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Electronics
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Advances in Image Processing, Artificial Intelligence and Intelligent Robotics
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Advances in Image Processing, Artificial Intelligence and Intelligent Robotics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 10433

Special Issue Editors

Prof. Dr. Vladimir László Tadić

E-Mail Website
Guest Editor
1. John von Neumann Faculty of Informatics, University of Obuda, Becsi ut 96/B., 1034 Budapest, Hungary
2. Institute of Information Technology, University of Dunaujvaros, Tancsics M. Str. 1/A, H-2401 Dunaujvaros, Hungary
Interests: image processing; computer vision; signal processing; electronics; robotics and soft computing methods
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Peter Odry

E-Mail Website
Guest Editor
1. Institute of Information Technology, University of Dunaujvaros, Tancsics M. Str. 1/A, H-2401 Dunaujvaros, Hungary
2. Symbolic Methods in Material Analysis and Tomography Research Group, Faculty of Engineering and Information Technology, University of Pecs, Boszorkany Str. 6, H-7624 Pecs, Hungary
Interests: robotics; fuzzy control; electrical engineering; optimization methods; electrical impedance tomography; control theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For many years, scientists and engineers have tried to make digital image processing as efficient as the human vision system. Recently, artificial intelligence, deep learning and soft computing methods have been involved in the development of various sophisticated image processing algorithms. Further, image processing plays a significant role in intelligent robotics, where the goal is the realization of precise, robust, and intelligent control solutions based on image information. Thus, the use of vision sensors and cameras in robotics inspired the deployment of effective applications in industry, agriculture, biology, medicine, etc.

The aim of this Special Issue is to give researchers the opportunity to provide new tendencies as well as the latest achievements and research directions, and to present their current work on the important problems in image processing, deep learning, soft computing, sensor fusion, robotic vision and applied industrial solutions in robotics.

In this Special Issue, original research articles, short communications, technical reports, perspectives, extended conference papers and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Two- and three-dimensional image processing;
  • Image segmentation and texture analysis;
  • Image filtering, restoration and enhancement;
  • Biomedical image processing;
  • Pattern recognition and shape detection;
  • Deep learning;
  • Soft computing and fuzzy techniques;
  • Sensor fusion;
  • Measurements;
  • Robot vision;
  • Intelligent and applied robotics;
  • Hardware and architectures for image processing and robotics;
  • Robust identification.

Prof. Dr. Vladimir Laslo Tadić
Prof. Dr. Peter Odry
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. Electronics 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

  • image processing
  • medical imaging
  • artificial intelligence
  • deep learning
  • soft computing
  • fuzzy logic
  • sensor fusion
  • measurements
  • robotic vision
  • industrial robotics
  • robust identification

Published Papers (10 papers)

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24 pages, 27317 KiB  
Article
An Image Retrieval Method for Lunar Complex Craters Integrating Visual and Depth Features
by Yingnan Zhang, Zhizhong Kang and Zhen Cao
Electronics 2024, 13(7), 1262; https://doi.org/10.3390/electronics13071262 - 28 Mar 2024
Viewed by 413
Abstract
In the geological research of the Moon and other celestial bodies, the identification and analysis of impact craters are crucial for understanding the geological history of these bodies. With the rapid increase in the volume of high-resolution imagery data returned from exploration missions, [...] Read more.
In the geological research of the Moon and other celestial bodies, the identification and analysis of impact craters are crucial for understanding the geological history of these bodies. With the rapid increase in the volume of high-resolution imagery data returned from exploration missions, traditional image retrieval methods face dual challenges of efficiency and accuracy when processing lunar complex crater image data. Deep learning techniques offer a potential solution. This paper proposes an image retrieval model for lunar complex craters that integrates visual and depth features (LC2R-Net) to overcome these difficulties. For depth feature extraction, we employ the Swin Transformer as the core architecture for feature extraction and enhance the recognition capability for key crater features by integrating the Convolutional Block Attention Module with Effective Channel Attention (CBAMwithECA). Furthermore, a triplet loss function is introduced to generate highly discriminative image embeddings, further optimizing the embedding space for similarity retrieval. In terms of visual feature extraction, we utilize Local Binary Patterns (LBP) and Hu moments to extract the texture and shape features of crater images. By performing a weighted fusion of these features and utilizing Principal Component Analysis (PCA) for dimensionality reduction, we effectively combine visual and depth features and optimize retrieval efficiency. Finally, cosine similarity is used to calculate the similarity between query images and images in the database, returning the most similar images as retrieval results. Validation experiments conducted on the lunar complex impact crater dataset constructed in this article demonstrate that LC2R-Net achieves a retrieval precision of 83.75%, showcasing superior efficiency. These experimental results confirm the advantages of LC2R-Net in handling the task of lunar complex impact crater image retrieval. Full article
(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)
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17 pages, 6023 KiB  
Article
Continuous Electrode Models and Application of Exact Schemes in Modeling of Electrical Impedance Measurements
by Zoltan Vizvari, Mihaly Klincsik, Peter Odry, Vladimir Tadic, Nina Gyorfi, Attila Toth and Zoltan Sari
Electronics 2024, 13(1), 66; https://doi.org/10.3390/electronics13010066 - 22 Dec 2023
Viewed by 564
Abstract
The crucial issue in electrical impedance (EI) measurements lies in the galvanic interaction between the electrodes and the investigated material. This paper brings together the basic and applied research experience and combines their results with excellent properties. Consequently, innovative precise methodologies have emerged, [...] Read more.
The crucial issue in electrical impedance (EI) measurements lies in the galvanic interaction between the electrodes and the investigated material. This paper brings together the basic and applied research experience and combines their results with excellent properties. Consequently, innovative precise methodologies have emerged, enabling the direct modeling of EI measurements, free from the inaccuracies often associated with numerical approaches. As an outcome of the efficiency and robustness of the applied method, the conductivity of the material and the electrodes are represented by a common piecewise function, which is used to solve the differential equation modeling of the EI measurement. Moreover, this allows the possibility for modeling the conductivity of electrodes with continuous functions, providing an important generalization of the Complete Electrode Model (CEM), which has been widely used so far. The effectiveness of the novel approach was showcased through two distinct case studies. In the first case study, potential functions within both the material and the electrodes were computed using the CEM. In the second case study, calculations were performed utilizing the newly introduced continuous electrode model. The simulation results suggest that the new method is a powerful tool for biological research, from in vitro experiments to animal studies and human applications. Full article
(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)
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16 pages, 37035 KiB  
Article
A Method for Visualization of Images by Photon-Counting Imaging Only Object Locations under Photon-Starved Conditions
by Jin-Ung Ha, Hyun-Woo Kim, Myungjin Cho and Min-Chul Lee
Electronics 2024, 13(1), 38; https://doi.org/10.3390/electronics13010038 - 20 Dec 2023
Viewed by 564
Abstract
Recently, many researchers have been studying the visualization of images and the recognition of objects by estimating photons under photon-starved conditions. Conventional photon-counting imaging techniques estimate photons by way of a statistical method using Poisson distribution in all image areas. However, Poisson distribution [...] Read more.
Recently, many researchers have been studying the visualization of images and the recognition of objects by estimating photons under photon-starved conditions. Conventional photon-counting imaging techniques estimate photons by way of a statistical method using Poisson distribution in all image areas. However, Poisson distribution is temporally and spatially independent, and the reconstructed image has a random noise in the background. Random noise in the background may degrade the quality of the image and make it difficult to accurately recognize objects. Therefore, in this paper, we apply photon-counting imaging technology only to the area where the object is located to eliminate the noise in the background. As a result, it can be seen that the image quality using the proposed method is better than that of the conventional method and the object recognition rate is also higher. Optical experiments were conducted to prove the denoising performance of the proposed method. In addition, we used the structure similarity index measure (SSIM) as a performance metric. To check the recognition rate of the object, we applied the YOLOv5 model. Finally, the proposed method is expected to accelerate the development of astrophotography and medical imaging technologies. Full article
(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)
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17 pages, 5495 KiB  
Article
Application of an Output Filtering Method for an Unstable Wheel-Driven Pendulum System Parameter Identification
by Chao-Chung Peng, Nai-Jen Cheng and Min-Che Tsai
Electronics 2023, 12(22), 4569; https://doi.org/10.3390/electronics12224569 - 08 Nov 2023
Viewed by 568
Abstract
This research aims to apply an output filtering method to conduct the system parameter identification of an unstable wheel-driven pendulum system. First, the nonlinear dynamic model of the system is established by utilizing the Lagrangian dynamic theorem. Next, the Least-Square (LS) is introduced [...] Read more.
This research aims to apply an output filtering method to conduct the system parameter identification of an unstable wheel-driven pendulum system. First, the nonlinear dynamic model of the system is established by utilizing the Lagrangian dynamic theorem. Next, the Least-Square (LS) is introduced for system parameter identification formulation. Nevertheless, considering the real scenario, the wheel displacement is acquired from encoders subject to quantization errors. The pitch angle of the pendulum cart is also accompanied by Gaussian noise. Therefore, using numerical differentiation for angular acceleration in the LS estimations directly would induce incorrect state information seriously. To address this practical issue, an output filtering method is considered. The developed parameter identification algorithm could attenuate the influence of the quantization effect as well as noisy data and thus obtain much more accurate parameter identification results. Comparative simulation reveals that the output filtering method has a superior parameter estimation performance than the direct numerical difference method. Full article
(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)
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19 pages, 8072 KiB  
Article
Stable and Efficient Reinforcement Learning Method for Avoidance Driving of Unmanned Vehicles
by Sun-Ho Jang, Woo-Jin Ahn, Yu-Jin Kim, Hyung-Gil Hong, Dong-Sung Pae and Myo-Taeg Lim
Electronics 2023, 12(18), 3773; https://doi.org/10.3390/electronics12183773 - 06 Sep 2023
Viewed by 754
Abstract
Reinforcement learning (RL) has demonstrated considerable potential in solving challenges across various domains, notably in autonomous driving. Nevertheless, implementing RL in autonomous driving comes with its own set of difficulties, such as the overestimation phenomenon, extensive learning time, and sparse reward problems. Although [...] Read more.
Reinforcement learning (RL) has demonstrated considerable potential in solving challenges across various domains, notably in autonomous driving. Nevertheless, implementing RL in autonomous driving comes with its own set of difficulties, such as the overestimation phenomenon, extensive learning time, and sparse reward problems. Although solutions like hindsight experience replay (HER) have been proposed to alleviate these issues, the direct utilization of RL in autonomous vehicles remains constrained due to the intricate fusion of information and the possibility of system failures during the learning process. In this paper, we present a novel RL-based autonomous driving system technology that combines obstacle-dependent Gaussian (ODG) RL, soft actor-critic (SAC), and meta-learning algorithms. Our approach addresses key issues in RL, including the overestimation phenomenon and sparse reward problems, by incorporating prior knowledge derived from the ODG algorithm. With these solutions in place, the ultimate aim of this work is to improve the performance of reinforcement learning and develop a swift, stable, and robust learning method for implementing autonomous driving systems that can effectively adapt to various environments and overcome the constraints of direct RL utilization in autonomous vehicles. We evaluated our proposed algorithm on official F1 circuits, using high-fidelity racing simulations with complex dynamics. The results demonstrate exceptional performance, with our method achieving up to 89% faster learning speed compared to existing algorithms in these environments. Full article
(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)
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17 pages, 2726 KiB  
Article
A Two-Stage Image Inpainting Technique for Old Photographs Based on Transfer Learning
by Mingju Chen, Zhengxu Duan, Lan Li, Sihang Yi and Anle Cui
Electronics 2023, 12(15), 3221; https://doi.org/10.3390/electronics12153221 - 25 Jul 2023
Viewed by 858
Abstract
To address the challenge of sparse old photo datasets, we apply transfer learning to image inpainting tasks. Specifically, we improve a two-stage image inpainting network that focuses on collaborative subtasks. We also design a transform module based on the cross-aggregation of windows to [...] Read more.
To address the challenge of sparse old photo datasets, we apply transfer learning to image inpainting tasks. Specifically, we improve a two-stage image inpainting network that focuses on collaborative subtasks. We also design a transform module based on the cross-aggregation of windows to improve long-distance contextual information acquisition in image inpainting and enhance the integrity of images in terms of structure and texture. Our improved two-stage network has a significantly better repair performance compared to that of the current common inpainting methods. We further apply transfer learning techniques by utilizing the improved two-stage image inpainting network as the base network and decoupling the generator into a feature extractor and classifier, which consist of an encoder and a decoder, respectively. We obtain a domain-invariant feature extractor through minimax game training using source and target domain data. This feature extractor can be combined with the original encoder to restore old photo images. To verify the effectiveness of our approach, we conducted comparative experiments. Our results show that the PSNR, SSIM, and FID indexes of the model using transfer learning are 11.8%, 2.96%, and 44.4% higher than those without transfer learning, respectively. These findings suggest that applying transfer learning techniques can be an effective solution to address the challenge of sparse old photo datasets in image inpainting tasks. Full article
(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)
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20 pages, 9710 KiB  
Article
Physiological Signal-Based Real-Time Emotion Recognition Based on Exploiting Mutual Information with Physiologically Common Features
by Ean-Gyu Han, Tae-Koo Kang and Myo-Taeg Lim
Electronics 2023, 12(13), 2933; https://doi.org/10.3390/electronics12132933 - 03 Jul 2023
Cited by 2 | Viewed by 1607
Abstract
This paper proposes a real-time emotion recognition system that utilizes photoplethysmography (PPG) and electromyography (EMG) physiological signals. The proposed approach employs a complex-valued neural network to extract common features from the physiological signals, enabling successful emotion recognition without interference. The system comprises three [...] Read more.
This paper proposes a real-time emotion recognition system that utilizes photoplethysmography (PPG) and electromyography (EMG) physiological signals. The proposed approach employs a complex-valued neural network to extract common features from the physiological signals, enabling successful emotion recognition without interference. The system comprises three stages: single-pulse extraction, a physiological coherence feature module, and a physiological common feature module. The experimental results demonstrate that the proposed method surpasses alternative approaches in terms of accuracy and the recognition interval. By extracting common features of the PPG and EMG signals, this approach achieves effective emotion recognition without mutual interference. The findings provide a significant advancement in real-time emotion analysis and offer a clear and concise framework for understanding individuals’ emotional states using physiological signals. Full article
(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)
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14 pages, 5945 KiB  
Article
A Transformer-Based Cross-Window Aggregated Attentional Image Inpainting Model
by Mingju Chen, Tingting Liu, Xingzhong Xiong, Zhengxu Duan and Anle Cui
Electronics 2023, 12(12), 2726; https://doi.org/10.3390/electronics12122726 - 19 Jun 2023
Cited by 2 | Viewed by 1197
Abstract
To overcome the fault of convolutional networks, which can be over-smooth, blurred, or discontinuous, a novel transformer network with cross-window aggregated attention is proposed. Our network as a whole is constructed as a generative adversarial network model, and by embedding the Window Aggregation [...] Read more.
To overcome the fault of convolutional networks, which can be over-smooth, blurred, or discontinuous, a novel transformer network with cross-window aggregated attention is proposed. Our network as a whole is constructed as a generative adversarial network model, and by embedding the Window Aggregation Transformer (WAT) module, we improve the information aggregation between windows without increasing the computational complexity and effectively obtain the image long-range dependencies to solve the problem that convolutional operations are limited by local feature extraction. First, the encoder extracts the multi-scale features of the image with convolution kernels of different scales; second, the feature maps of different scales are input into a WAT module to realize the aggregation between feature information and finally, these features are reconstructed by the decoder, and then, the generated image is input into the global discriminator, in which the discrimination between real and fake images is completed. It is experimentally verified that our designed Transformer window attention network is able to make the structured texture of the restored images richer and more natural when performing the restoration task of large broken or structurally complex images. Full article
(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)
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23 pages, 5335 KiB  
Article
Analysis of the Security Challenges Facing the DS-Lite IPv6 Transition Technology
by Ameen Al-Azzawi and Gábor Lencse
Electronics 2023, 12(10), 2335; https://doi.org/10.3390/electronics12102335 - 22 May 2023
Cited by 3 | Viewed by 1288
Abstract
This paper focuses on one of the most prominent IPv6 transition technologies named DS-Lite (Dual-Stack Lite). The aim was to analyze the security threats to which this technology might be vulnerable. The analysis is based on the STRIDE method, which stands for Spoofing, [...] Read more.
This paper focuses on one of the most prominent IPv6 transition technologies named DS-Lite (Dual-Stack Lite). The aim was to analyze the security threats to which this technology might be vulnerable. The analysis is based on the STRIDE method, which stands for Spoofing, Tampering, Repudiation, Information Disclosure, and Elevation of Privilege. A testbed was built for the DS-Lite topology using several virtual machines, which were created using CentOS Linux images. The testbed was used to perform several types of attacks against the infrastructure of DS-Lite, especially against the B4 (Basic Bridging Broadband) and the AFTR (Address Family Transition Router) elements, where it was shown that the pool of source ports can be exhausted in 14 s. Eventually, the most common attacks that DS-Lite is susceptible to were summarized, and methods for mitigating such attacks were proposed. Full article
(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)
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24 pages, 16929 KiB  
Perspective
Study on Automatic Electric Vehicle Charging Socket Detection Using ZED 2i Depth Sensor
by Vladimir Tadic
Electronics 2023, 12(4), 912; https://doi.org/10.3390/electronics12040912 - 10 Feb 2023
Cited by 4 | Viewed by 1740
Abstract
This article introduces the utilization of the ZED 2i depth sensor in a robot-based automatic electric vehicle charging application. The employment of a stereo depth sensor is a significant aspect in robotic applications, since it is both the initial and the fundamental step [...] Read more.
This article introduces the utilization of the ZED 2i depth sensor in a robot-based automatic electric vehicle charging application. The employment of a stereo depth sensor is a significant aspect in robotic applications, since it is both the initial and the fundamental step in a series of robotic operations, where the intent is to detect and extract the charging socket on the vehicle’s body surface. The ZED 2i depth sensor was utilized for scene recording with artificial illumination. Later, the socket detection and extraction were accomplished using both simple image processing and morphological operations in an object extraction algorithm with tilt angles and centroid coordinates determination of the charging socket itself. The aim was to use well-known, simple, and proven image processing techniques in the proposed method to ensure both reliable and smooth functioning of the robot’s vision system in an industrial environment. The experiments demonstrated that the deployed algorithm both extracts the charging socket and determines the slope angles and socket coordinates successfully under various depth assessment conditions, with a detection rate of 94%. Full article
(This article belongs to the Special Issue Advances in Image Processing, Artificial Intelligence and Intelligent Robotics)
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