Current Issue : January-March Volume : 2026 Issue Number : 1 Articles : 5 Articles
Artificial intelligence technologies, particularly machine learning and computer vision, are being increasingly utilized to preserve, restore, and create immersive virtual experiences with cultural artifacts and sites, thus aiding in conserving cultural heritage and making it accessible to a global audience. This paper examines the performance of Generative Adversarial Networks (GAN), especially Style-Based Generator Architecture (StyleGAN), as a deep learning approach for producing realistic images of Egyptian monuments. We used Sigmoid loss for Language–Image Pre-training (SigLIP) as a unique image–text alignment system to guide monument generation through semantic elements. We also studied truncation methods to regulate the generated image noise and identify the most effective parameter settings based on architectural representation versus diverse output creation. An improved discriminator design that combined noise addition with squeeze-and-excitation blocks and a modified MinibatchStdLayer produced 27.5% better Fréchet Inception Distance performance than the original discriminator models. Moreover, differential evolution for latent-space optimization reduced alignment mistakes during specific monument construction tasks by about 15%. We checked a wide range of truncation values from 0.1 to 1.0 and found that somewhere between 0.4 and 0.7 was the best range because it allowed for good accuracy while retaining many different architectural elements. Our findings indicate that specific model optimization strategies produce superior outcomes by creating betterquality and historically correct representations of diverse Egyptian monuments. Thus, the developed technology may be instrumental in generating educational and archaeological visualization assets while adding virtual tourism capabilities....
Using a camera system developed earlier for monitoring the behavior of lemmings under the snow, we are now able to record a large number of short image sequences from this rodent which plays a central role in the Arctic food web. Identifying lemming species in these images manually is wearisome and time-consuming. To perform this task, we present a deep neural network which has several million parameters to configure. Training a network of such an immense size with conventional methods requires a huge amount of data but a sufficiently large labeled dataset of lemming images is currently lacking. Another challenge is that images are obtained in darkness in the near infrared spectrum, causing the loss of some image texture information. We investigate whether these challenges can be tackled by a transfer learning approach in which a network is pretrained on a dataset of visible spectrum images that does not include lemmings. We believe this work provides a basis for moving toward developing intelligent software programs that can facilitate the analysis of videos by biologists....
Numerous imaging-based methods have been proposed for artifact monitoring and preservation, yet most rely on fixed-angle cameras or robotic platforms, leading to high cost and complexity. In this study, a portable monocular camera pose estimation and calibration framework is presented to capture artifact images from consistent viewpoints over time. The system is implemented on a Raspberry Pi integrated with a controllable three-axis gimbal, enabling untethered operation. Three methodological innovations are proposed. First, ORB feature extraction combined with a quadtree-based distribution strategy is employed to ensure uniform keypoint coverage and robustness under varying illumination conditions. Second, on-device processing is achieved using a Raspberry Pi, eliminating dependence on external power or high-performance hardware. Third, unlike traditional fixed setups or multi-degree-of-freedom robotic arms, real-time, low-cost calibration is provided, maintaining pose alignment accuracy consistently within three pixels. Through these innovations, a technically robust, computationally efficient, and highly portable solution for artifact preservation has been demonstrated, making it suitable for deployment in museums, exhibition halls, and other resource-constrained environments....
Snow accumulation on photovoltaic (PV) panels can cause significant energy losses in cold climates. While drone-based monitoring offers a scalable solution, real-world challenges like varying illumination can hinder accurate snow detection. We previously developed a YOLO-based drone system for snow coverage detection using a Fixed Thresholding segmentation method to discriminate snow from the solar panel; however, it struggled in challenging lighting conditions. This work addresses those limitations by presenting a reliable drone-based system to accurately estimate the Snow Coverage Percentage (SCP) over PV panels. The system combines a lightweight YOLOv11n-seg deep learning model for panel detection with an adaptive image processing algorithm for snow segmentation. We benchmarked several segmentation models, including MASK R-CNN and the state-ofthe- art SAM2 segmentation model. YOLOv11n-seg was selected for its optimal balance of speed and accuracy, achieving 0.99 precision and 0.80 recall. To overcome the unreliability of static thresholding under changing lighting, various dynamic methods were evaluated. Otsu’s algorithm proved most effective, reducing the absolute error of the mean in SCP estimation to just 1.1%, a significant improvement over the 13.78% error from the previous fixed-thresholding approach. The integrated system was successfully validated for realtime performance on live drone video streams, demonstrating a highly accurate and scalable solution for autonomous snow monitoring on PV systems....
Thermal imaging is a non-contact method for monitoring respiration activity during sleep. In this study, we evaluated its clinical application during overnight recordings in a sleep clinic. Five thermal cameras were used to detect breaths, the estimated respiration rate (RR), and inter-breath intervals (IBIs) in seven adults undergoing diagnostic polysomnography (PSG). Forty-five minutes of recordings were selected, consisting of 12 motionless and event-free segments. The thermal videos were processed using an adapted pre-existing thermal video processing algorithm. The respiration signals generated with the thermal cameras were validated against simultaneously recorded signals from the PSG system, the current gold standard for monitoring sleep. The results show a mean absolute error (MAE) ranging between 0.64 and 0.91 breaths per minute for the RR. Breath detection showed a sensitivity of 96.3%, and a precision of 94.1%. The MAE obtained between IBIs was 0.48 s, and the mean IBI variability difference recorded was 3.9 percentage points. In addition, the results from this clinical study show that the use of all five cameras and a single camera revealed no statistically significant differences, demonstrating the work towards a robust system. This first study of thermal cameras for the assessment of respiration in a clinical setting shows us the potential application of thermal imaging in clinical practice for respiration monitoring and establishes a foundation for further implementation in assessing sleep-disordered breathing....
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