Mixed-Signal Circuit Classification in a Pseudo-Random Testing Scheme

Pseudo-random testing techniques for mixed-signal circuits offer several advantages compared to explicit time-domain and frequency-domain test methods, especially in a BIST structure. To fully exploit these advantages a suitable choice of the pseudo-random input parameters should be done and an investigation on the accuracy of the circuit response samples needed to reduce the risk of misclassification should be carried out. Here these issues have been addressed for a testing scheme based on the estimation of the impulse response of the device under test (DUT) by means of input-output cross-correlation. Moreover, new acceptance criteria for the DUT are suggested which solve some ambiguity problems arising if the classification of the DUT as good or bad is based on a few samples of the cross-correlation function. Examples of application of the proposed techniques to real cases are also shown in order to assess the impact of the measurement system inaccuracies on the reliability of the test.     

Toward automatic phenotyping of developing embryos from videos.

We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was designed as the central component of a fully automated phenotyping system. The system contains three modules 1) a convolutional network trained to classify each pixel into five categories: cell wall, cytoplasm, nucleus membrane, nucleus, outside medium; 2) an energy-based model, which cleans up the output of the convolutional network by learning local consistency constraints that must be satisfied by label images; 3) a set of elastic models of the embryo at various stages of development that are matched to the label images.     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Surface adsorption of comb polymers by Monte Carlo simulations

This paper reports off-lattice Monte Carlo simulations of highly-branched comb homopolymers weakly adsorbed on a flat, featureless surface showing only covolume and dispersion interactions with the adsorbate. A minimal coarse-grained model, described by hard spheres connected by harmonic springs, was employed. The interaction energy of the adsorbed combs and linear chains is first discussed as a function of the molecular mass and of the number of beads in contact with the surface. The molecular size is then investigated as a function of backbone length and branching density at a fixed arm size. The apparent swelling exponents of the adsorbed combs are larger than those of the corresponding linear chains, and much larger than that of the free molecules. This result indicates a surface-induced stiffening of the comb backbone, further studied through the persistence length l_pers. It is found that l_pers increases upon adsorption over the free-molecule value, more so the larger is the branching density. Finally, the thickness of the adsorbed layer, the surface-induced molecular anisotropy and the molecular aspect ratio are investigated as a function of branching density and molecular mass.     

Polarization mode dispersion characterization of single-modeoptical fiber using backscattering technique

This paper presents a completely new method able to characterize polarization mode dispersion (PMD) properties of randomly birefringent single-mode fibers, using polarization sensitive backscattering technique. We show analytical relationships between evolution of polarization state of backscattered signal with respect to state of polarization of forward one. Our technique allows one to measure differential group delay, beat length, and correlation length at the same time over long single-mode fibers using only one fiber end. Experimental data fit very well with numerical results, confirming the capability of our technique for fast routine characterization of PMD during cabling, before and after installation