应用加权高斯模型的非理想稀疏信道估计

在高速通信系统中,由于多径信道通常存在一些小的散射体,使得抽头向量不满足理想的稀疏特性,导致经典的稀疏估计算法存在一定的性能损失。针对上述非理想稀疏特性问题,提出了一种基于酉变换近似消息传递(Unitary Transform Approximate Message Passing,UT-AMP)和加权高斯(Weighting-Gaussian,WG)先验模型的稀疏估计算法。首先,由非理想稀疏信道的构造分析,导出了WG先验模型和参数;其次,利用贝叶斯公式对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统进行因式分解和因子图建模,归纳了在消息传递框架内期望最大化(Expectation Maximization,EM)算法嵌入方式,推导了联合UT-AMP和EM的信道估计算法;最后,建立仿真环境对所提算法进行复杂度分析和数值仿真。仿真结果表明,所提算法能够以同阶复杂度实现信道估计性能和频带利用率的提升,具有很高的应用和推广价值。     

实时图像处理并行操作系统的微内核设计和实现

本文主要设计和实现了运行在数字信号处理阵列上支持中高层图像处理的实时图像处理并行操作系统（ＲＴＩＰＰＯＳ）微内核，该内核提供了任务分解模型，并发进程管理，支持局部消息传递，同步，设计了快速共享消息传递方式和它辅助性能，内核原语支持ＰＯＳＩＸ系统调用的Ｃ语言接口和抽象级的用户封装，有行为的高效性和预测性，本文是对ＲＴＩＰＰＯＳ微内核设计的综述。     

Maximum-likelihood parameter estimation for unsupervised stochasticmodel-based image segmentation

An unsupervised stochastic model-based approach to image segmentation is described, and some of its properties investigated. In this approach, the problem of model parameter estimation is formulated as a problem of parameter estimation from incomplete data, and the expectation-maximization (EM) algorithm is used to determine a maximum-likelihood (ML) estimate. Previously, the use of the EM algorithm in this application has encountered difficulties since an analytical expression for the conditional expectations required in the EM procedure is generally unavailable, except for the simplest models. In this paper, two solutions are proposed to solve this problem: a Monte Carlo scheme and a scheme related to Besag's (1986) iterated conditional mode (ICM) method. Both schemes make use of Markov random-field modeling assumptions. Examples are provided to illustrate the implementation of the EM algorithm for several general classes of image models. Experimental results on both synthetic and real images are provided.     

3D EM/MPM Image Segmentation Using an FPGA Embedded Design Implementation

This paper presents a Field Programmable Gate Array (FPGA) based embedded system which is used to achieve high speed segmentation of 3D images. Segmentation is performed using Expectation-Maximization (EM) with Maximization of Posterior Marginals (MPM) Bayesian algorithm. This algorithm segments the 3D image using neighboring pixels based on a Markov Random Field (MRF) model. In this system, the embedded processor controls a custom circuit which performs the MPM and portions of the EM algorithm. The embedded processor completes the EM algorithm and also controls image data transmission between host computer and on-board memory. The whole system has been implemented on Xilinx Virtex 6 FPGA and achieved over 100 times processing improvement compared to standard desktop computer. Three new techniques were the key to achieve this speed: Pipelined computational cores, sixteen parallel data paths and a novel memory interface for maximizing the external memory bandwidth.

     

衍射层析模型下穿墙雷达三维学习成像方法

针对现有穿墙雷达三维稀疏成像中,存在网格时延构建字典矩阵所需内存过大以及凸优化稀疏成像算法阈值参数不确定影响重建图像质量的问题,提出了一种基于衍射层析稀疏模型的学习近似消息传递三维成像方法。该方法在衍射层析成像算法上通过构造快速傅里叶变换算子来建立三维成像稀疏模型,然后修正近似消息传递算法求解稀疏解,并将其迭代过程映射成多层神经网络,最后通过数据驱动自适应学习多层神经网络中的可调参数,从而实现三维学习成像。仿真和实验数据处理结果表明,该方法不仅减小了系统所需内存,还避免了参数的人工调整对成像质量的影响。     

采用拉普拉斯尺度混合先验的结构化近似消息传递算法

为了准确有效地实现自然图像的压缩感知重构,提出一种使用拉普拉斯尺度混合（Laplacian Scale Mixture,LSM）先验的结构化近似消息传递（Approximate Message Passing,AMP）算法.利用LSM模型构建AMP算法的高阶统计约束,将压缩感知重构问题转化为先验信息估计问题和奇异值最小化问题.首先,用LSM分布刻画相似块矩阵奇异值的稀疏性,其中该稀疏性指示了图像块的相似性,因此LSM模型被用来描述图像的非局部相似结构;然后,通过期望最大化算法估计LSM模型的尺度参数,得到可靠的先验信息;最后,由AMP算法求解奇异值最小化问题,实现图像的精确重构.实验结果表明,提出的结构化AMP算法的图像重构质量优于多种主流的压缩感知图像重构算法.     

Approximate analysis of timer-controlled priority scheme in thesingle-service token-passing systems

This paper presents an approximate analysis of delay performance in the timer-controlled priority scheme of single-service token passing systems, where each priority queue is allowed to transmit one message at a time. This is the practical case of real-time networks such as those used in aircraft and spacecraft control, process control and factory communications. The token passing systems consist of N stations, each of which has (K+1) priority queues. The highest priority queues can transmit a message whenever the token arrives. A lower priority queue can transmit a message if the token rotation time (which was reset and restarted at the previous token arrival instant) is not expired when the token arrives. An analytical model is developed based on the independent and identically distributed random variables of conditional effective service times (time interval between two consecutive instants at which a priority queue has an opportunity to transmit a message) of each priority queue. The condition is assigned on the basis of the contribution of message transmission time to the effective service time. The approximate mean waiting time for each priority queue is determined using the well-known results from Kuehn (1979), who first introduced the conditional cycle time. The approximate analytical model is validated by comparison with the simulation results     

低信噪比下二维联合快速超分辨B-ISAR成像方法

双基地ISAR成像分辨率受限于信号带宽和方位积累时间,且成像质量受噪声影响严重.本文在充分考虑回波的二维联合稀疏特征基础上,提出二维联合字典下的矩阵复数近似消息传递超分辨快速成像算法.在构建双基地ISAR的二维联合超分辨成像模型基础上,首先通过向量化处理,将二维超分辨成像问题转换为复数基追踪去噪问题;其次通过两种策略实现复数基追踪去噪问题的快速求解,一是利用向量化与Kronecker积的关系,推导出矩阵形式复数近似消息传递算法,从而避免向量化处理带来的大矩阵运算量和大存储量问题;二是为进一步减少单次迭代的运算量,将矩阵乘法等效为二维快速傅里叶变换.最后,利用本文算法在迭代过程中对噪声阈值不断精确逼近,提高算法在低信噪比下的成像能力.仿真数据成像结果验证了本文算法的有效性.     

A Dual Ring Connectivity Model for VANET Under Heterogeneous Traffic Flow

Vehicular ad-hoc network (VANET) is characterized as a highly dynamic wireless network due to the dynamic connectivity of the network nodes. To achieve better connectivity under such dynamic conditions, an optimal transmission strategy is required to direct the information flow between the nodes. Earlier studies on VANET’s overlook the characteristics of heterogeneity in vehicle types, traffic structure, flow for density estimation, and connectivity observation. In this paper, we have proposed a heterogeneous traffic flow based dual ring connectivity model to enhance both the message disseminations and network connectivity. In our proposed model the availability of different types of vehicles on the road, such as, cars, buses, etc., are introduced in an attempt to propose a new communication structure for moving vehicles in VANETl under cooperative transmission in heterogeneous traffic flow. The model is based on the dual-ring structure that forms the primary and secondary rings of vehicular communication. During message disseminations, Slow speed vehicles (buses) on the secondary ring provide a backup path of communication for high speed vehicles (cars) moving on the primary ring. The Slow speed vehicles act as the intermediate nodes in the aforementioned connectivity model that helps improve the network coverage and end-to-end data delivery. For the evaluation and the implementation of dual-ring model a clustering routing scheme warning energy aware cluster-head is adopted that also caters for the energy optimization. The implemented dual-ring message delivery scheme under the cluster-head based routing technique does show improved network coverage and connectivity dynamics even under the multi-hop communication system.     

基于变量节点串行策略的SCMA低复杂度译码研究

稀疏码多址接入(SCMA)技术作为5G无线通信网络的一个竞争性的非正交多址方案,具有广阔的应用前景.但目前基于SCMA的上行链路均采用泛洪方案的消息传递算法(MPA)进行译码,无论是检测的复杂度还是收敛速度都不甚理想.提出一种改变信息更新策略的串行消息传递算法——S-MPA (serial MPA),按照变量节点的次序进行消息处理和传递,每一个变量节点同时进行校验消息的接收和变量消息的发送.理论和仿真结果表明,该算法不仅能保持良好的性能,而且也有较低的解码复杂度.     

Combining compile-time and run-time support for efficient softwaredistributed shared memory

We describe an integrated compile time and run time system for efficient shared memory parallel computing on distributed memory machines. The combined system presents the user with a shared memory programming model. The run time system implements a consistent shared memory abstraction using memory access detection and automatic data caching. The compiler improves the efficiency of the shared memory implementation by directing the run time system to exploit the message passing capabilities of the underlying hardware. To do so, the compiler analyzes shared memory accesses and transforms the code to insert calls to the run time system that provide it with the access information computed by the compiler. The run time system is augmented with the appropriate entry points to use this information to implement bulk data transfer and to reduce the overhead of run time consistency maintenance. In those cases where the compiler analysis succeeds for the entire program, we demonstrate that the combined system achieves performance comparable to that produced by compilers that directly target message passing. If the compiler analysis is successful only for parts of the program, for instance, because of irregular accesses to some of the arrays, the resulting optimizations can be applied to those parts for which the analysis succeeds. If the compiler analysis fails entirely, we rely on the run time maintenance of shared memory and thereby avoid the complexity and the limitations of compilers that directly target message passing. The result is a single system that combines efficient support for both regular and irregular memory access patterns     

Bayesian pixel classification using spatially variant finitemixtures and the generalized EM algorithm

A spatially variant finite mixture model is proposed for pixel labeling and image segmentation. For the case of spatially varying mixtures of Gaussian density functions with unknown means and variances, an expectation-maximization (EM) algorithm is derived for maximum likelihood estimation of the pixel labels and the parameters of the mixture densities, An a priori density function is formulated for the spatially variant mixture weights. A generalized EM algorithm for maximum a posteriori estimation of the pixel labels based upon these prior densities is derived. This algorithm incorporates a variation of gradient projection in the maximization step and the resulting algorithm takes the form of grouped coordinate ascent. Gaussian densities have been used for simplicity, but the algorithm can easily be modified to incorporate other appropriate models for the mixture model component densities. The accuracy of the algorithm is quantitatively evaluated through Monte Carlo simulation, and its performance is qualitatively assessed via experimental images from computerized tomography (CT) and magnetic resonance imaging (MRI).     

Multiscale fragile watermarking based on the Gaussian mixture model.

In this paper, a new multiscale fragile watermarking scheme based on the Gaussian mixture model (GMM) is presented. First, a GMM is developed to describe the statistical characteristics of images in the wavelet domain and an expectation-maximization algorithm is employed to identify GMM model parameters. With wavelet multiscale subspaces being divided into watermarking blocks, the GMM model parameters of different watermarking blocks are adjusted to form certain relationships, which are employed for the presented new fragile watermarking scheme for authentication. An optimal watermark embedding method is developed to achieve minimum watermarking distortion. A secret embedding key is designed to securely embed the fragile watermarks so that the new method is robust to counterfeiting, even when the malicious attackers are fully aware of the watermark embedding algorithm. It is shown that the presented new method can securely embed a message bit stream, such as personal signatures or copyright logos, into a host image as fragile watermarks. Compared with conventional fragile watermark techniques, this new statistical model based method modifies only a small amount of image data such that the distortion on the host image is imperceptible. Meanwhile, with the embedded message bits spreading over the entire image area through the statistical model, the new method can detect and localize image tampering. Besides, the new multiscale implementation of fragile watermarks based on the presented method can help distinguish some normal image operations such as JPEG compression from malicious image attacks and, thus, can be used for semi-fragile watermarking.     

基于混合消息传递和部分近似的联合信道估计MIMO-OFDM接收机

本文提出了一种基于混合消息传递和部分高斯近似(Partial Gaussian Approximation, PGA)的多用户干扰消除方法,并应用到联合信道估计MIMO-OFDM接收机中。由于多用户干扰模型中存在的“乘积-求和”结构,使得选择标准消息传递规则,如置信传播(Belief Propagation, BP),期望传播(Expectation Propagation, EP),平均场规则(Mean Field, MF),或者联合方法时只能在性能或复杂度方面有所取舍。现有根据标准消息传递规则得到的最优性能接收机复杂度高,而近似程度大的低复杂度接收机性能损失严重。本文根据多用户干扰模型的自身特点,对标准消息传递规则进行了修改,提出了一种基于混合消息传递规则和部分高斯近似的多用户干扰消除方法。依据信道估计过程中不同用户的信道权重,采用不同的消息传递规则,可以实现复杂度和性能的均衡调整。仿真结果表明,本文提出的多用户干扰消除方法,在性能接近已知最优接收机的情况下,能够大幅降低复杂度。      

Time discretization of continuous-time filters and smoothers forHMM parameter estimation

In this paper we propose algorithms for parameter estimation of fast-sampled homogeneous Markov chains observed in white Gaussian noise. Our algorithms are obtained by the robust discretization of stochastic differential equations involved in the estimation of continuous-time hidden Markov models (HMM's) via the EM algorithm. We present two algorithms: the first is based on the robust discretization of continuous-time filters that were recently obtained by Elliott to estimate quantities used in the EM algorithm; the second is based on the discretization of continuous-time smoothers, yielding essentially the well-known Baum-Welch re-estimation equations. The smoothing formulas for continuous-time HMM's are new, and their derivation involves two-sided stochastic integrals. The choice of discretization results in equations which are identical to those obtained by deriving the results directly in discrete time. The filter-based EM algorithm has negligible memory requirements; indeed, independent of the number of observations. In comparison the smoother-based discrete-time EM algorithm requires the use of the forward-backward algorithm, which is a fixed-interval smoothing algorithm and has memory requirements proportional to the number of observations. On the other hand, the computational complexity of the filter-based EM algorithm is greater than that of the smoother-based scheme. However, the filters may be suitable for parallel implementation. Using computer simulations we compare the smoother-based and filter-based EM algorithms for HMM estimation. We provide also estimates for the discretization error     

LDPC码串行解码算法建模

消息传递机制是影响LDPC码解码性能的重要因素。简要介绍了Gallager提出的LDPC码和基于洪水消息传递机制的和积解码算法。在此基础上引入基于校验节点的串行消息传递机制并提出具体可实现的解码算法模型。该串行解码算法按照校验节点一定的顺序进行消息处理和传递,保证更新的消息能够马上进入迭代过程,从而改善消息传递的收敛特性;同时充分利用消息间相互关联的特点,融合消息传递步骤,可以降低所需的存储容量,简化解码复杂度。分别应用洪水解码算法和串行解码算法在AWGN信道下进行性能仿真。仿真结果表明串行解码算法使解码性能得到明显提高。     

一种图像的隐藏信息还原方法

提出了一种基于遗传算法的机密信息还原方法.该方法首先确定嵌入扩频序列的长度:然后,采用遗传算法求得嵌入序列的最佳估计:进而,用最佳的估计序列还原出机密信息.实现机密信息的盲还原.该方法己在微机上进行模拟实验,实验结果表明,还原效果较好.     

Reducing location update and paging costs in a PCS network

Mobility tracking operations in a personal communication service (PCS) system are signaling consuming. Several strategies have been proposed in the literature to reduce both the location update (LU) and the paging (PG) costs. In this paper, we propose a location-tracking algorithm called three-location area (TrLA), combined with selective paging. In TrLA, the mobile terminal (MT) allocates the identification of three neighboring location areas (LAs) in its local memory. We call this set of three LAs, a big-location area (BLA). Each time the MT exits the BLA, it triggers an LU message to the system databases via a base station, in order to maintain the databases up to date. The MT also updates its cache memory. A two-step selective PG is also considered and compared with the single-step (or nonselective PG). An analytical model based on a semi-Markov process has been used to evaluate our proposal. This scheme is compared with the classical Global System for Mobile Communications (GSM) standard and the two-location algorithm proposal. As a result, this new scheme outperforms the cited strategies, and in our opinion, it can easily be implemented in existing standard cellular and personal communication systems     

基于串行策略的SCMA多用户检测算法

稀疏码多址接入(SCMA)作为一个前景广阔的5 G无线空口技术,能够满足海量连接的需求。针对现有SCMA通信系统都是基于并行策略的消息传递算法(MPA)进行多用户检测,存在信息收敛速度不理想的问题,该文提出一种串行策略的多用户检测算法。该算法以资源节点为序,按串行方式依次进行消息更新与传递,保证更新的消息能够立即进入当前迭代过程,改善了消息传递的收敛速度,相比并行策略的多用户检测算法,降低了算法复杂度;同时,充分利用消息间相互关联的特点,融合消息传递步骤,降低了存储器的要求。理论与仿真结果表明,该算法在误比特率(BER)性能与算法复杂度之间可以达到较理想的平衡。     

The MIT Alewife Machine

A variety of models for parallel architectures, such as shared memory, message passing, and data flow, have converged in the recent past to a hybrid architecture form called distributed shared memory (DSM). Alewife, an early prototype of such DSM architectures, uses hybrid software and hardware mechanisms to support coherent shared memory, efficient user level messaging, fine grain synchronization, and latency tolerance. Alewife supports up to 512 processing nodes connected over a scalable and cost effective mesh network at a constant cost per node. Four mechanisms combine to achieve Alewife's goals of scalability and programmability: software extended coherent shared memory provides a global, linear address space; integrated message passing allows compiler and operating system designers to provide efficient communication and synchronization; support for fine grain computation allows many processors to cooperate on small problem sizes; and latency tolerance mechanisms-including block multithreading and prefetching-mask unavoidable delays due to communication. Extensive results from microbenchmarks, together with over a dozen complete applications running on a 32-node prototype, demonstrate that integrating message passing with shared memory enables a cost efficient solution to the cache coherence problem and provides a rich set of programming primitives. Our results further show that messaging and shared memory operations are both important because each helps the programmer to achieve the best performance for various machine configurations