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Applied Sciences
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Signal Processing, Applications and Systems
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Signal Processing, Applications and Systems

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 November 2022) | Viewed by 8045

Special Issue Editors

Prof. Dr. Jun-Bao Li

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Guest Editor
School of Computing, Harbin Institute of Technology, Harbin 150001, China
Interests: machine learning; image processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jeng-Shyang Pan

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Guest Editor
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266510, China
Interests: multimedia processing; sensor fusion; machine learning; information hiding
Special Issues, Collections and Topics in MDPI journals
Dr. Meng Li

E-Mail Website
Guest Editor
Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
Interests: deep learning; machine learning; neuroscience
Special Issues, Collections and Topics in MDPI journals
Dr. Huanyu Liu

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Guest Editor
Faculty of Computing, Harbin Institute of Technology, Harbin 150006, China
Interests: image processing; image superresolution
Prof. Dr. Shi-Huang Chen

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, Shu-Te University, Kaohsiung 82445, Taiwan
Interests: wireless sensor network; vehicle communication; speech processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of sensor technology, multi-source data are gradually increasing. Currently, the application of unmanned equipment usually needs to deal with data from different sources and different fields. The fusion and integration of multi-source data is an important basis on which to improve the quality of data analysis. Many theories, methods, and techniques have been developed in the recent years. The deep learning and meta-heuristic approaches may promote the applications of machine learning for image and speech recognition. However, deep learning and meta-heuristic optimization for data fusion and analysis still requires further study. This Special Issue seeks to discuss the relative learning networks’ design, analysis, and algorithms of deep learning for multi-source data fusion with the applications of unmanned equipment.

This Special Issue includes “Signal Processing, Applications and Systems” in the scope of “multi-source data processing”.

Potential topics include:

  • Multi-source data fusion theory;
  • Deep learning networks for multi-source data;
  • Machine learning for signal processing;
  • Signal processing theory;
  • Image processing methods;
  • Radar processing and applications;
  • Other relative topics.

Prof. Dr. Jun-Bao Li
Prof. Dr. Jeng-Shyang Pan
Dr. Meng Li
Dr. Huanyu Liu
Prof. Dr. Shi-Huang Chen
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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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.

Keywords

  • multi-source data fusion
  • deep learning
  • machine learning
  • signal processing

Published Papers (3 papers)

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Research

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15 pages, 2223 KiB  
Article
Towards Improved Inertial Navigation by Reducing Errors Using Deep Learning Methodology
by Hua Chen, Tarek M. Taha and Vamsy P. Chodavarapu
Appl. Sci. 2022, 12(7), 3645; https://0-doi-org.brum.beds.ac.uk/10.3390/app12073645 - 5 Apr 2022
Cited by 4 | Viewed by 3121
Abstract
Autonomous vehicles make use of an Inertial Navigation System (INS) as part of vehicular sensor fusion in many situations including GPS-denied environments such as dense urban places, multi-level parking structures, and areas with thick tree-coverage. The INS unit incorporates an Inertial Measurement Unit [...] Read more.
Autonomous vehicles make use of an Inertial Navigation System (INS) as part of vehicular sensor fusion in many situations including GPS-denied environments such as dense urban places, multi-level parking structures, and areas with thick tree-coverage. The INS unit incorporates an Inertial Measurement Unit (IMU) to process the linear acceleration and angular velocity data to obtain orientation, position, and velocity information using mechanization equations. In this work, we describe a novel deep-learning-based methodology, using Convolutional Neural Networks (CNN), to reduce errors from MEMS IMU sensors. We develop a CNN-based approach that can learn from the responses of a particular inertial sensor while subject to inherent noise errors and provide near real-time error correction. We implement a time-division method to divide the IMU output data into small step sizes to make the IMU outputs fit the input format of the CNN. We optimize the CNN approach for higher performance and lower complexity that would allow its implementation on ultra-low power hardware such as microcontrollers. Our results show that we achieved up to 32.5% error improvement in straight-path motion and up to 38.69% error improvement in oval motion compared with the ground truth. We examined the performance of our CNN approach under various situations with IMUs of various performance grades, IMUs of the same type but different manufactured batch, and controlled, fixed, and uncontrolled vehicle motion paths. Full article
(This article belongs to the Special Issue Signal Processing, Applications and Systems)
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12 pages, 3164 KiB  
Article
Structural Damage Localization under Unknown Seismic Excitation Based on Mahalanobis Squared Distance of Strain Transmissibility Function
by Lijun Liu, Xin Zhang, Ying Lei and Zhupeng Zheng
Appl. Sci. 2022, 12(6), 3115; https://0-doi-org.brum.beds.ac.uk/10.3390/app12063115 - 18 Mar 2022
Viewed by 1448
Abstract
Due to the unpredictability of seismic excitation, the data-driven damage identification method, which only depends on the monitoring response data, has a good development prospect in structural health monitoring. In recent years, damage identification methods based on transmissibility function (TF) and Mahalanobis squared [...] Read more.
Due to the unpredictability of seismic excitation, the data-driven damage identification method, which only depends on the monitoring response data, has a good development prospect in structural health monitoring. In recent years, damage identification methods based on transmissibility function (TF) and Mahalanobis squared distance (MSD) have been widely studied. However, the existing methods are only applicable to damage warning. To overcome this limitation, an improved method for structural damage localization is proposed. Strain TF is used to eliminate the influence of unknown ambient excitation and unknown seismic excitation, which is more sensitive to local damage than traditional TF based on acceleration and displacement data. The MSD of strain TF is employed to construct a novel damage indicator that is used to identify the damage location. Two numerical simulations have been conducted to verify the feasibility of the method for damage localization and good anti-noise performance. In the case of the multi-damage condition, the novel damage indicator is performed to estimate the severity of damage to some extent. Full article
(This article belongs to the Special Issue Signal Processing, Applications and Systems)
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Review

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24 pages, 473 KiB  
Review
Gaussian Processes for Signal Processing and Representation in Control Engineering
by Adrian Dudek and Jerzy Baranowski
Appl. Sci. 2022, 12(10), 4946; https://0-doi-org.brum.beds.ac.uk/10.3390/app12104946 - 13 May 2022
Cited by 8 | Viewed by 2573
Abstract
The Gaussian process is an increasingly well-known type of stochastic process, which is a generalization of the Gaussian probability distribution. It allows us to model complex functions thanks to its flexibility, which would not be possible with the use of other tools. Gaussian [...] Read more.
The Gaussian process is an increasingly well-known type of stochastic process, which is a generalization of the Gaussian probability distribution. It allows us to model complex functions thanks to its flexibility, which would not be possible with the use of other tools. Gaussian processes also have a couple of other features that are used in various branches of automation with positive results, ranging from industrial processes to image processing. There are also many ways of setting up the Gaussian processes, which required knowledge on the topic and depend on the presented problem. Considerations on these topics lead to the conclusion that the current state of practical usefulness of Gaussian processes increases significantly, therefore the deepening of knowledge about the ways of its use is highly suggested. In this review, we present selected technical applications of Gaussian Processes allowing an understanding of their broad applicability. Full article
(This article belongs to the Special Issue Signal Processing, Applications and Systems)
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