Research

20 pages, 2104 KiB

Open AccessArticle

Personal Identification Using Long Short-Term Memory with Efficient Features of Electromyogram Biomedical Signals

by Yeong-Hyeon Byeon and Keun-Chang Kwak

Electronics 2023, 12(20), 4192; https://doi.org/10.3390/electronics12204192 - 10 Oct 2023

Viewed by 647

This study focuses on personal identification using bidirectional long short-term memory (LSTM) with efficient features from electromyogram (EMG) biomedical signals. Personal identification is performed by comparing and analyzing features that can be stably identified and are not significantly affected by noise. For this [...] Read more.

This study focuses on personal identification using bidirectional long short-term memory (LSTM) with efficient features from electromyogram (EMG) biomedical signals. Personal identification is performed by comparing and analyzing features that can be stably identified and are not significantly affected by noise. For this purpose, 13 efficient features, such as enhanced wavelength, zero crossing, and mean absolute value, were obtained from EMG signals. These features were extracted from segmented signals of a specific length. Then, the bidirectional LSTM was trained on the selected features as sequential data. The features were ranked based on their classification performance. Finally, the most effective features were selected, and the selected features were connected to achieve an improved classification rate. Two public EMG datasets were used to evaluate the proposed model. The first database was acquired from eight-channel Myo bands and was composed of EMG signals from 10 varying motions of 50 individuals. The total numbers of segments for the training and test sets were 30,000 and 20,000, respectively. The second dataset consisted of ten arm motions acquired from 40 individuals. A performance comparison of the dataset revealed that the proposed method exhibited good performance and efficiency compared to other well-known methods. Full article

(This article belongs to the Special Issue Advanced Technologies of Artificial Intelligence in Signal Processing)

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19 pages, 1532 KiB

Open AccessArticle

Polarization Direction of Arrival Estimation Using Dual Algorithms Based on Time-Frequency Cross Terms

by Shuai Shao, Aijun Liu, Xiuhong Wang and Changjun Yu

Electronics 2023, 12(17), 3575; https://doi.org/10.3390/electronics12173575 - 24 Aug 2023

Viewed by 722

Abstract

In radar array signal processing, weak nonstationary polarization signal direction of arrival (DOA) estimation is a challenging and significant issue when both strong and weak nonstationary signals coexist. Time-frequency (T-F) analysis is an effective method to deal with nonstationary signals. In the last [...] Read more.

In radar array signal processing, weak nonstationary polarization signal direction of arrival (DOA) estimation is a challenging and significant issue when both strong and weak nonstationary signals coexist. Time-frequency (T-F) analysis is an effective method to deal with nonstationary signals. In the last decade, spatial time-frequency distributions (STFDs) have been proposed for multiple dual-polarization antenna arrays and efficaciously used for nonstationary signal DOA estimation. In this article, we introduced a novelty means for estimating weak nonstationary polarization signal DOA by utilizing spatial polarimetric time-frequency distributions (SPTFDs) of cross terms when there are strong nonstationary polarization interference signals and additive Gaussian white noise. The cross terms’ SPTFDs are considered via a replaceability matrix for data covariance in multiple signal classification (MUSIC) and the estimation of signal parameters using the rotational invariance technique (ESPRIT). Combining the STFDs of cross terms with polarization information about weak nonstationary signals improves signal DOA estimation accuracy. The combined MUSIC and ESPRIT are used in the algorithm to further ensure the success probability and accuracy of DOA estimation. Through simulation analyses, the proposed algorithm is more suitable for the required application scenarios than other algorithms and is superior to other algorithms. Full article

(This article belongs to the Special Issue Advanced Technologies of Artificial Intelligence in Signal Processing)

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17 pages, 7641 KiB

Open AccessArticle

Lightweight Tunnel Defect Detection Algorithm Based on Knowledge Distillation

by Anfu Zhu, Bin Wang, Jiaxiao Xie and Congxiao Ma

Electronics 2023, 12(15), 3222; https://doi.org/10.3390/electronics12153222 - 26 Jul 2023

Cited by 1 | Viewed by 878

Abstract

One of the greatest engineering feats in history is the construction of tunnels, and the management of tunnel safety depends heavily on the detection of tunnel defects. However, the real-time, portability, and accuracy issues with the present tunnel defect detection technique still exist. [...] Read more.

One of the greatest engineering feats in history is the construction of tunnels, and the management of tunnel safety depends heavily on the detection of tunnel defects. However, the real-time, portability, and accuracy issues with the present tunnel defect detection technique still exist. The study improves the traditional defect detection technology based on the knowledge distillation algorithm, the depth pooling residual structure is designed in the teacher network to enhance the ability to extract target features. Next, the MobileNetv3 lightweight network is built into the student network to reduce the number and volume of model parameters. The lightweight model is then trained in terms of both features and outputs using a multidimensional knowledge distillation approach. By processing the tunnel radar detection photos, the dataset is created. The experimental findings demonstrate that the multidimensional knowledge distillation approach greatly increases the detection efficiency: the number of parameters is decreased by 81.4%, from 16.03 MB to 2.98 MB, while the accuracy is improved by 2.5%, from 83.4% to 85.9%. Full article

(This article belongs to the Special Issue Advanced Technologies of Artificial Intelligence in Signal Processing)

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19 pages, 3871 KiB

Open AccessArticle

A Source Seeking Method for the Implicit Information Field Based on a Balanced Searching Strategy

by Kun Liu, Yang Bi, Qi Zhang and Junfang Li

Electronics 2023, 12(14), 3027; https://doi.org/10.3390/electronics12143027 - 10 Jul 2023

Viewed by 612

Abstract

To address the issue of low efficiency in source seeking within implicit information fields, this paper proposes an autonomous sourcing method based on a balanced search strategy inspired by biological homing behaviors. At the outset of the research, the task of source seeking [...] Read more.

To address the issue of low efficiency in source seeking within implicit information fields, this paper proposes an autonomous sourcing method based on a balanced search strategy inspired by biological homing behaviors. At the outset of the research, the task of source seeking boiled down to a multi-objective convergence problem. By utilizing feasibility search behaviors as individual samples in evolutionary population, drawing on the principles of evolutionary algorithms, motion searching was integrated with population evolution to guide carriers towards completing source seeking tasks by solving multi-objective problems. Furthermore, the distribution entropy was also considered to measure the searching bias in the process of source seeking. In combination with the requirements of the source seeking process, a new method for balanced searching was designed. Ultimately, through theoretical analysis and simulation verification, we confirmed the effectiveness and rationality of this proposed method. Full article

(This article belongs to the Special Issue Advanced Technologies of Artificial Intelligence in Signal Processing)

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29 pages, 1470 KiB

Open AccessArticle

An Improved Modulation Recognition Algorithm Based on Fine-Tuning and Feature Re-Extraction

by Yibing Wang, Liang Zhou, Zhutian Yang, Longwen Wu, Zhendong Yin, Yaqin Zhao and Zhilu Wu

Electronics 2023, 12(9), 2134; https://doi.org/10.3390/electronics12092134 - 06 May 2023

Cited by 2 | Viewed by 1750

Abstract

Modulation recognition is an important technology in wireless communication systems. In recent years, deep learning-based modulation recognition algorithms, which can autonomously learn deep features and achieve superior recognition performance compared with traditional algorithms, have emerged. Yet, there are still certain limitations. In this [...] Read more.

Modulation recognition is an important technology in wireless communication systems. In recent years, deep learning-based modulation recognition algorithms, which can autonomously learn deep features and achieve superior recognition performance compared with traditional algorithms, have emerged. Yet, there are still certain limitations. In this paper, aiming at addressing the issue of poor recognition performance at low signal-to-noise ratios (SNRs) and the inability of deep features to effectively distinguish among all modulation types, we propose an optimization scheme for modulation recognition based on fine-tuning and feature re-extraction. In the proposed scheme, the network is firstly trained with the signals at high SNRs; then, the trained network is fine-tuned to the untrained network at low SNRs. Finally, on the basis of the features learned by the network, deeper features with enhanced discriminability for confused modulation types are obtained using feature re-extraction. The simulation results demonstrate that the proposed optimization scheme can maximize the performance of the neural network in the recognition of signals that are easily confused and at low SNRs. Notably, the average recognition accuracy of the proposed scheme was 91.28% within an SNR range of −8 dB to 18 dB, which is an improvement of 8% to 17% in comparison with four existing schemes. Full article

(This article belongs to the Special Issue Advanced Technologies of Artificial Intelligence in Signal Processing)

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21 pages, 571 KiB

Open AccessArticle

Joint Resource Allocation in a Two-Way Relaying Simultaneous Wireless Information and Power Transfer System

by Xuefei Ru, Gang Wang, Xiuhong Wang and Bo Li

Electronics 2023, 12(8), 1941; https://doi.org/10.3390/electronics12081941 - 20 Apr 2023

Cited by 2 | Viewed by 965

Abstract

Simultaneous wireless information and power transfer (SWIPT) technology provides an efficient solution to energy-limited communication terminals in a wireless network. However, in the current application scenario of SWIPT technology, relays use all the collected energy forwarding to assist communication between source nodes, while [...] Read more.

Simultaneous wireless information and power transfer (SWIPT) technology provides an efficient solution to energy-limited communication terminals in a wireless network. However, in the current application scenario of SWIPT technology, relays use all the collected energy forwarding to assist communication between source nodes, while ignoring the fact that relays can accumulate and store energy. This is of great practical significance in collaborative communication. By using a transmission scheme of a time-division broadcast for the direct transmission link, improvement of the system diversity gain through combining the technology at the receiver can be realized. Based on the above, we propose a two-way relay energy accumulation communication system, which is extended from a single relay system to a multi-relay system. The instantaneous transmission rate of the system link and the system equivalent profit are optimized by jointly optimizing the relay selection, time slot allocation factor, power splitting factor and relay transmit power. Compared with the comparison algorithm, the proposed algorithm shows a significant improvement in the performance of the system. Full article

(This article belongs to the Special Issue Advanced Technologies of Artificial Intelligence in Signal Processing)

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17 pages, 2441 KiB

Open AccessArticle

Pruning- and Quantization-Based Compression Algorithm for Number of Mixed Signals Identification Network

by Weiguo Shen, Wei Wang, Jiawei Zhu, Huaji Zhou and Shunling Wang

Electronics 2023, 12(7), 1694; https://doi.org/10.3390/electronics12071694 - 03 Apr 2023

Cited by 3 | Viewed by 1349

Abstract

Source number estimation plays an important role in successful blind signal separation. At present, the application of machine learning allows the processing of signals without the time-consuming and complex work of manual feature extraction. However, the convolutional neural network (CNN) for processing complex [...] Read more.

Source number estimation plays an important role in successful blind signal separation. At present, the application of machine learning allows the processing of signals without the time-consuming and complex work of manual feature extraction. However, the convolutional neural network (CNN) for processing complex signals has some problems, such as incomplete feature extraction and high resource consumption. In this paper, a lightweight source number estimation network (LSNEN), which can achieve a robust estimation of the number of mixed complex signals at low SNR (signal-to-noise ratio), is studied. Compared with other estimation methods, which require manual feature extraction, our network can realize the extraction of the depth feature of the original signal data. The convolutional neural network realizes complex mapping of modulated signals through the cascade of multiple three-dimensional convolutional modules. By using a three-dimensional convolution module, the mapping of complex signal convolution is realized. In order to deploy the network in the mobile terminal with limited resources, we further propose a compression method for the network. Firstly, the sparse structure network is obtained by the weight pruning method to accelerate the speed of network reasoning. Then, the weights and activation values of the network are quantified at a fixed point with the method of parameter quantization. Finally, a lightweight network for source number estimation was obtained, which was compressed from 12.92 MB to 3.78 MB with a compression rate of 70.74%, while achieving an accuracy of 94.4%. Compared with other estimation methods, the lightweight source number estimation network method proposed in this paper has higher accuracy, less model space occupation, and can realize the deployment of the mobile terminal. Full article

(This article belongs to the Special Issue Advanced Technologies of Artificial Intelligence in Signal Processing)

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17 pages, 6112 KiB

Open AccessArticle

ECG Signal Denoising Method Based on Disentangled Autoencoder

by Haicai Lin, Ruixia Liu and Zhaoyang Liu

Electronics 2023, 12(7), 1606; https://doi.org/10.3390/electronics12071606 - 29 Mar 2023

Cited by 1 | Viewed by 1765

Abstract

The electrocardiogram (ECG) is widely used in medicine because it can provide basic information about different types of heart disease. However, ECG data are usually disturbed by various types of noise, which can lead to errors in diagnosis by doctors. To address this [...] Read more.

The electrocardiogram (ECG) is widely used in medicine because it can provide basic information about different types of heart disease. However, ECG data are usually disturbed by various types of noise, which can lead to errors in diagnosis by doctors. To address this problem, this study proposes a method for denoising ECG based on disentangled autoencoders. A disentangled autoencoder is an improved autoencoder suitable for denoising ECG data. In our proposed method, we use a disentangled autoencoder model based on a fully convolutional neural network to effectively separate the clean ECG data from the noise. Unlike conventional autoencoders, we disentangle the features of the coding hidden layer to separate the signal-coding features from the noise-coding features. We performed simulation experiments on the MIT-BIH Arrhythmia Database and found that the algorithm had better noise reduction results when dealing with four different types of noise. In particular, using our method, the average improved signal-to-noise ratios for the three noises in the MIT-BIH Noise Stress Test Database were 27.45 db for baseline wander, 25.72 db for muscle artefacts, and 29.91 db for electrode motion artefacts. Compared to a denoising autoencoder based on a fully convolutional neural network (FCN), the signal-to-noise ratio was improved by an average of 12.57%. We can conclude that the model has scientific validity. At the same time, our noise reduction method can effectively remove noise while preserving the important information conveyed by the original signal. Full article

(This article belongs to the Special Issue Advanced Technologies of Artificial Intelligence in Signal Processing)

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21 pages, 7652 KiB

Open AccessEditor’s ChoiceArticle

An Image Object Detection Model Based on Mixed Attention Mechanism Optimized YOLOv5

by Guangming Sun, Shuo Wang and Jiangjian Xie

Electronics 2023, 12(7), 1515; https://doi.org/10.3390/electronics12071515 - 23 Mar 2023

Cited by 3 | Viewed by 1662

Abstract

As one of the more difficult problems in the field of computer vision, utilizing object image detection technology in a complex environment includes other key technologies, such as pattern recognition, artificial intelligence, and digital image processing. However, because an environment can be complex, [...] Read more.

As one of the more difficult problems in the field of computer vision, utilizing object image detection technology in a complex environment includes other key technologies, such as pattern recognition, artificial intelligence, and digital image processing. However, because an environment can be complex, changeable, highly different, and easily confused with the target, the target is easily affected by other factors, such as insufficient light, partial occlusion, background interference, etc., making the detection of multiple targets extremely difficult and the robustness of the algorithm low. How to make full use of the rich spatial information and deep texture information in an image to accurately identify the target type and location is an urgent problem to be solved. The emergence of deep neural networks provides an effective way for image feature extraction and full utilization. By aiming at the above problems, this paper proposes an object detection model based on the mixed attention mechanism optimization of YOLOv5 (MAO-YOLOv5). The proposed method fuses the local features and global features in an image so as to better enrich the expression ability of the feature map and more effectively detect objects with large differences in size within the image. Then, the attention mechanism is added to the feature map to weigh each channel, enhance the key features, remove the redundant features, and improve the recognition ability of the feature network towards the target object and background. The results show that the proposed network model has higher precision and a faster running speed and can perform better in object-detection tasks. Full article

(This article belongs to the Special Issue Advanced Technologies of Artificial Intelligence in Signal Processing)

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15 pages, 3544 KiB

Open AccessArticle

Deep-Learning-Based Recovery of Frequency-Hopping Sequences for Anti-Jamming Applications

by Jiawei Zhu, Anqiang Wang, Wei Wu, Zhijin Zhao, Yuting Xu, Rong Lei and Keqiang Yue

Electronics 2023, 12(3), 496; https://doi.org/10.3390/electronics12030496 - 18 Jan 2023

Cited by 7 | Viewed by 2456

Abstract

The frequency-hopping communication system has been widely used in anti-jamming communication due to its anti-interception and anti-jamming performance. With the increasingly complex electromagnetic environment, the frequency-hopping communication system needs more flexible frequency-hopping patterns to deal with interferences, which brings great challenges to the [...] Read more.

The frequency-hopping communication system has been widely used in anti-jamming communication due to its anti-interception and anti-jamming performance. With the increasingly complex electromagnetic environment, the frequency-hopping communication system needs more flexible frequency-hopping patterns to deal with interferences, which brings great challenges to the communication receiver. In this paper, an intelligent receiving scheme of frequency-hopping sequences is proposed, which combines time–frequency analysis with deep learning to realize an intelligent estimation of frequency-hopping sequences. A hybrid network module is designed by combining a convolutional neural network (CNN) with a gated recurrent unit (GRU). In the proposed network module, the combination of a residual network (ResNet) and squeeze and extraction (SE) improves the feature extraction and expression capabilities of the CNN network. The GRU network is proposed to solve the problem of dealing with signals with variant input lengths. A transfer learning scheme is further proposed to deal with communications systems with different frequency-hopping sets. Simulation results show that the proposed method has strong generalization ability and robustness, and the bit error rate (BER) performance of intelligent receiving is close to the receiving performance under ideal conditions. Full article

(This article belongs to the Special Issue Advanced Technologies of Artificial Intelligence in Signal Processing)

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18 pages, 10089 KiB

Open AccessFeature PaperArticle

A Novel Target Tracking Scheme Based on Attention Mechanism in Complex Scenes

by Yu Wang, Zhutian Yang, Wei Yang and Jiamin Yang

Electronics 2022, 11(19), 3125; https://doi.org/10.3390/electronics11193125 - 29 Sep 2022

Cited by 2 | Viewed by 1316

Abstract

In recent years, target tracking algorithms based on deep learning have realized significant progress, especially the Siamese neural network structure, which has a simple structure and excellent scalability. Although these methods provide excellent generalization capabilities, they fail to perform the task of learning [...] Read more.

In recent years, target tracking algorithms based on deep learning have realized significant progress, especially the Siamese neural network structure, which has a simple structure and excellent scalability. Although these methods provide excellent generalization capabilities, they fail to perform the task of learning target information discrimination smoothly due to being affected by distractors such as background clutter, occlusion, and target size. To solve this problem, in this paper we propose a newly improved Siamese network target tracking algorithm based on an attention mechanism. We introduce a channel attention module and a spatial attention module into the original network to improve the problem of insufficient semantic extraction ability of the convolutional layer of the tracking algorithm in complex environments. A channel attention mechanism enhances the feature extraction ability by using the network to learn the importance of each channel and establish the relationship between channels, while a spatial attention mechanism strengthens the feature extraction ability by establishing the importance of spatial position in locating the target or carrying out a certain degree of deformation. In this paper, the above two models are combined to improve the robustness of trackers without sacrificing tracking speed. We conduct a comprehensive experiment on the Object Tracking Benchmark dataset. The experimental results show that our algorithm outperforms other real-time trackers in both accuracy and robustness in most complex environments. Full article

(This article belongs to the Special Issue Advanced Technologies of Artificial Intelligence in Signal Processing)

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