Statistical intensity of amplitude scintillations on Earth-space X-band link

Amplitude fluctuation measurements made on a 30° of elevation earth-space link at 11.6 GHz using the Orbital Test Satellite transmissions are described. Results on the intensity and cumulative distribution of the scintillations between 0.01 Hz and 14 Hz are presented and show that for percentages of time less than 20% the peak amplitude deviations from the mean exceed the values derived using a log-normal distribution and the average r.m.s. value. For greater time percentages the agreement with a log-normal distribution is good.     

Rate of evolution of the specific surface area of surface snow layers

The snowpack can impact atmospheric chemistry by exchanging adsorbed or dissolved gases with the atmosphere. Modeling this impact requires the knowledge of the specific surface area (SSA) of snow and its variations with time. We have therefore measured the evolution of the SSA of eight recent surface snow layers in the Arctic and the French Alps, using CH4 adsorption at liquid nitrogen temperature (77 K). The SSA of fresh snow layers was found to decrease with time, from initial values in the range 613-1540 cm2/g to values as low as 257 cm2/g after 6 days. This is explained by snow metamorphism, which causes modifications in crystal shapes, here essentially crystal rounding and the disappearance of microstructures. A parametrization of the rate of SSA decrease is proposed. We fit the SSA decrease to an exponential law and find that the time constant alpha(exp) (day(-1)) depends on temperature according to alpha(exp) = 76.6 exp (-1708/7), with Tin kelvin. Our parametrization predicts that the SSA of a snow layer evolving at -40 degrees C will decrease by a factor of 2 after 14 days, while a similar decrease at -1 degrees C will only require 5 days. Wind was found to increase the rate of SSA decrease, but insufficient data did not allow a parametrization of this effect.     

Snow metamorphism as revealed by scanning electron microscopy

Current theories of snow metamorphism indicate that sublimating snow crystals have rounded shapes, while growing crystals have shapes that depend on growth rates. At slow growth rates, crystals are rounded. At moderate rates, they have flat faces with rounded edges. At fast growth rates, crystals have flat faces with sharp edges, and they have hollow faces at very fast growth rates. The main growth/sublimation mechanism is thought to be by the homogeneous nucleation of new layers at or near crystal edges. It was also suggested that the equilibrium shape of snow crystals would be temperature dependent: rounded above -10.5 degrees C, and faceted below. To test these paradigms, we have performed SEM investigations of snow samples having undergone metamorphism under natural conditions, and of snow samples subjected to isothermal metamorphism at -4 degrees and -15 degrees C in the laboratory. In general, current theories predicting crystal shapes as a function of growth rates, and of whether crystals are growing or sublimating, are verified. However, the transition in equilibrium shapes from rounded to faceted at -10.5 degrees C is not observed in our isothermal experiments that reveal a predominance of rounded shapes after more than a month of metamorphism at -4 and -15 degrees C. Some small crystals with flat faces that also have sharp angles at -15 degrees C, are observed in our isothermal experiments. These faces are newly formed, and contradict current theory. Several hypotheses are proposed to explain their occurrence. One is that they are due to sublimation at emerging dislocations.     

A framework for efficient regression tests on database applications

Regression testing is an important software maintenance activity to ensure the integrity of a software after modification. However, most methods and tools developed for software testing today do not work well for database applications; these tools only work well if applications are stateless or tests can be designed in such a way that they do not alter the state. To execute tests for database applications efficiently, the challenge is to control the state of the database during testing and to order the test runs such that expensive database reset operations that bring the database into the right state need to be executed as seldom as possible. This work devises a regression testing framework for database applications so that test runs can be executed in parallel. The goal is to achieve linear speed-up and/or exploit the available resources as well as possible. This problem is challenging because parallel testing needs to consider both load balancing and controlling the state of the database. Experimental results show that test run execution can achieve linear speed-up by using the proposed framework.     

A Petri-Net Approach to Modular Supervision With Conflict Resolution for Semiconductor Manufacturing Systems

In a semiconductor manufacturing system, particular human operations may violate desired requirements and lead to destructive failure. For such human-in-the-loop systems, this paper proposes a supervisory framework which guarantees that manual operations meet required specifications so as to prevent human errors in operation using Petri nets. Moreover, a modular technique with an intersection mechanism is proposed in order to cope with the state-space explosion problem of large-scale systems. A rapid thermal process in semiconductor manufacturing systems is provided to show the practicability of the proposed approach. Note to Practitioners - This work was motivated by the requirement of remote monitoring and supervision of semiconductor manufacturing systems. In such human-in-the-loop and large-scale systems, certain human operations may violate desired safety requirements and result in catastrophic failure. Moreover, the overall complexity of most existing Petri net modeling and analysis approaches significantly increases with the size of the considered systems. This paper suggests a modular supervisory scheme for modeling and synthesis of supervisory agents using Petri nets. The proposed method contributes a promising tool for preventing abnormal operations from being carried out to semiconductor manufacturing systems which, if appropriately modified, may be also applied to other types of discrete event systems.     

Chemical-Enhanced Washing for Remediation of Soils Contaminated with Marine Diesel Fuel in the Presence/Absence of Pb

The effects of the operating conditions, the initial concentrations of marine diesel fuel (MDF) and the coexisting Pb in the soil, and the ethylene diamine tetra acetic salt (EDTA) in solution on MDF removal by sodium dodecyl sulfate (SDS) washing were extensively investigated with the aim of optimizing the process parameters and determining the MDF removal efficiency by SDS under different contamination conditions. The experimental results from batch tests indicated that the majority of MDF was removed by SDS in the first 2?h, and its optimal pH was nearly neutral. Increasing the SDS concentration linearly increased the MDF removal efficiency. At a given SDS concentration, the removal efficiency was dependent on the existing forms of MDF in soils, and the free phase of MDF was found to be more easily removed than the adsorbed phase. MDF removal by SDS was significantly reduced by the coexisting Pb in soils, which likely forms a complexation with SDS and thereby enhances the partitioning of MDF in the soil by the re-adsorption of released MDF onto the hydrophobic tails of the adsorbed SDS. EDTA alone, or with SDS, could remove MDF, but the remaining MDF in the contaminated soil after EDTA washing became more difficult to be removed by SDS. Therefore, the EDTA washing followed by SDS washing is not recommended for MDF removal.     

A Wide Tuning Range Gm –C Continuous-Time Analog Filter

A CMOS operational transconductance amplifier (OTA) for low-power and wide tuning range filter application is proposed in this paper. The OTA can work from the weak inversion region to the strong inversion region to maximize the transconductance tuning range. The transconductance can be tuned by changing its bias current. A fifth-order Elliptic low-pass filter implemented with the OTAs was integrated by TSMC 0.18-mum CMOS process. The filter can operate with the cutoff frequency of 250 Hz to 1 MHz. The wide tuning range filter would be suitable for multi-mode applications, especially under the consideration of saving chip areas. The third-order inter-modulation (IM3) of -40 dB was measured over the tuning range with two tone input signals. The power consumption is 0.8 mW at 1-MHz cutoff frequency and 1.8-V supply voltage with the active area less than 0.3 mm²     

Work Function Tunability of Refractory Metal Nitrides by Lanthanum or Aluminum Doping for Advanced CMOS Devices

A lanthanum (La)-doped HfN is investigated as an n-type metal gate electrode on SiO₂ with tunable work function. The variation of La concentration in (HfinfinLa_1-x)N_y modulates the gate work function from 4.6 to 3.9 eV and remains stable after high-temperature annealing (900degC to 1000degC), which makes it suitable for n-channel MOSFET application. An ultrathin high-fc dielectric layer was formed at the metal/SiO₂ interface due to the (HfinfinLa_1-x)N_y and SiO₂ interaction during annealing. This causes a slight reduction in the effective oxide thickness and improves the tunneling current of the gate dielectric by two to three orders. We also report the tunability of TaN with Al doping, which is suitable for a p-type metal gate work function. Based on our results, several dual-gate integration processes by incorporating lanthanum or aluminum into a refractory metal nitride for CMOS technology are proposed.     

Artificial convolution neural network techniques and applications for lung nodule detection

We have developed a double-matching method and an artificial visual neural network technique for lung nodule detection. This neural network technique is generally applicable to the recognition of medical image pattern in gray scale imaging. The structure of the artificial neural net is a simplified network structure of human vision. The fundamental operation of the artificial neural network is local two-dimensional convolution rather than full connection with weighted multiplication. Weighting coefficients of the convolution kernels are formed by the neural network through backpropagated training. In addition, we modeled radiologists' reading procedures in order to instruct the artificial neural network to recognize the image patterns predefined and those of interest to experts in radiology. We have tested this method for lung nodule detection. The performance studies have shown the potential use of this technique in a clinical setting. This program first performed an initial nodule search with high sensitivity in detecting round objects using a sphere template double-matching technique. The artificial convolution neural network acted as a final classifier to determine whether the suspected image block contains a lung nodule. The total processing time for the automatic detection of lung nodules using both prescan and convolution neural network evaluation was about 15 seconds in a DEC Alpha workstation.     

Moving cast shadow detection by exploiting multiple cues

Background subtraction or temporal differencing is commonly applied on an image sequence for foreground/background segmentation. However, cast shadows of moving foreground objects in a scene often result in detection errors for many vision-based applications. To address this problem, the authors propose an algorithm exploiting the information of colour, shading, texture, neighbourhood and temporal consistency to detect shadows efficiently and adaptively. The experimental results show that the proposed method can detect the penumbra as well as the umbra in different kinds of scenarios under various illumination conditions.