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We provide quantitative information on the effects of selected inhomogeneities and anisotropies on the finite element solution of the forward electrocardiographic field problem. The geometry of the model is based on the Utah Torso model and includes epicardial fatpads, cardiac chambers, major arteries and veins, sternum, ribs, spine and clavicles. Experimentally measured epicardial potentials were used for the electrical source and solutions were computed using the finite element (FE) method. Simulations were performed varying the conductivities assigned to the different structures in the model including anisotropic configurations of the skeletal muscle. Results showed the anisotropic skeletal muscle had a significant impact on the distribution of electrocardiographic fields with an average of 13% percent difference front the homogeneous isotropic case.

Decision feedback equalization (DFE) is expected in digital TV receivers and other high error rate environments. Error propagation usually occurs in infrequent bursts. It is argued that the minimum mean-square error (MMSE) adaptation mechanism in the presence of error propagation will find a better answer than the solution computed in the absence of decision errors. The paper attempts to formalize this benefit during the design phase, by considering other (convex) performance measures than MMSE assuming perfect decisions. After all, any such modified objective is just a proxy for determining the optimal error rate. As discussed in Kennedy et al. (1987), error propagation is enhanced by large gains in the decision portion of the DFE portion. We consider a method to penalize these gains, but not in the unconstrained (perfect decision) MMSE sense.

Summary form only given, as follows. Channel equalization is one of the most challenging tasks in terrestrial digital TV and digital audio broadcasting. These broadcast channels are often subject to frequency selective, time varying fading and severe bandwidth limitations. Furthermore, each receiver has vastly different type of channel characteristics and signal-to-noise (and interference) ratios. Here the authors consider channel equalization and estimation problems from both transmitter and receiver perspective. Specifically, they examine various blind and semiblind equalization schemes, some of which are being implemented in chip sets for the next generation HDTV broadcasting.

This paper introduces a blind adaptive carrier phase offset recovery scheme based on the constant modulus (CM) algorithm and analyzes its behavior. The proposed CM-derotator (CMD) minimizes the dispersion of the real projection of the derotated signal to remove the phase offset of the signal. By analyzing the cost function, the performance of the CMD is investigated in various situations including its tracking ability, its behavior in the presence-of intersymbol interference, and when there are statistical dependencies between the in-phase and quadrature components.

Describes Uintah, a component-based visual problem-solving environment (PSE) that is designed to specifically address the unique problems of massively parallel computation on tera-scale computing platforms. Uintah supports the entire life-cycle of scientific applications by allowing scientific programmers to quickly and easily develop new techniques, debug new implementations and apply known algorithms to solve novel problems. Uintah is built on three principles: (1) as much as possible, the complexities of parallel execution should be handled for the scientist, (2) the software should be reusable at the component level, and (3) scientists should be able to dynamically steer and visualize their simulation results as the simulation executes. To provide this functionality, Uintah builds upon the best features of the SCIRun (Scientific Computing and Imaging Run-time) PSE and the DoE (Department of Energy) Common Component Architecture (CCA).

Blind signal separation is currently an active research topic, and many techniques for accomplishing it have been discussed in the literature. In his excellent survey article [4], Cardoso describes blind signal separation (BSS) as the recovery of “unobserved signals of ‘sources’ from several observed mixtures”. This paper describes a particular implementation, a recently constructed two-channel processor capable of operating on convolutive mixtures of QAM signals running at symbol rates as fast as 40 MHz. This processor has been applied to several practical problems, including the cancellation of cross-pole interference from a digital radio signal of interest and the diversity combination of signals received from two apertures or antenna feeds.

The reconstruction of bioelectric fields from non-invasive measurements can be used as a powerful new diagnostic tool in cardiology and neurology. Mathematically, the reconstruction of a bioelectric field can be modeled as an inverse problem for a potential equation. This problem is ill-posed and requires special treatment, in particular either regularization or an otherwise suitable restriction of the solution space.

We describe a new graduate program at the Univesity of Utah that we consider a first step towards the modernization of the University's curriculum in what we call "Computational Engineering and Science" (CES). Our goal is to provide a mechanism by which a graduate student can obtain integrated expertise and skills in all areas that are required for the solution of a particular computational problem.

The use of a decision feedback equalizer (DFE) that employs time-reversal of the received sequence in conjunction with a normal mode operation is considered for a packet transmission system. The bidirectional decision feedback equalizer (BiDFE) combines the output of the normal mode and time-reversal mode DFEs and is known to provide improved performance. This paper shows that, under the decision-error-free assumption, the BiDFE, with an infinite length MMSE-DFE in each stream, has a smaller performance penalty from the matched filter bound (MFB) when compared to each individual stream. The BiDFE tap coefficients are optimized to minimize the overall MSE and it is shown that the infinite length MMSE-BiDFE attains the MFB.

Computational problems in medicine often require a researcher to apply diverse skills in confronting problems involving very large data sets, three-dimensional complex geometries which must be modeled and visualized, large scale computing, and hefty amounts of numerical analysis. In this talk I will present recent research results in computational neuroscience, imaging, and cardiology. I will provide examples of several driving applications of steering and interactive visualization in cardiology (defibrillation simulation and device design), neuroscience (new inverse source localization techniques), and imaging (new methods for interactive visualization of large-scale 3D MRI and CT volumes, and introduce new methods for diffusion tensor imaging).

We discuss a proportional weight algorithm that is similar to least mean square (LMS). The distinction is that the new algorithm (called normalized sparse LMS, or NSLMS) has a time-varying vector step size, whose coefficients are proportional to the magnitudes of the current values of the tap estimates. We show that when the system to be identified is sparse, NSLMS converges faster than LMS (to the same asymptotic MMSE for both algorithms). We also discuss the effect of the initialization on the performance of NSLMS.

We consider a spatiotemporal method for source localization, taking advantage of the entire EEG time series to reduce the configuration space we must evaluate. The EEG data are first decomposed into signal and noise subspaces using a principal component analysis (PCA) decomposition. This partitioning allows us to easily discard the noise subspace, which has two primary benefits: the remaining signal is less noisy, and it has lower dimensionality. After PCA, we apply independent component analysis (ICA) on the signal subspace. The ICA algorithm separates multichannel data into activation maps due to temporally independent stationary sources. For each activation map we perform an EEG source localization procedure, looking only for a single dipole per map. By localizing multiple dipoles independently, we substantially reduce our search complexity and increase the likelihood of efficiently converging on the correct solution.

Researchers from several universities, national laboratories, software companies, and government funding agencies participated in the 1998 Scientific Integrated Development Environments for Knowledge, Information, and Computing Workshop. This meeting summarized the state-of-the-art in the area of problem-solving environments (PSEs) for scientific and engineering computation, and mapped out directions for future research. This report presents some of the results from the meeting and recommends promising areas for further work. It describes the need for PSEs, lists characteristics that many PSEs share and lists a few current systems. Design goals and future directions, with an emphasis on research issues, are outlined, followed by summary findings and conclusions.

Atherosclerosis is a progressive disease characterized by the accumulation of lipids and fibrous elements in the arteries and is a leading cause of heart disease and stroke in developed and developing countries.1 Animal models have become increasingly important tools for addressing key mechanistic and therapeutic questions that cannot be answered from human studies of atherosclerosis. However, the small-scale vascular structures in genetically engineered mice require labor-intensive histomorphometric techniques to quantify lesions. Recently, a new technique has emerged to image ex vivo blocks of soft tissue by staining tissue with metal solutions, then scanning with a microscopic computed tomography (microCT) instrument (Figure I in the online-only Data Supplement).2 This technique was originally applied to the study of the developing heart in embryos3 and fetuses (Figure II in the online-only Data Supplement) but can also be applied to the en bloc imaging of the heart, great vessels, and lesions thereof. By this method, tissues are left intact, but one can employ image analysis to create “virtual” histological …

OAK-B135 Final Report: Symposium on Adaptive Methods for Partial Differential Equations. Complex physical phenomena often include features that span a wide range of spatial and temporal scales. Accurate simulation of such phenomena can be difficult to obtain, and computations that are under-resolved can even exhibit spurious features. While it is possible to resolve small scale features by increasing the number of grid points, global grid refinement can quickly lead to problems that are intractable, even on the largest available computing facilities. These constraints are particularly severe for three dimensional problems that involve complex physics. One way to achieve the needed resolution is to refine the computational mesh locally, in only those regions where enhanced resolution is required. Adaptive solution methods concentrate computational effort in regions where it is most needed. These methods have been successfully applied to a wide variety of problems in computational science and engineering. Adaptive methods can be difficult to implement, prompting the development of tools and environments to facilitate their use. To ensure that the results of their efforts are useful, algorithm and tool developers must maintain close communication with application specialists. Conversely it remains difficult for application specialists who are unfamiliar with the methods to more » evaluate the trade-offs between the benefits of enhanced local resolution and the effort needed to implement an adaptive solution method. « less

Building systems that alter program behavior during execution based on user-specified criteria (computational steering systems) has been a recent research topic, particularly among the high-performance computing community [1–5]. To enable a computational steering system with powerful visualization capabilities to run in a distributed computational environment, a distributed infrastructure (or runtime system) is required. This infrastructure permits one to harness a variety of machines to collaborate on an interactive simulation. Building such an infrastructure requires devising strategies for coordinating execution across machines (concurrency control mechanisms), mechanisms for fast data transfer between machines, and mechanisms for user manipulation of remote execution.

In this paper, the problem of tonal disturbance rejection via direct adaptive control is considered. The key problem feature is compared with the usual adaptive tracking problem. Traditional model reference and pole placement approaches are attempted, and their advantages and drawbacks are given. We then propose an adaptive solution based on a new parametrization scheme which is unique to the given problem. Simulation studies are used to compare these different schemes.

Presents a case study in system identification for limit cycling systems. The focus of the paper is on (a) the use of a model structure derived from physical considerations and (b) the use of algorithms for the identification of component subsystems of this model structure. The physical process used in this case study is that of a reduced order model for combustion instabilities for lean premixed systems. The identification techniques applied in this paper are the use of linear system identification tools, time delay estimation and qualitative validation of model properties using harmonic balance and describing function methods. The novelty of the paper, apart from its practical application, is that closed loop limit cycle data is used together with a priori process structural knowledge to identify both linear dynamic forward and nonlinear feedback paths.

A standard method for noninvasively computing neurocortical potentials from potentials measured on the scalp surface is to solve the problem on a generalized geometry and map the results back to the true model. This solution to the inverse EEG problem has been employed using spherical and, more recently, generic cranial models as templates. In the case of the most complex spherical models, the patient's skin, bone, cerebrospinal fluid, gray matter and white matter surfaces are mapped onto concentric spheres. The simplicity of the spherical domain allows for an analytic solution to the surface mapping inverse problem; however, the inaccuracy of such a solution challenges its clinical value. Similarly, solving the problem on a predefined generic model also holds computational allure--the generic model can be hand-picked to reduce the ill-conditioning of the problem. However, we suggest that such results from generic models are still not sufficiently accurate to be of general clinical use. In our paper, we evaluate the impact of varying both model accuracy and model complexity on the inverse cortical mapping. Small modeling perturbations (as might be introduced from noisy or under-sampled data) are shown to have large and detrimental effects on the quality of the solution.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The aim of this study was to quantify the effects of selected inhomogeneities and anisotropies on computed electric potential fields associated with the electrocardiographic forward problem. The model construction was based on the Utah Torso model and included geometry for major anatomical structures such as subcutaneous fat, skeletal muscle, and lungs, as well as for epicardial fatpads, major arteries and veins, and the sternum, ribs, spine, and clavicles. Measured epicardial potentials served as the electrical source for solutions to the electrocardiographic forward problems computed using the finite element method (FEM). The geometry of the torso model for each simulation was constant, but different combinations of conductivities were assigned to individual organs or tissues. Comparisons of different conductivity combinations followed one of two basic schemes: 1) a homogeneous torso served as the reference against which we compared simulations with a single organ or tissue and assigned its nominal conductivity, or 2) a fully inhomogeneous torso served as the reference and we removed the effect of individual organs or tissues by assigning it the homogeneous conductivity value. When single inhomogeneities were added to an otherwise homogeneous isotropic model, anisotropic skeletal muscle (at a 15:1 anisotropy ratio) and the right and left lung had larger average effects (12.8, 12.7, and 12.1% relative error (RE), respectively) than the other inhomogeneities tested. Our results for removing single inhomogeneities show that the subcutaneous fat, the anisotropic skeletal muscle (with the degree of anisotropy equal to 7:1), and the lungs have larger average impacts on the body surface potential distributions than other elements of the model (with values of 14.9, 12.6, and 11.7% RE, respectively). The results also show that the size of the effect depended strongly on the distribution of epicardial potentials. The results of this study suggest that accurate representation of tissue inhomogeneity has a significant effect on the accuracy of the forward solution, with regions near the torso surface playing a larger role, in general, than those near the heart.

This paper describes the conceptual design and components of CALVIN an unmanned ground vehicle. In addition to a description of the design highlights, electronic, computer hardware and software, the paper also describes the design process. Safety, reliability, and durability considerations are included. The performance of the vehicle is documented. The paper also includes a cost estimate for the final product if it were to be duplicated.

SCIRun is a problem solving environment that allows the interactive construction, debugging, and steering of large-scale scientific computations. We review related systems and introduce a taxonomy that explores different computational steering solutions. Considering these approaches, we discuss why a tightly integrated problem solving environment, such as SCIRun, simplifies the design and debugging phases of computational science applications and how such an environment aids in the scientific discovery process.

Fundamental problems in electrophysiology can be studied by computationally modeling and simulating the associated microscopic and macroscopic bioelectric fields. To study such fields computationally, researchers have developed a variety of numerical and computational techniques. Advances in computer architectures have allowed researchers to model increasingly complex biophysical systems. Modeling such systems requires a researcher to apply a wide variety of computational and numerical methods to describe the underlying physics and physiology of the associated three-dimensional geometries. Issues naturally arise as to the accuracy and efficiency of such methods. In this paper we review computational and numerical methods of solving bioelectric field problems. The motivating applications represent a class of bioelectric field problems that arise in electrocardiography and electroencephalography.

We present the design, implementation and application of SCIRun, a scientific programming environment that allows the interactive construction, debugging, and steering of large-scale scientific computations. Using this “computational workbench,” a scientist can design and modify simulations interactively via a dataflow programming model. SCIRun enables scientists to design and modify model geometry, interactively change simulation parameters and boundary conditions, and interactively visualize geometric models and simulation results. We discuss the ubiquitous roles SCIRun plays as a computational tool (e.g. resource manager, thread scheduler, development environment), and how we have applied an object oriented design (implemented in C++) to the scientific computing process. Finally, we demonstrate the application of SCIRun to large scale problems in computational medicine.

This paper describes the design requirements for a QAM demodulator chip developed to be part of a set top convertor for digital cable television. The chip demodulates 64- and 256-QAM signal at a maximum bit rate of 44 Mb/s and uses blind acquisition techniques so that no training or pilot signal need be sent by the transmitter.

One of the fundamental problems in electroencephalography can be characterized by an inverse problem. Given a subset of electrostatic potentials measured on the surface of the scalp and the geometry and conductivity properties within the head, calculate the current vectors and potential fields within the cerebrum. Mathematically the generalized EEG problem can be stated as solving Poisson's equation of electrical conduction for the primary current sources. The resulting problem is mathematically ill-posed i.e., the solution does not depend continuously on the data, such that small errors in the measurement of the voltages on the scalp can yield unbounded errors in the solution, and, for the general treatment of a solution of Poisson's equation, the solution is non-unique. However, if accurate solutions the general treatment of a solution of Poisson's equation, the solution is non-unique. However, if accurate solutions to such problems could be obtained, neurologists would gain noninvasive accesss to patient-specific cortical activity. Access to such data would ultimately increase the number of patients who could be effectively treated for pathological cortical conditions such as temporal lobe epilepsy. In this paper, we present the effects of spatial adaptive refinement on the inverse EEG problem and show that the use of adaptive methods allow for significantly better estimates of electric and potential fileds within the brain through an inverse procedure. To test these methods, we have constructed several finite element head models from magneteic resonance images of a patient. The finite element meshes ranged in size from 2724 nodes and 12,812 elements to 5224 nodes and 29,135 tetrahedral elements, depending on the level of discretization. We show that an adaptive meshing algorithm minimizes the error in the forward problem due to spatial discretization and thus increases the accuracy of the inverse solution.

CMA fractionally spaced equalizers (CMA-FSEs) have been shown, under certain conditions, to be globally asymptotically convergent to a setting which provides perfect equalization. Such a result relies heavily on the assumptions of a white source and no channel noise (as is the case in much of the literature's analysis of CMA). Herein, we relax the white source assumption and examine the effect of source correlation on CMA. Analytic results are meshed with examples showing CMA-FSE source correlation effects. Techniques for finding all stationary and saddle points on the CMA-FSE error surface are presented using recent developments in the algebraic-geometry community. Upprättat; 1995; 20070109 (ysko)

Important problems in Computational Medicine exist that can benefit from the implementation of adaptive mesh refinement techniques. Biological systems are so inherently complex that only efficient models running on state of the art hardware can begin to simulate reality. To tackle the complex geometries associated with medical applications we present a general purpose mesh generation scheme based upon the Delaunay tessellation algorithm and an iterative point generator. In addition, automatic, two- and three-dimensional adaptive mesh refinement methods are presented that are derived from local and global estimates of the finite element error. Mesh generation and adaptive refinement techniques are utilized to obtain accurate approximations of bioelectric fields within anatomically correct models of the heart and human thorax. Specifically, we explore the simulation of cardiac defibrillation and the general forward and inverse problems in electrocardiography (ECG). Comparisons between uniform and adaptive refinement techniques are made to highlight the computational efficiency and accuracy of adaptive methods in the solution of field problems in computational medicine.

The constant modulus algorithm (CMA) is a simple and effective blind equalization algorithm that is generally implemented on feedforward linear adaptive equalizers. In this paper, we study the use of CMA on a decision feedback equalizer (CMA-DFE) for blind channel equalization. We analyze the convergence behavior of this highly nonlinear adaptive system. We also find a class of channels for which the decision directed DFE algorithm (DD-DFE) will not converge to the global optimum when initialized at the origin. For some of these channels, the CMA-DFE does not suffer the same ill-convergence.

We present the design, implementation and application of SCIRun, a scientific programming environment that allows the interactive construction, debugging and steering of large scale scientific computations. Using this "computational workbench," a scientist can design and modify simulations interactively via a dataflow programming model. SCIRun enables scientists to design and modify models and automatically change parameters and boundary conditions as well as the mesh discretization level needed for an accurate numerical solution. As opposed to the typical "off-line" simulation mode - in which the scientist manually sets input parameters, computes results, visualizes the results via a separate visualization package, then starts again at the beginning - SCIRun "closes the loop" and allows interactive steering of the design and computation phases of the simulation. To make the dataflow programming paradigm applicable to large scientific problems, we have identified ways to avoid the excessive memory use inherent in standard dataflow implementations, and have implemented fine-grained dataflow in order to further promote computational efficiency. In this paper, we describe applications of the SCIRun system to several problems in computational medicine. In addition, an we have included an interactive demo program in the form of an application of SCIRun system to a small electrostatic field problem.

While many application scenarios are not yet possible, grid computing will provide access to distributed resources with the same ease we now access electrical power. Clearly, the ability to analyze data within this distributed environment is critical to the success of the grid. To successfully analyze data, we need visualization tools. This special issue provides a glimpse at the future of grid visualization by exploring working prototypes that exploit distributed resources. These articles provide an insight into how visualization can effectively use, analyze, and explore grid-based data and applications. The included articles are briefly summarized.

This paper presents for the first time computer simulations of electrical defibrillation in the heart in a realistic model of cardiac structure and membrane ionic processes. The content of the paper focuses primarily on model development. Two models of defibrillation are presented here: a three-dimensional slice of the canine heart that encompasses both ventricular cavities, and a full rabbit heart model. The two sets of simulations employing the described models demonstrate transmural electrical activity in the heart during and after the shock that has not yet been observed experimentally due to present limitations in recording procedures. The models developed here are intended for exploration of the mechanisms for defibrillation as well as to address issues pertinent to the clinical procedure of defibrillation such as electrode location and shock waveform.

One of the fundamental problems in theoretical electrocardiography can be characterized by an inverse problem. In this paper, we present new methods for achieving better estimates of heart surface potential distributions in terms of torso potentials through an inverse procedure. First, an adaptive meshing algorithm is described which minimizes the error in the forward problem due to spatial discretization. We have found that since the inverse problem relies directly on the accuracy of the forward solution, adaptive meshing produces a more accurate inverse transfer matrix. Secondly, we introduce a new local regularization procedure. This method works by breaking the global transfer matrix into sub-matrices and performing regularization only on those sub-matrices which have large condition numbers. Furthermore, the regularization parameters are specifically 'tuned' for each sub-matrix using an a priori scheme based on the L-curve method. This local regularization method provides substantial increases in accuracy when compared to global regularization schemes. Finally, we present specific examples of the implementation of these schemes using models derived from magnetic resonance imaging data from a human subject.

Frequency-domain analysis is presented for the bias distribution in a reduced-order modeling experiment using either LMS (least mean square) or LS (least square)-type algorithms. The authors develop and prove a new theorem that allows the exact decomposition of the bias into a sum of components related to the eigenfilters of the input autocorrelation matrix. The new bias decomposition theorem allows accurate prediction of the bias as a function of the input power spectral density beyond results previously obtained for the case of large filter order. Furthermore, for low-order adaptive filters, eigenvalues and eigenfilters are shown to be fundamental tools for extracting the essential features of the filter input spectrum. >

An important aspect of the biophysical modeling of electrocardiographic fields is the effect of various tissue inhomogeneities and anisotropies on the calculated fields. In this paper we quantitatively assess the effects of different tissue conductivities using a thorax geometry based upon MRI scans and intraoperatively recorded heart potentials.

Electrocardiography has played an important role in the detection and characterization of heart function, both in normal and abnormal states. In this paper we present an inhomogeneous, anisotropic computer model of the human thorax for use in electrocardiography with emphasis on the calculation of transthoracic potential and current distributions. Knowledge of the current pathways in the thorax has many applications in electrocardiography and has direct utility in studies pertaining to cardiac defibrillation, forward and inverse problems, impedance tomography, and electrode placement in electrocardiography.

One of the central challenges facing visualization research is how to effectively enable knowledge discovery. An effective approach will likely combine application architectures that are capable of running on today's largest platforms to address the challenges posed by large data with visual data analysis techniques that help find, represent, and effectively convey scientifically interesting features and phenomena.

The problem of blind equalization was thought to have been solved by some `globally convergent' blind equalizers. However, the established local convergence of some blind equalizers demonstrates the possibility of ill-convergence in the true equalizer parameter space by these supposedly ideal algorithms. In this paper, we analyze the reason why an algorithm globally convergent in the combined channel and equalizer parameter space can have undesirable local convergence in the true equalizer parameter space. We demonstrate that the existing results on global convergence are implicitly based on an idealistic assumption that the equalizer is infinitely parameterized with infinite output delay. We show that despite the engineering intuition that such results should carry over to the finitely parametrized case; in reality the theory breaks down and local minima are common place. Thus, while the theories proving global convergence are elegant and positive, the reality is that for realizable systems such convergence behavior may not materialize.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

It is shown, through the analysis of nullspace for the channel convolution matrix, that the behavior of a finitely parameterized Godard equalizer in general can never achieve or approximate the desirable global convergence performance of an infinitely parameterized noncausal Godard equalizer. Nullspace theory is supported by a simple example showing ill-convergence of the Godard algorithm. >

Leading-edge science and engineering depend on advanced computing for understanding, prediction, and control. In response to these needs, the field of computational science and engineering (CS&E) is evolving rapidly, to the point that it is now widely considered to be a new discipline by itself and a third pillar of the scientific enterprise, a peer alongside theory and physical experiment. CS&E is unique in that it enables progress in virtually all other disciplines by providing windows of discovery when traditional means of research reach their limits. Because of its flexibility, computer simulation has become a universal tool. A simulation may serve as a virtual microscope that lets scientists observe the world of quantum physics much smaller than an atom, or it may be employed as a virtual telescope that allows us to explore how galaxies are forming in the universe. In this way, CS&E helps scientists to reach beyond our physical limitations in space and time. When physical experiments are too dangerou...

A common recursive identification scheme used in a class of adaptive systems problems involving blind channel equalization (but potentially usable elsewhere) is an algorithm due to Sato. The authors study the convergence properties of the Sato blind algorithm by characterizing the mean cost surface. The results show the important feature that the equalizer parameters may converge to parameter settings which fail to achieve the ideal objective which is to approximate the inverse with sufficient accuracy. A proof that a well-posed Sato algorithm can misbehave is presented. Examples are used to illustrate the results. >

A blind adaptive identification algorithm uses explicit measurements of the plant output coupled with only implicit knowledge of the input (in the form of its statistical properties) for updating the parameters of a (delayed) plant inverse model. Blind adaptive algorithms are recursive identification schemes used in communication systems, and are often designed via gradient descent minimization of certain memoryless, non-MSE (mean square error) continuous cost functions. It is shown that for these cost functions, there generally exist local minima that do not correspond to the ideal plant inverse identification once the algorithm has converged. The authors demonstrate the general nonglobality of gradient descent blind algorithms and the need for initialization within the regions of attractions of the desired minima in order to achieve the objective of successful identification. Furthermore, it is shown that global convergence in the total (channel plus equalizer) parameter space does not necessarily imply the same behavior in finite equalizer parameter space. >

A bold new effort to disrupt every gene in the mouse genome necessitates systematic, interdisciplinary approaches to analyzing patterning defects in the mouse embryo. We present a novel, rapid, and inexpensive method for obtaining high-resolution virtual histology for phenotypic assessment of mouse embryos. Using osmium tetroxide to differentially stain tissues followed by volumetric X-ray computed tomography to image whole embryos, isometric resolutions of 27 μm or 8 μm were achieved with scan times of 2 h or 12 h, respectively, using mid-gestation E9.5–E12.5 embryos. The datasets generated by this method are immediately amenable to state-of-the-art computational methods of organ patterning analysis. This technique to assess embryo anatomy represents a significant improvement in resolution, time, and expense for the quantitative, three-dimensional analysis of developmental patterning defects attributed to genetically engineered mutations and chemically induced embryotoxicity., Synopsis Developmental biology is entering the digital age, thanks to advancements in imaging technologies, instrumentation, and software. These advancements are converging with discoveries in developmental biology to deliver unprecedented insight into how human development is impacted by the products we use, the environment we live in, and our genetic composition. Industrial societies are becoming increasingly concerned with the exposure of women and their unborn fetuses to pharmaceuticals and commonplace household chemicals. In addition, understanding genetic causes of birth defects is now possible through the isolation of specific genes, which can be efficiently disrupted in embryos, and subsequently observed for birth defects. Such studies of embryotoxicity typically involve the use of mouse embryos. However, evaluation of mouse embryos in the past has involved expensive and cumbersome external inspection and thin sectioning for view under the microscope. As such, developmental biologists have eagerly anticipated the advent of tools that would allow them to routinely assess the complex and dynamic organization of embryos using techniques that are fast and inexpensive. In this article, the authors introduce a rapid, high-quality, and inexpensive technique for the three-dimensional visualization of mouse embryos using X-ray computed tomography that is ideally suited for researchers in pharmaceutical, industrial, and academic laboratories.