A medial axis based method for irregular grain shape representation in DEM simulations

This paper describes a novel method for representing arbitrary grain shapes in discrete element method (DEM) simulations. The method takes advantage of the efficient sphere contact treatment in DEM and approximates the overall grain shape by combining a number of overlapping spheres. The method is based on the medial axis transformation, which defines the set of spheres needed for total grain reconstruction. This number of spheres is then further diminished by selecting only a subset of reconstructing spheres and opting for a grain approximation rather than a full grain reconstruction. The effects of the grain approximating parameters on the key geometrical features of the grains and the overall mechanical response of the granular medium are monitored by an extensive sensitivity analysis. The results of DEM quasi-static oedometric compression on a granular sample of approximated grains exhibit a high level of accuracy even for a small number of spheres.     

Discrimination of corn from monocotyledonous weeds with ultraviolet (UV) induced fluorescence

In production agriculture, savings in herbicides can be achieved if weeds can be discriminated from crop, allowing the targeting of weed control to weed-infested areas only. Previous studies demonstrated the potential of ultraviolet (UV) induced fluorescence to discriminate corn from weeds and recently, robust models have been obtained for the discrimination between monocots (including corn) and dicots. Here, we developed a new approach to achieve robust discrimination of monocot weeds from corn. To this end, four corn hybrids (Elite 60T05, Monsanto DKC 26-78, Pioneer 39Y85 (RR), and Syngenta N2555 (Bt, LL)) and four monocot weeds (Digitaria ischaemum (Schreb.) I, Echinochloa crus-galli (L.) Beauv., Panicum capillare (L.), and Setaria glauca (L.) Beauv.) were grown either in a greenhouse or in a growth cabinet and UV (327 nm) induced fluorescence spectra (400 to 755 nm) were measured under controlled or uncontrolled ambient light intensity and temperature. This resulted in three contrasting data sets suitable for testing the robustness of discrimination models. In the blue-green region (400 to 550 nm), the shape of the spectra did not contain any useful information for discrimination. Therefore, the integral of the blue-green region (415 to 455 nm) was used as a normalizing factor for the red fluorescence intensity (670 to 755 nm). The shape of the normalized red fluorescence spectra did not contribute to the discrimination and in the end, only the integral of the normalized red fluorescence intensity was left as a single discriminant variable. Applying a threshold on this variable minimizing the classification error resulted in calibration errors ranging from 14.2% to 15.8%, but this threshold varied largely between data sets. Therefore, to achieve robustness, a model calibration scheme was developed based on the collection of a calibration data set from 75 corn plants. From this set, a new threshold can be estimated as the 85% quantile on the cumulative frequency curve of the integral of the normalized red fluorescence. With this approach the classification error was nearly constant (16.0% to 18.5%), thereby indicating the potential of UV-induced fluorescence to reliably discriminate corn from monocot weeds.     

Identification of linear systems captured in a feedback loop

The identification of model parameters using measured input-output data from linear systems operating in a closed loop is discussed. A frequency-domain maximum likelihood estimator which takes into account the correlation between the input and output disturbances is presented. Its properties are analyzed and illustrated by simulation examples and real measurements     

The effect of loading on surface roughness at the atomistic level

One of the key points to better understand the origins of friction is to know how two surfaces in contact adhere to one another. In this paper we present molecular dynamics (MD) simulations of two aluminium bodies in contact, exposed to a range of normal loads. The contact surfaces of both aluminium bodies have a self-affine fractal roughness, but the exact roughness varies from simulation to simulation. Both bodies are allowed to have an adhesive interaction and are fully deformable. Tracking important contact parameters (such as contact area, number of contact clusters, and contact pressure) during a simulation is challenging. We propose an algorithm (embedded within a parallel MD code) which is capable of accessing these contact statistics. As expected, our results show that contact area is increasing in proportion with applied load, and that a higher roughness reduces contact area. Contact pressure distributions are compared to theoretical models, and we show that they are shifted into the tensile regime due to the inclusion of adhesion in our model.     

Parametric modeling of an electromagnetic compression device with the proper generalized decomposition

Optimization of forming processes seeks an optimal choice of many process parameters. In Electromagnetic Material Forming (EMF), parameters associated to the geometry of the forming device or related to the generation of the pulsed currents have to be set, and are of primary importance to achieve the proper geometry of the formed part. Usual optimization procedures proceed by defining a trial choice of the set of parameters and then evaluate the optimality of a given cost function computed from a direct analysis. This iterative process requires many assessments of the cost function and may lead to a prohibitive computation cost since the direct analysis may involve a structural analysis. Others approaches have been proposed to circumvent this problem; based on a separated representation of the solution, the Proper Generalized Decomposition allows for a parametric resolution by introducing optimization parameters as extra-coordinates of the problem, hence the optimization procedure reduces to a simple post-treatment of the multidimensional numerical solution. The aim of this work is to develop a numerical tool dedicated to the optimization of the design of an electromagnetic compression device. This tool should enable to optimize process parameters of the generator and geometrical parameters of the electromagnetic forming device by solving the set of electromagnetic equations in quasistatics. To this end, we take advantage of the Proper Generalized Decomposition (PGD) to perform a parametric resolution. We show solutions computed with a parameterization of the discharged current, and with a parameterization of the geometry considering a multi-layered structure. Finally, an example of optimization procedure is shown on the latter solution, seeking the configuration maximising the radial component of the resultant compression force applied on the part to be formed.     

Bhattacharyya distance as a contrast parameter for statistical processing of noisy optical images

In many imaging applications, the measured optical images are perturbed by strong fluctuations or boise. This can be the case, for example, for coherent-active or low-flux imagery. In such cases, the noise is not Gaussian additive and the definition of a contrast parameter between two regions in the image is not always a straightforward task. We show that for noncorrelated noise, the Bhattacharyya distance can be an efficient candidate for contrast definition when one uses statistical algorithms for detection, location, or segmentation. We demonstrate with numerical simulations that different images with the same Bhattacharyya distance lead to equivalent values of the performance criterion for a large number of probability laws. The Bhattacharyya distance can thus be used to compare different noisy situations and to simplify the analysis and the specification of optical imaging systems.     

Time-dependent spectral analysis of epidemiological time-series with wavelets.

In the current context of global infectious disease risks, a better understanding of the dynamics of major epidemics is urgently needed. Time-series analysis has appeared as an interesting approach to explore the dynamics of numerous diseases. Classical time-series methods can only be used for stationary time-series (in which the statistical properties do not vary with time). However, epidemiological time-series are typically noisy, complex and strongly non-stationary. Given this specific nature, wavelet analysis appears particularly attractive because it is well suited to the analysis of non-stationary signals. Here, we review the basic properties of the wavelet approach as an appropriate and elegant method for time-series analysis in epidemiological studies. The wavelet decomposition offers several advantages that are discussed in this paper based on epidemiological examples. In particular, the wavelet approach permits analysis of transient relationships between two signals and is especially suitable for gradual change in force by exogenous variables.     

Nonparaxial analysis of continuous self-imaging gratings in oblique illumination

Tolerance in angles of continuously self-imaging gratings (CSIGs) is explored. The degradation in angle of the shape of the point-spread function is theoretically investigated and illustrated by simulations and experiments. The formalism presented is inspired by the one used for classical lenses and can be easily generalized to diffraction gratings. It turns out that well-designed CSIGs could be used for scanning optical systems requiring a large field of view.     

Failure analysis defect location on a real case 55 nm memory using dynamic power supply emulation

Static and dynamic techniques for defect location are well established in the failure analysis flow of a failing integrated circuit. When a circuit shows an overconsumption on power supply, the useful static techniques are laser stimulation (OBIRCH, TIVA, LIVA, etc.) or photoemission. When the electrical signature is a soft fail, a functional fault or a timing issue the analyst will use dynamic techniques like dynamic laser mapping (SDL, xVM, LVI, etc.), dynamic photoemission or internal probing (Ebeam, TRE, LVP, etc.) by applying a looping test sequence which emulates the fail.In this paper we will present a real case analysis on a circuit showing a static signature (over consumption) and also a functional fault. Both static and dynamic location techniques have been used for the defect location, plus a non conventional approach by applying a clocked power supply sequence to the circuit. A comparison is done between the different signatures and we show that dynamic power supply emulation can bring some additional information on the defect location which is not detected with the conventional static/dynamic approach.