Electromagnetic radiation induced non-chaotic behaviors in a Wilson neuron model

Ju, Zhutao; Lin, Yi; Chen, Bei; Wu, Hsuan-Chung; Chen, Mo; Xu, Quan

doi:10.1016/j.cjph.2022.03.012

Cited by 18 publications

(3 citation statements)

References 39 publications

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“…Researching the human biological field is one of the most important diagnostic techniques in modern medicine. The human biological field includes many acoustic [1]- [3], thermal [4], [5], and electromagnetic [6]- [8] radiations, the study of each of which makes it possible to identify the pathology of the functioning of the organism as a whole, individual organ, and organ systems. During the examination, diagnosticians receive huge amounts of data, the processing of which takes a long time.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of heart pathology using deep learning methods

Naizagarayeva,

Abdikerimova,

Shaikhanova

et al. 2023

IJECE

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<span lang="EN-US">In the directions of modern medicine, a new area of processing and analysis of visual data is actively developing - a radio municipality - a computer technology that allows you to deeply analyze medical images, such as computed tomography (CT), magnetic resonance imaging (MRI), chest radiography (CXR), electrocardiography and electrocardiography. This approach allows us to extract quantitative texture signs from signals and distinguish informative features to describe the heart's pathology, providing a personified approach to diagnosis and treatment. Cardiovascular diseases (SVD) are one of the main causes of death in the world, and early detection is crucial for timely intervention and improvement of results. This experiment aims to increase the accuracy of deep learning algorithms to determine cardiovascular diseases. To achieve the goal, the methods of deep learning were considered used to analyze cardiograms. To solve the tasks set in the work, 50 patients were used who are classified by three indicators, 13 anomalous, 24 nonbeat, and 1 healthy parameter, which is taken from the MIT-BIH Arrhythmia database.</span>

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Section: Literature Reviewmentioning

confidence: 99%

Detection of heart pathology using deep learning methods

Naizagarayeva,

Abdikerimova,

Shaikhanova

et al. 2023

IJECE

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“…They observed the coexistence of scroll in the network and investigated the complex attractor structure variations under the influence of external pulse-controlled memristors, pulse current stimulation, and their combined effects. Ju et al investigated the effects of electromagnetic radiation on a three-dimensional, non-autonomous Wilson neuron model based on memristors, and the study revealed various non-chaotic discharge behaviors, including different periodic, quasi-periodic, chaotic bursting, and periodic or quasi-periodic bursting patterns, arising from the influence of electromagnetic radiation [31]. Yu et al investigated a neural network consisting of three neurons under the influence of electromagnetic radiation, leading to the observation of diverse dynamic behaviors [32].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Analysis and FPGA Implementation of a New Fractional-Order Hopfield Neural Network System under Electromagnetic Radiation

Yu,

Lin,

et al. 2023

Biomimetics

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Fractional calculus research indicates that, within the field of neural networks, fractional-order systems more accurately simulate the temporal memory effects present in the human brain. Therefore, it is worthwhile to conduct an in-depth investigation into the complex dynamics of fractional-order neural networks compared to integer-order models. In this paper, we propose a magnetically controlled, memristor-based, fractional-order chaotic system under electromagnetic radiation, utilizing the Hopfield neural network (HNN) model with four neurons as the foundation. The proposed system is solved by using the Adomain decomposition method (ADM). Then, through dynamic simulations of the internal parameters of the system, rich dynamic behaviors are found, such as chaos, quasiperiodicity, direction-controllable multi-scroll, and the emergence of analogous symmetric dynamic behaviors in the system as the radiation parameters are altered, with the order remaining constant. Finally, we implement the proposed new fractional-order HNN system on a field-programmable gate array (FPGA). The experimental results show the feasibility of the theoretical analysis.

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“…Due to the introduction of chaotic dynamics, CNN has more dynamic characteristics. The complex dynamic characteristics of CNN make it widely used in information processing, algorithm optimization, image encryption and random number generators [17][18][19][20][21], etc.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Analysis and Audio Encryption Application in IoT of a Multi-Scroll Fractional-Order Memristive Hopfield Neural Network

Chen

et al. 2022

Fractal Fract

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Fractional-order chaotic systems are widely used in the field of encryption because of its initial value sensitivity and historical memory. In this paper, the fractional-order definition of Caputo is introduced based on a nonideal flux-controlled memristive Hopfield neural network model, when changing the parameters of the fractional-order memristive Hopfield neural network (FMHNN) can generate a different amount of multi-scroll attractors. Some dynamical behaviors are investigated by numerical simulation, especially analyzed coexistence and bifurcation under different orders and different coupling strengths. The results show that the chaotic system of FMHNN has abundant dynamic behaviors. In addition, a chaotic audio encryption scheme under a Message Queueing Telemetry Transport (MQTT) protocol is proposed and implemented by Raspberry Pi; the audio encryption system based on FMHNN has a broad future in intelligent home and other IoT applications.

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Electromagnetic radiation induced non-chaotic behaviors in a Wilson neuron model

Cited by 18 publications

References 39 publications

Detection of heart pathology using deep learning methods

Detection of heart pathology using deep learning methods

Dynamic Analysis and FPGA Implementation of a New Fractional-Order Hopfield Neural Network System under Electromagnetic Radiation

Dynamic Analysis and Audio Encryption Application in IoT of a Multi-Scroll Fractional-Order Memristive Hopfield Neural Network

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