control over the quantum states of individual luminescent nitrogen‐vacancy (NV) centres in nanodiamonds (NDs) is demonstrated by careful design of the crystal host: its size, surface functional groups, and interfacing substrate. By progressive etching of the ND host, the NV centres are induced to switch from latent, through continuous, to intermittent or “blinking” emission states. The blinking mechanism of the NV centre in NDs is elucidated and a qualitative model proposed to explain this phenomenon in terms of the centre electron(s) tunnelling to acceptor site(s). These measurements suggest that the substrate material and its proximity to the NV are responsible for the fluorescence intermittency.     

Contrast enhancement and robustness improvement of adaptive ultrasound imaging using forward-backward minimum variance beamforming

In adaptive ultrasound imaging, accurate estimation of the array covariance matrix is of great importance, and biases the performance of the adaptive beamformer. The more accurately the covariance matrix can be estimated, the better the resolution and contrast can be achieved in the ultrasound image. To this end, in this paper, we have used the forward-backward spatial averaging for array covariance matrix estimation, which is then employed in minimum variance (MV) weights calculation. The performance of the proposed forward-backward MV (FBMV) beamformer is tested on simulated data obtained using Field II. Data for two closely located point targets surrounded by speckle pattern are simulated showing the higher amplitude resolution of the FBMV beamformer in comparison to the forward-only (F-only) MV beamformers, without the need for diagonal loading. A circular cyst with a diameter of 6 mm and a phantom containing wire targets and two cysts with different diameters of 8 mm and 6 mm are also simulated. The simulations show that the FBMV beamformer, in contrast to the F-only MV, could estimate the background speckle statistics without the need for temporal smoothing, resulting in higher contrast for the FBMV-resulted image in comparison to the MV images. In addition, the effect of steering vector errors is investigated by applying an error of the sound speed estimate to the ultrasound data. The simulations show that the proposed FBMV beamformer presents a satisfactory robustness against data misalignment resulted from steering vector errors, outperforming the regularized F-only MV beamformer. These improvements are achieved without compromising the good resolution of the MV beamformer and resulted from more accurate estimation of the covariance matrix and consequently, the more accurate setting of the MV weights.     

To shrink or not to shrink: Hotelling's T2 chart based on shrunken covariance estimates

Several authors have studied the effect of parameter estimation on the performance of Phase II control charts and shown that large in‐control reference samples are necessary for the Phase II control charts to perform as desired. For higher dimensional data, even larger reference samples are required to achieve stable estimation of the in‐control parameters. Shrinkage estimation has been widely studied as a method to achieve stable estimation of the covariance matrix for high‐dimensional data. We investigate the average run length (ARL) distribution of the Hotelling T² chart when using a shrunken covariance matrix. Specifically, we explore the following questions: (1) Does the use of a shrinkage estimator of the covariance matrix result in reduced variability in the ARL performance of the T² chart? (2) Does the use of a shrinkage estimator of the covariance matrix result in a reduced occurrence of “strictly multivariate” false alarms on the T²chart? (3) How does shrinkage of the covariance matrix affect the out‐of‐control performance of the T² chart? We use a simulation study to investigate the use of shrinkage estimation with the Hotelling T² chart in Phase II. Our results indicate that, while shrinkage estimation affects the ARL performance of the T² chart, the benefits are small and occur in fairly specific circumstances. The benefits of shrinking may not justify the use of more advanced techniques.     

A computationally efficient approach for template matching-based image registration

Image registration using template matching is an important step in image processing. In this paper, a simple, robust and computationally efficient approach is presented. The proposed approach is based on the properties of a normalized covariance matrix. The main advantage of the proposed approach is that the image matching can be achieved without calculating eigenvalues and eigenvectors of a covariance matrix, hence reduces the computational complexity. The experimental results show that the proposed approach performs better in the presence of various noises and rigid geometric transformations.     

Effect of peritectic reaction on the migration of secondary dendrite arms in the presence of tertiary dendrites: analysis of a directionally solidified Sn–36 at.%Ni peritectic alloy

Directional solidification experiments have been performed on Sn–36 at.%Ni peritectic alloy in a constant temperature gradient at different growth velocities. Experimental result shows that a “sawtooth” morphology forms on secondary dendrite arms during the migration of secondary dendrites in the presence of tertiary dendrite arms. A theoretic model is therefore proposed to describe the formation of this “sawtooth” morphology in the peritectic solidification with tertiary dendrite arms taken into consideration. The migration of secondary dendrite arms is caused by remelting/solidification at the hot/cold sides of a liquid pool between secondary dendrite arms, which is a form of temperature gradient zone melting. And, the “sawtooth” morphology is ascribed to the difference in remelting velocity at the hot side of liquid pool during the migration of secondary dendrite arms due to the presence of tertiary dendrite arms. In addition, the proceeding of peritectic reaction can accelerate the formation of “sawtooth” morphology.