This paper proposes a novel technology to detect the orientation of an image relying on its contour which is noised to varying degrees. For the image orientation detection, most methods regard to the landscape image and the image taken of a single object. In these cases, the contours of these images are supposed to be immune to the noise. This paper focuses on the the contour noised after image segmentation. A polar orientation descriptor Orientation Context is viewed as a feature to describe the coarse distribution of the contour points. This descriptor is verified to be independent of translation, isotropic scaling, and rotation transformation by theory and experiment. The relative orientation depends on the minimum distance Roulette Distance between the descriptor of a template image and that of a test image. The proposed method is capable of detecting the direction on the interval from 0 to 359 degrees which is wider than the former contour-based means (Distance Phase [1], from 0 to 179 degrees). What's more, the results of experiments show that not only the normal binary image (Noise-0, Accuracy-1: 84.8%) (defined later) achieves more accurate orientation but also the binary image with slight contour noise (Noise-1, Accuracy-1: 73.5%) could obtain more precise orientation compared to Distance Phase (Noise-0, Accuracy-1: 56.3%; Noise-1, Accuracy-1: 27.5%). Although the proposed method (O(op2)) takes more time to detect the orientation than Distance Phase (O(st)), it could be realized including the preprocessing in real time test with a frame rate of 30.

The cationic dye, E-N-alkyl-4-[2-(4-alkyloxynaphthyl) ethenyl] quinolinium bromide has a wide-bodied chromophore with alkyl groups at opposite ends, i. e. CmH2m+1-D-π-A+-CnH2n+1 X-, where D and A are electron-donor and electron-acceptor groups respectively, π is a conjugated bridge and X- is the bromide counterion. It forms non-centrosymmetric Langmuir-Blodgett (LB) structures in which the packing is dependent upon the combination and compatibility of the alkyl groups. Films of two isomeric analogues where m + n = 34, have a layer thickness of 4.3 nm when the groups are significantly different (m = 12 and n = 22) which decreases to 2.5 nm when they are similar (m = 18 and n = 16). The latter adopts an interdigitating layer arrangement at both the upper and lower interfaces and it is the first example of a Lego-type structure with non-centrosymmetric alignment of the optically nonlinear chromophores. This is evidenced by the second-harmonic intensity, which increases quadratically with the number of layers, i. e. as .

Recently, there have been a number of studies on wavelength division multiplexing (WDM) systems designed to increase transmission capacity and flexibility. This has in turn made it necessary to design the optical loss for optical fiber networks over a wide wavelength range. However, there is no general optical loss design method that allows us to employ for an optical fiber network over a wide wavelength range. In this paper, we propose a novel optical loss design and estimation method based on multiple regression analysis and statistical loss design technology for designing new optical fiber networks and for upgrading those that have already been installed. We show that the method can be used for highly accurate optical loss design and estimation from the O- to the L-band (1260 nm to 1625 nm), except for the E-band, by using easily obtainable optical loss values at wavelengths of 1310 and 1550 nm, which are usually used for communication. Moreover, we also show that the accuracy of our proposed method can be improved by selecting proper additional predictor wavelengths.

Pairing computations on elliptic curves with odd prime degrees are rarely studied as low efficiency. Recently, Clarisse, Duquesne and Sanders proposed two new curves with odd prime embedding degrees: BW13-P310 and BW19-P286, which are suitable for some special cryptographic schemes. In this paper, we propose efficient methods to compute the optimal ate pairing on this types of curves, instantiated by the BW13-P310 curve. We first extend the technique of lazy reduction into the finite field arithmetic. Then, we present a new method to execute Miller's algorithm. Compared with the standard Miller iteration formulas, the new ones provide a more efficient software implementation of pairing computations. At last, we also give a fast formula to perform the final exponentiation. Our implementation results indicate that it can be computed efficiently, while it is slower than that over the (BLS12-P446) curve at the same security level.

Ultra wideband (UWB) technologies are expected to be used in ultra-high-speed wireless personal area networks (WPAN) and wireless body area networks (WBAN). UWB human electromagnetic phantoms are useful for performance evaluation of antennas mounted in the vicinity of a human body and channel assessment when a human body blocks a propagation path. Publications on UWB phantoms, however, have been limited so far. This paper describes the development of liquid UWB phantom material (aqueous solution of sucrose) and UWB arm and torso phantoms. The UWB phantoms are not intended to evaluate a specific absorption rate (SAR) in a human body, because UWB devices are supposed to transmit at very low power and thus should pose no human hazard.

Photonic crystal fiber (PCF) is a promising candidate for future transmission media due to its unobtainable features in a conventional single-mode fiber. We discuss some important problems to realize a PCF for transmission purpose. We also present recent progress on the PCF as a transmission media.

In this Letter, a new face recognition approach based on curvelets and local ternary patterns (LTP) is proposed. First, we observe that the curvelet transform is a new anisotropic multi-resolution transform and can efficiently represent edge discontinuities in face images, and that the LTP operator is one of the best texture descriptors in terms of characterizing face image details. This motivated us to decompose the image using the curvelet transform, and extract the features in different frequency bands. As revealed by curvelet transform properties, the highest frequency band information represents the noisy information, so we directly drop it from feature selection. The lowest frequency band mainly contains coarse image information, and thus we deal with it more precisely to extract features as the face's details using LTP. The remaining frequency bands mainly represent edge information, and we normalize them for achieving explicit structure information. Then, all the extracted features are put together as the elementary feature set. With these features, we can reduce the features' dimension using PCA, and then use the sparse sensing technique for face recognition. Experiments on the Yale database, the extended Yale B database, and the CMU PIE database show the effectiveness of the proposed methods.

In this Letter, a new iris recognition approach based on local Gabor orientation feature is proposed. On one hand, the iris feature extraction method using the traditional Gabor filters can cause time-consuming and high-feature dimension. On the other hand, we can find that the changes of original iris texture in angle and radial directions are more obvious than the other directions by observing the iris images. These changes in the preprocessed iris images are mainly reflected in vertical and horizontal directions. Therefore, the local directional Gabor filters are constructed to extract the horizontal and vertical texture characteristics of iris. First, the iris images are preprocessed by iris and eyelash location, iris segmentation, normalization and zooming. After analyzing the variety of iris texture and 2D-Gabor filters, we construct the local directional Gabor filters to extract the local Gabor features of iris. Then, the Gabor & Fisher features are obtained by Linear Discriminant Analysis (LDA). Finally, the nearest neighbor method is used to recognize the iris. Experimental results show that the proposed method has better iris recognition performance with less feature dimension and calculation time.

The linguistic Multi-Criteria Group Decision-Making (MCGDM) problem involves various types of uncertainties. To deal with this problem, a new linguistic MCGDM method combining cloud model and evidence theory is thus proposed. Cloud model is firstly used to handle the fuzziness and randomness of the linguistic concept, by taking both the average level and fluctuation degree of the linguistic concept into consideration. Hence, a method is presented to transform linguistic variables into clouds, and then an asymmetrical weighted synthetic cloud is proposed to aggregate the clouds of decision makers on each criterion. Moreover, evidence theory is used to handle the imprecision and incompleteness of the group assessment, with the belief degree and the ignorance degree. Hence, the conversion from the cloud to the belief degree is investigated, and then the evidential reasoning algorithm is adopted to aggregate the criteria values. Finally, the average utility is applied to rank the alternatives. A numerical example, which is given to confirm the validity and feasibility, also shows that the proposed method can take advantage of cloud model and evidence theory to efficiently deal with the uncertainties caused by both the linguistic concept and group assessment.