GUI Application Research Articles

Following the Writer’s Path to the Dynamically Coalescing Reactive Chains Design Pattern

Two recent studies addressed the problem of reducing transitional turbulence in applications developed in C# on .NET. The first study investigated this problem in desktop and Web GUI applications and the second in virtual and augmented reality applications using the Unity3D game engine. The studies used similar solution approaches, but both were somewhat embedded in the details of their applications and implementation platforms. This paper examines these two families of applications and seeks to extract the common aspects of their problem definitions and solution approaches and codify the problem-solution pair as a new software design pattern. To do so, the paper adopts Wellhausen and Fiesser’s writer’s path methodology and follows it systematically to discover and write the pattern, recording the reasoning at each step. To evaluate the pattern, the paper applies it to an arbitrary C#/.NET GUI application. The resulting design pattern is named Dynamically Coalescing Reactive Chains (DCRC). It enables the approach to transitional turbulence reduction to be reused across a range of related applications, languages, and user interface technologies. The detailed example of the writer’s path can assist future pattern writers in navigating through the complications and subtleties of the pattern-writing process.

PEMANFAATAN CHATBOT MENGGUNAKAN NATURAL LANGUAGE PROCESSING UNTUK PEMBELAJARAN DASAR-DASAR GUI TKINTER PADA BAHASA PEMROGRAMAN PYTHON

Along with the significant development of technology today, websites have become an alternative media as a means of promotion and information dissemination. The website has many facilities that can make things easier for users, one of which is a chatbot. Chatbots or what are commonly known as virtual assistants are able to understand and respond to user requests with the right answers. The use of the Python programming language is quite popular among novice programmers, its simple syntax and clear and logical code means that this language is often used for creating games, websites, prototyping and also for handling big data. One of the modules for creating Graphical User Interface (GUI) applications in Python is Tkinter. In this research, a chatbot will be created which aims to make it easier for users to find solutions to the problems they face when using the Python programming language in the Tkinter GUI module using Natural Language Processing in an interactive way.

A prospective approach for human-to-human interaction recognition from Wi-Fi channel data using attention bidirectional gated recurrent neural network with GUI application implementation

A prospective approach for human-to-human interaction recognition from Wi-Fi channel data using attention bidirectional gated recurrent neural network with GUI application implementation

Machine-learning based process monitoring for automated composites manufacturing

AbstractAutomated fibre placement (AFP) is an advanced robotic manufacturing technique which can overcome the challenges of traditional composite manufacturing. The interlaminar strength of AFP-manufactured composites depends on the in-situ thermal history during manufacturing. The thermal history is controlled by the choice of processing conditions and improper interfacial temperatures may result in insufficient bonding. Being able to better predict such maintenance issues in real-time is an important focus of smart manufacturing and Industry 4.0 to improve manufacturing operations. The data analysis of real-time temperature measurements during AFP composites manufacturing requires the temperature profiles from Finite Element Analysis (FEA) based simulations of the AFP process to better predict the quality of layup. However, the FEA simulations of the AFP process are computationally expensive. This study focuses on developing a digital tool enabling real-time process monitoring and predictive maintenance of the AFP process. The digital tool constitutes a machine learning-based surrogate model based on results from Finite Element Analysis (FEA) simulations of the AFP process to predict the in-situ thermal profile during AFP manufacturing. Multivariate Linear Regression, Multivariate Polynomial Regression, Support Vector Machine, Random Forest and Artificial Neural Network (ANN)-based models are compared to conclude that ANN based surrogate model performs best by predicting the important parameters of thermal profiles with a mean absolute percentage error of 1.56% on additional test data and reducing the time by four orders of magnitude as compared to FEA simulations. The predicted thermal profile can be compared with the real-time in-situ temperatures during manufacturing to predict the quality of the layup. A GUI application is developed to provide predicted thermal profiles data for analysis in conjunction with real-time temperatures during manufacturing enabling monitoring and predictive maintenance of the AFP process and paving way for the development of a digital twin of the AFP composites manufacturing process.

Development of a machine learning model for on-site evaluation of concrete compressive strength by SonReb

Coring or destructive testing is typically the default choice for the evaluation of concrete compressive strength in reinforced concrete (RC) structures. However, it can be impractical and/or not representative of all areas of interest in a structure. While various non-destructive test (NDT) methods can be correlated to concrete strength, the accuracy of any single NDT used for this purpose is generally low. The SonReb method, which combines ultrasonic pulse velocity readings and rebound number, has been shown to have improved accuracy over single test methods. However, empirical SonReb equations, calibrated to specific datasets using regression analysis, cannot necessarily be applied to concrete from other sources without introducing significant errors. This study presents a practical machine learning (ML) model for on-site concrete strength prediction. A large database was created from available literature along with new experimental test data. Three different ML models based on an adaptive neuro-fuzzy inference system (ANFIS) were developed along with a graphical user interface application to facilitate its use in the field. In addition to the ML models, linear and non-linear regression analyses were also conducted and compared with existing equations in the literature. The accuracy of each model was subsequently validated against core samples extracted from a reinforced concrete slab. The results show that the proposed ML model and non-linear regression provided the most reliable predictions of concrete strength of the validation specimen with a mean absolute error of less than 10 % compared with twelve core samples. The findings suggest that ML can be a potential tool to evaluate the in-place compressive strength of RC structures in combination with simple NDT methods.

A Model-Based Approach to Mobile Application Testing

Abstract Modeling the automated testing of mobile applications is a crucial aspect of mobile application automation testing. Due to the varied styles and complex interactions of mobile applications, automated modeling methods are urgently required, particularly in the context of their short development cycles, large numbers, and fast version iterations and updates. In this paper, we address the challenge of exploring mobile application behavior and state based on robotic testing environment without invading the application interior, and propose a method for automated exploration of GUI components and GUI events of applications combined with application domain knowledge to generate mobile application GUI semantic test models. Our results show that the proposed semantic model achieves 70.6% and 82.4% defect detection rate in the robot vision environment and simulation environment, respectively. Compared with the comparative testing method that can only find application crash defects, our method can explore both crash defects and functional anomalies with the application semantic understanding and domain knowledge, thereby extending the automated mobile application functional testing capability of mobile applications. In response to the limitations of mobile application automated testing modeling mentioned above, this paper introduces an automated testing method based on semantic models. It uses the proposed semantic testing model to guide the purposeful exploration of the tested application’s states. Subsequently, it generates positive and negative test cases based on the domain knowledge associated with the semantic model. This modeling approach leverages domain models in the mobile application field to conduct automated modeling tests imbued with functional significance, guided by domain knowledge. This optimization aims to address the shortcomings of current automated testing, particularly in terms of model reuse and test expansion.

Automation of Seating Plan for Examinations using Round-Robin Policy

Abstract: This paper introduces a Python-based Graphical User Interface (GUI) application designed to automate the seating arrangement process for examinations, specifically catering to the needs of examination departments. Leveraging the openpyxl and pandas libraries for Excel file manipulation and creating Dataframes the application aims to replace the laborious manual process traditionally employed by colleges. The main objective is to reduce the time and effort invested by examination departments in seating arrangement tasks. The user-friendly GUI simplifies data input, while the application's algorithm automates seating assignments, ensuring that no two students writing same exam can’t sit in same bench in optimal manner using excel and python . Results demonstrate a significant reduction in time spent on seating arrangement, with outputs including detailed room-wise seating sheets and comprehensive summaries listing roll numbers by regulation and branch. This innovative approach not only streamlines examination management but also enhances accuracy and efficiency.From this GUI we can add, edit and delete the halls in which the exams are being conducted.Further we input the excel sheet in which the roll numbers of students were there as per their branch and regulation as input and genrates formatted excel sheets that generate seating arrangement .Formatting the font and size of rows and columns and wrapping the columns when required are done automatically

Visual Servo Kinematic Control of Delta Robot using YOLOv5 Algorithm

Industrial delta robots with motion-and vision-base controllers, providing high-precision and fast/flexible motion, normally come with high cost and complex software. Low-cost delta robots with vision solution could be beneficial to vast industries to increase productivity and to reduce labor cost. A delta robot using a low-cost motion controller and an open-source vision system is developed to accomplish real-time visual servoing with high motion accuracy. In the low-cost motion controller, three parallel links’ upper-arm angles, computed from inverse kinematics for a given desired target position by a high-level computer, are used as reference position commands for three AC-motor drives. A low-level Arduino microcontroller is employed to convert these links’ angles to high-frequency pulses and on-off signals for synchronously controlling three motor angles and direction. Experimental results of a point-to-point motion tracking exhibit high-precision repeatability. Synchronous pulse generation from Arduino microcontroller and structural misalignments of parallel links are major challenges for achieving high motion accuracy. For the vision-based system, the YOLOv5 algorithm is implemented along with a Python GUI Application. Then, the visual-servo performance is evaluated on localization accuracy and recognition rate of 3-color objects. However, a partial object occlusion can reduce the visual classification rate. A sorting task of 4-category medicine boxes demonstrate a high-speed pick-and-place operation using the low-cost visual-servo system of this delta robot. Therefore, integration of low-cost visual servoing with this delta robot can revolutionize various industries, like automobile, pharmaceutical, and food sectors, in separating, sorting and packing applications.

Investigating the rendering capability of embedded devices for graphical-user-interfaces in mobile machines

Abstract This paper focuses on the alignment of Graphical-User-Interface (GUI) applications and embedded devices. The main contribution is a benchmark approach that enables measuring and comparing the GUI rendering capability of embedded devices and provides a performance-oriented GUI design recommendation for an embedded device. The benchmark is tailored to common GUI applications and resource-limited devices, which are usually mounted in mobile machines. GUI applications can only be rendered smoothly if these devices have sufficient performance. A general benchmark concept is described and modeled in Unified Modeling Language (UML) at first, followed by a prototypical implementation and evaluation. It is demonstrated that the benchmark approach is applicable to GUI applications with different levels of complexity as well as embedded devices of different performance classes.

Comparative Analysis Association and Prediction of Various Phenotypic Traits of Oryza Sativa

Understanding the genotype-phenotype relationship and accurately predicting breeding values are crucial aspects of crop improvement programs. This paper investigates the genetic basis ,association of phenotypic trait height and yield and predicts the phenotypic traits of Oryza Sativa (rice) through a comprehensive approach encompassing genome-wide association studies (GWAS), phylogenetic analysis, machine learning algorithms, and the development of a graphical user interface (GUI) application. Genotypic and phenotypic data were collected from the RiceVarMap database. The genotypic information consisted of gene variation IDs, while the phenotype data included plant height. Data preprocessing involved the creation of a sequence. fasta file and multiple sequence alignment using the ClustalW tool. A phylogenetic tree was then constructed to analyse the subpopulations of Oryza Sativa. Clustering techniques were applied to further explore the genetic relationships among the samples. A GWAS file was generated to identify associations between genotype and phenotype. Subsequently, machine learning algorithms were employed for the classification and prediction of genomic estimated breeding values (GEBV) for height and yield traits. Random Forest emerged as the most accurate algorithm with 85% accuracy. To facilitate user interaction and data exploration, a GUI application was developed using Flask, allowing users to access the phylogenetic tree, height, and yield information, GWAS results, and make predictions. We explored there is a strong positive association between phenotypic trait height and yield.

Modelling Small Artefact for Preservation – A Case Study of Perlis Heritage

Heritage preservation is essential for preserving historical sites and cultural artefact for future generations. However, they are prone to damages and destructions due to weather conditions and other factors. 3D models and reconstructions can aid in the conservation of historical sites and artefact. LiDAR (light detection and ranging) technology can be utilized to obtain accurate 3D representation of object or area of interest. This project aims to preserve one of the historical artefact in Perlis, Malaysia which is known as ‘Batu Nisan Acheh’ or the Acheh Gravestone by using 3D model and reconstruction. iPhone 13 Pro Max LiDAR scanner is used to collect the raw dataset of the artefact with Scaniverse application. MATLAB is employed for data processing which includes data filtering, noise reduction, downsampling and 3D surface reconstruction. In addition, a GUI application is also developed in enabling users to upload their desired point cloud files and produce its 3D model for future usage. Results show that the accuracy, effectiveness, and usability of heritage preservation initiatives are improved by combining iPhone 13 Pro Max LiDAR scanning with MATLAB processing, which is useful for virtual displays, restoration, and future study.

High Performance Software Architectures for Remote High-Speed Data Acquisition

There are various tools for real-time data acquisition and analysis of high-frequency signals. Usually, the tools for data acquisition and analysis are incompatible with each other, expensive, or require the utilization of various technologies. Designing a unique toolset for real-time data acquisition and analysis of high-frequency signals presents a large challenge, both in terms of hardware and software design. Some of the main challenges include fulfilling strict real-time performance requirements, using cost-effective hardware components, and designing a software architecture with minimal software latency for data sampling on an embedded platform and data analysis on a PC machine. This paper presents a software architecture design methodology for embedded platforms, including an RTOS and an Ethernet interface, along with the application architecture on the host side. To demonstrate the advantages of the proposed methodology, a prototype consisting of the STM32H747 microcontroller, FreeRTOS operating system, and a GUI application based on the Qt framework has been created. It has been shown that using the proposed architecture design methodology makes it possible to preserve the maximal hardware sampling rate utilizing a 100 Mbps Ethernet link. In the experiments, sampling rates up to 4.57 MSps for 10-bit resolution and real-time transfer of data over Ethernet link and visualization on host PC have been achieved with this specific MCU.

Spinach fungi guard: A deep learning-based software solution for swift detection and remediation of fungal diseases in spinach leaves

We have developed a software solution aimed at assisting farmers in quickly detecting and finding a cure for fungal diseases in spinach leaves. This solution utilizes Deep Learning techniques, specifically a Convolutional Neural Network (CNN), to effectively detect diseases caused by fungi in spinach leaves and suggest suitable remedies to the user. To train our model; we gathered a spinach fungi disease image dataset consisting of over 700 images categorized into six classes, including five diseases and one healthy category. Prior to model training, we resized the images and applied various image augmentation techniques to improve the robustness of the model. Using Keras, we constructed a sequential CNN model for disease classification. The model was trained on the dataset and evaluated on the validation set, achieving an impressive accuracy of 89.86 %. To provide an intuitive interface for end users, we implemented a PySide2-based GUI application that leverages the trained model to classify disease in spinach leaf images provided as input.Our software not only accurately classifies the disease but also suggests appropriate remedies or medications for the specific disease. Furthermore, it provides links to relevant products on various e-commerce sites, enabling users to conveniently purchase the required medications. This comprehensive solution empowers end users to analyze infected spinach leaf images, accurately classify diseases, and take necessary actions by applying appropriate remedies and acquiring the right medications. By swiftly detecting diseases and offering prompt remedies, our software aids in preserving the production of spinach and ensures farmers can effectively combat fungal diseases, ultimately benefiting the food, medicine, and skincare industries.

Speech data collection system for KUI, a Low resourced tribal language

<p>A new generation of speech translation technology is being developed to enable natural cross-language communication. Research efforts must focus on large vocabulary, spontaneous speech, and speaker variances to accommodate the varying demands of speech recognition technologies. These are important issues that need to be resolved for the general application of voice recognition in realistic settings. Most languages with limited resources don’t even have any speech data. Creating speech corpora is extremely difficult and time-consuming. Among all, KUI is regarded as one of the low-resource languages. In this paper, we developed the speech dataset for the KUI language to document and preserve their culture, tradition, and history for future generations. We also discuss the design, data collection procedures, and implementations and outline the different research possibilities using our KUI dataset. This paper mainly describes the GUI and method for the collection of KUI speech more quickly. In this section, the statistics of the people who helped and contributed to the collection of this KUI dataset have been provided. This study details a novel method of gathering data for any speech dataset. Using this process, we collected 60 hours of speech data sampled at 16 kHz by three different devices such as a Zoom recorder, Mobile, and Laptop from 80 different speakers. Each speaker contributed 500 sentences in the KUI language. A GUI application is designed to capture the speeches of numerous speakers in the KUI language. Several guidelines are proposed and used for the collection of the KUI speech dataset. All the guidelines are based on real-time experience gained during the data collection process by our team members.</p>

Development of a Graphic Interface Application for the Vision System of the Fruit Sorting Line

Introduction. At present, an intuitive graphical interface is an indispensable component of modern agricultural-oriented software products. Aim of the Article. The research is aimed at improving the efficiency of sorting apples by developing a graphical control interface for a vision system to recognize various defects and sort apples. Materials and Methods. The authors used methods of analysis, enumeration, comparison and synthesis of modern software solutions. Results. As a result of the research, there was created a graphical application of the software for the control unit of the machine vision system containing all the necessary tools for managing and optimizing costs when sorting apples into three commercial quality classes. Discussion and Conclusion. The graphical interface of the machine vision system was used in the line LSP-4 for sorting and packing apples. It was developed by Scientific and Practical Center NAS of Belarus for Agricultural Mechanization in 2020 and successfully passed state acceptance tests.

An automation solution to convert CAD engineering drawings into railroad station models

AbstractCreating a high‐fidelity railroad station model to match the physical details of hundreds of tracks and switches is never a trivial task. The manual modeling approach often costs engineers significant efforts and constrains the generality and extensivity of many advanced methods. Taking advantage that many stations are drawn proportionally into two‐dimensional drawing exchange format (DXF) files via state‐of‐the‐art computer‐aided design (CAD) techniques, this paper proposed an efficient solution to convert DXF files into meaningful station models. The proposed solution consists of two phases (1) converting graphic basic primitives without explicit engineering interpretations into recognizable railroad symbols and (2) modeling undirected railroad station graphs with necessary configurations such as endpoints and routes. The proposed solution is developed into a graphic user interface application with minimal user interactions and subsequently tested at several real‐world passenger stations in Asia for its validity, productivity, and applicability.

SpecUnPy: A python GUI/App for neutron spectrum unfolding

Bonner Sphere Spectrometers (BSS) are widely used for neutron spectrometry. Spectra are obtained by unfolding detector readings. In this work, a Python Graphical User Interface Application (GUI/App) for spectrum unfolding is presented; SpecUnPy. In this App, the user can choose three unfolding algorithms: SPUNIT/MLEM/GRAVEL. There is no limit for energy bins or detectors and after unfolding, a “.xlsx” file and a graphical comparison can be downloaded. This paper presents SpecunPy and some tests performed to validate it.

Soft computing based e-commerce website service quality index measurement

It is observed that there is a need for a quality measurement template system that is easy to understand, effective, easily updated according to different requests, contains enough variables, and gives fast results, which can be placed on websites except for general evaluation comments or 5-star rating evaluations after a customer makes a purchase from the online shopping website. For this reason, an inference system based on human reasoning is developed to measure practically the quality index of e-commerce shopping websites according to customer feedback in this study. The proposed system includes four subs and one main fuzzy inference system. 11 parameters affecting the problem are used in subsystems, while five main parameters are used in the main inference system. The main parameters are cargo and delivery performance (CDP), customer support and campaign performance (CSCP), ease of payment and return (EPR), interface quality (IQ), and privacy-security. The subsystems produce detailed outputs for the main parameters other than privacy-security. E-commerce shopping website service quality index is computed in the main system that processes the outputs of the subsystems. In addition, survey data is applied for verifying and supporting the study. Respondents in the survey were asked about the parameter values for their 1st and 2nd preferred shopping websites and it was observed that A3 e-commerce website has high quality index, and A1 e-shopping website has good quality index but should improve its CSCP dimensions. A4 website especially should improve its IQ and EPR dimensions and its quality index is not good compared to others. Finally, a graphical user interface application of the system is formed.

MATLAB GUI application for failure analysis of electrical power system faults

This paper presents a computational algorithm in MATLAB with the Guide tool to analyse symmetrical and asymmetrical faults in electrical power systems. Fault analysis is important in the design, planning and operation of power systems, and is used to specify interrupting devices and define operating strategies that do not violate short-circuit levels. It also highlights the importance of good fault detection and coordination of protections to consider the type of fault, where it occurs, the phases involved and the evolution of the fault type.

Fusion of experimental and synthetic data for reliable prediction of steel connection behaviour using machine learning

The development of robust prediction tools based on machine learning (ML) techniques requires the availability of complete, consistent, accurate, and numerous datasets. The application of ML in structural engineering has been limited since, although real size experiments provide complete and accurate data, they are time-consuming and expensive. On the other hand, validated finite element (FE) models provide consistent and numerous synthetic data. Depending on the complexity of the problem, they might require large computational time and cost, and could be subjected to uncertainties and limitation in prediction capability given they are approximations of real-world problems. Hybrid approaches to combine experimental and synthetic datasets have emerged as an alternative to improve the reliability of ML model predictions. In this paper, we explore two hybrid methods to propose a robust approach for the prediction of the extended hollo-bolt (EHB) connection strength, stiffness, and column face displacement: (1) supervised ML methods with data fusion (DF) where learning is optimized with particle swarm optimization (PSO), and (2) artificial neural networks (ANN) based method with model fusion (MF). Based on the analysis of a dataset that combines 22 tensile experimental results with 2000 synthetic datapoints based on FE models, we concluded that using the first method (ML with DF and PSO) is the most suitable method for the prediction of the connection behavior. The ANN-based method with MF shows to be a promising method for the characterization of the EHB connection, however, more extensive experimental data is required for its implementation. Finally, a graphical user interface application was developed and shared in a public repository for the implementation of the proposed hybrid model.