Search

International audience; Diffusion tensor imaging (DTI) is currently the unique imaging technique that can detect the structure of in-vivo human myocardium without invasivity and radiation. However, it is particularly sensitive to motions, especially respiratory motion that results in serious signal loss in diffusion-weighted (DW) images. This makes it impossible to accurately measure cardiac microscopic structural properties. To cope with such problem, this paper proposes an unsupervised dense-encoder-fusion-decoder network (DEFD-net) to compensate for signal loss in cardiac DW images, which allows investigating in-vivo myocardium structure more accurately. The DEFD-net consists of three modules, namely dense-encoder, fusion module and decoder module. The dense-encoder and decoder are trained firstly with DW images acquired at different trigger delays in an unsupervised manner for extracting local and global features. A fusion strategy is then designed to fuse the extracted features. Finally, the well-trained decoder is used to reconstruct the fused DW image from the fused features. To validate the superiority of the proposed method, comparison with existing methods such as PCAMIP, WIF and U2Fusion is performed on both simulated and acquired datasets. The experimental results showed that the proposed method effectively compensates for motion-induced signal loss in DW images, thus leading to much better DW image quality with respect to existing methods. Moreover, the subsequently derived myocardium fiber structure is more regular.

In this paper, passive generation of the multi-wavelength parabolic pulses (PPs) in tapered silicon nanowires (TSNs) is numerically investigated. The coupled inhomogeneous nonlinear Schrödinger equation (INLSE) is derived in the context of TSN to model the multi-wavelength PP generation. The TSN is designed based on the well-known self-similar theory that the group-velocity dispersion is decreased while the nonlinear coefficient is unchanged along the propagative direction. While the pump wavelengths for three input pulses are specially set with equal intervals of 18, 12, and 6 nm, the time-domain profiles are aligned at the same position. Simulation results show that except for wavelength interval, the waveguide length is also a critical factor that influences the quality of generated PP. Both of them reshape the pulse profile by the means of walk-off effect which depends on the cross-phase modulation between multiple pulses. By properly optimizing the input parameters of Gaussian pulses as well as the center wavelength interval and TSN length, we prove that the generation of two- even three-wavelength PPs with high-quality is feasible.

In recent years, with the opening of the “smart age” curtain, smart devices dominated by technologies such as robots, drones, and intelligent perception have gradually moved to the center of the Intelligent CPSS stage. However, the new security risks of the Intelligent CPSS have also become increasingly prominent. Especially in recent years, in Ukraine and Venezuela's power attack incidents, a series of related attacks always occur simultaneously. This is a multi-task compound attack. This paper designs a set of Cloud-Fog-Edge closed-loop feedback security risk prediction strategies for multi-task compound attacks based on the offensive and defensive ideas of intelligent games, combining classified deep Boltzmann machines and Markov time-varying models. This strategy can be used for various types of power intelligent system terminals, and realizes security risk prediction with modularity, interoperability, open interfaces and compliance with open standards. Interoperability with other safety equipment can also be achieved through standardized interfaces to form system security protection capabilities to meet the actual needs of the industry Internet system. Experimental results show that the method is superior to the typical traditional method.

A simple optic-fiber Fabry-Perot (FP) sensing technique was proposed and experimentally investigated by using polymer material to connect the ends of two singlemode fibers. Four different polymer materials (benzocyclobutene (BCB), UV88 (Relentless, China), Loctite3525 (HenKel, Germany), and NOA68 (Norland, USA)) filling the FP cavity were used to comparatively study the sensing performance of temperature, strain and refractive index. The result shows that the FP sensor with BCB has excellent repeatability with good linear response to temperature in a wide range from room temperature to 250 °C, which is much larger than that of other three materials (2)) and the best repeatability among the four polymer materials.

A microfiber interferometer based on a single-mode tapered-no-core single-mode (STNCS) fiber structure coated with a thin layer of graphene oxide (GO) was proposed and demonstrated as a relative humidity (RH) sensor. The STNCS fiber structure has a strong evanescent field, which can sensitively response to changes in the surrounding refractive index. The moisture-sensitive material GO was used to form the film on the no-core fiber by a dip impregnation method. The effects of GO concentration and the tapered waist diameter on RH sensitivity were studied experimentally. The experimental results showed that a thinner waist diameter provides higher RH sensitivity. When the concentration of GO solution is 0.01 mg/mL, the STNCS fiber structure with a waist diameter of 2.9 μm performs better in RH sensing; a high sensitivity of 0.461 nm/%RH in the range of 40-98% was achieved. The sensor has good stability with wavelength fluctuations of ±0.009 nm and ±0.008 nm over 60 mins at RHs of 50% and 80%, respectively. The proposed fiber optic humidity sensor has a high sensitivity over a wide measurement range and offers good stability showing promising potential application in the field of RH sensing.

© 2013 IEEE. Advanced driver assistance systems (ADAS) based on monocular vision are rapidly becoming a popular research subject. In ADAS, inter-vehicle distance estimation from an in-car camera based on monocular vision is critical. At present, related methods based on a monocular vision for measuring the absolute distance of vehicles ahead experience accuracy problems in terms of the ranging result, which is low, and the deviation of the ranging result between different types of vehicles, which is large and easily affected by a change in the attitude angle. To improve the robustness of a distance estimation system, an improved method for estimating the distance of a monocular vision vehicle based on the detection and segmentation of the target vehicle is proposed in this paper to address the vehicle attitude angle problem. The angle regression model (ARN) is used to obtain the attitude angle information of the target vehicle. The dimension estimation network determines the actual dimensions of the target vehicle. Then, a 2D base vector geometric model is designed in accordance with the image analytic geometric principle to accurately recover the back area of the target vehicle. Lastly, area-distance modeling based on the principle of camera projection is performed to estimate distance. The experimental results on the real-world computer vision benchmark, KITTI, indicate that our approach achieves superior performance compared with other existing published methods for different types of vehicles (including front and sideway vehicles).

Precise control of a coupling effect is critical for space-limited communication system applications. Dielectric metamaterials, which derive their electromagnetic properties from subwavelength structures, have emerged as a promising way to tune coupling effect due to their complex resonant modes. However, it has not yet achieved quantitative control of coupling effects in metamaterial designs, which is important for advanced engineering applications such as artificial intelligence antenna and reconfigurable camouflage. In this paper, all dielectric left-handed metamaterial operating in THz band based on the deep coupling of higher mode Mie resonance is presented. Distinguished from those designs published before, this left-handed metamaterial is composed only by one same structure, not two separated parts providing negative permittivity and permeability, respectively. By utilizing different Mie resonant modes, negative permittivity and permeability are accomplished via high order Mie resonant modes and the coupling between cubes. This work opens a gate to highly control of electromagnetic wave in dielectric metamaterial by using the coupling between the unit cells.

Functional magnetic resonance imaging (fMRI) has increasingly come to dominate brain mapping research, as it provides a dynamic view of brain matter. Feature selection or extraction methods play an important role in the successful application of machine learning techniques to classifying fMRI data by appropriately reducing the dimensionality of the data. While whole-brain fMRI data contains large numbers of voxels, the curse of dimensionality problem may limit the feature selection/extraction and classification performance of traditional methods. In this paper, we propose a novel framework based on a tensor neural network (TensorNet) to extract the essential and discriminative features from the whole-brain fMRI data. The tensor train model was employed to construct a simple and shallow neural network and compress a large number of network weight parameters. The proposed framework can avoid the curse of dimensionality problem, and allow us to extract effective patterns from the whole-brain fMRI data. Furthermore, it reveals a new perspective for analyzing complex fMRI data with a large numbers of voxels, through compressing the number of parameters in a neural network. Experimental results confirmed that our proposed classification framework based on TensorNet outperforms traditional methods based on an SVM classifier for multi-class fMRI data.