The subwavelength metal grating is designed and simulated by using the finite difference time domain(FDTD) *** transmission characteristics of subwavelength metal grating structure are studied based on the Lossy Drude...
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The subwavelength metal grating is designed and simulated by using the finite difference time domain(FDTD) *** transmission characteristics of subwavelength metal grating structure are studied based on the Lossy Drude dispersive medium *** influence of grating geometry parameters on the transmission characteristics are analyzed,especially for the zero transmission point location of the *** difference between the designed and conventional gratings is that the former introduces the resonant *** show that,compared with the single-layer grating,the triple-layer grating has a better filtering effect,and the transmission spectrum peak is in excess of 82%.It concludes that the transmission peak drift is analyzed by using the waveguide Fabry-Perot(F-P) cavity resonance effect,and the zero transmission point location of the spectrum is independent of the metal layer *** influence rules provide an available reference for the design of the band-pass filters.
Recently, support vector ranking has been adopted to address the challenging person re-identification problem. However, the ranking model based on ordinary global features cannot represent the significant variation of...
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ISBN:
(纸本)9781479975921
Recently, support vector ranking has been adopted to address the challenging person re-identification problem. However, the ranking model based on ordinary global features cannot represent the significant variation of pose and viewpoint across camera views. Thus, a novel ranking method which fuses the dense invariant features is proposed in this paper to model the variation of images across camera views. By maximizing the margin and minimizing the error score for the fused features, an optimal space for ranking has been learned. Due to the invariance of the dense invariant features and the fusion of the bidirectional features, the proposed method significantly outperforms the original support vector ranking algorithm and is competitive with state-of-the-art techniques on two challenging datasets, showing its potential for real-world person re-identification.
Herein,a new identity recognition method of multi-haptic pressure feature based on sparse representation was *** to the common dynamic features,the regional feature and the ratio of length *** of external bounding rec...
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Herein,a new identity recognition method of multi-haptic pressure feature based on sparse representation was *** to the common dynamic features,the regional feature and the ratio of length *** of external bounding rectangle(extracted by using the least area method) were *** subset of dynamic feature was optimized by correlation criterion,the sparse representation of haptic pressure was obtained according to the sparse basis(i.e.,wavelet basis),and the sparse feature vector was calculated by the Topelitz measurement *** that,the haptic pressure feature set was created by combining dynamic feature subset and sparse feature subset ***,Support Vector Machine(SVM) classifier identified more than two objects following the one to many rule and output the identification result according to the rule of majority voting,and the stability of features is studied by calculating the intraclass correlation coefficient(ICC) and coefficient of variation(C.V).Overall,the improved acuracy of identity recognition demonstrating the effectiveness and stability of the multihaptic pressure feature.
A method to induce multi-colors on an iron surface by using Circularly Polarized Femtosecond Laser (CPFL) processing was proposed. The subwavelength ripples were fabricated by CPFL scanning over the iron surface where...
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Despite numerous efforts on blur measurement of partially blurred images, there still lacks an effective blur measure that is both pixel-wise and locally sharp consistent. The paper proposes a novel method with two co...
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With the increasingly rapid developments in e-commerce, schemes for digital gift certificates have become prevalent electronic payment systems due to their practicality and simplicity. In 2002, Chan and Chang introduc...
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We proposed two whispered speech enhancement methods based on asymmetric cost functions in this paper to deal with the amplification and attenuation distortions of whispered speech *** modified Itakura-Saito(MIS)dis...
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We proposed two whispered speech enhancement methods based on asymmetric cost functions in this paper to deal with the amplification and attenuation distortions of whispered speech *** modified Itakura-Saito(MIS)distance function provides more penalties to speech amplification distortion,whereas the Kullback-Leibler(KL)divergence function gives more penalties to speech attenuation *** experimental results show that the MIS function based method achieves significant improvement of intelligibility in contrast to the conventional speech enhancement algorithms when the signal-to-noise ratio(SNR)falls below-6 dB,whereas the KL function based one achieves the similar result as the minimum mean square error(MMSE)speech enhancement *** results show that the effects of the amplification and attenuation distortions on the intelligibility of the enhanced whisper are different,where larger attenuation distortion may result in better intelligibility of speech with low ***,the attenuation distortion has small effects on intelligibility of speech with high SNR.
Database security prevents the disclosure of confidential data within a database to unauthorized users, and has become an urgent challenge for a tremendous number of database applications. Data encryption is a widely-...
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Noisy whisper contaminated by different types of noise are enhanced by compressive sensing method. The intelligibility performance of the enhanced whisper is measured using the short time objective index (STOI). To as...
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Mining from ambiguous data is very important in data mining. This paper discusses one of the tasks for mining from ambiguous data known as multi-instance problem. In multi-instance problem, each pattern is a labeled b...
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Mining from ambiguous data is very important in data mining. This paper discusses one of the tasks for mining from ambiguous data known as multi-instance problem. In multi-instance problem, each pattern is a labeled bag that consists of a number of unlabeled instances. A bag is negative if all instances in it are negative. A bag is positive if it has at least one positive instance. Because the instances in the positive bag are not labeled, each positive bag is an ambiguous. The mining aim is to classify unseen bags. The main idea of existing multi-instance algorithms is to find true positive instances in positive bags and convert the multi-instance problem to the supervised problem, and get the labels of test bags according to predict the labels of unknown instances. In this paper, we aim at mining the multi-instance data from another point of view, i.e., excluding the false positive instances in positive bags and predicting the label of an entire unknown bag. We propose an algorithm called Multi-Instance Covering kNN (MICkNN) for mining from multi-instance data. Briefly, constructive covering algorithm is utilized to restructure the structure of the original multi-instance data at first. Then, the kNN algorithm is applied to discriminate the false positive instances. In the test stage, we label the tested bag directly according to the similarity between the unseen bag and sphere neighbors obtained from last two steps. Experimental results demonstrate the proposed algorithm is competitive with most of the state-of-the-art multi-instance methods both in classification accuracy and running time.
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