Classification of Chaotic Signals of the Recurrence Matrix Using a Convolutional Neural Network and Verification through the Lyapunov Exponent

Nam, Jaehyeon; Kang, Jaeyoung

doi:10.3390/app11010077

Cited by 12 publications

(6 citation statements)

References 27 publications

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“…12C). Either the CNN is trained to classify different RPs [129][130][131][132], or to predict time series values [115]. Such combinations of RPs and RQA measures with machine learning were successfully applied for transition detection, monitoring, and anomaly detection [80,[133][134][135][136].…”

Section: Recurrence and Machine Learningmentioning

confidence: 99%

Trends in recurrence analysis of dynamical systems

Marwan

Kraemer

2023

Eur. Phys. J. Spec. Top.

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The last decade has witnessed a number of important and exciting developments that had been achieved for improving recurrence plot-based data analysis and to widen its application potential. We will give a brief overview about important and innovative developments, such as computational improvements, alternative recurrence definitions (event-like, multiscale, heterogeneous, and spatio-temporal recurrences) and ideas for parameter selection, theoretical considerations of recurrence quantification measures, new recurrence quantifiers (e.g. for transition detection and causality detection), and correction schemes. New perspectives have recently been opened by combining recurrence plots with machine learning. We finally show open questions and perspectives for futures directions of methodical research.

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Section: Recurrence and Machine Learningmentioning

confidence: 99%

Trends in recurrence analysis of dynamical systems

Marwan

Kraemer

2023

Eur. Phys. J. Spec. Top.

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“…Research efforts have demonstrated that CNNs are effective in studies and applications involving chaotic signals, such as chaotic biomedical signal analysis, speech processing, and chaos identification systems [48][49][50]. Moreover, the selection of CNNs allows us to gain useful theoretical insight into the proposed system.…”

Section: Cnn-based Receivermentioning

confidence: 99%

Design of an M-Ary DLCSK Communication System Using Deep Transfer Learning

Mobini,

Herceg,

Kaddoum

2023

IEEE Open J. Commun. Soc.

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Conventional coherent chaos-based communication systems require synchronization of chaotic signals, which is still practically unattainable in a noisy environment. Moreover, in non-coherent schemes, a part of the bit duration is spent sending non-information-bearing reference samples, which deteriorates the Bit Error Rate performance (BER) of these systems. To tackle these problems, this paper designs an M -ary Deep Learning Chaos Shift Keying (M -ary DLCSK) system. The proposed receiver uses a Convolutional Neural Network (CNN)-based classifier that recovers M -ary modulated data. The trained NN model grasps different chaotic maps, estimates channels, and classifies the received signals effectively. Moreover, we consider a Transfer Learning (TL) framework that enhances the noise performance and classification results. Due to the generalization capabilities of TL, the trained NN can work in different Signal-to-Noise Ratio (SNR) conditions without the need for re-training. We compare the BER performance, complexity, and bandwidth efficiency of the M -ary DLCSK receiver with existing receivers. The results demonstrate that the M -ary DLCSK receiver is the first practical system that achieves the theoretical BER performance of the coherent CSK systems under Rayleigh fading channels. Moreover, the proposed system provides a considerable performance advantage compared to the existing DL-based receivers under Rayleigh fading channels. For example, the BER performance of 8-ary DLCSK shows a gain of 0.1 over the Long Short-Term Memory (LSTM)-aided DNN systems at the target E b /N 0 = 14dB. These features make M -ary DLCSK an attractive candidate for several applications, such as Massive Multiple-Input Multiple Output (MIMO), Vehicle-to-everything (V2X), Quantum, and optical communication systems.

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“…In recent years, deep learning algorithms [14][15][16] represented by convolutional neural networks [17][18][19], recurrent neural networks [20] and generative adversarial networks have been widely used in many fields such as image classification [21,22], object detection [23], semantic segmentation [24,25], image retrieval [26], scene understanding [27], etc. and have made a leap forward compared with traditional methods.…”

Section: Image Forensicsmentioning

confidence: 99%

Scale-Adaptive Deep Matching Network for Constrained Image Splicing Detection and Localization

Xu¹,

Lv²,

Liu³

et al. 2022

Applied Sciences

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Constrained image splicing detection and localization (CISDL) is a newly formulated image forensics task that aims at detecting and localizing the source and forged regions from a series of input suspected image pairs. In this work, we propose a novel Scale-Adaptive Deep Matching (SADM) network for CISDL, consisting of a feature extractor, a scale-adaptive correlation module and a novel mask generator. The feature extractor is built on VGG, which has been reconstructed with atrous convolution. In the scale-adaptive correlation computation module, squeeze-and-excitation (SE) blocks and truncation operations are integrated to process arbitrary-sized images. In the mask generator, an attention-based separable convolutional block is designed to reconstruct richer spatial information and generate more accurate localization results with less parameters and computation burden. Last but not least, we design a pyramid framework of SADM to capture multiscale details, which can increase the detection and localization accuracy of multiscale regions and boundaries. Extensive experiments demonstrate the effectiveness of SADM and the pyramid framework.

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Classification of Chaotic Signals of the Recurrence Matrix Using a Convolutional Neural Network and Verification through the Lyapunov Exponent

Cited by 12 publications

References 27 publications

Trends in recurrence analysis of dynamical systems

Trends in recurrence analysis of dynamical systems

Design of an M-Ary DLCSK Communication System Using Deep Transfer Learning

Scale-Adaptive Deep Matching Network for Constrained Image Splicing Detection and Localization

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