Journals / CMC / Vol.57, No.1
Table of Content


  • ARTICLE

    Analyzing Cross-domain Transportation Big Data of New York City with Semi-supervised and Active Learning

    Huiyu Sun1,*, Suzanne McIntosh1
    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 1-9, 2018, DOI:10.32604/cmc.2018.03684
    Abstract The majority of big data analytics applied to transportation datasets suffer from being too domain-specific, that is, they draw conclusions for a dataset based on analytics on the same dataset. This makes models trained from one domain (e.g. taxi data) applies badly to a different domain (e.g. Uber data). To achieve accurate analyses on a new domain, substantial amounts of data must be available, which limits practical applications. To remedy this, we propose to use semi-supervised and active learning of big data to accomplish the domain adaptation task: Selectively choosing a small amount of datapoints from a new domain while… More >

  • ARTICLE

    Improved VGG Model for Road Traffic Sign Recognition

    Shuren Zhou1,2,*, Wenlong Liang1,2, Junguo Li1,2, Jeong-Uk Kim3
    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 11-24, 2018, DOI:10.32604/cmc.2018.02617
    Abstract Road traffic sign recognition is an important task in intelligent transportation system. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, it presents a road traffic sign recognition algorithm based on a convolutional neural network. In natural scenes, traffic signs are disturbed by factors such as illumination, occlusion, missing and deformation, and the accuracy of recognition decreases, this paper proposes a model called Improved VGG (IVGG) inspired by VGG model. The IVGG model includes 9 layers, compared with the original VGG model, it is added max-pooling… More >

  • ARTICLE

    Multi-task Joint Sparse Representation Classification Based on Fisher Discrimination Dictionary Learning

    Rui Wang1, Miaomiao Shen1,*, Yanping Li1, Samuel Gomes2
    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 25-48, 2018, DOI:10.32604/cmc.2018.02408
    Abstract Recently, sparse representation classification (SRC) and fisher discrimination dictionary learning (FDDL) methods have emerged as important methods for vehicle classification. In this paper, inspired by recent breakthroughs of discrimination dictionary learning approach and multi-task joint covariate selection, we focus on the problem of vehicle classification in real-world applications by formulating it as a multi-task joint sparse representation model based on fisher discrimination dictionary learning to merge the strength of multiple features among multiple sensors. To improve the classification accuracy in complex scenes, we develop a new method, called multi-task joint sparse representation classification based on fisher discrimination dictionary learning, for… More >

  • ARTICLE

    Snow Cover Mapping for Mountainous Areas by Fusion of MODIS L1B and Geographic Data Based on Stacked Denoising Auto-Encoders

    Xi Kan1, Yonghong Zhang2,*, Linglong Zhu2, Liming Xiao2, Jiangeng Wang3, Wei Tian4, Haowen Tan5
    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 49-68, 2018, DOI:10.32604/cmc.2018.02376
    Abstract Snow cover plays an important role in meteorological and hydrological researches. However, the accuracies of currently available snow cover products are significantly lower in mountainous areas than in plains, due to the serious snow/cloud confusion problem caused by high altitude and complex topography. Aiming at this problem, an improved snow cover mapping approach for mountainous areas was proposed and applied in Qinghai-Tibetan Plateau. In this work, a deep learning framework named Stacked Denoising Auto-Encoders (SDAE) was employed to fuse the MODIS multispectral images and various geographic datasets, which are then classified into three categories: Snow, cloud and snow-free land. Moreover,… More >

  • ARTICLE

    New Method for Computer Identification Through Electromagnetic Radiation

    Jun Shi1, Zhujun Zhang2, Yangyang Li1,*, Rui Wang1, Hao Shi1, Xile Li3
    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 69-80, 2018, DOI:10.32604/cmc.2018.03688
    Abstract The electromagnetic waves emitted from devices can be a source of information leakage and can cause electromagnetic compatibility (EMC) problems. Electromagnetic radiation signals from computer displays can be a security risk if they are intercepted and reconstructed. In addition, the leaks may reveal the hardware information of the computer, which is more important for some attackers, protectors and security inspection workers. In this paper, we propose a statistical distribution based algorithm (SD algorithm) to extracted eigenvalues from electromagnetic radiate video signals, and then classified computers by using classifier based on Bayesian and SVM. We can identify computers automatically and accurately… More >

  • ARTICLE

    Crowdsourcing-Based Framework for Teaching Quality Evaluation and Feedback Using Linguistic 2-Tuple

    Tiejun Wang1, Tao Wu1,*, Amir Homayoon Ashrafzadeh2, Jia He1
    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 81-96, 2018, DOI:10.32604/cmc.2018.03259
    Abstract Crowdsourcing is widely used in various fields to collect goods and services from large participants. Evaluating teaching quality by collecting feedback from experts or students after class is not only delayed but also not accurate. In this paper, we present a crowdsourcing-based framework to evaluate teaching quality in the classroom using a weighted average operator to aggregate information from students’ questionnaires described by linguistic 2-tuple terms. Then we define crowd grade based on similarity degree to distinguish contribution from different students and minimize the abnormal students’ impact on the evaluation. The crowd grade would be updated at the end of… More >

  • ARTICLE

    Method of Time Series Similarity Measurement Based on Dynamic Time Warping

    Lianggui Liu1,*, Wei Li1, Huiling Jia1
    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 97-106, 2018, DOI:10.32604/cmc.2018.03511
    Abstract With the rapid development of mobile communication all over the world, the similarity of mobile phone communication data has received widely attention due to its advantage for the construction of smart cities. Mobile phone communication data can be regarded as a type of time series and dynamic time warping (DTW) and derivative dynamic time warping (DDTW) are usually used to analyze the similarity of these data. However, many traditional methods only calculate the distance between time series while neglecting the shape characteristics of time series. In this paper, a novel hybrid method based on the combination of dynamic time warping… More >

  • ARTICLE

    Expression Preserved Face Privacy Protection Based on Multi-mode Discriminant Analysis

    Xiang Wang1, *, Chen Xiong1, Qingqi Pei1, Youyang Qu2
    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 107-121, 2018, DOI:10.32604/cmc.2018.03675
    Abstract Most visual privacy protection methods only hide the identity information of the face images, but the expression, behavior and some other information, which are of great significant in the live broadcast and other scenarios, are also destroyed by the privacy protection process. To this end, this paper introduces a method to remove the identity information while preserving the expression information by performing multi-mode discriminant analysis on the images normalized with AAM algorithm. The face images are decomposed into mutually orthogonal subspaces corresponding to face attributes such as gender, race and expression, each of which owns related characteristic parameters. Then, the… More >

  • ARTICLE

    Dynamical Interaction Between Information and Disease Spreading in Populations of Moving Agents

    Lingling Xia1, Bo Song2,3, Zhengjun Jing4, Yurong Song5,*, Liang Zhang1
    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 123-144, 2018, DOI:10.32604/cmc.2018.03738
    Abstract Considering dynamical disease spreading network consisting of moving individuals, a new double-layer network is constructed, one where the information dissemination process takes place and the other where the dynamics of disease spreading evolves. On the basis of Markov chains theory, a new model characterizing the coupled dynamics between information dissemination and disease spreading in populations of moving agents is established and corresponding state probability equations are formulated to describe the probability in each state of every node at each moment. Monte Carlo simulations are performed to characterize the interaction process between information and disease spreading and investigate factors that influence… More >

  • ARTICLE

    Modification of Nano Tourmaline Surface Treatment Agent and Its Performance on Negative Ion Release

    Guorui Huang1, Zhongkai Cui2, Pengfei Zhu1, Xiaoyun Liu1,*
    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 145-150, 2018, DOI:10.32604/cmc.2018.02947
    Abstract In this paper, a kind of wall fabric’s surface treatment agent modified with nonionic surfactant was reported. This surface treatment agent was prepared by using nano tourmaline powder dispersion in water with surfactant as dispersants by sand milling. Under the influence of different dispersants, the negative ions releasing amount of functional wall fabrics, the milling process and the storage stability of nano tourmaline powder dispersion were discussed. The results showed that nano tourmaline powder dispersion achieved the smallest average diameter of 44 nm and had best storage stability that the average diameter maintained below 200 nm in 17 days when… More >

  • ARTICLE

    Research on Operation of UAVs in Non-isolated Airspace

    Zhaoyue Zhang1, *, Jing Zhang2, Peng Wang1, Lei Chen3
    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 151-166, 2018, DOI:10.32604/cmc.2018.02890
    Abstract In order to explore the safe operation of UAVs in non-segregated airspace, a collision risk model for cylindrical UAVs based on conflict areas was constructed and the risk of conflict between manned and unmanned aerial vehicles was researched. According to the results of risk analysis, a strategy for solving the conflict of aircraft is proposed, and the risk assessment experiment of unmanned aerial vehicle (UAV) in non-isolated airspace conflict is carried out. The results show that under the experimental conditions, large unmanned aerial vehicles equipped with ADS-B, TCAS and other airborne sensing systems will indeed interfere with other aircraft in… More >

  • ARTICLE

    A Method for Improving CNN-Based Image Recognition Using DCGAN

    Wei Fang1,2, Feihong Zhang1,*, Victor S. Sheng3, Yewen Ding1
    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 167-178, 2018, DOI:10.32604/cmc.2018.02356
    Abstract Image recognition has always been a hot research topic in the scientific community and industry. The emergence of convolutional neural networks(CNN) has made this technology turned into research focus on the field of computer vision, especially in image recognition. But it makes the recognition result largely dependent on the number and quality of training samples. Recently, DCGAN has become a frontier method for generating images, sounds, and videos. In this paper, DCGAN is used to generate sample that is difficult to collect and proposed an efficient design method of generating model. We combine DCGAN with CNN for the second time.… More >

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