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  • Open Access

    ARTICLE

    Machine Learning Based Resource Allocation of Cloud Computing in Auction

    Jixian Zhang1, Ning Xie1, Xuejie Zhang1, Kun Yue1, Weidong Li2,*, Deepesh Kumar3

    CMC-Computers, Materials & Continua, Vol.56, No.1, pp. 123-135, 2018, DOI: 10.3970/cmc.2018.03728

    Abstract Resource allocation in auctions is a challenging problem for cloud computing. However, the resource allocation problem is NP-hard and cannot be solved in polynomial time. The existing studies mainly use approximate algorithms such as PTAS or heuristic algorithms to determine a feasible solution; however, these algorithms have the disadvantages of low computational efficiency or low allocate accuracy. In this paper, we use the classification of machine learning to model and analyze the multi-dimensional cloud resource allocation problem and propose two resource allocation prediction algorithms based on linear and logistic regressions. By learning a small-scale training More >

  • Open Access

    ARTICLE

    An Ensemble Based Hand Vein Pattern Authentication System

    M. Rajalakshmi*

    CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.2, pp. 209-220, 2018, DOI:10.3970/cmes.2018.114.209

    Abstract Amongst several biometric traits, Vein pattern biometric has drawn much attention among researchers and diverse users. It gains its importance due to its difficulty in reproduction and inherent security advantages. Many research papers have dealt with the topic of new generation biometric solutions such as iris and vein biometrics. However, most implementations have been based on small datasets due to the difficulties in obtaining samples. In this paper, a deeper study has been conducted on previously suggested methods based on Convolutional Neural Networks (CNN) using a larger dataset. Also, modifications are suggested for implementation using More >

  • Open Access

    ARTICLE

    Real-Time Moving Targets Detection in Dynamic Scenes

    Fan Li1, Yang Yang

    CMES-Computer Modeling in Engineering & Sciences, Vol.107, No.2, pp. 103-124, 2015, DOI:10.3970/cmes.2015.107.103

    Abstract The shift of the camera leads to unsteadiness of backgrounds in video sequences. The motion of camera will results in mixture of backgrounds and foregrounds motion. So it is a challenge for targets detection in dynamic scenes. A realtime moving target detection algorithm with low complexity in dynamic scenes is proposed in this paper. Sub-block based image registration is applied to remove the global motion of the video frame. Considering the blocks in one frame have different motion vectors, the global motion of each block is separately estimated. Then, a neighbor-based background modeling is applied More >

  • Open Access

    ARTICLE

    Matching Contours in Images through the use of Curvature, Distance to Centroid and Global Optimization with Order-Preserving Constraint

    Francisco P. M. Oliveira1, João Manuel R. S. Tavares1

    CMES-Computer Modeling in Engineering & Sciences, Vol.43, No.1, pp. 91-110, 2009, DOI:10.3970/cmes.2009.043.091

    Abstract This paper presents a new methodology to establish the best global match of objects' contours in images. The first step is the extraction of the sets of ordered points that define the objects' contours. Then, by using the curvature value and its distance to the corresponded centroid for each point, an affinity matrix is built. This matrix contains information of the cost for all possible matches between the two sets of ordered points. Then, to determine the desired one-to-one global matching, an assignment algorithm based on dynamic programming is used. This algorithm establishes the global More >

  • Open Access

    ARTICLE

    Improved Logistic Regression Algorithm Based on Kernel Density Estimation for Multi-Classification with Non-Equilibrium Samples

    Yang Yu1, Zeyu Xiong1,*, Yueshan Xiong1, Weizi Li2

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 103-118, 2019, DOI:10.32604/cmc.2019.05154

    Abstract Logistic regression is often used to solve linear binary classification problems such as machine vision, speech recognition, and handwriting recognition. However, it usually fails to solve certain nonlinear multi-classification problem, such as problem with non-equilibrium samples. Many scholars have proposed some methods, such as neural network, least square support vector machine, AdaBoost meta-algorithm, etc. These methods essentially belong to machine learning categories. In this work, based on the probability theory and statistical principle, we propose an improved logistic regression algorithm based on kernel density estimation for solving nonlinear multi-classification. We have compared our approach with More >

  • Open Access

    ARTICLE

    High Precision SAR ADC Using CNTFET for Internet of Things

    V. Gowrishankar1,*, K. Venkatachalam1

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 947-957, 2019, DOI:10.32604/cmc.2019.07749

    Abstract A high precision 10-bit successive approximation register analog to digital converter (ADC) designed and implemented in 32nm CNTFET process technology at the supply of 0.6V, with 73.24 dB SNDR at a sampling rate of 640 MS/s with the average power consumption of 120.2 μW for the Internet of things node. The key components in CNTFET SAR ADCs are binary scaled charge redistribution digital to analog converter using MOS capacitors, CNTFET based dynamic latch comparator and simple SAR digital code error correction logic. These techniques are used to increase the sampling rate and precision while ensuring More >

  • Open Access

    ARTICLE

    Design of Feedback Shift Register of Against Power Analysis Attack

    Yongbin Zhao1,*, XuYang1, RanranLi1

    CMC-Computers, Materials & Continua, Vol.58, No.2, pp. 517-527, 2019, DOI:10.32604/cmc.2019.03680

    Abstract Stream ciphers based on linear feedback shift register (LFSR) are suitable for constrained environments, such as satellite communications, radio frequency identification devices tag, sensor networks and Internet of Things, due to its simple hardware structures, high speed encryption and lower power consumption. LFSR, as a cryptographic primitive, has been used to generate a maximum period sequence. Because the switching of the status bits is regular, the power consumption of the LFSR is correlated in a linear way. As a result, the power consumption characteristics of stream cipher based on LFSR are vulnerable to leaking initialization More >

  • Open Access

    ARTICLE

    Spatial Quantitative Analysis of Garlic Price Data Based on ArcGIS Technology

    Guojing Wu1, Chao Zhang1,*, Pingzeng Liu1, Wanming Ren2, Yong Zheng2, Feng Guo1, Xiaowei Chen3, Russell Higgs4

    CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 183-195, 2019, DOI:10.32604/cmc.2019.03792

    Abstract In order to solve the hidden regional relationship among garlic prices, this paper carries out spatial quantitative analysis of garlic price data based on ArcGIS technology. The specific analysis process is to collect prices of garlic market from 2015 to 2017 in different regions of Shandong Province, using the Moran's Index to obtain monthly Moran indicators are positive, so as to analyze the overall positive relationship between garlic prices; then using the geostatistical analysis tool in ArcGIS to draw a spatial distribution Grid diagram, it was found that the price of garlic has a significant… More >

  • 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 More >

  • Open Access

    ARTICLE

    A Higher Order Synergistic Damage Model for Prediction of Stiffness Changes due to Ply Cracking in Composite Laminates

    Chandra Veer Singh1,*

    CMC-Computers, Materials & Continua, Vol.34, No.3, pp. 227-249, 2013, DOI:10.3970/cmc.2013.034.227

    Abstract A non-linear damage model is developed for the prediction of stiffness degradation in composite laminates due to transverse matrix cracking. The model follows the framework of a recently developed synergistic damage mechanics (SDM) approach which combines the strengths of micro-damage mechanics and continuum damage mechanics (CDM) through the so-called constraint parameters. A common limitation of the current CDM and SDM models has been the tendency to over-predict stiffness changes at high crack densities due to linearity inherent in their stiffness-damage relationships. The present paper extends this SDM approach by including higher order damage terms in More >

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