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Over the last several decades, the increasing data traffic in telecom, datacom, and high-performance computing has led to multi-gigabytes-per-second data transmission speeds in multiservice switches (MSSs), such as core routers and cross-connect switches [1]. A particularly challenging problem in this regard is the transmission of high-speed electrical data through the backplane in MSSs due to the high attenuation of current backplane materials and the limited bandwidth of backplane connectors [2].

This paper proposes a novel strategy for creating generative models of stochastic link responses starting from limited available data. Whereas state-of-the-art techniques, e.g., based on generalized polynomial chaos expansions, require a considerable amount of (expensive) input data, here we start from a small set of “training” responses. These responses are obtained either from simulations or measurements to construct a comprehensive stochastic model. Using this model, new response samples can be generated with a distribution as similar as possible to the real data distribution, for use in Monte Carlo-like analyses. The methodology first uses the standard Vector Fitting algorithm to fit the S-parameter data with rational functions having common poles. Then, a generative model for the residues is created by means of principal component analysis and kernel density estimation. An a posteriori selection of passive samples is performed on the generated data to ensure the new samples are physically consistent. The proposed modeling approach is applied to a commercial connector and to a set of differential striplines. Both are concatenated to produce the stochastic analysis of a complete link. Comparisons on the prediction of time-domain responses are also provided.

This paper addresses the problem of constructing a generative statistical model for an interconnect starting from a limited set of S-parameter samples, which are obtained by simulating or measuring the interconnect for a few random realizations of its stochastic physical properties. These original samples are first converted into a pole-residue representation with common poles. The corresponding residues are modeled as a correlated stochastic process by means of principal component analysis and kernel density estimation. The obtained model allows generating new samples with similar statistics as the original data. A passivity check is performed over the generated samples to retain only passive data. The proposed approach is applied to a representative coupled microstrip line example.

Ivo Abraham,1–3 Jan De Geest,2 Wim De Geest,2 Elke De Troy,4 Karen MacDonald3 1Center for Health Outcomes and PharmacoEconomic Research, University of Arizona, Tucson, AZ, USA; 2TheraSolve, Diepenbeek, Belgium; 3Matrix45, Tucson, AZ, USA; 4Jessa Ziekenhuis, Hasselt, Belgium Background: Unintended forgetfulness is the most common cause of medication nonadherence. MemoPatch® is an electronic skin patch intended to deliver discreet tactile medication reminder stimuli. This study aimed 1) to evaluate, within an experimental setup, the detectability and acceptability of fifteen continuous bipolar pulse signals; 2) to identify variables, if any, associated with differential perception of the candidate reminder signals; and 3) to collect safety data as reported by subjects or observed by staff. Methods: This was a laboratory experiment involving 147 healthy adult volunteers (55.1% female, 98.0% Caucasian, with age 41.8±16.0 years, body mass index [BMI] 24.7±4.4, upper body adiposity 28.5%±8.3% body fat, and skin impedance 367.6±140.8Ω) and using an experimental version of the MemoPatch®. Following four training signals administered in fixed order, subjects were exposed to a set of fifteen randomly sequenced signals varying in rise and fall time, width, and current, to be rated in terms of detectability ("too weak", "appropriate", or "too strong") and acceptability. Results: Ratings of "appropriate" were virtually independent of such variables as sex, BMI, upper body adiposity, and skin impedance at the patch location. Five signals were rated as "appropriate" by ≥67% of subjects and acceptable by ≥95% of subjects, virtually independently of the indicators of interest, and were retained as candidate signals for use in next stages of development and commercialization. Nine adverse events, none serious, were observed in six subjects. Conclusion: This study yielded five effective and safe candidate signals for potential use in the MemoPatch® device, all equally considered to be of appropriate detectability and high acceptability, in an experimental context. The signals were independent from, and therefore highly robust relative to, sex, BMI, upper body adiposity, and skin impedance at the patch site, lending additional generalizability to the signals and hence their potential relevance to broad commercial application. Keywords: adherence, satisfaction, persistence, forgetfulness, medication

This paper presents the frequency domain characterization of very high bandwidth connectorized traces and a millimeter wave rat race coupler. These connectorized differential grounded coplanar waveguide traces, essential for the testability of high speed integrated circuits, have a measured flat frequency response up to 67 GHz which indicates correct connector footprint and transmission line design. The differential traces narrow down to a chip scale pitch of 150 μm allowing direct flip chip connections. This enabling the testing of millimeter wave integrated circuits without the need for probing. Furthermore, a 50 GHz rat race coupler was fabricated to generate a differential clock from a single ended clock source.

Patient non-adherence to prescribed medication regimens is a significant problem and affects clinical treatment outcomes. The MemoPatch™ medical device, currently in development, is an electronic skin patch intended to deliver tactile medication reminder signals. Fifty volunteers completed a laboratory experiment that evaluated the detectability and appraisal thresholds of five split signals; specifically, the current thresholds (in mA) at which a signal was detected (threshold T1), was considered sufficiently detectable to serve as a reminder signal (threshold T2), and became too strong as a reminder signal (threshold T3). Signals were selected under consideration of three data points: T1Max and T2Max (defined as, resp., the maximum current observed at T1 and T2) and T3Pct90 (the T3 current at the 90(th) percentile). A signal was considered to be useable in future versions of the MemoPatch™ device if it met the constraint that (T3Pct90-T2Max) should not be negative. One signal met the constraint requirement as its T3Pct90-T2Max=0.96mA.

Patient non-adherence to prescribed medication regimens is a significant problem and affects clinical treatment outcomes. The MemoPatch™ medical device, currently in development, is an electronic skin patch intended to deliver tactile medication reminder signals. Fifty volunteers completed a laboratory experiment that evaluated the detectability and appraisal thresholds of five split signals; specifically, the current thresholds (in mA) at which a signal was detected (threshold T1), was considered sufficiently detectable to serve as a reminder signal (threshold T2), and became too strong as a reminder signal (threshold T3). Signals were selected under consideration of three data points: T1Max and T2Max (defined as, resp., the maximum current observed at T1 and T2) and T3Pct90 (the T3 current at the 90th percentile). A signal was considered to be useable in future versions of the MemoPatch™ device if it met the constraint that (T3Pct90-T2Max) should not be negative. One signal met the constraint requirement as its T3Pct90-T2Max=0.96mA.

A new automated circuit-modeling tool is presented for arbitrary planar transmission lines. The tool builds compact, parameterized, analytical models based on multiple full-wave 2D electro-magnetic (EM) simulations. The transmission line parameters are stored as a multidimensional function of frequency and geometrical parameters. The modeling algorithm combines adaptive data selecting and modeling techniques. The circuit models combine EM-accuracy and generality, and circuit simulation speed and flexibility.

A new adaptive technique is presented for building multidimensional parameterized analytical models for planar interconnection structures with a pre-defined accuracy and based on full-wave electromagnetic (EM) simulations. The models can be incorporated in a circuit simulator and the time required to calculate the circuit representation of a practical network is reduced by several orders of magnitude compared to full EM simulations. Furthermore, the accuracy of the results is significantly better compared to the results obtained with the circuit models used in state of the art CAD tools.