Yifan Yin¹, Chunliu He¹, Biao Xu², Zhiyong Li^1,*

Molecular & Cellular Biomechanics, Vol.16, Suppl.1, pp. 57-57, 2019, DOI:10.32604/mcb.2019.05732

Abstract The tissue composition and morphological structure of atherosclerotic plaques determine its stability or vulnerability. Intravascular optical coherence tomography (IVOCT) has rapidly become the method of choice for assessing the pathology of the coronary arterial wall in vivo due to its superior resolution. However, in clinical practice, the analysis of plaque composition of OCT images mainly relies on the interpretation of images by well-trained experts, which is a time-consuming, labor-intensive procedure and it is also subjective. The purpose of this study is to use the Convolutional neural network (CNN) method to automatically extract the best feature information from the OCT images… More >

Chaoyang Xia¹, Jing Peng¹, Zongqing Ma², Xiaojie Li^1,*

Journal of Information Hiding and Privacy Protection, Vol.1, No.3, pp. 109-117, 2019, DOI:10.32604/jihpp.2019.07198

Abstract Cardiomyopathy is one of the most serious public health threats. The precise structural and functional cardiac measurement is an essential step for clinical diagnosis and follow-up treatment planning. Cardiologists are often required to draw endocardial and epicardial contours of the left ventricle (LV) manually in routine clinical diagnosis or treatment planning period. This task is time-consuming and error-prone. Therefore, it is necessary to develop a fully automated end-to-end semantic segmentation method on cardiac magnetic resonance (CMR) imaging datasets. However, due to the low image quality and the deformation caused by heartbeat, there is no effective tool for fully automated end-to-end… More >

Yaocheng Zheng¹, Weiwei Zhang^1,*, Xuncheng Wu¹, Bo Zhao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 559-576, 2019, DOI:10.32604/cmes.2019.05684

Abstract Vision-based technologies have been extensively applied for on-street parking space sensing, aiming at providing timely and accurate information for drivers and improving daily travel convenience. However, it faces great challenges as a partial visualization regularly occurs owing to occlusion from static or dynamic objects or a limited perspective of camera. This paper presents an imagery-based framework to infer parking space status by generating 3D bounding box of the vehicle. A specially designed convolutional neural network based on ResNet and feature pyramid network is proposed to overcome challenges from partial visualization and occlusion. It predicts 3D box candidates on multi-scale feature… More >

Princy Matlani^1,*, Manish Shrivastava¹

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 427-458, 2019, DOI:10.32604/cmes.2019.04985

Abstract Real-time wild smoke detection utilizing machine based identification method is not produced proper accuracy, and it is not suitable for accurate prediction. However, various video smoke detection approaches involve minimum lighting, and it is required for the cameras to identify the existence of smoke particles in a scene. To overcome such challenges, our proposed work introduces a novel concept like deep VGG-Net Convolutional Neural Network (CNN) for the classification of smoke particles. This Deep Feature Synthesis algorithm automatically generated the characteristics for relational datasets. Also hybrid ABC optimization rectifies the problem related to the slow convergence since complexity is reduced.… More >

M. Schanz¹

CMES-Computer Modeling in Engineering & Sciences, Vol.58, No.2, pp. 109-130, 2010, DOI:10.3970/cmes.2010.058.109

Abstract Boundary Element formulations in time domain suffer from two problems. First, for hyperbolic problems not too much fundamental solutions are available and, second, the time stepping procedure is expensive in storage and has stability problems for badly chosen time step sizes. The first problem can be overcome by using the Convolution Quadrature Method (CQM) for time discretisation. This as well improves the stability. However, still the storage requirements are large. A recently published reformulation of the CQM by Banjai and Sauter [Rapid solution of the wave equation in unbounded domains, SIAM J. Numer. Anal., 47, 227-249] reduces the time stepping… More >

Pinjie Xu², Ce Li^1,2,*, Liguo Zhang^3,4, Feng Yang^1,2, Jing Zheng^1,5, Jingwu Feng²

Journal on Artificial Intelligence, Vol.1, No.1, pp. 9-18, 2019, DOI:10.32604/jai.2019.06157

Abstract Detecting the underground disease is very crucial for the roadbed health monitoring and maintenance of transport facilities, since it is very closely related to the structural health and reliability with the rapid development of road traffic. Ground penetrating radar (GPR) is widely used to detect road and underground diseases. However, it is still a challenging task due to data access anywhere, transmission security and data processing on cloud. Cloud computing can provide scalable and powerful technologies for large-scale storage, processing and dissemination of GPR data. Combined with cloud computing and radar detection technology, it is possible to locate the underground… More >

Liming Chen¹, Bosi Wang^1,*, Weijie Yu¹, Xu Fan¹

Journal of New Media, Vol.1, No.3, pp. 115-126, 2019, DOI:10.32604/jnm.2019.08581

Abstract With the development of multimedia presentation technology, image acquisition technology and the Internet industry, long-distance communication methods have changed from the previous letter, the audio to the current audio/video. And the proportion of video in work, study and entertainment keeps increasing, high-definition video is getting more and more attention. Due to the limits of the network environment and storage capacity, the original video must be encoded to be efficiently transmitted and stored. High Efficient Video Coding (HEVC) requires a large amount of time to recursively traverse all possible quantization parameter values of the coding unit in the adaptive quantization process.… More >

Jingcheng Chen¹, Zhili Zhou^1,2,*, Zhaoqing Pan¹, Ching-nung Yang³

Journal of New Media, Vol.1, No.2, pp. 87-99, 2019, DOI:10.32604/jnm.2019.06582

Abstract Recently, image representations derived by convolutional neural networks (CNN) have achieved promising performance for instance retrieval, and they outperform the traditional hand-crafted image features. However, most of existing CNN-based features are proposed to describe the entire images, and thus they are less robust to background clutter. This paper proposes a region of interest (RoI)-based deep convolutional representation for instance retrieval. It first detects the region of interests (RoIs) from an image, and then extracts a set of RoI-based CNN features from the fully-connected layer of CNN. The proposed RoI-based CNN feature describes the patterns of the detected RoIs, so that… More >

Caining Zhang¹, Huaguang Li², Xiaoya Guo³, David Molony⁴, Xiaopeng Guo², Habib Samady⁴, Don P. Giddens^4,5, Lambros Athanasiou⁶, Rencan Nie^2,*, Jinde Cao^3,*, Dalin Tang^1,*,7

Molecular & Cellular Biomechanics, Vol.16, No.2, pp. 153-161, 2019, DOI:10.32604/mcb.2019.06873

Abstract Cardiovascular diseases are closely associated with deteriorating atherosclerotic plaques. Optical coherence tomography (OCT) is a recently developed intravascular imaging technique with high resolution approximately 10 microns and could provide accurate quantification of coronary plaque morphology. However, tissue segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective. To overcome these limitations, two automatic segmentation methods for intracoronary OCT image based on support vector machine (SVM) and convolutional neural network (CNN) were performed to identify the plaque region and characterize plaque components. In vivo IVUS and OCT coronary plaque data from 5… More >

Junhao Yang¹, Chunxiao Chen^1,*, Qingyang Zang¹, Jianfei Li¹

Molecular & Cellular Biomechanics, Vol.15, No.4, pp. 203-214, 2018, DOI:10.32604/mcb.2018.03824

Abstract Pathological slide is increasingly applied in the diagnosis of breast tumors despite the issues of large amount of data, slow viewing and high subjectivity. To overcome these problems, a micrograph recognition method based on convolutional neural network is proposed for pathological slide of breast tumor. Combined with multi-channel threshold and watershed segmentation, a sample database including single cell, adhesive cell and invalid cell was established. Then, the convolution neural network with six layers is constructed, which has ability to classify the stained breast tumor cells with accuracy of more than 90%, and evaluate the proliferation level with relative error of… More >