In this paper, one-element and six-element antenna arrays, conformal to electrically large cylinders of finite length, are simulated. When the length of conducting cylinder changes, the proposed antenna maintains stab...
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In this paper, one-element and six-element antenna arrays, conformal to electrically large cylinders of finite length, are simulated. When the length of conducting cylinder changes, the proposed antenna maintains stable radiation patterns. Moreover, the proposed antenna array provides end-fire radiation patterns with a high gain and high front-to-back ratio.
Using the spherical vector wave functions, an analytical solution to the scattering of a chiral-coated dielectric sphere with plane wave is presented. Applying the boundary conditions on each interface among the diele...
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ISBN:
(纸本)9781467317993
Using the spherical vector wave functions, an analytical solution to the scattering of a chiral-coated dielectric sphere with plane wave is presented. Applying the boundary conditions on each interface among the dielectric sphere, chiral medium and free space, the expansion coefficients for the internal and the scattered fields are determined. The influences of the chiral-coated spherical shell and the chiral parameter on radar cross section (RCS) are discussed numerically.
This paper presents a novel stacked patch antenna design with stable radiation and high gain characteristics. The presented antenna provides the bandwidth from 2.4 GHz to 2.483GHz. This bandwidth is realized by stacki...
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This paper presents a novel stacked patch antenna design with stable radiation and high gain characteristics. The presented antenna provides the bandwidth from 2.4 GHz to 2.483GHz. This bandwidth is realized by stacking the copper sheet. The bandwidth of the stacked antenna is narrow, but results in stable radiation patterns and high gain in the operating band, and the main gain can achieve 8.9dBi.
Power analysis attack is a powerful cryptanalytic technique aiming to extract the secret data from a cryptographic device by collecting and analyzing power consumption traces. In this paper, we present a practical pow...
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A novel bandstop Frequency Selective Surface (FSS) is presented, in which a unit cell consists of two metallic of spiral strips of different lengths and a dielectric substrate in the middle. Both simulation and measur...
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A novel bandstop Frequency Selective Surface (FSS) is presented, in which a unit cell consists of two metallic of spiral strips of different lengths and a dielectric substrate in the middle. Both simulation and measurement are presented to show that the FSS can realize size reduction and ultra-wideband response. By changing the lengths of some outer strips the miniaturization amount and bandwidth can be adjusted. Moreover, all these responses are stable with respect to different incident angles, which make the proposed structure more valuable for practical application.
Surface deformation of an object can be measured by digital shearography through non-contact measurement with a simple device. It is always difficult to remove the noise of the speckle interference. Consequently extra...
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Surface deformation of an object can be measured by digital shearography through non-contact measurement with a simple device. It is always difficult to remove the noise of the speckle interference. Consequently extracting phase from a single fringe pattern without carrier-frequency is complex. Based on gray extremum, an improved method for phase extraction is proposed by using the improved image binarization algorithm in this paper. Through comparing the average value of eight neighbourhood points around each pixel with threshold value, the quality of binary image can be improved, and then the skeleton line can be extracted. Finally a smooth extremum image can be obtained. Experimental results show that the proposed method is simple, convenient and reliable.
As the development of network applications, old technologies like identifying traffic based on "well-known" TCP or UDP port numbers or based on the payload inspection were already inaccurate or wasteful of t...
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As the development of network applications, old technologies like identifying traffic based on "well-known" TCP or UDP port numbers or based on the payload inspection were already inaccurate or wasteful of time and storage space. To get over the challenges that referred above, statistical technology and machine learning (ML) are introduced to traffic classification. The paper focuses on emerging hybrid research - blend of network and data mining techniques. We summarize ML approaches briefly, review significant works that cover the period from 2004 TO 2010 and categorize them by ML methods and major contribution. Considered data stream characteristics of network traffic flow, we proposed a Traffic Classification Scheme based on Data Stream Clustering Techniques, which has the capability to achieve real-time traffic classification, application clusters evolution analysis, and outline analysis for massive, high-speed, dynamic traffic flows. As an expansion, further research in this field is discussed at the end.
A typical optical-mechanical integration instrument system integrity development based on multi-attribute comprehensive evaluation method is proposed in this *** multi-attribute comprehensive evaluation process is sti...
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A typical optical-mechanical integration instrument system integrity development based on multi-attribute comprehensive evaluation method is proposed in this *** multi-attribute comprehensive evaluation process is stipulated and the new evaluation system for optical-mechanical-electrical instrument is *** evaluation system includes numerous secondary indices based on seven primary evaluation *** realizes the comprehensive evaluation for the new system quality by index model which is made according to the multiple attribute features of the integrity development *** method can not only inspect the product quality,but also can rapidly and effectively perform a comprehensive evaluation for the instrument integrity development capacity and effect.
Energy consumption and time delay, i.e., the amount of energy and time consumed by all the nodes for transmitting data, are two critical factors for estimating the efficiency of algorithms in wireless sensor networks....
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Energy consumption and time delay, i.e., the amount of energy and time consumed by all the nodes for transmitting data, are two critical factors for estimating the efficiency of algorithms in wireless sensor networks. We focus on the application of time-driven data gathering, where all nodes act as sources at the same time to transmit data to an immobile sink node. We propose an efficient algorithm called DGNC for this scenario using inter-session network coding and opportunistic routing. Besides, we theoretically quantify the energy and time delay saving that our algorithm could offer in a rectangular grid network, and demonstrate the fact by simulation results in random networks, which DGNC could offer significant benefits in terms of both energy consumption and time delay in timedriven data gathering.
The importance of incremental learning in changing environments has been acknowledged in recent years. In this paper we present an ensemble learning method for supervised learning with drifting concepts. The method em...
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The importance of incremental learning in changing environments has been acknowledged in recent years. In this paper we present an ensemble learning method for supervised learning with drifting concepts. The method employs hypothesis test as mechanism for detecting concept drift and learns a base classifier for each new training data chunk. Former classifiers deemed as usable by the hypothesis test mechanism and the new classifiers are integrated to form the final classifiers ensemble for prediction. The main focus of the work is to identify the usability of base classifiers that representing the same or similar concept with the current one, make full use of the older valid information together with the newer examples to improve classification accuracy, and avoid the interference of classifiers representing conflictive concepts with the current one. Experiments with simulated concept drift scenarios compared the proposed method with other approaches. The results showed that the method could consistently recognize different types of drift, adapt quickly to these changes to maintain its performance level, and utilize the former knowledge to improve its performance for recurring context.
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