Detecting machine-readable codes in industrial manufacturing is a critical yet challenging task, as it directly impacts both defect detection and broader visual anomaly detection. The complexity of visual data, coupled with high-speed production processes generating vast volumes of information, makes accurate and efficient detection essential for ensuring product quality and operational efficiency. This paper takes significant steps toward addressing these challenges by identifying and categorizing key issues related to machine-readable code defect detection, introducing the first publicly available and challenging dataset as a benchmark for future studies, and exploring techniques to enhance both detection performance and data storage efficiency. Experimental results demonstrate that leveraging the YUV color space, combined with data compression, significantly improves detection accuracy while minimizing storage requirements. This work highlights the importance of balancing data complexity, storage optimization, and detection reliability, laying a strong foundation for future advancements in defect detection, anomaly identification, and cost-efficient industrial automation.

Solar Active Regions are characterised by their intense magnetic activity, which often leads to solar phenomena such as solar flares, and coronal mass ejections (CMEs). With the recent advancement of computing technologies and the huge integration of Artificial Intelligence (AI), many approaches have been proposed for forecasting solar eruptions using machine learning. In this study, we propose the use of a Temporal Convolutional Network (TCN) for predicting whether an active region will be flaring in a specific window of time and defining the flare class. The dataset is categorised into three different subsets based on the flare class and trained separately with the same TCN architecture to apply late fusion. The proposed solar flare prediction ensemble (EoFNets) is based on both the physical characteristics of the active region (EoFPhyNet) and geometric features (EoFGeoNet). Experimental results show that TCN outperforms long short-term memory (LSTM) in three cases. Our main aim is to deploy deep-learning-based approaches onboard for faster and more accurate real-time monitoring as well as leveraging the higher sampling rates for improved time-series predictions. Many major benefits can be realised if the deep learning models can be implemented onboard, including a sizeable reduction in the volume of downlinked data, and improved system latency. However, implementing deep learning models in space can be a critical task, as most approaches require high computational and memory resources, both of which are limited in typical spacecraft onboard data handling systems. Nevertheless, the EoFNets network outlined in this paper has been optimised to fit the resource constraints of a space platform deployed at the extreme edge far from Earth. Two low-power hardware targets are considered, namely the IntelMovidius MyriadX and Rockchip RK3588S. To the best of our knowledge, this is the first time that such a TCN network has been proposed for solar flare forecasting.

In recent years, Deep Neural Networks (DNNs) approaches have outperformed traditional techniques for several computer vision problems. This has been made possible by the increase of computational resources represented by Graphical Processing Units (GPU) that allow training using large datasets and the availability of deep learning accelerators for inference. On the other hand, the attitude determination accuracy requirements for spacecraft are increasing. The most accurate attitude determination sensor for spacecraft is the so-called star sensor or star tracker. With the increase in low-cost satellite platforms such as CubeSats, research into the improvement of star sensor accuracy for low-power and low-cost sensor architectures remains a relevant subject. In this context, we examine several methods for noise reduction and star detection for improving centroiding performance. More specifically, an efficient and robust denoising method for star images using an Auto-Encoder (AE) is proposed. This method enhances the image quality for systems sensitive to noise. Furthermore, an accurate and lightweight algorithm based on an existing YOLO (You Only Look Once) architecture is proposed to detect the location of stars in the image. In this work, the YOLO bounding boxes are used to describe the space region around the stars. Subsequently, the star centroid within the bounding box is computed using the COG (Center Of Gravity) method. This method removes the need for centroiding algorithms sliding over the entire image area. An extensive comparison of the proposed denoising technique with other traditional filters confirms that the proposed method resists all noise models and reconstructs well the corrupted images. Experiments show that the proposed YOLO-based star detector achieves high accuracy with a lightweight architecture without any extra latency.