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Laser-based light detection and ranging (LIDAR) offers a powerful tool to real-timely map spatial information with exceptional accuracy and owns various applications ranging from industrial manufacturing, and remote sensing, to airborne and in-vehicle missions. Over the past two decades, the rapid advancements of optical frequency combs have ushered in a new era for LIDAR, promoting measurement precision to quantum noise limited level. For comb LIDAR systems, to further improve the comprehensive performances and reconcile inherent conflicts between speed, accuracy, and ambiguity range, innovative demodulation strategies become crucial. Here we report a dispersive Fourier transform (DFT) based LIDAR method utilizing phase-locked Vernier dual soliton laser combs. We demonstrate that after in-line pulse stretching, the delay of the flying pulses can be identified via the DFT-based spectral interferometry instead of temporal interferometry or pulse reconstruction. This enables absolute distance measurements with precision starting from 262 nm in single shot, to 2.8 nm after averaging 1.5 ms, in a non-ambiguity range over 1.7 km. Furthermore, our DFT-based LIDAR method distinctly demonstrates an ability to completely eliminate dead zones. Such an integration of frequency-resolved ultrafast analysis and dual-comb ranging technology may pave a way for the design of future LIDAR systems. Here the authors develop a dispersive Fourier transform (DFT) based LIDAR method utilizing phase-locked Vernier dual soliton laser combs and demonstrate improved precision in the measurements. [ABSTRACT FROM AUTHOR]

Numerous bismuth-based semiconductors (BBSs) with sophisticated and desirable structures used as photocatalysts for efficient photocatalytic degradation of water organic contaminants have attracted considerable attention. However, regulating the crystal phases and phase transition of BBSs for promoted photocatalytic performance is ignored. Herein, the unique crystal structure and band structure features of each typical BBSs, and the vital roles on phase controlling of each phase were systematically presented based on the classification of BBSs. Notably, the critical factors for the phase transition of BBSs and intrinsic driving forces endowed by phases of BBSs for enhanced photocatalytic performance of organic contaminants removal were also elucidated. This review will provide systematical guidelines and horizons for regulating the crystal phase and phase transition of BBSs, promoting photocatalytic degradation and mineralization of organic contaminants. [ABSTRACT FROM AUTHOR]

Influence of the ion irradiation at elevated temperatures on the novel preceramic paper-derived SiCf/SiC laminated composites fabricated by spark plasma sintering was investigated. The composites were subjected to high-dose simultaneous irradiation of silicon (Si) ions at 400 °C. Irradiation induces amorphization of the damaged area up to 30 dpa, and then recrystallization of amorphous regions with increasing dose occur. No evidence of cavities was found in SiCf/SiC composites irradiated by silicon ions up to 100 dpa. The irradiated composites pre-injected with 5000 appm He displays low swelling (~ 0.3%). The irradiation with Si ions may suppress the cavity nucleation and they growth in SiC layers. [ABSTRACT FROM AUTHOR]

Surface plasmons in graphene provide a compelling strategy for advanced photonic technologies thanks to their tight confinement, fast response and tunability. Recent advances in the field of all-optical generation of graphene's plasmons in planar waveguides offer a promising method for high-speed signal processing in nanoscale integrated optoelectronic devices. Here, we use two counter propagating frequency combs with temporally synchronized pulses to demonstrate deterministic all-optical generation and electrical control of multiple plasmon polaritons, excited via difference frequency generation (DFG). Electrical tuning of a hybrid graphene-fibre device offers a precise control over the DFG phase-matching, leading to tunable responses of the graphene's plasmons at different frequencies across a broadband (0 ~ 50 THz) and provides a powerful tool for high-speed logic operations. Our results offer insights for plasmonics on hybrid photonic devices based on layered materials and pave the way to high-speed integrated optoelectronic computing circuits. Nano-photonic devices based on 2D materials offer a potential solution for the miniaturization of optical computing technologies. Here, the authors demonstrate the implementation of high-speed logic operations via the all-optical generation and electrical control of multiple plasmon polaritons in a hybrid graphene device. [ABSTRACT FROM AUTHOR]

Among the factors which restrict the large-scale utilization of magnesium-based hydride as a hydrogen storage medium, the high operating temperature, slow kinetics, and air stability in particular are key obstacles. In this work, a novel method, namely hydriding combustion synthesis plus short-term mechanical milling followed by air exposure, was proposed to synthesize air stable and autocatalytic magnesium nickel hydride (Mg2NiH4), which shows excellent hydrogen absorption/desorption kinetics, capacity retention and oxidation resistance. The short-term-milled Mg2NiH4 can desorb 2.97 wt.% hydrogen within 500 s at 230 °C. Even after exposure under air atmosphere for 67 days, it can still desorb 2.88 wt.% hydrogen within 500 s at 230 °C. The experimental and theoretical results both indicated that the surface of as-milled Mg2NiH4 was easy to be oxidized under air atmosphere. However, the in-situ formed Ni during air exposure of Mg2NiH4 improved the hydrogen desorption kinetics, and the formed surface passivation layer maintained the hydrogen storage capacity and avoided further poisoning, which we called autocatalytic and self-protective effect. Such a novel dual effect modified the reaction activity and oxidation resistance of the air-exposed Mg2NiH4. Our findings provide useful insights into the design and preparation of air stable metal-based hydride for large-scale utilization and long-term storage. [ABSTRACT FROM AUTHOR]

With the development of deep learning technology, many deep learning methods have been applied to font recognition and generation. However, few studies focus on font inpainting problems. This paper is dedicated to repairing damaged fonts based on style to repair damaged fonts in a better way. In this paper, we propose a CGAN (Conditional Generative Adversarial Nets)-based font repair method. This paper uses the content accuracy and style similarity of the repaired image as an evaluation index to evaluate the accuracy of the restored style font. The font content proposed by the paper based on CGAN network repair style is similar with the correct content. [ABSTRACT FROM AUTHOR]

To study the dynamic characteristics of the pipeline end termination (PLET) installation operation based on pipelaying under full coupling effect, a numerical model for PLET installation based on S-laying with the vessel–cable–PLET–pipeline coupling in time domain is proposed in this study. The whole model mainly consists of four parts, which are the pipelaying vessel, the abandonment and recovery (A&R) cable, the pipeline and the PLET. The finite element method is proposed for the pipeline and the cable, while the hydrodynamic forces on the PLET are simulated by the Morison equation. The pipelaying vessel is controlled by a dynamic positioning system. The proposed model is validated by OrcaFlex software and then used to carry out numerical simulations of PLET installation based on practical operations. The static and dynamic responses of each part in different operation phases and under different wave and current directions are analyzed. The results show that the PLET position in different phases as well as the direction of wave and current during installation has great effect on forces and motions of the system parts. Moreover, the dynamic responses of different parts are apparently connected, so the coupling effect cannot be ignored. [ABSTRACT FROM AUTHOR]

This paper presents the mechanism and control design of a micro-motion stage, which employs the right-angle flexure hinges and piezoelectric actuators (PZT). Aiming at the mechanism with the characteristics of a large stroke and three degrees of freedom, analytical models of statics and dynamics are established; especially the coupling motions of stage are investigated, which are verified by finite element analysis simulation. Via open-loop experiment, the decoupling property is well certified. Owing to the hysteresis of PZT, the dynamic equation of system with Bouc–Wen hysteresis model is proposed, which is identified through the Least squares. Moreover, a closed-loop controller of proportion integral derivative combined with the inverse hysteresis model-based feedforward is developed to reduce the nonlinearity and uncertainty, which can improve the positioning accuracy. Besides, the single-axis and multi-axis motions are tested. Experimental results reveal that the stage has a well-decoupling performance, and the effectiveness of proposed Bouc–Wen model is validated under open-loop control. Furthermore, the micro-motion performance in single- and multi-axis motions can be achieved as well. [ABSTRACT FROM AUTHOR]

This paper presents a novel wrapper feature selection algorithm for classification problems, namely hybrid genetic algorithm (GA)- and extreme learning machine (ELM)-based feature selection algorithm (HGEFS). It utilizes GA to wrap ELM to search for the optimum subsets in the huge feature space, and then, a set of subsets are selected to make ensemble to improve the final prediction accuracy. To prevent GA from being trapped in the local optimum, we propose a novel and efficient mechanism specifically designed for feature selection problems to maintain GA’s diversity. To measure each subset’s quality fairly and efficiently, we adopt a modified ELM called error-minimized extreme learning machine (EM-ELM) which automatically determines an appropriate network architecture for each feature subsets. Moreover, EM-ELM has good generalization ability and extreme learning speed which allows us to perform wrapper feature selection processes in an affordable time. In other words, we simultaneously optimize feature subset and classifiers’ parameters. After finishing the search process of GA, to further promote the prediction accuracy and get a stable result, we select a set of EM-ELMs from the obtained population to make the final ensemble according to a specific ranking and selecting strategy. To verify the performance of HGEFS, empirical comparisons are carried out on different feature selection methods and HGEFS with benchmark datasets. The results reveal that HGEFS is a useful method for feature selection problems and always outperforms other algorithms in comparison. [ABSTRACT FROM AUTHOR]

A physical model test has been conducted to study the oil diffusion from the submarine pipeline under water flow. The crude oil in the flume is spilled from a leakage point of the pipeline and diffused from the seabed to the surface. By the non-contact optical measuring technology, an image acquisition and data analysis system is designed to explore the spilled mechanism and characteristic. The oil trajectory, velocity and the rising time to the surface are obtained through this system. The influence of the water flow and the spilled discharge on the behavior of the spilled oil are analyzed from both qualitative and quantitative perspectives. The sensitivity study of the characteristic physical quantities to various factors are presented afterward. The spilled oil under water is mainly distributed in the form of the scattered particles with different sizes. The rising process of the oil can be divided into three stages: full, dispersion and aggregation period. The spilled discharge is the primary factor affecting the rising time of the oil particles. In the rising process of the oil particles, the vertical velocity of the oil is mainly affected by the spilled discharge, and the transverse velocity is more dependent on the water velocity. The deviation of the transverse oil velocity is much larger than that of the rising time and the vertical oil velocity. The study can provide a theoretical reference for the prediction system of oil spill emergency. [ABSTRACT FROM AUTHOR]

In order to improve the working stroke of micro/nano-transmission platform (MNTP) , a unidirectional multi-driven compliant mechanism based on the right-angle flexure hinges is designed. Applying the compliant mechanisms, elastic mechanics, and combining the working principle of MNTP, the mechanics models of bridge-type amplification mechanism (BTAM) and MNTP are built. Stiffness matrices, stress and strain, natural frequency, and sensitivity are derived. Also, the posture state equations of MNTP are established to predict the mechanism behavior. Then a few simulation calculations through Matlab software are carried out. In terms of Ansys 14.5 software, the simulation motions of the MNTP are acquired. The MNTP is machined through wire cut electrical discharge machining, and the measurement experiments of MNTP are established to verify the computational predictions, and the ability of the proposed mechanism with large working stroke is demonstrated in the experimental study. Finally, the theoretical values, finite element values and experimental values are comparatively analyzed, especially the output results of X/Y-direction in single-driven and double-driven mode. As a result, the reasonability of theoretical model of MNTP is verified. And a reference basis in the study of large stroke micro platform is provided. [ABSTRACT FROM AUTHOR]

A facile one-step polyol method is employed to synthesize the Ag nanoparticles (NPs) in large scale. The Ag NPs with different average diameter (from 52 to 120 nm) and particle size distribution are prepared by changing the mass ratio of AgNO and PVP. Furthermore, the as-obtained Ag NPs are prepared as conductive inks, which could be screen printed on various flexible substrates and formed as conductive patterns after sintering treatment. During the reaction process, PVP is used as the capping reagent for preventing the agglomeration of Ag NPs, and the influence of the mass ratio of AgNO and PVP to the size distribution of Ag NPs is investigated. The results of electronic properties reveal that the conductivity of printed patterns is highly dependent on the size distribution of as-obtained Ag NPs. Among all the samples, the optimal conductivity is obtained when the mass ratio of AgNO and PVP is 1:0.4. Subsequently, the sintering time and temperature are further investigated for obtaining the best conductivity; the optimal electrical resistivity value of 3.83 μΩ · cm is achieved at 160 °C for 75 min, which is close to the resistivity value of the bulk silver (1.58 μΩ · cm). Significantly, there are many potential advantages in printed electronics applications because of the as-synthesized Ag NPs with a low sintering temperature and low electrical resistivity. [ABSTRACT FROM AUTHOR]

The reconstruction of flame from the captured images is a difficult and computationally expensive problem. Reconstruction from color images will keep the colorful appearance, as is beneficial for visually realistic flame modeling. Most of existing color-image-based methods rebuild three density fields from RGB intensities; however, these methods suffer from the color distortion problem due to the high correlation of RGB intensities. A novel method for 3D flame reconstruction using color temperature is presented in this paper. Color-temperature mapping is calculated to avoid color distortion; this method maps the RGB intensities into the color temperature and its joint intensity. We improve the multiplication reconstruction with visual hull restriction so that the energy distribution is more reasonable, which allows avoidance of the impossible zones. Experimental results indicate that our approach is efficient in the visually plausible 3D flame generation and produces better color restorations. [ABSTRACT FROM AUTHOR]

Voice-based biometric security systems involving only neutral speech have achieved promising performance. However, the speakers are very likely to fail the recognition when the test data exhibit multiple emotions. This paper aimed to address the mismatch of the emotional states between training and testing speech. We discuss different modeling strategies that incorporate the emotions (affects) of speakers into the training stage of a Mandarin-based speaker recognition system and propose an alternative approach, which could optimize the utilization of the limited affective speech. The training speeches are partitioned and clustered by the trends of the prosodic variations. Multiple models are built based on the clustered speech for a given speaker. The prosodic differences are characterized by a combination of features that describe the changes of the fundamental frequencies and energy contours. The experiments were carried out based on the Mandarin Affective Speech Corpus. The result shows 73.37 % improvement in recognition rate over that of the traditional speaker verification tasks relatively and also achieves 63.53 % higher in performance over the structural training-based systems relatively. [ABSTRACT FROM AUTHOR]

Social network sites, such as Facebook and Twitter, are gaining increasing popularity nowadays by providing a convenient platform for sharing and consuming information of all kinds. While the ever increasing information sources on the social network sites hold tremendous promise, how to select user interested information becomes nontrivial as users are easily overloaded by a vast amount candidate information sources. Furthermore, as the information providers are autonomous entities in an open social network environment, they may spread information that is unreliable or completely fake. Hence, technological advances are in demand to recommend information sources to social network users that both match their interests and come from reliable information sources. We develop a novel social media recommendation framework, referred to as GCCR, to tackle the above central challenges. GCCR is coined based on the key technologies that supports the proposed framework: Graph summarization, Content-based approach, Clustering, and Recommendation. A user-centric strategy is adopted that exploits the historical behavior of a set of seed users as evidence to assess the trustworthiness of different information providers. A two-phase process that employs graph summarization and content-based clustering is developed to partition users into different interest groups. The interest group information is then used for recommendation purpose. We perform extensive experiments on real-world social network data to assess the effectiveness of the proposed GCCR framework. [ABSTRACT FROM AUTHOR]

Generating computer animated flames is a difficult and computationally expensive problem. Dynamic textures provide an effective means for extrapolating and synthesizing dynamic flames, but aggravates color distortion due to the high correlation of RGB components. A novel method for dynamic flame texture synthesis using color temperature is proposed in this paper. Firstly, the color-temperature mapping is calculated by using the Planck's law and two-color pyrometric technique to avoid color distortion. Secondly, a novel dynamic texture model is presented to transform the RGB space into temperature space. Finally, the dynamic flames editing is presented to support physical temperature adjustment. Experimental results illustrate that our approach is effective to synthesize visually plausible dynamic flames without color distortions and to edit dynamic flames with intuitive physical interpretation. [ABSTRACT FROM AUTHOR]

Understanding the correlation between physicochemical properties and morphology of nanostructures is a prerequisite for widespread applications of nanomaterials in environmental application areas. Herein, we illustrated that the uniform-sized SnO@C hollow nanoparticles were large-scale synthesized by a facile hydrothermal method. The size of the core-shell hollow nanoparticles was about 56 nm, and the shell was composed of a solid carbon layer with a thickness of 2 ~ 3 nm. The resulting products were characterized in terms of morphology, composition, and surface property by various analytical techniques. Moreover, the SnO@C hollow nanoparticles are shown to be effective adsorbents for removing four different dyes from aqueous solutions, which is superior to the pure hollow SnO nanoparticles and commercial SnO. The enhanced mechanism has also been discussed, which can be attributed to the high specific surface areas after carbon coating. [ABSTRACT FROM AUTHOR]

Automatic facial expression recognition has made considerable gains in the body of research available due to its vital role in human-computer interaction. So far, research on this problem or problems alike has proposed a wide verity of techniques and algorithms for both information representation and classification. Very recently, Farajzadeh et al. in Int J Pattern Recognit Artif Intell 25(8):1219-1241, () proposed a novel information representation approach that uses machine-learning techniques to derive a set of new informative and descriptive features from the original features. The new features, so called meta probability codes (MPC), have shown a good performance in a wide range of domains. In this paper, we aim to study the performance of the MPC features for the recognition of facial expression via proposing an MPC-based framework. In the proposed framework any feature extractor and classifier can be incorporated using the meta-feature generation mechanism. In the experimental studies, we use four state-of-the-art information representation techniques; local binary pattern, Gabor-wavelet, Zernike moment and facial fiducial point, as the original feature extractors; and four multiclass classifiers, support vector machine, k-nearest neighbor, radial basis function neural network, and sparse representation-based classifier. The results of the extensive experiments conducted on three facial expression datasets, Cohn-Kanade, JAFFE, and TFEID, show that the MPC features promote the performance of facial expression recognition inherently. [ABSTRACT FROM AUTHOR]