Although NMR has the ability to investigate biological systems non-destructively, it suffers from low sensitivity compared to other analytical techniques. Therefore, optimizing radio frequency (RF) coils to improve se...
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Although NMR has the ability to investigate biological systems non-destructively, it suffers from low sensitivity compared to other analytical techniques. Therefore, optimizing radio frequency (RF) coils to improve sensitivity is an area of significant focus in NMR Sensitivity can be improved for mass- and volume-limited samples by reducing, coil diameter, and adding sensing layers to the coil. This paper demonstrates that around skin depth, coil sensitivity can be increased with additional sensing layers, by using multilayered scroll coil geometries. While solenoids have demonstrated increased sensitivity over conventional coils (Helmholtz, Saddle, and Birdcage), their dimensions are limited by wire diameter, and difficulty in fabrication at reduced dimensions. The novel scroll geometry avails of microfabrication techniques to provide flexibility in manufacturing coils at very reduced dimensions.
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.
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.
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...
Grasslands are the largest of the Earth's four major vegetation types and are among the most agriculturally productive lands. Grassland management practices alter biophysical factors, such as plant species composi...
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We have fabricated erbium-doped porous silicon (PSi) devices and demonstrated stable room-temperature electroluminescence (EL) at 1.54 μm under both forward and reverse bias conditions. Erbium was infiltrated into th...
We have fabricated erbium-doped porous silicon (PSi) devices and demonstrated stable room-temperature electroluminescence (EL) at 1.54 μm under both forward and reverse bias conditions. Erbium was infiltrated into the nanostructured matrix of PSi (≤ 10 19 cm —3 ) by cathodic electrochemical migration of the ions followed by high temperature annealing (950–1100 °C). The devices exhibit an exponential EL dependence in both bias conditions as a function of driving current and voltage. External quantum efficiencies of 0.01% have been obtained. The EL intensity in reverse bias and the transport properties of the devices show large temperature dependences. From the differences in temperature, electrical, and EL characteristics in forward and reverse biases, we believe that different excitation mechanisms are responsible for the EL.
In this study we present the fabrication, the optical characterization and the electrical operation of a high quality, nanometer-sized, silicon-based light modulator made of nematic liquid crystals (LC) encapsulated i...
In this study we present the fabrication, the optical characterization and the electrical operation of a high quality, nanometer-sized, silicon-based light modulator made of nematic liquid crystals (LC) encapsulated into porous silicon (PSi) multilayer matrix. We first demonstrate the infiltration procedure and characterize the cell's reflectance and photoluminescence (PL) before and after infiltration. Then we examine the LC molecular alignment, and show that their long axis is positioned along the pore walls. Therefore, the electrical control of an interference filter is achieved by applying a field horizontal to the pore's directionality. We also demonstrate that by applying up to 10 V the molecules reorient, the effective refractive index is modulated by as much as Δ n = 0.15, and the reflectance spectra is red shifted.
A variety of alternate training strategies for implementing the dual heuristic programming (DHP) method of approximate dynamic programming in the neurocontrol context are explored. The DHP method of controller trainin...
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A variety of alternate training strategies for implementing the dual heuristic programming (DHP) method of approximate dynamic programming in the neurocontrol context are explored. The DHP method of controller training has been successfully demonstrated by a number of authors on a variety of control problems in recent years, but no unified view of the implementation details of the method has yet emerged. A number of options are described for sequencing the training of the controller and critic networks in DHP implementations. Results are given about their relative efficiency and the quality of the resulting controllers for two benchmark control problems.
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