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Entropy Algorithms Using Deep Learning for Signal Processing

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 12964

Special Issue Editor


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Guest Editor
Department of Embedded Systems Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea
Interests: remote sensing; deep learning; artificial intelligence; image processing; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Signal processing is important in our daily lives as electronic devices, such as computers, radios, videos, mobile phones, are all enhanced by image and video processing. Signal processing is a part of computer science and information entropy that models and analyzes data representations of the physical world. Now, signal processing is starting to make waves in deep learning. Deep learning for signal data needs extra stages when compared to using deep learning or machine learning for other data sets. Signal data without any contamination is hard to achieve and signal data normally has meaningful noise and variability. This Special Issue calls for recent studies on various image, video, and signal processing algorithms that are based on deep learning and information entropy. Papers of both theoretical and applicative nature are welcome, as well as contributions regarding new image and video processing techniques for the entropy research community. Major topics of interest, by no means exclusive, are:

Keywords:

  • Deep learning for signal processing;
  • Machine learning for signal processing;
  • Multi-channel imaging;
  • Sensor size, channel number, and dynamic range;
  • Modeling of signal processing;
  • Compression approach;
  • Entropy-based video coding;
  • Prediction and redundancy for video coding;
  • Noise removal approach;
  • GPU-based methods for signal processing;
  • Signal quality assessment.

Dr. Gwanggil Jeon
Guest Editor

Manuscript Submission Information

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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. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

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Published Papers (7 papers)

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Editorial

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2 pages, 158 KiB  
Editorial
Entropy Algorithms Using Deep Learning for Signal Processing
by Gwanggil Jeon
Entropy 2023, 25(1), 23; https://doi.org/10.3390/e25010023 - 23 Dec 2022
Viewed by 1043
Abstract
Image and video processing operatons are significant in our life as most electronic devices, such as PCs and mobiles, are all developed by signal processing [...] Full article
(This article belongs to the Special Issue Entropy Algorithms Using Deep Learning for Signal Processing)

Research

Jump to: Editorial

37 pages, 17703 KiB  
Article
Chaotic Mapping-Based Anti-Sorting Radio Frequency Stealth Signals and Compressed Sensing-Based Echo Signal Processing Technology
by Jinwei Jia, Limin Liu, Yuying Liang, Zhuangzhi Han and Xuetian Wang
Entropy 2022, 24(11), 1559; https://doi.org/10.3390/e24111559 - 29 Oct 2022
Cited by 2 | Viewed by 1244
Abstract
Radio frequency (RF) stealth anti-sorting technology can improve the battlefield survival rate of radar and is one of the research hotspots in the radar field. In this study, the signal design principle of anti-sequential difference histogram (SDIF) sorting was explored for the main [...] Read more.
Radio frequency (RF) stealth anti-sorting technology can improve the battlefield survival rate of radar and is one of the research hotspots in the radar field. In this study, the signal design principle of anti-sequential difference histogram (SDIF) sorting was explored for the main sorting algorithm of the SDIF. Furthermore, we designed a piecewise linear chaotic system with interval number parameterization based on random disturbance and proposed a method to modulate the repetition period of widely spaced signal pulses using a chaotic system. Then, considering the difficulty of the traditional signal processing method to measure the velocity of the highly random anti-sorting signals designed in this paper, we used compressed sensing (CS) technology to process the echoes of the signals to solve the velocity and distance of the detection targets. Finally, simulation verification was performed from the correctness of the signal design principle, the performance of the chaotic system, the anti-sorting performance of the designed signals and the recovery and reconstruction performance of the signals by CS. The results show that: (a) the signal design principle presented in this paper can guide the signal design correctly; (b) the performance of the piecewise linear chaotic system with interval number parameterization is better than that of the classical one-dimensional chaotic system; (c) the anti-sorting signal modulated by the chaotic system can achieve anti-SDIF sorting, and the anti-sorting signals designed in this paper can be processed to obtain the velocity and distance of the targets. Full article
(This article belongs to the Special Issue Entropy Algorithms Using Deep Learning for Signal Processing)
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16 pages, 922 KiB  
Article
PCQNet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
by Xiuwen Bao, Ming Jiang, Wenhao Fang and Chunming Zhao
Entropy 2022, 24(8), 1066; https://doi.org/10.3390/e24081066 - 2 Aug 2022
Cited by 3 | Viewed by 1537
Abstract
Multi-user multiple-input multiple-output (MU-MIMO) technology can significantly improve the spectral and energy efficiencies of wireless networks. In the uplink MU-MIMO systems, the optimal precoder design at the base station utilizes the Lagrange multipliers method and the centralized iterative algorithm to minimize the mean [...] Read more.
Multi-user multiple-input multiple-output (MU-MIMO) technology can significantly improve the spectral and energy efficiencies of wireless networks. In the uplink MU-MIMO systems, the optimal precoder design at the base station utilizes the Lagrange multipliers method and the centralized iterative algorithm to minimize the mean squared error (MSE) of all users under the power constraint. The precoding matrices need to be fed back to the user equipment to explore the potential benefits of the joint transceiver design. We propose a CNN-based compression network named PCQNet to minimize the feedback overhead. We first illustrate the effect of the trainable compression ratios and feedback bits on the MSE between the original precoding matrices and the recovered ones. We then evaluate the block error rates as the performance measure of the centralized implementation with an optimal minimum mean-squared error (MMSE) transceiver. Numerical results show that the proposed PCQNet achieves near-optimal performance compared with other quantized feedback schemes and significantly reduces the feedback overhead with negligible performance degradation. Full article
(This article belongs to the Special Issue Entropy Algorithms Using Deep Learning for Signal Processing)
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22 pages, 7078 KiB  
Article
Efficient Entropic Security with Joint Compression and Encryption Approach Based on Compressed Sensing with Multiple Chaotic Systems
by Jingya Wang, Xianhua Song and Ahmed A. Abd El-Latif
Entropy 2022, 24(7), 885; https://doi.org/10.3390/e24070885 - 27 Jun 2022
Cited by 8 | Viewed by 1743
Abstract
This paper puts forward a new algorithm that utilizes compressed sensing and two chaotic systems to complete image compression and encryption concurrently. First, the hash function was utilized to obtain the initial parameters of two chaotic maps, which were the 2D-SLIM and 2D-SCLMS [...] Read more.
This paper puts forward a new algorithm that utilizes compressed sensing and two chaotic systems to complete image compression and encryption concurrently. First, the hash function was utilized to obtain the initial parameters of two chaotic maps, which were the 2D-SLIM and 2D-SCLMS maps, respectively. Second, a sparse coefficient matrix was transformed from the plain image through discrete wavelet transform. In addition, one of the chaotic sequences created by 2D-SCLMS system performed pixel transformation on the sparse coefficient matrix. The other chaotic sequences created by 2D-SLIM were utilized to generate a measurement matrix and perform compressed sensing operations. Subsequently, the matrix rotation was combined with row scrambling and column scrambling, respectively. Finally, the bit-cycle operation and the matrix double XOR were implemented to acquire the ciphertext image. Simulation experiment analysis showed that the compressed encryption scheme has advantages in compression performance, key space, and sensitivity, and is resistant to statistical attacks, violent attacks, and noise attacks. Full article
(This article belongs to the Special Issue Entropy Algorithms Using Deep Learning for Signal Processing)
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13 pages, 1286 KiB  
Article
A Complex-Valued Self-Supervised Learning-Based Method for Specific Emitter Identification
by Dongxing Zhao, Junan Yang, Hui Liu and Keju Huang
Entropy 2022, 24(7), 851; https://doi.org/10.3390/e24070851 - 21 Jun 2022
Cited by 5 | Viewed by 1746
Abstract
Specific emitter identification (SEI) refers to distinguishing emitters using individual features extracted from wireless signals. The current SEI methods have proven to be accurate in tackling large labeled data sets at a high signal-to-noise ratio (SNR). However, their performance declines dramatically in the [...] Read more.
Specific emitter identification (SEI) refers to distinguishing emitters using individual features extracted from wireless signals. The current SEI methods have proven to be accurate in tackling large labeled data sets at a high signal-to-noise ratio (SNR). However, their performance declines dramatically in the presence of small samples and a significant noise environment. To address this issue, we propose a complex self-supervised learning scheme to fully exploit the unlabeled samples, comprised of a pretext task adopting the contrastive learning concept and a downstream task. In the former task, we design an optimized data augmentation method based on communication signals to serve the contrastive conception. Then, we embed a complex-valued network in the learning to improve the robustness to noise. The proposed scheme demonstrates the generality of handling the small and sufficient samples cases across a wide range from 10 to 400 being labeled in each group. The experiment also shows a promising accuracy and robustness where the recognition results increase at 10–16% from 10–15 SNR. Full article
(This article belongs to the Special Issue Entropy Algorithms Using Deep Learning for Signal Processing)
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20 pages, 7315 KiB  
Article
Multi-Scale Mixed Attention Network for CT and MRI Image Fusion
by Yang Liu, Binyu Yan, Rongzhu Zhang, Kai Liu, Gwanggil Jeon and Xiaoming Yang
Entropy 2022, 24(6), 843; https://doi.org/10.3390/e24060843 - 19 Jun 2022
Cited by 7 | Viewed by 2243
Abstract
Recently, the rapid development of the Internet of Things has contributed to the generation of telemedicine. However, online diagnoses by doctors require the analyses of multiple multi-modal medical images, which are inconvenient and inefficient. Multi-modal medical image fusion is proposed to solve this [...] Read more.
Recently, the rapid development of the Internet of Things has contributed to the generation of telemedicine. However, online diagnoses by doctors require the analyses of multiple multi-modal medical images, which are inconvenient and inefficient. Multi-modal medical image fusion is proposed to solve this problem. Due to its outstanding feature extraction and representation capabilities, convolutional neural networks (CNNs) have been widely used in medical image fusion. However, most existing CNN-based medical image fusion methods calculate their weight maps by a simple weighted average strategy, which weakens the quality of fused images due to the effect of inessential information. In this paper, we propose a CNN-based CT and MRI image fusion method (MMAN), which adopts a visual saliency-based strategy to preserve more useful information. Firstly, a multi-scale mixed attention block is designed to extract features. This block can gather more helpful information and refine the extracted features both in the channel and spatial levels. Then, a visual saliency-based fusion strategy is used to fuse the feature maps. Finally, the fused image can be obtained via reconstruction blocks. The experimental results of our method preserve more textual details, clearer edge information and higher contrast when compared to other state-of-the-art methods. Full article
(This article belongs to the Special Issue Entropy Algorithms Using Deep Learning for Signal Processing)
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21 pages, 2697 KiB  
Article
On Architecture Selection for Linear Inverse Problems with Untrained Neural Networks
by Yang Sun, Hangdong Zhao and Jonathan Scarlett
Entropy 2021, 23(11), 1481; https://doi.org/10.3390/e23111481 - 9 Nov 2021
Cited by 4 | Viewed by 1970
Abstract
In recent years, neural network based image priors have been shown to be highly effective for linear inverse problems, often significantly outperforming conventional methods that are based on sparsity and related notions. While pre-trained generative models are perhaps the most common, it has [...] Read more.
In recent years, neural network based image priors have been shown to be highly effective for linear inverse problems, often significantly outperforming conventional methods that are based on sparsity and related notions. While pre-trained generative models are perhaps the most common, it has additionally been shown that even untrained neural networks can serve as excellent priors in various imaging applications. In this paper, we seek to broaden the applicability and understanding of untrained neural network priors by investigating the interaction between architecture selection, measurement models (e.g., inpainting vs. denoising vs. compressive sensing), and signal types (e.g., smooth vs. erratic). We motivate the problem via statistical learning theory, and provide two practical algorithms for tuning architectural hyperparameters. Using experimental evaluations, we demonstrate that the optimal hyperparameters may vary significantly between tasks and can exhibit large performance gaps when tuned for the wrong task. In addition, we investigate which hyperparameters tend to be more important, and which are robust to deviations from the optimum. Full article
(This article belongs to the Special Issue Entropy Algorithms Using Deep Learning for Signal Processing)
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