Monolithic millimeter-wave diode grid frequency multiplier arrays

Monolithic diode frequency multiplier arrays, including barrier-N-N(+) (BNN) doubler, multi-quantum-barrier-varactor (MQBV) tripler, Schottky-quantum-barrier-varactor (SQBV) tripler, and resonant-tunneling-diode (RTD) tripler arrays, have been successfully fabricated with yields between 85 and 99 percent. Frequency doubling and/or tripling have been observed for all the arrays. Output powers of 2.4-2.6 W (eta = 10-18 percent) at 66 GHz with the BNN doubler and 3.8-10 W (eta = 1.7-4 percent) at 99 GHz with the SQBV tripler have been achieved

Spanning Trees on Graphs and Lattices in d Dimensions

The problem of enumerating spanning trees on graphs and lattices is considered. We obtain bounds on the number of spanning trees $N_{ST}$ and establish inequalities relating the numbers of spanning trees of different graphs or lattices. A general formulation is presented for the enumeration of spanning trees on lattices in $d\geq 2$ dimensions, and is applied to the hypercubic, body-centered cubic, face-centered cubic, and specific planar lattices including the kagom\'e, diced, 4-8-8 (bathroom-tile), Union Jack, and 3-12-12 lattices. This leads to closed-form expressions for $N_{ST}$ for these lattices of finite sizes. We prove a theorem concerning the classes of graphs and lattices ${\cal L}$ with the property that $N_{ST} \sim \exp(nz_{\cal L})$ as the number of vertices $n \to \infty$, where $z_{\cal L}$ is a finite nonzero constant. This includes the bulk limit of lattices in any spatial dimension, and also sections of lattices whose lengths in some dimensions go to infinity while others are finite. We evaluate $z_{\cal L}$ exactly for the lattices we considered, and discuss the dependence of $z_{\cal L}$ on d and the lattice coordination number. We also establish a relation connecting $z_{\cal L}$ to the free energy of the critical Ising model for planar lattices ${\cal L}$.Comment: 28 pages, latex, 1 postscript figure, J. Phys. A, in pres

Enhancing complex-network synchronization

Heterogeneity in the degree (connectivity) distribution has been shown to suppress synchronization in networks of symmetrically coupled oscillators with uniform coupling strength (unweighted coupling). Here we uncover a condition for enhanced synchronization in directed networks with weighted coupling. We show that, in the optimum regime, synchronizability is solely determined by the average degree and does not depend on the system size and the details of the degree distribution. In scale-free networks, where the average degree may increase with heterogeneity, synchronizability is drastically enhanced and may become positively correlated with heterogeneity, while the overall cost involved in the network coupling is significantly reduced as compared to the case of unweighted coupling.Comment: 4 pages, 3 figure

An Examination Of The Roles Of State School Psychology Consultants

With the increasing visibility of state school psychology consultants (SSPCs) across the nation, there is a pressing need to understand their roles and functions relative to serving their stakeholders. In addition, it is unclear whether current SSPC job responsibilities are aligned with the National Association of School Psychologists’ (NASP) practice model, which can help ensure the quality of school psychological service delivery. A qualitative case study design was conducted with interviews to examine the job responsibilities of SSPCs (e.g., provision of consultation, policy guidance, professional development, coordination of professional resources and services). The qualitative analysis revealed three main themes: (a) service provision, (b) collaborative roles and efforts, and (c) systems improvement across the state, which were aligned with different levels of domains in the NASP practice model. The findings can help inform the roles and responsibilities of SSPCs and the development of new SSPC functions. Implications for conceptualization of the SSPC initiative in relation to the NASP practice model for future practice are discussed.&nbsp

Monolithic millimeter-wave diode array beam controllers: Theory and experiment

In the current work, multi-function beam control arrays have been fabricated and have successfully demonstrated amplitude control of transmitted beams in the W and D bands (75-170 GHz). While these arrays are designed to provide beam control under DC bias operation, new designs for high-speed electronic and optical control are under development. These arrays will fill a need for high-speed watt-level beam switches in pulsed reflectometer systems under development for magnetic fusion plasma diagnostics. A second experimental accomplishment of the current work is the demonstration in the 100-170 GHz (D band) frequency range of a new technique for the measurement of the transmission phase as well as amplitude. Transmission data can serve as a means to extract ('de-embed') the grid parameters; phase information provides more complete data to assist in this process. Additional functions of the array beam controller yet to be tested include electronically controlled steering and focusing of a reflected beam. These have application in the areas of millimeter-wave electronic scanning radar and reflectometry, respectively

Ascertaining price formation in cryptocurrency markets with machine learning

The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using machine learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a machine learning approach to predict the direction of the mid-price changes on the upcoming tick. We show that there are universal features amongst cryptocurrencies which lead to models outperforming asset-specific ones. We also show that there is little point in feeding machine learning models with long sequences of data points; predictions do not improve. Furthermore, we solve the technical challenge to design a lean predictor, which performs well on live data downloaded from crypto exchanges. A novel retraining method is defined and adopted towards this end. Finally, the trade-off between model accuracy and frequency of training is analyzed in the context of multi-label prediction. Overall, we demonstrate that promising results are possible for cryptocurrencies on live data, by achieving a consistent 78% accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs. US dollars