2018-2019 IJAC Top Reviews

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During the past two years, IJAC has published a series of high-level reviews by world-famous scholars, including Prof. Brian D. O. Anderson, Prof. Amir Hussain, Prof. Wei He, Prof. En-Hong Chen, etc. The fields cover Autonomous Driving, Social Networks, Intelligent Robots, and so on. Download full text now!




This paper firstly gives an over- view of the three key technologies of a 3D VTSMs: multi-camera placement, multi-camera calibration and pose estimation for multi-copters. Then, some representative 3D visual tracking systems for multicopters are introduced. Finally, the future development of the 3D VTSMs is analyzed and summarized.


Open AccessA Survey on 3D Visual Tracking of Multicopters

Qiang Fu, Xiang-Yang Chen, Wei He

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In this paper, we give an overview of recent advances in deep learning-based models and methods that have been applied to single image super-resolution tasks. We also summarize, compare and discuss various models from the past and present for comprehensive understanding and finally provide open problems and possible directions for future research.


Deep Learning Based Single Image Super-resolution: A Survey

Viet Khanh Ha, Jin-Chang Ren, Xin-Ying Xu, Sophia Zhao, Gang Xie, Valentin Masero, Amir Hussain

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A fundamental aspect of society is the exchange and discussion of opinions between individuals, occurring in situations as varied as company boardrooms, elementary school classrooms and online social media. After a very brief introduction to the established results of the most fundamental opinion dynamics models, which seek to mathematically capture observed social phenomena, a brief discussion follows on several recent themes pursued by the authors building on the fundamental ideas. 


Open AccessRecent Advances in the Modelling and Analysis of Opinion Dynamics on Influence Networks

Brian D. O. Anderson, Mengbin Ye

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In this paper, we provide a literature review of key crowdsourcing technologies including crowdsourcing platforms and tools, crowdsourcing frameworks, and techniques in terms of open call generation, rewarding, crowd qualification for working, organization structure of crowds, solution evaluation, workflow and quality control and indicate the challenges of integrating crowdsourcing with a PDD process. We also explore the necessary techniques and tools to support the crowdsourcing PDD process. Finally, we propose some key guidelines for coping with the aforementioned challenges in the crowdsourcing PDD process.


Open AccessKey Crowdsourcing Technologies for Product Design and Development

Xiao-Jing Niu, Sheng-Feng Qin, John Vines, Rose Wong, Hui Lu

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This paper introduces the application of deep learning in healthcare extensively. We focus on 7 application areas of deep learning, which are electronic health records (EHR), electrocardiography (ECG), electroencephalogram (EEG), community healthcare, data from wearable devices, drug analysis and genomics analysis. In addition, we analyze the merits and drawbacks of the existing works, analyze the existing challenges, and discuss future trends.


Applying Deep Learning to Individual and Community Health Monitoring Data: A Survey

Zhen-Jie Yao, Jie Bi, Yi-Xin Chen

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In this paper, the existing contour detection approaches are reviewed and roughly divided into three categories: pixel-based, edge-based, and region-based. In addition, since the traditional contour detection approaches have achieved a high degree of sophistication, the deep convolutional neural networks (DCNNs) have good performance in image recognition, therefore, the DCNNs based contour detection approaches are also covered in this paper. Moreover, the future development of contour detection is analyzed and predicted.


An Overview of Contour Detection Approaches

Xin-Yi Gong, Hu Su, De Xu, Zheng-Tao Zhang, Fei Shen, Hua-Bin Yang

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In recent years, a large number of relatively advanced and often ready-to-use robotic hardware components and systems have been developed for small-scale use. As these tools are mature, there is now a shift towards advanced applications. These often require automation and demand reliability, efficiency and decisional autonomy. New software tools and algorithms for artificial intelligence (AI) and machine learning (ML) can help here. However, since there are many software-based control approaches for small-scale robotics, it is rather unclear how these can be integrated and which approach may be used as a starting point. Therefore, this paper attempts to shed light on existing approaches with their advantages and disadvantages compared to established requirements.


Software for Small-scale Robotics: A Review

Tobias Tiemerding, Sergej Fatikow

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Based on working experiences in and reviews on intelligent robot studies both in China and abroad, the authors summarized researches on key and leading technologies related to human-robot collaboration, driverless technology, emotion recognition, brain-computer interface, bionic software robot and cloud platform, big data network, etc. The development trend of intelligent robot was discussed, and reflections on and suggestions to intelligent robot development in China were proposed. 


Current Researches and Future Development Trend of Intelligent Robot: A Review

Tian-Miao Wang, Yong Tao, Hui Liu

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In this paper, we review the recent advances on information diffusion analysis in social networks and its applications. Specifically, we first shed light on several popular models to describe the information diffusion process in social networks, which enables three practical applications, i.e., influence evaluation, influence maximization and information source detection. Then, we discuss how to evaluate the authority and influence based on network structures. After that, current solutions to influence maximization and information source detection are discussed in detail, respectively. Finally, some possible research directions of information diffusion analysis are listed for further study.


Study on Information Diffusion Analysis in Social Networks and Its Applications

Biao Chang, Tong Xu, Qi Liu, En-Hong Chen

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This paper attempts to investigate the interpretation of traffic scene in autonomous driving from an event reasoning view. To reach this goal, we study the most relevant literatures and the state-of-the-arts on scene representation, event detection and intention prediction in autonomous driving. In addition, we also discuss the open challenges and problems in this field and endeavor to provide possible solutions.


A Survey of Scene Understanding by Event Reasoning in Autonomous Driving

Jian-Ru Xue, Jian-Wu Fang, Pu Zhang

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The study and application of methods for incorporating nonuniform and delayed information in state estimation techniques are important topics to advance in soft sensor development.Therefore, this paper presents a review of these methods and proposes a taxonomy that allows a faster selection of state estimator in this type of applications. The classification is performed according to the type of estimator, method, and used tool. Finally, using the proposed taxonomy, some applications reported in the literature are described.


State Estimation Using Non-uniform and Delayed Information: A Review

Jhon A. Isaza, Hector A. Botero, Hernan Alvarez

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