The use of distribution-based data representation to handle large-scale scientific datasets is a promising approach. Distribution-based approaches often transform a scientific dataset into many distributions, each of which is calculated from a small...
The increase of data collection in various domains calls for an adaptation of methods of visualization to tackle magnitudes exceeding the number of available pixels on screens and challenging interactivity. This growth of datasets size has been suppo...
The Reeb graph of a scalar function that is defined on a domain gives a topologically meaningful summary of that domain. Reeb graphs have been shown in the past decade to be of great importance in geometric processing, image processing, computer grap...
Large-scale geospatial data have accumulated worldwide in the past decades. However, various data formats often result in a geospatial data sharing problem in the geographical information system community. Despite the various methodologies proposed i...
Large-scale distributed systems have the advantages of high processing speeds and large communication bandwidths over the network. The processing of huge real-world data within a time constraint becomes tricky, due to the complexity of data parallel...
Remote sensing big data (RSBD) is generally characterized by huge volumes, diversity, and high dimensionality. Mining hidden information from RSBD for different applications imposes significant computational challenges. Clustering is an important dat...
A lack of memory can lead to job failures or increase processing times for garbage collection. However, if too much memory is provided, the processing time is only marginally reduced, and most of the memory is wasted. Many big data processing tasks a...
Interferometric synthetic aperture radar (InSAR) has developed rapidly over the past years and is considered as an important method for surface deformation monitoring, benefiting from growing data quantities and improving data quality. However, the h...
Considering the problem that the timing synchronization calculation in high-speed remote sensing signal reception is complex and it is difficult for it to be parallel, this paper deduces and designs a parallel timing error estimation and correction s...
It is well known that network-based parallel data processing algorithms are well suited to implementation in reconfigurable hardware recurring to either Field-Programmable Gate Arrays (FPGA) or Programmable Systems-on-Chip (PSoC). The intrinsic paral...
Curve fitting to unorganized data points is a very challenging problem that arises in a wide variety of scientific and engineering applications. Given a set of scattered and noisy data points, the goal is to construct a curve that corresponds to the...
Airborne Light Detection and Ranging (LiDAR) topographic data provide highly accurate digital terrain information, which is used widely in applications like creating flood insurance rate maps, forest and tree studies, coastal change mapping, soil and...
Cloud Computing has emerged as a powerful and promising way for running high performance computing (HPC) jobs. Most HPC jobs are designed under multi-processes paradigm and involve frequent communication and synchronization among parallel processes....
The parallel system is a kind of scientific research method based on an artificial system and computational experiments, which can not only reflect the dynamic process of the real system but also optimize its control process in real time. Given the r...
A novel visualization technique is proposed for the sum of ranking differences method (SRD) based on parallel coordinates. An axis is defined for each variable, on which the data are depicted row-wise. By connecting data, the lines may intersect. The...
Significant changes in the global climate are a focus of widespread concern, with profound implications for economies, daily life, and sustainable development. Analyzing and forecasting these trends relies heavily on meteorological data, which typica...
As the largest ellipsoid (LE) data fusion algorithm can only be applied to two-sensor system, in this contribution, parallel fusion structure is proposed to introduce the LE algorithm into a multisensor system with unknown cross-covariances, and thre...
In this paper, an automatic speech emotion recognition (SER) task of classifying eight different emotions was experimented using parallel based networks trained using the Ryeson Audio-Visual Dataset of Speech and Song (RAVDESS) dataset. A combination...
With the increasing demand for high precision in numerical simulations using computational fluid dynamics (CFD), the use of large-scale grids for discretized solutions has become a trend, resulting in an explosive growth of flow-field data size. To a...
The growing adoption of artificial intelligence in space communications has imposed increasingly demanding efficiency requirements on deep learning training for tasks such as intelligent signal processing and resource allocation. However, performance...
Efficient matching of incoming events of data streams to persistent queries is fundamental to event stream processing systems in wireless sensor networks. These applications require dealing with high volume and continuous data streams with fast proce...
Visual analysis provides a powerful approach for exploring multidimensional spatiotemporal data, yet it faces significant challenges in simultaneously representing temporal and spatial attributes. A key limitation is the difficulty of visually encodi...
Spatial neighboring analysis is an indispensable part of geo-raster spatial analysis. In the big data era, high-resolution raster data offer us abundant and valuable information, and also bring enormous computational challenges to the existing focal...
Experts in agriculture have conducted considerable work on rice plant protection. However, in-depth exploration of the plant disease problem has not been performed. In this paper, we find the trend of rice diseases by using the cascade parallel rando...
This paper proposes a framework for the wireless sensor data acquisition using a team of Unmanned Aerial Vehicles (UAVs). Scattered over a terrain, the sensors detect information about their surroundings and can transmit this information wirelessly o...
This paper proposes and experimentally evaluates a multi-level CPU-oriented execution method for high-throughput portion-based processing of file-backed Variant Call Format (VCF) data and automated mutation classification. The approach is based on a...
Trajectory clustering algorithms analyze the movement trajectory of the target objects to mine the potential movement trend, regularity, and behavioral patterns of the object. Therefore, the trajectory clustering algorithm has a wide range of applica...
Excessive information significantly increases the mental burden on operators of critical monitoring services such as maritime and air traffic control. In these fields, vessels and aircraft have sensors that transmit data to a control center. Because...
As the world has become increasingly digitalized in recent years, high-dimensional data with geographical location coordinate attributes, mainly referring to latitude and longitude, have been accumulated and spread to many disciplines. It is challeng...
The volume of telemetry data is gradually increasing, both because of the increasingly larger number of parameters involved, and the use of higher sampling frequencies. Efficient data compression schemes are therefore needed in space telemetry system...
The accurate and reliable acquisition of measurement information is very important for the stable operation of power systems, especially the operation status information of new energy stations. With the increasing proportion of new energy stations in...
The Sniffin’ Sticks Olfactory Identification Test is a tool for measurement of olfactory performance developed in Germany and validated in several countries. This research aims to develop the Spanish version of the Sniffin’ Sticks Olfactory Identific...
Anomaly detection systems, also known as intrusion detection systems (IDSs), continuously monitor network traffic aiming to identify malicious actions. Extensive research has been conducted to build efficient IDSs emphasizing two essential characteri...
Generated and collected data have been rising with the popularization of technologies such as Internet of Things, social media, and smartphone, leading big data term creation. One class of big data hidden information is causality. Among the tools to...
Density-based spatial clustering of applications with noise (DBSCAN) is a density-based clustering algorithm that has the characteristics of being able to discover clusters of any shape, effectively distinguishing noise points and naturally supportin...
The paper describes PCDM and PCDM4MP as new tools and commands capable of exploring large datasets. They select variables based on identifying the absolute values of Pearson’s pairwise correlation coefficients between a chosen response variable...
With the trend of high-resolution imaging, computational costs of image matching have substantially increased. In order to find the compromise between accuracy and computation in real-time applications, we bring forward a fast and robust matching alg...
We present a survey of results concerning Lempel–Ziv data compression on parallel and distributed systems, starting from the theoretical approach to parallel time complexity to conclude with the practical goal of designing distributed algorithms with...
Sequence similarity searches have been widely used in the analyses of metagenomic sequencing data. Finding homologous sequences in a reference database enables the estimation of taxonomic and functional characteristics of each query sequence. Because...
Meta-few-shot learning algorithms, such as Model-Agnostic Meta-Learning (MAML) and Almost No Inner Loop (ANIL), enable machines to learn complex tasks quickly with limited data and based on previous experience. By maintaining the inner loop head of t...
The article discusses approaches to building parallel data storage systems in high- performance clusters. The features of building data structures in parallel file systems for various applied tasks are analyzed. Approaches are proposed to improve the...
With the development of polarimetric synthetic aperture radar (PolSAR), quantitative parameter inversion has been seen great progress, especially in the field of soil parameter inversion, which has achieved good results for applications. However, Pol...
The Internet of Things is used in many application areas in our daily lives. Ensuring the security of valuable data transmitted over the Internet is a crucial challenge. Hash functions are used in cryptographic applications such as integrity, authent...
Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can...
This paper presents a novel numerical method for simulation controlled-source audio-magnetotellurics (CSAMT) and radio-magnetotellurics (CSRMT) data. These methods are widely used in mineral exploration. Interpretation of the CSAMT and CSRMT data col...
This paper is on the optimization of computing resources to process geospatial image data in a cloud computing infrastructure. Parallelization was tested by combining two different strategies: image tiling and multi-threading. The objective here was...
The paper discusses the use of parallel computation to obtain rough set approximations from large-scale information systems where missing data exist in both condition and decision attributes. To date, many studies have focused on missing condition da...
As recent heterogeneous systems comprise multi-core CPUs and multiple GPUs, efficient allocation of multiple data-parallel applications has become a primary goal to achieve both maximum total performance and efficiency. However, the efficient orchest...
An emerging reality is the development of smart buildings and cities, which improve residents’ comfort. These environments employ multiple sensor networks, whose data must be acquired and processed in real time by multiple rule engines, which t...
Personalized medicine is an aspect of the P4 medicine (predictive, preventive, personalized and participatory) based precisely on the customization of all medical characters of each subject. In personalized medicine, the development of medical treatm...
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