Precise Data Processing Research Articles

Assessing the health and yield of argan trees in Morocco’s unique ecosystem: a multispectral and machine learning approach

ABSTRACT This research is focused on developing an AI model that utilizes multispectral camera data from the Souss-Massa region. The model aims to estimate the yield and identify a range of diseases in Argan trees through meticulous on-field investigations. Initially, the work is focused on understanding the resistance of Argan plants against different diseases, based on non-irrigated and irrigated Argan trees as well as planted ones. The results show that disease resistance is high in the case of non-irrigated Argan trees and low in the case of irrigated trees. In addition, we conducted a detailed study of the Argan trees to provide a comprehensive view of the plant’s health. Utilizing machine-learning techniques, the yield estimation model suggests that it is possible to achieve up to 97% accuracy in yield estimation, processing data at an impressive rate of 33 images per second. After careful consideration and analysis, we have concluded that machine counting is the most suitable technique for Argan plants. Machine counting and disease detection offer high precision, fast and efficient data processing and cost-effectiveness. Additionally, it is less labour-intensive and can be easily integrated into the existing production process.

Digital Implementation of Chaotic Systems UsingNvidia Jetson AGX Orin and Custom DAC Converter

Digital implementation of chaotic systems has numerous advantages, offering higher flexibility and computational accuracy compared to traditional analog systems. This is particularly important for engineering applications. Digital solutions are also more cost-effective. In this study, the Nvidia Jetson AGX Orin board and a 16-bit converter board are used for the digital realization of chaotic systems. The Nvidia Jetson AGX Orin provides high processing power and energy efficiency, while the 16-bit converter card ensures precise data processing. This setup allows for advanced, flexible, reliable, and economical solutions, enhancing various engineering applications, such as modeling, simulation, and control of complex systems. Digital platforms also facilitate parametric investigations and optimization processes, enabling more in-depth analysis.

Machine Learning in Geosciences: A Review of Complex Environmental Monitoring Applications

This is a systematic literature review of the application of machine learning (ML) algorithms in geosciences, with a focus on environmental monitoring applications. ML algorithms, with their ability to analyze vast quantities of data, decipher complex relationships, and predict future events, and they offer promising capabilities to implement technologies based on more precise and reliable data processing. This review considers several vulnerable and particularly at-risk themes as landfills, mining activities, the protection of coastal dunes, illegal discharges into water bodies, and the pollution and degradation of soil and water matrices in large industrial complexes. These case studies about environmental monitoring provide an opportunity to better examine the impact of human activities on the environment, with a specific focus on water and soil matrices. The recent literature underscores the increasing importance of ML in these contexts, highlighting a preference for adapted classic models: random forest (RF) (the most widely used), decision trees (DTs), support vector machines (SVMs), artificial neural networks (ANNs), convolutional neural networks (CNNs), principal component analysis (PCA), and much more. In the field of environmental management, the following methodologies offer invaluable insights that can steer strategic planning and decision-making based on more accurate image classification, prediction models, object detection and recognition, map classification, data classification, and environmental variable predictions.

DELIVERY AUTOMATION ROBOT USING ARDUINO MICROCONTROLLER

The Delivery Automation Robot project aims to develop a robust and efficient autonomous delivery robot for last-mile logistics operations. Built upon a foundation of sturdy hardware components including a four-wheel metal chassis and large wheels, the Delivery Automation Robot ensures stability and durability while navigating diverse terrains. Integrated with advanced control systems such as the L298n motor driver shield, NODEMCU, and Arduino Uno, the robot offers precise movement control and data processing capabilities. Equipped with essential sensors like the magnetometer HMC5883 and NEO 6m GPS module, the MDAR achieves accurate localization and navigation, crucial for optimizing delivery routes. Additionally, wireless communication facilitated by the HC06 Bluetooth module enables remote monitoring and control. Overall, the Delivery Automation Robot represents an innovative solution poised to revolutionize last-mile delivery processes, enhancing efficiency and reliability in logistics operations. Keywords: four-wheel chassis, L298n motor driver, NODEMCU, Arduino Uno, magnetometer HMC5883, NEO 6m GPS module, HC06 Bluetooth module.

Integration and Innovation Path Analysis of Enterprise Marketing Data Management Based on Deep Learning

INTRODUCTION: To explore the integration and innovation path of enterprise marketing data management based on deep learning to adapt to today's competitive business environment. With the continuous development of information technology, enterprises are faced with a large amount of marketing data, and how to efficiently manage and integrate these data has become an essential issue for enterprises to improve their market competitiveness. Deep learning, as a necessary technical means of artificial intelligence, provides enterprises with more intelligent and precise data processing tools.OBJECTIVES: The primary purpose of the study is to solve the problems of marketing data management in traditional enterprises and to achieve better integration and management of data through deep learning technology. Specifically, the goal is to explore the potential of deep learning in improving data processing efficiency and accurately analyzing user behavior and trends. By achieving these goals, organizations can better understand market needs, develop more effective marketing strategies, and stand out in a competitive marketplace.METHODS: This study adopts a comprehensive approach, including a literature review, case study, and empirical analysis of deep learning algorithms. First, the main issues of current enterprise marketing data management and the latest progress in deep learning were understood through an in-depth study of the literature in related fields. Second, several enterprise cases were selected to gain a deeper understanding of the challenges and needs of enterprises in marketing data management through field research and data collection. Finally, a series of deep learning algorithms were designed and implemented to validate their effectiveness in real-world applications and analyze their impact on data integration and innovation paths.RESULTS: The results of the study show that deep learning has significant advantages in enterprise marketing data management. By using deep learning algorithms, enterprises are able to handle large-scale marketing data more efficiently and achieve intelligent data integration and accurate analysis. This not only improves the efficiency of data processing but also provides enterprises with deeper market insights that help develop more targeted marketing strategies.CONCLUSION: The results of the study are of guiding significance for enterprises to realize data-driven marketing decision-making, which provides strong support for enterprises to maintain their competitive advantages in the highly competitive market. Future research can further explore the application of deep learning in different industries and scenarios, as well as how to optimize deep learning algorithms further to meet the changing needs of enterprises.

AN INTERACTION MODEL OF RELAY PROTECTION MEASURING TRANSDUCERS WITH COMPUTING SYSTEMS VIA IIOT TECHNOLOGY

"The object of this research is the interaction model between measuring transducers and computing systems, particularly focusing on its application within the framework of Industrial Internet of Things (IIoT) and Cloud technologies. The imperative driving this study is the increasing demand for precise and efficient data acquisition, protection and processing in various industrial sectors. The research aims to address this need by elucidating an interaction model that optimizes measuring computing systems through IIoT and Cloud technologies. As a result of our research, a comprehensive interaction model that encompasses the entire data flow process was established. This model integrates data acquisition, analytics, real-time communication protocols, and secure data transmission methodologies. It offers a structured framework for acquiring, transmitting, and analyzing data with high accuracy and efficiency. The significance of our results lies in their capacity to resolve critical challenges in data acquisition and processing of the electrical power data, as well as monitoring and protection against electrical faults. These results hold practical value under diverse industrial conditions, including manufacturing, energy production and energy distribution. Keywords: relay protection, open architecture, encryption, data processing, data visualization, internet of things "

High-resolution structural imaging of volcanoes using improved muon tracking

SUMMARY Muography is increasingly used to image the density distribution of volcanic edifices, complementing traditional geophysical tomographies. Here, we present a new muon data processing algorithm, and apply it to a new generation of scintillator-based muon detectors, to image the relative density distribution in La Soufrière de Guadeloupe volcano (Lesser Antilles, France). Our processing method iteratively searches for the best fit of each muon trajectory, accounting for all the hits registered by the detector related to the particular muon event. We test the performance of our algorithm numerically, simulating the interaction of muons with our detector and accounting for its exact assemblage including the scintillator bars and lead shielding. We find that our new data processing mitigates the impact of spurious signals coming from secondary particles, and improves the amount of successfully reconstructed events. The resulting 2-D muon images at La Soufrière have higher angular resolution than previous ones and capture the heterogeneous structure of the dome. They show density anomalies located on the summit southern region, which includes a boiling acid lake and degassing fractures, where the rock is the most porous and fumarolic activity is ongoing. This work shows the importance of combining numerical simulations of muon propagation with precise raw data processing to obtain high-quality results. It is also a first step towards fully assessing the noise contamination sources when performing muon tomography, and their correction, prior to geophysical interpretations.

NEW FUNCTIONALITIES OF THE WEIGH-IN-MOTION SYSTEM: iWIM SOLUTION

This paper presents new functionalities for systems weighing vehicles in motion, which result from the integration of various measurement technologies and the use of a precise registration and data processing system. The utilized registration track provides accurate measurements of basic vehicle parameters such as axle load and vehicle speed and makes it possible to determine the location of the vehicle passing the weighing station, calculate the width of the tire tread, and detect twin (double) wheels. A key functionality is the assessment of the reliability of the measurement, taking into account, among other factors, vehicle movement dynamics and the ambient conditions during the measurements. For this purpose, additional measurements of road surface temperature and wind speed and direction were introduced. The technological solutions used and the proprietary data processing algorithms are described. Tests carried out to verify the effectiveness of the proposed algorithms and to assess the significance of the influence of the isolated factors permitted confirmation of the validity of the proposed solutions.

A novel single-particle multiple-signal sensor array combined with multidimensional data mining for the detection of tricarboxylic acid cycle metabolites and discrimination of cells.

Tricarboxylic acid (TCA) metabolites in cancer cells show amarked difference from those in normal cells. Herein, we report a single-particle multiple-signal lanthanide/europium-based metal-organic framework (Tb/Eu MOF) sensor array for the detection of TCA metabolites and discrimination of cancer cells. In the presence of TCA metabolite, 6 characteristic peaks of Tb/Eu MOF showed dramatic changes due to host-guest interactions, allowing sensor array-based qualitative and quantitative detection to be performed. In the qualitative detection ability test, 18 TCA metabolites at 4 concentrations (50μM, 100μM, 200μM, 300μM) were accurately discriminated by the sensor array via linear discriminant analysis (LDA). Significantly, these 4 concentrations include the clinical detection criteria for most TCA metabolites. In the quantitative detection ability test, a good linear relationship between Euclidean distances and the concentrations of L-valine (Val) could be obtained in the range of 50 to 500μM (R2 = 0.9755). On this basis, theprovided method was successfully applied for theclassification of 2 normal cells and 5 cancer cells via principal components analysis (PCA), LDA and aradial basis function neural network (RBFN). What's more, by verifying the weight coefficient of each point, detection and discrimination results are proved as a trustworthy balanced evaluation of multiple factors. Depending on precise data processing, theexperimental operation was simplified on the premise of ensuring accuracy, soour method is a meaningful exploration for array design.

Two-colour dissipative solitons and breathers in microresonator second-harmonic generation

Frequency conversion of dissipative solitons associated with the generation of broadband optical frequency combs having a tooth spacing of hundreds of giga-hertz is a topical challenge holding the key to practical applications in precision spectroscopy and data processing. The work in this direction is underpinned by fundamental problems in nonlinear and quantum optics. Here, we present the dissipative two-colour bright-bright and dark-dark solitons in a quasi-phase-matched microresonator pumped for the second-harmonic generation in the near-infrared spectral range. We also found the breather states associated with the pulse front motion and collisions. The soliton regime is found to be typical in slightly phase-mismatched resonators, while the phase-matched ones reveal broader but incoherent spectra and higher-order harmonic generation. Soliton and breather effects reported here exist for the negative tilt of the resonance line, which is possible only via the dominant contribution of second-order nonlinearity.

Impact of ambiguity resolution on phase center offsets and hardware delay estimation for BDS-3 inter-satellite links

The Chinese BeiDou navigation satellite system (BDS) has already completed its three phases and developed into a global navigation satellite system for open positioning, navigation, and timing services. The BDS-3 satellites feature the inter-satellite link (ISL). The ISL observation-related error model and ambiguity resolution for L-band observation are the crucial factors in precise data processing. In this study, we present for the first time the impact of ambiguity resolution on phase center offsets (PCOs) and hardware delay estimation of BDS-3 inter-satellite links. Two weeks’ L-band observations from 99 globally distributed ground stations and Ka-band ISL observations are collected for experimental validation and analysis. First, network solutions with and without ISL observations are conducted to investigate the role of ISL observation in ambiguity resolution. Afterward, ISL observation-related errors, mainly PCOs and hardware delays, are estimated by processing L-band with ISL observations with and without ambiguity resolution to analyze the impact of ambiguity resolution on these two factors. Finally, orbit accuracy in the network solution is assessed to further validate the effectiveness of the estimated PCOs and hardware delays in our experiment. The result indicates that introducing the ISL can slightly improve the fixing rate compared to only L-band observations from 83.7% to 84.3%. Furthermore, ambiguity resolution has a positive influence on the stability of estimated PCOs and hardware delays in turn, although the root mean square (RMS) values basically remain unchanged. The standard deviation (STD) of the x-offset is reduced from 0.021 m to 0.012 m, a significant improvement of about 43%, and 0.022 m–0.016 m, with an improvement of about 27%, for the y-offset. There is a slight improvement of about 8% for z-offset. Similarly, around 10% improvement in the STD for hardware delays can be achieved while the RMS values almost stay the same except for C40. Orbit determination from network solutions shows high accuracy compared to the public products for the satellite with good geometry configuration, which further validates our estimates for ISL PCOs and hardware delays.

Prediction of Gender-Biased Perceptions of Learners and Teachers Using Machine Learning

Computers have enabled diverse and precise data processing and analysis for decades. Researchers of humanities and social sciences are increasingly adopting computational tools such as artificial intelligence (AI) and machine learning (ML) to analyse human behaviour in society by identifying patterns within data. In this regard, this paper presents the modelling of teachers and students’ perceptions regarding gender bias in text books through AI. The data was collected from 470 respondents through a questionnaire using five different themes. The data was analysed with support vector machines (SVM), decision trees (DT), random forest (RF) and artificial neural networks (ANN). The experimental results show that the prediction of perceptions regarding gender varies according to the theme and leads to the different performances of the AI techniques. However, it is observed that when data from all the themes are combined, the best results are obtained. The experimental results show that ANN, on average, demonstrates the best performance by achieving an accuracy of 87.2%, followed by RF and SVM, which demonstrate an accuracy of 84% and 80%, respectively. This paper is significant in modelling human behaviour in society through AI, which is a significant contribution to the field.

Risk Area Alert Through Heterogeneous Mobile Networks: A New Approach to Fight COVID-19 and Beyond

COVID-19 has now been sweeping the whole world, and fundamentally affecting our daily life. An effective mechanism to further fight against COVID-19 and prevent the spread of this pandemic is to alert people when they are in the vicinity of areas with a high infection risk, yielding them to adjust their routes and consequently, leave these areas. Inspired by the fact that mobile communication networks are capable of precise positioning, data processing and information broadcasting, as well as are available for almost every person, in this article, we propose a mobile network assisted Risk arEa ALerting scheme, named REAL, which exploits heterogeneous mobile networks to alert users who are in/near to the areas with high risks of COVID-19 infection. Specifically, in REAL scheme, all base stations (BSs) periodically estimate their serving users' locations, which are then analyzed by macro BSs (MBSs) to identify risk areas. Next, each MBS transmits the information about risk areas to small BSs (SBSs), which in their turn adjust the beamforming direction to cover these areas and send alerts to users located therein. Simulation results validate the effectiveness of the proposed REAL scheme. In addition, some key challenges associated with implementing REAL are discussed at the end.

Практический метод реализации нечетких запросов для реляционных баз данных

Information systems that use databases are flexible to the extent that they allow users to request the data they need. SQL is limited to precise data processing and does not directly express fuzzy concepts of natural language. Therefore, giving SQL some flexibility can help users improve interaction with information systems without requiring them to learn a completely new language. The task of reducing the labor intensity of the process of integrating the mechanisms of fuzzy requests to existing information systems is urgent. The article shows the limitations of clear queries, considers various forms of fuzzy queries. Known approaches to implementing fuzzy queries to relational databases were analyzed. Provides a detailed analysis of fuzzy queries, as well as their conversion to standard SQL queries using MySQL. The proposed method for implementing the ability to work with fuzzy queries is based on expanding a clear database with stored functions, without changing the structure and composition of its tables. The advantages of the method are: increased readability and understanding of SQL queries; ease of integration with existing databases of information systems; flexible adjustment of the linguistic variable membership function in accordance with the needs of the database user. The application of the method is shown by the example of adapting a MySQL database. The proposed adaptation method can be widely used to implement fuzzy queries to databases of various DBMS that support work with stored functions.

Study of LEAP® 5000 Deadtime and Precision via Silicon Pre-Sharpened-Microtip™ Standard Specimens.

Atom probe tomography (APT) is a technique that has expanded significantly in terms of adoption, dataset size, and quality during the past 15 years. The sophistication used to ensure ultimate analysis precision has not kept pace. The earliest APT datasets were small enough that deadtime and background considerations for processing mass spectrum peaks were secondary. Today, datasets can reach beyond a billion atoms so that high precision data processing procedures and corrections need to be considered to attain reliable accuracy at the parts-per-million level. This paper considers options for mass spectrum ranging, deadtime corrections, and error propagation as applied to an extrinsic-silicon standard specimen to attain agreement for silicon isotopic fraction measurements across multiple instruments, instrument types, and acquisition conditions. Precision consistent with those predicted by counting statistics is attained showing agreement in silicon isotope fraction measurements across multiple instruments, instrument platforms, and analysis conditions.

Google earth engine based computational system for the earth and environment monitoring applications during the COVID-19 pandemic using thresholding technique on SAR datasets

Observing the earth and environmental conditions during the COVID-19 pandemic lockdown along with travel restrictions headed to worse circumstance. These scenarios amplified the hurdles of flood management. In order to resolves these issues, an efficient and resilient geospatial framework with unconventional systems is also required for the generation of instantaneous results. Hence to avoid these deficiencies, the google earth engine based computational system integrated with analytical tools for large-scale data handling is introduced for the earth and environmental monitoring applications. The present study proposes a working model for geospatial data processing to understand socio-demographic implications with a web-based analytical interface. The research introduces a histogram-based thresholding approach for real-time surface water mapping along with precise data processing and analysis for automated monitoring. The study integrates geospatial datasets to a enhanced data processing methods in a web-based platform to deliver the required results for extensive planning and decision making. Furthermore, a similar type of work can be undertaken for other disaster management applications.

Noises in Double-Differenced GNSS Observations

Precise data processing from the Global Navigation Satellite Systems (GNSS) reference station network is mainly based on a combination of double-differenced carrier phase and code observations. This approach allows most of the measurement errors to be removed or reduced and is characterized as the most accurate method. However, creating observation differences between two receivers and two satellites increases the measurement noise of the observations by a factor of 2. As a result, it increases the impact of the incorrect definition of the noise characteristic on the results of the estimation of the unknowns in the positioning model. This is especially important in Multi-GNSS solutions, which integrate measurements from different systems, for which the stochastic parameters of observation may differ significantly. In this paper, the authors prepared a complex analysis of the noise type in double-differenced GNSS (GPS, GLONASS and Galileo) observations, both carrier phase and code ones, with a 1 s sampling interval. The Autocorrelation Function (ACF) method, the Lomb–Scargle (L-S) periodogram method, and the Allan variance (AVAR) method were used. The results that were obtained for the weekly set of measurement data showed that, depending on the system and type of observation, the noise level and its type are significantly different. Among the code measurements, the lowest noise levels were obtained for the GPS C5Q and Galileo C7Q/C8Q observations, with the standard deviations not exceeding ±10 cm, while the noisiest observations were for the GLONASS C1C and C2C signals, which had standard deviations of about ±90 cm and ±45 cm, respectively. For the carrier phase observations, each signal type was characterized by very similar noise levels of ±1.5–3.5 mm. The ACF analysis showed that 1 Hz double-differenced GNSS data can only be treated as being not correlated to time for carrier phase observations; for code observations, an irrelevant autocorrelation may be considered for measurement intervals greater than 20 s. Depending on the GNSS signals, the spectral index k varies in a range from −1.3 to −0.2 for code data and k = 0.0 in the case of phase data. Using the modified Allan deviation (MDEV) allows for specific noise types for each signal and GNSS system to be determined. All of the code observations were characterized by either flicker PM or white PM. In the case of the phase observations, they were all uniquely characterized by white PM (GPS and Galileo or by white PM and flicker PM (GLONASS).

Processing of Multicrystal Diffraction Patterns in Macromolecular Crystallography Using Serial Crystallography Programs

Cryocrystallography is a widely used method for determining the crystal structure of macromolecules. This technique uses a cryoenvironment, which significantly reduces the radiation damage to the crystals and has the advantage of requiring only one crystal for structural determination. In standard cryocrystallography, a single crystal is used for collecting diffraction data, which include single-crystal diffraction patterns. However, the X-ray data recorded often may contain diffraction patterns from several crystals. The indexing of multicrystal diffraction patterns in cryocrystallography requires more precise data processing techniques and is therefore time consuming. Here, an approach for processing multicrystal diffraction data using a serial crystallography program is introduced that allows for the integration of multicrystal diffraction patterns from a single image. Multicrystal diffraction data were collected from lysozyme crystals and processed using the serial crystallography program CrystFEL. From 360 images containing multicrystal diffraction patterns, 1138 and 691 crystal lattices could be obtained using the XGANDALF and MOSFLM indexing algorithms, respectively. Using this indexed multi-lattice information, the crystal structure of the lysozyme could be determined successfully at a resolution of 1.9 Å. Therefore, the proposed approach, which is based on serial crystallography, is suitable for processing multicrystal diffraction data in cryocrystallography.

Impact of higher-order ionospheric delay on the reliability of RTK ambiguity estimation

The ionospheric delay is one of the major error sources in GNSS precise data processing. In most scenarios, only the first-order ionosphere delay was fully considered, which contributes to about 99% of the total ionosphere delay. Recent research reveals that the higher-order ionospheric (HOI) delay can also introduce up to 2 cm biases for the GPS L1 observations. This bias is generally not considered in the underlying function model and consequently, it introduces unintentional biases, which may degrade the reliability of the ambiguity estimation in long-baseline RTK. This study examined the impact of the HOI on the observations and the ambiguity estimation procedure. We considered the HOI bias in the underlying model to evaluate the unintentional HOI bias on the success rate (SR) of ambiguity estimation. The numerical results showed that the double-differenced HOI effect reaches about 7 mm in the ionospheric active period, and can affect the ambiguity estimation success rate by up to 30% in the GNSS data processing and the HOI impact is more significant when a new satellite arose. The presence of the HOI biases may change about 0.3% ambiguity fixing results. In addition, the HOI bias can also introduce several millimeters biases in positioning solutions.

Evaluation of BDS-2 and BDS-3 Satellite Atomic Clock Products and Their Effects on Positioning

The BeiDou Navigation Satellite System (BDS) has completed third phase construction and currently provides global services, with a mixed constellation of BDS-2 and BDS-3. The newly launched BDS-3 satellites are equipped with rubidium and passive hydrogen maser (PHM) atomic clocks. The performance of atomic clocks is one of the cores of satellite navigation system, which will affect the performance of positioning, navigation and timing (PNT). In this paper, we systematically analyze the characteristics of BDS-2 and BDS-3 atomic clocks, based on more than one year of precise satellite clock products and broadcast ephemeris. Firstly, the results of overlapping Allan variations demonstrate that BDS-3 Rb and PHM clocks improve better in stability than BDS-2 Rb clock and are comparable to GPS IIF Rb and Galileo PHM clocks. Accordingly, the STDs of BDS-3 broadcast satellite clock are better than GPS and BDS-2, which are at the same level with that of Galileo. Secondly, the inter-system bias (ISB) between BDS-2 and BDS-3 is analyzed by satellite clock datum comparison and precise point positioning (PPP). Surprisingly, the discrepancy between BDS-2 and BDS-3 satellite clock datum has a great difference between products that could reach up to about 10 ns for WHU satellite clock products and broadcast ephemeris. Moreover, the ISBs between BDS-2 and BDS-3 satellite clocks are quite stable over one-year periods. Thirdly, due to the improved stability of BDS-3 atomic clock, the 68% positioning accuracy is better than 0.65 m at 10 min for BDS-3 PPP, based on broadcast ephemeris. Besides, the non-negligible bias between BDS-2 and BDS-3 will greatly affect the BDS precise data processing. The accuracy of positioning is greatly improved when considering the ISB.