Multiply-rooted multiscale models for large-scale estimation

Divide-and-conquer or multiscale techniques have become popular for solving large statistical estimation problems. The methods rely on defining a state which conditionally decorrelates the large problem into multiple subproblems, each more straightforward than the original. However this step cannot be carried out for asymptotically large problems since the dimension of the state grows without bound, leading to problems of computational complexity and numerical stability. In this paper, we propose a new approach to hierarchical estimation in which the conditional decorrelation of arbitrarily large regions is avoided, and the problem is instead addressed piece-by-piece. The approach possesses promising attributes: it is not a local method-the estimate at every point is based on all measurements; it is numerically stable for problems of arbitrary size; and the approach retains the benefits of the multiscale framework on which it is based: a broad class of statistical models, a stochastic realization theory, an algorithm to calculate statistical likelihoods, and the ability to fuse local and nonlocal measurements.     

Likelihood calculation for a class of multiscale stochastic models,with application to texture discrimination

A class of multiscale stochastic models based on scale-recursive dynamics on trees has previously been introduced. Theoretical and experimental results have shown that these models provide an extremely rich framework for representing both processes which are intrinsically multiscale, e.g., 1/f processes, as well as 1D Markov processes and 2D Markov random fields. Moreover, efficient optimal estimation algorithms have been developed for these models by exploiting their scale-recursive structure. The authors exploit this structure in order to develop a computationally efficient and parallelizable algorithm for likelihood calculation. They illustrate one possible application to texture discrimination and demonstrate that likelihood-based methods using the algorithm achieve performance comparable to that of Gaussian Markov random field based techniques, which in general are prohibitively complex computationally.     

Establishing multiscale models for simulating whole limb estimates of electric fields for osseointegrated implants

Although the survival rates of warfighters in recent conflicts are among the highest in military history, those who have sustained proximal limb amputations may present additional rehabilitation challenges. In some of these cases, traditional prosthetic limbs may not provide adequate function for service members returning to an active lifestyle. Osseointegration has emerged as an acknowledged treatment for those with limited residual limb length and those with skin issues associated with a socket together. Using this technology, direct skeletal attachment occurs between a transcutaneous osseointegrated implant (TOI) and the host bone, thereby eliminating the need for a socket. While reports from the first 100 patients with a TOI have been promising, some rehabilitation regimens require 12-18 months of restricted weight bearing to prevent overloading at the bone-implant interface. Electrically induced osseointegration has been proposed as an option for expediting periprosthetic fixation and preliminary studies have demonstrated the feasibility of adapting the TOI into a functional cathode. To assure safe and effective electric fields that are conducive for osseoinduction and osseointegration, we have developed multiscale modeling approaches to simulate the expected electric metrics at the bone-implant interface. We have used computed tomography scans and volume segmentation tools to create anatomically accurate models that clearly distinguish tissue parameters and serve as the basis for finite element analysis. This translational computational biological process has supported biomedical electrode design, implant placement, and experiments to date have demonstrated the clinical feasibility of electrically induced osseointegration.     

Multiscale analysis of microvascular blood flow: a multiscale entropy study of laser Doppler flowmetry time series

Processes regulating the cardiovascular system (CVS) are numerous. Each possesses several temporal scales. Their interactions lead to interdependences across multiple scales. For the CVS analysis, different multiscale studies have been proposed, mostly performed on heart rate variability signals (HRV) reflecting the central CVS; only few were dedicated to data from the peripheral CVS, such as laser Doppler flowmetry (LDF) signals. Very recently, a study implemented the first computation of multiscale entropy for LDF signals. A nonmonotonic evolution of multiscale entropy with two distinctive scales was reported, leading to a markedly different behavior from the one of HRV. Our goal herein is to confirm these results and to go forward in the investigations on origins of this behavior. For this purpose, 12 LDF signals recorded simultaneously on the two forearms of six healthy subjects are processed. This is performed before and after application of physiological scales-based filters aiming at isolating previously found frequency bands linked to physiological activities. The results obtained with signals recorded simultaneously on two different sites of each subject show a probable central origin for the nonmonotonic behavior. The filtering results lead to the suggestion that origins of the distinctive scales could be dominated by the cardiac activity.     

3G前途光明道路曲折

3G（第三代移动通信）将给人们带来的是惊喜呢，抑或是灾难？这样难以捉摸的问题正在令全世界的电信运营商和业界分析家们为之困惑、彷徨或忧心忡忡；而对于那些在3G领域内急欲成为“世界第一”的商家而言更是令他们伤透了脑筋，尤其是当眼看3G无线技术的魅力已在现实世界里逐渐消失的今天，更令他们惊慌失措，莫衷一是。3G技术对不同的人具有不同的含义。一方面，3G技术也许是二十一世纪最伟大的技术发明创造之一；另一方面，它也许是目前世界上吹得最大的牛皮。总而言之，3G道路是坎坷不平、充满陷阱和误区。任何人试探沿着这条在地图…     

A multiscale analytical correction technique for two-dimensional thermal models of AlGaN/GaN HEMTs

Two-dimensional approaches are widely used in the numerical thermal models of AlGaN/GaN high electron mobility transistors (HEMTs) to reduce the high computational cost of the three-dimensional approaches. The aforementioned simplified models predict inaccurate device temperatures with significant overestimation of the thermal resistance. In order to take advantage of the computational efficiency of the two-dimensional models, a correction procedure is necessary for the accurate representation of the actual device temperatures. In this paper, a novel correction method is introduced for this purpose. Correction technique presented in this study can be used to improve the accuracy of the multiscale numerical thermal device models.     

Learning graphical models for stationary time series

Probabilistic graphical models can be extended to time series by considering probabilistic dependencies between entire time series. For stationary Gaussian time series, the graphical model semantics can be expressed naturally in the frequency domain, leading to interesting families of structured time series models that are complementary to families defined in the time domain. In this paper, we present an algorithm to learn the structure from data for directed graphical models for stationary Gaussian time series. We describe an algorithm for efficient forecasting for stationary Gaussian time series whose spectral densities factorize in a graphical model. We also explore the relationships between graphical model structure and sparsity, comparing and contrasting the notions of sparsity in the time domain and the frequency domain. Finally, we show how to make use of Mercer kernels in this setting, allowing our ideas to be extended to nonlinear models.     

等待3G

今年三月，新一届政府组成后，信息产业部新任部长王旭东面临的首要任务是3G执照的发放，国内外的设备制造商们无不企盼3G牌照的早日发放能给疲软低迷的通信市场注入一针“强心剂”。 虽然目前全球有上百个3G网络正在或将要建设，但远未达到人们当初的预期。不可否认，3G业务在技术上，如终端、传输、速率、覆盖等方面都存在一些问题，但这都是网络在建设初期发生的正常现象，2G的GSM、CDMAOne也曾遇到过类似情况，只有当网络大规模运行后，逐步对设备进行调整和改进。因此，技术问题不是制约3G发展的主要因素。 从3G网络建设资…     

Platelets: a multiscale approach for recovering edges and surfaces in photon-limited medical imaging

The nonparametric multiscale platelet algorithms presented in this paper, unlike traditional wavelet-based methods, are both well suited to photon-limited medical imaging applications involving Poisson data and capable of better approximating edge contours. This paper introduces platelets, localized functions at various scales, locations, and orientations that produce piece-wise linear image approximations, and a new multiscale image decomposition based on these functions. Platelets are well suited for approximating images consisting of smooth regions separated by smooth boundaries. For smoothness measured in certain H?lder classes, it is shown that the error of m-term platelet approximations can decay significantly faster than that of m-term approximations in terms of sinusoids, wavelets, or wedgelets. This suggests that platelets may outperform existing techniques for image denoising and reconstruction. Fast, platelet-based, maximum penalized likelihood methods for photon-limited image denoising, deblurring and tomographic reconstruction problems are developed. Because platelet decompositions of Poisson distributed images are tractable and computationally efficient, existing image reconstruction methods based on expectation-maximization type algorithms can be easily enhanced with platelet techniques. Experimental results suggest that platelet-based methods can outperform standard reconstruction methods currently in use in confocal microscopy, image restoration, and emission tomography.     

Virtual view generation for 3D digital video

Virtual reality systems use digital models to provide interactive viewing. We present a 3D digital video system that attempts to provide the same capabilities for actual performances such as dancing. Recreating the original dynamic scene in 3D, the system allows photorealistic interactive playback from arbitrary viewpoints using video streams of a given scene from multiple perspectives     

Deconvolution filter design for fractal signal transmissionsystems: a multiscale Kalman filter bank approach

A deconvolution filtering design is proposed for the 1/f fractal signal transmission systems, with its design philosophy being based on multiscale Kalman deconvolution filter bank equipped in the analysis/synthesis wavelet filter bank, The role of wavelet transformation for 1/f fractal signal process is exploited as a multiscale whitening filter for removing the properties of self-similarity and long-range dependence from the fractal signals     

3-d modeling from a single view of a symmetric object

3-D technologies are considered as the next generation of multimedia applications. Currently, one of the challenges faced by 3-D applications is the shortage of 3-D resources. To solve this problem, many 3-D modeling methods are proposed to directly recover 3-D geometry from 2-D images. However, these methods on single view modeling either require intensive user interaction, or are restricted to a specific kind of object. In this paper, we propose a novel 3-D modeling approach to recover 3-D geometry from a single image of a symmetric object with minimal user interaction. Symmetry is one of the most common properties of natural or manmade objects. Given a single view of a symmetric object, the user marks some symmetric lines and depth discontinuity regions on the image. Our algorithm first finds a set of planes to approximately fit to the object, and then a rough 3-D point cloud is generated by an optimization procedure. The occluded part of the object is further recovered using symmetry information. Experimental results on various indoor and outdoor objects show that the proposed system can obtain 3-D models from single images with only a little user interaction.     

Geometric deconvolution: a meta-algorithm for limited view computed tomography

Images reconstructed using a limited number of projections spanning a narrow angular range suffer from a systematic geometric distortion due to the two-dimensional point spread function of the reconstruction process. Applying the projection theorem, we show that the problem of removing this distortion reduces to that of estimating the one-dimensional spread function and deconvolving projections computed for a complementary set of new angles from the initial reconstruction. A second reconstruction is performed using the deconvolved projections along with the original set of projections, thus incorporating wider angular coverage. We present here initial results of such geometric deconvolution performed via inverse filtering using fast Fourier transform techniques. While the results are noisy due to well-known problems associated with inverse filtering, they illustrate the plausibility of the underlying ideas.     

Petri nets and integrality relaxations: A view of continuous Petri net models

Petri nets are formalisms for the modeling of discrete event dynamic systems (DEDS). The integrality of the marking and of the transitions firing counters is a clear reflection of this. To reduce the computational complexity of the analysis or synthesis of Petri nets, two relaxations have been introduced at two different levels: (1) at net level, leading to continuous net systems; (2) at state equation level, which has allowed to obtain systems of linear inequalities, or linear programming problems. These relaxations are mainly related to the fractional firing of transitions, which implies the existence of non-integer markings. We give an overview of this emerging field. It is focused on the relationship between the properties of (discrete) PNs and the corresponding properties of their continuous approximation. Through the interleaving of qualitative and quantitative techniques, surprising results can be obtained from the analysis of these continuous systems. For these approximations to be "acceptable", it is necessary that large markings (populations) exist. It can also be seen, however, that not every populated net system can be continuized. In fact, there exist systems with "large" populations for which continuation does not make sense. The possibility of expressing nonlinear behaviors may lead to deterministic continuous differential systems with complex behaviors.     

Equivalent circuit models in current-mode circuits for time delay calculations

In this paper time delay calculations for current-mode circuits are considered and equivalent circuit models for delay estimation are developed. Three different equivalent circuit structures for the Core Circuit used in the multipurpose IC DU-TCC1209 are examined separately; however the relation obtained for the time delay can be applied to any CMOS current-mode circuit. The proposed calculation methods are verified with SPICE using 0.35 μm TSMC MOSIS technology parameters and with bench-test measurements using DU-TCC1209.     

Grid computing: a STOPE view

Grid computing is emerging as the foundation upon which virtual organizations can be built. Such organizations are becoming of increasing importance for tackling various projects, both in academic and in business fields. This paper is concerned with presenting an integrated view of the grid to readers interested in understanding it, or perhaps in developing it further or making use of it in the future. The target view is based on the STOPE (strategy/technology/organization/people/environment) framework that has previously been used to integrate the issues of various information technology problems over its well‐defined domains. For strategy, the view considers the reasons associated with the need for the grid, including grid services and grid benefits. For technology, the technical components of the grid and their functions are taken into account. For organization, the organizations concerned with the development, technology production, service provisioning and use of the grid are considered. For people, those associated with the grid in the related organizations are taken into account. For the environment, rules, practices and support associated with the grid are discussed. Finally, the paper emphasizes questions that need to be answered in order to contribute to the future development of the grid. Copyright © 2006 John Wiley & Sons, Ltd.     

Analytical models for the base transit time of a bipolar transistor with double base epilayers

arriers when transiting the base region and reduce the base transit time. From the simulation results, the base transit time reaches a minimal value when the ratio of L2/L2is about 2.     

双外延基区BJT基区渡越时间的模型研究

The doping profile function of a double base epilayer is constructed according to drift-diffusion theory. Then an analytical model for the base transit time τb is developed assuming a small-level injection based on the characteristics of the 4H-SiC material and the principle of the 4H-SiC BJTs. The device is numerically simulated and validated based on two-dimensional simulation models. The results show that the built-in electric field generated by the double base epilayer configuration can accelerate the carriers when transiting the base region and reduce the base transit time. From the simulation results, the base transit time reaches a minimal value when the ratio of L2/L1 is about 2.     

Intelligent sensors: a functional view

This paper presents the generic model of intelligent instruments and specifies it for intelligent sensors, from a functional point of view, i.e., from the point of view of the services they offer to the system designer. Basic services are concerned with data estimation and data characterization, both based on local transducers and on the availability of remote signals. Advanced services are concerned with data validation, through fault detection and isolation procedures, and with fault tolerance, by means of accommodation and reconfiguration strategies. Finally, the integration of intelligent sensors in distributed control systems is discussed.     

TwinMARM: two-stage multiscale adaptive regression methods for twin neuroimaging data

Twin imaging studies have been valuable for understanding the relative contribution of the environment and genes on brain structures and their functions. Conventional analyses of twin imaging data include three sequential steps: spatially smoothing imaging data, independently fitting a structural equation model at each voxel, and finally correcting for multiple comparisons. However, conventional analyses are limited due to the same amount of smoothing throughout the whole image, the arbitrary choice of smoothing extent, and the decreased power in detecting environmental and genetic effects introduced by smoothing raw images. The goal of this paper is to develop a two-stage multiscale adaptive regression method (TwinMARM) for spatial and adaptive analysis of twin neuroimaging and behavioral data. The first stage is to establish the relationship between twin imaging data and a set of covariates of interest, such as age and gender. The second stage is to disentangle the environmental and genetic influences on brain structures and their functions. In each stage, TwinMARM employs hierarchically nested spheres with increasing radii at each location and then captures spatial dependence among imaging observations via consecutively connected spheres across all voxels. Simulation studies show that our TwinMARM significantly outperforms conventional analyses of twin imaging data. Finally, we use our method to detect statistically significant effects of genetic and environmental variations on white matter structures in a neonatal twin study.