Mark H. Weatherspoon

Binder-free Co–Mn composite oxide for Li–air battery electrode

Binder-free Co–Mn composite oxide was successfully deposited at low temperature on a woven substrate through a combination of electroless and electrolytic steps. The principle of the approach was illustrated with cobalt metal and a successful thin film metal oxide formation was supported by CV, XRD and XPS data. The viability of the binder-free Co–Mn oxide electrode was tested as a Li–air battery electrocatalyst and yielded an initial specific capacity of up to 2000 mA h g−1 and survived multiple charge–discharge–recharge cycles. The oxidation time in the electrolytic oxidation-step was found to affect the battery discharge period. In comparison to the conventional polymeric binder methods, the present binder-free method is potentially adaptable to a roll–roll continuous processing approach.

Accurate and efficient small-signal modeling of active devices using artificial neural networks

Artificial neural networks (ANNs) are presented for the accurate and efficient small-signal modeling of active devices. Models are developed using measured data and are valid over ranges of parameters such as frequency, bias, and ambient temperature. Once generated, these ANN models are inserted into commercial microwave circuit simulators where they can be used for computer-aided design (CAD) and optimization of microwave/MM-wave circuits. Also, the developed ANN models can give physical insight into device behavior and scaling properties when used in conjunction with an equivalent circuit approach. An advantage of the ANN modeling approach is that it provides substantial data storage reduction over previously used modeling techniques without loss of accuracy. With increased model accuracy, the potential of first-pass design success may be realized, resulting in cost savings and decreased time-to-market for new products.

Experimental validation of generalized equations for FET cold noise source design

This work advances the capabilities of accurately quantifying the microwave noise temperature that exits port one of a two-port active device when port two is terminated with a complex load of known temperature. This noise temperature is of interest when characterizing and designing field-effect transistor (FET)-based cold noise sources or active cold loads that can be used as radiometer calibration reference standards. Unlike prior efforts, noise wave theory is used herein to derive an equation that embodies the complete effects of load mismatch, thus providing a new expression that correctly predicts the available noise temperature exiting port one of the two-port device. An electronic tuner is used to vary the impedance presented to port two of an on-wafer device while the port-one noise temperature is measured. The measured forward noise parameters of the device, the tuner impedance, and the tuner temperature (usually ambient) are used to compute the predicted output noise temperature values. Good agreement was observed between the noise wave-based predicted noise temperature and the measured noise temperature. Equations are developed for achieving minimum noise temperature, and a procedure including simulations and a flowchart are also presented for the design of FET-based synthetic cold loads.

An overview of reconfigurable antennas: Design, simulation, and optimization

Reconfigurable antenna implementation can be separated into three main processes: Antenna design, simulation, and optimization. The objective of this paper is to provide insight into the selections that must be made to implement a reconfigurable antenna, serving as a guideline for future reconfigurable antenna designers as to what needs to be done before a model is constructed, such as choosing a suitable antenna structure and reconfiguration technology, a proper simulation environment, and an adequate optimization algorithm that will allow smooth transitions between reconfigurable states. These selections are directly dependent on the given project requirements. Several options are presented for each case, and particular selections are made based on desired characteristics.

Small-Signal Modeling of Microwave MESFETs Using RBF-ANNs

This paper presents a comprehensive approach to accurate and efficient modeling of microwave active devices such as metal semiconductor field effect transistors (MESFETs) using artificial neural networks (ANNs). A radial basis function (RBF)-ANN model is developed for S-parameters and equivalent circuit parameters (ECPs) of MESFETs. The training and testing data for these models are obtained from the measured two-port scattering parameters and extracted ECPs of a 0.25 times 200 mum (4 times 50 mum) gallium arsenide MESFET. A four- input eight-output ANN is used to model the S-parameters of a microwave MESFET versus bias, temperature, and frequency, and a three-input eight-output ANN is used to model the ECPs of a microwave MESFET versus bias and temperature. Comparisons of measured and modeled data are presented, and the results show very good agreement. The average relative errors using the RBF-ANN models for the S-parameters and ECPs were 0.81% and 0.77%, respectively, which both represent about 60% reduction in error when compared to backpropagation ANN models of similar parameters of the same device.

Synthesizing antenna array sidelobe levels and null placements using the Cross Entropy method

This paper describes the synthesis of linear antenna arrays using the cross entropy (CE) method to produce array responses with minimum peak sidelobe levels (SLL) and position nulls at specific angular locations. The CE method is a very simple, yet highly efficient approach to solving complex multi-objective and multi-extremal optimization problems. CE seeks to adaptively estimate an optimal sampling distribution which generates solutions in the neighborhood of the globally optimal solution by minimizing the cross entropy (or Kullback-Leibler divergence) between current best estimates and the overall estimate of the optimal distribution. The performance of CE is comparable to other popular optimization techniques such as the genetic algorithm, simulated annealing and particle swarm optimization.

Supervised and Unsupervised Methods for Stock Trend Forecasting

Stock forecasting is a major component of any finance institution because predictions of future prices, indices, volumes and many more values are often incorporated into the economic decision-making process. Although there are many different approaches out there, this paper will compare unsupervised classification techniques such as k-means clustering with supervised learning algorithms such as support vector machines (SVMs). In our study, a list of stock prices taken from historical data of the S&P 500 is used as our testbed. These prices will be categorized as increasing or decreasing in price on a weekly basis. The goal of this study is to determine the best method for forecasting the trend of stock prices.

Optimization of a multi-band reconfigurable microstrip line-fed rectangular patch antenna using Self-Organizing Maps

The objective of this work is to demonstrate how clustering techniques such as Kohonen Self-Organizing Maps (SOM) can dramatically reduce the amount of time necessary for optimizing the reconfigurable aperture. Herein, a microstrip line-fed rectangular patch antenna is targeted as the reconfigurable aperture of interest. The patch element is divided into smaller elements, which by themselves are too small to radiate efficiently, yet in combination become a viable radiating structure. A 3-D finite-difference time-domain (FDTD) full-wave analysis utilizing perfectly matched layers is performed to determine the broadband return loss of a reconfigured aperture representing the optimization goal of our SOM. Simulations were performed for a large subset of possible state combinations of the reconfigurable aperture in order to cluster in an unsupervised way the states of the reconfigured apertures using SOM subject to broadband return loss. The SOM is shown to be an efficient method for reconfiguring the aperture from one state to the next within its respective cluster for large populations of feasible reconfigurable states.

Vector corrected on-wafer measurements of noise temperature

On-wafer noise temperature measurements are performed using receiver noise parameters for error correction. Accuracy in one-port measurements of on-wafer noise temperature made with commercial systems is demonstrated without using isolators. Equations for correcting mismatch errors are properly applied to the on-wafer environment as part of the available vector noise temperature equation. To test the measurement system, known off-wafer noise sources were used to obtain predictable on-wafer noise temperatures. These on-wafer noise temperatures were then measured and compared to predictions. Measured test results, presented for a C-band solid-state cold noise source and a pair of microwave solid-state noise diodes, are shown to be in good agreement with the predicted on-wafer noise temperature of the same sources with worst-case disagreement of 7.4%. Measured on-wafer device under test results, presented for a microwave monolithic integrated circuit active cold load, were in good agreement with values predicted from measured forward noise parameters.

Vector corrected noise temperature measurements

A new one-port technique for measuring noise temperature is presented that uses receiver noise parameters for error correction. Improved accuracy in one-port measurements of noise temperature made with commercial systems is demonstrated without using isolators. Equations for correcting mismatch errors are developed as part of the available vector noise temperature equation. Results, presented for a C-band solid-state cold noise source and a pair of microwave solid-state noise diodes, are shown to be in good agreement with radiometric measurements of the same sources.