Xianzhe Zhang^1,3,∗, Sheng Zhou^2,†, Jun Fang^1,‡, Yanling Ni^3,§

Computer Systems Science and Engineering, Vol.35, No.4, pp. 247-256, 2020, DOI:10.32604/csse.2020.35.247

Abstract Bidding for construction projects is a very important and representative field in the industry. The system of bidding for construction projects has made considerable progress over the years due to the accumulation of experience and many contributions to the field. Nowadays, with the rapid development of information technology and the intellectualization of the bidding system for construction projects, the accumulation and processing of data has become an essential element of its development. In order to manage the bidding system of construction engineering reasonably, this paper proposes a system based on image processing and pattern recognition… More >

Li-Hong Juang^1,*, Yi-Hua Zhao²

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 291-301, 2020, DOI:10.31209/2020.100000164

Abstract Speech recognition is one of the most convenient forms of human beings engaging in the exchanging of information. In this research, we want to make robots understand human language and communicate with each other through the human language, and to realize man–machine interactive and humanoid– robot interactive. Therefore, this research mainly studies NAO robots’ speech recognition and humanoid communication between double -humanoid robots. This paper introduces the future direction and application prospect of speech recognition as well as its basic method and knowledge of speech recognition fields. This research also proposes the application of the… More >

Xin Liu^1,*, Yujuan Si^1,2, Di Wang¹

Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 341-351, 2020, DOI:10.31209/2019.100000104

Abstract As a biological signal existing in the human living body, the electrocardiogram (ECG) contains abundantly personal information and fulfils the basic characteristics of identity recognition. It has been widely used in the field of individual identification research in recent years. The common process of identity recognition includes three steps: ECG signals preprocessing, feature extraction and processing, beat classification recognition. However, the existing ECG classification models are sensitive to limitations of database type and extracted features dimension, which makes classification accuracy difficult to improve and cannot meet the needs of practical applications. To tackle the problem,… More >

Rongyu Chen¹, Lili Pan^{1, *}, Cong Li¹, Yan Zhou¹, Aibin Chen¹, Eric Beckman²

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1691-1706, 2020, DOI:10.32604/cmc.2020.011706 - 20 August 2020

Abstract With the development of Deep Convolutional Neural Networks (DCNNs), the extracted features for image recognition tasks have shifted from low-level features to the high-level semantic features of DCNNs. Previous studies have shown that the deeper the network is, the more abstract the features are. However, the recognition ability of deep features would be limited by insufficient training samples. To address this problem, this paper derives an improved Deep Fusion Convolutional Neural Network (DF-Net) which can make full use of the differences and complementarities during network learning and enhance feature expression under the condition of limited… More >

Huiping Jiang^{1, *}, Zequn Wang¹, Rui Jiao¹, Shan Jiang²

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1453-1465, 2020, DOI:10.32604/cmc.2020.011793 - 20 August 2020

Abstract Emotion recognition systems are helpful in human–machine interactions and Intelligence Medical applications. Electroencephalogram (EEG) is closely related to the central nervous system activity of the brain. Compared with other signals, EEG is more closely associated with the emotional activity. It is essential to study emotion recognition based on EEG information. In the research of emotion recognition based on EEG, it is a common problem that the results of individual emotion classification vary greatly under the same scheme of emotion recognition, which affects the engineering application of emotion recognition. In order to improve the overall emotion… More >

Zhaoquan Gu¹, Yu Su¹, Chenwei Liu¹, Yinyu Lyu¹, Yunxiang Jian¹, Hao Li², Zhen Cao³, Le Wang^{1, *}

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1437-1452, 2020, DOI:10.32604/cmc.2020.011834 - 20 August 2020

Abstract The license plate recognition system (LPRS) has been widely adopted in daily life due to its efficiency and high accuracy. Deep neural networks are commonly used in the LPRS to improve the recognition accuracy. However, researchers have found that deep neural networks have their own security problems that may lead to unexpected results. Specifically, they can be easily attacked by the adversarial examples that are generated by adding small perturbations to the original images, resulting in incorrect license plate recognition. There are some classic methods to generate adversarial examples, but they cannot be adopted on More >

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 >