Showing papers by "National University of Defense Technology published in 2015"

IBS: an illustrator for the presentation and visualization of biological sequences.

Abstract: Summary: Biological sequence diagrams are fundamental for visualizing various functional elements in protein or nucleotide sequences that enable a summarization and presentation of existing information as well as means of intuitive new discoveries. Here, we present a software package called illustrator of biological sequences (IBS) that can be used for representing the organization of either protein or nucleotide sequences in a convenient, efficient and precise manner. Multiple options are provided in IBS, and biological sequences can be manipulated, recolored or rescaled in a user-defined mode. Also, the final representational artwork can be directly exported into a publication-quality figure. Availability and implementation: The standalone package of IBS was implemented in JAVA, while the online service was implemented in HTML5 and JavaScript. Both the standalone package and online service are freely available at http://ibs.biocuckoo.org. Contact: renjian.sysu@gmail.com or xueyu@hust.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise

Abstract: Heart rate monitoring using wrist-type photoplethysmographic signals during subjects’ intensive exercise is a difficult problem, since the signals are contaminated by extremely strong motion artifacts caused by subjects’ hand movements. So far few works have studied this problem. In this study, a general framework, termed TROIKA, is proposed, which consists of signal decomposiTion for denoising, sparse signal RecOnstructIon for high-resolution spectrum estimation, and spectral peaK trAcking with verification. The TROIKA framework has high estimation accuracy and is robust to strong motion artifacts. Many variants can be straightforwardly derived from this framework. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/h showed that the average absolute error of heart rate estimation was 2.34 beat per minute, and the Pearson correlation between the estimates and the ground truth of heart rate was 0.992. This framework is of great values to wearable devices such as smartwatches which use PPG signals to monitor heart rate for fitness.

Surface transfer doping induced effective modulation on ambipolar characteristics of few-layer black phosphorus

Abstract: Black phosphorus, a fast emerging two-dimensional material, has been configured as field effect transistors, showing a hole-transport-dominated ambipolar characteristic. Here we report an effective modulation on ambipolar characteristics of few-layer black phosphorus transistors through in situ surface functionalization with caesium carbonate (Cs2CO3) and molybdenum trioxide (MoO3), respectively. Cs2CO3 is found to strongly electron dope black phosphorus. The electron mobility of black phosphorus is significantly enhanced to similar to 27 cm(2)V(-1) s(-1) after 10 nm Cs2CO3 modification, indicating a greatly improved electron-transport behaviour. In contrast, MoO3 decoration demonstrates a giant hole-doping effect. In situ photoelectron spectroscopy characterization reveals significant surface charge transfer occurring at the dopants/black phosphorus interfaces. Moreover, the surface-doped black phosphorus devices exhibit a largely enhanced photodetection behaviour. Our findings coupled with the tunable nature of the surface transfer doping scheme ensure black phosphorus as a promising candidate for further complementary logic electronics.

Quality and productivity outcomes relating to continuous integration in GitHub

Abstract: Software processes comprise many steps; coding is followed by building, integration testing, system testing, deployment, operations, among others. Software process integration and automation have been areas of key concern in software engineering, ever since the pioneering work of Osterweil; market pressures for Agility, and open, decentralized, software development have provided additional pressures for progress in this area. But do these innovations actually help projects? Given the numerous confounding factors that can influence project performance, it can be a challenge to discern the effects of process integration and automation. Software project ecosystems such as GitHub provide a new opportunity in this regard: one can readily find large numbers of projects in various stages of process integration and automation, and gather data on various influencing factors as well as productivity and quality outcomes. In this paper we use large, historical data on process metrics and outcomes in GitHub projects to discern the effects of one specific innovation in process automation: continuous integration. Our main finding is that continuous integration improves the productivity of project teams, who can integrate more outside contributions, without an observable diminishment in code quality.

In situ synthesis of graphitic-C3N4 nanosheet hybridized N-doped TiO2 nanofibers for efficient photocatalytic H2 production and degradation

Abstract: Graphitic carbon nitride nanosheets (g-C3N4 NSs) hybridized nitrogen doped titanium dioxide (N-TiO2) nanofibers (GCN/NT NFs) have been synthesized in situ via a simple electrospinning process combined with a modified heat-etching method. The prepared GCN/NT NFs were characterized by a variety of methods and their photocatalytic activities were evaluated by hydrogen (H2) production from water splitting and degradation of rhodamine B in aqueous solution. It was found that the GCN/NT NFs have a mesoporous structure, composed of g-C3N4 NSs and N-doped TiO2 crystallites. The g-C3N4 NSs synthesized after heat-etching were found to be embedded in, and covered, the hybrid NFs to form stable interfaces. The partial decomposition of g-C3N4 releases its nitrogen content which eventually gets doped into the nearby TiO2 skeleton. The GCN/NT NFs give a high photocatalytic H2 production rate of 8,931.3 μmol·h−1·g−1 in aqueous methanol solution under simulated solar light. Such a highly efficient photocatalytic performance can be ascribed to the combined effects of g-C3N4 NSs and N-doped TiO2 with enhanced light absorption intensity and improved electron transport ability. Also, the large surface area of the mesoporous NFs minimizes light reflection on the surface and provides more surface-active sites. This work highlights the potential of quasi-one dimensional hybrid materials in the field of solar energy conversion.

Multiobjective Reinforcement Learning: A Comprehensive Overview

Abstract: Reinforcement learning (RL) is a powerful paradigm for sequential decision-making under uncertainties, and most RL algorithms aim to maximize some numerical value which represents only one long-term objective. However, multiple long-term objectives are exhibited in many real-world decision and control systems, so recently there has been growing interest in solving multiobjective reinforcement learning (MORL) problems where there are multiple conflicting objectives. The aim of this paper is to present a comprehensive overview of MORL. The basic architecture, research topics, and naive solutions of MORL are introduced at first. Then, several representative MORL approaches and some important directions of recent research are comprehensively reviewed. The relationships between MORL and other related research are also discussed, which include multiobjective optimization, hierarchical RL, and multiagent RL. Moreover, research challenges and open problems of MORL techniques are suggested.

Orbital-Angular-Momentum-Based Electromagnetic Vortex Imaging

Abstract: A novel radar imaging technique based on orbital angular momentum (OAM) modulation is presented. First, the generation of electromagnetic (EM) vortex wave, which carries the OAM, using incrementally phased uniform circular array (UCA) is introduced, and factors that affect the phase-front distribution are analyzed. Subsequently, echo signal models of both multiple-in–multiple-out and multiple-in–single-out modes are established. The target images are obtained using the fast Fourier transform (FFT) and back-projection methods. Simulation results demonstrate that orbital angular momentum has the prospect for acquiring the azimuth information of radar target. The signal of both OAM modulation and frequency modulation can be used to obtain two-dimensional radar target image. The work can benefit the development of novel information-rich radar based on orbital angular momentum, as well as radar target recognition.

New Measurement of Antineutrino Oscillation with the Full Detector Configuration at Daya Bay

Abstract: We report a new measurement of electron antineutrino disappearance using the fully constructed Daya Bay Reactor Neutrino Experiment. The final two of eight antineutrino detectors were installed in the summer of 2012. Including the 404 days of data collected from October 2012 to November 2013 resulted in a total exposure of 6.9×10^5 GW_(th) ton days, a 3.6 times increase over our previous results. Improvements in energy calibration limited variations between detectors to 0.2%. Removal of six ^(241)Am−^(13)C radioactive calibration sources reduced the background by a factor of 2 for the detectors in the experimental hall furthest from the reactors. Direct prediction of the antineutrino signal in the far detectors based on the measurements in the near detectors explicitly minimized the dependence of the measurement on models of reactor antineutrino emission. The uncertainties in our estimates of sin 2^2θ_(13) and |Δm^2_(ee)| were halved as a result of these improvements. An analysis of the relative antineutrino rates and energy spectra between detectors gave sin^2 2θ_(13)=0.084±0.005 and |Δm^2_(ee)|=(2.42±0.11)×10^(−3) eV^2 in the three-neutrino framework.

A Dynamically Optimized SSVEP Brain–Computer Interface (BCI) Speller

Abstract: The aim of this study was to design a dynamically optimized steady-state visually evoked potential (SSVEP) brain-computer interface (BCI) system with enhanced performance relative to previous SSVEP BCIs in terms of the number of items selectable on the interface, accuracy, and speed. In this approach, the row/column (RC) paradigm was employed in a SSVEP speller to increase the number of items. The target is detected by subsequently determining the row and column coordinates. To improve spelling accuracy, we added a posterior processing after the canonical correlation analysis (CCA) approach to reduce the interfrequency variation between different subjects and named the new signal processing method CCA-RV, and designed a real-time biofeedback mechanism to increase attention on the visual stimuli. To achieve reasonable online spelling speed, both fixed and dynamic approaches for setting the optimal stimulus duration were implemented and compared. Experimental results for 11 subjects suggest that the CCA-RV method and the real-time biofeedback effectively increased accuracy compared with CCA and the absence of real-time feedback, respectively. In addition, both optimization approaches for setting stimulus duration achieved reasonable online spelling performance. However, the dynamic optimization approach yielded a higher practical information transfer rate (PITR) than the fixed optimization approach. The average online PITR achieved by the proposed adaptive SSVEP speller, including the time required for breaks between selections and error correction, was 41.08 bit/min. These results indicate that our BCI speller is promising for use in SSVEP-based BCI applications.

Workflow scheduling in cloud: a survey

Abstract: To program in distributed computing environments such as grids and clouds, workflow is adopted as an attractive paradigm for its powerful ability in expressing a wide range of applications, including scientific computing, multi-tier Web, and big data processing applications. With the development of cloud technology and extensive deployment of cloud platform, the problem of workflow scheduling in cloud becomes an important research topic. The challenges of the problem lie in: NP-hard nature of task-resource mapping; diverse QoS requirements; on-demand resource provisioning; performance fluctuation and failure handling; hybrid resource scheduling; data storage and transmission optimization. Consequently, a number of studies, focusing on different aspects, emerged in the literature. In this paper, we firstly conduct taxonomy and comparative review on workflow scheduling algorithms. Then, we make a comprehensive survey of workflow scheduling in cloud environment in a problem---solution manner. Based on the analysis, we also highlight some research directions for future investigation.

A Self-Adaptive Parameter Selection Trajectory Prediction Approach via Hidden Markov Models

Abstract: Trajectory prediction of objects in moving objects databases (MODs) has garnered wide support in a variety of applications and is gradually becoming an active research area. The existing trajectory prediction algorithms focus on discovering frequent moving patterns or simulating the mobility of objects via mathematical models. While these models are useful in certain applications, they fall short in describing the position and behavior of moving objects in a network-constraint environment. Aiming to solve this problem, a hidden Markov model (HMM)-based trajectory prediction algorithm is proposed, called Hidden Markov model-based Trajectory Prediction (HMTP). By analyzing the disadvantages of HMTP, a self-adaptive parameter selection algorithm called HMTP $\ast$ is proposed, which captures the parameters necessary for real-world scenarios in terms of objects with dynamically changing speed. In addition, a density-based trajectory partition algorithm is introduced, which helps improve the efficiency of prediction. In order to evaluate the effectiveness and efficiency of the proposed algorithms, extensive experiments were conducted, and the experimental results demonstrate that the effect of critical parameters on the prediction accuracy in the proposed paradigm, with regard to HMTP $\ast$ , can greatly improve the accuracy when compared with HMTP, when subjected to randomly changing speeds. Moreover, it has higher positioning precision than HMTP due to its capability of self-adjustment.

Adaptive Dynamic Surface Control of a Class of Nonlinear Systems With Unknown Direction Control Gains and Input Saturation

Abstract: In this paper, adaptive neural network based dynamic surface control (DSC) is developed for a class of nonlinear strict-feedback systems with unknown direction control gains and input saturation. A Gaussian error function based saturation model is employed such that the backstepping technique can be used in the control design. The explosion of complexity in traditional backstepping design is avoided by utilizing DSC. Based on backstepping combined with DSC, adaptive radial basis function neural network control is developed to guarantee that all the signals in the closed-loop system are globally bounded, and the tracking error converges to a small neighborhood of origin by appropriately choosing design parameters. Simulation results demonstrate the effectiveness of the proposed approach and the good performance is guaranteed even though both the saturation constraints and the wrong control direction are occurred.

Median robust extended local binary pattern for texture classification

Abstract: Local Binary Patterns (LBP) are among the most computationally efficient amongst high-performance texture features. However, LBP is very sensitive to image noise and is unable to capture macrostructure information. To best address these disadvantages, in this paper we introduce a novel descriptor for texture classification, the Median Robust Extended Local Binary Pattern (MRELBP). In contrast to traditional LBP and many LBP variants, MRELBP compares local image medians instead of raw image intensities. We develop a multiscale LBP-type descriptor by efficiently comparing image medians over a novel sampling scheme, which can capture both microstructure and macrostructure. A comprehensive evaluation on benchmark datasets reveals MRELBP's remarkable performance (robust to gray scale variations, rotation changes and noise) relative to state-of-the-art algorithms, but nevertheless at a low computational cost, producing the best classification scores of 99.82%, 99.38% and 99.77% on three popular Outex test suites. Furthermore, MRELBP is also shown to be highly robust to image noise including Gaussian noise, Gaussian blur, Salt-and-Pepper noise and random pixel corruption.

A Bilateral CFAR Algorithm for Ship Detection in SAR Images

Abstract: A bilateral constant false alarm rate (CFAR) algorithm for ship detection in synthetic aperture radar (SAR) images is proposed in this letter. Compared to the standard CFAR algorithm, the proposed algorithm can reduce the influence of SAR ambiguities and sea clutter, by means of a combination of the intensity distribution and the spatial distribution of SAR images. The spatial distribution plays an equally important role as the intensity distribution. It is estimated before ship detection by a new kernel density estimation algorithm proposed in this letter. The experimental results of typical SAR images show that the algorithm is effective.

Giant electrical modulation of magnetization in Co40Fe40B20/Pb(Mg1/3Nb2/3)0.7Ti0.3O3(011) heterostructure.

Abstract: We report a giant electric-field control of magnetization (M) as well as magnetic anisotropy in a Co40Fe40B20(CoFeB)/Pb(Mg1/3Nb2/3)0.7Ti0.3O3(PMN-PT) structure at room temperature, in which a maximum relative magnetization change (ΔM/M) up to 83% with a 90° rotation of the easy axis under electric fields were observed by different magnetic measurement systems with in-situ electric fields. The mechanism for this giant magnetoelectric (ME) coupling can be understood as the combination of the ultra-high value of anisotropic in-plane piezoelectric coefficients of (011)-cut PMN-PT due to ferroelectric polarization reorientation and the perfect soft ferromagnetism without magnetocrystalline anisotropy of CoFeB film. Besides the giant electric-field control of magnetization and magnetic anisotropy, this work has also demonstrated the feasibility of reversible and deterministic magnetization reversal controlled by pulsed electric fields with the assistance of a weak magnetic field, which is important for realizing strain-mediated magnetoelectric random access memories.

Wait for it: determinants of pull request evaluation latency on GitHub

Abstract: The pull-based development model, enabled by git and popularised by collaborative coding platforms like Bit Bucket, Gitorius, and GitHub, is widely used in distributed software teams. While this model lowers the barrier to entry for potential contributors (since anyone can submit pull requests to any repository), it also increases the burden on integrators (i.e., Members of a project's core team, responsible for evaluating the proposed changes and integrating them into the main development line), who struggle to keep up with the volume of incoming pull requests. In this paper we report on a quantitative study that tries to resolve which factors affect pull request evaluation latency in GitHub. Using regression modeling on data extracted from a sample of GitHub projects using the Travis-CI continuous integration service, we find that latency is a complex issue, requiring many independent variables to explain adequately.

Hierarchical and Networked Vehicle Surveillance in ITS: A Survey

Abstract: Traffic surveillance has become an important topic in intelligent transportation systems (ITSs), which is aimed at monitoring and managing traffic flow. With the progress in computer vision, video-based surveillance systems have made great advances on traffic surveillance in ITSs. However, the performance of most existing surveillance systems is susceptible to challenging complex traffic scenes (e.g., object occlusion, pose variation, and cluttered background). Moreover, existing related research is mainly on a single video sensor node, which is incapable of addressing the surveillance of traffic road networks. Accordingly, we present a review of the literature on the video-based vehicle surveillance systems in ITSs. We analyze the existing challenges in video-based surveillance systems for the vehicle and present a general architecture for video surveillance systems, i.e., the hierarchical and networked vehicle surveillance, to survey the different existing and potential techniques. Then, different methods are reviewed and discussed with respect to each module. Applications and future developments are discussed to provide future needs of ITS services.

Adaptive NN Control of a Class of Nonlinear Systems With Asymmetric Saturation Actuators

Abstract: In this note, adaptive neural network (NN) control is investigated for a class of uncertain nonlinear systems with asymmetric saturation actuators and external disturbances. To handle the effect of nonsmooth asymmetric saturation nonlinearity, a Gaussian error function-based continuous differentiable asymmetric saturation model is employed such that the backstepping technique can be used in the control design. The explosion of complexity in traditional backstepping design is avoided using dynamic surface control. Using radial basis function NN, adaptive control is developed to guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of origin by appropriately choosing design constants. The effectiveness of the proposed control is demonstrated in the simulation study.

SAR Target Recognition via Joint Sparse Representation of Monogenic Signal

Abstract: In this paper, the classification via sprepresentation and multitask learning is presented for target recognition in SAR image. To capture the characteristics of SAR image, a multidimensional generalization of the analytic signal, namely the monogenic signal, is employed. The original signal can be then orthogonally decomposed into three components: 1) local amplitude; 2) local phase; and 3) local orientation. Since the components represent the different kinds of information, it is beneficial by jointly considering them in a unifying framework. However, these components are infeasible to be directly utilized due to the high dimension and redundancy. To solve the problem, an intuitive idea is to define an augmented feature vector by concatenating the components. This strategy usually produces some information loss. To cover the shortage, this paper considers three components into different learning tasks, in which some common information can be shared. Specifically, the component-specific feature descriptor for each monogenic component is produced first. Inspired by the recent success of multitask learning, the resulting features are then fed into a joint sparse representation model to exploit the intercorrelation among multiple tasks. The inference is reached in terms of the total reconstruction error accumulated from all tasks. The novelty of this paper includes 1) the development of three component-specific feature descriptors; 2) the introduction of multitask learning into sparse representation model; 3) the numerical implementation of proposed method; and 4) extensive comparative experimental studies on MSTAR SAR dataset, including target recognition under standard operating conditions, as well as extended operating conditions, and the capability of outliers rejection.

Mixing-related low frequency oscillation of combustion in an ethylene-fueled supersonic combustor

Abstract: The present work investigated combustion instabilities inside an ethylene-fueled scramjet combustor mounted on a Mach 2.1 direct-connect test facility with an inflow stagnation temperature of 846 K. Effects of fueling schemes on the combustion stability characteristics were examined. The experimental results suggest that the oscillation modes correlate with mixing status closely. For the cases with a quasi-steady thermal throat or stable shock trains, flame fluctuation exists in a mode of thermo-acoustic type oscillation with a broad frequency range. For the cases with a transient thermal throat, if a fuel/air premixed region from the injection to the cavity flameholder exists, the cavity pilot flame could reignite the fuel/air mixture and undergo a process similar to deflagration–detonation transition (DDT). This process couples with the flame quenching upstream of the injection location, and a DDT-type low frequency oscillation can be formed.

A Miniaturized Absorptive Frequency Selective Surface

Abstract: A miniaturized absorptive frequency selective surface (MAFSS) is presented in this letter, composed of a layer of miniaturized resistive surface placed above a metallic bandpass FSS. The MAFSS performs as a bandpass filter at operation band around 0.92 GHz, and acts as an absorber over a wide out-of-band 3–9 GHz. Moreover, due to its miniaturized elements, the MAFSS exhibits the property of eliminating the grating lobe in absorption band when illuminated by oblique incident wave. Numerical and experimental results have been given.

Towards photodetection with high efficiency and tunable spectral selectivity: graphene plasmonics for light trapping and absorption engineering

Abstract: Plasmonics can be used to improve absorption in optoelectronic devices and has been intensively studied for solar cells and photodetectors. Graphene has recently emerged as a powerful plasmonic material. It shows significantly less loss compared to traditional plasmonic materials such as gold and silver and its plasmons can be tuned by changing the Fermi energy with chemical or electrical doping. Here we propose the use of graphene plasmonics for light trapping in optoelectronic devices and show that the excitation of localized plasmons in doped, nanostructured graphene can enhance optical absorption in its surrounding medium including both bulky and two-dimensional materials by tens of times, which may lead to a new generation of photodetectors with high efficiency and tunable spectral selectivity in the mid-infrared and THz ranges.

Convolutional neural network-based image representation for visual loop closure detection

Abstract: Deep convolutional neural networks (CNN) have recently been shown in many computer vision and pattern recognition applications to outperform by a significant margin state-of-the-art solutions that use traditional hand-crafted features. However, this impressive performance is yet to be fully exploited in robotics. In this paper, we focus one specific problem that can benefit from the recent development of the CNN technology, i.e., we focus on using a pre-trained CNN model as a method of generating an image representation appropriate for visual loop closure detection in SLAM (simultaneous localization and mapping). We perform a comprehensive evaluation of the outputs at the intermediate layers of a CNN as image descriptors, in comparison with state-of-the-art image descriptors, in terms of their ability to match images for detecting loop closures. The main conclusions of our study include: (a) CNN-based image representations perform comparably to state-of-the-art hand-crafted competitors in environments without significant lighting change, (b) they outperform state-of-the-art competitors when lighting changes significantly, and (c) they are also significantly faster to extract than the state-of-the-art hand-crafted features even on a conventional CPU and are two orders of magnitude faster on an entry-level GPU.

Z-scan measurement of the nonlinear refractive index of monolayer WS(2).

Abstract: Transition metal dichalcogenides (TMDCs), such as tungsten disulfide (WS(2)), are layered materials with strong in-plane bonding and weak out-of-plane interactions enabling exfoliation into two-dimensional layers of single unit cell thickness. Recent advances in nanoscale materials characterization and few layer TMDCs' unique optical properties make them a research hot-spot in nonlinear optics. In this work, the nonlinear refractive index of monolayer WS(2) has been characterized with Z-scan measurement under 800nm femtosecond pulsed laser excitation, and a value of n2 ≃ (8.1 ± 0.41) × 10(-13)m(2)/W is obtained. A shift from saturable absorption to reverse saturable absorption was observed at higher input pump intensities in the experiments. The transition process was analyzed using a phenomenological model based on two photon absorption, and the two photon absorption coefficient was estimated about (3.7±0.28)×10(-6)m/W.