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Artificial Intelligence Based Multi-Source Information Processing and Fusion
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Artificial Intelligence Based Multi-Source Information Processing and Fusion

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 20341

Special Issue Editors

Prof. Dr. En Li

E-Mail Website
Guest Editor
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Interests: robotics control; embedded system; sensor technology
Special Issues, Collections and Topics in MDPI journals
Dr. Lei Yang

E-Mail Website
Guest Editor
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: robot vision; intelligent detection; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yuan Li

E-Mail Website
Guest Editor
Beijing Institute of Technology, Beijing, China
Interests: computer vision; intelligent robotics; natural language processing; data mining

Special Issue Information

Dear Colleagues,

Sensors and data are the most basic fundamentals needed to be designed for an intelligent system. In today’s digital world, different sensors have been designed for information sensing which bring the urgent demand to attain a comprehensive and accurate perception of different scenarios from multi-source information. Sensor fusion could integrate multiple information sources to obtain more reliable and accurate information for decision-making system. It has been a popular scheme in science and technology to collect data at different levels or from different perspectives which has wide applications, such as robotic system, and healthcare applications, IoT, etc.

This Special Issue aims to publish original technical papers and review papers on recent technologies that focus on sensor fusion methods and various applications in computer science and intelligent systems.

Potential topics include, but are not limited to, the following:

  • Data fusion of multiple sensors
  • Artificial intelligence in sensor fusion
  • Sensor fusion in robotic system
  • Sensor fusion in healthcare applications
  • Sensor fusion in IoT
  • Application scenarios of sensor fusion
  • AI-based information processing

Prof. Dr. En Li 
Prof. Dr. Lei Yang
Prof. Dr. Yuan Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

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16 pages, 5057 KiB  
Article
A Detection and Tracking Method Based on Heterogeneous Multi-Sensor Fusion for Unmanned Mining Trucks
by Haitao Liu, Wenbo Pan, Yunqing Hu, Cheng Li, Xiwen Yuan and Teng Long
Sensors 2022, 22(16), 5989; https://doi.org/10.3390/s22165989 - 11 Aug 2022
Cited by 10 | Viewed by 2162
Abstract
There exist many difficulties in environmental perception in transportation at open-pit mines, such as unpaved roads, dusty environments, and high requirements for the detection and tracking stability of small irregular obstacles. In order to solve the above problems, a new multi-target detection and [...] Read more.
There exist many difficulties in environmental perception in transportation at open-pit mines, such as unpaved roads, dusty environments, and high requirements for the detection and tracking stability of small irregular obstacles. In order to solve the above problems, a new multi-target detection and tracking method is proposed based on the fusion of Lidar and millimeter-wave radar. It advances a secondary segmentation algorithm suitable for open-pit mine production scenarios to improve the detection distance and accuracy of small irregular obstacles on unpaved roads. In addition, the paper also proposes an adaptive heterogeneous multi-source fusion strategy of filtering dust, which can significantly improve the detection and tracking ability of the perception system for various targets in the dust environment by adaptively adjusting the confidence of the output target. Finally, the test results in the open-pit mine show that the method can stably detect obstacles with a size of 30–40 cm at 60 m in front of the mining truck, and effectively filter out false alarms of concentration dust, which proves the reliability of the method. Full article
(This article belongs to the Special Issue Artificial Intelligence Based Multi-Source Information Processing and Fusion)
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19 pages, 6098 KiB  
Article
Application of Improved CycleGAN in Laser-Visible Face Image Translation
by Mingyu Qin, Youchen Fan, Huichao Guo and Mingqian Wang
Sensors 2022, 22(11), 4057; https://doi.org/10.3390/s22114057 - 27 May 2022
Cited by 2 | Viewed by 2317
Abstract
CycleGAN is widely used in various image translations, such as thermal-infrared–visible-image translation, near-infrared–visible-image translation, and shortwave-infrared–visible-image translation. However, most image translations are used for infrared-to-visible translation, and the wide application of laser imaging has an increasingly strong demand for laser–visible image translation. In [...] Read more.
CycleGAN is widely used in various image translations, such as thermal-infrared–visible-image translation, near-infrared–visible-image translation, and shortwave-infrared–visible-image translation. However, most image translations are used for infrared-to-visible translation, and the wide application of laser imaging has an increasingly strong demand for laser–visible image translation. In addition, the current image translation is mainly aimed at frontal face images, which cannot be effectively utilized to translate faces at a certain angle. In this paper, we construct a laser-visible face mapping dataset; in case of the gradient dispersion of the objective function of the original adversarial loss, the least squares loss function is used to replace the cross-entropy loss function and an identity loss function is added to strengthen the network constraints on the generator. The experimental results indicate that the SSIM value of the improved model increases by 1.25%, 8%, 0, 8%, the PSNR value is not much different, and the FID value decreases by 11.22, 12.85, 43.37 and 72.19, respectively, compared with the CycleGAN, Pix2pix, U-GAN-IT and StarGAN models. In the profile image translation, in view of the poor extraction effect of CycleGAN’s original feature extraction module ResNet, the RRDB module is used to replace it based on the first improvement. The experimental results show that, compared with the CycleGAN, Pix2pix, U-GAN-IT, StarGAN and the first improved model, the SSIM value of the improved model increased by 3.75%, 10.67%, 2.47%, 10.67% and 2.47%, respectively; the PSNR value increased by 1.02, 2.74, 0.32, 0.66 and 0.02, respectively; the FID value reduced by 26.32, 27.95, 58.47, 87.29 and 15.1, respectively. Subjectively, the contour features and facial features were better conserved. Full article
(This article belongs to the Special Issue Artificial Intelligence Based Multi-Source Information Processing and Fusion)
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20 pages, 13666 KiB  
Article
Research on Trajectory Tracking Control of Inspection UAV Based on Real-Time Sensor Data
by Mingbo Yang, Ziyang Zhou and Xiangming You
Sensors 2022, 22(10), 3648; https://doi.org/10.3390/s22103648 - 11 May 2022
Cited by 3 | Viewed by 1932
Abstract
In power inspection, uncertainties, such as wind gusts in the working environment, affect the trajectory of the inspection UAV (unmanned aerial vehicle), and a sliding mode adaptive robust control algorithm is proposed in this paper to solve this problem. For the nonlinear and [...] Read more.
In power inspection, uncertainties, such as wind gusts in the working environment, affect the trajectory of the inspection UAV (unmanned aerial vehicle), and a sliding mode adaptive robust control algorithm is proposed in this paper to solve this problem. For the nonlinear and under-driven characteristics of the inspection UAV system, a double closed-loop control system which includes a position loop and attitude loop is designed. Lyapunov stability analysis is used to determine whether the designed system could finally achieve asymptotic stability. Sliding-mode PID control and a backstepping control algorithm are applied to analyze the superiority of the control algorithm proposed in this paper. A PX4 based experimental platform system is built and experimental tests were carried out under outdoor environment. The effectiveness and superiority of the control algorithm are proposed in this paper. The experimental results show that the sliding mode PID control can achieve good accuracy with smaller computing costs. For nonlinear interference, the sliding mode adaptive robust control strategy can achieve higher trajectory tracking accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence Based Multi-Source Information Processing and Fusion)
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20 pages, 3824 KiB  
Article
Multi-level Hierarchical Complex Behavior Monitoring System for Dog Psychological Separation Anxiety Symptoms
by Huasang Wang, Othmane Atif, Jirong Tian, Jonguk Lee, Daihee Park and Yongwha Chung
Sensors 2022, 22(4), 1556; https://doi.org/10.3390/s22041556 - 17 Feb 2022
Cited by 7 | Viewed by 3679
Abstract
An increasing number of people own dogs due to the emotional benefits they bring to their owners. However, many owners are forced to leave their dogs at home alone, increasing the risk of developing psychological disorders such as separation anxiety, typically accompanied by [...] Read more.
An increasing number of people own dogs due to the emotional benefits they bring to their owners. However, many owners are forced to leave their dogs at home alone, increasing the risk of developing psychological disorders such as separation anxiety, typically accompanied by complex behavioral symptoms including excessive vocalization and destructive behavior. Hence, this work proposes a multi-level hierarchical early detection system for psychological Separation Anxiety (SA) symptoms detection that automatically monitors home-alone dogs starting from the most fundamental postures, followed by atomic behaviors, and then detecting separation anxiety-related complex behaviors. Stacked Long Short-Term Memory (LSTM) is utilized at the lowest level to recognize postures using time-series data from wearable sensors. Then, the recognized postures are input into a Complex Event Processing (CEP) engine that relies on knowledge rules employing fuzzy logic (Fuzzy-CEP) for atomic behaviors level and higher complex behaviors level identification. The proposed method is evaluated utilizing data collected from eight dogs recruited based on clinical inclusion criteria. The experimental results show that our system achieves approximately an F1-score of 0.86, proving its efficiency in separation anxiety symptomatic complex behavior monitoring of a home-alone dog. Full article
(This article belongs to the Special Issue Artificial Intelligence Based Multi-Source Information Processing and Fusion)
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15 pages, 1011 KiB  
Article
A Method of Short Text Representation Fusion with Weighted Word Embeddings and Extended Topic Information
by Wenfu Liu, Jianmin Pang, Qiming Du, Nan Li and Shudan Yang
Sensors 2022, 22(3), 1066; https://doi.org/10.3390/s22031066 - 29 Jan 2022
Cited by 4 | Viewed by 2293
Abstract
Short text representation is one of the basic and key tasks of NLP. The traditional method is to simply merge the bag-of-words model and the topic model, which may lead to the problem of ambiguity in semantic information, and leave topic information sparse. [...] Read more.
Short text representation is one of the basic and key tasks of NLP. The traditional method is to simply merge the bag-of-words model and the topic model, which may lead to the problem of ambiguity in semantic information, and leave topic information sparse. We propose an unsupervised text representation method that involves fusing word embeddings and extended topic information. Following this, two fusion strategies of weighted word embeddings and extended topic information are designed: static linear fusion and dynamic fusion. This method can highlight important semantic information, flexibly fuse topic information, and improve the capabilities of short text representation. We use classification and prediction tasks to verify the effectiveness of the method. The testing results show that the method is valid. Full article
(This article belongs to the Special Issue Artificial Intelligence Based Multi-Source Information Processing and Fusion)
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Review

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19 pages, 4721 KiB  
Review
A Review of the Methods on Cobb Angle Measurements for Spinal Curvature
by Chen Jin, Shengru Wang, Guodong Yang, En Li and Zize Liang
Sensors 2022, 22(9), 3258; https://doi.org/10.3390/s22093258 - 24 Apr 2022
Cited by 23 | Viewed by 5921
Abstract
Scoliosis is a common disease of the spine and requires regular monitoring due to its progressive properties. A preferred indicator to assess scoliosis is by the Cobb angle, which is currently measured either manually by the relevant medical staff or semi-automatically, aided by [...] Read more.
Scoliosis is a common disease of the spine and requires regular monitoring due to its progressive properties. A preferred indicator to assess scoliosis is by the Cobb angle, which is currently measured either manually by the relevant medical staff or semi-automatically, aided by a computer. These methods are not only labor-intensive but also vary in precision by the inter-observer and intra-observer. Therefore, a reliable and convenient method is urgently needed. With the development of computer vision and deep learning, it is possible to automatically calculate the Cobb angles by processing X-ray or CT/MR/US images. In this paper, the research progress of Cobb angle measurement in recent years is reviewed from the perspectives of computer vision and deep learning. By comparing the measurement effects of typical methods, their advantages and disadvantages are analyzed. Finally, the key issues and their development trends are also discussed. Full article
(This article belongs to the Special Issue Artificial Intelligence Based Multi-Source Information Processing and Fusion)
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