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  • Open Access

    ARTICLE

    Rare Bird Sparse Recognition via Part-Based Gist Feature Fusion and Regularized Intraclass Dictionary Learning

    Jixin Liu1,*, Ning Sun1,2, Xiaofei Li1, Guang Han1, Haigen Yang1, Quansen Sun3

    CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 435-446, 2018, DOI: 10.3970/cmc.2018.02177

    Abstract Rare bird has long been considered an important in the field of airport security, biological conservation, environmental monitoring, and so on. With the development and popularization of IOT-based video surveillance, all day and weather unattended bird monitoring becomes possible. However, the current mainstream bird recognition methods are mostly based on deep learning. These will be appropriate for big data applications, but the training sample size for rare bird is usually very short. Therefore, this paper presents a new sparse recognition model via improved part detection and our previous dictionary learning. There are two achievements in our work: (1) after the… More >

  • Open Access

    ARTICLE

    An Optimized Labeling Scheme for Reachability Queries

    Xian Tang1,*, Ziyang Chen2, Haiyan Zhang3, Xiang Liu1, Yunyu Shi1, Asad Shahzadi4

    CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 267-283, 2018, DOI:10.3970/cmc.2018.01839

    Abstract Answering reachability queries is one of the fundamental graph operations. Existing approaches either accelerate index construction by constructing an index that covers only partial reachability relationship, which may result in performing cost traversing operation when answering a query; or accelerate query answering by constructing an index covering the complete reachability relationship, which may be inefficient due to comparing the complete node labels. We propose a novel labeling scheme, which covers the complete reachability relationship, to accelerate reachability queries processing. The idea is to decompose the given directed acyclic graph (DAG) G into two subgraphs, G1 and G2. For G1, we… More >

  • Open Access

    ARTICLE

    An Abnormal Network Flow Feature Sequence Prediction Approach for DDoS Attacks Detection in Big Data Environment

    Jieren Cheng1,2, Ruomeng Xu1,*, Xiangyan Tang1, Victor S. Sheng3, Canting Cai1

    CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 95-119, 2018, DOI:10.3970/cmc.2018.055.095

    Abstract Distributed denial-of-service (DDoS) is a rapidly growing problem with the fast development of the Internet. There are multitude DDoS detection approaches, however, three major problems about DDoS attack detection appear in the big data environment. Firstly, to shorten the respond time of the DDoS attack detector; secondly, to reduce the required compute resources; lastly, to achieve a high detection rate with low false alarm rate. In the paper, we propose an abnormal network flow feature sequence prediction approach which could fit to be used as a DDoS attack detector in the big data environment and solve aforementioned problems. We define… More >

  • Open Access

    ARTICLE

    A Fusion Steganographic Algorithm Based on Faster R-CNN

    Ruohan Meng1,2, Steven G. Rice3, Jin Wang4, Xingming Sun1,2,*

    CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 1-16, 2018, DOI:10.3970/cmc.2018.055.001

    Abstract The aim of information hiding is to embed the secret message in a normal cover media such as image, video, voice or text, and then the secret message is transmitted through the transmission of the cover media. The secret message should not be damaged on the process of the cover media. In order to ensure the invisibility of secret message, complex texture objects should be chosen for embedding information. In this paper, an approach which corresponds multiple steganographic algorithms to complex texture objects was presented for hiding secret message. Firstly, complex texture regions are selected based on a kind of… More >

  • Open Access

    ARTICLE

    Fingerprint Liveness Detection from Different Fingerprint Materials Using Convolutional Neural Network and Principal Component Analysis

    Chengsheng Yuan1,2,3, Xinting Li3, Q. M. Jonathan Wu3, Jin Li4,5, Xingming Sun1,2

    CMC-Computers, Materials & Continua, Vol.53, No.4, pp. 357-372, 2017, DOI:10.3970/cmc.2017.053.357

    Abstract Fingerprint-spoofing attack often occurs when imposters gain access illegally by using artificial fingerprints, which are made of common fingerprint materials, such as silicon, latex, etc. Thus, to protect our privacy, many fingerprint liveness detection methods are put forward to discriminate fake or true fingerprint. Current work on liveness detection for fingerprint images is focused on the construction of complex handcrafted features, but these methods normally destroy or lose spatial information between pixels. Different from existing methods, convolutional neural network (CNN) can generate high-level semantic representations by learning and concatenating low-level edge and shape features from a large amount of labeled… More >

  • Open Access

    ARTICLE

    Fast and High-Resolution Optical Inspection System for In-Line Detection and Labeling of Surface Defects

    M. Chang1,2,3, Y. C. Chou1,2, P. T. Lin1,2, J. L. Gabayno2,4

    CMC-Computers, Materials & Continua, Vol.42, No.2, pp. 125-140, 2014, DOI:10.3970/cmc.2014.042.125

    Abstract Automated optical inspection systems installed in production lines help ensure high throughput by speeding up inspection of defects that are otherwise difficult to detect using the naked eye. However, depending on the size and surface properties of the products such as micro-cracks on touchscreen panels glass cover, the detection speed and accuracy are limited by the imaging module and lighting technique. Therefore the current inspection methods are still delegated to a few qualified personnel whose limited capacity has been a huge tradeoff for high volume production. In this study, an automated optical technology for in-line surface defect inspection is developed… More >

  • Open Access

    ARTICLE

    Matrix Crack Detection in Composite Plate with Spatially Random Material Properties using Fractal Dimension

    K. Umesh1, R. Ganguli1

    CMC-Computers, Materials & Continua, Vol.41, No.3, pp. 215-240, 2014, DOI:10.3970/cmc.2014.041.215

    Abstract Fractal dimension based damage detection method is investigated for a composite plate with random material properties. Composite material shows spatially varying random material properties because of complex manufacturing processes. Matrix cracks are considered as damage in the composite plate. Such cracks are often seen as the initial damage mechanism in composites under fatigue loading and also occur due to low velocity impact. Static deflection of the cantilevered composite plate with uniform loading is calculated using the finite element method. Damage detection is carried out based on sliding window fractal dimension operator using the static deflection. Two dimensional homogeneous Gaussian random… More >

  • Open Access

    ARTICLE

    Effect of Electric Field on the Response of Clamped-FreeMagnetostrictive/Piezoelectric/Magnetostrictive Laminates

    Kotaro Mori1, Fumio Narita1, Yasuhide Shindo1

    CMC-Computers, Materials & Continua, Vol.23, No.3, pp. 187-200, 2011, DOI:10.3970/cmc.2011.023.187

    Abstract This work deals with the response of clamped-free magnetostrictive/piezoelectric/magnetostrictive laminates under electric field both numerically and experimentally. The laminate is fabricated using two magnetostrictive Terfenol-D layers and a soft piezoelectric PZT layer. Easy axis of Terfenol-D layers is length direction, while the polarization of PZT layer is the thickness direction. The magnetostriction of the Terfenol-D layers bonded to the upper and lower surfaces of the PZT layer is first measured. Next, a nonlinear finite element analysis is employed to evaluate the second-order magnetoelastic constants in the Terfenol-D layers bonded to the PZT layer using measured data. The induced magnetic field… More >

  • Open Access

    ARTICLE

    Applications of the Phase-Coded Generalized Hough Transform to Feature Detection, Analysis, and Segmentation of Digital Microstructures

    Stephen R. Niezgoda1, Surya R. Kalidindi1,2

    CMC-Computers, Materials & Continua, Vol.14, No.2, pp. 79-98, 2009, DOI:10.3970/cmc.2009.014.079

    Abstract The generalized Hough transform is a common technique for feature detection in image processing. In this paper, we develop a size invariant Hough framework for the detection of arbitrary shapes in three dimensional digital microstructure datasets. The Hough transform is efficiently implemented via kernel convolution with complex Hough filters, where shape is captured in the magnitude of the filter and scale in the complex phase. In this paper, we further generalize the concept of a Hough filter by encoding other parameters of interest (e.g. orientation of plate or fiber constituents) in the complex phase, broadening the applicability of Hough transform… More >

  • Open Access

    ARTICLE

    Anomaly Detection

    Nadipuram R. Prasad1, Salvador Almanza-Garcia1, Thomas T. Lu2

    CMC-Computers, Materials & Continua, Vol.14, No.1, pp. 1-22, 2009, DOI:10.3970/cmc.2009.014.001

    Abstract The paper presents a revolutionary framework for the modeling, detection, characterization, identification, and machine-learning of anomalous behavior in observed phenomena arising from a large class of unknown and uncertain dynamical systems. An evolved behavior would in general be very difficult to correct unless the specific anomalous event that caused such behavior can be detected early, and any consequence attributed to the specific anomaly following its detection. Substantial investigative time and effort is required to back-track the cause for abnormal behavior and to recreate the event sequence leading to such abnormal behavior. The need to automatically detect anomalous behavior is therefore… More >

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