Solar Photovoltaic (PV) systems are increasingly vital for enhancing energy security worldwide. However, their efficiency and power output can be significantly reduced by hotspots and snail trails, predominantly caused by cracks in PV modules. This article introduces a novel methodology for the automatic segmentation and analysis of such anomalies, utilizing unsupervised sensing algorithms coupled with 3D Augmented Reality (AR) for enhanced visualization. The methodology outperforms existing segmentation techniques, including Weka and the Meta Segment Anything Model (SAM), as demonstrated through computer simulations. These simulations were conducted using the Cali-Thermal Solar Panels and Solar Panel Infrared Image Datasets, with evaluation metrics such as the Jaccard Index, Dice Coefficient, Precision, and Recall, achieving scores of 0.76, 0.82, 0.90, 0.99, and 0.76, respectively. By integrating drone technology, the proposed approach aims to revolutionize PV maintenance by facilitating real-time, automated solar panel detection. This advancement promises substantial cost reductions, heightened energy production, and improved performance of solar PV installations. Furthermore, the innovative integration of unsupervised sensing algorithms with 3D AR visualization opens new avenues for future research and development in the field of solar PV maintenance.

Recently many studies have shown the effectiveness of using augmented reality (AR) and virtual reality (VR) in biomedical image analysis. However, they are not automating the COVID level classification process. Additionally, even with the high potential of CT scan imagery to contribute to research and clinical use of COVID-19 (including two common tasks in lung image analysis: segmentation and classification of infection regions), publicly available data-sets are still a missing part in the system care for Algerian patients. This article proposes designing an automatic VR and AR platform for the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic data analysis, classification, and visualization to address the above-mentioned challenges including (1) utilizing a novel automatic CT image segmentation and localization system to deliver critical information about the shapes and volumes of infected lungs, (2) elaborating volume measurements and lung voxel-based classification procedure, and (3) developing an AR and VR user-friendly three-dimensional interface. It also centered on developing patient questionings and medical staff qualitative feedback, which led to advances in scalability and higher levels of engagement/evaluations. The extensive computer simulations on CT image classification show a better efficiency against the state-of-the-art methods using a COVID-19 dataset of 500 Algerian patients. The developed system has been used by medical professionals for better and faster diagnosis of the disease and providing an effective treatment plan more accurately by using real-time data and patient information.

Several oil spill disasters have been reported in the last decade, posing a major threat to the marine ecosystem, damaging marine life, vital for protecting the environment, and reducing economic losses. In order to reduce or clean the oil spill, one needs to create a cost-effective oil spill detection system, including its source, the spill extent, the quantity estimate, the range of probable transport paths, and weather and sea conditions. Thermal and polarimetric imagery are emerging sensing modalities that show the potential for enhanced contrast in situations where conventional imaging, such as microwave, hyper-spectral, and visible imaging, has recently been researched. There is a need to compare existing thermal and polarimetric images since there is little work and data in this area. Current studies have shown some improvement in oil spill technique development. Even with the additional availability of new techniques, these steps are limited by cloud cover and lack of contrast. This article will investigate thermal and polarimetric cameras' usage for tracking 3-D oil spills in the sea by developing robust unsupervised oil sensing algorithms. It involves introducing: 1) an oil spill segmentation framework designed for thermal and polarimetric imagery; 2) a multidensity oil spill region enhancement and 3-D thickness visualization algorithm; and 3) a qualitative and quantitative oil spill analysis approach. Comparisons with existing algorithms demonstrate the effectiveness of the proposed algorithms.

Backlit images are from an excessive reflection of light being opposite to a capturing device. The existing image enhancement methods cannot be directly applied to the backlit images because they are not designed to enhance both broad light and dark regions simultaneously. Moreover, the techniques have several limitations about over-saturation or losing contrast. This paper presents a single backlit image enhancement method based on the novel full-piecewise non-linear automatic stretching, without input parameter provided by a user, e.g., gamma and so on. The computer simulation results confirm that the proposed approach can (i) undoubtedly reveal hidden details in the dark region; (ii) preserve features and color of common and light (over-brightness) regions, and (iii) increase a local contrast of dark areas. The proposed approach tested on Li’s backlit image database and several backlit images from commercial devices. The simulation results demonstrate the efficiency of the proposed approach and its advantages over the cutting-edge backlit image enhancement methods in perceptual quality.