Events of a seismic nature followed by catastrophic floods caused by tsunami waves (the incidence of which has increased in recent decades) have an important impact on the populations of littoral regions. On the coast...
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Events of a seismic nature followed by catastrophic floods caused by tsunami waves (the incidence of which has increased in recent decades) have an important impact on the populations of littoral regions. On the coast of Japan and Kamchatka, it takes nearly 20 min for tsunami waves to approach the nearest dry land after an offshore seismic event. This paper addresses an important question of fast simulation of tsunami wave propagation by mapping the algorithms in use in field-programmable gate arrays (FPGAs) with the help of high-level synthesis (HLS). Wave propagation is described by the shallow water system, and for numerical treatment the MacCormack scheme is used. The MacCormack algorithm is a direct difference scheme at a three-point stencil of a "cross" type;it happens to be appropriate for FPGA-based parallel implementation. A specialized calculator was designed. The developed software was tested for precision and performance. Numerical tests computing wave fronts show very good agreement with the available exact solutions (for two particular cases of the sea bed topography) and with the reference code. As the result, it takes just 17.06 s to simulate 1600 s (3200 time steps) of the wave propagation using a 3000 x 3200 computation grid with a VC709 board. The step length of the computational grid was chosen to display the simulation results in sufficient detail along the coastline. At the same time, the size of data arrays should provide their free placement in the memory of FPGA chips. The rather high performance achieved shows that tsunami danger could be correctly evaluated in a few minutes after seismic events.
This paper provides a comprehensive exploration of physics-informed neural networks and their core features. It delves into their role in tackling inverse problems inherent in ordinary differential equation-based mode...
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This paper provides a comprehensive exploration of physics-informed neural networks and their core features. It delves into their role in tackling inverse problems inherent in ordinary differential equation-based models. Within this context, we introduce a two-group epidemiological model, elucidating its fundamental attributes. The central objective of this research is to accurately estimate the model parameters for both groups in the epidemiological model. We offer a detailed exposition of the adopted methodology, providing insights into the algorithm and the techniques employed for its implementation. Through this analysis, we illuminate the complexities of our study, contributing to the growing body of knowledge in this field, which intersects epidemiology and neural network-based parameter estimation for an enriched understanding of infectious disease dynamics.
Protein interactions and cellular responses are fundamental pillars of molecular systems biology. Decoding these complex signaling pathways requires advanced computational methods. One promising direction of algorithm...
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ISBN:
(纸本)9798400704246
Protein interactions and cellular responses are fundamental pillars of molecular systems biology. Decoding these complex signaling pathways requires advanced computational methods. One promising direction of algorithm development is using graph algorithms to identify proteins involved in signaling pathways. Despite the availability of tools, many researchers grapple with software and user experience constraints. In response, we have developed the Signaling Pathway Reconstruction Analysis Streamliner (SPRAS), a robust containerized framework that enables users to easily reconstruct signaling pathways by connecting proteins of interest within molecular interaction networks. It seamlessly integrates graph algorithms designed for pathway reconstruction with downstream visualization and clustering analysis. We contribute and integrate three random-walk-based algorithms to SPRAS, including one algorithm we developed for large networks and two other algorithms that appear in the literature. Random walk approaches have been highly successful in predicting candidate proteins involved in a signaling pathway, and integrating them into SPRAS will greatly expand the framework's ability for pathway reconstruction. We illustrate their importance by using the random walk algorithms now available in SPRAS to explore potential proteins involved in cell-cell fusion in flies. In our computational experiments, five fly proteins appeared in multiple reconstructed pathways, suggesting a potential role for them in cell-cell fusion. With the addition of these new algorithms, SPRAS will become an essential tool for unraveling the mysteries of biological interactions.
When the software realizes the FIR filter, it usually carries on the cyclic shift to the multiple input data according to the FIR filtering formula. After the completion of the shift, it can get the filtering result b...
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ISBN:
(纸本)9781450397148
When the software realizes the FIR filter, it usually carries on the cyclic shift to the multiple input data according to the FIR filtering formula. After the completion of the shift, it can get the filtering result by multiplying with the FIR parameters. The higher the filtering order, the more the filtering channels, the longer the software running time will be consumed. In order to save software running time, this paper proposes a software implementation method which called copying the filter parameters. The FIR parameters are copied once, and only the latest input data is assigned in the operation process. It can directly multiply the data and parameters to get the filtering result, so as to save the software running time.
When shooting by airborne or vehicular cameras, the problem of image dithering is inevitable and it will lead to the dithering of frames in the videos captured by these cameras. On the basis of the analysis on camera ...
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ISBN:
(纸本)9781784660291
When shooting by airborne or vehicular cameras, the problem of image dithering is inevitable and it will lead to the dithering of frames in the videos captured by these cameras. On the basis of the analysis on camera motion model, the feature points were firstly found in videos after adaptively smoothing the videos. In terms of the fact that motion homogeneity was always included in the blocks extracted from the adjacent frames, the feature points in the current frame then were able to be found by matching blocks, once the feature points in the reference frames were determined. Based on these steps, the proposed algorithm is robust and effective to extract the feature points from videos. Compared with other methods, the proposed method works more accurately, steadily, and effectively, especially for the case that includes violent illumination.
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