一种基于小波变换域的有意义图像盲水印算法

提出了一种基于小波变换域的有意义图像盲水印算法。算法中利用乘性隐藏方法在原始图像小波高频系数中嵌入一幅二值图像信息，通过拉普拉斯分布对原始图像小波变换域高频系数的统计分布特性进行建模，然后采用最大似然(ML)法推导得出了一个盲水印检测器，并且利用该检测器实现了一个有意义图像的盲水印算法。实验表明该算法具有较好的隐蔽性和鲁棒性。     

输入为Markov序列时的多用户检测算法

考虑当用户序列存在时间相关性时的多用户检测，并假设这种相关性可以用Markov链描述，在传统的线性最大似然检测器中嵌入一个隐Markov模型估计过程。因为输入序列是Markov链，检测器的输出可以看成是被噪声污染的Markov序列，Markov模型估计子用于估计用户序列及其转移概率，而估计得到的用户序列用来更新检测器的估计。因此，检测器和用户序列可以通过迭代的方式求解。仿真结果显示本文算法能充分利用信道输入的时间相关性．效果优于传统的最大似然线性检测器。     

Simultaneous parameter estimation and segmentation of Gibbs randomfields using simulated annealing

An adaptive segmentation algorithm is developed which simultaneously estimates the parameters of the underlying Gibbs random field (GRF)and segments the noisy image corrupted by additive independent Gaussian noise. The algorithm, which aims at obtaining the maximum a posteriori (MAP) segmentation is a simulated annealing algorithm that is interrupted at regular intervals for estimating the GRF parameters. Maximum-likelihood (ML) estimates of the parameters based on the current segmentation are used to obtain the next segmentation. It is proven that the parameter estimates and the segmentations converge in distribution to the ML estimate of the parameters and the MAP segmentation with those parameter estimates, respectively. Due to computational difficulties, however, only an approximate version of the algorithm is implemented. The approximate algorithm is applied on several two- and four-region images with different noise levels and with first-order and second-order neighborhoods     

Efficient detection methods for amplify-and-forward relay-aided device-to-device systems with full-rate space-time block code

Relay-aided device-to-device (D2D) communication is a promising technology for the next-generation cellular network. We study the transmission schemes for an amplify-and-forward relay-aided D2D system which has multiple antennas. To circumvent the prohibitive complexity problem of traditional maximum likelihood (ML) detection for full-rate space-time block code (FSTBC) transmission, two low-complexity detection methods are proposed, i.e., the detection methods with the ML-combining (MLC) algorithm and the joint conditional ML (JCML) detector. Particularly, the method with the JCML detector reduces detection delay at the cost of more storage and performs well with parallel implementation. Simulation results indicate that the proposed detection methods achieve a symbol error probability similar to that of the traditional ML detector for FSTBC transmission but with less complexity, and the performance of FSTBC transmission is significantly better than that of spatial multiplexing transmission. Diversity analysis for the proposed detection methods is also demonstrated by simulations.     

Boosting learning and inference in Markov logic through metaheuristics

Markov Logic (ML) combines Markov networks (MNs) and first-order logic by attaching weights to first-order formulas and using these as templates for features of MNs. State-of-the-art structure learning algorithms in ML maximize the likelihood of a database by performing a greedy search in the space of structures. This can lead to suboptimal results because of the incapability of these approaches to escape local optima. Moreover, due to the combinatorially explosive space of potential candidates these methods are computationally prohibitive. We propose a novel algorithm for structure learning in ML, based on the Iterated Local Search (ILS) metaheuristic that explores the space of structures through a biased sampling of the set of local optima. We show through real-world experiments that the algorithm improves accuracy and learning time over the state-of-the-art algorithms. On the other side MAP and conditional inference for ML are hard computational tasks. This paper presents two algorithms for these tasks based on the Iterated Robust Tabu Search (IRoTS) metaheuristic. The first algorithm performs MAP inference and we show through extensive experiments that it improves over the state-of-the-art algorithm in terms of solution quality and inference time. The second algorithm combines IRoTS steps with simulated annealing steps for conditional inference and we show through experiments that it is faster than the current state-of-the-art algorithm maintaining the same inference quality.     

Development of moving window state and parameter estimators under maximum likelihood and Bayesian frameworks

Estimation of slowly varying model parameters/unmeasured disturbances is of paramount importance in process monitoring, fault diagnosis, model based advanced control and online optimization. The conventional approach to estimate drifting parameters is to artificially model them as a random walk process and estimate them simultaneously with the states. However, this may lead to a poorly conditioned problem, where the tuning of the random walk model becomes a non-trivial exercise. In this work, the moving window parameter estimator of Huang et al. [1] is recast as a moving window maximum likelihood (ML) estimator. The state can be estimated within the window using any recursive Bayesian estimator. It is assumed that, when the model parameters are perfectly known, the innovation sequence generated by the chosen Bayesian estimator is a Gaussian white noise process and is further used to construct a likelihood function that treats the model parameters as unknowns. This leads to a well conditioned problem where the only tuning parameter is the length of the moving window, which is much easier to select than selecting the covariance of the random walk model. The ML formulation is further modified to develop a maximum a posteriori (MAP) cost function by including arrival cost for the parameter. Efficacy of the proposed ML and MAP formulations has been demonstrated by conducting simulation studies and experimental evaluation. Analysis of the simulation and experimental results reveals that the proposed moving window ML and MAP estimators are capable of tracking the drifting parameters/unmeasured disturbances fairly accurately even when the measurements are available at multiple rates and with variable time delays.     

一种基于置信度稳定性的SCMA多用户检测算法

稀疏码分多址(即非正交多址)(Sparse Code Multiple Access,SCMA) 技术,具有在有限频谱资源下过载通信的特点,能够显著提升频谱利用率。得益于稀疏码分多址码本的稀疏性,消息传递算法(Message Passing Algorithm,MPA)成为经典多用户检测算法。在传统MPA方法中,尽管与最大似然译码具有相近的误比特率(Bit Error Ratio,BER)性能,但指数运算的复杂度仍然很高。据此,设计一种基于置信度的动态边缘选择更新方法,以减少不必要的节点运算。每次迭代中,利用因子图模型中功能节点到变量节点的置信度稳定性信息,动态判定是否需要节点更新运算。仿真结果表明,动态边缘选择方案使得算法的复杂度得到显著降低,并且能够与BER取得良好的均衡。     

A comparative study of heuristic algorithms: GA and UMDA in spatially multiplexed communication systems

A performance comparison of genetic algorithm (GA) and the univariate marginal distribution algorithm (UMDA) as decoders in multiple input multiple output (MIMO) communication system is presented in this paper. While the optimal maximum likelihood (ML) decoder using an exhaustive search method is prohibitively complex, simulation results show that the GA and UMDA optimized MIMO detection algorithms result in near optimal bit error rate (BER) performance with significantly reduced computational complexity. The results also suggest that the heuristic based MIMO detection outperforms the vertical bell labs layered space time (VBLAST) detector without severely increasing the detection complexity. The performance of UMDA is found to be superior to that of GA in terms of computational complexity and the BER performance.     

Directionlet-based method using the Gaussian mixture prior to SAR image despeckling

In this article, a new denoising algorithm is proposed based on the directionlet transform and the maximum a posteriori (MAP) estimation. The detailed directionlet coefficients of the logarithmically transformed noise-free image are considered to be Gaussian mixture probability density functions (PDFs) with zero means, and the speckle noise in the directionlet domain is modelled as additive noise with a Gaussian distribution. Then, we develop a Bayesian MAP estimator using these assumed prior distributions. Because the estimator that is the solution of the MAP equation is a function of the parameters of the assumed mixture PDF models, the expectation-maximization (EM) algorithm is also utilized to estimate the parameters, including weight factors and variances. Finally, the noise-free SAR image is restored from the estimated coefficients yielded by the MAP estimator. Experimental results show that the directionlet-based MAP method can be successfully applied to images and real synthetic aperture radar images to denoise speckle.     

Iterative parameter estimate with batched binary-valued observations基于给定二值观测样本数据的迭代参数估计算法

In this paper, we consider linear system identification with batched binary-valued observations. We constructed an iterative parameter estimate algorithm to achieve the maximum likelihood (ML) estimate. The first interesting result was that there exists at most one finite ML solution for this specific maximum likelihood problem, which was induced by the fact that the Hessian matrix of the log-likelihood function was negative definite under binary data and Gaussian system noises. The global concave property and local strongly concave property of the log-likelihood function were obtained. Under mild conditions on the system input, we proved that the ML function has a unique maximum point. The second main result was that the ML estimate was consistent under persistent excitation inputs, which infers the effectiveness of ML estimate. Finally, the proposed iterative estimate algorithm converged to a fixed vector with an exponential rate that was proved by constructing a Lyapunov function. A more interesting result was that the limit of the iterative algorithm achieved the maximization of the ML function. Numerical simulations are illustrated to support the theoretical results obtained in this paper well.     

Spark下的并行多标签最近邻算法

随着大数据时代的到来,大规模多标签数据挖掘方法受到广泛关注。多标签最近邻算法MLKNN是一种简单高效、应用广泛的多标签分类方法,其分类精度在很多应用中都高于其他常见的多标签学习方法。然而随着需要处理的数据规模越来越大,传统串行ML-KNN算法已经难以满足大数据应用中时间和存储空间上的限制。结合Spark的并行机制和其基于内存的迭代计算特点,提出了一种基于Spark并行框架的ML-KNN算法SML-KNN。在Map阶段分别找到待预测样本每个分区的K近邻,随后Reduce阶段根据每个分区的近邻集合确定最终的K近邻,最后并行地对近邻的标签集合进行聚合,通过最大化后验概率准则输出待预测样本的目标标签集合。串行和并行环境下的对比实验结果表明,SML-KNN在保证分类精度的前提下性能与计算资源呈近似线性关系,提高了ML-KNN算法对大规模多标签数据的处理能力。     

高速列车非线性模型的极大似然辨识

提出高速列车非线性模型的极大似然(Maximum likelihood, ML)辨识方法,适合于高速列车在非高斯噪声干扰下的非线性模型的参数估计.首先,构建了描述高速列车单质点力学行为的随机离散非线性状态空间模型,并将高速列车参数的极大似然(ML)估计问题转化为期望极大(Expectation maximization, EM)的优化问题; 然后,给出高速列车状态估计的粒子滤波器和粒子平滑器的设计方法,据此构造列车的条件数学期望,并给出最大化该数学期望的梯度搜索方法,进而得到列车参数的辨识算法,分析了算法的收敛速度; 最后,进行了高速列车阻力系数估计的数值对比实验. 结果表明, 所提出的辨识方法的有效性.     

Spatial random tree grammars for modeling hierarchal structure in images with regions of arbitrary shape

We present a novel probabilistic model for the hierarchical structure of an image and its regions. We call this model spatial random tree grammars (SRTGs). We develop algorithms for the exact computation of likelihood and maximum a posteriori (MAP) estimates and the exact expectation-maximization (EM) updates for model-parameter estimation. We collectively call these algorithms the center-surround algorithm. We use the center-surround algorithm to automatically estimate the maximum likelihood (ML) parameters of SRTGs and classify images based on their likelihood and based on the MAP estimate of the associated hierarchical structure. We apply our method to the task of classifying natural images and demonstrate that the addition of hierarchical structure significantly improves upon the performance of a baseline model that lacks such structure.     

Symbol Detection Based on Back Tracking Search Algorithm in MIMO-NOMA Systems

One of the most important methods used to cope with multipath fading effects, which cause the symbol to be received incorrectly in wireless communication systems, is the use of multiple transceiver antenna structures. By combining the multi-input multi-output (MIMO) antenna structure with non-orthogonal multiple access (NOMA), which is a new multiplexing method, the fading effects of the channels are not only reduced but also high data rate transmission is ensured. However, when the maximum likelihood (ML) algorithm that has high performance on coherent detection, is used as a symbol detector in MIMO NOMA systems, the computational complexity of the system increases due to higher-order constellations and antenna sizes. As a result, the implementation of this algorithm will be impractical. In this study, the backtracking search algorithm (BSA) is proposed to reduce the computational complexity of the symbol detection and have a good bit error performance for MIMO-NOMA systems. To emphasize the efficiency of the proposed algorithm, simulations have been made for the system with various antenna sizes. As can be seen from the obtained results, a considerable reduction in complexity has occurred using BSA compared to the ML algorithm, also the bit error performance of the system is increased compared to other algorithms.     

Multilevel discrete time system identification in large scale systems†

Hierarchical decomposition is considered to be one of the most powerful and offective tools to deal with complexity. Hierarchical system theory, which deals with system decomposition and coordination, can be used to decentralize and reduce the computational efforts requirements for many large-scale problems. This is achieved by decomposing the original system problem into several lower order easier to handle sub-problems, which are then coordinated such that the overall system objectives are met. In this work a hierarchical system theory approach to the discrete-time system identification problem is considered for stochastic large-scale system applications. A set of sequential discrete-time hierarchical identification algorithms, suitable for known and unknown system noise moments, are first obtained using a maximum a posteriori (MAP) approach with covariance matching and maximum likelihood (ML) methods. This is conducted in a two level hierarchical structure with two principles of coordination. Next, the hierarchical system identification algorithms are extended to multilevel hierarchical structures based on system characteristics of priority of action, spatial structure and time behaviour. This results in multilevel and composite multilevel coordinated system identification procedures, where each subsystem unit can be treated independently in reaching the overall system optimality. Application of these algorithms for the purpose of decentralization and reduction of computational requirements as well as adaptation to structural changes in growth and merger are considered.     

A Robust High-Order Mixed L2-Linfty Estimation for Linear-in-the-Parameters Models

A new algorithm called Mixed L2-Linfty (ML2) estimation is proposed in this paper; it combines both the weighted least squares and the worst-case parameter estimations together as the cost function and strikes the right balance between them. A robust ML2 algorithm and a practical approximate robust ML2 algorithm are also developed under disturbance signals. The properties of the new robust ML2 algorithm are analyzed and the simulation results are given to show the convergence and the validity.     

基于TOA非相干检测UWB测距系统的性能分析

鉴于非相干CRB对于分析非相干TOA性能有很大参考价值,将UWB测距系统中接收端信号经过平方律后进行包络检波所得的信号拟合成指数衰减的曲线,利用最大似然估计推导出非相干检测的CRB,并利用TC、MES算法验证其正确性。对影响TOA性能的非相干检测参数进行了分析,得出积分窗口的选取必须在TOA性能和算法收敛速度上进行折衷的结论。提出了一种新的分析信道时延扩展对CRB影响的方法,最后得出结论CRB随着时延扩展的增大而增大。     

On-line retrainable neural networks: improving the performance ofneural networks in image analysis problems

A novel approach is presented in this paper for improving the performance of neural-network classifiers in image recognition, segmentation, or coding applications, based on a retraining procedure at the user level. The procedure includes: 1) a training algorithm for adapting the network weights to the current condition; 2) a maximum a posteriori (MAP) estimation procedure for optimally selecting the most representative data of the current environment as retraining data; and 3) a decision mechanism for determining when network retraining should be activated. The training algorithm takes into consideration both the former and the current network knowledge in order to achieve good generalization. The MAP estimation procedure models the network output as a Markov random field (MRF) and optimally selects the set of training inputs and corresponding desired outputs. Results are presented which illustrate the theoretical developments as well as the performance of the proposed approach in real-life experiments.     

Exchange rates determination based on genetic algorithms using Mendel’s principles: Investigation and estimation under uncertainty

A genetic algorithm using Mendel’s principle (Mendel-GA), in which the random assignment of alleles from parents to offsprings is implied by the Mendel genetic operator, is proposed for the exchange rates determination problem. Besides the traditional genetic operators of selection, crossover, and mutation, Mendel’s principles are included, in the form of an operator in the genetic algorithm’s evolution process. In the quantitative analysis of exchange rates determination, the Mendel-GA examines the exchange rate fluctuations at the short-run horizon. Specifically, the aim is to revisit the determination of high-frequency exchange rates and examine the differences between the method of genetic algorithms and that of the traditional estimation methods. A simulation with a given initial conditions has been devised in MATLAB, and it is shown that the Mendel-GA can work valuably as a tool for the exchange rates estimation modelling with high-frequency data.     

An EM approach to mineral analysis using natural gamma rays

We describe here a method for the analysis of materials on a conveyor belt using the natural gamma spectra collected with a BGO (Bismuth Germanate) gamma ray detector. This detector collects gamma ray emissions from the Potassium (K), Uranium (U), and Thorium (Th) atoms in the materials. Based on these data, and using a Poisson model for the data generation, a statistical model is proposed and an approximate maximum likelihood (ML) technique based on the expectation-maximization (EM) algorithm is then used to estimate the amount of each of the three elements in the material. The statistical model is further refined to incorporate parameters of drift in the detector and an estimation technique for this is developed and tested against real data. The Cramér–Rao lower bounds for the estimators are calculated.