The AlGaN/GaN heterostructure is important for both electronic and optoelectronic devices. In addition to having a large band gap, the heterostructure has a strong piezoelectric effect and a large spontaneous polariza...
The AlGaN/GaN heterostructure is important for both electronic and optoelectronic devices. In addition to having a large band gap, the heterostructure has a strong piezoelectric effect and a large spontaneous polarization. This allows one to incorporate a large electric field (>106 V/cm) and high sheet charge (>1013 cm−2) without doping. Theoretical studies are done to examine how polarization effects controls the sheet charge density. The studies also focus on how interface roughness, aluminum mole fraction in the barrier, impurity scattering, sheet charge density, and phonon scattering influence mobility. Results are compared with experimental studies on samples grown by both molecular beam epitaxy (MBE) and metalorganic chemical vapor deposition (MOCVD). We find that interface roughness is a dominant source of scattering in the samples reported. Due to the variation in growth techniques we find that the MBE samples have a smoother interface quality compared to the MOCVD samples. By carefully fitting the experimental data we present results on interface roughness parameters for MBE and MOCVD samples.
Summary form only given. We report the room temperature operation of a novel unipolar self-organized quantum dot FIR laser (13.3 /spl mu/m) based on transitions between discrete bound electron states in self-organized...
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Summary form only given. We report the room temperature operation of a novel unipolar self-organized quantum dot FIR laser (13.3 /spl mu/m) based on transitions between discrete bound electron states in self-organized dots. A typical photoluminescence spectrum of InGaAs-GaAs self-organized quantum dots is shown.
We classify an input space according to the outputs of a real-valued function. The function is not given, but rather examples of the function. We contribute a consistent classifier that avoids the unnecessary complexi...
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ISBN:
(纸本)0262122413
We classify an input space according to the outputs of a real-valued function. The function is not given, but rather examples of the function. We contribute a consistent classifier that avoids the unnecessary complexity of estimating the function.
Photoconductance spectroscopy was used to probe the effects of quantum confinement in nanocrystalline (nc)-Si/amorphous (a)-SiO2 superlattices (SLs). A Metal-Oxide-Semiconductor (MOS)-like structure with the nc-Si SL ...
Photoconductance spectroscopy was used to probe the effects of quantum confinement in nanocrystalline (nc)-Si/amorphous (a)-SiO2 superlattices (SLs). A Metal-Oxide-Semiconductor (MOS)-like structure with the nc-Si SL incorporated in the oxide was fabricated to study charging/discharging processes in Si nanocrystals. The fine structure observed in photoconductance spectra at low temperatures was interpreted in terms of singularities in the carrier density of states, possibly due to energy quantization. In addition, a low-resistance sample exhibited photocurrent oscillations with a frequency of several kHz, which could be a manifestation of sequential resonant carrier tunneling in the nc-Si/a-SiO2 SL.
Under deregulation, the formation of electricity markets is a topic of great interest in the power industry and in financial institutions worldwide. Using derivative financial instruments (including options) becomes i...
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Under deregulation, the formation of electricity markets is a topic of great interest in the power industry and in financial institutions worldwide. Using derivative financial instruments (including options) becomes important for hedging against uncertainty and managing risk-limiting exposure to adverse market conditions. Black and Scholes' equation is often used to value options, but its validity is questionable due to assumptions that may not hold for electricity, most notably the assumption of log-normally distributed prices for the underlying commodity. In this research, a put options market for electricity is modeled. Adaptive agents trade in this market to maximize profit. They are not forced to use an explicit economic or financial model (e.g., Black-Scholes) in their valuation. A genetic algorithm (GA) is used to find alternate valuations that are used to generate buy and sell signals. The results show that it is possible to evolve profitable valuations for use with buying and selling options in this simple model. Reasons for and implications of this finding (e.g., that Black-Scholes may not be a good method for pricing electricity derivatives) are discussed.
Subband adaptive filtering has been recently studied by a large number of researchers. The main alternatives are structures with critical sampling and noncritical sampling, and structures that use local errors and glo...
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Subband adaptive filtering has been recently studied by a large number of researchers. The main alternatives are structures with critical sampling and noncritical sampling, and structures that use local errors and global error in the adaptive algorithm. In this paper a theoretical convergence analysis for the case of an oversampled subband adaptive altering structure with local errors is presented. The convergence rate and misadjustment of the algorithm can be estimated from the results of this analysis. computer simulations are presented to illustrate the convergence behavior of the subband adaptive algorithm and to verify the theoretical results.
This paper introduces reconfigurable computing and MorphoSys, which is a reconfigurable system. It also explains the architecture of its reconfigurable hardware part. Then, it presents two spreadsheet models for the o...
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ISBN:
(纸本)0780365429
This paper introduces reconfigurable computing and MorphoSys, which is a reconfigurable system. It also explains the architecture of its reconfigurable hardware part. Then, it presents two spreadsheet models for the operation of this reconfigurable device. The first spreadsheet performs the modelling through formulas, while the second does it numerically. These spreadsheet models serve as design and debugging tools.
In a psychophysical experiment, a wideband, 4-ms noise is compared with spectrally smoothed versions of the noise. To isolate on the magnitude spectrum, the phase spectrum is controlled by assigning the same random ph...
Summary form only given. Recent experimental measurements from InP and InAlAs avalanche photodiodes (APDs) with thin multiplication regions, collected by J.C. Campbell and collaborators at the University of Texas at A...
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Summary form only given. Recent experimental measurements from InP and InAlAs avalanche photodiodes (APDs) with thin multiplication regions, collected by J.C. Campbell and collaborators at the University of Texas at Austin, show that, for a fixed gain the excess noise factor is significantly lower than that predicted by the conventional McIntyre theory. The observed dependence of the noise on the multiplication-region width cannot be explained using the conventional theory in which the excess noise factor is a function only of the mean gain and the ionization coefficient ratio. In the dead-space-multiplication theory (DSMT), a carrier must travel a certain distance, called the dead space, before gaining sufficient energy for impact ionisation to occur. Because this dead space regularizes the ionisation locations, the randomness of the avalanching mechanism is reduced. For thin multiplication-region APDs, this effect is proportionally higher and thus the noise is lower. We applied the DSMT to the experimental results for GaAs and AlGaAs and more recently for InP and InAlAs APDs. We were able to fit the ionization coefficients associated with devices of various thicknesses, as a function of the electric field, within the confines of a single exponential model.
Presents a neural-based approach to classifying and estimating the statistical parameters of speckle noise found in biomedical ultrasound images. Speckle noise, a very complex phenomenon, has been modeled in a variety...
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Presents a neural-based approach to classifying and estimating the statistical parameters of speckle noise found in biomedical ultrasound images. Speckle noise, a very complex phenomenon, has been modeled in a variety of different ways: and there is currently no clear consensus as to its precise statistical characteristics. In this study, different neural network architectures are used to classify ultrasound images contaminated with three types of noise, based upon three one-parameter statistical distributions. At the same time: the parameter is estimated. It is expected that accurate characterization of ultrasound speckle noise will benefit existing post-processing methods, and may lead to new refinements in these techniques.
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