Danilo Di Febo

Practical EBG application to multilayer PCB: impact on signal integrity

In this paper, signal transmission performance of single ended and differential striplines between two parallel GND planes with embedded electromagnetic band gap (EBG) structure for noise isolation in high speed digital printed circuit boards (PCB) are studied. The performances in terms of |S11|, |S21|, |Sdd21| and |Scc21| are considered in function of the stack up cross section and position above the EBG. Practical considerations for the layout strategies are drawn.

Experimental validation of common-mode filtering performances of planar electromagnetic band-gap structures

An experimental validation of an electromagnetic band-gap (EBG) based common mode filter is given in this paper. The proposed layout technique is based on planar EBG structures altered by removing the connecting bridges between adjacent patches. The patches are properly dimensioned for ensuring the presence of frequency notches at the frequencies that should be filtered in the common-mode transfer function. The notch frequency is associated with the first resonant mode of the patch.

EBG-Based Common-Mode Microstrip and Stripline Filters: Experimental Investigation of Performances and Crosstalk

The aim of this study is to analyze from a modeling and experimental point of view the filter effectiveness and the crosstalk among signal traces crossing the same common-mode filter based on electromagnetic bandgap structures in a modern server design configurations. Both microstrips and striplines are considered in the study, detailing the differences among them in the design step as well as in filter geometry and response. Simulation models and experimental setups are carefully described, and the numerical and measurement results are compared and discussed.

Challenges in developing a multidimensional Feature Selective Validation implementation

Feature Selective Validation (FSV) was incorporated as core component of IEEE Std 1597.1. Recently, discussions have moved from ‘is quantitative comparison of data needed?’ to ‘how can this be applied to multidimensional data?’ The purpose of this paper is to present some of the latest thinking about the key challenges in developing a 1D model to unlimited dimensionality. In particular, the paper will present: (1) a revised mathematical framework for FSV, e.g. using tensor notation to simplify the mathematical representation. (2) A discussion on how the performance of n Dimensional FSV can be verified against human perception. (3) A review on the effects of data that oscillates between positive and negative data points, e.g. transients.

Removable EBG-Based Common-Mode Filter for High-Speed Signaling: Experimental Validation of Prototype Design

A new common-mode filter structure based on planar electromagnetic bandgap (EBG) technologies is designed, fabricated, and measured. It is based on a previously proposed geometry, implementing a sequence of two or more EBGs resonating at a filtering frequency; however, the new filter is placed on the top of the PCB as a standalone component, instead of being included within the PCB stack up. The filter can be easily removed and substituted by another one that is designed to filter a different frequency. The replacement design should maintain the same external size of the component as the original filter, which can be achieved by choosing the permittivity of the dielectric as well as the relationship among the EBG parameters appropriately. The electromagnetic behavior of the filter is simulated and a prototype structure is fabricated and measured. Results are compared to validate the design concept and procedure.

Comparison of Data With Multiple Degrees of Freedom Utilizing the Feature Selective Validation Method

The feature selective validation method has been shown to provide results that are in broad agreement with the visual assessment of a group of engineers for line, 1-D, data. An implementation using 2-D Fourier transforms and derivatives have been available for some years, but verification of the performance has been difficult to obtain. Further, that approach does not naturally scale well for 3-D and higher degrees of freedom, particularly if there are sizable differences in the number of points in the different directions. This paper describes an approach based on repeated 1-D FSV analyses that overcomes those challenges. The ability of the 2-D case to mirror user perceptions is demonstrated using the LIVE database. Its extension to n-dimensions is also described and includes a suggestion for weighting the algorithm based on the number of data points in a given “direction.”

Applying the Analytic Hierarchy Process (AHP) to an FSV-Based Comparison of Multiple Datasets

This paper presents research extending the feature selective validation (FSV) method to quantitatively compare multiple datasets. The analytic hierarchy process is introduced to weight different FSV results when comparison is conducted by comparing multiple numerical results with their referencing counterparts. Only pairwise comparisons are required which simplify the process of comprehensive validation. The efficiency of this approach is demonstrated by the validation of a simplified crosstalk model.

EMC Analysis of Axle Counters in the Italian Railway Network

The need of transport interoperability in Europe is a relevant issue nowadays. Particular interest is given to the signaling systems. The TEN-T project focuses one of its workpackages on “EMC Axle Counter Validation.” This work developed in the frame of the TEN-T project develops a procedure to assess the frequency spectrum of the magnitude of the magnetic field, due to the traction current, at the track level. Full-wave 3-D numerical models are developed to predict the field values. The measuring system is previously tested and then used for a measurement campaign. Results are reported in terms of tables and frequency spectra.

Investigating Confidence Histograms and Classification in FSV: Part II-Float FSV

One important aspect of the feature selective validation (FSV) method is that it classifies comparison data into a number of natural-language categories. This allows comparison data generated by FSV to be compared with equivalent “visual” comparisons obtained using the visual rating scale. Previous research has shown a close relationship between visual assessment and FSV generated data using the resulting confidence histograms. In all cases, the category membership functions are “crisp”: that is data on the FSV-value axis falls distinctly into one category. The companion paper to this Investigating Confidence histograms and Classification in FSV: Part I. Fuzzy FSV investigated whether allowing probabilistic membership of categories could improve the comparison between FSV and visual assessment. That paper showed that such an approach produced limited improvement and, as a consequence, showed that FSV confidence histograms are robust to flexibility in category boundaries. This paper investigates the effect of redefining some, but not all, category boundaries based around the mode category. This “float” approach does show some improvement in the comparison between FSV and visual assessment.One important aspect of the feature selective validation (FSV) method is that it classifies comparison data into a number of natural-language categories. This allows comparison data generated by FSV to be compared with equivalent “visual” comparisons obtained using the visual rating scale. Previous research has shown a close relationship between visual assessment and FSV generated data using the resulting confidence histograms. In all cases, the category membership functions are “crisp”: that is data on the FSV value axis fall distinctly into one category. An important open question in FSV-based research, and for validation techniques generally, is whether allowed variability in these crisp category membership functions could further improve agreement with the visual assessment. A similar and related question is how robust is FSV to variation in the categorization algorithm. This paper and its associated “part II” present research aimed at developing a better understanding of the categorization of both visual and FSV data using nonsquare or variable boundary category membership functions. This first paper investigates the level of improvement to be expected by applying fuzzy logic to location of the category boundaries. The result is limited improvement to FSV, showing that FSV categorization is actually robust to variations in category boundaries.

Downsampled and Undersampled Datasets in Feature Selective Validation (FSV)

Feature selective validation (FSV) is a heuristic method for quantifying the (dis)similarity of two datasets. The computational burden of obtaining the FSV values might be unnecessarily high if datasets with large numbers of points are used. While this may not be an important issue per se it is an important issue for future developments in FSV such as real-time processing or where multidimensional FSV is needed. Coupled with the issue of dataset size, is the issue of datasets having “missing” values. This may come about because of a practical difficulty or because of noise or other confounding factors making some data points unreliable. These issues relate to the question “what is the effect on FSV quantification of reducing or removing data points from a comparison-i.e., down- or undersampling data?” This paper uses three strategies to achieve this from known datasets. This paper demonstrates, through a representative sample of 16 pairs of datasets, that FSV is robust to changes providing a minimum dataset size of approximately 200 points is maintained. It is robust also for up to approximately 10% “missing” data, providing this does not result in a continuous region of missed data.