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In this paper, we propose a lossless encryption method based on dual images. A cosine perturbation is added to the generalized voltage-controlled memristor, thus constituting a generalized voltage-controlled cosine memristor. And the characteristics of the memristor are analyzed and introduced into a four-dimensional cellular neural network, thus constituting the memristor-based four-dimensional cellular neural network (M4DCNN). The parameters of the M4DCNN are adjusted to achieve a hyperchaotic state for the diffusion and scrambling stages of the image. On the key, the partial key is obtained by K-means algorithm to make the key more random. In the scrambling phase, perturbed logistic chaotic map is introduced in this paper, which makes the image pixels better satisfy the avalanche effect. Through security analysis and comparison with other encryption methods, it is found that the encryption method shows to have good encryption effect in various indexes such as key sensitivity, key space, pixel correlation, information entropy, resistance to shear attack, resistance to noise attack, UACI and NPCR analysis. [ABSTRACT FROM AUTHOR]

The purpose of this research is to investigate the synchronization and control for uncertain memristor-based cellular neural network and its application in secure communication. To address the issue, a novel sliding mode function is designed, on which the system states can effectively converge to the equilibrium point after reaching the sliding mode surface. Moreover, the corresponding controller is constructed by employing the proposed sliding mode function. The proposed control strategy achieves the synchronization of the uncertain memristor-based cellular neural network, and effectively addresses the integral saturation existing in traditional one. In addition, the control performance, including convergence speed, control accuracy, robustness and security, are significantly enhanced. Furthermore, the stability of the system is discussed based on Lyapunov theory. Finally, comparative tests and application examples are presented to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In the internet era, image encryption technology plays a crucial role in ensuring the security of image information. Aiming at the problem that low-dimensional chaotic encryption has a relatively small key space and is vulnerable to chosen-plain attacks, this paper proposed a novel colour image block encryption algorithm based on the cellular neural network and the Chua's chaotic system. Firstly, the plain is separated into three primary colours. And Fourier transform is applied to achieve optical transformation, generating six chaotic sequences iteratively. Secondly, the chaotic sequences were combined in pairs. And then combined with the plain to form three sequences, which were used in the diffusion process. Finally, the improved Chua's chaotic system is used to scramble the image. Both diffusion and scrambling are performed synchronously during encryption. Through the above operation, we get the final colour encrypted picture. The experimental results show that the algorithm has excellent encryption effect and can resist common attacks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this work we propose a new mathematical model of 2D cellular neural networks (CNN) in terms of the lattice FitzHugh-Nagumo equations with boundary feedback. The model features discrete Laplacian operators and periodic boundary feedback instead of the interior-clamped or mean-field feedback. We first prove the globally dissipative dynamics of the solutions through a priori uniform estimates. In the main result we show that the 2D cellular neural networks are exponentially synchronized if the computable threshold condition is satisfied by the synaptic gap signals of pairwise boundary cells of the grid and the system parameters with a linear feedback coupling coefficient tunable in applications. [ABSTRACT FROM AUTHOR]

This study ventures into the captivating realm of visual illusion art, where the enigmatic principles of visual perception are harnessed to enhance artistic creativity. Through mathematical modeling of the visual perception process, we uncover the essence of visual illusions and their profound impact on art and design. Leveraging cellular neural networks, this research merges dynamic processes with the WC visual illusion and color space models to craft a novel visual illusion neural network model adept at reproducing the nuances of visual illusion art. Our investigation into the application of visual illusions in art design reveals a notable affinity for the “transparent” quality, achieving a 57% certainty level and embodying the art’s ethereal nature. Furthermore, we identify significant correlations between interactive effects, color coordination, design structure, visual impact, and the overarching quality of art designs, with correlation indices of 0.508, 0.487, 0.535, and 0.602, respectively. This work highlights visual illusion’s pivotal role in propelling the field of art and design forward, thereby enriching the tapestry of human experience.

This paper presents the control design framework for the hybrid synchronization (HS) and parameter identification of the 3-cell cellular neural network. The cellular neural network (CNN) of this kind has increasing practical importance but due to its strong chaotic behavior and the presence of uncertain parameters make it difficult to design a smooth control framework. Sliding mode control (SMC) is very helpful for this kind of environment where the systems are nonlinear and have uncertain parameters and bounded disturbances. However, conventional SMC offers a dangerous chattering phenomenon, which is not acceptable in this scenario. To get chattering-free control, smooth higher-order SMC formulated on the smooth super twisting algorithm (SSTA) is proposed in this article. The stability of the sliding surface is ensured by the Lyapunov stability theory. The convergence of the error system to zero yields hybrid synchronization and the unknown parameters are computed adaptively. Finally, the results of the proposed control technique are compared with the adaptive integral sliding mode control (AISMC). Numerical simulation results validate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

For a graph Γ , a subset S ⊆ V Γ is known to be a dominating set, if every x ∈ V Γ \ S has at least one neighbor in D. The domination number γ (Γ) is merely the size of a smallest dominating set in Γ . The strong product P r ⊠ P s of two paths P r and P s is known as the king's graph. Interestingly, the king's graph is isomorphic to the two-parametric family of cellular neural network (CNNs). In Asad et al. (Alex Eng J 66:957–977, 2023), the authors retrieved certain structural characteristics of CNNs from their minimal dominating sets. They conjectured in Problem 8.3 that the domination number of P r ⊠ P s is ⌈ r 3 ⌉ ⌈ s 3 ⌉ . This paper solves Problem 8.3 by providing a proof to the conjecture. This result, in turn, reveals interesting topological properties such as an optimal routing for this class of neural networks. [ABSTRACT FROM AUTHOR]

The paper considers a class of discrete-time cellular neural networks (DT-CNNs) obtained by applying Euler's discretization scheme to standard CNNs. Let T be the DT-CNN interconnection matrix which is defined by the feedback cloning template. The paper shows that a DT-CNN is convergent, i.e. each solution tends to an equilibrium point, when T is symmetric and, in the case where T + E n is not positive-semidefinite, the step size of Euler's discretization scheme does not exceed a given bound ( E n is the n × n unit matrix). It is shown that two relevant properties hold as a consequence of the local and space-invariant interconnecting structure of a DT-CNN, namely: (1) the bound on the step size can be easily estimated via the elements of the DT-CNN feedback cloning template only; (2) the bound is independent of the DT-CNN dimension. These two properties make DT-CNNs very effective in view of computer simulations and for the practical applications to high-dimensional processing tasks. The obtained results are proved via Lyapunov approach and LaSalle's Invariance Principle in combination with some fundamental inequalities enjoyed by the projection operator on a convex set. The results are compared with previous ones in the literature on the convergence of DT-CNNs and also with those obtained for different neural network models as the Brain-State-in-a-Box model. Finally, the results on convergence are illustrated via the application to some relevant 2D and 1D DT-CNNs for image processing tasks. [ABSTRACT FROM AUTHOR]

A new fractional-order cellular neural network (CNN) system is solved using the Adomian decomposition method (ADM) with the hyperbolic tangent activation function in this paper. The equilibrium point is analyzed in this CNN system. The dynamical behaviors are studied as well, using a phase diagram, bifurcation diagram, Lyapunov Exponent spectrum (LEs), and spectral entropy (SE) complexity algorithm. Changing the template parameters and the order values has an impact on the dynamical behaviors. The results indicate that rich dynamical properties exist in the system, such as hyperchaotic attractors, chaotic attractors, asymptotic periodic loops, complex coexisting attractors, and interesting state transition phenomena. In addition, the digital circuit implementation of this fractional-order CNN system is completed on a digital signal processing (DSP) platform, which proves the accuracy of ADM and the physical feasibility of the CNN system. The study in this paper offers a fundamental theory for the fractional-order CNN system as it applies to secure communication and image encryption. [ABSTRACT FROM AUTHOR]

A new tri-cellular neural network(CNN) system based on double memristors is constructed which used a hyperbolic tangent function instead of the conventional segmentation function in this paper. The multiple equilibrium points existing in the CNN system are analyzed. Through Lyapunov exponential spectrum, bifurcation diagram, phase diagram, SE complexity and digital circuit implementation, the rich and complex dynamical characteristics of the double-MR-based CNN system are presented. Interestingly, changing different parameters and initial values, the system has multiple coexisting attractors which include periodic-periodic attractors, periodic-chaotic attractors, and chaotic-chaotic attractors. Finally, a hardware circuit of the memristive cellular neural network is designed and built on the basis of a DSP platform to verify the implementability of the network model. The improved double-MR-based Cellular neural network system provides a theoretical foundation in other fields of application, especially for secure communications. [ABSTRACT FROM AUTHOR]

Summary: High latency and power consumption are two major problems that need to be addressed in convolutional neural networks (CNN). In this paper, the convolutional layer is replaced with a discrete‐time cellular neural network (CellNN) to overcome these problems. Multiple configurations of CellNNs are trained in a framework called TensorFlow to classify objects from the CIFAR‐10 database. Effects of the number of iterations, the number of channels, batch normalization, and activation functions on the classification accuracies are presented. It is shown that TensorFlow is a tool that is capable of training discrete‐time CellNNs. Although the accuracies of the proposed networks on CIFAR‐10 are slightly lesser than the existing CNNs, with reduced parameters and multiply‐accumulates (MACs), power consumption and computation time of our networks will be less than CNNs. [ABSTRACT FROM AUTHOR]

This paper presents a novel algorithm for image denoising using an improved nonlinear diffusion PDE model and a cellular neural network (CeNN) scheme. In particular, the images corrupted with multiplicative (speckle) noise have been considered. The proposed generalized-order nonlinear diffusion (GOND) model is solved through a suitable cellular neural network (CeNN) approach. The CeNN templates act like edge-preserving filters to reduce the multiplicative noise. The present study also gives a convergence analysis of the proposed CeNN based solution scheme. Further, the proposed scheme is numerically validated on synthetic, medical, and real SAR images. The obtained results demonstrate that the proposed algorithm provides a better way to deal with speckle noise and suppresses the staircase effects. To broaden the simulation results, the proposed method is applied to images corrupted with Gaussian, Rayleigh, and gamma noise. The proposed method for SSIM values interpret 0.2dB to 0.4dB better than state-of-the-art methods and comparative results in terms of PSNR in most test cases. [ABSTRACT FROM AUTHOR]

This work presents a cellular neural network (CNN) learning tool based on the Center of Mass Algorithm (CMA), which provides a friendly graphical user interface. As original contributions of this work, the learning tool here developed for CNN features a few adaptations in CMA to improve the training process for grayscale image filtering, such as decreasing learning rates and training with multiple image sets, and comprises many graphical user interface facilities for CNN designers. The training capability of the developed tool is validated through several filter applications, among which lowpass and highpass filters with finite or infinite impulse responses. A comparative analysis is performed between the theoretical responses of the filters and the results obtained from the simulation of a CMOS analog CNN configured by the parameters determined through the learning tool. The training process was very fast for the lowpass filters and acceptable RMS pixel errors have been obtained for all examples, confirming the reliability of the learning tool. [ABSTRACT FROM AUTHOR]

This paper introduces a contour detection scheme to detect object contours in medical images. A new PDE model is designed by including a fractional-order regularization term, making it robust against noise and maintaining the regularity of level set function (LSF) during evolution. A cellular neural network (CeNN) model is used to solve the proposed contour detection PDE. The main advantages of using the CeNN-based approach are that it wipes out the requirement of a reinitialization of level set and can be implemented efficiently on parallel chips. Finally, an experimental study is carried out, which exhibits the feasibility of the proposed approach in contour detection from a set of medical images. [ABSTRACT FROM AUTHOR]

In texture classification, local binary pattern (LBP) is currently one of the most widely-concerned feature encoding models. Most existing LBP-based texture classification methods are usually limited to single-kind texture features. In fact, an across-domain fusion of LBP features with other features, such as image contours, could be another potential path to promote texture classification. To enhance the feature modeling ability of LBP-based methods, this paper firstly designs a Cellular Neural Network (CellNN) with recurrent convolutions, initially trained by a simplified simulated-annealing algorithm, to extract informative image contours. For better reliability, a new three-channel contour extractor of deep CellNNs (i. e. , dCellNNs) is proposed. This extractor contains the initially-trained CellNNs of more than three layers, and it is further optimized by fine-tuning parameters. Moreover, a new weighted-base algorithm is designed to fulfill the fusion of the multi-scale texture features by LBPs and the contour features by dCellNNs to enhance feature representation. Finally, these enhanced features are concatenated together to generate the final multi-scale features of given texture image. On texture datasets KTH, Brodatz, OTC12 and UIUC, experiment results verify that the across-domain fusion of multi-scale LBPs and dCellNNs is efficient in capturing & enhancing texture features. With moderate feature dimensionality and computational costs, it could improve texture classification, acquiring an obvious accuracy increase on previous state-of-the-art ones, e.g. , a rise of 2.58% on KTH-TIPS2b, a rise of 3.11% on Brodatz, a rise of 0.71% on OTC12 and a rise of 0.42% on UIUC. • A multi-scale across-domain feature fusion of textures and contours is proposed. • The first deep model of CellNN (dCellNNs) with two-stage training is proposed. • The three-channel texture contour extractor of dCellNNs is designed. • A weighted-base fusing is designed for the across-domain fusion of features. [ABSTRACT FROM AUTHOR]

Digital dentistry has become an integral part of our practice today, with artificial intelligence (AI) playing the predominant role. The present systematic review was intended to detect the accuracy of landmarks identified cephalometrically using machine learning and artificial intelligence and compare the same with the manual tracing (MT) group. According to the PRISMA-DTA guidelines, a scoping evaluation of the articles was performed. Electronic databases like Doaj, PubMed, Scopus, Google Scholar, and Embase from January 2001 to November 2022 were searched. Inclusion and exclusion criteria were applied, and 13 articles were studied in detail. Six full-text articles were further excluded (three articles did not provide a comparison between manual tracing and AI for cephalometric landmark detection, and three full-text articles were systematic reviews and meta-analyses). Finally, seven articles were found appropriate to be included in this review. The outcome of this systematic review has led to the conclusion that AI, when employed for cephalometric landmark detection, has shown extremely positive and promising results as compared to manual tracing., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Rauniyar et al.)