Abdu Gumaei^{1, 2, *}, Mabrook Al-Rakhami^{1, 2}, Hussain AlSalman², Sk. Md. Mizanur Rahman³, Atif Alamri^{1, 2}

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1033-1057, 2020, DOI:10.32604/cmc.2020.011740 - 20 August 2020

Abstract Human activity recognition is commonly used in several Internet of Things applications to recognize different contexts and respond to them. Deep learning has gained momentum for identifying activities through sensors, smartphones or even surveillance cameras. However, it is often difficult to train deep learning models on constrained IoT devices. The focus of this paper is to propose an alternative model by constructing a Deep Learning-based Human Activity Recognition framework for edge computing, which we call DL-HAR. The goal of this framework is to exploit the capabilities of cloud computing to train a deep learning model More >

Jeong-Sik Park¹, Seok-Hoon Kim^2,*

Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 149-159, 2020, DOI:10.31209/2019.100000136

Abstract Variety types of smart home devices have a main function of a human-machine interaction by speech recognition. Speech recognition system may be vulnerable to rapidly changing noises in home environments. This study proposes an efficient noise cancellation approach to eliminate the noises directly on the devices in real time. Firstly, we propose an advanced voice activity detection (VAD) technique to efficiently detect speech and non-speech regions on the basis of spectral property of speech signals. The VAD is then employed to enhance the conventional spectral subtraction method by steadily estimating noise signals in non-speech regions. More >

Jinfeng Li¹, Li Feng^{1, *}, Longqing Zhang², Hongning Dai¹, Lei Yang¹, Liwei Tian¹

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 639-651, 2020, DOI:10.32604/cmc.2020.010309 - 23 July 2020

Abstract Fueled by the booming online games, there is an increasing demand for monitoring online games in various settings. One of the application scenarios is the monitor of computer games in school computer labs, for which an intelligent game recognition method is required. In this paper, a method to identify game processes in accordance with private working sets (i.e., the amount of memory occupied by a process but cannot be shared among other processes) is introduced. Results of the W test showed that the memory sizes occupied by the legitimate processes (e.g., the processes of common… More >

Xu Han^{1, #}, Huijun Yang^{1, 4, *}, Qiufeng Shen^{1, #}, Jiangtao Yang², Huihui Liang¹, Cancan Bao¹, Shuang Cang³

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 579-596, 2020, DOI:10.32604/cmc.2020.011262 - 23 July 2020

Abstract Although predecessors have made great contributions to the semantic segmentation of 3D indoor scenes, there still exist some challenges in the debris recognition of terrain data. Compared with hundreds of thousands of indoor point clouds, the amount of terrain point cloud is up to millions. Apart from that, terrain point cloud data obtained from remote sensing is measured in meters, but the indoor scene is measured in centimeters. In this case, the terrain debris obtained from remote sensing mapping only have dozens of points, which means that sufficient training information cannot be obtained only through… More >

Yugang Li^{1, *}, Haibo Sun¹

Journal on Artificial Intelligence, Vol.2, No.2, pp. 103-112, 2020, DOI:10.32604/jai.2020.010203 - 15 July 2020

Abstract Scene text recognition (STR) is the task of recognizing character sequences in natural scenes. Although STR method has been greatly developed, the existing methods still can't recognize any shape of text, such as very rich curve text or rotating text in daily life, irregular scene text has complex layout in two-dimensional space, which is used to recognize scene text in the past Recently, some recognizers correct irregular text to regular text image with approximate 1D layout, or convert 2D image feature mapping to one-dimensional feature sequence. Although these methods have achieved good performance, their robustness More >

Desheng Zheng^{1, *}, Ziyong Ran¹, Zhifeng Liu¹, Liang Li², Lulu Tian³

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1885-1895, 2020, DOI:10.32604/cmc.2020.010070 - 30 June 2020

Abstract In the sorting system of the production line, the object movement, fixed angle of view, light intensity and other reasons lead to obscure blurred images. It results in bar code recognition rate being low and real time being poor. Aiming at the above problems, a progressive bar code compressed recognition algorithm is proposed. First, assuming that the source image is not tilted, use the direct recognition method to quickly identify the compressed source image. Failure indicates that the compression ratio is improper or the image is skewed. Then, the source image is enhanced to identify More >

Yajie Sun^{1, 2, *}, Yanqing Yuan², Qi Wang², Sai Ji^{1, 2}, Lihua Wang³, Shao’en Wu⁴

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1647-1656, 2020, DOI:10.32604/cmc.2020.06331 - 30 June 2020

Abstract In order to identify the location and magnitude of the impact force accurately, determine the damage range of the structure and accelerate the health monitoring of key components of the composite, this paper studies the location and magnitude of the impact force of composite plates by an inverse method. Firstly, a PZT sensor mounted on the material plate is used to collect the response signal generated by the impact force, which is from several impact locations, and establish transfer functions between the impact location and the PZT sensor. Secondly, this paper applies several forces to More >

Yuheng Sun¹, Ye Mu^{1, 2, 3, 4, *}, Qin Feng⁵, Tianli Hu^{1, 2, 3, 4}, He Gong^{1, 2, 3, 4}, Shijun Li^{1, 2, 3, 4}, Jing Zhou⁶

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 1317-1328, 2020, DOI:10.32604/cmc.2020.010510 - 10 June 2020

Abstract In large-scale deer farming image analysis, K-means or maximum betweenclass variance (Otsu) algorithms can be used to distinguish the deer from the background. However, in an actual breeding environment, the barbed wire or chain-link fencing has a certain isolating effect on the deer which greatly interferes with the identification of the individual deer. Also, when the target and background grey values are similar, the multiple background targets cannot be completely separated. To better identify the posture and behaviour of deer in a deer shed, we used digital image processing to separate the deer from the… More >

Weibo Yang¹, Weidong Liu^{2, *}, Jinming Liu³, Mingyang Zhang⁴

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 515-525, 2020, DOI:10.32604/cmc.2020.09801 - 20 May 2020

Abstract The Convolutional Neural Network (CNN) is a widely used deep neural network. Compared with the shallow neural network, the CNN network has better performance and faster computing in some image recognition tasks. It can effectively avoid the problem that network training falls into local extremes. At present, CNN has been applied in many different fields, including fault diagnosis, and it has improved the level and efficiency of fault diagnosis. In this paper, a two-streams convolutional neural network (TCNN) model is proposed. Based on the short-time Fourier transform (STFT) spectral and Mel Frequency Cepstrum Coefficient (MFCC) More >

Shuren Zhou^{1, *}, Le Chen¹, Vijayan Sugumaran²

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1545-1561, 2020, DOI:10.32604/cmc.2020.09867 - 30 April 2020

Abstract The two-stream convolutional neural network exhibits excellent performance in the video action recognition. The crux of the matter is to use the frames already clipped by the videos and the optical flow images pre-extracted by the frames, to train a model each, and to finally integrate the outputs of the two models. Nevertheless, the reliance on the pre-extraction of the optical flow impedes the efficiency of action recognition, and the temporal and the spatial streams are just simply fused at the ends, with one stream failing and the other stream succeeding. We propose a novel More >