Jinhua Cui^1,2, Yuanyuan Zhang³, Zhiping Cai^1,*, Anfeng Liu⁴, Yangyang Li⁵

CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 17-35, 2018, DOI:10.3970/cmc.2018.055.017

Abstract While smart devices based on ARM processor bring us a lot of convenience, they also become an attractive target of cyber-attacks. The threat is exaggerated as commodity OSes usually have a large code base and suffer from various software vulnerabilities. Nowadays, adversaries prefer to steal sensitive data by leaking the content of display output by a security-sensitive application. A promising solution is to exploit the hardware visualization extensions provided by modern ARM processors to construct a secure display path between the applications and the display device. In this work, we present a scheme named SecDisplay for trusted display service, it… More >

Ruohan Meng^1,2, Steven G. Rice³, Jin Wang⁴, Xingming Sun^1,2,*

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 >

P. PattunnaRajam^{1, *}, Reeba korah², G. Maria Kalavathy³

CMC-Computers, Materials & Continua, Vol.54, No.3, pp. 251-268, 2018, DOI:10.3970/cmc.2018.054.251

Abstract This paper presents an automated POCOFAN-POFRAME algorithm that partitions large combinational digital VLSI circuits for pseudo exhaustive testing. In this paper, a simulation framework and partitioning technique are presented to guide VLSI circuits to work under with fewer test vectors in order to reduce testing time and to develop VLSI circuit designs. This framework utilizes two methods of partitioning Primary Output Cone Fanout Partitioning (POCOFAN) and POFRAME partitioning to determine number of test vectors in the circuit. The key role of partitioning is to identify reconvergent fanout branch pairs and the optimal value of primary input node N and fanout… More >

Muhammad Shoaib Arif¹, Mairaj Bibi², Adnan Jhangir³

CMC-Computers, Materials & Continua, Vol.54, No.2, pp. 181-195, 2018, DOI:10.3970/cmc.2018.054.181

Abstract This communique is opted to study the approximate solution of the Algebraic Lyapunov equation on the manifold of positive-definite Hermitian matrices. We choose the geodesic distance between -AHX - XA and P as the cost function, and put forward the Extended Hamiltonian algorithm (EHA) and Natural gradient algorithm (NGA) for the solution. Finally, several numerical experiments give you an idea about the effectiveness of the proposed algorithms. We also show the comparison between these two algorithms EHA and NGA. Obtained results are provided and analyzed graphically. We also conclude that the extended Hamiltonian algorithm has better convergence speed than the… More >

Y. A. Amer¹, A. M. S. Mahdy^{1, 2, *}, E. S. M. Youssef¹

CMC-Computers, Materials & Continua, Vol.54, No.2, pp. 161-180, 2018, DOI:10.3970/cmc.2018.054.161

Abstract In this paper we are looking forward to finding the approximate analytical solutions for fractional integro-differential equations by using Sumudu transform method and Hermite spectral collocation method. The fractional derivatives are described in the Caputo sense. The applications related to Sumudu transform method and Hermite spectral collocation method have been developed for differential equations to the extent of access to approximate analytical solutions of fractional integro-differential equations. 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 >

Chengsheng Yuan^1,2,3, Xinting Li³, Q. M. Jonathan Wu³, Jin Li^4,5, Xingming Sun^1,2

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 >

Mahesh S. Shinde^1,*, Kishor M. Ashtankar²

CMC-Computers, Materials & Continua, Vol.53, No.3, pp. 207-217, 2017, DOI:10.32604/cmc.2017.053.223

Abstract This paper presents simulation study on Milled Grooved conformal cooling channels (MGCCC) in injection molding. MGCCC has a more effective cooling surface area which helps to provide efficient cooling as compared to conventional cooling. A case study of Encloser part is investigated for cycle time reduction and quality improvement. The performance designs of straight drilled are compared with MGCCC by using Autodesk Moldflow Insight (AMI) 2016. The results show total 32.1% reduction of cooling time and 9.86% reduction of warpage in case of MGCCC as compared to conventional cooling. 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 >

L.S. Melro^1,2, L.R. Jensen¹

CMC-Computers, Materials & Continua, Vol.53, No.2, pp. 109-127, 2017, DOI:10.3970/cmc.2017.053.111

Abstract Molecular dynamics simulations were applied in order to calculate the Young’s modulus of graphene functionalized with carboxyl, hydroxyl, carbonyl, hydrogen, methyl, and ethyl groups. The influence of the grafting density with percentages of 3, 5, 7, and 10% and the type of distribution as a single cluster or several small clusters were also studied. The results show that the elastic modulus is dependent on the type of functional groups. The increasing coverage density also evidenced a decrease of the Young’s modulus, and the organization of functional groups as single cluster showed a lesser impact than for several small clusters. Furthermore,… More >