A bandwidth enhancement method for microstrip antennas

Bandwidth enhancement methods for electromagnetically coupled microstrip dipoles are discussed. It is demonstrated that if parasitic metallic strips are incorporated in the structure either co-planar and parallel to the embedded microstrip transmission line open end, or between the transmission line and the microstrip dipole, then substantial bandwidth enhancement results. Experimental verification of this model is introduced for a bandwidth definition based on the frequency range which satisfies a voltage standing-wave ratio (VSWR) < 2 criterion. Also, experimentalbar{E}- andbar{H}-plane patterns verify the theoretical model which accounts for radiation from the microstrip dipole, the parasitics, and the transmission line.     

Red cell distortion and conceptual basis of diffusing capacity estimates: finite element analysis

To understand the effects of dynamic shape distortion of red blood cells (RBCs) as it develops under high-flow conditions on the standard physiological and morphometric methods of estimating pulmonary diffusing capacity, we computed the uptake of CO across a two-dimensional geometric capillary model containing a variable number of equally spaced RBCs. RBCs are circular or parachute shaped, with the same perimeter length. Total CO diffusing capacity (DLCO) and membrane diffusing capacity (DMCO) were calculated by a finite element method. DLCO calculated at two levels of alveolar PO2 were used to estimate DMCO by the Roughton-Forster (RF) technique. The same capillary model was subjected to morphometric analysis by the random linear intercept method to obtain morphometric estimates of DMCO. Results show that shape distortion of RBCs significantly reduces capillary diffusive gas uptake. Shape distortion exaggerates the conceptual errors inherent in the RF technique (J. Appl. Physiol. 79: 1039-1047, 1995); errors are exaggerated at a high capillary hematocrit. Shape distortion also introduces additional error in morphometric estimates of DMCO caused by a biased sampling distribution of random linear intercepts; errors are exaggerated at a low capillary hematocrit.     

Dynamics and control of ZCZVT boost converters

In this paper, the small-signal mathematical model of a zero-current-zero-voltage-transition (ZCZVT) soft-switching boost power converter is proposed. It shows that the ZCZVT boost converter exhibits better dynamic behavior than the conventional pulsewidth modulated boost converter. The input-to-output voltage conversion ratio of the ZCZVT soft-switching converters lies in a range which is related to the load. Based on the derived model, a classical controller and a modified integral variable structure controller are designed to achieve output voltage regulation and line voltage disturbance rejection. The experimental results regarding converters performances for two controllers are compared by experimental results.     

Piecewise empirical model (PEM) of resistive memory for pulsed analog and neuromorphic applications

With the end of Dennard scaling and the ever-increasing need for more efficient, faster computation, resistive switching devices (ReRAM), often referred to as memristors, are a promising candidate for next generation computer hardware. These devices show particular promise for use in an analog neuromorphic computing accelerator as they can be tuned to multiple states and be updated like the weights in neuromorphic algorithms. Modeling a ReRAM-based neuromorphic computing accelerator requires a compact model capable of correctly simulating the small weight update behavior associated with neuromorphic training. These small updates have a nonlinear dependence on the initial state, which has a significant impact on neural network training. Consequently, we propose the piecewise empirical model (PEM), an empirically derived general purpose compact model that can accurately capture the nonlinearity of an arbitrary two-terminal device to match pulse measurements important for neuromorphic computing applications. By defining the state of the device to be proportional to its current, the model parameters can be extracted from a series of voltages pulses that mimic the behavior of a device in an analog neuromorphic computing accelerator. This allows for a general, accurate, and intuitive compact circuit model that is applicable to different resistance-switching device technologies. In this work, we explain the details of the model, implement the model in the circuit simulator Xyce, and give an example of its usage to model a specific \(\hbox {Ta}/\hbox {TaO}_{\mathrm{x}}\) device.     

Theoretical investigations of the <Emphasis Type="Italic">g</Emphasis> factors and the hyperfine structure constants of the Cr<Superscript>4+</Superscript> and Mn<Superscript>5+</Superscript> centrs in silicon

The g factors and the hyperfine structure constants of the Cr⁴⁺ and Mn⁵⁺ centers in silicon are theoretically investigated using the perturbation formulas of these parameters for 3d ² ions in tetrahedra. In the calculations, both the contributions from the crystal-field and charge transfer mechanisms are taken into account based on the cluster approach. The calculated results show agreement with the observed values. It is found that the relative importance of the charge transfer contributions to the g-shift Δg = g − g _s (where g _s = 2.0023 is the spin-only value) increases significantly with increasing the valence state of the impurity ion.     

Vertical p-i-n polysilicon diode with antifuse for stackable field-programmable ROM

A field-programmable, stackable memory cell using 0.15-/spl mu/m technology is demonstrated. Vertical polycrystalline silicon diodes are stacked on top of one another, with tungsten (with TiN adhesion film) interconnect wires. An SiO/sub 2/ antifuse film separates the top of each diode from the TiN-W films. The cell is programmed when sufficient biasing voltage is applied to break down the antifuse, connecting the diode to tungsten. The cell is unprogrammed when the antifuse is intact. Cell fabrication and performance are described.     

Interference rejection using the time-dependent constant modulusalgorithm (CMA) and the hybrid CMA/spectral correlation discriminator

Two new blind adaptive filtering algorithms for interference rejection using time-dependent filtering structures are presented. The time-dependent structure allows the adaptive filter to outperform the conventional adaptive filter implemented with a time-independent structure for filtering of cyclostationary communication signals. At the same time, the blind adaption algorithms allow the filters to operate without the use of an external training signal. The first algorithm applies the CMA to an unconstrained time-dependent filtering structure. The second algorithm applies the CMA to a spectral correlation discriminator, which is constrained to select signals with unique spectral correlation characteristics. Using computer simulations, it is shown that the blind time-dependent filtering algorithms can provide mean-square errors (MSEs) and bit error rates (BERs) that are significantly lower than the MSEs and BERs provided using conventional time-independent adaptive filters. It is also shown that these processors can outperform the nonblind training-sequence directed time-independent adaptive filter     

Decision learning about production control as machines break down in a flexible manufacturing system

During manufacturing, there are many situations that can affect production performance. Such situations include machine breakdowns, rush orders, order changes, and order delays. When such issues occur, one has to make decisions to try to maintain production efficiency.Human decisions tend to be too late and incomplete in such contingencies. Thus a system that can make better decisions in time to maintain production performance is needed. To achieve this objective, the intelligent decision system described in this paper integrates artificial intelligence, an optimization technique, and simulation to solve such problems. The decision-making logic of the intelligent decision system is described by event graphs. It imitates the manner of human thinking.Self-learning of the decision-making process is used to strengthen the decision quality. In this study, a method of rule induction is applied to build up the self-learning system. There are two subsystems included in this system. One is rule generation and the other is knowledge management. A case for machine breakdowns is presented and discussed. A series of tests designed to validate the self-learning system are presented. These demonstrate that a rule induction method is suitable for constructing the self-learning.