Pulse Coupled Neural Network Research Articles

FusionCPP: Cooperative fusion of infrared and visible light images based on PCNN and PID control systems

The imaging results of infrared and visible light sensors are highly complementary. Thus, combining the two images can compensate for the shortcomings of a single sensor. However, previous fusion methods have not fully utilized the collaborative guidance of infrared and visible images in the fusion process, leading to a poor correlation between extracted features and cannot guarantee feature fusion accuracy. In order to address these challenges, this paper proposes a collaborative fusion method for infrared and visible images based on pulse-coupled neural networks (PCNN) and proportional-integral-derivative (PID) control systems, called FusionCPP. First, a coupled neural network with a dual pulse structure is designed for the feature extraction section. Unlike traditional PCNN, this network includes two pulse generators, one of which is utilized to extract the salient features of the image, while the other is employed to extract the common pulse layer of the source image and multiply the pulse layer by the coupling iteration value to obtain the base layer. Second, a detail layer is obtained by subtracting the base layer from the source image. This method accurately extracts salient and detailed features based on the coupling between source images. Finally, a closed-loop PID control system is utilized as the fusion strategy in the image fusion task, which judges the difference between the fusion result and the source image in real-time through the feedback effect of the system. The controller adaptively adjusts the fusion weight based on the difference to fuse image features into the new image accurately. Experimental results indicate that the proposed FusionCPP can maintain significant contrast and rich textures. Besides, the proposed FusionCPP has extended to multi-focus image fusion and target detection tasks to verify its effectiveness.

Multi-Focus Image Fusion via PAPCNN and Fractal Dimension in NSST Domain

Multi-focus image fusion is a popular technique for generating a full-focus image, where all objects in the scene are clear. In order to achieve a clearer and fully focused fusion effect, in this paper, the multi-focus image fusion method based on the parameter-adaptive pulse-coupled neural network and fractal dimension in the nonsubsampled shearlet transform domain was developed. The parameter-adaptive pulse coupled neural network-based fusion rule was used to merge the low-frequency sub-bands, and the fractal dimension-based fusion rule via the multi-scale morphological gradient was used to merge the high-frequency sub-bands. The inverse nonsubsampled shearlet transform was used to reconstruct the fused coefficients, and the final fused multi-focus image was generated. We conducted comprehensive evaluations of our algorithm using the public Lytro dataset. The proposed method was compared with state-of-the-art fusion algorithms, including traditional and deep-learning-based approaches. The quantitative and qualitative evaluations demonstrated that our method outperformed other fusion algorithms, as evidenced by the metrics data such as QAB/F, QE, QFMI, QG, QNCIE, QP, QMI, QNMI, QY, QAG, QPSNR, and QMSE. These results highlight the clear advantages of our proposed technique in multi-focus image fusion, providing a significant contribution to the field.

Enhancement method for non-uniform scattering images of turbid underwater based on neural network

Due to the influence of light absorption and scattering in the water medium, underwater images often appear with different degrees of blur, color distortion, and so on. The physical model-based method needs to establish image degradation processes and improve image quality by assuming and estimating complex parameters, which limits the generalization ability and practical application effect of these methods. To solve the above problems, this paper proposes an image enhancement method of non-uniform scattering in turbid underwater based on neural network. First, the high-frequency and low-frequency components of underwater images were obtained through the difference of gaussian (DOG) and bilateral filtering (BF), respectively. Then, for the high-frequency component, pulse coupled neural networks (PCNN) to improve the edge details of the image. For low-frequency components, it is carried out in two steps: an adaptive automatic white balance and polynomial regression (AAWBPR) algorithm is proposed, which can effectively eliminate the color bias under different color temperatures and improve the brightness of images; the adaptive color and contrast enhancement (ACCE) method is used to further enhance the color and contrast of images. Finally, Decom-Net and the improved U-Net method are used to reduce the errors caused by the non-uniform scattering effect, weaken the noise interference, and enhance the overall effect of images. Extensive experiments show that the proposed method can effectively improve the quality of turbid underwater non-uniform scattering images, enhance the contrast of images, reduce the complexity of parameters, and has good generalization performance and practical application effects.

Brain image fusion using the parameter adaptive-pulse coupled neural network (PA-PCNN) and non-subsampled contourlet transform (NSCT)

Medical imaging has played an essential role in medicine and disease diagnosis. Medical images from a single modality contain limited data about the organ. On the other hand, images from different modalities contain valuable structural and functio nal data about an organ. Medical image fusion (MIF) strategies integrate complementary information from two medical images captured using distinct modalities. This paper offered a new multimodal MIF approach using the parameter-adaptive pulse-coupled neural networks (PA-PCNN) within the non-subsampled contourlet transform (NSCT). The NSCT decomposes those images into high- and low-frequency bands. PA-PCNN combines those bands. The fused image was created using the inverse of the NSCT approach. To prove the proposed approach’s performance, we appoint a variety of medical images like computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission CT (SPECT), and positron emission tomography (PET). Our experiments use five fusion metrics to validate the proposed approach’s performance, such as entropy (EN), mutual information (MI), weighted edge information (QAB/F\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{AB/F}$$\\end{document}), nonlinear correlation information entropy (Qncie\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{ncie}$$\\end{document}), and average gradient (AG). Outcomes show that the proposed approach achieves high overall performance in visual and objective characteristics when compared with five well-known MIF methods. The average values for EN, MI, QAB/F\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{AB/F}$$\\end{document}, Qncie\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{ncie}$$\\end{document}, and AG with the proposed approach are 5.2144,3.1282,.6600,.8071, and 8.9874, respectively.

Bionic visual navigation model for enhanced template matching and loop closing in challenging lighting environments

In recent years, various computational models have been proposed in neuroscience, but only a few have been utilized in robot visual navigation through engineering approaches. Besides, these engineering methods lack environmental adaptability, especially in the aspect of visual place recognition in weak or uncontrollable light fluctuations. To address this issue and enhance the performance of visual template matching and map loop closure detection in challenging lighting environments, this paper proposed a bionic visual navigation model that combines two neural network architectures, the Pulse Coupled Neural Network (PCNN) and Continuous Attractor Neural Network (CANN). In our navigation model, the visual features of the environments are encoded as temporal information by the spiking model and input into the local view cells for visual template matching. Additionally, we utilize the pose cells network, which incorporates the similarity between current and previous templates, along with odometry data, to encode spatial information and store it as an experience map. To validate the effectiveness of the proposed model, we conducted evaluations on datasets collected within our library, campus, and an open-source office dataset. The experimental results reveal that our algorithm increases the F1-score of template matching by approximately 10.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\% $$\\end{document}, 35.8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\% $$\\end{document}, 61.7%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\% $$\\end{document}, and 1.9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\% $$\\end{document} in each dataset, compared to the conventional RatSLAM method. Furthermore, our algorithm generates a more accurate map that closely correlates with the real-world trajectory without compromising on computation time. The results suggest that our bionic visual navigation model is reliable for both standard and extreme lighting conditions.

A Novel Adaptively Optimized PCNN Model for Hyperspectral Image Sharpening

Hyperspectral satellite imagery has developed rapidly over the last decade because of its high spectral resolution and strong material recognition capability. Nonetheless, the spatial resolution of available hyperspectral imagery is inferior, severely affecting the accuracy of ground object identification. In the paper, we propose an adaptively optimized pulse-coupled neural network (PCNN) model to sharpen the spatial resolution of the hyperspectral imagery to the scale of the multispectral imagery. Firstly, a SAM-CC strategy is designed to assign hyperspectral bands to the multispectral bands. Subsequently, an improved PCNN (IPCNN) is proposed, which considers the differences of the neighboring neurons. Furthermore, the Chameleon Swarm Optimization (CSA) optimization is adopted to generate the optimum fusion parameters for IPCNN. Hence, the injected spatial details are acquired in the irregular regions generated by the IPCNN. Extensive experiments are carried out to validate the superiority of the proposed model, which confirms that our method can realize hyperspectral imagery with high spatial resolution, yielding the best spatial details and spectral information among the state-of-the-art approaches. Several ablation studies further corroborate the efficiency of our method.

Estimation of interlayer textural relationships to discriminate the benignancy/malignancy of brain tumors

A computer-aided diagnosis system is a popular tool to predict the risk factors of brain tumors. However, the existing techniques are unable to provide high detection accuracy due to the inability of capturing the hidden features of brain tumors. In this view, the present work decomposes the MR modality images into different layers using non-sub-sampled shearlet transformation (NSST). Following this, the proposed detection pipeline employs adaptive pulse-coupled neural network (A-PCNN) module and a fuzzy c-means (FCM) clustering algorithm for enhancement and segmentation of suspicious regions respectively. Interlayer textural relationships of tumors are estimated in terms of the layer-wise difference of the gray-level cooccurrence matrix (GLCM), similarity indices, and entropy distributions. Thenceforth, those feature coefficients are coded with binary sequences to express the textural homogeneity/randomness of tumors. Finally, these handcrafted multi-scale feature quantifiers are fed to some standard classifiers such as the k-nearest neighbor (kNN) technique, the linear square support vector machine (LS-SVM), and the decision tree (DT) for discriminating the state of benignancy/malignancy of tumors. Experimental results ensure that the proposed detection model shows superior performance compared to existing methods.

Research on liver cancer segmentation method based on PCNN image processing and SE-ResUnet

As one of the malignant tumors with high mortality, the initial symptoms of liver cancer are not obvious. In addition, the liver is the largest internal organ of the human body, and its structure and distribution are relatively complex. Therefore, in order to help doctors judge liver cancer more accurately, this paper proposes a variant model based on Unet network. Before segmentation, the image is preprocessed, and Pulse Coupled Neural Network (PCNN) algorithm is used to filter the image adaptively to make the image clearer. For the segmentation model, the SE module is used as the input of the residual network, and then its output is connected to the Unet model through bilinear interpolation to perform the down-sampling and up-sampling operations. The dataset is a combination of Hainan Provincial People's Hospital and some public datasets Lits. The results show that this method has better segmentation performance and accuracy than the original Unet method, and the dice coefficient, mIou and other evaluation indicators have increased by at least 2.1%, which is a method that can be applied to cancer segmentation.

Simulation analysis of visual perception model based on pulse coupled neural network

Pulse-coupled neural networks perform well in many fields such as information retrieval, depth estimation and object detection. Based on pulse coupled neural network (PCNN) theory, this paper constructs a visual perception model framework and builds a real image reproduction platform. The model firstly analyzes the structure and generalization ability of neural network multi-class classifier, uses the minimax criterion of feature space as the splitting criterion of visual perception decision node, which solves the generalization problem of neural network learning algorithm. In the simulation process, the initial threshold is optimized by the two-dimensional maximum inter-class variance method, and in order to improve the real-time performance of the algorithm, the fast recurrence formula of neural network is derived and given. The PCNN image segmentation method based on genetic algorithm is analyzed. The genetic algorithm improves the loop termination condition and the adaptive setting of model parameters of PCNN image segmentation algorithm, but the PCNN image segmentation algorithm still has the problem of complexity. In order to solve this problem, this paper proposed an IGA-PCNN image segmentation method combining the improved algorithm and PCNN model. Firstly, it used the improved immune genetic algorithm to adaptively obtain the optimal threshold, and then replaced the dynamic threshold in PCNN model with the optimal threshold, and finally used the pulse coupling characteristics of PCNN model to complete the image segmentation. From the coupling characteristics of PCNN, junction close space of image and gray level characteristics, it determined the local gray mean square error of image connection strength coefficient. The feature extraction and object segmentation properties of PCNN come from the spike frequency of neurons, and the number of neurons in PCNN is equal to the number of pixels in the input image. In addition, the spatial and gray value differences of pixels should be considered comprehensively to determine their connection matrix. Digital experiments show that the multi-scale multi-task pulse coupled neural network model can shorten the total training time by 17 h, improve the comprehensive accuracy of the task test data set by 1.04%, and shorten the detection time of each image by 4.8 s compared with the series network model of multiple single tasks. Compared with the traditional PCNN algorithm, it has the advantages of fast visual perception and clear target contour segmentation, and effectively improves the anti-interference performance of the model.

A pansharpening method combining iterative filtering and NSST-NSML-PAPCNN to optimize spatial detail extraction and injection

ABSTRACT Spatial injection-based pansharpening methods are prone to spatial or spectral distortions in pansharpening images due to insufficient extraction of spatial details and a mismatch between the amount of spatial detail information injected and the required amount. To this end, this paper proposes a pansharpening method that optimizes spatial detail extraction and injection. Firstly, a method to optimize the amount of spatial detail injection is proposed, that is, to extract the high-frequency information of the image through iterative filtering and determine the optimal number of iterations based on the global analysis of the method. Then, to fully extract and combine the spatial detail information of the source image, the detailed high-frequency image extracted corresponding to the optimal iterative filtering times is decomposed by non-subsampled shearlet transform (NSST), and a new multi-scale sum-modified-Laplacian (NSML) as an external stimulus to a parameter-adaptive pulse-coupled neural network model (PAPCNN). A fusion rule based on multi-scale morphological gradients is designed to extract a small amount of detailed information for the low-frequency subband. The fused spatial detail image can be obtained by combining the fused low-frequency and high-frequency subbands and inverse NSST transformation. Finally, pansharpening can be realized by combining spatial detail image, injection coefficient, and MS image. In this paper, many experiments are carried out on the QuickBird, GeoEye-1, and WorldView-4 datasets, and quantitative and qualitative comparisons are made with eight advanced methods. Experimental results show that the method proposed in this paper can achieve better fusion results.

Image Segmentation Combining Pulse Coupled Neural Network and Adaptive Glowworm Algorithm

Image segmentation is one of the key steps of target recognition. In order to improve the accuracy of image segmentation, an image segmentation algorithm combining Pulse Coupled Neural Network(PCNN) and adaptive Glowworm Algorithm(GA) is proposed. The algorithm retains the advantages of the GA. Introduce the adaptive moving step size and the population optimal value as adjustment factors. Enhance the ability to solve the global optimal value, and takes the weighted sum of the cross entropy, information entropy and compactness of the image as the fitness function of the GA. Maintain the diversity of image features and improving the accuracy of image segmentation. Experimental results show that compared with other algorithms, the segmented image obtained by this algorithm has better visual effect and the segmentation performance has the best comprehensive performance. For the seven gray-scale images in the Berkeley segmentation dataset, the segmentation effect is improved by 10.85% compared with TDE algorithm, 9.22% compared with GA algorithm, and 22.58% compared with AUTO algorithm.

An improved pulse coupled neural networks model for semantic IoT

In recent years, the Internet of Things (IoT) has gradually developed applications such as collecting sensory data and building intelligent services, which has led to an explosion in mobile data traffic. Meanwhile, with the rapid development of artificial intelligence, semantic communication has attracted great attention as a new communication paradigm. However, for IoT devices, however, processing image information efficiently in real time is an essential task for the rapid transmission of semantic information. With the increase of model parameters in deep learning methods, the model inference time in sensor devices continues to increase. In contrast, the Pulse Coupled Neural Network (PCNN) has fewer parameters, making it more suitable for processing real-time scene tasks such as image segmentation, which lays the foundation for real-time, effective, and accurate image transmission. However, the parameters of PCNN are determined by trial and error, which limits its application. To overcome this limitation, an Improved Pulse Coupled Neural Networks (IPCNN) model is proposed in this work. The IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons, and all its parameters are set adaptively, which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of images. Experimental segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation Datasets. The IPCNN method achieves a better segmentation result without training, providing a new solution for the real-time transmission of image semantic information.

Discrimination of neutrons and gamma-rays in plastic scintillator based on spiking cortical model

In this study, a spiking cortical model (SCM) based n-γ discrimination method is proposed. The SCM-based algorithm is compared with three other methods, namely: (i) the pulse-coupled neural network (PCNN), (ii) the charge comparison, and (iii) the zero-crossing. The objective evaluation criteria used for the comparison are the FoM-value and the time consumption of discrimination. Experimental results demonstrated that our proposed method outperforms the other methods significantly with the highest FoM-value. Specifically, the proposed method exhibits a 34.81% improvement compared with the PCNN, a 50.29% improvement compared with the charge comparison, and a 110.02% improvement compared with the zero-crossing. Additionally, the proposed method features the second-fastest discrimination time, where it is 75.67% faster than the PCNN, 70.65% faster than the charge comparison and 38.4% slower than the zero-crossing. Our study also discusses the role and change pattern of each parameter of the SCM to guide the selection process. It concludes that the SCM's outstanding ability to recognize the dynamic information in the pulse signal, improved accuracy when compared to the PCNN, and better computational complexity enables the SCM to exhibit excellent n-γ discrimination performance while consuming less time.

LI-DWT- and PD-FC-MSPCNN-Based Small-Target Localization Method for Floating Garbage on Water Surfaces

Typically, the process of visual tracking and position prediction of floating garbage on water surfaces is significantly affected by illumination, water waves, or complex backgrounds, consequently lowering the localization accuracy of small targets. Herein, we propose a small-target localization method based on the neurobiological phenomenon of lateral inhibition (LI), discrete wavelet transform (DWT), and a parameter-designed fire-controlled modified simplified pulse-coupled neural network (PD-FC-MSPCNN) to track water-floating garbage floating. First, a network simulating LI is fused with the DWT to derive a denoising preprocessing algorithm that effectively reduces the interference of image noise and enhances target edge features. Subsequently, a new PD-FC-MSPCNN network is developed to improve the image segmentation accuracy, and an adaptive fine-tuned dynamic threshold magnitude parameter V and auxiliary parameter P are newly designed, while eliminating the link strength parameter. Finally, a multiscale morphological filtering postprocessing algorithm is developed to connect the edge contour breakpoints of segmented targets, smoothen the segmentation results, and improve the localization accuracy. An effective computer vision technology approach is adopted for the accurate localization and intelligent monitoring of water-floating garbage. The experimental results demonstrate that the proposed method outperforms other methods in terms of the overall comprehensive evaluation indexes, suggesting higher accuracy and reliability.

Riesz-Laplace Wavelet Transform and PCNN Based Image Fusion

Important information perceived by human vision comes from the low-level features of the image, which can be extracted by the Riesz transform. In this study, we propose a Riesz transform based approach to image fusion. The image to be fused is first decomposed using the Riesz transform. Then the image sequence obtained in the Riesz transform domain is subjected to the Laplacian wavelet transform based on the fractional Laplacian operators and the multi-harmonic splines. After Laplacian wavelet transform, the image representations have directional and multi-resolution characteristics. Finally, image fusion is performed, leveraging Riesz-Laplace wavelet analysis and the global coupling characteristics of pulse coupled neural network (PCNN). The proposed approach has been tested in several application scenarios, such as multi-focus imaging, medical imaging, remote sensing full-color imaging, and multi-spectral imaging. Compared with conventional methods, the proposed approach demonstrates superior performance on visual effects, contrast, clarity, and the overall efficiency.

Improved PCNN Polarization Image Denoising Method Based on Grey Wolf Algorithm and Non-Subsampled Contourlet Transform

In this article, an adaptive pulse-coupled neural network (PCNN) polarization image denoising method based on Grey Wolf Optimization (GWO) and Non-Subsampled Contourlet Transform(NSCT) is proposed. Different from the traditional PCNN denoising method, the captured polarization image was firstly devised by the NSCT and enforced band-decomposition to denoised by PCNN. The evaluable index of the image was used for quantitative analysis. Then, GWO is used to update PCNN inherent voltage constant and attenuation time constant and neurons connected intensity factor three model parameters, after looking for multiple optimal solutions, and then to polarized image denoising to achieve the best effect. This method not only avoids the image edge blurring caused by the traditional image denoising method, but also solves the problem that the parameters of the PCNN are difficult to accurately estimate. Hence, it is more suitable for polarization images containing noise. The experiment and the quantitative analysis of image evaluation indices showed that NSCT-GWO-PCNN effectively suppresses the noise in polarization image by reducing salt-and-pepper noise while protecting edges.

Heterogeneous Quasi-Continuous Spiking Cortical Model for Pulse Shape Discrimination

The present study introduces the heterogeneous quasi-continuous spiking cortical model (HQC-SCM) method as a novel approach for neutron and gamma-ray pulse shape discrimination. The method utilizes specific neural responses to extract features in the falling edge and delayed fluorescence parts of radiation pulse signals. In addition, the study investigates the contributions of HQC-SCM’s parameters to its discrimination performance, leading to the development of an automatic parameter selection strategy. As HQC-SCM is a chaotic system, a genetic algorithm-based parameter optimization method was proposed to locate local optima of HQC-SCM’s parameter solutions efficiently and robustly in just a few iterations of evolution. The experimental results of this study demonstrate that the HQC-SCM method outperforms traditional and state-of-the-art pulse shape discrimination algorithms, including falling edge percentage slope, zero crossing, charge comparison, frequency gradient analysis, pulse-coupled neural network, and ladder gradient methods. The outstanding discrimination performance of HQC-SCM enables plastic scintillators to compete with liquid and crystal scintillators’ neutron and gamma-ray pulse shape discrimination ability. Additionally, the HQC-SCM method outperforms other methods when dealing with noisy radiation pulse signals. Therefore, it is an effective and robust approach that can be applied in radiation detection systems across various fields.

IoT based secured data monitoring system for renewable energy fed micro grid system

Recently, the Wireless Sensor Network (WSN) plays a prominent role in many applications for efficiently monitoring the performance of the system. As the conventional power grid faces multiple issues in the process of power transmission, a smart grid based Internet of Things (IoT), which uses integrated electrical and communication infrastructure is employed in this study. The micro grid using Renewable Energy (RE) resources like wind and solar power is preferred in this work. The major objective of this study is to have perfect monitoring and control of the overall power grid using WSN. By connecting a smart micro grid with IoT, the issue of voltage violation and grid instability caused by power insufficiency is resolved. The IoT-based network integrates grid with the distributed source through WSN, which senses the data and forwards the gathered data to the cloud while taking a backup for future monitoring. Before forwarding, the data is highly encrypted using a hybrid approach known as Advanced Encryption Standard (AES) and Rivest Cipher 4 (RC4).The hybrid encryption generates improved ratio of transaction success with respect to malicious ratio indicating the secured data transmission preventing the intrusion of hackers. Simultaneously, the secured data is transmitted at high speed by making use of Pulse Coupled Neural Network (PCNN) along with Remora Optimization algorithm. The inclusion of optimization for the effective selection of the hyperparameters of neural network ensures the generation of global optimal solution which in turn enables the efficient identification of shortest path. The hardware in this system uses Arduino controller and the proposed approach aids in efficient monitoring of smart grid parameters facilitating uninterrupted power availability.

Resolution improvement of photothermal microscopy by the modulated difference method.

Photothermal microscopy (PTM) was developed to image non-fluorescent objects. In the past two decades, PTM has reached single-particle and single-molecule sensitivity and has been used in the fields of material science and biology. However, PTM is a far-field imaging method whose resolution is restricted by the diffraction limits. This Letter reports a resolution improvement approach for photothermal microscopy called modulated difference PTM (MD-PTM), which utilizes Gaussian and doughnut formalism heating beams that are modulated at the same frequency but are of opposite phase to generate the photothermal signal. Furthermore, the opposite phase characteristics of the photothermal signals are applied to determine the objective profile from the PTM magnitude, and this helps to improve the lateral resolution of PTM. The lateral resolution is related to the difference coefficient between the Gaussian and doughnut heating beams; an increase in the difference coefficient causes a larger sidelobe of the MD-PTM amplitude, which readily forms an artifact. A pulse-coupled neural network (PCNN) is employed for phase image segmentations of MD-PTM. We experimentally study the micro-imaging of gold nanoclusters and crossed nanotubes using MD-PTM, and the results indicate that MD-PTM has merit in terms of improving the lateral resolution.

Retinex-MPCNN: A Retinex and Modified Pulse coupled Neural Network based method for low-illumination visible and infrared image fusion

To overcome detail loss problem of infrared and low-illumination visible light image fusion, this paper proposes a novel fusion framework based on Modified Pulse Coupled Neural Network (MPCNN) and Retinex theory. First, MPCNN is designed to segment original images into regions with different weights. Second, a novel Retinex-MPCNN algorithm is proposed to enhance low-illumination visible light image, details of which can be clearer. Then, a specific weighted fusion strategy based on region segmentations of MPCNN is designed to fuse the infrared and enhanced visible light images. Different from average fusion strategy, we introduce an illumination item to increase the attention for low-illumination areas, thereby preserves more details to the fusion image. Experimental results on TNO dataset demonstrate that our proposed method can generate fusion images with clear contour and structure information. Compared with existing fusion methods, our method achieves better performance both in subjective and objective assessment.