Reliability estimation for probabilistic switching circuits

A simple technique for the reliability estimation of switching circuits is developed. A Table is prepared for reliability expressions of basic logic modules. These expressions are then used for the reliability evaluation of complex switching circuits.     

Improved iterative joint detection and estimation through variational Bayesian inference

We propose an improved variational Bayesian (VB) receiver for orthogonal frequency-division multiplexing (OFDM) systems over frequency-selective block-fading channels. Conventional VB receivers provide distribution-estimates for the channel and information symbols iteratively and jointly. The proposed scheme is different from conventional VB inference in that the VB iterative receiver also exploits the hard channel estimate extracted from previous iterations to update the channel and symbol distributions. In this way, we reduce the impact of channel uncertainty on the decoder performance by means of a modified formulation of the VB formalism. The adequacy of the proposed approach compared to classically used VB receivers is demonstrated by simulations.     

MAP-based probabilistic diffusion method for correspondence andline field estimation

A new maximum a posteriori (MAP)-based probabilistic diffusion method is proposed for the estimation of dense correspondence and line fields. The proposed algorithm reflects the joint probabilistic distributions of the neighbourhood in the Markov random field, and implements them by using a plane configuration model. The probabilistic diffusion scheme showed fast convergence and improved object boundaries in the estimated fields     

A probabilistic resource estimation and semi-reservation scheme forflow oriented multimedia wireless networks

Currently, multimedia is urgently expected in wireless networks. One of the most important and complicated issues is quality of service guarantees in third-generation wireless networks. The purpose of this article is to propose a probabilistic resource estimation and semi-reservation scheme in considering the probability of real usage made by the mobile host. This scheme can efficiently improve the connection blocking probability, connection dropping probability, and bandwidth utilization     

Unifying regularization and Bayesian estimation methods for enhanced imaging with remotely sensed Data-part I: theory

The problem of estimating, from one sampled realization of the remotely sensed data signal, the power spatial spectrum pattern (SSP) of the wave field scattered from the probing surface is treated as it is required for enhanced radar imaging of the remotely sensed scenes. Specifically, we propose to unify the Bayesian estimation strategy with the maximum-entropy (ME) information-theoretic principle for incorporating the prior knowledge through developing the fused Bayesian-regularization (FBR) technique for SSP estimation. The first aspect of the proposed approach concerns the ME-based incorporating the a priori information about the geometrical properties of an image to tailor the metrics structure in the solution space to the problem at hand. The second aspect alleviates the problem ill-posedness associated with preserving the boundary values, calibration, and spectral a priori fixed model properties of an image through the regularizing projection constraints imposed on the solution. When applied to SSP estimation without incorporating the metrics and regularization considerations, the procedure leads to the previously derived maximum-likelihood method. When such considerations are incorporated, the optimal FBR technique leads to a new nonlinear imaging algorithm that implies adaptive formation of the second-order sufficient statistics of the data, their smoothing, and projection applying the composite regularizing window operator. We provide analytical techniques to find these statistics and windows, and the optimal FBR estimator itself. Numerical recipes, performance issues, and simulation examples are treated in a companion paper.     

Parameter estimation in TV image restoration using variational distribution approximation.

In this paper, we propose novel algorithms for total variation (TV) based image restoration and parameter estimation utilizing variational distribution approximations. Within the hierarchical Bayesian formulation, the reconstructed image and the unknown hyper parameters for the image prior and the noise are simultaneously estimated. The proposed algorithms provide approximations to the posterior distributions of the latent variables using variational methods. We show that some of the current approaches to TV-based image restoration are special cases of our framework. Experimental results show that the proposed approaches provide competitive performance without any assumptions about unknown hyper parameters and clearly outperform existing methods when additional information is included.     

A probabilistic framework for next best view estimation in a cluttered environment

In this article, we present an information gain-based variant of the next best view problem for occluded environment. Our proposed method utilizes a belief model of the unobserved space to estimate the expected information gain of each possible viewpoint. More precise, this belief model allows a more precise estimation of the visibility of occluded space and with that a more accurate prediction of the potential information gain of new viewing positions. We present experimental evaluation on a robotic platform for active data acquisition, however due to the generality of our approach it also applies to a wide variety of 3D reconstruction problems. With the evaluation done in simulation and on a real robotic platform, exploring and acquiring data from different environments we demonstrate the generality and usefulness of our approach for next best view estimation and autonomous data acquisition.     

Unifying regularization and Bayesian estimation methods for enhanced imaging with remotely sensed Data-part II: implementation and performance issues

The fused Bayesian-regularization (FBR) method from a companion paper provides a rigorous theoretical formalism for optimal estimation of the power spatial spectrum pattern (SSP) of the wave field scattered from the probing surface as it is required for enhanced radar imaging of the remotely sensed scenes. Being nonlinear and solution-dependent, the optimal FBR method requires extremely complex nonlinear solution-dependent operator inversions and, therefore, cannot be recommended as a numerically realizable estimator of the SSP. Here, we design a family of robust easy-to-implement FBR algorithms, provide the relevant computational recipes, and discuss their performances. We comment on the practical aspects of the robustified FBR estimators, such as numerical implementation and improvement in the output SNR. The advantage in using the proposed robust FBR method is demonstrated through simulations of enhancing the SAR images formed using the conventional matched filtering of the trajectory signal.     

A new variational method for erythrocyte velocity estimation in wide-field imaging in vivo

Measuring erythrocyte velocity in individual microvessels has important applications for biomedical and functional imaging. Recent multiphoton fluorescence microscopy approaches require injecting fluorescent tracers; moreover, only one or few vessels can be imaged at a time. To overcome these shortcomings, we used CCD-based optical imaging of intrinsic absorption changes in macroscopic vascular networks to record erythrocytes' trajectories over several mm (2) of cortical surface. We then demonstrate the feasibility of erythrocyte velocity estimation from such wide-field data, using two robust, independent, algorithms. The first one is a recently published Radon transform-based algorithm that estimates erythrocyte velocity locally. We adapt it to data obtained in wide-field imaging and show, for the first time, its performance on such datasets. The second ("fasttrack") algorithm is novel. It is based on global energy minimization techniques to estimate the full spatiotemporal erythrocytes' trajectories inside vessels. We test the two algorithms on both simulated and biological data, obtained in rat cerebral cortex in a spreading depression experiment. On vessels with medium-slow erythrocyte velocities both algorithms performed well, allowing their usage as benchmark one for another. However, our novel fasttrack algorithm outperformed the other one for higher velocities, as encountered in the arterial network.     

MIMO-OFDM系统中基于变分贝叶斯EM算法的联合符号检测与信道估计

基于变分贝叶斯期望最大化(VBEM, variational Bayes expectation maximization)算法和Turbo原理,提出了时变信道条件下MIMO-OFDM系统中的联合符号检测与信道估计算法.设计的软入软出空时检测器在采用列表球形译码避免穷尽搜索的同时,考虑了信道估计误差方差矩阵的影响;利用空时检测获得的发送信号后验概率分布估计,推出了新的Kalman前向后向递归信道估计器.仿真结果表明,在时变多径信道条件下,提出的算法比传统EM算法和面向判决算法更加具有顽健性.     

Blind deconvolution using a variational approach to parameter, image, and blur estimation.

Following the hierarchical Bayesian framework for blind deconvolution problems, in this paper, we propose the use of simultaneous autoregressions as prior distributions for both the image and blur, and gamma distributions for the unknown parameters (hyperparameters) of the priors and the image formation noise. We show how the gamma distributions on the unknown hyperparameters can be used to prevent the proposed blind deconvolution method from converging to undesirable image and blur estimates and also how these distributions can be inferred in realistic situations. We apply variational methods to approximate the posterior probability of the unknown image, blur, and hyperparameters and propose two different approximations of the posterior distribution. One of these approximations coincides with a classical blind deconvolution method. The proposed algorithms are tested experimentally and compared with existing blind deconvolution methods.     

A variational Bayesian approach to number of sources estimation for multichannel blind deconvolution

Most traditional multichannel blind deconvolution algorithms rely on some assumptions on the mixing model, e.g. the number of sources is known a priori; and the mixing environment is noise-free. Unfortunately, these assumptions are not necessarily true in practice. In this paper, we will relax the assumption placed on the number of sources by studying a state space mixing model where the number of sources is assumed to be unknown but not greater than the number of sensors. Based on this mixing model, we will formulate the estimation of the number of sources problem as a model order selection problem. Model comparison, as a common method of model order selection, usually involves the evaluation of multi-variable integrals which is computationally intractable. A variational Bayesian method is therefore used to overcome this multi-variable integral issue. The problem is solved by approximating the true, complicated posteriors with a set of independent, simple, tractable posteriors. To realize the objective of optimal approximation, we maximize an objective function called negative free energy. We will derive a variational Bayesian algorithm, in which the number of sources will be estimated through two approaches: automatic relevance determination and comparison of the optimized negative free energy. The proposed variational Bayesian algorithm will be evaluated on both artificially generated examples, and practical signals.     

Unifying mesh- and tree-based programmable interconnect

We examine the traditional, symmetric, Manhattan mesh design for field-programmable gate-array (FPGA) routing along with tree-of-meshes (ToM) and mesh-of-trees (MoT) based designs. All three networks can provide general routing for limited bisection designs (Rent's rule with p<1) and allow locality exploitation. They differ in their detailed topology and use of hierarchy. We show that all three have the same asymptotic wiring requirements. We bound this tightly by providing constructive mappings between routes in one network and routes in another. For example, we show that a (c,p) MoT design can be mapped to a (2c,p) linear population ToM and introduce a corner turn scheme which will make it possible to perform the reverse mapping from any (c,p) linear population ToM to a (2c,p) MoT augmented with a particular set of corner turn switches. One consequence of this latter mapping is a multilayer layout strategy for N-node, linear population ToM designs that requires only /spl Theta/(N) two-dimensional area for any p when given sufficient wiring layers. We further show upper and lower bounds for global mesh routes based on recursive bisection width and show these are within a constant factor of each other and within a constant factor of MoT and ToM layout area. In the process we identify the parameters and characteristics which make the networks different, making it clear there is a unified design continuum in which these networks are simply particular regions.     

A constrained variational principle for direct estimation and smoothing of the diffusion tensor field from complex DWI

In this paper, we present a novel constrained variational principle for simultaneous smoothing and estimation of the diffusion tensor field from complex valued diffusion-weighted images (DWI). The constrained variational principle involves the minimization of a regularization term of L(P) norms, subject to a nonlinear inequality constraint on the data. The data term we employ is the original Stejskal-Tanner equation instead of the linearized version usually employed in literature. The complex valued nonlinear form leads to a more accurate (when compared to the linearized version) estimate of the tensor field. The inequality constraint requires that the nonlinear least squares data term be bounded from above by a known tolerance factor. Finally, in order to accommodate the positive definite constraint on the diffusion tensor, it is expressed in terms of Cholesky factors and estimated. The constrained variational principle is solved using the augmented Lagrangian technique in conjunction with the limited memory quasi-Newton method. Experiments with complex-valued synthetic and real data are shown to depict the performance of our tensor field estimation and smoothing algorithm.     

统一用户管理在企业信息化中的价值及框架

背景随着信息系统在企业运行中的全面渗透,企业建立了各种IT应用系统,包括CRM、ERP、OA、HR、SCM、PLM 等,还包括各种IT资源服务如数据库、目录、邮件、网络接入系统、支持系统等。每一个应用系统都对应一个特定的用户群体,以往应用系统都储存了自身的用户信     

Unifying keywords and visual contents in image retrieval

We're interested in using keywords and visual content together in image retrieval. We used a seamless joint querying and relevance feedback scheme based on keywords and lowlevel visual content, incorporating keyword similarities. We developed an algorithm for a learned word similarity matrix and conducted experiments that validated our approach     

Wi—Fi手机：有线与无线的统一

它可以在移动通信网络和宽带互联网连接之间平滑切换,在宽带互联网上利用VoIP技术实现语音服务;它兼具移动电话的机动和灵活性,及固定电话的成本优势;而且2006 年,它的价格不再令人望而生畏。     

Unifying perspective on log-domain filter synthesis

A unifying framework is presented for the in-depth understanding of the seemingly unrelated state-space-based and translinear methods proposed for the synthesis of externally linear but internally nonlinear logdomain filters. The translinear methods exploit either the 'dynamic translinear principle' or the Bernoulli Cell dynamics. Light is shed in an insightful manner on key interconnections between Frey's nodal state-space-based synthesis relations, translinear loop-based synthesis conditions and the archetypal linear filter dynamics by considering the form of the state variables used in each case.