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Information Fusion and Its Applications for Smart Sensing
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Information Fusion and Its Applications for Smart Sensing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 16188

Special Issue Editors

Dr. Xinlei Chen

E-Mail Website
Guest Editor
Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Beijing 518055, China
Interests: AIoT; artificial intelligence; pervasive computing; cyber physical system; robotics; urban sensing; brain computer interface; human computer interface
Special Issues, Collections and Topics in MDPI journals
Dr. Le Yang

E-Mail Website
Guest Editor
School of Information and Communications Engineering, Xi'an Jiaotong University, Xi’an, China
Interests: machine learning; computer vision; dynamic neural network; resource-efficient learning
Dr. Tong Qin

E-Mail Website
Guest Editor
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: optical data processing and application; state estimation; spacecraft control
Dr. Chun Hu

E-Mail Website
Guest Editor
Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology, Beijing 100081, China
Interests: smart sensing; advanced sensing mechanism; meteorological information sensing

Special Issue Information

Dear Colleagues,

Nowadays, sensing systems (SenSys) play important roles in our daily lives and have been applied to perform complex tasks in smart environments, such as indoor localization and navigation, anonymous environment monitoring, human-machine interactive sensing, and even fine-grained activity and gesture recognition, which will offer intelligent and advanced services to improve quality of life.

In modern times, smart sensing systems tend to apply techniques, such as multi-sensors, multi-source, multiprocess information fusion, and other advanced synergism methods for more accurate sensing. Information fusion is evolving as a promising technology in the field of smart sensing, which further facilitates the development of other research areas, including the Internet of Things, intelligent unmanned systems, mobile computing, etc. In recent years, many researchers have attempted to acquire better information and fuse sensing data based on signal processing and estimation theory, statistical information theory, decision theory, etc. Nevertheless, many researchers are currently focusing on utilizing artificial intelligence (AI) technologies, such as fuzzy logic and neural networks, to accomplish information fusion and establish a smart and collaborative sensing system. With this advancement, intelligent information fusion technologies can extend their applications to smart sensing and smart environments, such as human-machine interactive sensing, intrusion detection, and autonomous environment monitoring, improving the quality of human lives.

This Special Issue aims to collate original research articles from researchers in both academia and the relevant industries, in order to share their research contributions to information fusion and its applications for smart sensing with the scientific community. Studies are expected to report the innovative ideas and solutions of information fusion methods, Moreover, this Special Issue encourages submissions that explore new and compelling mobile scenarios and applications. This Special Issue will allow readers to identify recent advances in information fusion and its applications for smart sensing. Review articles discussing state-of-the-art innovations are also welcome.

Potential topics include but are not limited to the following:

  • Complex information fusion methods for smart sensing;
  • The security and privacy of sensing networks;
  • Fairness, equity, and transparency issues in IoT and CPS;
  • Machine learning and deep learning of sensor data;
  • Computer vision for resource-constrained and mobile platforms;
  • Modeling of Big Data from multi-sensor systems;
  • Artificial intelligence technology in multi-sensor information fusion;
  • Data fusion based on artificial intelligence;
  • Integration of fuzzy logic and neural network interfaces in distributed sensors;
  • Protocols and standards for smart sensing;
  • Data acquisition and storage in collaborative sensors;
  • Advanced intelligent sensing principles for multi-sensor coupling;
  • Multi-functional sensor design and testing;
  • Resource-efficient machine learning and AI for mobile devices;
  • Systems for location and context sensing and awareness;
  • Mobile computing support for pervasive computing.

Dr. Xinlei Chen
Dr. Le Yang
Dr. Tong Qin
Dr. Chun Hu
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. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Published Papers (7 papers)

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Research

13 pages, 5581 KiB  
Article
Teacher–Student Model Using Grounding DINO and You Only Look Once for Multi-Sensor-Based Object Detection
by Jinhwan Son and Heechul Jung
Appl. Sci. 2024, 14(6), 2232; https://doi.org/10.3390/app14062232 - 7 Mar 2024
Cited by 2 | Viewed by 3392
Abstract
Object detection is a crucial research topic in the fields of computer vision and artificial intelligence, involving the identification and classification of objects within images. Recent advancements in deep learning technologies, such as YOLO (You Only Look Once), Faster-R-CNN, and SSDs (Single Shot [...] Read more.
Object detection is a crucial research topic in the fields of computer vision and artificial intelligence, involving the identification and classification of objects within images. Recent advancements in deep learning technologies, such as YOLO (You Only Look Once), Faster-R-CNN, and SSDs (Single Shot Detectors), have demonstrated high performance in object detection. This study utilizes the YOLOv8 model for real-time object detection in environments requiring fast inference speeds, specifically in CCTV and automotive dashcam scenarios. Experiments were conducted using the ‘Multi-Image Identical Situation and Object Identification Data’ provided by AI Hub, consisting of multi-image datasets captured in identical situations using CCTV, dashcams, and smartphones. Object detection experiments were performed on three types of multi-image datasets captured in identical situations. Despite the utility of YOLO, there is a need for performance improvement in the AI Hub dataset. Grounding DINO, a zero-shot object detector with a high mAP performance, is employed. While efficient auto-labeling is possible with Grounding DINO, its processing speed is slower than YOLO, making it unsuitable for real-time object detection scenarios. This study conducts object detection experiments using publicly available labels and utilizes Grounding DINO as a teacher model for auto-labeling. The generated labels are then used to train YOLO as a student model, and performance is compared and analyzed. Experimental results demonstrate that using auto-generated labels for object detection does not lead to degradation in performance. The combination of auto-labeling and manual labeling significantly enhances performance. Additionally, an analysis of datasets containing data from various devices, including CCTV, dashcams, and smartphones, reveals the impact of different device types on the recognition accuracy for distinct devices. Through Grounding DINO, this study proves the efficacy of auto-labeling technology in contributing to efficiency and performance enhancement in the field of object detection, presenting practical applicability. Full article
(This article belongs to the Special Issue Information Fusion and Its Applications for Smart Sensing)
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19 pages, 5288 KiB  
Article
Theoretical and Simulation Analysis of a Thin Film Temperature Sensor Error Model for In Situ Detection in Near Space
by Guoyan Wang, Chun Hu and Dezhi Zheng
Appl. Sci. 2023, 13(10), 5954; https://doi.org/10.3390/app13105954 - 12 May 2023
Cited by 1 | Viewed by 1939
Abstract
Near space environment is the airspace at 20–100 km, where complex conditions such as low temperature, low pressure, high wind speed, and solar radiation exist. Temperature, as one of the most important meteorological parameters, is crucial for space activities. However, the accuracy of [...] Read more.
Near space environment is the airspace at 20–100 km, where complex conditions such as low temperature, low pressure, high wind speed, and solar radiation exist. Temperature, as one of the most important meteorological parameters, is crucial for space activities. However, the accuracy of traditional temperature sensors is low, and the influence of complex environments makes the error of conventional temperature measurement methods more extensive. Therefore, we designed a new microbridge temperature sensor to reduce solar radiation and achieve a fast response. Additionally, through simulation analysis, we investigated the three factors influencing the temperature errors of Joule heat, solar radiation heat, and aerodynamic heat. Additionally, the influence of temperature error is reduced by optimizing the installation position of the sensor. The error value in the actual measurement value is removed through the temperature error model to realize the high-accuracy detection of the near space temperature. Full article
(This article belongs to the Special Issue Information Fusion and Its Applications for Smart Sensing)
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Graphical abstract

18 pages, 8967 KiB  
Article
Clustering-Based Segmented Regression for Particulate Matter Sensor Calibration
by Sijie Liu, Xinyu Liu and Pei Lu
Appl. Sci. 2022, 12(24), 12934; https://doi.org/10.3390/app122412934 - 16 Dec 2022
Viewed by 1710
Abstract
Nowadays, sensor-based air pollution sensing systems are widely deployed for fine-grained pollution monitoring. In-field calibration plays an important role in maintaining sensory data quality. Determining the model structure is challenging using existing methods of variable global fitting models for in-field calibration. This is [...] Read more.
Nowadays, sensor-based air pollution sensing systems are widely deployed for fine-grained pollution monitoring. In-field calibration plays an important role in maintaining sensory data quality. Determining the model structure is challenging using existing methods of variable global fitting models for in-field calibration. This is because the mechanism of interference factors is complex and there is often insufficient prior knowledge on a specific sensor type. Although Artificial-Neuron-Net-based (ANN-based) methods ignore the complex conditions above, they also have problems regarding generalization, interpretability, and calculation cost. In this paper, we propose a clustering-based segmented regression method for particulate matter (PM) sensor in-field calibration. Interference from relative humidity and temperature are taken into consideration in the particulate matter concentration calibration model. Samples for modeling are divided into clusters and each cluster has an individual multiple linear regression equation. The final calibrated result of one sample is calculated from the regression model of the cluster the sample belongs to. The proposed method is evaluated under in-field deployment and performs better than a global multiple regression method both on PM2.5 and PM10 pollutants with, respectively, at least 16% and 9% improvement ratio on RMSE error. In addition, the proposed method is insensitive to reduction of training data and increase in cluster number. Moreover, it may bear lighter calculation cost, less overfitting problems and better interpretability. It can improve the efficiency and performance of post-deployment sensor calibration. Full article
(This article belongs to the Special Issue Information Fusion and Its Applications for Smart Sensing)
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20 pages, 4423 KiB  
Article
Design and Optimization of Interdigital Capacitive Humidity Sensor with Highly Sensitive and Dynamic Response Time
by Dapeng Li, Aobei Chen, Dezhi Zheng, Zhongxiang Li, Rui Na, Fei Cui and Xiaojun Yang
Appl. Sci. 2022, 12(23), 12362; https://doi.org/10.3390/app122312362 - 2 Dec 2022
Cited by 8 | Viewed by 2457
Abstract
Humidity sensors are widely used in various fields of life. In meteorological detection, the sensor must have high sensitivity and fast dynamic response time due to extreme environmental interference. However, the sensitive mechanism of the humidity sensor determines that the dynamic response time [...] Read more.
Humidity sensors are widely used in various fields of life. In meteorological detection, the sensor must have high sensitivity and fast dynamic response time due to extreme environmental interference. However, the sensitive mechanism of the humidity sensor determines that the dynamic response time will inevitably be increased while improving the sensitivity, which undoubtedly creates difficulties for sensor design. This article takes the interdigitated capacitive humidity sensor as the research object and proposes an optimal design scheme for the sensor that considers high dynamic response time and sensitivity. By constructing the sensor’s theoretical mathematical model, the influence of each structure is analyzed. The theoretical model has been verified by finite element simulation to have an accuracy higher than 95%. The article constructs the sensor optimization objective equation based on this model. Through analysis, within the range of structural parameters set in the article, to improve the sensitivity and reduce the dynamic response time of the sensor, the width and spacing of the interdigital electrodes should have a minimum value of 3 μm and a maximum value of 14 μm, respectively. The thickness of the electrode layer and the moisture-sensitive layer should be flexibly adjusted according to the application to ensure the lowest value of the optimization objective function. To further improve the sensor’s performance, the article optimizes the electrode structure and heating strategy of the sensor heating layer, which not only enhances the uniformity of heat transfer but also increases the optimal heat transfer area by 6% compared with the traditional scheme. Full article
(This article belongs to the Special Issue Information Fusion and Its Applications for Smart Sensing)
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19 pages, 2478 KiB  
Article
A Fast Calibration Method for Sensors of Atmospheric Detection System
by Aobei Chen, Dapeng Li, Dezhi Zheng, Zhongxiang Li and Rui Na
Appl. Sci. 2022, 12(22), 11733; https://doi.org/10.3390/app122211733 - 18 Nov 2022
Cited by 3 | Viewed by 1990
Abstract
To meet the needs of a large number of high-altitude meteorological detections, we need to perform fast, high-precision, and high-reliability calibrations of the sensors in the atmospheric detection system (ADS). However, using the traditional method to calibrate the sensor with high precision often [...] Read more.
To meet the needs of a large number of high-altitude meteorological detections, we need to perform fast, high-precision, and high-reliability calibrations of the sensors in the atmospheric detection system (ADS). However, using the traditional method to calibrate the sensor with high precision often takes a lot of time and increases the cost of workforce and material resources. Therefore, a method for realizing fast sensor calibration under the current system hardware conditions is required. A physical field model of Tube–Air–ADS is proposed for the first time, and the transfer function is obtained by combining the system identification, which provides the possibility for dynamic analysis of the calibration system. A Multi-Criteria Adaptive (MCA) PID controller design method is proposed, which provides a new idea for the parameter design of the controller. It controls the amplitude and switching frequency of the controller’s output signal, ensuring the safe and stable operation of the calibration system. Combined with the hardware parameters of the system, we propose the Variable Precision Steady-State Discrimination (VPSSD) method, which can further shorten the calibration time. Comparing and analyzing the current simulation results under Matlab/Simulink, the proposed MCA method, compared with other PID controller design methods, ensures the stable operation of the calibration system. At the same time, compared with the original system, the calibration time is shortened to 47.7%. Combined with the VPSSD method, the calibration time further shortens to 38.7 s. Full article
(This article belongs to the Special Issue Information Fusion and Its Applications for Smart Sensing)
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15 pages, 3900 KiB  
Article
Multi-Spacecraft Tracking and Data Association Based on Uncertainty Propagation
by Xingyu Zhou, Shuo Wang and Tong Qin
Appl. Sci. 2022, 12(15), 7660; https://doi.org/10.3390/app12157660 - 29 Jul 2022
Cited by 8 | Viewed by 1616
Abstract
This paper proposed a novel multi-spacecraft tracking and data association method based on the orbit uncertainty propagation. The proposed method makes full use of the dynamic information and thus the data association performance is enhanced. The proposed method is divided into three portions, [...] Read more.
This paper proposed a novel multi-spacecraft tracking and data association method based on the orbit uncertainty propagation. The proposed method makes full use of the dynamic information and thus the data association performance is enhanced. The proposed method is divided into three portions, i.e., the uncertainty propagation, the data association, and the orbit estimation. The second-order solutions derived for state and measurement prediction, on which to base the optimal association, are set up. The optimal association is solved by the contract network algorithm to reduce the computing cost. Finally, a second-order extended Kalman filter is designed to estimate the orbit of each spacecraft. The proposed method is successfully applied for solving a four-spacecraft tracking problem. Simulations show that all the four targets are well tracked. The method demonstrates close to 100% data association precision. The proposed method is proved to be efficient and effective to solve the multi-spacecraft tracking problem. Full article
(This article belongs to the Special Issue Information Fusion and Its Applications for Smart Sensing)
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18 pages, 4667 KiB  
Article
Rapid Construction of Aerocapture Attainability Sets Using Sequential Convex Programming
by Rui Teng, Hongwei Han and Jilin Chen
Appl. Sci. 2022, 12(13), 6437; https://doi.org/10.3390/app12136437 - 24 Jun 2022
Cited by 1 | Viewed by 1734
Abstract
This paper proposes a novel method to efficiently construct the aerocapture attainability set based on convex optimization. Using dimensionality reduction, constructing the attainability set is equivalent to solving a set of discrete points on its boundary. As solving each of the boundary points [...] Read more.
This paper proposes a novel method to efficiently construct the aerocapture attainability set based on convex optimization. Using dimensionality reduction, constructing the attainability set is equivalent to solving a set of discrete points on its boundary. As solving each of the boundary points is a typical nonlinear optimal control problem, a sequential convex programming method is adopted. The efficiency and accuracy of the proposed algorithm is demonstrated by high-fidelity numerical results. This is the first time that the configuration of the aerocapture attainability set is precisely described by the state variables at atmospheric exit. Since the quantification of the set is significant for assessing the feasibility of performing an aerocapture maneuver, the proposed method can be used as a reliable tool for systematic design for aerocapture mission. Full article
(This article belongs to the Special Issue Information Fusion and Its Applications for Smart Sensing)
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