Nematic Liquid Crystal Carbon Nanotube Composite Materials for Designing RF Switching Devices

Radio frequency microelectromechanical systems (RF MEMS) devices are microdevices used to switch or modify signals from the RF to millimeter wave (mmWave) frequency range. Liquid crystals (LCs) are widely used as electro-optic modulators for display devices. An electric field-induced electrical conductivity modulation of pure LC media is quite low which makes it difficult to use for RF MEMS switching applications. Currently, RF MEMS devices are characterized as an excellent option between solid-state and electromechanical RF switches to provide high isolation, low insertion loss, low power usage, excellent return loss, and large frequency band. However, commercial usage is low due to their lower switching speed, reliability, and repeatability. This research presents an electrical conductivity enhancement through the use of carbon nanotube (CNT) doping of LCs to realize a high-performance RF LC-CNT switching device. This thesis presents simulations of an RF switch using a coplanar waveguide (CPW) with a LC-CNT composite called 4-Cyano-4’-pentylbiphenyl multi-walled nanotube (5CB-MWNT) that is suitable for RF applications. The electrical conductivity modulation and RF switch performance of the 5CB-MWNT composite is determined using Finite Element Analysis (FEA). The simulations will present data on the coplanar waveguide’s s-parameters at the input and output ports S11 and S21 to measure return and insertion loss respectively, two key parameters for determining any RF switch’s performance. Furthermore, this thesis presents applications for improving tunable phased antenna arrays using the LC-CNT composite to allow for beam steering with high-gain and directivity to provide a broad 3D scannable coverage of an area. Tunable antennas are an important characteristic for 5G applications to achieve an optimal telecommunication system to prevent overcrowding of antennas and reduce overall system costs. This research investigates various device geometries with 5CB-MWNT to realize the best performing RF device for RF applications and 5G telecommunication systems. This research presents return and insertion loss data for three waveguide device configurations: CPW, coplanar waveguide grounded (CPWG), and finite ground coplanar waveguide grounded (FG-CPWG). The best results are shown using the CPW configuration. The return loss for the LC-CNT device showed a 5 dB improvement from -7.5 dB to -12.5 dB when using the LC-CNT signal line device. The insertion loss for this configuration showed a much more consistent 0 to -0.3 dB insertion loss value with much less noise when using the LC-CNT device compared to the -0.3 to -1 dB insertion loss value with heavy noise when using the Au signal line device. For the other two configurations the return loss and insertion loss value stayed the same indicating there is no loss in performance when using the LC-CNT switching mechanism. This is ideal due to the benefits that the LC-CNT switching mechanism provides like device reliability and increased switching speeds

MMD-based Variable Importance for Distributional Random Forest

Distributional Random Forest (DRF) is a flexible forest-based method to estimate the full conditional distribution of a multivariate output of interest given input variables. In this article, we introduce a variable importance algorithm for DRFs, based on the well-established drop and relearn principle and MMD distance. While traditional importance measures only detect variables with an influence on the output mean, our algorithm detects variables impacting the output distribution more generally. We show that the introduced importance measure is consistent, exhibits high empirical performance on both real and simulated data, and outperforms competitors. In particular, our algorithm is highly efficient to select variables through recursive feature elimination, and can therefore provide small sets of variables to build accurate estimates of conditional output distributions

The Study of Correlation Structures of DNA Sequences: A Critical Review

The study of correlation structure in the primary sequences of DNA is reviewed. The issues reviewed include: symmetries among 16 base-base correlation functions, accurate estimation of correlation measures, the relationship between $1/f$ and Lorentzian spectra, heterogeneity in DNA sequences, different modeling strategies of the correlation structure of DNA sequences, the difference of correlation structure between coding and non-coding regions (besides the period-3 pattern), and source of broad distribution of domain sizes. Although some of the results remain controversial, a body of work on this topic constitutes a good starting point for future studies.Comment: LaTeX, two figures, postscript is expected to be 46 pages. To appear in the special issue of Computer & Chemistry (1997

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

MMD-based Variable Importance for Distributional Random Forest

Distributional Random Forest (DRF) is a flexible forest-based method to estimate the full conditional distribution of a multivariate output of interest given input variables. In this article, we introduce a variable importance algorithm for DRFs, based on the well-established drop and relearn principle and MMD distance. While traditional importance measures only detect variables with an influence on the output mean, our algorithm detects variables impacting the output distribution more generally. We show that the introduced importance measure is consistent, exhibits high empirical performance on both real and simulated data, and outperforms competitors. In particular, our algorithm is highly efficient to select variables through recursive feature elimination, and can therefore provide small sets of variables to build accurate estimates of condi-tional output distributions