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  • Open Access

    ARTICLE

    V-Shaped Monopole Antenna with Chichena Itzia Inspired Defected Ground Structure for UWB Applications

    Naeem Ahmad Jan1, Saad Hassan Kiani2, Fazal Muhammad2, Daniyal Ali Sehrai2, Amjad Iqbal3, Muhammad Tufail4, Sunghwan Kim5, *

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 19-32, 2020, DOI:10.32604/cmc.2020.011091

    Abstract Due to rapid growth in wireless communication technology, higher bandwidth requirement for advance telecommunication systems, capable of operating on two or higher bands with higher channel capacities and minimum distortion losses is desired. In this paper, a compact Ultra-Wideband (UWB) V-shaped monopole antenna is presented. UWB response is achieved by modifying the ground plane with Chichen Itzia inspired rectangular staircase shape. The proposed V-shaped is designed by incorporating a rectangle, and an inverted isosceles triangle using FR4 substrate. The size of the antenna is 25 mm×26 mm×1.6 mm. The proposed V-shaped monopole antenna produces bandwidth response of 3 GHz Industrial,… More >

  • Open Access

    ARTICLE

    Predicting Concrete Compressive Strength Using Deep Convolutional Neural Network Based on Image Characteristics

    Sanghyo Lee1, Yonghan Ahn2, Ha Young Kim3, *

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 1-17, 2020, DOI:10.32604/cmc.2020.011104

    Abstract In this study, we examined the efficacy of a deep convolutional neural network (DCNN) in recognizing concrete surface images and predicting the compressive strength of concrete. A digital single-lens reflex (DSLR) camera and microscope were simultaneously used to obtain concrete surface images used as the input data for the DCNN. Thereafter, training, validation, and testing of the DCNNs were performed based on the DSLR camera and microscope image data. Results of the analysis indicated that the DCNN employing DSLR image data achieved a relatively higher accuracy. The accuracy of the DSLR-derived image data was attributed to the relatively wider range… More >

  • Open Access

    ARTICLE

    Information Classification and Extraction on Official Web Pages of Organizations

    Jinlin Wang1, Xing Wang1, *, Hongli Zhang1, Binxing Fang1, Yuchen Yang1, Jianan Liu2

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 2057-2073, 2020, DOI:10.32604/cmc.2020.011158

    Abstract As a real-time and authoritative source, the official Web pages of organizations contain a large amount of information. The diversity of Web content and format makes it essential for pre-processing to get the unified attributed data, which has the value of organizational analysis and mining. The existing research on dealing with multiple Web scenarios and accuracy performance is insufficient. This paper aims to propose a method to transform organizational official Web pages into the data with attributes. After locating the active blocks in the Web pages, the structural and content features are proposed to classify information with the specific model.… More >

  • Open Access

    ARTICLE

    Identification of Weather Phenomena Based on Lightweight Convolutional Neural Networks

    Congcong Wang1, 2, 3, Pengyu Liu1, 2, 3, *, Kebin Jia1, 2, 3, Xiaowei Jia4, Yaoyao Li1, 2, 3

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 2043-2055, 2020, DOI:10.32604/cmc.2020.010505

    Abstract Weather phenomenon recognition plays an important role in the field of meteorology. Nowadays, weather radars and weathers sensor have been widely used for weather recognition. However, given the high cost in deploying and maintaining the devices, it is difficult to apply them to intensive weather phenomenon recognition. Moreover, advanced machine learning models such as Convolutional Neural Networks (CNNs) have shown a lot of promise in meteorology, but these models also require intensive computation and large memory, which make it difficult to use them in reality. In practice, lightweight models are often used to solve such problems. However, lightweight models often… More >

  • Open Access

    ARTICLE

    Bilateral Collaborative Optimization for Cloud Manufacturing Service

    Bin Xu1, 2, Yong Tang1, Yi Zhu1, Wenqing Yan1, Cheng He3, Jin Qi1, *

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 2031-2042, 2020, DOI:10.32604/cmc.2020.011149

    Abstract Manufacturing service composition of the supply side and scheduling of the demand side are two important components of Cloud Manufacturing, which directly affect the quality of Cloud Manufacturing services. However, the previous studies on the two components are carried out independently and thus ignoring the internal relations and mutual constraints. Considering the two components on both sides of the supply and the demand of Cloud Manufacturing services at the same time, a Bilateral Collaborative Optimization Model of Cloud Manufacturing (BCOM-CMfg) is constructed in this paper. In BCOM-CMfg, to solve the manufacturing service scheduling problem on the supply side, a new… More >

  • Open Access

    ARTICLE

    A Distributed Covert Channel of the Packet Ordering Enhancement Model Based on Data Compression

    Lejun Zhang1, Tianwen Huang1, Xiaoyan Hu1, Zhijie Zhang1, Weizheng Wang2, Donghai Guan3, *, Chunhui Zhao1, 4, Seokhoon Kim5

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 2013-2030, 2020, DOI:10.32604/cmc.2020.011219

    Abstract Covert channel of the packet ordering is a hot research topic. Encryption technology is not enough to protect the security of both sides of communication. Covert channel needs to hide the transmission data and protect content of communication. The traditional methods are usually to use proxy technology such as tor anonymous tracking technology to achieve hiding from the communicator. However, because the establishment of proxy communication needs to consume traffic, the communication capacity will be reduced, and in recent years, the tor technology often has vulnerabilities that led to the leakage of secret information. In this paper, the covert channel… More >

  • Open Access

    ARTICLE

    A Recommendation Approach Based on Bayesian Networks for Clone Refactor

    Ye Zhai1, *, Dongsheng Liu1, Celimuge Wu2, Rongrong She1

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1999-2012, 2020, DOI:10.32604/cmc.2020.09950

    Abstract Reusing code fragments by copying and pasting them with or without minor adaptation is a common activity in software development. As a result, software systems often contain sections of code that are very similar, called code clones. Code clones are beneficial in reducing software development costs and development risks. However, recent studies have indicated some negative impacts as a result. In order to effectively manage and utilize the clones, we design an approach for recommending refactoring clones based on a Bayesian network. Firstly, clone codes are detected from the source code. Secondly, the clones that need to be refactored are… More >

  • Open Access

    ARTICLE

    Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

    Kui Fu1, Jiansheng Peng1, 2, *, Hanxiao Zhang2, Xiaoliang Wang3, Frank Jiang4

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1977-1997, 2020, DOI:10.32604/cmc.2020.09882

    Abstract Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the… More >

  • Open Access

    ARTICLE

    A Recommendation Method for Highly Sparse Dataset Based on Teaching Recommendation Factorization Machines

    Dunhong Yao1, 2, 3, Shijun Li4, *, Ang Li5, Yu Chen6

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1959-1975, 2020, DOI:10.32604/cmc.2020.010186

    Abstract There is no reasonable scientific basis for selecting the excellent teachers of the school’s courses. To solve the practical problem, we firstly give a series of normalization models for defining the key attributes of teachers’ professional foundation, course difficulty coefficient, and comprehensive evaluation of teaching. Then, we define a partial weight function to calculate the key attributes, and obtain the partial recommendation values. Next, we construct a highly sparse Teaching Recommendation Factorization Machines (TRFMs) model, which takes the 5-tuples relation including teacher, course, teachers’ professional foundation, course difficulty, teaching evaluation as the feature vector, and take partial recommendation value as… More >

  • Open Access

    ARTICLE

    Research on Vehicle Routing Problem with Soft Time Windows Based on Hybrid Tabu Search and Scatter Search Algorithm

    Jinhui Ge1, Xiaoliang Liu2, *, Guo Liang3

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1945-1958, 2020, DOI:10.32604/cmc.2020.010977

    Abstract With the expansion of the application scope of social computing problems, many path problems in real life have evolved from pure path optimization problems to social computing problems that take into account various social attributes, cultures, and the emotional needs of customers. The actual soft time window vehicle routing problem, speeding up the response of customer needs, improving distribution efficiency, and reducing operating costs is the focus of current social computing problems. Therefore, designing fast and effective algorithms to solve this problem has certain theoretical and practical significance. In this paper, considering the time delay problem of customer demand, the… More >

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