MODELLING ERRORS IN X-RAY FLUOROSCOPIC IMAGING SYSTEMS USING PHOTOGRAMMETRIC BUNDLE ADJUSTMENT WITH A DATA-DRIVEN SELF-CALIBRATION APPROACH

X-ray imaging is a fundamental tool of routine clinical diagnosis. Fluoroscopic imaging can further acquire X-ray images at video frame rates, thus enabling non-invasive in-vivo motion studies of joints, gastrointestinal tract, etc. For both the qualitative and quantitative analysis of static and dynamic X-ray images, the data should be free of systematic biases. Besides precise fabrication of hardware, software-based calibration solutions are commonly used for modelling the distortions. In this primary research study, a robust photogrammetric bundle adjustment was used to model the projective geometry of two fluoroscopic X-ray imaging systems. However, instead of relying on an expert photogrammetrist’s knowledge and judgement to decide on a parametric model for describing the systematic errors, a self-tuning data-driven approach is used to model the complex non-linear distortion profile of the sensors. Quality control from the experiment showed that 0.06 mm to 0.09 mm 3D reconstruction accuracy was achievable post-calibration using merely 15 X-ray images. As part of the bundle adjustment, the location of the virtual fluoroscopic system relative to the target field can also be spatially resected with an RMSE between 3.10 mm and 3.31 mm

Building an Assessment Use Argument for sign language: the BSL Nonsense Sign Repetition Test

In this article, we adapt a concept designed to structure language testing more effectively, the Assessment Use Argument (AUA), as a framework for the development and/or use of sign language assessments for deaf children who are taught in a sign bilingual education setting. By drawing on data from a recent investigation of deaf children's nonsense sign repetition skills in British Sign Language, we demonstrate the steps of implementing the AUA in practical test design, development and use. This approach provides us with a framework which clearly states the competing values and which stakeholders hold these values. As such, it offers a useful foundation for test-designers, as well as for practitioners in sign bilingual education, for the interpretation of test scores and the consequences of their use

Cue Reactivity in Active Pathological, Abstinent Pathological, and Regular Gamblers

Twenty-one treatment-seeking pathological gamblers, 21 pathological gamblers in recovery, and 21 recreational gamblers watched two video-taped exciting gambling scenarios and an exciting roller-coaster control scenario while their arousal (heart rate and subjective excitement) and urge to gamble were being measured. The gamblers did not differ significantly in cue-elicited heart rate elevations or excitement. However, the active pathological gamblers reported significantly greater urges to gamble across all cues compared to the abstinent pathological gamblers and, with marginal significance (p = 0.06), also compared to the social gamblers. Further exploration of these findings revealed that active pathological gamblers experience urges to gamble in response to exciting situations, whether or not they are gambling related, whereas abstinent and social gamblers only report urges to an exciting gambling-related cue. This suggests that for pathological gamblers excitement itself, irrespective of its source, may become a conditioned stimulus capable of triggering gambling behavior. Implications for treatment and future research are discussed

The genera Melanothamnus Bornet & Falkenberg and Vertebrata S.F. Gray constitute well-defined clades of the red algal tribe Polysiphonieae (Rhodomelaceae, Ceramiales).

Polysiphonia is the largest genus of red algae, and several schemes subdividing it into smaller taxa have been proposed since its original description. Most of these proposals were not generally accepted, and currently the tribe Polysiphonieae consists of the large genus Polysiphonia (190 species), the segregate genus Neosiphonia (43 species), and 13 smaller genera (< 10 species each). In this paper, phylogenetic relationships of the tribe Polysiphonieae are analysed, with particular emphasis on the genera Carradoriella, Fernandosiphonia, Melanothamnus, Neosiphonia, Polysiphonia sensu stricto, Streblocladia and Vertebrata. We evaluated the consistency of 14 selected morphological characters in the identified clades. Based on molecular phylogenetic (rbcL and 18S genes) and morphological evidence, two speciose genera are recognized: Vertebrata (including the type species of the genera Ctenosiphonia, Enelittosiphonia, Boergeseniella and Brongniartella) and Melanothamnus (including the type species of the genera Fernandosiphonia and Neosiphonia). Both genera are distinguished from other members of the Polysiphonieae by synapomorphic characters, the emergence of which could have provided evolutionarily selective advantages for these two lineages. In Vertebrata trichoblast cells are multinucleate, possibly associated with the development of extraordinarily long, photoprotective, trichoblasts. Melanothamnus has 3-celled carpogonial branches and plastids lying exclusively on radial walls of the pericentral cells, which similarly may improve resistance to damage caused by excessive light. Other relevant characters that are constant in each genus are also shared with other clades. The evolutionary origin of the genera Melanothamnus and Vertebrata is estimated as 75.7-95.78 and 90.7-138.66 Ma, respectively. Despite arising in the Cretaceous, before the closure of the Tethys Seaway, Melanothamnus is a predominantly Indo-Pacific genus and its near-absence from the northeastern Atlantic is enigmatic. The nomenclatural implications of this work are that 46 species are here transferred to Melanothamnus, six species are transferred to Vertebrata and 13 names are resurrected for Vertebrata

The Genetics and Genomics of Virus Resistance in Maize

Viruses cause significant diseases on maize worldwide. Intensive agronomic practices, changes in vector distribution, and the introduction of vectors and viruses into new areas can result in emerging disease problems. Because deployment of resistant hybrids and cultivars is considered to be both economically viable and environmentally sustainable, genes and quantitative trait loci for most economically important virus diseases have been identified. Examination of multiple studies indicates the importance of regions of maize chromosomes 2, 3, 6, and 10 in virus resistance. An understanding of the molecular basis of virus resistance in maize is beginning to emerge, and two genes conferring resistance to sugarcane mosaic virus, Scmv1 and Scmv2, have been cloned and characterized. Recent studies provide hints of other pathways and genes critical to virus resistance in maize, but further work is required to determine the roles of these in virus susceptibility and resistance. This research will be facilitated by rapidly advancing technologies for functional analysis of genes in maize

Phylogenetic Analysis of Seven WRKY Genes across the Palm Subtribe Attaleinae (Arecaceae) Identifies Syagrus as Sister Group of the Coconut

BACKGROUND:The Cocoseae is one of 13 tribes of Arecaceae subfam. Arecoideae, and contains a number of palms with significant economic importance, including the monotypic and pantropical Cocos nucifera L., the coconut, the origins of which have been one of the "abominable mysteries" of palm systematics for decades. Previous studies with predominantly plastid genes weakly supported American ancestry for the coconut but ambiguous sister relationships. In this paper, we use multiple single copy nuclear loci to address the phylogeny of the Cocoseae subtribe Attaleinae, and resolve the closest extant relative of the coconut. METHODOLOGY/PRINCIPAL FINDINGS:We present the results of combined analysis of DNA sequences of seven WRKY transcription factor loci across 72 samples of Arecaceae tribe Cocoseae subtribe Attaleinae, representing all genera classified within the subtribe, and three outgroup taxa with maximum parsimony, maximum likelihood, and Bayesian approaches, producing highly congruent and well-resolved trees that robustly identify the genus Syagrus as sister to Cocos and resolve novel and well-supported relationships among the other genera of the Attaleinae. We also address incongruence among the gene trees with gene tree reconciliation analysis, and assign estimated ages to the nodes of our tree. CONCLUSIONS/SIGNIFICANCE:This study represents the as yet most extensive phylogenetic analyses of Cocoseae subtribe Attaleinae. We present a well-resolved and supported phylogeny of the subtribe that robustly indicates a sister relationship between Cocos and Syagrus. This is not only of biogeographic interest, but will also open fruitful avenues of inquiry regarding evolution of functional genes useful for crop improvement. Establishment of two major clades of American Attaleinae occurred in the Oligocene (ca. 37 MYBP) in Eastern Brazil. The divergence of Cocos from Syagrus is estimated at 35 MYBP. The biogeographic and morphological congruence that we see for clades resolved in the Attaleinae suggests that WRKY loci are informative markers for investigating the phylogenetic relationships of the palm family

Summary and Recommendations from Working Group 1: model uncertainty representations in convection-permitting / shorter lead-time / limited-area ensembles

WG3 discussed both the pros and cons of existing schemes as Working group 1 considered the treatment of model uncertainty (MU) in high-resolution ensembles, at grid spacings of order 1-5 km. These systems are often run for regional weather forecasting, perhaps over a single country, and for lead times of up to 5 days. Looking ahead, ECMWF’s strategy seeks to deliver global medium-range ensemble forecasts with 3-4 km grid spacings by 2030. It is questionable for what grid spacing we should dispense with a deep convection parameterization, but it will be either switched off or damped in these systems, such that deep convection can be assumed to be dominated by explicit motions. One of the problems with limited-area ensemble systems at this scale is that spread depends not only on the modelling system itself but also on the variability inherited from the large-scale boundary conditions. There is often thought to be a lack of spread in our high-resolution EPS (ensemble prediction systems), but this could reflect a lack of diversity on larger scales. The relative importance of lateral-boundary diversity and the model uncertainty mechanisms is regime dependent. The lateral boundaries will generally be more important in midlatitude winter but less so for summertime convection in relatively weak synoptic flow

An Analysis and Improvement of the Predictive Control Integrating Component

integrator wind-up and, therefore, it is recommended that separate weighting be used with a modified integrating component predictive controller. The separate weighting also improves the designers intuition with respect to tuning the controller, significantly reducing the time required to generate desired closed loop responses. References Clarke, D. W., and Mohtadi, C, 1987, &quot;Properties of Generalized Predictive Control,&quot; World Congress IFAC, Munich. Cutler, C. R., and Ramaker, B. L., 1979, &quot;Dynamic Matrix Control-A Computer Control Algorithm,&quot; A.I.Ch.E., 86th National Meeting, Apr. Kurfess, T. R., Whitney, D. E., and Brown, M. L., 1988, &quot;Verification of a Dynamic Grinding Model,&quot; ASME JOURNAL OF DYNAMIC SYSTEMS, MEAS-UREMENT, AND CONTROL, Dec., Vol. 110, Kurfess, T. R., 1989 &quot;Predictive Control of a Robotic Weld Bead Grinding System,&quot; Ph.D. thesis, MIT Department of Mechanical Engineering. Kurfess, T. R., and Whitney, D. E., 1989, &quot;Predictive Control of a Robotic Grinding System,&quot; Proceedings of the NMTBA Eastern Manufacturing Technology Conference, Hartford, CT, Oct. Kurfess, T. R., Whitney, D. E., 1989, &quot;An Analysis and Improvement of the Predictive Control Integrating Component,&quot; ASME JOURNAL OF DYNAMIC SYS-TEMS, MEASUREMENT, AND CONTROL, submitted Dec. Kwakernaak, H., and Sivan, R., 1972 Introduction The usefulness of observers for real-time state estimation of linear dynamic systems based on measured system outputs is well known. Procedures for designing observers Another approach to robust state estimation has centered upon the fact that the estimated state is often used for feedback control. Hence, the criterion for observer design in these cases is to reduce the effect of modeling errors on the controlled system response. The work of The current work on robust state estimation using observers is motivated by the need to estimate pressure and temperature fields in thermoplastic injection molding processes, based on a few measurement locations in the mold cavity. Robustness of the estimate to errors in the process model is essential for this application given the complexity of the process. The initial use of the estimated pressure and temperature fields is for more effective process monitoring rather than for feedback control. The robustness of the state estimates obtained using observers, in the presence of system modeling error, is examined in this paper following the procedure of Determination of State Estimation Error Bound • Consider the linear time-invariant system described by x{t)=Ax(t) + Bu(t) y(t)=Cx(t) (1) subject to the initial condition x(0) = x 0 where A, B, and C are (nxn), (nxp), and (mxn) matrices, respectively, and x(t), u{t), and y(t) are («xl), (pxl) and (m x 1) vectors, respectively. A full order observer is designed Copyright © 1993 by ASME based on this model to estimate the state x(t). The observer is described by x(t) =AJt(t) +B c u(t)+L(y(t) -y(t)) y(t)=Cx(t) (2) subject to the initial condition Note that modeling errors are permitted only in the A and B matrices and not in the C matrix. Let the estimation error be defined by Manipulation of subject to the initial condition e(0) = x(0)-x(0) = e 0 (5) The eigenvalues of the augmented system described by (1) and (4) are those of A and F c . We assume that the input u{f) is bounded in magnitude and that all the eigenvalues of A have negative real parts, thus ensuring that the estimation error is bounded if all the eigenvalues of F c also have negative real parts. The solution of where M being the modal matrix corresponding to F c and A a diagonal matrix with the eigenvalues of F c as the diagonal elements. Extension of the results obtained here to the case of repeated eigenvalues is relatively straightforward. Taking norms of both sides of Eq. (6), we get C[ being the real part of the observer pole farthest to the right in the complex plane, assumed to be negative here. Id represents the Euclidean norm of any (n x 1) vector v and IIP! represents the spectral norm of any (n x ri) matrix P above. Also, k(M) is the condition number of the (n x ri) matrix M and is equal to IIMII. HAT 1 ! Note that the expression within curly brackets on the right hand side of Eq. (7) depends on the observer eigenvalues and not on the eigenvectors associates with these eigenvalues. The dependence of the state estimation error bound on these eigenvectors is solely via the condition number k(M) of the modal matrix corresponding to F c . Therefore, for competing observer designs with the same eigenvalues, the only difference is in the modal matrix M. The other terms within the curly brackets would be identical for such competing designs. Equation The result obtained here that the eigenvectors corresponding to the observer eigenvalues be chosen to be as nearly mutually orthogonal as possible to reduce the norm of the state estimation error seems to be a natural extension of a result obtained by The suggested observer design guideline does not address the issue of observer eigenvalue selection despite the fact that eigenvalue selection affects the estimation error. Thus, selection of observer eigenvalues without reference to consequences for estimation error may well lead to more robust observer designs being overlooked. Futhermore, Eq. (7) provides only a bound on the estimation error norm. Therefore, it is possible that even if two observer designs differ only in their eigenvector selections, the actual state estimation error norm may in some cases be lower for the design which yields a higher value of k(M) and hence of the error bound. This is less likely to occur, however, if the difference in the values of k(M) for the competing designs is large. Finally, the results obtained here are valid only for cases where the C matrix is known exactly. The procedure for eigenvector selection and observer gain computation follows that of D&apos;Azzo and Houpis (1988). Since the eigenvectors and reciprocal eigenvectors of a matrix are known to be mutually orthogonal, the procedure begins with selection of the reciprocal eigenvectors of F c to be as nearly orthogonal as possible and normalized to have Euclidean norms of unity. S(\ i ) = (A c T -\ i IC T ) for the n specified eigenvalues of F c . At this point in the observer design, the available freedom in eigenvector assignment is used to obtain as nearly mutually orthogonal a set of reciprocal eigenvectors as is possible. The observer gain matrix is then given by Example of Observer Design Consider one dimensional heat conduction in a bar insulated at both ends, governed by the equation where c is the thermal diffusivity of the bar and u(r, t) is the temperature at the location r and time t. It is assumed here that two temperature sensors are located on the bar, one at each end. Using the two measurements provided by the sensors, we need to estimate the temperature distribution in the bar. It is also assumed that the initial temperature distribution in the bar may be unknown. A third order lumped parameter approximation of the distributed parameter system is developed using the modal expansion method. This lumped parameter model is described in a normalized form by The elements of x are the normalized weighting factors on the responses of the corresponding modes, c&apos; is a normalized version of c. It is assumed that the actual value of c&apos; is 0.11, while for observer design, a value of 0.09 is assumed, indicating about 18 percent error. The elements of the C matrix depend only on the boundary conditions and the form of the partial differential Eq. and yields a condition number of the modal matrix of F c , after equilibration, of 3.43. In design 2, the reciprocal eigenvectors are chosen to get a poorer condition number of the modal matrix of F c , equal to 31.44. The observer gain matrix for this design is given by It should be noted here, as an indication of the restricted nature of the results of There is no guarantee, however, that the norm of the state estimation error will always be lower if the observer is designed as indicated here. In fact, if the initial state estimation error vector is dominated by one component, or if the errors in some of the parameters of the A and B matrices are dominant over the others, the relationship between the state estimation error norms may not be the same as the relationship between the error bounds indicated by Eq. Conclusions In this paper, we have derived an expression for an upper bound on the norm of the estimation error for an observer, in the presence of errors in the system A and B matrices and in the estimated initial conditions. It is shown that, in designing observers for multi-output systems using eigenstructure assignment, if the eigenvectors of the F c matrix are chosen to be as nearly mutually orthogonal as possible, a smaller bound on the state estimation error is obtained and thus may lead to more accurate state estimation. This is demonstrated by means of an example. The approach presented seems most appropriate in the absence of any a priori information on the initial state or the nature of the modeling errors. References Introduction This paper is concerned with the problem of identifying the input-output relationship of an unknown nonlinear dynamical system. Classical adaptive control of deterministic linear systems whose state variables are not all observed makes use of the separation principle (Narendra and Annaswamy, 1989) which says, in effect, that the problems of constructing an observer and parameter estimator can be considered separately. When the system is not observable it is not possible to construct an observer to recover the full state. Furthermore, when the system is nonlinear the separation principle no longer applies, and hence conventional adaptive identification and control techniques offer little hope of effective control of partially observed nonlinear systems. In this paper we show that these difficulties can be avoided by using neural networks instead. Neural networks are already successfully applied in control theory and system identification. In a recent paper, Narandra and Parthasarathy (1990) formalized a unified approach to solving nonlinear identification and control problems using multilayered neural networks. Chen (1990) applied multilayer neural network to nonlinear self-tuning tracking problems. Chu et al. (1990) implemented a Hopfield network on identifying time-varying linear systems. Various learning architectures for training neural net controller are outlined in Psaltis et al. (1988) and some interesting applications of neural networks in adaptive control can be found in Goldenthal an