Reporting of Two or More Races In the 1999 American Community Survey

This study presents data on race, collected at selected sites throughout the country for the 1999 American Community Survey (ACS). In particular, the distribution of the population by race and Hispanic or Latino origin is examined, as are the reporting of multiple races, number of races, and major race combinations and the extent to which the race and Hispanic/Latino questions were not answered. Although the ACS sites were not intended to be a nationally representative sample, the study's results provide important insights into what might be learned from Census 2000.

"Reporting of Two or More Races in the 1999 American Community Survey"

This paper investigates the causes of western Germany's remarkably poor performance since 1992. The paper challenges the view that the poor record of the nineties, particularly the marked deterioration in public finances since unification, might be largely attributable to unification. Instead, the analysis highlights the role of ill-timed and overly ambitious fiscal consolidation in conjunction with tight monetary policies of an exceptional length and degree. The issue of fiscal sustainability and Germany's fiscal and monetary policies are assessed both in the light of economic theory and in comparison to the best practices of other more successful countries. The analysis concludes that Germany's dismal record of the nineties must not be seen as a direct and apparently inevitable result of unification. Rather, the record arose as a perfectly unnecessary consequence of unsound macro demand policies conducted under the Bundesbank's dictate in response to it, policies that caused the severe and protracted de-stabilization of western Germany in the first place.

Dynamic selection and estimation of the digital predistorter parameters for power amplifier linearization

© © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a new technique that dynamically estimates and updates the coefficients of a digital predistorter (DPD) for power amplifier (PA) linearization. The proposed technique is dynamic in the sense of estimating, at every iteration of the coefficient's update, only the minimum necessary parameters according to a criterion based on the residual estimation error. At the first step, the original basis functions defining the DPD in the forward path are orthonormalized for DPD adaptation in the feedback path by means of a precalculated principal component analysis (PCA) transformation. The robustness and reliability of the precalculated PCA transformation (i.e., PCA transformation matrix obtained off line and only once) is tested and verified. Then, at the second step, a properly modified partial least squares (PLS) method, named dynamic partial least squares (DPLS), is applied to obtain the minimum and most relevant transformed components required for updating the coefficients of the DPD linearizer. The combination of the PCA transformation with the DPLS extraction of components is equivalent to a canonical correlation analysis (CCA) updating solution, which is optimum in the sense of generating components with maximum correlation (instead of maximum covariance as in the case of the DPLS extraction alone). The proposed dynamic extraction technique is evaluated and compared in terms of computational cost and performance with the commonly used QR decomposition approach for solving the least squares (LS) problem. Experimental results show that the proposed method (i.e., combining PCA with DPLS) drastically reduces the amount of DPD coefficients to be estimated while maintaining the same linearization performance.Peer ReviewedPostprint (author's final draft

3-D distributed memory polynomial behavioral model for concurrent dual-band envelope tracking power amplifier linearization

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a new 3-D behavioral model to compensate for the nonlinear distortion arising in concurrent dual-band (DB) Envelope Tracking (ET) Power Amplifiers (PAs). The advantage of the proposed 3-D distributed memory polynomial (3D-DMP) behavioral model, in comparison to the already published behavioral models used for concurrent dual-band envelope tracking PA linearization, is that it requires a smaller number of coefficients to achieve the same linearity performance, which reduces the overall identification and adaptation computational complexity. The proposed 3D-DMP digital predistorter (DPD) is tested under different ET supply modulation techniques. Moreover, further model order reduction of the 3D-DMP DPD is achieved by applying the principal component analysis (PCA) technique. Experimental results are shown considering a concurrent DB transmission of aWCDMA signal at 1.75GHz and a 10-MHz bandwidth LTE signal at 2.1 GHz. The performance of the proposed 3D-DMP DPD is evaluated in terms of linearity, drain power efficiency, and computational complexity.Peer ReviewedPostprint (author's final draft

Independent digital predistortion parameters estimation using adaptive principal component analysis

©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents an estimation/adaptation method based on the adaptive principal component analysis (APCA) technique to guarantee the identification of the minimum necessary parameters of a digital predistorter. The proposed estimation/adaptation technique is suitable for online field-programmable gate array or system on chip implementation. By exploiting the orthogonality of the resulting transformed matrix obtained with the APCA technique, it is possible to reduce the number of coefficients to be estimated which, at the same time, has a beneficial regularization effect by preventing ill-conditioning or overfitting problems. Therefore, this identification/adaptation method enhances the robustness of the parameter estimation and simplifies the adaptation by reducing the number of estimated coefficients. Due to the orthogonality of the new basis, these parameters can be estimated independently, thus allowing for scalability. Experimental results will show that it is possible to determine the minimum number of parameters to be estimated in order to meet the targeted linearity levels while ensuring a robust well-conditioned identification. Moreover, the results will show how thanks to the orthogonality property of the new basis functions, the coefficients of the digital predistorter can be estimated independently. This allows to tradeoff the digital predistorter adaptation time versus performance and hardware complexity.Peer ReviewedPostprint (author's final draft

Prevalence of Rickettsia and Bartonella species in Spanish cats and their flleas

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Obtención de biogás como alternativa ecológica para el cuidado del medio ambiente.

Aun en pleno Siglo XXI el gas natural no es accesible para todos los habitantes de nuestro país y del mundo en general, en muchos lugares se utiliza el gas butano, pero este puede ser muy peligroso. Con este trabajo pretendemos realizar un pequeño biodigestor, de bajo costo, cuya fuente de energía es de fácil obtención, prácticamente inagotable y con un fácil manejo. Este dispositivo permitiría que más personas tuvieran acceso a esta fuente de energía; además de reducir la cantidad de basura que, al no ser utilizada, resulta contaminante. Los biodigestores de uso doméstico son una excelente opción para generar un tipo de combustible llamado biogás. El biogás es una mezcla constituida por metano CH4 en una proporción que oscila entre un 50% y un 70% y dióxido de carbono conteniendo pequeñas proporciones de gases como hidrógeno, nitrógeno y sulfuro de hidrógeno. El biogás está producido por bacterias durante el proceso de biodegradación de material orgánico en condiciones anaeróbicas (sin aire). El metano producido por bacterias es el último eslabón en una cadena de microorganismos que degradan material orgánico y devuelven los productos de la descomposición al medio ambiente. El proceso que genera biogás se lleva a cabo gracias a un biodigestor que es un sistema que permite la descomposición anaerobia de desechos orgánicos para generar biogás (gas metano) en este proceso intervienen bacterias denominadas metanogénicas y también se pueden aprovechar los residuos como abono

Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become increasingly important to detect center-involved diabetic macular edema (ci-DME). However, center-involved diabetic macular edema is diagnosed using optical coherence tomography (OCT), which is not generally available at screening sites because of cost and workflow constraints. Instead, screening programs rely on the detection of hard exudates in color fundus photographs as a proxy for DME, often resulting in high false positive or false negative calls. To improve the accuracy of DME screening, we trained a deep learning model to use color fundus photographs to predict ci-DME. Our model had an ROC-AUC of 0.89 (95% CI: 0.87-0.91), which corresponds to a sensitivity of 85% at a specificity of 80%. In comparison, three retinal specialists had similar sensitivities (82-85%), but only half the specificity (45-50%, p<0.001 for each comparison with model). The positive predictive value (PPV) of the model was 61% (95% CI: 56-66%), approximately double the 36-38% by the retinal specialists. In addition to predicting ci-DME, our model was able to detect the presence of intraretinal fluid with an AUC of 0.81 (95% CI: 0.81-0.86) and subretinal fluid with an AUC of 0.88 (95% CI: 0.85-0.91). The ability of deep learning algorithms to make clinically relevant predictions that generally require sophisticated 3D-imaging equipment from simple 2D images has broad relevance to many other applications in medical imaging

Multi-dimensional LUT-based digital predistorter for concurrent dual-band envelope tracking power amplifier linearization

©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a multi lookup table (LUT) implementation scheme for the 3D distributed memory polynomial (3D-DMP) behavioral model used in Digital Predistortion (DPD) linearization for concurrent dual-band envelope tracking (ET) power amplifiers (PAs). The proposed 3DDistributed Memory LUTs (3D-DML) architecture is suitable for efficient FPGA implementation. In order to optimize the linearization performance as well as to reduce the number of resources of the 3D-DML model, a new variant of the Orthogonal Matching Pursuit (OMP) algorithm is proposed to properly select the best LUTs. Experimental results show that the proposed strategy reduces the number of LUTs (i.e. the number of coefficients) while meeting the targeted linearity levels.Peer ReviewedPostprint (author's final draft

Partial least squares identification of multi look-up table digital predistorters for concurrent dual-band envelope tracking power amplifiers

©208 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a technique to estimate the coefficients of a multiple-look-up table (LUT) digital predistortion (DPD) architecture based on the partial least-squares (PLS) regression method. The proposed 3-D distributed memory LUT architecture is suitable for efficient FPGA implementation and compensates for the distortion arising in concurrent dual-band envelope tracking power amplifiers. On the one hand, a new variant of the orthogonal matching pursuit algorithm is proposed to properly select only the best LUTs of the DPD function in the forward path, and thus reduce the number of required coefficients. On the other hand, the PLS regression method is proposed to address both the regularization problem of the coefficient estimation and, at the same time, reducing the number of coefficients to be estimated in the DPD feedback identification path. Moreover, by exploiting the orthogonality of the PLS transformed matrix, the computational complexity of the parameters' identification can be significantly simplified. Experimental results will prove how it is possible to reduce the DPD complexity (i.e., the number of coefficients) in both the forward and feedback paths while meeting the targeted linearity levels.Peer ReviewedPostprint (author's final draft