Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this paper.Here,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its performance.Then,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation platforms.Subsequently,it is used toward secure communication application scenarios.Taking it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)model.Eventually,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

Image Hiding with High Robustness Based on Dynamic Region Attention in the Wavelet Domain

Hidden capacity,concealment,security,and robustness are essential indicators of hiding algorithms.Currently,hiding algorithms tend to focus on algorithmic capacity,concealment,and security but often overlook the robustness of the algorithms.In practical applications,the container can suffer from damage caused by noise,cropping,and other attacks during transmission,resulting in challenging or even impossible complete recovery of the secret image.An image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding algorithms.In this proposed algorithm,a secret image of size 256×256 is first decomposed using an eight-level Haar wavelet transform.The wavelet transform generates one coefficient in the approximation component and twenty-four detail bands,which are then embedded into the carrier image via a hiding network.During the recovery process,the container image is divided into four non-overlapping parts,each employed to reconstruct a low-resolution secret image.These lowresolution secret images are combined using densemodules to obtain a high-quality secret image.The experimental results showed that even under destructive attacks on the container image,the proposed algorithm is successful in recovering a high-quality secret image,indicating that the algorithm exhibits a high degree of robustness against various attacks.The proposed algorithm effectively addresses the robustness issue by incorporating both spatial and channel attention mechanisms in the multi-scale wavelet domain,making it suitable for practical applications.In conclusion,the image hiding algorithm introduced in this study offers significant improvements in robustness compared to existing algorithms.Its ability to recover high-quality secret images even in the presence of destructive attacksmakes it an attractive option for various applications.Further research and experimentation can explore the algorithm’s performance under different scenarios and expand its potential applications.

On Developing College Students' Ability of Reading Comprehension

Reading has become one of the four essential skills in English study,without reading,it is difficult for the students to support independent and self-directed learning.With the arrival of the Information Age,our reading materials gradually change from paper materials to electronic ones.So it becomes more and more important for college students to speed up their reading and develop a good reading habit in order to acquire as much information as possible.

Effect of Microstructures and Mechanical Properties of Large Deformation High Entropy Alloy CoCrCuFeNi in Semi-solid Isothermal Heat Treatment

The semi-solid slurries of the CoCrCuFeNi high entropy alloy(HEA)were fabricated through the recrystallization and partial melting(RAP)process by cold-rolling and partial remelting.The temperature range of the semi-solid region and the relationship between the liquid fraction and the temperature were determined by the differential scanning calorimetry(DSC)curve.The effect of isothermal temperature and holding time on the evolution of the microstructure and mechanical properties of the rolled samples was analyzed.The results show that the microstructure was significantly deformed,and the tensile strength has been increased by 107%after 63%rolling deformation of the CoCrCuFeNi high entropy alloy(HEA).The high-entropy alloy after cold rolling was maintained at 1150 and 1300℃for 20,30,60,and 120 minutes respectively,the plasticity has been improved compared with the rolled high entropy alloy.The optimal plasticity was reached 13.7%and 7.9%at 1150℃and 1300℃for 30 minutes,respectively.After semi-solid isothermal heat treatment,the grain morphology changed from dendritic of as-cast or rolled to spherulite and the grain size increased significantly with time and the holding temperature increased.

Research on the Large Precision Instrument Error Correction Model under the Perspectives of Stability and Robustness

In this paper, we conduct research on the large precision instrument error correction model under the perspectives of stability androbustness. It is one of the effective methods to improve the instruments accuracy using error correction technology, but at present, a lot of errorcorrection is limited to the system error modifi cation, only a small number of the instruments to an error in the dynamic error correction timely,device on the instrument precision sensors, apparently complicate the instrument structure. To fully system error correction that will affect theprecision of instrument mainly random error. Instrument is the main task of error correction is to use a certain method to compensate separableinstruments each component part of a deterministic system error, so the key problems of error correction as is the requirement of equipmentstructure stability is good, with this to ensure that the instrument error of the uncertainty, so that the fundamental fl aw. Under this basis, this paperproposes the novel countermeasure of the issues that is innovative.

Research on the Automation Control Information System Model based on the Chaos and Nonlinear Transformation

In this paper, we conduct research on the automation control information system model based on the Chaos and nonlineartransformation. Electronic control automation system in the process of social and economic development plays the key role is obvious, it caneffectively encourage industry automation level is improved greatly, at the same time, electronic automation system control to the larger extenton saving the cost of enterprise. Electronic systems control automation industrial enterprises should also improve their in technical innovationability, to have independent intellectual property rights of electronic automation engineering control system. This paper combines the Chaos andnonlinear transformation principles to optimize the traditional system that is meaningful.

Research on the Precision Instrument Reliability and Antiinterference Mode from the Intrusion Detection Angle based on Rough Set

In this paper, we conduct research on precision instrument reliability and anti-interference mode from the intrusion detectionangle based on rough set. Ultra-precision machining applications range from soft metal to hardened steel, stainless steel, high speed steel,cemented carbide and some other hard processing material, to the semiconductor, glass, ceramics and the other hard brittle non-metallic materials,almost all materials available ultra-precision machining technology for the processing. To enhance the robustness of the mentioned equipment,we consider the integration of the rough set and intrusion detection to modify the corresponding issues that will optimize the traditional methodsfrom systematical level which holds special signifi cance.

Research on the Novel Computer Network Intrusion Detection Model based on Improved Particle Swarm Optimization Algorithm

In this paper, we conduct research on the novel computer network intrusion detection model based on improved particle swarmoptimization algorithm. TCP fl ood attack, UDP fl ood attack, ICMP fl ood attack, deformity of message attack, the application layer attack is themost typical DDOS attacks, DDOS attacks are also changing to upgrade at the same time, scholars research on DDOS attack defense measuresbecome more and more has the application value and basic practical signifi cance. Network security protection is a comprehensive project, nomatter what measures to take that safety is always relative, so as the network security administrator, should change with the network securitysituation and the security requirements, moderate to adjust security policies, so as to achieve the target. Under this basis, we propose the newperspective on the IDS system that will then enhance the robustness and safetiness of the overall network system.

A novel image super-resolution reconstruction algorithm based on improved GANs and gradient penalty

Purpose–Conventional image super-resolution reconstruction by the conventional deep learning architectures suffers from the problems of hard training and gradient disappearing.In order to solve such problems,the purposeof this paperis to proposea novel image super-resolutionalgorithmbasedon improved generative adversarial networks(GANs)with Wasserstein distance and gradient penalty.Design/methodology/approach–The proposed algorithm first introduces the conventional GANs architecture,the Wasserstein distance and the gradient penalty for the task of image super-resolution reconstruction(SRWGANs-GP).In addition,a novel perceptual loss function is designed for the SRWGANs-GP to meet the task of image super-resolution reconstruction.The content loss is extracted from the deep model’s feature maps,and such features are introduced to calculate mean square error(MSE)for the loss calculation of generators.Findings–To validate the effectiveness and feasibility of the proposed algorithm,a lot of compared experiments are applied on three common data sets,i.e.Set5,Set14 and BSD100.Experimental results have shown that the proposed SRWGANs-GP architecture has a stable error gradient and iteratively convergence.Compared with the baseline deep models,the proposed GANs models have a significant improvement on performance and efficiency for image super-resolution reconstruction.The MSE calculated by the deep model’s feature maps gives more advantages for constructing contour and texture.Originality/value–Compared with the state-of-the-art algorithms,the proposed algorithm obtains a better performance on image super-resolution and better reconstruction results on contour and texture.

Motor imagery-based EEG signals classification by combining temporal and spatial deep characteristics

Purpose-In order to improve the weak recognition accuracy and robustness of the classification algorithm for brain-computer interface(BCI),this paper proposed a novel classification algorithm for motor imagery based on temporal and spatial characteristics extracted by using convolutional neural networks(TSCNN)model.Design/methodology/approach-According to the proposed algorithm,a five-layer neural network model was constructed to classify the electroencephalogram(EEG)signals.Firstly,the author designed a motor imagery-based BCI experiment,and four subjects were recruited to participate in the experiment for the recording of EEG signals.Then,after the EEG signals were preprocessed,the temporal and spatial characteristics of EEG signals were extracted by longitudinal convolutional kernel and transverse convolutional kernels,respectively.Finally,the classification of motor imagery was completed by using two fully connected layers.Findings-To validate the classification performance and efficiency of the proposed algorithm,the comparative experiments with the state-of-the-arts algorithms are applied to validate the proposed algorithm.Experimental results have shown that the proposed TS-CNN model has the best performance and efficiency in the classification of motor imagery,reflecting on the introduced accuracy,precision,recall,ROC curve and F-score indexes.Originality/value-The proposed TS-CNN model accurately recognized the EEG signals for different tasks of motor imagery,and provided theoretical basis and technical support for the application of BCI control system in the field of rehabilitation exoskeleton.

Facial expression recognition based on bidirectional gated recurrent units within deep residual network

Purpose-recent years,facial expression recognition has been widely used in human machine interaction,clinical medicine and safe driving.However,there is a limitation that conventional recurrent neural networks can only learn the time-series characteristics of expressions based on one-way propagation information.Design/methodology/approach-To solve such limitation,this paper proposes a novel model based on bidirectional gated recurrent unit networks(Bi-GRUs)with two-way propagations,and the theory of identity mapping residuals is adopted to effectively prevent the problem of gradient disappearance caused by the depth of the introduced network.Since the Inception-V3 network model for spatial feature extraction has too many parameters,it is prone to overfitting during training.This paper proposes a novel facial expression recognition model to add two reduction modules to reduce parameters,so as to obtain an Inception-W network with better generalization.Findings-Finally,the proposed model is pretrained to determine the best settings and selections.Then,the pretrained model is experimented on two facial expression data sets of CKþand Oulu-CASIA,and the recognition performance and efficiency are compared with the existing methods.The highest recognition rate is 99.6%,which shows that the method has good recognition accuracy in a certain range.Originality/value-By using the proposed model for the applications of facial expression,the high recognition accuracy and robust recognition results with lower time consumption will help to build more sophisticated applications in real world.