Desktop Applications Research Articles

Desktop Application for Traceability System on The Printed Circuit Board (PCB) Storage Process

This paper discusses the development of desktop applications for traceability systems. The application was developed to facilitate data recording and tracking in an electronics manufacturing company's storage process of Printed Circuit Board (PCB) products. The application is developed using the Visual Basic language and Microsoft Excel databases. Additionally, the application is integrated with a barcode scanner to simplify the data entry process from PCBs and employee ID cards. Through the trial process conducted on the developed application, it has generally functioned in accordance with the development goals. Program control validation has been tested through several application access attempts from users registered as operators and administrators. The application has successfully recorded data from inbound and outbound processes, demonstrating storage and tracking functionality. Furthermore, the application has displayed the actual status data of the PCBs present in the warehouse. In terms of user satisfaction, seven users stated that this application was effective and efficient compared to the manual data recording process previously used by the company. This result was obtained from a questionnaire after the application was implemented in the company warehouse.

Application Design of A Multiple Power Source Monitoring System Based On The Internet Of Things (IOT)

At the present time, technology is developing rapidly in various scientific fields. People continue to develop and research the latest technologies to make human life easier. One of them is in the field of IoT (Internet of Things) technology. The use of electrical devices in each power source has different electrical power consumption. And this often happens, so when using electrical equipment, each power source is a tool for monitoring the use of electrical power, so that the use of electrical power in multiple power sources is in accordance with the required power. Therefore, a tool is designed that can facilitate the performance of activities to monitor the use of electric power, the results of which can be displayed through LCD 16X2 and can be informed through the Internet. The purpose of this research is to design an IoT-based electric power monitoring system to facilitate the monitoring of electric power consumption in multiple IoT-based power sources. The method used in data collection is quantitative method. With the collection of several components needed, which are designed in this study such as voltage sensors, Ethernet shields, hub switches and Arduino Uno microcontrollers. This research will monitor IoT-based power, and can be monitored via the Internet in the form of a graphical display on a desktop application or in the form of a monitoring web page. The results showed that the average error value in testing the voltage sensor was 0.02%, the sensor for current readings had an error value of 0.19%, and the value of power was 0.18%. So it can be concluded that by having a fairly small difference and error, this tool is said to be quite good and then by testing the monitoring application system on the web page that is made capable of sending, storing and displaying data on the monitoring web page in real time every minute even with different power sources, in tests conducted with two different power sources and can be measured properly, so that testing the web monitoring application can be said to be good because it can monitor electrical power of multiple power sources.

Vehicle Tracking System for Small Scale Areas Using Wireless Sensor Network

A real-time vehicle tracking system is designed for specific covering zones based on wireless sensor network (WSN) technology. In this designed WSN infrastructure the location of the vehicles is tracked using a Neo-6MV2 UBLOX GPS module which is placed on the vehicles. Furthermore, the Enhanced ShockBurst protocol is employed to transmit wirelessly acquired location data to a master/center (destination) node using the NRF24101+PA LNA modules. The location information of the vehicle is graphically represented for a security center using an ADO.net-based desktop application. It is used to record the vehicle location information at certain time intervals (minutes) in the MSSQL database. This system intimates the system administrator about the long ques/waiting time of vehicles outside the premeditated area. After the successful deployment of the proposed WSN system, it has been substantiated that the proposed system can be used as an alternative to other wireless network infrastructures like WIFI or GSM for the small-scale areas vehicle tracking.

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.

Mitigating Debugger-based Attacks to Java Applications with Self-Debugging

Java bytecode is a quite high-level language and, as such, it is fairly easy to analyze and decompile with malicious intents, e.g., to tamper with code and skip license checks. Code obfuscation was a first attempt to mitigate malicious reverse engineering based on static analysis. However, obfuscated code can still be dynamically analyzed with standard debuggers to perform step-wise execution and to inspect (or change) memory content at important execution points, e.g., to alter the verdict of license validity checks. Although some approaches have been proposed to mitigate debugger-based attacks, they are only applicable to binary compiled code and none address the challenge of protecting Java bytecode. In this paper, we propose a novel approach to protect Java bytecode from malicious debugging. Our approach is based on automated program transformation to manipulate Java bytecode and split it into two binary processes that debug each other (i.e., a self-debugging solution). In fact, when the debugging interface is already engaged, an additional malicious debugger cannot attach. To be resilient against typical attacks, our approach adopts a series of technical solutions, e.g., an encoded channel is shared by the two processes to avoid leaking information, an authentication protocol is established to avoid Man-in-the-Middle attacks and the computation is spread between the two processes to prevent the attacker to replace or terminate either of them. We test our solution on 18 real-world Java applications, showing that our approach can effectively block the most common debugging tasks (either with the Java debugger or the GNU debugger) while preserving the functional correctness of the protected programs. While the final decision on when to activate this protection is still up to the developers, the observed performance overhead was acceptable for common desktop application domains.

Identifying Antitubercular Peptides via Deep Forest Architecture with Effective Feature Representation.

Tuberculosis (TB) is a severe disease caused by Mycobacterium tuberculosis that poses a significant threat to human health. The emergence of drug-resistant strains has made the global fight against TB even more challenging. Antituberculosis peptides (ATPs) have shown promising results as a potential treatment for TB. However, conventional wet lab-based approaches to ATP discovery are time-consuming and costly and often fail to discover peptides with desired properties. To address these challenges, we propose a novel machine learning-based framework called ATPfinder that can significantly accelerate the discovery of ATP. Our approach integrates various efficient peptide descriptors and utilizes the deep forest algorithm to construct the model. This neural network-like cascading structure can effectively process and mine features without complex hyperparameter tuning. Our experimental results show that ATPfinder outperforms existing ATP prediction tools, achieving state-of-the-art performance with an accuracy of 89.3% and an MCC of 0.70. Moreover, our framework exhibits better robustness than baseline algorithms commonly used for other sequence analysis tasks. Additionally, the excellent interpretability of our model can assist researchers in understanding the critical features of ATP. Finally, we developed a downloadable desktop application to simplify the use of our framework for researchers. Therefore, ATPfinder can facilitate the discovery of peptide drugs and provide potential solutions for TB treatment. Our framework is freely available at https://github.com/lantianyao/ATPfinder/ (data sets and code) and https://awi.cuhk.edu.cn/dbAMP/ATPfinder.html (software).

Bridging the GAP: Selenium and RPA for Unparalleled Automation

In the ever-changing technological landscape of today, the incorporation of test automation has become an essential element in the realm of software development and quality assurance. Selenium, known for its robust capabilities in web application testing, has long served as a fundamental pillar in the field of test automation. Simultaneously, Robotic Process Automation (RPA) has experienced increased utilization in order to streamline repetitive business processes across various industries. However, the true potential of these two automation approaches lies in their integration, which provides an opportunity for unparalleled efficiency, productivity, and comprehensive test coverage. This article delves into the synergistic relationship between Selenium and RPA, examining how their combination ushers in a new era of test automation. We explore the seamless integration of Selenium within RPA frameworks and demonstrate how it expands automation beyond web applications, encompassing desktop applications and diverse systems. Our research outlines practical use cases, technical implementation, and the benefits of this amalgamation. Furthermore, we discuss its potential to revolutionize not only software testing but also broader business processes.The fusion of Selenium and RPA signifies a transformative shift in test automation, enabling organizations to bridge the gap between isolated testing efforts and comprehensive automation, ultimately resulting in unparalleled efficiency and reliability.

Real-Time Protozoa Detection from Microscopic Imaging Using YOLOv4 Algorithm

Protozoa detection and classification from freshwaters and microscopic imaging are critical components in environmental monitoring, parasitology, science, biological processes, and scientific research. Bacterial and parasitic contamination of water plays an important role in society health. Conventional methods often rely on manual identification, resulting in time-consuming analyses and limited scalability. In this study, we propose a real-time protozoa detection framework using the YOLOv4 algorithm, a state-of-the-art deep learning model known for its exceptional speed and accuracy. Our dataset consists of objects of the protozoa species, such as Bdelloid Rotifera, Stylonychia Pustulata, Paramecium, Hypotrich Ciliate, Colpoda, Lepocinclis Acus, and Clathrulina Elegans, which are in freshwaters and have different shapes, sizes, and movements. One of the major properties of our work is to create a dataset by forming different cultures from various water sources like rainwater and puddles. Our network architecture is carefully tailored to optimize the detection of protozoa, ensuring precise localization and classification of individual organisms. To validate our approach, extensive experiments are conducted using real-world microscopic image datasets. The results demonstrate that the YOLOv4-based model achieves outstanding detection accuracy and significantly outperforms traditional methods in terms of speed and precision. The real-time capabilities of our framework enable rapid analysis of large-scale datasets, making it highly suitable for dynamic environments and time-sensitive applications. Furthermore, we introduce a user-friendly interface that allows researchers and environmental professionals to effortlessly deploy our YOLOv4-based protozoa detection tool. We conducted f1-score 0.95, precision 0.92, sensitivity 0.98, and mAP 0.9752 as evaluating metrics. The proposed model achieved 97% accuracy. After reaching high efficiency, a desktop application was developed to provide testing of the model. The proposed framework’s speed and accuracy have significant implications for various fields, ranging from a support tool for paramesiology/parasitology studies to water quality assessments, offering a powerful tool to enhance our understanding and preservation of ecosystems.

НЕЙРОСЕТЕВАЯ СИСТЕМА РАСЧЕТА УРОЖАЙНОСТИ ПОДСОЛНЕЧНИКА

The article is devoted to the solution of the problem of developing a neural network system for calculating the yield of sunflower. The developed system is designed to simplify, automate and speed up the inspector's work on calculating the biological yield of sunflower. When assessing the biological yield, most of the time is taken by counting the seeds of a sunflower head. It is proposed to solve this problem using a trained convolutional neural network. The developed system allows you to add the name of a new calculation area, use phone cameras to obtain images of sunflowers (for mobile applications), add images to the system, search for and select the required subspecies of sunflower from the directory, obtain the values of the average weight of seeds of the selected subtype of sunflower, set the area of the determined sowing field, calculate the yield based on the added images, receive the result of calculating the biological yield of sunflower, and also receive previously calculated yield values by name from the device's memory and by the year of the assessment. The Python programming language was chosen to solve the problem of counting seeds in a sunflower head and developing a neural network system. The Roboflow system was used to create a sample with segmented images of sunflower heads. TenserFlow was used as a machine learning library. Keras and OpenCV libraries were used for segmentation and object recognition in the image. Google Colab was used as the development environment for the neural network system. The mobile application was implemented in Java using the Android Studio development environment. The desktop application was implemented in C# using the Visual Studio development environment on Windows Forms technology. The developed neural network system, based on the image transferred to the input of the neural network, detects a sunflower head, segments part of the image with the head, cutting out this area, then counts the seeds in the head and calculates the yield. The YOLOv8 convolutional neural network was used to segment the sunflower head. Using the Roboflow service, a data sample of 10,000 sunflower images was formed. Part of the data (70%) was used for training, and the rest of the data was used for testing the model. Testing of the neural network system showed its efficiency and the possibility of further practical use. Thus, the developed system allows accelerating the assessment of sunflower yield by automating this process.

Conceptual design vulnerability assessment of the housing light roofs to strong winds

Hurricanes are one of the most significant causes of human and material losses in the Caribbean region. These events have demonstrated their devastating impact on housing and infrastructure. The assessment of the vulnerability of buildings with light roofs, at the initial design stage, is considered to be a fundamental step in the mitigation of these damages and losses. This paper presents the introduction of an indicator-based vulnerability assessment in an effort to mitigate these damages in advance. This indicator facilitates the design team's decision to select the appropriate light roof alternative subject to strong winds at the conceptual stage of the process. The indicators that contribute to the conceptual assessment of vulnerability were identified based on a comprehensive review of the literature and numerical simulations of the risk scenarios using CFD/FEM software’s. The ranking of indicator weights was determined by the Kano method according to experts' opinions. A desktop application has been developed for the assessment of the vulnerability of light roof variants for buildings at the conceptual design stage. The results reported in a case study demonstrate the viability of the desktop application based on the vulnerability indicator to assist decision making in the conceptual design stage.

Planetary orbital mapping and mosaicking (POMM) integrated open source software environment

Several Open Source planetary orbital mapping and utility image processing software packages, including, VICAR, AFIDS, ISIS, GDAL, and GeoTIFF, have been integrated into a single software environment (POMM), where package programs can be run independently from a Linux command line, or combined in synergistic scripts that facilitate advanced trans-package applications. Several integrated scripts have been prepared to simplify some of the more difficult database building tasks such as (1) image co-registration (for stacking and time series analysis), (2) mosaicking (for regional analysis), and (3) map-projection of raw Planetary Data System (PDS) images for selected Mars and Luna sensor systems. A user interface/GUI is provided for the three planetary applications, but the underlying scripts can also be modified by an advanced user and run at the command-line. POMM is Open Source software available in “docker” container and “Yum-Install” versions that support cross-platform installation on Windows, Apple, and Linux products using available desktop applications and/or virtual containers. However, installation on systems requiring emulation can be slow, and the installation process can be challenging.

An extended reality platform for inclusion of adults on the autism spectrum: a position paper

Extended reality (XR) enables both new opportunities but also introduces new barriers for inclusion in society. Furthermore, XR is less researched than web, desktop and mobile applications. This position paper presents the concept of an XR platform for inclusion, with the purpose to make people on the autism spectrum and with other disabilities, more independent of help from others in everyday life situations. Based on previous research, our position is that, through current and future XR technologies combined with civic and artificial intelligence, it is possible to create individually personalised support for this purpose, grounded in practice to ensure validation.

Desktop aplikacija za razmenu kriptozaštićenog govora

Introduction/purpose: All data exchanged over the Internet as well as other computer networks should be considered exposed to various types of security threats. In light of this, the transmission of voice over applications that do not use any type of crypto-protection allows anyone to discern the content of communication. Since voice transmission requires as little delay as possible, various protocols are used to enable crypto protected real-time communication. This paper presents one solution in a desktop application variant. Methods: The essence in voice exchange systems as well as in other systems where real-time communication is necessary is the establishment of a crypto-protected session which is a virtual secure channel for communication to which only the communicating parties have access. Voice sessions in the application are established with the SIP (Session Initiation Protocol) protocol. The sessions are further protected using the ZRTP (Zimmerman Real-time Transport Protocol) protocol. FusionPBX was used as the SIP server (registrar) for testing purpouses. The application is developed in C++ language using the Qt framework. Results: The final version of the application demostrates that ZRTP and SIP protocols are well suited for establishing crypto protected voice communications with low delay. Conclusion: This solution provides cryptographic functions for data secrecy and the management of cryptographic keys. Improving the solution with digital signatures and certificates will result in additional cryptographic functions: data integrity and personal identification. With this improvement, this solution will be able to withstand modern security threats with low delay.

Information support for the adjustment process of grain harvester

The article discusses many external harvesting factors and adjustable parameters of the working parts of the TORUM combine harvester. A description is given of the technologies used to develop a program - a highly specialized database management system (DMS) - for information support of the process of adjusting a combine harvester, namely the system programming language Rust and modern web technologies for creating desktop applications with a graphical user interface. A hint system is described to improve the user experience for interacting with domain data. The “Settings” subsystem, used for preliminary configuration of combine harvesters, is examined in detail, and the Entity relationship is shown - a diagram of the database tables related to this subsystem. The software allows you to work with the database in two operating modes: in user mode for viewing the knowledge of interest in the subject area and in expert mode for editing, adding and deleting this knowledge. The implementation of the software system under consideration (expert system) can reduce time and improve the quality of decisions made and, as a result, will increase the shift productivity of combine harvesters.

АВИАТЕХПОМ: СОСТОЯНИЕ И ПЕРСПЕКТИВЫ

This paper discusses the results of a project to create an intelligent decision support system for troubleshooting aircraft failures and malfunctions by the Aviation Technical Assistant (AviaTekhPom). The main implemented algorithms, functions, architecture and prototype of the system are described, as well as the further direction of the project development. The Sukhoi Superjet power supply system was chosen as the target object for testing solutions. The current version of the system provides processing of information presented in cards of accounting of malfunctions of aviation equipment and troubleshooting manuals, based on approaches known as rule-based reasoning and case-based reasoning. For preparation and modification of knowledge bases, a tabular formalism of knowledge representation is used, which provides the possibility of using publicly available tabular editors and CSV format. The developed algorithms are implemented in the form of conceptual prototypes of a desktop application and a chatbot.

Web system for automatic irrigation in a greenhouse

The irrigation system in greenhouses is a fundamental aspect in crop production since it is responsible for suplaying water to the plants. The San Sebastián greenhouse is irrigated by drip irrigation, the main characteristic of which is to provide the necessary amount of water and nutrients for the crops. The technological development has impacted irrigation systems in protected agriculture, allowing greenhouses to implement irrigation systems to automatically start and finish the irrigation process. This article describes the project based on a webbased system and a desktop application backed from realtime interface module through automated humidity and temperature sensors, with the objective of improving irrigation productivity and product quality. With the development of the Project, the irrigation automation process, the modules of the web system and the results produced by the system are exhibit. For this project, the Prototypes model was used using the PHP programming language.

Vegetation and Non-Vegetation Classification Using Object Detection Techniques and Deep Learning from Low/Mixed Resolution Satellite Images

Vegetation cover classification using mixed or low-resolution scalar images is challenging. Fortunately, recently deep learning object detection methods have emerged as a replacement to the conventional machine learning methods for the detection and classification of land use and land cover. This paper presents a deep learning object detection approach for land use and land cover detection using low/mixed resolution satellite images acquired from Google Earth satellite images. Google Earth images are accessible freely using the Google Earth Pro desktop application. Our dataset consists of two (02) classes (vegetation and non-vegetation) with a total of 450 labeled images captured from different parts of Pakistan. We present a comparison of the recent anchor-free object detection model YOLOX with the anchor-based object detection model YOLOR for solving real-time problems. The end-to-end differentiability, efficient GPU utilization, and absence of hand-crafted parameters make anchor-free models a compelling choice in object detection, and yet not been explored on Land cover classification using satellite images. Our experimental study shows that YOLOX delivers an overall accuracy of 83.50% on Vegetation and 86% on Non-Vegetation classes, which outperformed YOLOR by 30% on Vegetation classes and 34% on non-Vegetation classes for our dataset. We also show how an object detection system can be used for Vegetation and Non-Vegetation classification tasks, which can then be used for change monitoring and assisting in developing geographical maps using low/mixed resolution freely available satellite images.

Implementation of digital gis models in desktop applications developed on the basis of the «QT» framework

This article explores the implementation of digital geoinformation systems (GIS) models in desktop applications using the Qt framework. This highlights the importance of GIS technologies in various fields and the need for powerful desktop applications capable of efficiently processing spatial data. Integration of GIS models into Qt-based applications provides a number of advantages, such as efficient data management, spatial analysis and user-friendly interfaces. The article highlights the cross-platform compatibility of the Qt framework, which allows you to deploy GIS-enabled applications on multiple operating systems. In conclusion, it should be noted that the introduction of digital GIS models into desktop applications developed using the Qt framework provides significant benefits to both developers and users. This allows users to access, analyze and visualize geospatial data, thereby improving decision-making processes. The combination of GIS and Qt capabilities allows developers to create reliable and user-friendly applications to meet the growing demand for spatial data management and analysis tools. The list of sources used includes links to the official Qt and Esri websites, as well as relevant publications related to integration, GIS and noise mapping using ArcGIS.

КОЛАБОРАТИВНЕ ПРОЄКТУВАННЯ ПЕРСОНАЛІЗОВАНОЇ ГІБРИДНОЇ LMS З ВИКОРИСТАННЯМ ВІРТУАЛЬНОЇ МАШИНИ В ХМАРНОМУ СЕРЕДОВИЩІ

A teacher currently needs to acquire a huge amount of educational information and knowledge as part of his/her practice for the purposes of teaching, self-study, publishing, research, language support and related activities. In the academic environment, so-called learning management systems (LMSs), especially Moodle in the European Union, have proven suitable for the processing of educational content under e-Learning. LMSs usually function as online platform, at the same time as a supplement to academic information systems (AIS) on the local servers of universities. These are used by a larger number of participants, so these do not always meet the pedagogical needs of the teacher in all the activities he/she does. For example, these do not allow multilingual support, multiple searches and mass processing of e-Learning content. There are a large number of articles in the scientific literature describing the design of add-on modules for LMS. The authors offer their own approach to solving the problem of processing educational information to support a virtual team's work. As part of collaborative research in the field of technology-enhanced education and e-Learning design, the international team in the V4 EDUPORT project is implementing a personalized hybrid system on the IT infrastructure consisting of a specially designed desktop application WPad or web application (PIKS) and a common shared remote computer (virtual computer on a cloud server with Windows 2022). In this paper, the researchers share their experiences gained with their universities' academic LMSs (Moodle and AIS) and compare their personalization capabilities level to the teacher level and their use in the cloud. Part of current and future research is the development of the PIKS internet application, which operates as a multi-functional communication channel for e-Learning purposes (including use for the visually impaired). .

Information System for Ukrainian Text Voiceover Based on Nlp and Machine Learning Methods

During the research, an information system for voicing Ukrainian-language text was developed based on NLP and machine learning methods. The created information system is implemented in the form of a desktop application, which allows the process of voicing the Ukrainian-language text. The created system included all stages of software development: the design process, the implementation process, and the testing process. For the feasibility of creating this system, already existing software solutions on the market were analysed, their advantages and disadvantages were listed, which were subsequently taken into account to create a new system. During the system analysis of the system, a goal tree, a decision tree, and examples of context diagrams with process decomposition are given. One of the stages of the design of the economic part, where the budget that will be spent on the implementation of the system is analysed, all tax and administrative costs are calculated, development strategies are also analysed and the development strategy of the existing product with accompanying solutions and the product development strategy are selected. After that, an assessment was made for the feasibility of creating the designed system, it’s payback and profit. The object of the research is the process of the voiceover system of the Ukrainian-language text based on NLP and machine learning methods. The subject of the research is the methods and means of the Ukrainian-language text voicing system process based on NLP and machine learning methods. The purpose of the research is to create an information system for voicing Ukrainian- language text based on NLP and machine learning methods. The result of the work is a ready-to- implement information system for voicing Ukrainian-language text based on NLP and machine learning methods, an analytical review of literary and online sources related to the topic of voicing Ukrainian- language text based on NLP and machine learning methods, a systematic analysis of the research object, analysis and selection of software tools for system implementation, practical implementation of the system, economic justification of system implementation activities.