Robert W. Techentin

High-frequency characterization of power/ground-plane structures

In this paper, we describe a strategy to characterize power and ground-plane structures using a full cavity-mode frequency-domain resonator model. We develop insights into modal analysis and introduce a novel technique to suppress modal impedances, minimizing both transfer and input impedances. The influence of port locations on the Z matrix is evaluated.

Ka-band (35 GHz) 3-stage SiGe HBT low noise amplifier

We present design, simulation, and measurement of a Ka-band (35 GHz) low noise amplifier (LNA) fabricated in a 120 GHz f t /f m a x SiGe BiCMOS technology (IBM 7HP). To our knowledge, this is the first demonstration of a Ka-band LNA in a SiGe technology, representing the first of a set of desired building blocks for integrating a Ka-band transmit and receive (T/R) module in a single chip environment. At 35 GHz, the 3-stage LNA exhibited 15.1 dB gain, -5.9 dBm output compression (P1dB), 9 dBm third order intercept (IP3), and 5.6 dB noise figure at 25.6 mW DC power. Peak gain and bandwidth of the LNA were found to be 19.0 dB and 10.7 GHz respectively at a center frequency of 31.3 GHz.

Development of a Semi-synthetic Dataset as a Testbed for Big-Data Semantic Analytics

We have developed a large semi-synthetic, semantically rich dataset, modeled after the medical record of a large medical institution. Using the highly diverse data.gov data repository and a multivariate data augmentation strategy, we can generate arbitrarily large semi-synthetic datasets which can be used to test new algorithms and computational platforms. The construction process and basic data characterization are described. The databases, as well as code for data collection, consolidation, and augmentation are available for distribution.

A Rapid Link Analysis Technique Using Four-Port Scattering Parameters

A rapid link analysis technique is presented which simulates link performance using 4-port S-parameter models and pseudorandom driver stimulus. The mathematical technique and validation through simulation and hardware measurements of eye diagrams are presented.

Gene Expression Signatures Characterized by Longitudinal Stability and Interindividual Variability Delineate Baseline Phenotypic Groups with Distinct Responses to Immune Stimulation

Human immunity exhibits remarkable heterogeneity among individuals, which engenders variable responses to immune perturbations in human populations. Population studies reveal that, in addition to interindividual heterogeneity, systemic immune signatures display longitudinal stability within individuals, and these signatures may reliably dictate how given individuals respond to immune perturbations. We hypothesize that analyzing relationships among these signatures at the population level may uncover baseline immune phenotypes that correspond with response outcomes to immune stimuli. To test this, we quantified global gene expression in peripheral blood CD4+ cells from healthy individuals at baseline and following CD3/CD28 stimulation at two time points 1 mo apart. Systemic CD4+ cell baseline and poststimulation molecular immune response signatures (MIRS) were defined by identifying genes expressed at levels that were stable between time points within individuals and differential among individuals in each state. Iterative differential gene expression analyses between all possible phenotypic groupings of at least three individuals using the baseline and stimulated MIRS gene sets revealed shared baseline and response phenotypic groupings, indicating the baseline MIRS contained determinants of immune responsiveness. Furthermore, significant numbers of shared phenotype-defining sets of determinants were identified in baseline data across independent healthy cohorts. Combining the cohorts and repeating the analyses resulted in identification of over 6000 baseline immune phenotypic groups, implying that the MIRS concept may be useful in many immune perturbation contexts. These findings demonstrate that patterns in complex gene expression variability can be used to define immune phenotypes and discover determinants of immune responsiveness.

Characterization of semi-synthetic dataset for big-data semantic analysis

Over the past decade, the use of semantic databases has served as the basis for storing and analyzing complex, heterogeneous, and irregular data. While there are similarities with traditional relational database systems, semantic data stores provide a rich platform for conducting non-traditional analyses of data. In support of new graph analytic algorithms and specialized graph analytic hardware, we have developed a large semi-synthetic, semantically rich dataset. The construction of this dataset mimics the real-world scenario of using relational databases as the basis for semantic data construction. In order to achieve real-world variable distributions and variable dependencies, data.gov data was used as the basis for developing an approach to build arbitrarily large semi-synthetic datasets. The intent of the semi-synthetic dataset is to serve as a testbed for new semantic graph analyses and computational software/hardware platforms. The construction process and basic data characterization is described. All code related to the data collection, consolidation, and augmentation are available for distribution.