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Nonlinear Signal and Image Processing: Current Trends and Future Directions
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Nonlinear Signal and Image Processing: Current Trends and Future Directions

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

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 7146

Special Issue Editor

Prof. Dr. Bin Gao

E-Mail Website
Guest Editor
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
Interests: audio and image processing; social signal processing; multi-physics mathematical modeling; non-destructive evaluation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nonlinear Signal and Image Processing is an overarching field of research that focuses on the physical-mathematical foundations and practical applications of sensors, as well as interpretation, signal processing, and artificial intelligent algorithms. The natural sensing information of Nonlinearity enables us to learn, reason, and act. It bridges the boundary between theory and application, developing novel, theoretically inspired methodologies that target both longstanding and emergent Nonlinear Signal and Image Processing applications.

The focus of this Special Issue is on the use of this research and its proposed meaning for physics with regard to sensors, nonlinear and non-Gaussian signal processing methodologies combined with convex and non-convex optimization, sensor-based machine learning/deep learning neural networks, and image and video spatial–time processing. It encompasses new theoretical frameworks for interpreting sensing in Nonlinear signal processing (e.g., latent component analysis, tensor factorization, Bayesian methods) coupled with information-theoretic learning. The processing of a variety of modalities includes audio, chemical, bio-signals, electromagnetic thermal multi-physics signals, images, multispectral, and video, among others. In addition, the sensor applications are encouraged by using Nonlinear Signal and Image Processing as well as an interpretable deep learning structure. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen sensor data are captured.

Dr. Bin Gao
Guest Editor

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.

Keywords

  • nonlinear signal processing
  • image and video processing
  • machine learning
  • sensor signal processing
  • physical and math modeling
  • sensor applications
  • matrix and tensor factorization
  • statistic signal processing
  • pattern recognition

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Published Papers (3 papers)

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Research

25 pages, 3636 KiB  
Article
A Residual-Dense-Based Convolutional Neural Network Architecture for Recognition of Cardiac Health Based on ECG Signals
by Alaa E. S. Ahmed, Qaisar Abbas, Yassine Daadaa, Imran Qureshi, Ganeshkumar Perumal and Mostafa E. A. Ibrahim
Sensors 2023, 23(16), 7204; https://doi.org/10.3390/s23167204 - 16 Aug 2023
Cited by 4 | Viewed by 2015
Abstract
Cardiovascular disorders are often diagnosed using an electrocardiogram (ECG). It is a painless method that mimics the cyclical contraction and relaxation of the heart’s muscles. By monitoring the heart’s electrical activity, an ECG can be used to identify irregular heartbeats, heart attacks, cardiac [...] Read more.
Cardiovascular disorders are often diagnosed using an electrocardiogram (ECG). It is a painless method that mimics the cyclical contraction and relaxation of the heart’s muscles. By monitoring the heart’s electrical activity, an ECG can be used to identify irregular heartbeats, heart attacks, cardiac illnesses, or enlarged hearts. Numerous studies and analyses of ECG signals to identify cardiac problems have been conducted during the past few years. Although ECG heartbeat classification methods have been presented in the literature, especially for unbalanced datasets, they have not proven to be successful in recognizing some heartbeat categories with high performance. This study uses a convolutional neural network (CNN) model to combine the benefits of dense and residual blocks. The objective is to leverage the benefits of residual and dense connections to enhance information flow, gradient propagation, and feature reuse, ultimately improving the model’s performance. This proposed model consists of a series of residual-dense blocks interleaved with optional pooling layers for downsampling. A linear support vector machine (LSVM) classified heartbeats into five classes. This makes it easier to learn and represent features from ECG signals. We first denoised the gathered ECG data to correct issues such as baseline drift, power line interference, and motion noise. The impacts of the class imbalance are then offset by resampling techniques that denoise ECG signals. An RD-CNN algorithm is then used to categorize the ECG data for the various cardiac illnesses using the retrieved characteristics. On two benchmarked datasets, we conducted extensive simulations and assessed several performance measures. On average, we have achieved an accuracy of 98.5%, a sensitivity of 97.6%, a specificity of 96.8%, and an area under the receiver operating curve (AUC) of 0.99. The effectiveness of our suggested method for detecting heart disease from ECG data was compared with several recently presented algorithms. The results demonstrate that our method is lightweight and practical, qualifying it for continuous monitoring applications in clinical settings for automated ECG interpretation to support cardiologists. Full article
(This article belongs to the Special Issue Nonlinear Signal and Image Processing: Current Trends and Future Directions)
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12 pages, 2679 KiB  
Article
A New Method for Training CycleGAN to Enhance Images of Cold Seeps in the Qiongdongnan Sea
by Yuanheng Li, Shengxiong Yang, Yuehua Gong, Jingya Cao, Guang Hu, Yutian Deng, Dongmei Tian and Junming Zhou
Sensors 2023, 23(3), 1741; https://doi.org/10.3390/s23031741 - 3 Feb 2023
Cited by 2 | Viewed by 2011
Abstract
Clear underwater images can help researchers detect cold seeps, gas hydrates, and biological resources. However, the quality of these images suffers from nonuniform lighting, a limited range of visibility, and unwanted signals. CycleGAN has been broadly studied in regard to underwater image enhancement, [...] Read more.
Clear underwater images can help researchers detect cold seeps, gas hydrates, and biological resources. However, the quality of these images suffers from nonuniform lighting, a limited range of visibility, and unwanted signals. CycleGAN has been broadly studied in regard to underwater image enhancement, but it is difficult to apply the model for the further detection of Haima cold seeps in the South China Sea because the model can be difficult to train if the dataset used is not appropriate. In this article, we devise a new method of building a dataset using MSRCR and choose the best images based on the widely used UIQM scheme to build the dataset. The experimental results show that a good CycleGAN could be trained with the dataset using the proposed method. The model has good potential for applications in detecting the Haima cold seeps and can be applied to other cold seeps, such as the cold seeps in the North Sea. We conclude that the method used for building the dataset can be applied to train CycleGAN when enhancing images from cold seeps. Full article
(This article belongs to the Special Issue Nonlinear Signal and Image Processing: Current Trends and Future Directions)
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14 pages, 5359 KiB  
Article
Study on In-Service Inspection of Nuclear Fuel Assembly Failure Using Ultrasonic Plate Wave
by Xiang Xiao, Guo Zheng Zhou, Ke Qing Wang, Feng Xi and Kun Zeng
Sensors 2022, 22(19), 7606; https://doi.org/10.3390/s22197606 - 7 Oct 2022
Cited by 4 | Viewed by 1841
Abstract
As protection for nuclear power plants is quite necessary, the nuclear fuel is sealed in zirconium alloy thin wall cladding. During service, fuel rods might be damaged caused by wall-thickness thinning, cladding corrosion and cracking, etc. This will cause the coolant to enter [...] Read more.
As protection for nuclear power plants is quite necessary, the nuclear fuel is sealed in zirconium alloy thin wall cladding. During service, fuel rods might be damaged caused by wall-thickness thinning, cladding corrosion and cracking, etc. This will cause the coolant to enter into the fuel rod, which may lead to the failure of the fuel assembly. However, current diagnostic methods have limitations due to the special structure of the fuel assembly and the underwater and radioactive environment. In this paper, a novel inspection method is proposed to recognize the failure of a fuel rod. The fuel rod failure can be detected based on the presence or absence of coolant inside the fuel rod by using an ultrasonic plate wave. The inspection model and process algorithm are proposed for in-service inspection. The relationship between signal and scanning position is established and analyzed. Both ultrasound field simulation and experiment have been carried out for validation. The corresponding results illustrate that the failed nuclear fuel rod of the whole fuel assembly (including the internal rods) can be effectively detected without the influence of the near-field region by using the proposed method. Full article
(This article belongs to the Special Issue Nonlinear Signal and Image Processing: Current Trends and Future Directions)
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