Data Processing System Research Articles

Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak.

Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. In this study, we process high-speed camera data, at rates exceeding 100 kfps, on in situ field-programmable gate array (FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate control signals in real time. Our system utilizes a convolutional neural network (CNN) model, which predicts the n = 1 MHD mode amplitude and phase using camera images with better accuracy than other tested non-deep-learning-based methods. By implementing this model directly within the standard FPGA readout hardware of the high-speed camera diagnostic, our mode tracking system achieves a total trigger-to-output latency of 17.6 μs and a throughput of up to 120 kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment demonstrates an FPGA-based high-speed camera data acquisition and processing system, enabling application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains.

Results of the Sports Talent Identification test for Management Information Systems: Borneo Sports Talent ID Software Implementation

This research resulted in a data processing system application program for sports talent identification test results aged 11-15 years, which can accelerate the provision of information about the classification of potentially gifted children and recommendations for sports branches based on anthropometric data parameters and biometric data. The sample in the study was 50 students in Samarinda Kota and Samarinda Ulu sub-districts using the cluster area sampling technique. The data collection technique uses a talent identification test battery instrument. In completing this research, the author uses a database-based programming language in the Borneo Sports Talent ID software. The results showed: (1) Borneo Sports Talent ID can be used as a system for processing data on talent identification results effectively and efficiently, (2) Talent classifications were found, namely: 10% of children have potential, 22% of children have sufficient potential, 32% of children have less potential, and 36% of children have no potential, (3) Recommendations for potential sports: volleyball, rowing, soccer, fencing, swimming, gymnastics, and rock climbing, and athletics in running, jumping, and throwing events.

INFORMATION SYSTEM FOR DATA PROCESSING IN SPORTS USING THE RANDOM FOREST METHOD

A huge amount of data is collected and generated in modern sports. This data can be used to improve athletes' performance, make more informed coaching and strategic decisions, and increase fan engagement. However, processing, analyzing, and interpreting this data can be challenging. This article is devoted to the development of an information system for data processing in the sports sector using the random forest method. The system aims to ensure efficient collection, processing, and analysis of large amounts of data generated during sports competitions, training, and interaction with fans and other stakeholders. Research methods. This article proposes an information system (IS) for data processing in the sports industry using the Random Forest (RF) method. As one of the machine learning methods, it is well suited for working with large amounts of data and complex classification and prediction tasks. The proposed IS consists of three main components. The data collection module accumulates data from various sources such as sensors, GPS trackers, websites, and social networks. The data processing module cleans, normalizes, and transforms the data to prepare it for analysis. The data analysis module uses the RF method to analyze data, predict outcomes, identify patterns, and make decisions. The conducted research has shown that the proposed IS can be an effective tool for predicting the results of sports competitions with high accuracy, identifying patterns in the data that can be useful for coaches and athletes to improve their training and strategy, personalizing training programs and recommendations for athletes, increasing the level of fan engagement by providing them with personalized content and forecasts. The proposed IS based on the random forest method is a powerful tool for processing and analyzing data in the sports industry. Its use can lead to improved athletes' performance, more informed coaching and strategic decisions, and increased fan engagement. One of the most powerful and accurate machine learning methods, the random forest method, allows for reliable analysis and forecasting based on various types of data, including player statistics, match results, physiological indicators, and fan behavior data. The article describes the stages of creating an information system: from data collection to data processing, storage, and analysis.

Microcalorimetric study of the bactericidal action of titanium tetrachloride on the Pseudomonas aeruginosa growth. Potential implications in orthopaedic surgery

With the rising use of metallic implants worldwide, bacterial infection has become a major concern due to its significant impact on patient health and national healthcare budgets. This study aimed to evaluate the sensitivity of Pseudomonas aeruginosa, a common bacterium responsible for implant surgery infections, to different concentrations of titanium tetrachloride (TiCl4) solutions. Microcalorimetry was used to analyze the heat output produced by bacteria metabolism when exposed to TiCl4 at concentrations ranging from 0 to 10 mM, as well as a saturated solution. The inner chamber of the calorimeter was set to the physiological temperature of the human body (309.65 K), and the heat output produced by bacteria metabolism was collected at intervals of 22.2 s for 48 h using a data acquisition and processing system. The data was then represented as thermograms. The results showed that higher concentrations of TiCl4 had a bactericidal effect on the growth of Pseudomonas aeruginosa. This study supports the potential use of higher concentrations of this compound in surgical implants to reduce the risk of infection development due to its bactericidal properties.

AI Revolution in Exit Polls: Enhancing Accuracy, Efficiency, and Inclusivity

Exit polls have long been integral to understanding electoral dynamics, offering insights into voter behavior and preferences. Traditional exit poll methodologies, involving manual data collection and in-person interviews, face significant challenges in accuracy, sample size, and inclusivity. This paper explores the transformative impact of Artificial Intelligence (AI) on the exit polling process. By leveraging neural networks for enhanced sampling, employing automated Voice-AI for data collection, and utilizing Natural Language Processing (NLP) for real-time data analysis, AI significantly improves the efficiency, accuracy, and inclusivity of exit polls. The paper discusses the technical aspects of implementing AI-driven exit polls, including the architecture of neural networks, real-time data processing systems, and machine learning models for prediction. Additionally, it addresses the advantages of AI, such as cost-effectiveness and reduced human biases, while acknowledging challenges like algorithmic bias, privacy concerns, and the complexities of accurately interpreting voter behavior. As AI continues to evolve, it promises to further enhance the reliability and comprehensiveness of exit polls, providing valuable tools for understanding public opinion in democratic processes

Using the Autoproc Toolkit for Data Processing and Analysis

A typical diffraction experiment yields several pictures and datasets from various crystals within ‎a brief timeframe. This poses a barrier for the efficient operation of advanced synchrotron ‎beamlines and the following data processing. Novice users, in particular, may have a sense of ‎being inundated by the tables, graphs, and numerical information that is provided to them via ‎different data-processing systems and software packages. Here, we outline many common ‎obstacles that users face while processing a collection of photos to generate a processed dataset. ‎Our main emphasis is on the challenges that often occur while transitioning from the earliest ‎phases of converting experimental data collecting into an interpreted electron density model. ‎Examining certain data properties during processing may frequently help solve or identify ‎challenges such as unforeseen crystal formations, issues with crystal manipulation, and ‎suboptimal selection of data-collection techniques. Ultimately, it is important to distinguish ‎between concerns that are beyond of one's immediate control after the experiment has concluded ‎and those that can be dealt with later.‎ Presenting auto PROC, an innovative software solution ‎that combines external processing packages with new tools and an automated workflow script. ‎This program is designed to help users effectively handle data that is impacted by the issues ‎outlined above. It specifically focuses on automating the processing of multi-sweep data sets ‎produced from multi-axis gonio metrics. It offers consumers both instruction and important ‎insights into the processing of data that is not done online.‎

The Renoir Dataflow Platform: Efficient Data Processing without Complexity

Today, data analysis drives the decision-making process in virtually every human activity. This demands for software platforms that offer simple programming abstractions to express data analysis tasks and that can execute them in an efficient and scalable way. State-of-the-art solutions range from low-level programming primitives, which give control to the developer about communication and resource usage, but require significant effort to develop and optimize new algorithms, to high-level platforms that hide most of the complexities of parallel and distributed processing, but often at the cost of reduced efficiency.To reconcile these requirements, we developed Renoir, a novel distributed data processing platform written in Rust. Renoir provides a high-level dataflow programming model as mainstream data processing systems. It supports static and streaming data, it enables data transformations, grouping, aggregation, iterative computations, and time-based analytics, and it provides all these features incurring in a low overhead.In this paper, we present the programming model and the implementation details of Renoir. We evaluate it under heterogeneous workloads. We compare it with state-of-the-art solutions for data analysis and high-performance computing, as well as alternative research products, which offer different programming abstractions and implementation strategies. Renoir programs are compact and easy to write: developers need not care about low-level concerns such as resource usage, data serialization, concurrency control, and communication. At the same time, Renoir consistently presents comparable or better performance than competing solutions, by a large margin in several scenarios.We conclude that Renoir offers a good tradeoff between simplicity and performance, allowing developers to easily express complex data analysis tasks and achieve high performance and scalability.

Water Quality Monitoring System Based on the Internet of Things (IoT) for Vannamei Shrimp Farming

As Internet of Things (IoT) technology develops, water quality monitoring systems for Vannamei shrimp farms have become more inventive and straightforward. The prototype IoT system monitors and controls the pool using sensors that can measure water quality parameters, such as temperature, pH, and salinity. The research aimed to design an automated water quality monitoring system for Vannamei shrimp aquaculture. The research used the E-4052C sensor, DS18B20 sensor, and DFRobot V1.0 sensor as data transmitting hardware (transmitter) and the receiving hardware microcontroller NodeMCU ESP32 as data processing, management, and control system tools. Then, the system used a Wi-Fi network to transfer data from the microcontroller to the Message Queue Telemetry Transport (MQTT) server as a data cloud. Several software programs, including Telegram, Node-Red, and ThingSpeak, help Android devices display real-time data. Test results for the accuracy of the sensor’s reading on water pH are 99.71, with an error rate of 0.29%. Meanwhile, the accuracy of the temperature sensor is 98.03 with an error rate of 1.7%. On the other hand, the accuracy of the salinity sensor is 99.49, with an error rate of 0.41%. The results indicate that all sensors have excellent performance. The real-time monitoring display and Android Telegram notification functions are good, and the automatic water quality monitoring tool is successfully operating in the Vannamei shrimp pool in Pamandati, South Konawe District, Southeast Sulawesi ssProvince, Indonesia.

The analysis of aerobics intelligent fitness system for neurorobotics based on big data and machine learning

In modern society, people's pace of life is fast, and the pressure is enormous, leading to increasingly prominent issues such as obesity and sub-health. Traditional fitness methods cannot meet people's needs to a certain extent. Therefore, this work aims to use technology to change people's lifestyles and compensate for traditional fitness methods' shortcomings. Firstly, this work overviews neurorobotics, providing neural perception and control functions for aerobics intelligent fitness system. Secondly, the connection between big data and machine learning (ML), big data technology products, and the ML process are discussed. The Spark big data platform builds node data for calculation, and the decision tree algorithm is used for data preprocessing. These are important for future intelligent fitness analysis. This work proposes an aerobics intelligent fitness system based on neurorobotics technology and big data analysis and develops a recommendation system for the best fitness exercise. This system utilizes neural perception and control functions, combined with big data and ML technology, to solve the obesity and sub-health problems faced by people in fast-paced and high-pressure lifestyles. By harnessing the computational capabilities of the Spark big data platform and applying the decision tree algorithm for data preprocessing, the system can furnish users with personalized fitness plans and optimization recommendations. This work conducts a model performance study on 35 % aerobic fitness data on intelligent fitness Android v1.0.8 to evaluate the system's data processing ability and training effectiveness. Moreover, the aerobics intelligent fitness system models based on neurorobotics, big data, and ML are evaluated. The results indicate that normalizing the data using the Min-Max method leads to a decrease in the F1 value and a reduction in data set errors. Consequently, the dataset studied by the system model is beneficial to improving the work efficiency of the aerobics intelligent fitness system. After the comprehensive human quality of the system model is evaluated, the actual average score of the comprehensive human quality of the 13 users tested before the aerobics intelligent fitness system test is 91.44, and the average prediction score is 90.88. The results of the two tests are similar. Thus, using the intelligent fitness system can enable the user to obtain system feedback according to the actual training effect, thereby playing a guiding role in the intelligent fitness of aerobics for the user. This work designs and implements the aerobics intelligent fitness system close to the human body's training effect, further enhancing the specialization and individualization of sports and fitness.

Identification of Slovak Tourists’ Attitudes Towards Digitalization for Circular Economy and Waste Management

Abstract The research paper explores the perspectives of a chosen group of Slovak tourists on how digitization drives circular economy practices and promotes efficient waste management in tourism. The study involves data from 103 participants, who expressed their opinions on sustainability concepts like digitization, innovation, circular economy, and waste management in tourism, using a 5-point Likert scale. The research’s main objective is to understand sentiments and attitudes towards integrating digitization and innovation for sustainable practices in the tourism sector. The findings reveal differences in attitudes among respondents. Visual aids like box plots are utilized to help interpret the results and emphasize how factors like interest in new technologies impact respondents’ attitudes. Implementing digital technologies in tourism plays a key role in creating efficient waste management systems and reducing the environmental burden. Data tracking and processing systems enable detailed analysis of waste generation patterns, providing valuable information to optimize collection and recycling processes. Digital technologies contribute to the overall reduction of the impact of tourism on the environment through electronic reservations, intelligent transport systems and other innovative solutions. These initiatives increase waste management efficiency and minimise tourism’s negative ecological impact, contributing to more sustainable and ecologically responsible tourism.

Implementasi Structured System Analysis and Design Method pada Sistem Informasi Retribusi Pasar

Mojopanggung market is a traditional market located in mojopanggung sub-district, giri sub-district, banyuwangi regency. Based on the field survey, there are problems with the data processing system for market retribution income reports managed by market treasurers who stell have a conventional approach in processing payment data recorded in books and microsoft excel. This results in the current system having many weaknesses and limitations, increasing the risk of data loss. In addition, providing information to recapitulation officers in the field of market management becomes difficult. Therefore, it is necessary to design a website-based market levy income information system, which can handle problems that arise, where the system can be used to monitor market levy income report data, as well as facilitate the provision of information to recapitulation officers in the field of market management the design of a website-based market levy income information system was built by applying the structured system analysis and design method (SSADM). From the results of research that has been carried out, every company or government agency is encouraged to utilize information technology as a means of supporting business activities. A part from that, designing a website-based market levy income information system is very necessary and needed, in order to make it easier to provide information related to market traders’ dependents, as well as reporting levy income results and activities for monitoring data, so that in can produce levy income report data more easily and effectively

BlockEdge: Building Bridges to Safe and Efficient Distributed Systems

Abstract: In the ever-evolving landscape of distributed systems, ensuring safety, efficiency, and scalability remains a paramount challenge. BlockEdge emerges as a pioneering framework designed to address these critical issues by leveraging the principles of blockchain technology and advanced consensus mechanisms. This abstract outlines the key features, innovations, and potential impacts of BlockEdge on the realm of distributed computing. BlockEdge integrates blockchain's immutable ledger properties with a novel consensus algorithm tailored for distributed systems. Unlike traditional blockchain applications that prioritize decentralization for financial transactions, BlockEdge focuses on enhancing the performance and reliability of distributed applications. The framework employs a hybrid consensus model that combines Byzantine Fault Tolerance (BFT) with Proof-of-Stake (PoS), optimizing both security and energy efficiency. A standout feature of BlockEdge is its modular architecture, which allows seamless interoperability between different types of distributed networks. This modularity facilitates the integration of various consensus protocols, catering to the specific needs of diverse applications, from IoT networks to large-scale data processing systems. By enabling secure and efficient cross-chain communication, BlockEdge effectively mitigates the silo effect prevalent in current distributed system designs.

Big data processing and analysis platform based on deep neural network model

Users are increasingly turning to big data processing systems to extract valuable information from massive datasets as the field of big data grows. Data analytics platforms are used by e-commerce enterprises to improve product suggestions and model business processes. In order to meet the needs of large-scale data center operation and maintenance management, Internet companies often use Flink to process log data. This paper takes the big data processing and analysis platforms built by Internet financial companies and large banks as examples, and implants a stock prediction model based on Deep Neural Network (DNN). In this context, this paper completes the following work: 1) The research status of big data processing and analysis platforms at home and abroad is introduced. 2) Drawing on the modular design idea, the commercial bank big data platform is designed and the functions of each sub-module are introduced. Then the basic principle and structure of Convolutional Neural Networks (CNN) are expounded. 3) The optimal parameters of Convolutional Neural Networks are selected through experiments, and then the trained model is used for experiments. It can be seen that the stock prediction model proposed in this article has a higher prediction accuracy compared to existing models, which also verifies the validity of the proposed model. Input the data and compare the obtained results with the actual results, and finally show that the model in this paper has a good performance on stock prediction.

An Overview On: Exam Ease with Online Exam Portal

Our online Examination System is a special kind of program where examination of the coed are often taken during a correct and efficient manner. Online Examination System consists of some Question(MCQ) types & and also the student are to give the right answers of these question by selecting the suitable choice. Once a solution is submitted (it won't possible to return back to it question). An internet examination system could be a web-based examination system where examinations are given online either through the net or intranet employing a automatic data processing system. The principle objective of this online examination system is to effectively evaluate the scholar thoroughly through a completely automated system that not only reduces the specified time but also obtains fast and accurate results

Case study using APDMS and RPEMS for SAWL pipes and benefits for offshore pipelay

This paper reviews an Automatic Pipe Dimension Measurement System (APDMS) and a Robotic Pipe End Measurement System (RPEMS) which can measure 19 and 9 parameters with the help of 72 and 6 measurement sensors respectively. APDMS and RPEMS are integrated into one optical measurement system, using laser triangulation, coupled with Systems, Applications and Products in Data Processing (SAP) integration and its own calibration modulus which provides an accurate solution to all pipe measurements. Two longitudinal submerged arc welded offshore projects were executed for an offshore pipe lay with close dimensional tolerances measured using the APDMS for one project and RPEMS for the other project. The radii data, assessed from the newly calculated common centre of all cross-sectional data, was used to line up the two adjacent pipes in a faster and more accurate way. By knowing the shape of the pipe ends based on this radii data, the best relative rotation of these two pipes can be determined and any misalignment will be verified and will stay within the requirements. The extensive database available for each pipe helped to match the line pipes with similar or close out-of-roundness at pipe ends resulting in good fitment of the pipes and ultimately quality welds, together with more pipe joints per day, resulting in a faster completion of the project. This paper shows the significance of using both systems.

Autonomous Artificial Olfactory Sensor Systems with Homeostasis Recovery via a Seamless Neuromorphic Architecture.

Neuromorphic olfactory systems have been actively studied in recent years owing to their considerable potential in electronic noses, robotics, and neuromorphic data processing systems. However, conventional gas sensors typically have the ability to detect hazardous gas levels but lack synaptic functions such as memory and recognition of gas accumulation, which are essential for realizing human-like neuromorphic sensory system. In this study, a seamless architecture for a neuromorphic olfactory system capable of detecting and memorizing the present level and accumulation status of nitrogen dioxide (NO2) during continuous gas exposure, regulating a self-alarm implementation triggered after 147 and 85 s at a continuous gas exposure of 20 and 40ppm, respectively. Thin-film-transistor type gas sensors utilizing carbon nanotube semiconductors detect NO2 gas molecules through carrier trapping and exhibit long-term retention properties, which are compatible with neuromorphic excitatory applications. Additionally, the neuromorphic inhibitory performance is also characterized via gas desorption with programmable ultraviolet light exposure, demonstrating homeostasis recovery. These results provide a promising strategy for developing a facile artificial olfactory system that demonstrates complicated biological synaptic functions with a seamless and simplified system architecture.

A general neural membrane computing model

Neural P systems are a class of distributed parallel computing models inspired by the way neurons cooperate in biological neural networks. Artificial neural networks integrate biological neural networks with mathematical models, which have powerful information processing capabilities. This work adopts the structure and data processing method of neural networks and integrates them with membrane computing to improve the neural P systems in data processing. A general neural membrane computing (GNMC) model is developed to realize the flow and manipulation of data in the form of objects and rules in the neuron. Glial cells are introduced into the GNMC model at the cellular level to modulate the states of the connected neurons. The trigger conditions, the activation mechanisms and the dissolve-reconnect rules make the GNMC model more efficient in handling objects. The multiple objects and multiple topological structures enable the GNMC model to handle different practical problems. The Turing universality of the GNMC model is proved as number generating and accepting devices. Furthermore, a small universal GNMC model with 71 neurons, fewer than that of the existing state-of-the-art neural P systems, is constructed for computing functions.

Application of neural network algorithm in computer data information processing system

In order to solve the problem that the data processing frequency of the traditional centralized data processing system is low, which leads to the poor feedback effect of massive data, this paper proposes a computer data information processing system based on neural network algorithm. Based on the original system hardware, the system replaces the data processor and increases the total number of the processor, so as to realize the distributed synchronous processing of massive data. In the aspect of software design, the interaction between system units and modules is formed through the protocol to improve the data communication mode of the system; Calculate the Euclidean distance and set the distributed processing mode of the system; According to the definition of cloud computing, the classification function is used to determine the constraints, establish the processing frequency equation, and realize the rapid processing of massive data. The experimental results show that the processing frequency of the system designed in this paper is more than 50 % higher than that of the traditional system, which solves the problem of low processing frequency caused by weak analysis ability and slow response speed in the traditional system. Conclusioncompared with the traditional system, the system designed in this paper has faster processing frequency for massive data and better feedback effect to users.

Rationale for the creation of multispectral data-processing system shock reconnaissance UAVs for use in modern warfare

In recent years, in the technically developed countries of the world, special attention has been paid to the development of the theory and practice of warfare in a single information space, changing views on the training and use of armed forces in modern wars and conflicts. An important aspect in the combat management chain is the distribution and transmission of information to support the decisions of the command and control of the armed forces. The purpose of the work is to identify typical tasks solved using a multispectral information and computing system of a reconnaissance and strike unmanned aerial vehicle (UAV). The analysis of tasks and the target load of UAVs in modern wars is carried out. The data on modern UAVs are systematized, in particular, on the payload used, the features of combat use in military conflicts, the main disadvantages in the functioning of control systems to achieve specific combat tasks. The modern world concepts and technologies for the development of integrated UAV information systems are presented. The hierarchical principle of obtaining information in a multispectral system and its complex processing are considered. The result of the functioning of a set of sensors in complex information processing will be the possibility of forming solutions that reduce the amount of information transmitted through data transmission channels and simplify the operator's work.

Application of mobile laser measurement system in railway inspection

Abstract Given the time constraints and demanding precision requirements of railway measurements, traditional measurement methods struggle to meet the inspection needs for the future expansion and renovation of extensive rail networks. This paper presents a railway data collection and processing system developed by using mobile laser scanning technology. To address the issue of error accumulation exceeding predefined limits in the positioning algorithm, we conducted a comprehensive study and utilized smoothing algorithms to correct acceleration and attitude angles. This optimization process not only enhances trajectory data but also improves the accuracy of the point cloud. The results indicate that this system holds significant importance in the realms of railway digital survey and design, as well as digital renovation and related areas.