Cloud Data Processing Research Articles

Tomato Stem and Leaf Segmentation and Phenotype Parameter Extraction Based on Improved Red Billed Blue Magpie Optimization Algorithm

In response to the structural changes of tomato seedlings, traditional image techniques are difficult to accurately quantify key morphological parameters, such as leaf area, internode length, and mutual occlusion between organs. Therefore, this paper proposes a tomato point cloud stem and leaf segmentation framework based on Elite Strategy-based Improved Red-billed Blue Magpie Optimization (ES-RBMO) Algorithm. The framework uses a four-layer Convolutional Neural Network (CNN) for stem and leaf segmentation by incorporating an improved swarm intelligence algorithm with an accuracy of 0.965. Four key phenotypic parameters of the plant were extracted. The phenotypic parameters of plant height, stem thickness, leaf area and leaf inclination were analyzed by comparing the values extracted by manual measurements with the values extracted by the 3D point cloud technique. The results showed that the coefficients of determination (R2) for these parameters were 0.932, 0.741, 0.938 and 0.935, respectively, indicating high correlation. The root mean square error (RMSE) was 0.511, 0.135, 0.989 and 3.628, reflecting the level of error between the measured and extracted values. The absolute percentage errors (APE) were 1.970, 4.299, 4.365 and 5.531, which further quantified the measurement accuracy. In this study, an efficient and adaptive intelligent optimization framework was constructed, which is capable of optimizing data processing strategies to achieve efficient and accurate processing of tomato point cloud data. This study provides a new technical tool for plant phenotyping and helps to improve the intelligent management in agricultural production.

Features of ensuring cyber resilience of national information resources, communication and technological systems that ensure the functioning of government authorities under the conditions of the martial law

Risks and threats of unauthorized access to state information resources, communication and technological systems are identified. The procedure and conditions for the administration of state information resources hosted on cloud resources and data processing centers located outside of Ukraine are detailed. The set of regulatory acts approved by the Government of Ukraine with the aim of strengthening the cyber resilience of national information resources was analyzed, the procedure for responding to cyber incidents and cyber attacks, the procedure for searching and identifying potential vulnerabilities of information (automated), electronic communication, information and communication systems, and electronic communication networks was regulated. The regulatory acts adopted during 2023 by the Administration of the State Service of Special Communications and Information Protection with the aim of strengthening the cyber resilience of state information resources, automated systems for managing technological processes, administration of the state register of critical information infrastructure objects are summarized. The content of methodological recommendations devoted to the issue of responding by cyber security entities to various types of events in cyberspace, increasing the level of cyber protection of electronic document management systems, and strengthening the protection of critical infrastructure objects has been revealed. It is concluded that the process of managing vulnerabilities is aimed at their constant detection, which can be corrected by implementing patches and configuring security parameters. Further directions of improving vulnerability management processes as an important component of the system of ensuring cyber resilience of national information resources, communication and technological systems that ensure the functioning of state authorities are outlined.

Smart Fuel Monitoring for Agricultural Fleet Operations Using Tilt Sensors and Cloud Data Processing

This paper presents a novel Internet of Things (IoT)-based fuel monitoring and tracking system designed to address limitations of traditional fuel gauges and enhance fleet management efficiency. The system leverages an accelerometer and Arduino microcontroller to measure fuel level by detecting tilt angles within the tank. This data, along with real-time location from GPS, is transmitted to a cloud platform for processing and visualization. A mobile application, "Tracer," empowers users with insights into fuel level, location on a map, and distance traveled. Graphical fuel consumption patterns further aid in optimization strategies. This solution offers real-time data for improved fuel efficiency, proactive route planning, and overall fleet management. The proposed system is successfully implemented IOT based technology. This sys in Ada fruit IO cloud issued to provide information about vehicle location, fuel level in tank and distance travelled by the vehicle.

Development and Validation of a Point Cloud Data Processing Algorithm for Obstacle Recognition in Double Hull Block

<i>Shipyards have recently been experiencing severe labor shortages, prompting increased adoption of production automation systems to mitigate this issue. This paper introduces a point cloud data acquisition and processing system designed to support automation operations within double-hull block environments. The acquisition system utilizes LiDAR sensors and is built as a portable device capable of conducting 360-degree scans inside double-hull blocks. The processing system integrates the RANSAC algorithm for plane recognition and a voxelization algorithm for object detection, enabling accurate identification of obstacles within the double-hull block. To validate the system, a full-scale test bench was constructed to replicate actual working conditions. Experimental results indicated that the system could detect the positions of various obstacles within the test bench with an accuracy of up to 100 mm, which is sufficient for the implementation of automation systems. The findings from this research are expected to facilitate the adoption of production automation in shipyards, enhancing productivity and addressing labor shortages in the industry.</i>

Massive Point Cloud Processing for Efficient Construction Quality Inspection and Control.

The construction of large-scale civil infrastructures requires massive spatiotemporal data to support the management and control of scheduling, quality control, and safety monitoring. Existing artificial-intelligence-based data processing algorithms rely heavily on experienced engineers to adjust the parameters of data processing, which is inefficient and time-consuming when dealing with huge datasets. Limited studies have compared the performance of different algorithms on a unified dataset. This study proposes a framework and evaluation system for comparing different data processing policies for processing huge spatiotemporal data in construction quality control. The proposed method compares the combination of multiple types of algorithms involved in the processing of massive point cloud data. The performance of data processing strategies is evaluated through this framework, and the optimal point cloud processing strategies are explored based on registration accuracy and data fidelity. Results show that a reasonable choice of combinations of point cloud sampling, filtering, and registration algorithms can significantly improve the efficiency of point cloud data processing and satisfy engineering demands for data accuracy and completeness. The proposed method can be applied to the civil engineering problem of processing a large amount of point cloud data and selecting the optimal processing method.

Research on New Method for Safety Testing of Steel Structures—Combining 3D Laser Scanning Technology with FEA

This paper introduces a novel approach to assessing structural safety, specifically aimed at evaluating the safety of existing structures. Firstly, a point cloud model of the existing commercial complex was captured utilizing three-dimensional (3D) laser scanning technology. Subsequently, an intelligent method for identifying holes within the point cloud model was proposed, built upon a YOLO v5-based framework, to ascertain the dimensions and locations of holes within the commercial complex. Secondly, Poisson surface reconstruction, coupled with partially self-developed algorithms, was employed to reconstruct the surface of the structure, facilitating the three-dimensional geometric reconstruction of the commercial complex. Lastly, a finite element model of the framed structure with holes was established using the reconstructed 3D model, and a safety analysis was conducted. The research findings reveal that the YOLO v5-based intelligent hole identification method significantly enhances the level of intelligence in point cloud data processing, reducing manual intervention time and boosting operational efficiency. Furthermore, through Poisson surface reconstruction and the self-developed algorithms, we have successfully achieved automated surface reconstruction, where the resulting geometric model accurately reflects the dimensional information of the commercial complex. Additionally, the maximum uniformly distributed surface load that the floor slabs within the framed structure with holes can withstand should not exceed 17.7 kN/m2, and its vertical deformation resistance stiffness is approximately 71.6% of that of a frame without holes.

Optimal attempt scheduling and aborting in heterogenous system performing asynchronous multi-attempt mission

Multi-attempt mission aborting systems have recently received significant attention from the reliability community. Existing models mostly assume parallel or sequential execution of multiple attempts, incurring great cost or low mission success probability (MSP), respectively. This paper advances the state of the art by considering a new model, where system components may be activated with certain delay allowing to activate next one before the previous component leaves the operation, balancing the expected cost of lost components (ECC) and MSP. Each component may abort its attempt according to an individual aborting policy defined by two parameters (the number of survived shocks and an operation time threshold) or upon receiving a common abort command. Because components may have different shock resistances and performance rates, their activation order can affect both MSP and ECC. Thus, we formulate and solve the optimal attempt scheduling and aborting policy (SAP) problem, which determines the vector of component activation times and the individual attempt aborting policy for each component to minimize the expected mission losses (EML). The EML, a function of MSP and ECC, is evaluated using a new numerical procedure. A detailed case study of a cloud data processing system is provided to demonstrate the proposed model.

FLApy: A Python package for evaluating the 3D light availability heterogeneity within forest communities

Abstract Light availability (LAv) dictates a variety of biological and ecological processes across a range of spatiotemporal scales. Quantifying the spatial pattern of LAv in three‐dimensional (3D) space can promote the understanding of microclimates that are critical to fine‐scale species distribution. However, there is still a lack of tools that are robust to evaluate spatiotemporal heterogeneity of LAv in forests. Here, we propose the Forest Light Analyzer python package (FLApy), an open‐source computational tool designed for the analysis of intra‐forest LAv variation across multiple spatial scales. FLApy is freely invoked by Python, facilitating the processing of LiDAR point cloud data into a 3D data container constructed by voxels, as well as traversal calculations related to the LAv regime by high performance synthetic hemispherical algorithm. Furthermore, FLApy incorporates 37 indicators, enabling users to expediently export and visualize LAv patterns and the evaluation of heterogeneity of LAv at two scales (voxel scale and 3D‐cluster scale) for a range of fine‐scale ecological study purposes. To validate the efficacy of the FLApy, we employed a simulated point cloud dataset that simulates forests (varying in canopy closure). Furthermore, to evaluate real world forest, we executed the standard workflow of FLApy utilizing drone‐derived data from three subtropical evergreen broad‐leaved forest dynamics plots within the Ailao Mountain Reserve. Our findings underscore that a series of indices derived from FLApy provide a robust characterization of light availability heterogeneity within diverse forest settings. Additionally, when juxtaposed with conventional monitoring techniques, the metrics offered by FLApy demonstrated better generality in our field assessments. FLApy offers ecologists a solution for rapid quantification of understory light 3D‐regimes across multiple scales, addressing the disparity between traditional manual approaches and the precision required for contemporary ecological studies. Moreover, FLApy provides robust support for the establishment and expansion of heterogeneity indices based on 3D micro‐environments, enhancing our understanding of the largely uncharted 3D structural patterns. Anticipated outcomes suggest that FLApy will enhance our knowledge concerning the intra‐forest climatic conditions into a 3D context, proving pivotal in the delineation of microhabitats and the development of detailed 3D‐scale species distribution models.

Study on Morphometrical Urban Aerodynamic Roughness Multi-Scale Exploration Using LiDAR Remote Sensing

This study proposes the use of light detection and ranging (LiDAR) remote sensing (RS) to support morphometric research for estimating the aerodynamic roughness length (z0 ) of building placement on various scales. A LiDAR three-dimensional point cloud (3DPC) data processing graphical user interface (GUI) was developed to explore the z0 and related urban canopy parameters (UCPs) in the Incheon metropolitan area in South Korea. The results show that multi-scale urban aerodynamic roughness exploration is viable and can address differences in urban building data at various spatial resolutions. Although validating morphological multi-scale UCPs using dense tall towers is challenging, emerging low-cost and efficient methods can serve as substitutes. However, further efforts are required to link the measured z0 to building form regulations, such as floor area ratio, and expand RS research to obtain more quantitative and qualitative knowledge.

Fusion of Multiple Basic Element Features for Airborne LiDAR in-house Surveys

When using airborne LiDAR point clouds for city modelling and road extraction, point cloud classification is a crucial step. There are numerous ways for classifying point clouds, but there are still issues like redundant multi-dimensional feature vector data and poor point cloud classification in intricate situations. A point cloud classification method built on the fusing of multikernel feature vectors is suggested as a solution to these issues. The technique employs random forest to classify point cloud data by merging colour information, and it extracts feature vectors based on point primitives and object primitives, respectively. In this study, a densely populated area was chosen as the study area. Light airborne LIDAR mounted on a delta wing was used to collect point cloud data at a low altitude (170 m) over a dense cross-course. The point cloud data were then combined, corrected, and enhanced with texture data, and the houses were vectorized on the point cloud. The accuracy of the results was then assessed. With a median inaccuracy of 4.8 cm and a point cloud data collection rate of 83.3%, using airborne LIDAR to measure house corners can significantly lighten the labour associated with external house corner measurements.This test extracts the texture information of point cloud data through the efficient processing of high-density point cloud data, providing a reference for the application of texture information of airborne LIDAR data and a clear understanding of its accuracy.

Utilizing Python for Scalable Data Processing in Cloud Environments

In the age of big data and cloud computing, enterprises need to effectively analyze enormous datasets to get meaningful insights and stay ahead. Python, a popular programming language, is a strong tool for cloud-scale data processing. This research study examines Python's integration with cloud platforms and its effects on performance and efficiency in scalable data processing. The study introduces scalable data processing and cloud computing. It then discusses Python's ecosystem, including Dask, Apache Spark with PySpark, TensorFlow, and PyTorch for data processing and machine learning. The study also examines Python's interoperability with cloud services like AWS, Google Cloud Platform, and Microsoft Azure in data input, transformation, and analysis. Many case studies and real-world applications demonstrate how Python has been used in banking, healthcare, and e-commerce. Python is useful for managing massive amounts of data, streamlining processing processes, and scaling cloud applications, as shown in the case studies. The report also analyzes Python-based cloud systems' performance indicators and cost consequences, revealing best practices and possible issues. The article explores Python's involvement in cloud computing trends and technology. Serverless architectures, Docker and Kubernetes, and Python interaction with cloud-native tools and services are examples. These patterns show how data processing is changing and how Python is improving to meet current data needs. This study concludes that Python is a reliable and scalable cloud data processing option. The language's strengths, alignment with cloud technologies, and practical applications in many areas are covered in detail. The results indicate that Python's versatility and cloud scalability provide a robust foundation for handling and analyzing massive datasets, enabling better decision-making and innovation across fields.

Health of Things Melanoma Detection System—detection and segmentation of melanoma in dermoscopic images applied to edge computing using deep learning and fine-tuning models

According to the World Health Organization (WHO), melanoma is a type of cancer that affects people globally in different parts of the human body, leading to deaths of thousands of people every year worldwide. Intelligent diagnostic tools through automatic detection in medical images are extremely effective in aiding medical diagnosis. Computer-aided diagnosis (CAD) systems are of utmost importance for image-based pre-diagnosis, and the use of artificial intelligence–based tools for monitoring, detection, and segmentation of the pathological region are increasingly used in integrated smart solutions within smart city systems through cloud data processing with the use of edge computing. This study proposes a new approach capable of integrating into computational monitoring and medical diagnostic assistance systems called Health of Things Melanoma Detection System (HTMDS). The method presents a deep learning–based approach using the YOLOv8 network for melanoma detection in dermatoscopic images. The study proposes a workflow through communication between the mobile device, which extracts captured images from the dermatoscopic device and uploads them to the cloud API, and a new approach using deep learning and different fine-tuning models for melanoma detection and segmentation of the region of interest, along with the cloud communication structure and comparison with methods found in the state of the art, addressing local processing. The new approach achieved satisfactory results with over 98% accuracy for detection and over 99% accuracy for skin cancer segmentation, surpassing various state-of-the-art works in different methods, such as manual, semi-automatic, and automatic approaches. The new approach demonstrates effective results in the performance of different intelligent automatic models with real-time processing, which can be used in affiliated institutions or offices in smart cities for population use and medical diagnosis purposes.

Designing a low-cost wireless sensor network for particulate matter monitoring: Implementation, calibration, and field-test

Poor air quality can provoke severe impacts on health, necessitating environmental monitoring of atmospheric particulate matter (PM) to assess potential threats to human well-being. However, traditional continuous air quality monitoring systems are often costly and time-consuming in data treatment. Lately, there is a growing trend towards the use of low-cost wireless PM sensors, providing more detailed information than standard systems. This paper presents a system designed to measure air quality, specifically, a wireless sensor network composed of a distributed sensor network linked to a cloud system. The proposed system can efficiently measure air quality as it is cost-effective, small-sized, and consumes little power. Sensor nodes based on low-power long range (LoRa) motes transmit field measurement data to the cloud via a gateway, and a cloud computing system is implemented to store, monitor, process, and visualise the data. Advanced techniques were included in our cloud for data processing and analysis to optimise the detection of PM. Laboratory and field tests in the historic Riotinto mine validate the system's viability, offering real-time air quality information for nearby populations. Once calibrated, sensors demonstrate high accuracy, presenting mean error of −0.3% and low deviation (R2 = 0.96) when compared to regulatory systems for both low (<10 μgPM10/m3) and hazardous concentrations (300 μgPM10/m3), which makes them perfect as early warning systems for atmospheric pollution in mining.

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

Internet of Things (IoT) devices often operate with limited resources while interacting with users and their environment, generating a wealth of data. Machine learning models interpret such sensor data, enabling accurate predictions and informed decisions. However, the sheer volume of data from billions of devices can overwhelm networks, making traditional cloud data processing inefficient for IoT applications. This paper presents a comprehensive survey of recent advances in models, architectures, hardware, and design requirements for deploying machine learning on low-resource devices at the edge and in cloud networks. Prominent IoT devices tailored to integrate edge intelligence include Raspberry Pi, NVIDIA’s Jetson, Arduino Nano 33 BLE Sense, STM32 Microcontrollers, SparkFun Edge, Google Coral Dev Board, and Beaglebone AI. These devices are boosted with custom AI frameworks, such as TensorFlow Lite, OpenEI, Core ML, Caffe2, and MXNet, to empower ML and DL tasks (e.g., object detection and gesture recognition). Both traditional machine learning (e.g., random forest, logistic regression) and deep learning methods (e.g., ResNet-50, YOLOv4, LSTM) are deployed on devices, distributed edge, and distributed cloud computing. Moreover, we analyzed 1000 recent publications on “ML in IoT” from IEEE Xplore using support vector machine, random forest, and decision tree classifiers to identify emerging topics and application domains. Hot topics included big data, cloud, edge, multimedia, security, privacy, QoS, and activity recognition, while critical domains included industry, healthcare, agriculture, transportation, smart homes and cities, and assisted living. The major challenges hindering the implementation of edge machine learning include encrypting sensitive user data for security and privacy on edge devices, efficiently managing resources of edge nodes through distributed learning architectures, and balancing the energy limitations of edge devices and the energy demands of machine learning.

A Brief Discussion on the Overall Classification Algorithm of Airborne LiDAR Point Cloud

Abstract. Point cloud classification is the most important problem in airborne LiDAR point cloud data processing. In recent years, classification strategies with new theoretical background keep emerging, so it is urgent to make a more systematic and detailed summary of existing point cloud filtering algorithms, so that relevant researchers can have a more macroscopic and clear understanding of various algorithms and their advantages and disadvantages. Based on the characteristics of airborne LiDAR point cloud data, this paper combs the general process of point cloud classification. This paper summarizes the current mainstream classification methods and analyses their application effects in different scenarios, aiming at exploring and customizing suitable point cloud classification methods according to specific purpose objectives or industry standards. The point cloud classification is classified into three-level classification strategy. The first-level classification is gross error elimination, the second-level classification is point cloud filtering, which is to distinguish ground points from non-ground points. The third-level classification is to extract thematic point clouds from non-ground points according to application requirements. At present, the primary and secondary classification methods are relatively diverse and mature, reaching a certain level of application, while the tertiary classification is still in the initial stage of exploration, and large-scale application is not widespread.

AQUA: Analytics-driven quantum neural network (QNN) user assistance for software validation

This paper proposes a novel analytics-driven user assistance software validation approach for quantum neural network (QNN) codes. The proposed analytics-driven QNN user assistance (AQUA) for software validation considers user interactive feedback for constructing efficient QNN software. Our proposed AQUA is based on dynamic software testing and analysis due to undetermined qubit states in QNN which is hard to be tracked via static software analysis. AQUA is for plotting gradient variances to determine whether the QNN software suffers from local minima situations, which are called barren plateaus in QNN. By utilizing AQUA software validation, the stability, feasibility, and explainability of QNN software can be tested. AQUA has been tested using real-world case study with quantum convolutional neural network software for point cloud data processing in autonomous driving applications.

Optimizing Performance in Distributed Cloud Architectures: A Review of Optimization Techniques and Tools

This research paper presents a groundbreaking hybrid transactional/analytical processing (HTAP) architecture designed to revolutionize real-time point cloud data processing, particularly in autonomous driving environments. Integrating elements from both columnar and row-based tables within a spatial database, the proposed architecture offers unparalleled efficiency in managing and updating point cloud data in real-time. The architecture's distributed nature operates through a seamless synergy of Edge and Cloud components. The Edge segment operates within the Robot Operating System (ROS) environment of the vehicle, while the Cloud counterpart functions within the PostgreSQL environment of cloud services. The communication between these components is facilitated by Kafka, ensuring rapid and reliable data transmission. A pivotal aspect of the proposed system lies in its ability to autonomously detect changes in point cloud data over time. This is achieved through a sophisticated algorithm that analyzes dissimilarities in the data, triggering real-time updates in areas where high dissimilarity is detected. The system ensures the maintenance of the latest state of point cloud data, contributing significantly to the generation of safe and optimized routes for autonomous vehicles. In terms of optimization, the paper demonstrates how the HTAP architecture achieves real-time online analytical processing through query parallelization in a distributed database cluster. The system's efficacy is evaluated through simulations conducted in the CloudSim framework, showcasing its scalability, adaptability, and robustness in handling point cloud data processing for a single vehicle. While acknowledging the achievement of the proposed architecture, certain limitations are recognized. The study highlights the need for further investigation into the system's performance under simultaneous analysis and updates from multiple vehicles. Additionally, ensuring seamless scalability and robustness for uninterrupted operation and expansion during runtime is identified as an area requiring further development.

Influence of VF and SOR-Filtering Methods on Tree Height Inversion Using Unmanned Aerial Vehicle LiDAR Data

Forests, as the main body of the terrestrial ecosystem, have long been focal points for accurate structural parameter extraction. Among these parameters, tree height is a fundamental measurement factor that plays an important role in monitoring forest structure and biomass. The emergence of unmanned aerial vehicle light detection and ranging (UAV-LiDAR) technology has provided a strong guarantee of the acquisition of forest tree height parameters. However, UAV-LiDAR point cloud data have problems such as a large volume and data redundancy, and different point cloud data processing methods have different effects. Based on voxel filtering (VF) and statistical outlier removal (SOR)point cloud data processing experimental analysis, this study explored the influence of different filtering methods on the forest tree height inversion efficiency and accuracy. First, the point cloud data processed by VF is significantly better than that of SOR in terms of point cloud number, file size, running time, etc. The number of point clouds for VF decreased by an average of 96.91% compared with the original point clouds. Second, the VF tree height inversion accuracy was better than the tree height inversion data using SOR. The average accuracy of VF was 96.24%, while that of SOR was 94.17%. In summary, VF can effectively reduce data redundancy and improve tree height inversion accuracy.

Digital Restoration of Historical Buildings by Integrating 3D PC Reconstruction and GAN Algorithm

Historical architecture is an important carrier of cultural and historical heritage in a country and region, and its protection and restoration work plays a crucial role in the inheritance of cultural heritage. However, the damage and destruction of buildings urgently need to be repaired due to the ancient age of historical buildings and the influence of natural environment and human factors. Therefore, an artificial intelligence repair technology based on three-dimensional (3D) point cloud reconstruction and generative adversarial networks was proposed to improve the precision and efficiency of repair work. Firstly, in-depth research on the principles and algorithms of 3D point cloud data processing and generative adversarial networks should be conducted. Secondly, a digital restoration framework was constructed by combining these two artificial intelligence technologies to achieve precise and efficient restoration of historical buildings through continuous adversarial learning processes. The experimental results showed that the errors in the restoration of palace buildings, defense walls, pagodas, altars, temples, and mausoleums were 0.17, 0.12, 0.13, 0.11, and 0.09, respectively. The technique can significantly reduce the error while maintaining the high precision repair effect. This technology with artificial intelligence as the core has excellent accuracy and stability in the digital restoration. It provides a new technical means for the digital restoration of historical buildings and has important practical significance for the protection of cultural heritage.

ACCURACY RESEARCH OF THE EFFECT OF LOCAL SURFACE MODELING METHODS ON THE GENERATION OF 3D MODELS OF HISTORICAL OBJECTS WITH ROCK-BASED BUILDING STRUCTURES USING OPEN-SOURCE SOFTWARE

Abstract. The paper describes the effect of the local surface modelling methods known as plane, quadric and triangulation on the generation of the 3D mesh models of the historical objects with rock-based building structures using Cloudcompare, an open-source software for point cloud data processing. The methodology begins with data collection of the 3D test objects using terrestrial laser scanning technology, the pre-processing of the point cloud data, the point cloud subsampling process, the process of generating of the local surface modelling data, the process of generating the 3D mesh models and finally, the analysis, which involves the 3D surface deviation analysis between the generated 3D mesh models. The overall results shows that there are no significant differences between the 3D mesh models that was generated from all the three local surface modelling methods. The histogram analysis shows that the plane and the quadric local surface modelling methods is the best methods to be used in the research where the 3D test object to be modelled contains curvy surfaces without sharp edges and corners.