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.
A variable-speed power conversion system is considered where a permanent magnet generator (PMG) driven by an IC engine supplies power to an electronic inverter. The AC voltage from the PMG is typically diode-rectified...
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A variable-speed power conversion system is considered where a permanent magnet generator (PMG) driven by an IC engine supplies power to an electronic inverter. The AC voltage from the PMG is typically diode-rectified into a DC link, which is utilized by the inverter to produce constant-frequency, constant-voltage output. These "electronic gensets" can be smaller, lighter and have higher performance than their fixed-speed counterparts with synchronous alternators under field control. Such attributes are attractive for mobile and stand-by power applications. The added flexibility of a variable-speed genset system must be met with suitable techniques for directing the speed at which the engine should operate for a given electrical load. Constraints on torque, speed, and DC link voltage must additionally be met. This paper reviews conventional methods, and presents a new technique utilizing the operating power of the system as an input to a power-speed "map" for the system to follow. Experimental results are included.
The problem of robust filtering design for uncertain linear systems with guaranteed peak-to-peak performance is addressed in this paper. The uncertain parameters are assumed to belong to convex bounded domains (i.e. p...
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The problem of robust filtering design for uncertain linear systems with guaranteed peak-to-peak performance is addressed in this paper. The uncertain parameters are assumed to belong to convex bounded domains (i.e. polytope type uncertainty). The aim is to design a full-order stable linear filter that minimizes the worst-case peak value of the filtering error output signal with respect to all magnitude bounded noise inputs, in such way that the filtering error system remains robustly stable. The minimization provides an upper bound to the L ∞ induced gain ( ℓ ∞ for discrete-time systems) of the filtering error system. The conditions for the existence of such robust filter are provided in terms of linear matrix inequalities, allowing the use of standard convex optimization procedures to solve the problem. Both continuousand discrete-time systems are considered. The formulation presented is illustrated by examples.
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