Research on Parallel Computing Algorithms for Accounting Data Processing and its Exploration in Platform Practice

In a previous article, we presented development and verification of an integral transport equation-based deterministic algorithm for computing three-dimensional brachytherapy dose distributions. Recently, we have included fluorescence radiation physics and parallel computation to the standing algorithms so that we can compute dose distributions for a large set of seeds without resorting to the superposition methods. The introduction of parallel computing capability provided a means to compute the dose distribution for multiple seeds in a simultaneous manner. This provided a way to study strong heterogeneity and shadow effects induced by the presence of multiple seeds in an interstitial brachytherapy implant. This article presents the algorithm for computing fluorescence radiation, algorithm for parallel computing, and display results for an 81-seed implant that has a perfect and imperfect lattice. The dosimetry data for a single model 6711 seeds is presented for verification and heterogeneity factor computations using simultaneous and superposition techniques are presented.

Poly- and perfluoroalkyl acids (PFAAs) are a large family of widespread contaminants of worldwide concern and well-known as "forever chemicals". Direct emission of PFAAs from the fluorochemical industry is a crucial source of PFAA pollutants in the environment. This study implemented nontarget analysis and comprehensive characterization for a category of new PFAA contaminants, i.e., iodinated PFAAs (IPFAAs), in fluorochemical industry wastewater and relevant contaminated river water by liquid chromatography-high-resolution mass spectrometry with a cascade precursor ion exclusion (PIE) strategy and in-house developed data extraction and processing algorithms. A total of 26 IPFAAs (including 2 isomers of an IPFAA) were found and identified with tentative molecular structures. Semiquantification of the IPFAAs was implemented, and the total concentrations of IPFAAs were 0.16-285.52 and 0.15-0.17 μg/L in wastewater and river water, respectively. The high concentrations in association with the predicted ecotoxicities and environmental behaviors demonstrate that these IPFAAs are worthy of more concern and further in-depth research. The cascade PIE strategy along with the data extraction and processing algorithms can be extended to nontarget analysis for other pollutants beyond IPFAAs. The nontarget identification and characterization outcomes provide new understanding on the environmental occurrence and pollution status of IPFAAs from a comprehensive perspective.

Implementation of Image Enhancement Algorithms and Recursive Ray Tracing using CUDA

This book chapter explores the application of machine learning (ML) and deep learning (DL) algorithms for real-time data processing and decision-making within the context of smart cities. As urban environments evolve, the ability to harness vast amounts of data from diverse sourcessuch as sensors, IoT devices, and digital platformshas become essential for optimizing urban operations and improving quality of life. The chapter delves into the challenges and opportunities presented by these technologies, focusing on issues such as scalability, real-time performance, data quality, and the integration of heterogeneous systems. It also examines the critical role of model explainability, transparency, and ethical considerations in AI-driven decision-making processes. Moreover, the chapter discusses the importance of addressing uncertainty in smart city decision-making through advanced decision support systems, enabling cities to adapt dynamically to changing conditions. By investigating these themes, the chapter aims to provide comprehensive insights into the future of smart city systems powered by ML and DL, highlighting their potential to revolutionize urban management.

The paper deals with the choice of the algorithm for processing the data of the load sensor of the crane weight measuring system being developed. The requirements for the weight measuring system are based on the operating conditions of the crane (the nature of the dynamic process, the limitation of the weighing time) and the planned use of information about the weight of goods (the required accuracy). A comparison of 5 signal processing algorithms is presented: direct averaging, search for extremes with two different modes of pre-smoothing, nonlinear regression, and averaging using a short-term memory buffer. A series of experiments was conducted to compare the methods. The data was obtained on the KMG-201 bridge crane. Based on the results of numerical evaluation of the results of experimental data processing, conclusions are drawn about the applicability of the considered algorithms for analyzing the dynamic process of the crane lifting mechanism. The algorithms for finding extremes, regardless of the pre-smoothing modes, did not show the required accuracy (the relative error was 5%). The averaging algorithm using a short-term memory buffer showed the highest accuracy (the relative error was 1%). The influence of the parameters of the data reception board on the result of the algorithms is estimated. The parameters varied in the range: bit depth 10-12 bits, sensor polling frequency 4.1-256 Hz. Most experiments showed an increase in accuracy with increasing bit depth. Also, according to the results of the experiments, it was found that an increase in the sensor polling frequency of more than 64 Hz in the conditions of this dynamic process does not affect the accuracy of determining the weight of the load. The study showed the need for further work in the direction of determining the optimal parameters of the averaging algorithm with a short-term memory buffer and determining the limit of increasing accuracy with increasing the bit depth of the data reception board.

This article presents the results of analysis of measuring ECG signals processing using two new filtering algorithms for low-frequency and high-frequency interference. The presence of such interferences leads to a decrease in the accuracy of the selection of informative components of the measuring signals. To eliminate low-frequency and high-frequency interference, the results of evaluating the effectiveness of two new algorithms for ECG signals are presented. The processing algorithms are based on a polynomial approximation of the high-pass filter and the wide-band notch filter cascade using Newton polynomials. On the basis of these processing algorithms, its performance was analyzed on a priori models of ECG signals and interference, and on the basis of quantitative indicators, its effectiveness was compared with respect to known algorithms. It was found that processing algorithms based on Newton polynomials significantly improve the quality of filtering of measuring ECG signals and indicators of signal-to-noise ratio and mean square error.

Satellite image data generated from remote sensors around the world have different resolutions and are processed at varying levels from Level 0 to Level 3, with each level containing vast amounts of information. Due to the problem of data size, many ocean science researchers use L3 images, which have a spatial resolution of 4 km or 9 km. However, in order to overcome problems such as red tides or to analyze the marine ecosystem based on ocean color satellite research, researchers must generate data by changing the parameters of images at various levels. There is also a need for immediate access to satellite image information using analytical and visualization tools. Considering those requirements, TuPiX-OC (Turning PiXels into knowledge and science-OceanColor) provides an environment to design and execute algorithms for data processing and analysis of satellite image data by data type. TuPix-OC, which has a distributed architecture, is an analytical platform that supports data import, level conversion, DB integration, analysis and processing, and visualization. TuPiX-OC stores satellite data in a massive storage device, and provides an online platform for satellite data conversion/analysis/ visualization. For satellite data processing, TuPiX-OC converts NASA-provided binary files into files that can be analyzed by users. Moreover, TuPiX-OC includes various algorithms for satellite data selection and utilization of satellite images. Preliminary Experiments of TuPiX-OC's satellite image data processing capability showed that it was able to process 35 times as many images as the open source software SeaDAS.

(1) Tree-rings are a challenge for modern ecological research dealing with 'time' and 'complexity'. (2) Machine learning appears to be an efficient tool to extract knowledge from data sets showing a high degree of complexity. (3) The software package PC/BEAGLE, using the evolutionary learning algorithm, is able to find some high quality rules for Nelder design poplar plantations. (4) Poplar trees growing in thin stands show a strong negative temperature signal. (5) 'Beaupré' trees are almost always more massive than 'Unal' trees, except for the first years after planting in the wider planting distances.

The purpose of the research is differentiation of recent geodynamic processes within the Carpathian Mountains on the basis of freely available GNSS data. Methodology. The methodology included GNSS data collection, processing and analysis. An algorithm for processing was proposed, which consisted of 5 main stages: transformation of data into an internal format, verification of time series for compliance with requirements, determination of horizontal velocities, division of the GNSS network into triangles, and determination of deformation parameters. Results. This study presents a comprehensive analysis of recent geodynamic processes based on GNSS data freely available from the Nevada Geological Survey. Taking into account the requirements for time series, 50 GNSS stations were selected and processed. In general, absolute and regional velocities were obtained and analysed during 2000–2023. Regional velocities of horizontal movements were used to calculate the deformation tensor and deformation parameters. The results of the study are consistent and correlate well with the studies of other scientists. The obtained results confirm the presence of active geodynamic processes within the Carpathians. Originality. The proposed approach made it possible to estimate the main deformation parameters (value and direction of deformation axes, total shear and dilation) within the Carpathian Mountains. This makes it possible to analyse and predict recent geodynamic processes in the region. Practical significance. On the basis of the calculated values, maps of the distribution of vectors of absolute and regional horizontal velocities, total shear rates, dilatation rates, and rotation rates were constructed.

The method of shear wave elastography Supersonic Shear Imaging is presented. A scheme for implementing this method on the Verasonics research system and an algorithm for processing and outputting data as an elasticity map are developed. Physical modelling was carried out with a CIRS model 049 elasticity phantom on the Verasonics research system using SSI and SWEI methods, and on the Supersonic Aixplorer ultrasound system. The results were compared and shown the method presented to be applicable for the research system.

Wireless sensor networks, by providing an unprecedented way of interacting with the physical environment, have become a hot topic for research over the last few years. As with any new technology, results from real experimentations using these networks are still scarce, as real deployments are either costly, or still unfeasible in the current state of technology. There is therefore an increasing need for simulation tools allowing the testing of different architectures, communication protocols or information processing algorithms in sensor networks. In this paper, we investigate a simulation framework for the testing of data processing in wireless sensor network applications. In a first stage, data is generated using partial differential equations, allowing the modeling of a large panel of physical phenomena. In a second stage, sensing unit operating system and network constraints are simulated using an instance of a versatile simulator to account for the platform characteristics. Insights provided by the proposed simulation frame are illustrated by a set of experiments on a heat source detection task.

Demand for near-real-time (NRT) satellite ocean observations is rapidly increasing with the development of operational oceanography and its application such as ocean monitoring for offshore oil exploitation, fish resources management of detection of surface pollution. This paper demonstrates that useful NRT ocean color measurements can be deducted form the blue, red, near-infrared and SWIR bands of Ve/spl acute/ge/spl acute/tation on board the SPOT satellites. The data processing is based on a simple and primary algorithm. In particular, the atmospheric correction algorithm described by Fuognie, et al., (2001) is based on the use of the near-infrared B3 band to estimate aerosol amount and, assuming a unique maritime model, to extrapolate the aerosol signal to the B0 blue spectral band, the only one that is sensitive to marine reflectance. In order to increase the spatial coverage which can be sometimes very limited due to cloud coverage, a dedicated merging algorithm is used. An operational product, based on these processing algorithms is currently distributed on a commercial basis as the ocean color component into the Catsat package. Some algorithmic improvements have been studied to improve the atmospheric correction using 2 spectral bands for the selection of the aerosol model among nine. They are illustrated by results obtained using acquisitions over the Canal of Mozambique in June 2000.

Nowadays, a lot of autonomous unmanned underwater vehicles (AUV) are created in the Institute of Marine Technology Problem FEB RAS. Most of all include side-scan sonar (SSS) EdgeTechSonar with 32-bit resolution. Robots solve different problems. There are bottom map construction, monitoring of underwater channels, tubes, mineral exploration. Signal processing requires complex numerical resources. Unfortunately, AUV doesn’t solve these problems on the board independently. Real signal requires additional processing. Hence, sonar data is processed in the laboratory. Currently researchers have large databases of AUV. Data processing in the laboratory is inconvenient. If engineer adds additional processing algorithm he changes all local software. In this paper authors use cloud approach for solving this problem. The data from SSS EdgeTechSonar upload directly to the cloud service. Afterwards, user chooses any filter for sonar image processing. The cloud service includes a database of sonar images with its geolocation. Moreover, user uses different filters for image processing. Filters in cloud service are executed with parallel technology for fastest processing. There are 512 parallel processors with mpi-technology and several GPU-processors. Choice of technology depends on filter properties. Authors research some filters for sonar image processing and design special parallel algorithms for each of them.

Speed of data processing is regarded as a crucial parameter in the nuclear physics experiment, where a dead time elimination or reduction is needed. In the case of computer-based systems, fast data processing algorithms are necessary to be exploited for saving a computational time and to reduce a total duration of the experiment. Time of measurement is one of the key parameters for implementation of specific experiments. Fast pulse processing algorithm (FPPA) of detector events was developed in LabVIEW™ and is capable to run up to 70% faster compared to the performance of previously used algorithm. The results show increased performance and applicability in complex experiments. Detailed description of the overall algorithm with relevant data flow processing and its utilization in Mössbauer spectrometer is presented.

Plant architecture characteristics contribute significantly to the microclimate within peanut canopies, affecting weed suppression as well as incidence and severity of foliar and soil-borne diseases. However, plant canopy architecture is difficult to measure and describe quantitatively. In this study, a ground-based LiDAR sensor was used to scan rows of peanut plants in the field, and a data processing and analysis algorithm was developed to extract feature indices to describe the peanut canopy architecture. A data acquisition platform was constructed to carry the ground-based LiDAR and an RGB camera during field tests. An experimental field was established with three peanut cultivars at Oklahoma State University's Caddo Research Station in Fort Cobb, OK in May and the data collections were conducted once each month from July to September 2015. The ground-based LiDAR used for this research was a line-scan laser scanner with a scan-angle of 100°, an angle resolution of 0.25°, and a scanning speed of 53 ms. The collected line-scanned data were processed using the developed image processing algorithm. The canopy height, width, and shape/density were evaluated. Euler number, entropy, cluster count, and mean number of connected objects were extracted from the image and used to describe the shape of the peanut canopies. The three peanut cultivars were then classified using the shape features and indices. A high correlation was also observed between the LiDAR and ground-truth measurements for plant height. This approach should be useful for phenotyping peanut germplasm for canopy architecture.