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Non-contact visual monitoring of vital signs in neonatology has been demonstrated by several recent studies in ideal scenarios where the baby is calm and there is no medical or parental intervention. Similar to contact monitoring methods (e.g., ECG, pulse oximeter) the camera-based solutions suffer from motion artifacts. Therefore, during care and the infants' active periods, calculated values typically differ largely from the real ones. In this way, our main contribution to existing remote camera-based techniques is to detect and classify such situations with a high level of confidence. Our algorithms can not only evaluate quiet periods, but can also provide continuous monitoring. Altogether, our proposed algorithms can measure pulse rate, breathing rate, and to recognize situations such as medical intervention or very active subjects using only a single camera, while the system does not exceed the computational capabilities of average CPU-GPU-based hardware. The performance of the algorithms was evaluated on our database collected at the Ist Dept. of Neonatology of Pediatrics, Dept of Obstetrics and Gynecology, Semmelweis University, Budapest, Hungary. [ABSTRACT FROM AUTHOR]

Activity Chain Optimization (ACO) is the task of finding a minimum-cost tour that visits exactly one location for each required activity while respecting time window constraints. We develop an exact algorithm that efficiently solves the ACO problem in all practical cases that involve hundreds of locations offering up to 10–15 activities and returns the optimal route with minimal time spent traveling and waiting. We also introduce a greedy heuristic that simulates human decision-making for comparison. Our experimental results highlight the practical significance of our work as we can reduce travel and wait times on 45 realistic Budapest inner-city routing problems by 16.65% on average compared to our baseline. Our algorithms' computational and memory requirements for solving practical ACO instances are shown to be low enough to be employed on embedded devices, e.g. smartphones and navigation systems. [ABSTRACT FROM AUTHOR]

Cardiac magnetic resonance imaging (CMR) is a widely used non-invasive imaging modality for evaluating cardiovascular diseases. CMR is the gold standard method for left and right ventricular functional assessment due to its ability to characterize myocardial structure and function and low intra- and inter-observer variability. However the post-processing segmentation during the functional evaluation is time-consuming and challenging. A fully automated segmentation method can assist the experts; therefore, they can do more efficient work. In this paper, a regression-based fully automated method is presented for the right- and left ventricle segmentation. For training and evaluation, our dataset contained MRI short-axis scans of 5570 patients, who underwent CMR examinations at Heart and Vascular Center, Semmelweis University Budapest. Our approach is novel and after training the state-of-the-art algorithm on our dataset, our algorithm proved to be superior on both of the ventricles. The evaluation metrics were the Dice index, Hausdorff distance and volume related parameters. We have achieved average Dice index for the left endocardium: 0.927, left epicardium: 0.940 and right endocardium: 0.873 on our dataset. We have also compared the performance of the algorithm to the human-level segmentation on both ventricles and it is similar to experienced readers for the left, and comparable for the right ventricle. We also evaluated the proposed algorithm on the ACDC dataset, which is publicly available, with and without transfer learning. The results on ACDC were also satisfying and similar to human observers. Our method is lightweight, fast to train and does not require more than 2 GB GPU memory for execution and training. • A novel regression-based method for the fully-automatic segmentation of CMR short-axis images on both ventricles. • The algorithm performance is similar to an experienced radiologist on the left and right ventricle. • Our algorithm is more general because our dataset was more comprehensive and diverse compared to the other datasets used for developing segmentation algorithms. It does not contain only healthy subjects but a wide scale of cardiovascular diseases. • More accurate results than the baselines, we trained Bai's method from scratch on our dataset. • Lightweight model, requires fewer memory and computation to train and use the model. [ABSTRACT FROM AUTHOR]