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.

Digital Twin is the seamless data integration between a physical and virtual machine in either direction. Emotion recognition in healthcare is becoming increasingly important due to recent developments in Machine Learning methods. However, it may face technical problems such as limited datasets, occlusion and lighting issues, identifying key features, incorrect emotion classification, high implementation costs, head posture, and a person’s cultural background. This paper proposes a novel approach based on facial expression and body movement recognition for emotion recognition. It uses three devices (Kinect 1, Kinect 2, and RGB HD camera) to construct a new bi-modal database containing 17 participants’ performances of six emotional states. Two mono-modal classifiers have been developed to obtain sufficient state information based on facial expression and body motion analysis. The system’s performance is assessed using three algorithms: Bagged Trees, k -Nearest Neighbors ( k -NN), and Support Vector Machine (Linear and Cubic). The acquired findings demonstrate the excellent performance of the suggested method and the effectiveness of the proposed features, particularly the combination of 3D distance and 3D angle, in characterising and identifying emotions. Results obtained using Kinect 2 marginally surpass those with Kinect 1. Comparing 2D RGB and RGB-D data reveals that the depth information significantly raises the recognition rate. RGB-D features can be used to represent emotions, but there are discrepancies between RGB and RG-D data.

For children with autism spectrum disorder (ASD), innovative computer-assisted solutions can provide useful teaching, learning, and support tools. Speech, video, narrative, and a mix of 2D/3D images of virtual objects shown on screens and mobile phones can help to improve the learning experience. Augmented Reality (AR) presents a valuable technology for achieving certain educational objectives. This study presents a gesture and voice-based learning framework for children with ASD that offers interactivity, engagement, and visual support during school/cognitive therapy sessions. The proposed prototype was designed to increase children's engagement and focus during the session and achieve a high level of social interaction and inclusion. It was implemented in accordance with the requirements of specialists from regional rehabilitation centres for children with ASD and structured activities and AR-based games were defined. A multi-baseline feasibility pilot study was carried out with 18 children, aged 2–12 years, diagnosed with ASD and with learning difficulties (concentration and language development problems, social interaction and inclusion), and 3 therapists/parents. Preliminary qualitative and quantitative results show significantly higher engagement and concentration time using the proposed AR-computer system compared to a non-computer condition. In addition to improved receptive vocabulary and social interaction for the children, the prototype reduces teacher workload while engaging the children.

Although medical imaging technology has persisted in evolving over the last decades, the techniques and technologies used for analytical and visualisation purposes have remained constant. Manual or semi-automatic segmentation is, in many cases, complicated. It requires the intervention of a specialist and is time-consuming, especially during the Coronavirus disease (COVID-19) pandemic, which has had devastating medical and economic consequences. Processing and visualising medical images with advanced techniques represent medical professionals’ breakthroughs. This paper studies how augmented reality (AR) and artificial intelligence (AI) can transform medical practice during COVID-19 and post-COVID-19 pandemic. Here we report an augmented reality visualisation and interaction platform; it covers the whole process from uploading chest Ct-scan images to automatic segmentation-based deep learning, 3D reconstruction, 3D visualisation, and manipulation. AR provides a more realistic 3D visualisation system, allowing doctors to effectively interact with the generated 3D model of segmented lungs and COVID-19 lesions. We use the U-Net Neural Network (NN) for automated segmentation. The statistical measures obtained using the Dice score, pixel accuracy, sensitivity, G-mean, and specificity are 0.749, 0.949, 0.956, 0.955, and 0.954, respectively. The user-friendliness and usability are objectified by a formal user study that compared our augmented reality-assisted design to the standard diagnosis setup. One hundred and six doctors and medical students, including eight senior medical lecturers, volunteered to assess our platform. The platform could be used as an aid diagnosis tool to identify and analyse the COVID-19 infectious or as a training tool for residents and medical students. The prototype can be extended to other pulmonary pathologies.

To comprehend a scene, and use and interact with objects in an augmented reality (AR) application correctly, Degrees of Freedom (6 DoF) object pose estimation is a crucial task. By supplying exact 3D pixel coordinates, the incorporation of depth photos has resulted in substantial gains. However, depth photos are not always simple to find; for instance, mobile phones and tablets, which are common devices for augmented reality apps, do not provide any depth information. As a result, a lot of study is focused on determining the poses of known objects using simple RGB images. 3D Poorly textured object detection is a challenging task due to their lack of distinctive features, limited appearance variations, ambiguity with the background, occlusion challenges, lighting and reflection effects, and lack of distinct and unique features. In this study, we created a framework to automate the workflow for predicting brain tumour segmentation and 6 DoF phantom head localisation. Our framework named Hologram-Tumour ’HOLOTumour’ provides an augmented-reality rendering with 3D AR visualisation and interaction, generates the 3D model of the brain with a segmented tumour based on Geodesic-Aided Chan-Vese Model employing a local Magnetic Resonance Imaging (MRI) dataset, and outputs the phantom head 6 DoF pose estimation using generalisable object pose estimator, Gen6D, employing only RGB images for a texture-less object. Based on the comparison study including the accuracy and inference runtimes scores, the 6 DoF pose estimation of the printed phantom head and brain tumour segmentation achieve promising performance. Medical professionals may identify brain tumours with the help of the HOLOTumour platform, which can also be used for professional practice and training in medicine.