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Many modern applications of online changepoint detection require the ability to process high-frequency observations, sometimes with limited available computational resources. Online algorithms for detecting a change in mean often involve using a moving window, or specifying the expected size of change. Such choices affect which changes the algorithms have most power to detect. We introduce an algorithm, Functional Online CuSUM (FOCuS), which is equivalent to running these earlier methods simultaneously for all sizes of windows, or all possible values for the size of change. Our theoretical results give tight bounds on the expected computational cost per iteration of FOCuS, with this being logarithmic in the number of observations. We show how FOCuS can be applied to a number of different changes in mean scenarios, and demonstrate its practical utility through its state-of-the-art performance at detecting anomalous behaviour in computer server data. [ABSTRACT FROM AUTHOR]

Image Coregistration for InSAR processing is a time-consuming procedure that is usually processed in batch mode. With the availability of low-energy GPU accelerators, processing at the edge is now a promising perspective. Starting from the individuation of the most computationally intensive kernels from existing algorithms, we decomposed the cross-correlation problem from a multilevel point of view, intending to design and implement an efficient GPU-parallel algorithm for multiple settings, including the edge computing one. We analyzed the accuracy and performance of the proposed algorithm—also considering power efficiency—and its applicability to the identified settings. Results show that a significant speedup of InSAR processing is possible by exploiting GPU computing in different scenarios with no loss of accuracy, also enabling onboard processing using SoC hardware. [ABSTRACT FROM AUTHOR]

Purpose: To assess the validity of the results of a freely available online Deep Learning segmentation tool and its sensitivity to noise introduced by cataract. Methods: The OCT images were collected with a Spectralis SD-OCT (Heidelberg Engineering, Heidelberg, Germany) as part of normal clinical practice. Data were segmented using a freely available online tool called Relayer (https://www.relayer.online/), based on a cross-platform Deep Learning segmentation architecture specifically adapted for retinal OCT images. The segmentations were read into MATLAB (The MathWorks, Natick, MA, USA) and analyzed. Results: There was an excellent agreement between the ETDRS measurements obtained from the two algorithms. Upon visual inspection, the segmentation based on Deep Learning obtained with Relayer appeared more accurate except in one case of apparent good quality image showing interrupted segmentations in some of the B-scans. Conclusion: A freely available online Deep Learning segmentation tool showed good and promising performance in healthy retinas before and after cataract surgery, proving robust to optical degradation of the image from media opacities. [ABSTRACT FROM AUTHOR]

Pseudopterygium is a non-progressive conjunctival adhesion to the peripheral cornea secondary to a corneal-limbus damage. According to the literature, the main etiology is a previous eye trauma. Nevertheless, this could be biased by the existence of other underdiagnosed causes of pseudopterygium, some of which may have severe consequences for the integrity of the eye and patient's life. This comprehensive literature review was performed based on a search on the PubMed and Google Scholar databases of relevant pseudopterygium published papers according to our current knowledge and seeks to gather the existing evidence about its diverse etiologies and clinical features, as well as to propose a diagnostic algorithm to simplify its correct approach. [ABSTRACT FROM AUTHOR]

This study presents the new advances achieved in the field of harbour agitation climate assessment. Based on the improvement of an elliptic mild-slope model (MSP), which realistically reconstructs waves inside any-sized basin, represented by high-detailed unstructured meshes, and forced by real-shaped outer spectral data. A new solver is proposed for high performance runs, which allow fast agitation hindcast for statistical downtime analysis within an iterative and multi-scenario approach. Also, a realistic assimilation of partial reflection processes in quays/docks/wharfs/breakwaters is proposed. The model has been successfully validated in several harbours of special relevance in Spain with in situ measurements, through the assimilation of the hybrid downscaling (Camus et al., 2011) technique combined with monochromatic-based wave spectral reconstruction. • A fast, reliable and integral method for harbour agitation assessment is presented. • Important improvements for an elliptic mild slope numerical (agitation) model are presented. • A new algorithm for partial wave reflection is proposed and validated. • The method is validated with in situ measurements for 17 important harbours along Spanish coast, achieving good numerical predictions. • The method can easily handle any past/present/future harbour geometry, for any alternative and basin configuration/quay typology. [ABSTRACT FROM AUTHOR]

The synergy between Artificial Intelligence and the Edge Computing paradigm promises to transfer decision-making processes to the periphery of sensor networks without the involvement of central data servers. For this reason, we recently witnessed an impetuous development of devices that integrate sensors and computing resources in a single board to process data directly on the collection place. Due to the particular context where they are used, the main feature of these boards is the reduced energy consumption, even if they do not exhibit absolute computing powers comparable to modern high-end CPUs. Among the most popular Artificial Intelligence techniques, clustering algorithms are practical tools for discovering correlations or affinities within data collected in large datasets, but a parallel implementation is an essential requirement because of their high computational cost. Therefore, in the present work, we investigate how to implement clustering algorithms on parallel and low-energy devices for edge computing environments. In particular, we present the experiments related to two devices with different features: the quad-core UDOO X86 Advanced+ board and the GPU-based NVIDIA Jetson Nano board, evaluating them from the performance and the energy consumption points of view. The experiments show that they realize a more favorable trade-off between these two requirements than other high-end computing devices. [ABSTRACT FROM AUTHOR]