An EM algorithm for estimating SPECT emission and transmission parameters from emissions data only

A maximum-likelihood (ML) expectation-maximization (EM) algorithm (called EM-IntraSPECT) is presented for simultaneously estimating single photon emission computed tomography (SPECT) emission and attenuation parameters from emission data alone. The algorithm uses the activity within the patient as transmission tomography sources, with which attenuation coefficients can be estimated. For this initial study, EM-IntraSPECT was tested on computer-simulated attenuation and emission maps representing a simplified human thorax as well as on SPECT data obtained from a physical phantom. Two evaluations were performed. First, to corroborate the idea of reconstructing attenuation parameters from emission data, attenuation parameters (mu) were estimated with the emission intensities (lambda) fixed at their true values. Accurate reconstructions of attenuation parameters were obtained. Second, emission parameters lambda and attenuation parameters mu were simultaneously estimated from the emission data alone. In this case there was crosstalk between estimates of lambda and mu and final estimates of lambda and mu depended on initial values. Estimates degraded significantly as the support extended out farther from the body, and an explanation for this is proposed. In the EM-IntraSPECT reconstructed attenuation images, the lungs, spine, and soft tissue were readily distinguished and had approximately correct shapes and sizes. As compared with standard EM reconstruction assuming a fix uniform attenuation map, EM-IntraSPECT provided more uniform estimates of cardiac activity in the physical phantom study and in the simulation study with tight support, but less uniform estimates with a broad support. The new EM algorithm derived here has additional applications, including reconstructing emission and transmission projection data under a unified statistical model.     

An EM algorithm for wavelet-based image restoration

This paper introduces an expectation-maximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a reconstruction with low-complexity, expressed in the wavelet coefficients, taking advantage of the well known sparsity of wavelet representations. Previous works have investigated wavelet-based restoration but, except for certain special cases, the resulting criteria are solved approximately or require demanding optimization methods. The EM algorithm herein proposed combines the efficient image representation offered by the discrete wavelet transform (DWT) with the diagonalization of the convolution operator obtained in the Fourier domain. Thus, it is a general-purpose approach to wavelet-based image restoration with computational complexity comparable to that of standard wavelet denoising schemes or of frequency domain deconvolution methods. The algorithm alternates between an E-step based on the fast Fourier transform (FFT) and a DWT-based M-step, resulting in an efficient iterative process requiring O(NlogN) operations per iteration. The convergence behavior of the algorithm is investigated, and it is shown that under mild conditions the algorithm converges to a globally optimal restoration. Moreover, our new approach performs competitively with, in some cases better than, the best existing methods in benchmark tests.     

The cascade EM algorithm

The estimate-maximize (EM) algorithm is an iterative method for finding maximum-likelihood parameter estimates from incomplete data. The authors develop an extension of the EM algorithm that may be useful in accelerating the algorithm and in simplifying the computations involved. The extension works with an intermediate complete data specification, and performs intermediate steps at some iterations. The authors consider the problem of parameter identification of a continuous-time linear dynamic system given discrete-time observations, and show that the proposed algorithm accelerates the convergence of the EM algorithm and simplifies the computations involved     

一种基于对象的动态视频超分辨率复原算法

从视频序列中复原高分辨率的运动对象在众多研究领域具有重要的应用意义.本文针对动态视频中整体运动的刚性或准刚性对象,提出一种基于对象的超分辨率复原方案,首先引入基于6参数仿射模型的对象跟踪和匹配算法,用于视频中运动对象的自动跟踪和匹配.进而将该运动模型与最大后验概率(MAP)算法相结合实现了所跟踪对象的超分辨率复原.对仿真和实测序列的实验结果表明,这种基于对象的处理方法能够实现更为准确的运动估计,因而收到了更好的复原效果.     

An efficient dynamic multicast traffic-grooming algorithm for WDM networks

With the growth of multi-granularity multicast applications, there comes into being a huge gap between the bandwidth of a wavelength provided and a multicast traffic required in the wavelength division multiplexing (WDM) networks. The dynamic multicast traffic-grooming is an effective way for WDM networks to improve the wavelength utilization and decrease the traffic blocking probability. A novel switching node architecture with the multicast switching matrix and traffic-grooming fabric is studied in the paper. Then, an efficient dynamic multicast traffic-grooming algorithm is proposed for the architecture. According to the ratio of network available grooming port number to network transceiver number, the proposed algorithm estimates whether the traffic-grooming port is a scarce resource for input traffic and chooses the appropriate grooming strategy. If the traffic-grooming port is scarce, the minimized use grooming port strategy is designed for the coming traffic. On the contrary, the minimized use node transceiver strategy is applied for the coming traffic. Simulation results show that the proposed algorithm can groom traffic efficiently with low blocking probability and high network throughput constraint by limiting number of node transceivers and grooming ports.     

An adaptive clustering algorithm for dynamic heterogeneous wireless sensor networks

Wireless Networks - In the heterogeneous wireless sensor networks, most algorithms assume that nodes are heterogeneous in terms of their initial energy (we refer to as static energy heterogeneity)....     

零平均动态控制算法的研究

常见的电压控制型BUCK直流变换电路,实际输出电压与参考电压存在一定偏差,对于对电源精度要求较高的设备来说,常用的控制算法无法满足要求。基于该问题,文中提出了一种数字控制算法——零平均动态控制算法,使实际输出电压的平均值可与参考电压保持一致。采用Simplorer软件对传统电压控制型BUCK电路和基于零平均动态控制BUCK电路分别进行了仿真与分析,验证了零平均动态控制算法的有效性。     

一种改进的动态帧时隙ALOHA算法

射频识别系统的最大缺点之一是当标签碰撞时的低标签识别效率。传统的射频识别防碰撞算法在标签数量庞大时,解读标签所需的时隙数会成倍增加。为了解决这一问题,文章提出一种改进的动态时隙ALOHA算法,它根据标签数调整每帧中的时隙数,以使系统能以高吞吐量工作,缩短读取时间,提高了整个系统的效率。     

信息优先级保护的动态频谱分配算法

为提高海上无人艇编队无线网络频谱利用率,同时满足不同优先级信息的传输需求,提出了一种信息优先级保护的动态频谱分配算法。算法采用完全信息动态博弈模型,引入异步分布式定价（Asynchronous Distributed Pricing,ADP）算法设计效用函数。鉴于传统ADP算法在有较多通信余量时干扰价格定价过高,改进干扰价格定义。为体现优先级对分配的影响,在效用函数中加入信息权重。对效用函数成本部分进一步更改,可在实现高优先级信息优先传输的同时,依据通信速率需求合理分配信道通信容量。经过仿真验证,所提算法在吞吐量和可靠性方面优于基于节点优先级的分配算法。     

Penalized minimum matching distance-guided EM algorithm

A novel and efficient mixture model fitting technique, called penalized minimum matching distance-guided expectation–maximization (EM) algorithm, is proposed. Penalized minimum matching distance is used to find the number of mixture components very accurately. We illustrate the excellent performance of the penalized minimum matching distance-guided EM algorithm with experiments involving Gaussian mixtures.     

Adaptive MLSDE using the EM algorithm

The theory of adaptive sequence detection incorporating estimation of channel and related parameters is studied in the context of maximum-likelihood (ML) principles in a general framework based on the expectation and maximization (EM) algorithm. A generalized ML sequence detection and estimation (GMLSDE) criterion is derived based on the EM approach, and it is shown how the per-survivor processing and per-branch processing methods emerge naturally from GMLSDE. GMLSDE is developed into a real time detection/estimation algorithm using the online EM algorithm with coupling between estimation and detection. By utilizing Titterington's (1984) stochastic approximation approach, different adaptive ML sequence detection and estimation (MLSDE) algorithms are formulated in a unified manner for different channel models and for different amounts of channel knowledge available at the receiver. Computer simulation results are presented for differentially encoded quadrature phase-shift keying in frequency flat and selective fading channels, and comparisons are made among the performances of the various adaptive MLSDE algorithms derived earlier     

An effective algorithm for implementing perfectly matched layers in time-domain finite-element simulation of open-region EM problems

An algorithm is presented to implement perfectly matched layers (PMLs) for the time-domain finite-element (TDFE) simulation of two-dimensional open-region electromagnetic scattering and radiation problems. The proposed algorithm is based on the TDFE solution of a special vector wave equation similar to the one in an anisotropic and dispersive medium. The impact of the PML on the stability of the resultant TDFE solution is studied for a variety of temporal discretization schemes, and it is shown that the proposed algorithm for implementing PML can support unconditionally stable TDFE schemes. Both the total- and the scattered-field formulations are described, and numerical simulations of radiation and scattering problems are presented to validate the proposed PML algorithm for the mesh truncation of the TDFE solution.     

一种改进的高动态GPS载波组合跟踪环路算法

在高动态的接收环境下,GPS接收机接收信号载频上会产生很大的多普勒频移及其变化率,传统的GPS载波跟踪环无法保证可靠的跟踪。在综合分析了常规跟踪方案的利弊后,提出一种新的基于四相鉴频（FQFD）牵引的二阶叉积自动频率控制环（CPAFC）辅助三阶锁相环（PLL）高动态跟踪环路算法。通过美国喷气推进实验室（JPL）高动态载体模型测试表明,该算法在高动态环境下不仅能快速牵入和锁定载波信号,而且在高达100g/s的加加速度作用过程中能持续、精确跟踪,实现导航电文的正常解调。     

An EM Algorithm for Ion-Channel Current Estimation

Parameter estimation of a continuous-time Markov chain observed through a discrete-time memoryless channel is studied. An expectation-maximization (EM) algorithm for maximum likelihood estimation of the parameter of this hidden Markov process is developed and applied to a simple example of modeling ion-channel currents in living cell membranes. The approach follows that of Asmussen, Nerman and Olsson, and Ryden, for EM estimation of an underlying continuous-time Markov chain.     

基于IMM-EKF的高动态“北斗”导航信号频率估计算法

高动态环境下的"北斗"导航信号含有较大的多普勒频率及其变化率,传统锁相环(PLL)在跟踪时难以保证较高的跟踪精度.在分析高动态环境下"北斗"信号模型的基础上,提出了一种基于交互式多模型-扩展卡尔曼滤波(IMM-EKF)的自适应滤波算法,对载波相位及其高阶分量进行估计.IMM-EKF采用多个跟踪模型来解决滤波过程中单个模型不准确的问题,并结合改进的Sage-Husa自适应算法,在线估计和修正过程噪声及测量噪声的统计特性,增强了滤波的稳定性.仿真结果表明,IMM-EKF相比于PLL和EKF,估计精度更高,算法稳定性更强.     

EPON中一种改进的动态带宽分配算法

对经典的EBA算法进行了改进,提出了一种有效地利用空闲时间(idle time)来传输数据的动态带宽分配算法--N-EBA算法.仿真结果表明,N-EBA算法具有良好的平均包时延和较高的带宽利用率.     

一种带有可控动态参数的优化覆盖算法

能耗与覆盖问题是无线传感器网络研究领域的基本问题,也是一个重点问题。针对传感器节点所呈现的同构性特点,提出了一种带有可控动态参数的优化覆盖算法（OCCDP）。该算法首先给出了3节点联合覆盖时,最大无缝覆盖率的求解过程;其次,给出了在监测区域内存在传感器节点覆盖时,覆盖质量期望值求解方法以及与邻居节点进行覆盖比对时覆盖率的判定方法;当存在冗余覆盖时,给出了任意传感器节点处于冗余节点覆盖时的覆盖率的计算过程;最后,通过仿真实验与其他算法在覆盖质量和网络生存周期等方面进行对比,其性能指标平均提升了11.02%和13.27%,从而验证了提出算法的有效性和可行性。     

改进的高效MIMO-OFDM系统EM信道估计算法

针对MIMO-OFDM系统中期望最大化(EM)信道估计算法在高信噪比(SNR)下带来的误差地板(EF)现象,且OFDM符号的数据传输效率随发射天线数的增加而明显降低,提出一种改进的高效EM信道估计算法。该算法首先引入一种准确的等效信号模型并推导出一种修正的EM算法,改善了EM算法在高SNR下的性能;在多个OFDM间利用相位正交导频序列来提高数据传输效率,同时进行联合信道估计以提高估计性能。仿真实验验证了所提算法具有更高的信道估计性能和更高的数据传输效率。     

EM algorithm for image segmentation initialized by a tree structurescheme

In this correspondence, the objective is to segment vector images, which are modeled as multivariate finite mixtures. The underlying images are characterized by Markov random fields (MRFs), and the applied segmentation procedure is based on the expectation-maximization (EM) technique. We propose an initialization procedure that does not require any prior information and yet provides excellent initial estimates for the EM method. The performance of the overall segmentation is demonstrated by segmentation of simulated one-dimensional (1D) and multidimensional magnetic resonance (MR) brain images.