In this research, application of fuzzy logic as an intelligent quality strategy for controlling a two-axis sun tracking solar energy system. Solar panels are most effective when oriented perpendicular to the sun's journey across the sky. The two-axis sun tracking solar energy technology relies on software for its architecture. In this research, a method for optimizing the output power of a solar panel in a photovoltaic system by using a combination of two fuzzy controllers, one for tracking the sun and one for finding the point of optimum power production is applied. Using two strategically placed DC motors, the solar panels' horizontal and vertical orientations may be adjusted to follow the sun throughout the day. To obtain the maximum power output of the solar panels, a DC-DC converter is used. Resultantly, the proposed system uses innovative fuzzy logic to maximize total output by making the most of both the amount of photovoltaic energy consumed and the duration of time during which solar energy is available.

Improving Efficiency of Panel Using Solar Tracker Controlled Through Fuzzy Logic

Homography estimation of infrared and visible images is a highly challenging task in computer vision. Recently, the deep learning homography estimation methods have focused on the plane, while ignoring the details in the image, resulting in the degradation of the homography estimation performance in infrared and visible image scenes. In this work, we propose a detail-aware deep homography estimation network to preserve more detailed information in images. First, we design a shallow feature extraction network to obtain meaningful features for homography estimation from multi-level multi-dimensional features. Second, we propose a Detail Feature Loss (DFL), which utilizes refined features for computation and retains more detailed information while reducing the influence of unimportant features, enabling effective unsupervised learning. Finally, considering that the evaluation indicators of the previous homography estimation tasks are difficult to reflect severe distortion or the workload of manually labelling feature points is too large, we propose an Adaptive Feature Registration Rate (AFRR) to adaptive extraction of image pair feature points to calculate the registration rate. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods on synthetic benchmark dataset and real dataset.

/pdf/detail-aware-deep-homography-estimation-for-infrared-and-7b7jht2q.pdf

Detail-Aware Deep Homography Estimation for Infrared and Visible Image

Under this work, we propose a novel method for analyzing heart sounds to find cardiovascular illnesses. The phonocardiography (PCG) signal is divided into four pieces by the heart sound segmentation process: S1, systolic interval, S2, and diastolic interval. One may argue that it is an essential stage in the automated analysis of PCG signals. Mechanical activity of the circulatory system is conveyed via heart sounds. This data comprises the subject’s precise physiological condition as well as any short-term variations connected to the respiratory cycle. This paper’s focus is on an issue that is currently open: how to analyze noises and extract physiological state changes while keeping short-term variability. 

Phonocardiographic Signal Analysis for the Detection of Cardiovascular Diseases

The estimation of systems is based on the analysis of data obtained after experimenting about the behavior of a real process in response to an external disturbance to find a scheme that allows the mathematical stages to be modeled. This is the importance of its in-depth study, since if it is carried out correctly, re-directing resources to other fields is one of the many benefits of its applications. This research work seeks to highlight the importance of correcting the displacements that satellites in orbit may experience, due to the influence of various agents, both of their own functioning and those generated by the environment in which they operate. That is why, the technique of systems estimation should be presented as a very useful option because of the analysis tools of the MATLAB program. Putting-in orbit of a team of these characteristics implies investment of strong amounts of time, money, and technology; likewise maintaining its initial orientation leads to the analysis of new parameters without mentioning the great problem generated in the orbit of the Earth, if for reasons other than those considered “normal,” this orientation varied and the implemented processes did not fulfill their function. The necessary driver for our project is a PID control since a control law can compensate with the integral and derivative actions. The necessary tool for the development of our controller is from MATLAB. With this tool, we can reach our goal, since it shows us the trajectory of the roots as well as the necessary graphs showing control characteristics such as the maximum overshoot, stabilization time, and percentage of system error, among others. 

PID Control and Estimation of the Attitude Model Applied to Geostationary Satellites

Multimodal medical image fusion is a perennially prominent research topic that can obtain informative medical images and aid radiologists in diagnosing and treating disease more effectively. However, the recent state-of-the-art methods extract and fuse features by subjectively defining constraints, which easily distort the exclusive information of source images. To overcome these problems and get a better fusion method, this study proposes a 2D data fusion method that uses salient structure extraction (SSE) and a swift algorithm via normalized convolution to fuse different types of medical images. First, salient structure extraction (SSE) is used to attenuate the effect of noise and irrelevant data in the source images by preserving the significant structures. The salient structure extraction is performed to ensure that the pixels with a higher gradient magnitude impact the choices of their neighbors and further provide a way to restore the sharply altered pixels to their neighbors. In addition, a Swift algorithm is used to overcome the excessive pixel values and modify the contrast of the source images. Furthermore, the method proposes an efficient method for performing edge-preserving filtering using normalized convolution. In the end,the fused image are obtained through linear combination of the processed image and the input images based on the properties of the filters. A quantitative function composed of structural loss and region mutual data loss is designed to produce restrictions for preserving data at feature level and the structural level. Extensive experiments on CT-MRI images demonstrate that the proposed algorithm exhibits superior performance when compared to some of the state-of-the-art methods in terms of providing detailed information, edge contour, and overall contrasts. 

Effective image fusion strategies in scientific signal processing disciplines: Application to cancer and carcinoma treatment planning

Image fusion is mandatory to enhance the object recognition in images by concatenating many sources of planetoid, aerial and ground-based imaging systems with other related data set. Image fusion is the methodology of overlapping at least a couple of pictures so that their combination enhances the overall information content. In literature, there are many algorithms, whose objective is the fusion of images to improve the characteristics of the final image. This work primarily presents a contrast between thee cosine harmonic transform of wavelet transform, guided filter algorithm and discrete form of Wavelet transform. It can be stated with simple observation that the image fusion techniques based on guided refining or masking retrieve a good visual resultant image. Their performance is evaluated on Standard “Gun” dataset with the help of three most widely used performance evaluation metrics. The experimentation and work done on MATLAB, validates the superiority of DCHWT based fusion algorithm over others.

Infrared and Visible Image Fusion for Concealed Weapon Detection using Transform and Spatial Domain Filters

Clinicians often have to switch amongst radiographic scans in order to trace out patterns in various tissue striations. The conglomerated view of structural and anatomical view in medical scans can facilitate the physicians to execute precise diagnosis, intraoperative guidance, and planning preoperative procedures. Due to inherent physical limitations, source images have prevalence of noise and ambient light. This results in lower contrast and limited visual perception of striations and tissues in fused radiographic images. This paper proposes a concatenated filtering image fusion approach employing space segmentation and non-prior-based contrast enhancement. The latent row rank theory approach implements sub-space segmentation addressing the issue of noise removal, and the non-local-prior-based enhancement removes the ambient light from source images fortifying edge details and information. This complex fusion framework is designed in non-sub-sampled contourlet transform which exhibits computational efficiency. The final fused image obtained using local Laplacian energy fusion rule results in improved localisation of structural and anatomical details of brain tissue and outperforms high-performing fusion methods in literature both objectively with high fusion rate along with better quality visual results.

An Efficient Medical Assistive Diagnostic Algorithm for Visualisation of Structural and Tissue Details in CT and MRI Fusion

Underwater image enhancement is necessary to study aquatic flora and fauna. However, due to light absorption and scattering, acquired subaqueous images are gravely hazed and degraded. This results in low contrast of the underwater image. In literature, many algorithms aim to dehaze and enhance an image’s quality. The NLP-GRM method aims to design an efficient algorithm that carries out superior results under a different environmental condition in terms of visual analysis and objective evaluation. Our approach integrates the gradient residual minimization (GRM) and then on local prior to a new method called NLP-GRM. Initially, an underwater image is processed through a nonlocal prior to dehaze the image by color assumption. The nonlocal prior (NLP) output works with robust GRM, reinforcing the edge strength and detail in the image post removal of underwater haze. The execution of the NLP-GRM algorithm has been observed by quantitative metrics and subjectively as well. The experimental results demonstrate that the NLP-GRM is superior to existing underwater enhancement methods.

Image Enhancement Using Nonlocal Prior and Gradient Residual Minimization for Improved Visualization of Deep Underwater Image

The synthesis of visual information from multiple medical imaging inputs to a single fused image without any loss of detail and distortion is known as multimodal medical image fusion. It improves the quality of biomedical images by preserving detailed features to advance the clinical utility of medical imaging meant for the analysis and treatment of medical disorders. This study develops a novel approach to fuse multimodal medical images utilizing anisotropic diffusion (AD) and non-subsampled contourlet transform (NSCT). First, the method employs anisotropic diffusion for decomposing input images to their base and detail layers to coarsely split two features of input images such as structural and textural information. The detail and base layers are further combined utilizing a sum-based fusion rule which maximizes noise filtering contrast level by effectively preserving most of the structural and textural details. NSCT is utilized to further decompose these images into their low and high-frequency coefficients. These coefficients are then combined utilizing the principal component analysis/Karhunen-Loeve (PCA/KL) based fusion rule independently by substantiating eigenfeature reinforcement in the fusion results. An NSCT-based multiresolution analysis is performed on the combined salient feature information and the contrast-enhanced fusion coefficients. Finally, an inverse NSCT is applied to each coefficient to produce the final fusion result. Experimental results demonstrate an advantage of the proposed technique using a publicly accessible dataset and conducted comparative studies on three pairs of medical images from different modalities and health. Our approach offers better visual and robust performance with better objective measurements for research development since it excellently preserves significant salient features and precision without producing abnormal information in the case of qualitative and quantitative analysis. 

Medical Image Fusion Based on Anisotropic Diffusion and Non-Subsampled Contourlet Transform

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Infrared and Visible Image Fusion for Concealed Weapon Detection using Transform and Spatial Domain Filters

An Efficient Medical Assistive Diagnostic Algorithm for Visualisation of Structural and Tissue Details in CT and MRI Fusion

Image Enhancement Using Nonlocal Prior and Gradient Residual Minimization for Improved Visualization of Deep Underwater Image

Medical Image Fusion Based on Anisotropic Diffusion and Non-Subsampled Contourlet Transform