Removing interference components in time–frequency representations using morphological operators

Time–frequency representations have been of great interest in the analysis and classification of non-stationary signals. The use of highly selective transformation techniques is a valuable tool for obtaining accurate information for studies of this type. The Wigner-Ville distribution has high time and frequency selectivity in addition to meeting some interesting mathematical properties. However, due to the bi-linearity of the transform, interference terms emerge when the transform is applied over multi-component signals. In this paper, we propose a technique to remove cross-components from the Wigner-Ville transform using image processing algorithms. The proposed method exploits the advantages of non-linear morphological filters, using a spectrogram to obtain an adequate marker for the morphological processing of the Wigner-Ville transform. Unlike traditional smoothing techniques, this algorithm provides cross-term attenuations while preserving time–frequency resolutions. Moreover, it could also be applied to distributions with different interference geometries. The method has been applied to a set of different time–frequency transforms, with promising results.     

Analysis of cultured neuronal networks using intraburst firing characteristics

It is an open question whether neuronal networks, cultured on multielectrode arrays, retain any capability to usefully process information (learning and memory). A necessary prerequisite for learning is that stimulation can induce lasting changes in the network. To observe these changes, one needs a method to describe the network in sufficient detail, while stable in normal circumstances. We analyzed the spontaneous bursting activity that is encountered in dissociated cultures of rat neocortical cells. Burst profiles (BPs) were made by estimating the instantaneous array-wide firing frequency. The shape of the BPs was found to be stable on a time scale of hours. Spatiotemporal detail is provided by analyzing the instantaneous firing frequency per electrode. The resulting phase profiles (PPs) were estimated by aligning BPs to their peak spiking rate over a period of 15 min. The PPs reveal a stable spatiotemporal pattern of activity during bursts over a period of several hours, making them useful for plasticity and learning studies. We also show that PPs can be used to estimate conditional firing probabilities. Doing so, yields an approach in which network bursting behavior and functional connectivity can be studied.     

Probing the electromagnetic field distribution within a metallic nanodisk

A Co nanolayer is used as a local probe to evaluate the vertical inhomogeneous distribution of the electromagnetic (EM) field within a resonant metallic nanodisk. Taking advantage of the direct relation between the magneto-optical activity and the electromagnetic field intensity in the Co layer, it is shown that the nonuniform EM distribution within the nanodisk under plasmon resonant conditions has maximum values close to the upper and lower flat faces, and a minimum value in the middle.     

Pixel selection for near-infrared chemical imaging (NIR-CI) discrimination between fish and terrestrial animal species in animal protein by-product meals

This paper proposes a method based on near-infrared hyperspectral imaging for discriminating between terrestrial and fish species in animal protein by-products used in livestock feed. Four algorithms (Mahalanobis distance, Kennard-Stone, spatial interpolation, and binning) were compared in order to select an appropriate subset of pixels for further partial least squares discriminant analysis (PLS-DA). The method was applied to a set of 50 terrestrial and 40 fish meals analyzed in the 1000-1700 nm range. Models were then tested using an external validation set comprising 45 samples (25 fish and 20 terrestrial). The PLS-DA models obtained using the four subset-selection algorithms yielded a classification accuracy of 99.80%, 99.79%, 99.85%, and 99.61%, respectively. The results represent a first step for the analysis of mixtures of species and suggest that NIR-CI, providing valuable information on the origin of animal components in processed animal proteins, is a promising method that could be used as part of the EU feed control program aimed at eradicating and preventing bovine spongiform encephalopathy (BSE) and related diseases.     

DNA aptamers binding to multiple prevalent M-types of Streptococcus pyogenes

This paper describes the selection of high affinity DNA aptamers binding to multiple M-types of the pathogenic species Streptococcus pyogenes (Group A Streptococcus or GAS). Unlike common aptamer selection techniques that use purified molecules of a monoclonal cell population as targets, this work has achieved the selection of aptamers against the various M-types of S. pyogenes. Cell mixtures containing equal numbers of the 10 most prevalent S. pyogenes M-types were incubated with 80-nucleotide DNA libraries, centrifuged, and washed to separate cell-bound from unbound DNA sequences. The DNA bound to the cells was amplified using the polymerase chain reaction, and the amplicons were tested for their binding to the target cells. The amplicons were also used as new DNA libraries for subsequent rounds of selection. Cloning, sequencing, and subsequent analysis of selected aptamers showed that they bind preferentially to GAS over other common and related bacteria. Resultant DNA aptamers showed strong and preferential binding to GAS, including the 10 most prevalent GAS M-types and another 10 minor M-types tested. Estimated K(d) values were in the range of 4 to 86 nM. Two aptamers, 20A24P and 15A3P (with estimated binding dissociation constants of 9 and 10 nM, respectively), are particularly promising. These aptamers could potentially be used to improve the detection of GAS, a pathogen that is the causative agent of many infectious diseases, most notably strep throat.     

Semiparametric additive models under symmetric distributions

In this paper we discuss estimation and diagnostic procedures in semiparametric additive models with symmetric errors in order to permit distributions with heavier and lighter tails than the normal ones, such as Student-t, Pearson VII, power exponential, logistics I and II, and contaminated normal, among others. Such models belong to the general class of statistical models GAMLSS proposed by Rigby and Stasinopoulos (Appl. Stat. 54:507–554, 2005). A back-fitting algorithm to attain the maximum penalized likelihood estimates (MPLEs) by using natural cubic smoothing splines is presented. In particular, the score functions and Fisher information matrices for the parameters of interest are expressed in a similar notation of that used in parametric symmetric models. Sufficient conditions on the existence of the MPLEs are presented as well as some inferential results and discussions on degrees of freedom and smoothing parameter estimation. Diagnostic quantities such as leverage, standardized residual and normal curvatures of local influence under two perturbation schemes are derived. A real data set previously analyzed under normal linear models is reanalyzed under semiparametric additive models with symmetric errors.     

Multimode polarization degree of mixture optical states in integrated directional couplers

Mixture multimode optical field classical states propagating in N?×?N integrated directional couplers are analyzed by using the density matrix formalism in a N-dimensional optical space. These mutimode optical fields present a kind of generalized polarization and accordingly a definition of a multimode polarization degree is proposed. It is based on the distance measure between a mixture state and an unpolarized state in a N-dimensional optical space so that in the case N=2 the standard polarization degree is recovered. It is shown that directional couplers can reduce or increase remarkably the multimode polarization degree of a mixture state. Likewise a simple measurement technique, based on Y junctions, of this multimode polarization degree is proposed. Finally all the results can be formally extended to the special case of multimode single photon quantum states.     

Characterization and Physical Explanation of Energetic Particles on Planck HFI Instrument

The Planck High Frequency Instrument (HFI) has been surveying the sky continuously from the second Lagrangian point (L2) between August 2009 and January 2012. It operates with 52 high impedance bolometers cooled at 100 mK in a range of frequency between 100 GHz and 1 THz with unprecedented sensitivity, but strong coupling with cosmic radiation. At L2, the particle flux is about 5 \(\hbox {cm}^{-2}\,\hbox {s}^{-1}\) and is dominated by protons incident on the spacecraft. Protons with an energy above 40 MeV can penetrate the focal plane unit box causing two different effects: glitches in the raw data from direct interaction of cosmic rays with detectors (producing a data loss of about 15 % at the end of the mission) and thermal drifts in the bolometer plate at 100 mK adding non-Gaussian noise at frequencies below 0.1 Hz. The HFI consortium has made strong efforts in order to correct for this effect on the time ordered data and final Planck maps. This work intends to give a view of the physical explanation of the glitches observed in the HFI instrument in-flight. To reach this goal, we performed several ground-based experiments using protons and \(\alpha \) particles to test the impact of particles on the HFI spare bolometers with a better control of the environmental conditions with respect to the in-flight data. We have shown that the dominant part of glitches observed in the data comes from the impact of cosmic rays in the silicon die frame supporting the micro-machined bolometric detectors propagating energy mainly by ballistic phonons and by thermal diffusion. The implications of these results for future satellite missions will be discussed.