Ya Tu¹, Yun Lin¹, Jin Wang^2,3,*, Jeong-Uk Kim⁴

CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 243-254, 2018, DOI:10.3970/cmc.2018.01755

Abstract Deep Learning (DL) is such a powerful tool that we have seen tremendous success in areas such as Computer Vision, Speech Recognition, and Natural Language Pro-cessing. Since Automated Modulation Classification (AMC) is an important part in Cognitive Radio Networks, we try to explore its potential in solving signal modula-tion recognition problem. It cannot be overlooked that DL model is a complex mod-el, thus making them prone to over-fitting. DL model requires many training data to combat with over-fitting, but adding high quality labels to training data manually is not always cheap and accessible, especially in real-time system, which may counter… More >

Liang Yang^1,2, Caizhuang Wang^3,*, Shiwei Lin^2,*, Yang Cao², Xiaoheng Liu¹

CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 177-188, 2018, DOI:10.3970/cmc.2018.055.177

Abstract Understanding of metal oxidation is very critical to corrosion control, catalysis synthesis, and advanced materials engineering. Metal oxidation is a very complex phenomenon, with many different processes which are coupled and involved from the onset of reaction. In this work, the initial stage of oxidation on titanium surface was investigated in atomic scale by molecular dynamics (MD) simulations using a reactive force field (ReaxFF). We show that oxygen transport is the dominant process during the initial oxidation. Our simulation also demonstrate that a compressive stress was generated in the oxide layer which blocked the oxygen transport perpendicular to the Titanium… More >

G. Jayaprakash¹, M. P. Muthuraj^2,*

CMC-Computers, Materials & Continua, Vol.54, No.1, pp. 83-102, 2018, DOI:10.3970/cmc.2018.054.083

Abstract This paper discusses the applicability of relevance vector machine (RVM) based regression to predict the compressive strength of various self compacting concrete (SCC) mixes. Compressive strength data various SCC mixes has been consolidated by considering the effect of water cement ratio, water binder ratio and steel fibres. Relevance vector machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM has an identical functional form to the support vector machine, but provides probabilistic classification and regression. RVM is based on a Bayesian formulation of a linear model with an appropriate… More >

Susom Dutta¹, A. Ramach,ra Murthy², Dookie Kim³, Pijush Samui⁴

CMC-Computers, Materials & Continua, Vol.53, No.2, pp. 157-174, 2017, DOI:10.3970/cmc.2017.053.167

Abstract In the present scenario, computational modeling has gained much importance for the prediction of the properties of concrete. This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete (SCC). Three models, namely, Extreme Learning Machine (ELM), Adaptive Neuro Fuzzy Inference System (ANFIS) and Multi Adaptive Regression Spline (MARS) have been employed in the present study for the prediction of compressive strength of self compacting concrete. The contents of cement (c), sand (s), coarse aggregate (a), fly ash (f), water/powder (w/p) ratio and superplasticizer (sp) dosage have been taken as inputs… More >

Ravikumar Gurusamy¹, Dr Vijayan Subramaniam²

CMC-Computers, Materials & Continua, Vol.53, No.2, pp. 91-108, 2017, DOI:10.3970/cmc.2017.053.091

Abstract A new method for the denoising, extraction and tumor detection on MRI images is presented in this paper. MRI images help physicians study and diagnose diseases or tumors present in the brain. This work is focused towards helping the radiologist and physician to have a second opinion on the diagnosis. The ambiguity of Magnetic Resonance (MR) image features is solved in a simpler manner. The MRI image acquired from the machine is subjected to analysis in the work. The real-time data is used for the analysis. Basic preprocessing is performed using various filters for noise removal. The de-noised image is… More >

R. Praveena¹, S. Nirmala²

CMC-Computers, Materials & Continua, Vol.53, No.1, pp. 37-47, 2017, DOI:10.3970/cmc.2017.053.039

Abstract DSP operation in a Biomedical related therapeutic hardware need to be performed with high accuracy and with high speed. Portable DSP hardware’s like pulse/heart beat detectors must perform with reduced operational power due to lack of conventional power sources. This work proposes a hybrid biomedical hardware chip in which the speed and power utilization factors are greatly improved. Multipliers are the core operational unit of any DSP SoC. This work proposes a LUT based unsigned multiplication which is proven to be efficient in terms of high operating speed. For n bit input multiplication n*n memory array of 2n bit size… More >

Zhengong Zhou¹, Shuang Tian^1,2, Jiawei Zhang³

CMC-Computers, Materials & Continua, Vol.52, No.2, pp. 105-121, 2016, DOI:10.3970/cmc.2016.052.105

Abstract The methods of numerical simulation and test are combined to analyze the impact behavior of glass fiber reinforced aluminum alloy laminate (GLARE). A new failure criteria is proposed to obtain the impact failure of GLARE, and combined with material progressive damage method by writing code of LS-DYNA. Low velocity impact test of GLARE is employed to validate the feasibility of the finite element model established. The simulation results have been shown that progressive damage finite element model established is reliable. Through the application of the finite element model established, the delamination of GLARE evolution progress is simulated, various failure modes… More >

Q.W. Yang¹, S.G. Du^1,2

CMC-Computers, Materials & Continua, Vol.47, No.3, pp. 217-236, 2015, DOI:10.3970/cmc.2015.047.217

Abstract Size effect is a major issue in concrete structures and occurs in concrete in any loading conditions. In this study, size effect on concrete cubic compressive strength is modeled with a back-propagation neural network. The main advantage in using an artificial neural network (ANN) technique is that the network is built directly from experimental data without any simplifying assumptions via the self-organizing capabilities of the neural network. The proposed ANN model is verified by using 27 experimental data sets collected from the literature. For the large specimens, a modified ANN is developed in the paper to further improve the forecast… More >

S.D. Akbarov^1,2, M.I. Ismailov³

CMC-Computers, Materials & Continua, Vol.45, No.2, pp. 75-106, 2015, DOI:10.3970/cmc.2015.045.075

Abstract The subject of the paper is the study of the dynamics of the moving load acting on the hydro-elastic system consisting of the elastic plate, compressible viscous fluid and rigid wall. Under this study the motion of the plate is described by linear elastodynamics, and the motion of the compressible viscous fluid is described by the linearized Navier-Stokes equations. Numerical results are obtained for the case where the material of the plate is steel, but the fluid material is Glycerin. According to these results, corresponding conclusions related to the influence of the problem parameters, such as fluid viscosity, plate thickness,… More >

Ang Li¹, Guo-jian Shao^1,2, Pei-rong Du³, Sheng-yong Ding¹, Jing-bo Su⁴

CMC-Computers, Materials & Continua, Vol.45, No.1, pp. 17-38, 2015, DOI:10.3970/cmc.2015.045.017

Abstract This paper investigates the failure behaviors of stratified rocks under uniaxial compression using a digital image processing (DIP) based finite difference method (FDM). The two-dimensional (2D) mesostructure of stratified rocks, represented as the internal spatial distribution of two main rock materials (marble and greenschist), is first identified with the DIP technique. And then the binaryzation image information is used to generate the finite difference grid. Finally, the failure behaviors of stratified rock samples are simulated by FDM considering the inhomogeneity of rock materials. In the DIP, an image segmentation algorithm based on seeded region growing (SRG) is proposed, instead of… More >