2017

• Record 301 of
  
  Title:Traveling wave deflector design for femtosecond streak camera
  
  Author(s):Pei, Chengquan(1); Wu, Shengli(1); Luo, Duan(2,3); Wen, Wenlong(2); Xu, Junkai(2,3); Tian, Jinshou(2,5); Zhang, Minrui(2,3); Chen, Pin(2,3); Chen, Jianzhong(1); Liu, Rong(4)
  Source: Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment  Volume: 855  Issue:   DOI: 10.1016/j.nima.2017.02.062  Published: May 21, 2017  
  Abstract:In this paper, a traveling wave deflection deflector (TWD) with a slow-wave property induced by a microstrip transmission line is proposed for femtosecond streak cameras. The pass width and dispersion properties were simulated. In addition, the dynamic temporal resolution of the femtosecond camera was simulated by CST software. The results showed that with the proposed TWD a femtosecond streak camera can achieve a dynamic temporal resolution of less than 600 fs. Experiments were done to test the femtosecond streak camera, and an 800 fs dynamic temporal resolution was obtained. Guidance is provided for optimizing a femtosecond streak camera to obtain higher temporal resolution. © 2017 Elsevier B.V.
  Accession Number: 20171203475341
• Record 302 of
  
  Title:Projected clustering via robust orthogonal least square regression with optimal scaling
  
  Author(s):Zhang, Rui(1); Nie, Feiping(1); Li, Xuelong(2)
  Source: Proceedings of the International Joint Conference on Neural Networks  Volume: 2017-May  Issue:   DOI: 10.1109/IJCNN.2017.7966199  Published: June 30, 2017  
  Abstract:The orthogonal least square regression (OLSR) serves as a pretty significant problem for the dimensionality reduction. Due to lack of the scale change in OLSR, the scaling term is at first introduced to OLSR to build up a novel orthogonal least square regression with optimal scaling (OLSR-OS) problem. However, OLSR-OS is still sensitive to the outliers, such that associated results could be fallacious. To strengthen the robustness of OLSR-OS, we propose an original robust OLSR-OS (ROLSR-OS) problem in 2,1-norm. To tackle a more ill-defined situation, ROLSR-OS in 2,1-norm can be further extended to ROLSR-OS in capped 2-norm. Besides, the associated ROLSR-OS methods could be derived by solving the re-weighted counterparts of ROLSR-OS problems in both norms. Moreover, the equivalence between the re-weighted counterparts and the original ROLSR-OS problems is also provided along with the convergence analysis of the proposed ROLSR-OS methods. Accordingly, both the optimal scaling and weight can be achieved automatically via the proposed ROLSR-OS approaches. Specifically, the proposed ROLSR-OS methods are self-adaptive, such that the smaller weight would be automatically assigned to the term with larger outliers to enhance the robustness. Consequently, projected clustering and modified projected clustering under the proposed ROLSR-OS problems are further investigated both theoretically and experimentally. © 2017 IEEE.
  Accession Number: 20174204273986
• Record 303 of
  
  Title:Self-weighted spectral clustering with parameter-free constraint
  
  Author(s):Zhang, Rui(1); Nie, Feiping(1); Li, Xuelong(2)
  Source: Neurocomputing  Volume: 241  Issue:   DOI: 10.1016/j.neucom.2017.01.085  Published: June 7, 2017  
  Abstract:The constrained spectral clustering (or known as the semi-supervised spectral clustering) focuses on enhancing the clustering capability by utilizing the side information. In this paper, a novel constrained spectral clustering method is proposed based on deriving a sparse parameter-free similarity. Different from other works, the proposed method transforms the given pairwise constraints into the intrinsic graph similarity and the penalty graph similarity respectively instead of incorporating them into one single similarity. Besides, the optimal weight can be automatically achieved to balance the graph optimization problems between the intrinsic graph and the penalty graph. Equipped with a general framework of efficiently unraveling the bi-objective optimization, the proposed method could obtain both ratio cut and normalized cut clusterings via updating the weighted Laplacian matrix until convergence. Moreover, the proposed method is equivalent to the spectral clustering, when no side information is provided. Consequently, the effectiveness and the superiority of the proposed method are further verified both analytically and empirically. © 2017 Elsevier B.V.
  Accession Number: 20170903385458
• Record 304 of
  
  Title:Few-layered MoS2 as a saturable absorber for a passively Q-switched Er: YAG laser at 1.6 μm
  
  Author(s):Xia, Hongwang(1); Li, Ming(2,3); Li, Tao(1); Zhao, Shengzhi(1); Li, Guiqiu(1); Yang, Kejian(1)
  Source: Applied Optics  Volume: 56  Issue: 10  DOI: 10.1364/AO.56.002766  Published: April 1, 2017  
  Abstract:The passively Q-switched Er: YAG laser at 1.6 μm was achieved with a YAG-based MoS2 saturable absorber (SA) for the first time. The saturable absorption properties of the MoS2 SA near 1.6 μm were investigated. Under an absorbed pump power of 8.09 W, an average output power of 1.08 W with a pulse duration of 1.138 μs and a repetition rate of 46.6 kHz was obtained, corresponding to an optical conversion efficiency of 40.67%. The pulse energy and peak power were calculated to be 23.08 μJ and 20.28 W, respectively. © 2017 Optical Society of America.
  Accession Number: 20171403528214
• Record 305 of
  
  Title:A novel generation scheme of ultra-short pulse trains with multiple wavelengths
  
  Author(s):Su, Yulong(1,2,3); Hu, Hui(1,3); Feng, Huan(1); Li, Lu(1,2,3); Han, Biao(1,3); Wen, Yu(4); Wang, Yishan(1); Si, Jinhai(2); Xie, Xiaoping(1); Wang, Weiqiang(1,3)
  Source: Optics Communications  Volume: 389  Issue:   DOI: 10.1016/j.optcom.2016.12.044  Published: April 15, 2017  
  Abstract:We demonstrate a novel scheme based on active mode locking combined with four-wave mixing (FWM) to generate ultra-short pulse trains at high repetition rate with multiple wavelengths for applications in various fields. The obtained six wavelengths display high uniformity both in temporal and frequency domain. Pulses at each wavelength are mode locked with pulse duration of ~44.37 ps, signal-to-noise ratio (SNR) of ~47.89 dB, root-mean-square (RMS) timing jitter of ~552.7 fs, and the time-bandwidth product of ~0.68 at repetition rate of 1 GHz. The experimental results show this scheme has promising usage in optical communications, optical networks, and fiber sensing. © 2016 Elsevier B.V.
  Accession Number: 20165203192473
• Record 306 of
  
  Title:Efficient modulation of orthogonally polarized infrared light using graphene metamaterials
  
  Author(s):Cui, Yudong(1,2); Zeng, Chao(2)
  Source: Journal of Applied Physics  Volume: 121  Issue: 14  DOI: 10.1063/1.4980029  Published: April 14, 2017  
  Abstract:We propose an efficient modulation of linearly polarized infrared light using graphene metamaterials (GMMs) by exploiting the phase-coupled plasmon-induced transparency (PIT) mechanism. Because of the phase-coupling effect in GMMs, pronounced PIT peaks can be simultaneously obtained for the orthogonally polarized light through tuning of the Fermi level in graphene. Taking advantage of such polarization-selective PIT spectral responses and precise phase management, a dual-polarization GMM modulator is successfully achieved with ultra-high modulation depths of ∼32 dB at 10 μm and ∼28 dB at 12.45 μm for the x- and y-polarized light beams, respectively. The underlying principle of the proposal is well explained and verified by using transfer matrix method. The proposed scheme provides new opportunities for developing graphene-integrated high-performance electro-optical modulation, switching, and other optoelectronics applications. © 2017 Author(s).
  Accession Number: 20171503568129
• Record 307 of
  
  Title:Natural healing behavior of gamma radiation induced defects in multicomponent phosphate glasses used for high energy UV lasers
  
  Author(s):He, Quanlong(1,2); Xue, Yaoke(3); Wang, Pengfei(1); Sun, Mengya(1,2); Lu, Min(1); Peng, Bo(1)
  Source: Optical Materials Express  Volume: 7  Issue: 9  DOI: 10.1364/OME.7.003284  Published: 2017  
  Abstract:Obvious healing behavior of gamma radiation induced defects in multicomponent phosphate glass was observed at room temperature. The recovery of the defects depends on the ratio of H3BO3/SiO2 in the investigated glasses, the total gamma radiation dose, and the time of ageing at room temperature. Meanwhile, the synchronous decreases of PO3-EC and POHC defects contribute to the corresponding recovery of the transmittance change at 385 nm and 525 nm, which could be described by the charge transfer. Besides, a general model of the healing mechanism associated with the release and capture of the electrons between PO3- EC and POHC defects in these phosphate glass was proposed. © 2017 Optical Society of America.
  Accession Number: 20173604116492
• Record 308 of
  
  Title:The Recognition of the Point Symbols in the Scanned Topographic Maps
  
  Author(s):Miao, Qiguang(1); Xu, Pengfei(2); Li, Xuelong(3); Song, Jianfeng(1); Li, Weisheng(4); Yang, Yun(5,6)
  Source: IEEE Transactions on Image Processing  Volume: 26  Issue: 6  DOI: 10.1109/TIP.2016.2613409  Published: June 2017  
  Abstract:It is difficult to separate the point symbols from the scanned topographic maps accurately, which brings challenges for the recognition of the point symbols. In this paper, based on the framework of generalized Hough transform (GHT), we propose a new algorithm, which is named shear line segment GHT (SLS-GHT), to recognize the point symbols directly in the scanned topographic maps. SLS-GHT combines the line segment GHT (LS-GHT) and the shear transformation. On the one hand, LS-GHT is proposed to represent the features of the point symbols more completely. Its R-table has double level indices, the first one is the color information of the point symbols, and the other is the slope of the line segment connected a pair of the skeleton points. On the other hand, the shear transformation is introduced to increase the directional features of the point symbols; it can make up for the directional limitation of LS-GHT indirectly. In this way, the point symbols are detected in a series of the sheared maps by LS-GHT, and the final optimal coordinates of the setpoints are gotten from a series of the recognition results. SLS-GHT detects the point symbols directly in the scanned topographic maps, totally different from the traditional pattern of extraction before recognition. Moreover, several experiments demonstrate that the proposed method allows improved recognition in complex scenes than the existing methods. © 2017 IEEE.
  Accession Number: 20171903647517
• Record 309 of
  
  Title:Data preprocessing methods for robust Fourier ptychographic microscopy
  
  Author(s):Zhang, Yan(1,2); Pan, An(1,2); Lei, Ming(1); Yao, Baoli(1,3)
  Source: Optical Engineering  Volume: 56  Issue: 12  DOI: 10.1117/1.OE.56.12.123107  Published: December 1, 2017  
  Abstract:Fourier ptychographic microscopy (FPM) is a recently developed computational imaging technique that achieves gigapixel images with both high resolution and large field-of-view. In the current FPM experimental setup, the dark-field images with high-angle illuminations are easily overwhelmed by stray lights and background noises due to the low signal-to-noise ratio, thus significantly degrading the achievable resolution of the FPM approach. We provide an overall and systematic data preprocessing scheme to enhance the FPM's performance, which involves sampling analysis, underexposed/overexposed treatments, background noises suppression, and stray lights elimination. It is demonstrated experimentally with both US Air Force (USAF) 1951 resolution target and biological samples that the benefit of the noise removal by these methods far outweighs the defect of the accompanying signal loss, as part of the lost signals can be compensated by the improved consistencies among the captured raw images. In addition, the reported nonparametric scheme could be further cooperated with the existing state-of-the-art algorithms with a great flexibility, facilitating a stronger noise-robust capability of the FPM approach in various applications. © 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).
  Accession Number: 20180904840307
• Record 310 of
  
  Title:Learning Sampling Distributions for Efficient Object Detection
  
  Author(s):Pang, Yanwei(1); Cao, Jiale(1); Li, Xuelong(2)
  Source: IEEE Transactions on Cybernetics  Volume: 47  Issue: 1  DOI: 10.1109/TCYB.2015.2508603  Published: January 2017  
  Abstract:Object detection is an important task in computer vision and machine intelligence systems. Multistage particle windows (MPW), proposed by Gualdi et al., is an algorithm of fast and accurate object detection. By sampling particle windows (PWs) from a proposal distribution (PD), MPW avoids exhaustively scanning the image. Despite its success, it is unknown how to determine the number of stages and the number of PWs in each stage. Moreover, it has to generate too many PWs in the initialization step and it unnecessarily regenerates too many PWs around object-like regions. In this paper, we attempt to solve the problems of MPW. An important fact we used is that there is a large probability for a randomly generated PW not to contain the object because the object is a sparse event relative to the huge number of candidate windows. Therefore, we design a PD so as to efficiently reject the huge number of nonobject windows. Specifically, we propose the concepts of rejection, acceptance, and ambiguity windows and regions. Then, the concepts are used to form and update a dented uniform distribution and a dented Gaussian distribution. This contrasts to MPW which utilizes only on region of support. The PD of MPW is acceptance-oriented whereas the PD of our method (called iPW) is rejection-oriented. Experimental results on human and face detection demonstrate the efficiency and the effectiveness of the iPW algorithm. The source code is publicly accessible. © 2016 IEEE.
  Accession Number: 20160201782760
• Record 311 of
  
  Title:A Bayesian-adaboost model for stock trading rule discovery
  
  Author(s):Kong, Zhoufan(1); Yang, Jie(1); Huang, Qinghua(1); Li, Xuelong(2)
  Source: Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017  Volume: 2018-January  Issue:   DOI: 10.1109/CISP-BMEI.2017.8302138  Published: July 2, 2017  
  Abstract:Detecting the trading patterns with different technical indicators from the historical financial data is an efficient way to forecast the trading decisions in the financial market. In most cases, the trading patterns which consist of some specific combinations of technical indicators are significant in predicting the efficient trading decisions. However, discovering those combinations is a rather challenge assignment. In this paper, we propose a novel method to detect the trading patterns and later the Naive bayes with Adaboost method was employed to determine the trading decisions. The proposed method has been implemented on two historical stock datasets, the experimental results demonstrate that the proposed algorithm outperforms the other three algorithms and could provide a worthwhile reference for the financial investments. © 2017 IEEE.
  Accession Number: 20182205244172
• Record 312 of
  
  Title:Learning deep event models for crowd anomaly detection
  
  Author(s):Feng, Yachuang(1,2); Yuan, Yuan(1); Lu, Xiaoqiang(1)
  Source: Neurocomputing  Volume: 219  Issue:   DOI: 10.1016/j.neucom.2016.09.063  Published: January 5, 2017  
  Abstract:Abnormal event detection in video surveillance is extremely important, especially for crowded scenes. In recent years, many algorithms have been proposed based on hand-crafted features. However, it still remains challenging to decide which kind of feature is suitable for a specific situation. In addition, it is hard and time-consuming to design an effective descriptor. In this paper, video events are automatically represented and modeled in unsupervised fashions. Specifically, appearance and motion features are simultaneously extracted using a PCANet from 3D gradients. In order to model event patterns, a deep Gaussian mixture model (GMM) is constructed with observed normal events. The deep GMM is a scalable deep generative model which stacks multiple GMM-layers on top of each other. As a result, the proposed method acquires competitive performance with relatively few parameters. In the testing phase, the likelihood is calculated to judge whether a video event is abnormal or not. In this paper, the proposed method is verified on two publicly available datasets and compared with state-of-the-art algorithms. Experimental results show that the deep model is effective for abnormal event detection in video surveillance. © 2016 Elsevier B.V.
  Accession Number: 20164903088954