Data Processing Algorithms Research Articles

Анализ и документирование требований для программного комплекса по проведению вычислительных экспериментов и численному исследованию системы «расходомерная трубка – жидкость» кориолисова расходомера

<p>Item. This article describes the stages of development of a software package designed to conduct a series of computational experiments on numerical modeling of the “flow tube – liquid” system of a Coriolis flow meter and automation of algorithms for subsequent data processing and visualization. The purpose of this article is to formulate and document the requirements for a software package, and its design based on modern approaches to the development of software products. Methods. The structure of requirements for the software package was developed based on the Software Requirements Specification template adopted in the Rational Unified Process software development methodology, in accordance with the classification of requirements for FURPS+ software systems. Python 3 and the interpreted programming language of the MATLAB environment were used to develop the complex routines. Results. A structure of requirements for the software package has been developed, including functional and non-functional requirements. A software package consisting of three subprograms has been developed, its structure, main components and capabilities are described, and examples of use are demonstrated. Conclusions. The software package solves the problems of automating modeling algorithms, processing and visualizing data obtained as a result of experiments. It can be used in engineering and scientific research related to the analysis of the performance of Coriolis flow meters.</p>

ISB recommendations on the definition, estimation, and reporting of joint kinematics in human motion analysis applications using wearable inertial measurement technology

There is widespread and growing use of inertial measurement technology for human motion analysis in biomechanics and clinical research. Due to advancements in sensor miniaturization, inertial measurement units can be used to obtain a description of human body and joint kinematics both inside and outside the laboratory. While algorithms for data processing continue to improve, a lack of standard reporting guidelines compromises the interpretation and reproducibility of results, which hinders advances in research and development of measurement and intervention tools. To address this need, the International Society of Biomechanics approved our proposal to develop recommendations on the use of inertial measurement units for joint kinematics analysis. A collaborative effort that incorporated feedback from the biomechanics community has produced recommendations in five categories: sensor characteristics and calibration, experimental protocol, definition of a kinematic model and subject-specific calibration, analysis of joint kinematics, and quality assessment. We have avoided an overly prescriptive set of recommendations for algorithms and protocols, and instead offer reporting guidelines to facilitate reproducibility and comparability across studies. In addition to a conceptual framework and reporting guidelines, we provide a checklist to guide the design and review of research using inertial measurement units for joint kinematics.

DEVELOPMENT OF AN ALGORITHM FOR ADAPTING A MATHEMATICAL MODEL OF THE PROCESS OF MIXING AND MELTING COPPER CONCENTRATES

The paper describes the information base of the system, which is formed by algorithms of centralized control, automated analytical control system and control data of material flows. The data processing algorithm according to a special program generates additional information necessary to solve the control tasks of the system and includes algorithms for processing analytical data information, as well as data for monitoring material flows, calculations of additional electric furnace variables, loading of charge into furnace bunkers and technological variables by department. The simulation model of the functioning of the complex "technological process – control system" is implemented in the form of a package of interconnected software modules. The package implementing the simulation model is divided into three complexes: a set of programs for "Data collection and processing"; a complex for "Optimal process control"; a set of programs for "optimal energy regime management".

MSCT-semiotics of vertebrae in patients with cervical spine stenosis

Introduction The number of surgical interventions on the cervical spine for stenosis has been constantly increasing. This fact proves that there is a need for careful preoperative preparation that would consider the complexity of the intervention and the age of the patients.Purpose To substantiate the need to include the MSCT data processing algorithm of bone tissue density of vertebral bodies and arches to assess their quality for planning osteoplastic decompressive laminoplasty in patients with cervical spine stenosis due to degenerative changes.Material and methods This single-center retrospective study investigated qualitative and quantitative characteristics of the spine with radiography and multislice computed tomography (MSCT) in 82 patients with degenerative diseases of the cervical spine and associated spinal canal stenosis (CSS).Results and discussion The data obtained indicate a tendency for the total density of the cervical vertebrae to increase from C3 to C5 and to decrease caudally, with minimal density in C7 without signs of osteoporosis. A similar trend is characteristic of trabecular bone. The density of the osteon layer of the vertebral arch cortex differs significantly from the density of the outer and inner plates. The total density of the compact layer of the vertebral arch cortex exceeds 785.15 ± 38.4 HU.Conclusion The data obtained justify the need to include the study of the density of vertebral bodies, vertebral arches, and its thickness in the MSCT data processing algorithm to develop a plan for surgical intervention in patients with cervical spine stenosis in order to obtain objective data on the quality of the bone.

Using cloud computing techniques to map the geographic extent of informal settlements in the greater Cape Town Metropolitan Area

Although remote sensing approaches offer unprecedented opportunities to understand urban land cover dynamics including informal settlements areal extent, challenges such as spectral confusions still persist, particularly when segregating land cover types like informal settlements from planned formal settlements. The improvements in Earth Observation (EO) data analytic tools such as introduction of Google Earth Engine (GEE) cloud computing platform, provide prospects to improve separability of these settlements from other urban land cover classes, via their advanced data processing and filtering algorithm, which allows for the synergic use of multisource and multi-temporal data, thus improving detection and monitoring of these settlements. This study harnessed the advance data analytic powers of GEE cloud computing platform coupled with higher resolution Sentinel-2 data to map the geographical extent of informal settlement in the Cape Town Metropolitan Area. The classification yielded six land cover classes: formal settlements, informal settlements, water, bare or built-up areas, vegetated lands, and croplands. Built-up formal settlement was the most dominant class, accounting for 70% of the total Cape Town surface area, while open water was the least dominant, accounting for 2%. Informal settlements accounted for approximately 7% of all settlements. Although overall accuracy was within acceptable limits (68%), some classes, such as vegetated lands and formal settlements, reported low class accuracies due to spectral similarities with other classes. The findings highlight the importance of the GEE platform, as well as the interaction of contextual and spectral characteristics, as well as various sentinel-2 derived water, built up, and vegetation indices in mapping informal settlements. These findings are critical for the facilitation of improved urban planning, provision of services and assisting in alleviating social as well as environmental issues within the Cape Town Metropolitan area.

Prognostic Efficacy of the Albumin-Bilirubin Score and Treatment Outcomes in Hepatocellular Carcinoma: A Large-Scale, Multi-Center Real-World Database Study

<b><i>Introduction:</i></b> Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality globally, with treatment outcomes closely tied to liver function. This study evaluates the prognostic utility of the albumin-bilirubin (ALBI) score compared to the traditional Child-Pugh (CP) grading, leveraging real-world evidence from a large-scale, multi-center database. <b><i>Methods:</i></b> The Liver Cancer IN Korea (LINK) research network, a multi-center initiative, retrospectively collected electronic health records from three academic hospitals in South Korea, encompassing HCC patients diagnosed between 2015 and 2020. Inclusion criteria mandated at least one HCC treatment and excluded patients with other primary cancer diagnoses. The study followed patients until death, the last visit, or June 2021, employing standardized data processing and rule-based algorithms for data consistency. The prognostic efficacy of ALBI scores and CP scores was compared through time-dependent receiver operating characteristic (ROC) curves and the inverse probability censoring weighting method. <b><i>Results:</i></b> From 25,248 newly diagnosed patients, 10,297 were included, with 65.82% having hepatitis B etiology and a mean follow-up of 27.49 months. Patients’ classification by modified ALBI (mALBI) grade at diagnosis revealed: grade 1 (48.87%), 2a (20.50%), 2b (24.54%), and 3 (5.17%), with a minimal percentage missing (0.92%). Transarterial therapy (54.07%) and tyrosine kinase inhibitors (84.14% as the first-line systemic therapy) were predominant treatments. The ALBI score demonstrated greater prognostic efficacy than the CP score in long-term outcomes, with time-dependent area under the ROC curve analysis showing a score of 0.71 for ALBI versus 0.67 for CP at 60 months. Furthermore, higher mALBI grades were significantly associated with poorer survival outcomes, as indicated by both univariate and multivariate Cox proportional regression model analyses (<i>p</i> < 0.001). <b><i>Conclusions:</i></b> The study confirmed the ALBI score’s superior prognostic ability over the CP score, especially evident in long-term outcomes, suggesting a shift toward more nuanced liver function assessment tools in real-world clinical practice.

Application of GIS and Artificial Intelligence in Military Operations: Prospects and Challenges

Geographic Information System (GIS) and Remote Sensing have been considered significant in the military due to their spatiality in nature. Recent military developments have seen various military institutions depending on spatial mapping tools, for the purpose of command, control, communication and coordination in military operations. In this study, the qualitativeanalytical method was used to illustrate the applications of GIS in military operations, drawing lessons from land based military developments from selected studies. An online survey was conducted to extract information from a sample of forty (40) students undertaking War Studies at the University of Zimbabwe, who were conveniently selected for the study. In-depth interviews were also done with two (2) military experts from the university. Data collected was analyzed using descriptive statistics and qualitative interpretation of recent developments in military GIS. The benefits of GIS and machine learning in military operations included access to information; improved data management; information dissemination; improved terrain analysis; object, target or pattern recognition; quick data processing algorithms; autonomous systems; and predictive analysis. The study also notes some challenges associated with GIS and machine learning in military operations include expensive to set up which constituted of respondents; training requirements; cyber-security; software issues; offensive in nature, as well as misinterpretation of situations. The study recommends the need for human-AI collaboration and the development of a regulatory framework at national, regional and international level in ensuring sustainable implementation of GIS technology in military operations.

Обробка даних на основі швидкого перетворення Фур’є в кореляційному течешукачі

Fast Fourier transform (FFT) algorithms are widely used in correlation leak detectors (CD) in the implementation of digital data processing. The combination of the computa-tional efficiency of FFT with modern high-performance mobile computing means pro-vides acceptable data processing time during leak detection. Due to the increase in the general wear and tear of pipeline networks in many countries, the requirements for reliabil-ity and efficiency in detecting and repairing damage are increasing. Meeting these re-quirements means complicating data processing algorithms to more fully take into ac-count the diversity of the acoustic environment. This is the reason for the special interest in FFT as a basic, universal algorithm, on the basis of which it is promising to further develop data processing in CD. The article describes the experience of the authors in the application of FFT in parametric CD, in particular, the use of FFT in spectral evaluation and digital filtering of signals, calculations of digital filters, calculation and further pro-cessing of correlation functions is considered.

Efficient Soil Health Monitoring and Irrigation Management System Using Fuzzy Logic

Abstract: The need for sustainable agriculture is acutely felt due to ever increasing global water crisis. Efficient irrigation management is crucial in this regard. The proposed system employs a network of soil moisture sensor distributed over a agricultural field, where continuous acquisition of real time data on soil moisture levels is done. Soil moisture measurement is a vital parameter in terms of precision agriculture, which affects the irrigation scheduling for ensuring optimal crop growth. Traditional methods related face challenges adapting to changing soil patterns at different conditions. The system proposes application of fuzzy logic algorithm for processing of data. Key advantages of using fuzzy logic in soil moisture measurement include improved precision in moisture detection, enhanced ability to make more informed irrigation decisions. The proposed system delivers a model for promoting water conservation, enhancing crop productivity and maintaining soil health.

Internet of things weather monitoring system

The IoT Weather Monitoring System aims to develop a comprehensive and efficient solution for real-time weather data collection, analysis, and dissemination using the Internet of Things (IoT) technology. With the increasing need for accurate and up-to-date weather information, this project leverages interconnected sensors, data processing techniques, and cloud computing to create a reliable and accessible weather monitoring system. The system comprises a network of weather sensors strategically deployed in various locations to capture meteorological parameters such as temperature, humidity, air pressure, and rainfall. These sensors are integrated with microcontrollers and wireless communication modules to transmit data to a central data hub. The data hub employs advanced data processing algorithms to clean, aggregate, and analyze the incoming data streams, ensuring the accuracy and quality of the information. To enhance user accessibility, the processed weather data is made available through a user- friendly web application and mobile interface. Users can view current weather conditions, historical trends, and receive alerts for severe weather events based on the analyzed data. Additionally, the system's cloud-based architecture allows for scalability and remote management, ensuring seamless operation and easy expansion to accommodate growing demands.

Web Application for Company Stock Price Prediction

This study contributes to predicting stock prices. In this paper, we have provided a novel machine learning and deep learning technique for stock prediction, which capitalizes on the complex emotional patterns of investors that can be extracted from Twitter data. By joining two separate datasets, such as Twitter sentiments and the associated stock prices, we have applied complex algorithms and evaluated this method, which classifies a user’s sentiment into a positive and negative one. It was possible to address the complexity of investor behavior within the highly unstable stock market environment. One of the most important problems that are encountered by developers is gaining the possibility to properly capture stock price fluctuations in the temporal dimension. It implies the necessity to utilize such reliable evaluation metrics as precision-recall factors for classification purpose and root mean squared error for regression purpose. by using these metrics, the performance of the predictive model can be evaluated in detail, which allows to draw meaningful permanent conclusions within the context of how well it reflects the complex relationship between investor sentiment and market dynamics. The results of the evaluation conducted as part of the process of validating the proposed hypothesis show that, indeed, the predictive accuracy improves significantly from the use of machine learning and deep learning algorithms if paired with Twitter users’ emotional sentiment data. This combination enhances the predictive capacity of the model and provides meaningful insights into how the changes in stock prices are impacted by the sentiment of investors. Moreover, in the process of this study, the explanation and detail consideration of the algorithms for feature engineering and data processing were provided, which stands crucial for increasing the predictive model’s accuracy The study has a valuable implication for both the financial analysis and technical spheres due to the resolution of these methods and presents a model for further research activities in stock prediction. To conclude, this interdisciplinary strategy that combines sentiment analysis and stock prediction not only advances our understanding of the investor behavior but also brings the opportunity for novel applications in the emerging field at the juncture of technology and finance. As a result, the current research sheds light on the intricate connection between the dynamics of the stock market and investor sentiment, thus offering new insights that can better inform predictive modeling research in the financial industry.

Vehicle Accident Alerting System

This journal presents a detailed exploration of the design, development, and implementation of a vehicle accident alerting system aimed at significantly enhancing road safety and improving emergency response to accidents. The system employs a multi-faceted approach, integrating advanced sensor technology, real-time data processing algorithms, and efficient communication protocols to detect accidents promptly and notify emergency services and relevant stakeholders. The core components of the system include sensors strategically installed within vehicles to monitor motion, orientation, and environmental conditions. These sensors provide raw data, which is processed in real-time using sophisticated algorithms to detect patterns indicative of potential accidents. The system incorporates confirmation mechanisms to reduce false positives and accurately assess the severity of detected events. Key Words: Accident detection, alert system, GPS, GSM, Accelerometer, Android application.

Generating realistic data through modeling and parametric probability for the numerical evaluation of data processing algorithms in two-dimensional chromatography

BackgroundComprehensive two-dimensional chromatography generates complex data sets, and numerous baseline correction and noise removal algorithms have been proposed in the past decade to address this challenge. However, evaluating their performance objectively is currently not possible due to a lack of objective data. ResultTo tackle this issue, we introduce a versatile platform that models and reconstructs single-trace two-dimensional chromatography data, preserving peak parameters. This approach balances real experimental data with synthetic data for precise comparisons. We achieve this by employing a Skewed Lorentz-Normal model to represent each peak and creating probability distributions for relevant parameter sampling. The model's performance has been showcased through its application to two-dimensional gas chromatography data where it has created a data set with 458 peaks with an RMSE of 0.0048 or lower and minimal residuals compared to the original data. Additionally, the same process has been shown in liquid chromatography data. SignificanceData analysis is an integral component of any analytical method. The development of new data processing strategies is of paramount importance to tackle the complex signals generated by state-of-the-art separation technology. Through the use of probability distributions, quantitative assessment of algorithm performance of new algorithms is now possible. Therefore, creating new opportunities for faster, more accurate, and simpler data analysis development.

Enabling 3D CT-scanning of cultural heritage objects using only in-house 2D X-ray equipment in museums

Visualizing the internal structure of museum objects is a crucial step in acquiring knowledge about the origin, state, and composition of cultural heritage artifacts. Among the most powerful techniques for exposing the interior of museum objects is computed tomography (CT), a technique that computationally forms a 3D image using hundreds of radiographs acquired in a full circular range. However, the lack of affordable and versatile CT equipment in museums, combined with the challenge of transporting precious collection objects, currently keeps this technique out of reach for most cultural heritage applications. We propose an approach for creating accurate CT reconstructions using only standard 2D radiography equipment already available in most larger museums. Specifically, we demonstrate that a combination of basic X-ray imaging equipment, a tailored marker-based image acquisition protocol, and sophisticated data-processing algorithms, can achieve 3D imaging of collection objects without the need for a costly CT imaging system. We implemented this approach in the British Museum (London), the J. Paul Getty Museum (Los Angeles), and the Rijksmuseum (Amsterdam). Our work paves the way for broad facilitation and adoption of CT technology across museums worldwide.

Spectroscopy Detection and Imaging System Based on Line Array Single Photon Detectors

Abstract. Single-photon detectors, with their exceptional sensitivity, provide a reliable means for single-photon-level detection, demonstrating significant advantages in detecting weak signals in complex environments compared to traditional detectors. With the continuous advancement in semiconductor manufacturing technology, single-photon detectors based on linear array configurations have emerged and rapidly developed. This study utilizes a linear array single-photon detector in free-running mode combined with a scanning mechanism to design and implement a spectral detection and imaging system. Through the spectral scanning unit, this system successfully achieves precise spectral detection in the 890 nm to 1710 nm range, with a spectral resolution better than 2 nm. Utilizing the imaging scanning unit, the system effectively performs target spectral imaging under single and multiple wavelength conditions at 1064 nm, 1310 nm, and 1520 nm. By optimizing algorithms for data processing, the system can achieve rapid and accurate spectral detection and imaging even under low-light conditions where the average photon count per pixel is less than 3. The results of this study are expected to provide strong technical support for the application of spectral imaging technology in the field of high-speed detection and imaging.

Application of network data security based on wireless sensor technology in the development of digital sharing economy

Nowadays, when the existing algorithms are fused in the feature function network feature, they do not consider the different effects of different features on the saliency detection task. In order to solve this problem, this paper proposes a saliency feature fusion network based on multi-focus mechanism to complete the saliency detection task. While recognizing the important role of the network, it must also be realized that because most network users do not understand the details of network composition and network system analysis, the security of computer network data is relatively low. Unscrupulous hackers use their network technology and software to invade unauthorized networks and hosts without authorization. Therefore, network data security is very important. The competition and monopoly of the digital sharing economy platform has always been a hot topic of increasing attention from domestic and foreign economists. The platform economy is based on an efficient data collection and transmission system, a digital platform supported by continuously developing computing power and powerful data processing algorithms, which greatly promotes the development of social productivity. The core trend of the development of China's digital economy is the ecologicalization of the digital sharing economy. Small industrial platforms rely on massive user resources collected by a large platform to quickly connect the upper and lower channels of the industry to transform and form a new industry ecosystem.

Automatic classification of smart sensor data for evaluating machine tool efficiency

The assessment of production plant efficiency is crucial for optimizing the operational performance of manufacturing systems. In traditional facilities, automated data collection is limited and information primarily relies on operators declarations, which are prone to inaccuracy. There is therefore a need for readily accessible digital alternatives. This paper introduces a cost-effective method for classifying the status of machine tools using smart sensors to monitor their primary doors with minimal integration, and a streamlined algorithm for efficient data processing. The innovative algorithm was conceived using data collected in over 3 months in a manufacturing plant comprising 50 diverse machine tools engaged in batch production for the automotive industry, and is based on non-dimensional thresholds, making it suitable for generic applications requiring classification of repetitive patterns. Also, a realistic simulator was developed to provide reliable data for algorithm accuracy evaluation. The classification performance was fully tested using synthetic data, showing very good accuracy. In addition, the performance of the algorithm was compared to basic machine learning approaches further proving the validity of the proposed method. Ultimately, the classification algorithm was employed to assess the Overall Equipment Effectiveness (OEE) of the real plant machines, which were closely aligned with the estimates provided by the enterprise management.

A Hybrid Optimization Model for Efficient Detection and Classification of Malware in the Internet of Things

The proliferation of Internet of Things (IoT) devices and their integration into critical infrastructure and business operations has rendered them susceptible to malware and cyber-attacks. Such malware presents a threat to the availability and reliability of IoT devices, and a failure to address it can have far-reaching impacts. Due to the limited resources of IoT devices, traditional rule-based detection systems are often ineffective against sophisticated attackers. This paper addressed these issues by designing a new framework that uses a machine learning (ML) algorithm for the detection of malware. Additionally, it also employed sequential detection architecture and evaluated eight malware datasets. The design framework is lightweight and effective in data processing and feature selection algorithms. Moreover, this work proposed a classification model that utilizes one support vector machine (SVM) algorithm and is individually tuned with three different optimization algorithms. The employed optimization algorithms are Nuclear Reactor Optimization (NRO), Artificial Rabbits Optimization (ARO), and Particle Swarm Optimization (PSO). These algorithms are used to explore a diverse search space and ensure robustness in optimizing the SVM for malware detection. After extensive simulations, our proposed framework achieved the desired accuracy among eleven existing ML algorithms and three proposed ensemblers (i.e., NRO_SVM, ARO_SVM, and PSO_SVM). Among all algorithms, NRO_SVM outperforms the others with an accuracy rate of 97.8%, an F1 score of 97%, and a recall of 99%, and has fewer false positives and false negatives. In addition, our model successfully identified and prevented malware-induced attacks with a high probability of recognizing new evolving threats.

Development and Impact Analysis of a Multi-Pandemic Real-Time Data Dashboard: A Comparative Analytical Approach

This research presents the development and implementation of a Multi Pandemic Real-Time Data Dashboard, designed to facilitate enhanced comparative analysis across various global pandemics. Utilizing a combination of real-time data acquisition, statistical modeling, and predictive analytics, the dashboard provides a dynamic platform for analyzing pandemic trends and outcomes. Our methodology involved integrating diverse data sources, employing mathematical models such as the SIR (Susceptible, Infected, Recovered) model and machine learning algorithms for data processing and trend prediction. Key findings indicate that the dashboard effectively identifies patterns in pandemic spread and impact, offering valuable insights for public health decision-making. The research underscores the significance of advanced data analytics in managing public health crises, highlighting the dashboard’s potential in aiding proactive pandemic response and policy formulation.

Weak signal detection technique based on Durbin-Watson test and one-bit sampling.

Correlation-based detection techniques are widely used in the weak periodic signal detection field. Traditionally, they are based on extracting the correlation of a weak signal from noise. Considering the impact of a weak signal on the randomness of background noise, this article takes the opposite approach and proposes a weak signal detection technique based on the Durbin-Watson (DW) test and one-bit sampling, detecting the weak signal due to the extent to which the randomness of noise is affected. The randomness of noise is analyzed through the DW test, which is a method for detecting the randomness of data sequences through first-order autocorrelation. One-bit sampling is adopted to reduce the complexity of the sampling circuit and data processing algorithm. The effectiveness of the DW test in the situation of one-bit sampling is demonstrated through simulation and analysis. Simulation results show that the proposed technique is capable of detecting weak sinusoidal and square-wave signals with a signal-to-noise ratio (SNR) above -30 dB, and the frequency or SNR of a weak signal can be further estimated based on mutual constraints. The measured results confirm the capability. In addition, the factors of coherent sampling, noise bandwidth, and comparator threshold that influence the performance of the proposed technique are simulated and discussed in detail.