The paper describes an ultra-wideband (UWB) antenna developed for impulse ground penetrating radar (GPR) applications. The antenna is a modified bow-tie antenna designed for short-range GPR applications such as road i...
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The paper describes an ultra-wideband (UWB) antenna developed for impulse ground penetrating radar (GPR) applications. The antenna is a modified bow-tie antenna designed for short-range GPR applications such as road inspection and non-destructive testing of concrete. For these applications, the transmitted pulses should have minimal ringing to avoid masking of targets. This can be achieved by the application of resistive loading at the expense of radiation efficiency. We have attempted to improve a conventional bow-tie antenna usually used in GPR by reducing the antenna size and minimizing ringing, while at the same time improving the antenna radiation efficiency.
An 8/spl times/8 pixel array has been designed and fabricated for broadband THz detection. Each pixel consists of a 500/spl times/500 /spl mu/m thin film absorber on a SiN membrane with thermopile temperature readout....
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An 8/spl times/8 pixel array has been designed and fabricated for broadband THz detection. Each pixel consists of a 500/spl times/500 /spl mu/m thin film absorber on a SiN membrane with thermopile temperature readout. The expected performances - NEP = 4.10/sup -9/ W/Hz/sup 1/2/ and 10 ms response time - were compared with test results.
Although the Korringa-Kohn-Rostoker coherent-potential approximation (KKR-CPA) is used widely to configurationally average and get electronic structures and energies of disordered alloys, a single-site CPA misses loca...
Although the Korringa-Kohn-Rostoker coherent-potential approximation (KKR-CPA) is used widely to configurationally average and get electronic structures and energies of disordered alloys, a single-site CPA misses local environment effects, including short-range order (SRO). A proposed nonlocal CPA (NLCPA) recovers translational invariance of the effective medium via k-space coarse graining from the dynamical cluster approximation (DCA), where corrections are systematic as cluster size increases. We implement a first-principles KKR-NLCPA/DCA and show the effects of environment, including SRO, on the electronic structures of fcc CuAu and bcc NiAl.
Because of emergence of Semantic Web, It make possible for machines to understand the meaning of resources on the Web. The widespread availability of machine understandable information will impact on Information retri...
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FLAVERS is a finite-state verification approach that allows an analyst to incrementally add constraints to improve the precision of the model of the system being analyzed. Except for trivial systems, however, it is im...
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ISBN:
(纸本)9780769521633
FLAVERS is a finite-state verification approach that allows an analyst to incrementally add constraints to improve the precision of the model of the system being analyzed. Except for trivial systems, however, it is impractical to compute which constraints should be selected to produce precise results for the least cost. Thus, constraint selection has been a manual task, guided by the intuition of the analyst. In this paper, we investigate several heuristics for selecting task automaton constraints, a kind of constraint that tends to reduce infeasible task interactions. We describe an experiment showing that one of these heuristics is extremely effective at improving the precision of the analysis results without significantly degrading performance.
Ontology plays an important role on the Semantic Web. In this paper, we propose a method, AOIWD, of acquiring ontology information from Web documents. The AOIWD method employs data mining techniques combined with infe...
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A high-assurance system is largely dependent on the quality of its underlying software. software quality models can provide timely estimations of software quality, allowing the detection and correction of faults prior...
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A high-assurance system is largely dependent on the quality of its underlying software. software quality models can provide timely estimations of software quality, allowing the detection and correction of faults prior to operations. A software metrics-based quality prediction model may depict overfitting, which occurs when a prediction model has good accuracy on the training data but relatively poor accuracy on the test data. We present an approach to address the overfitting problem in the context of software quality classification models based on genetic programming (GP). The problem has not been addressed in depth for GP-based models. The presence of overfitting in a software quality classification model affects its practical usefulness, because management is interested in good performance of the model when applied to unseen software modules, i.e., generalization performance. In the process of building GP-based software quality classification models for a high-assurance telecommunications system, we observed that the GP models were prone to overfitting. We utilize a random sampling technique to reduce overfitting in our GP models. The approach has been found by many researchers as an effective method for reducing the time of a GP run. However, in our study we utilize random to reduce overfitting with the aim of improving the generalization capability of our GP models.
With the explosive growth of the World Wide Web, millions of documents are published and accessed on-line. Statistics show that a significant part of Web text information is encoded in Web images. Since Web images hav...
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Regression testing is an important part of software quality assurance. We work to extend regression testing to include regression benchmarking, which applies benchmarking to detect regressions in performance. Given th...
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We report a quantum interference and imaging experiment which shows quantitatively that entangled two-photon violate the EPR inequality. This measurement provides a direct way to distinguish quantum entanglement from ...
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