Development Of Graphical Interface Research Articles

System Dynamics Modeling: Technological Solution to Evaluating Cold-Chain Meat Packaging Scenarios

A cold-chain meat packaging project was developed for a meat product company in northwestern Mexico that moves high volumes of fresh meat into national and international markets. The objective of the present research is to evaluate the supply process for three types of thermo-shrinkable polyethylene bags to provide a technological solution for high-volume meat packaging based on a graphical user interface. A system dynamics (SD) methodology is developed in seven stages to generate a technological solution: (1) system mapping; (2) causal diagram construction; (3) stock, flow modeling, and equations; (4) model simulation; (5) model validation; (6) scenarios and multicriteria analysis; and (7) graphical user interface development. The main result for the company was a technological solution that could communicate with decision-makers and the proposed graphical user interface. Future optimistic and pessimistic scenarios were self-evaluated based on the current situation related to three thermo-shrinkable bags used for selling high volumes of fresh meat. In these solutions, previously simulated costs and savings can be implemented in a real situation. Quantitative graphical user interface data can be observed to adequately manage box and bag inventories and minimize costs. Using SD enables the development of technological solutions in complex environments with robust simulations and models that offer data to people interested in the system under study.

A High-Level Adaptation Toolkit for Unified Integration of Software Systems with Neural Interfaces

The paper is devoted to the urgent problem of automating the process of integrating different types of neurointerfaces into the infrastructure of the Internet of Things. Due to the low-level nature of these devices and related tools, integrating neurointerfaces with a wide range of IoT devices is a complex task requiring specialized knowledge and skills in the fields of neuroscience and signal processing. To tackle the significant challenge of automating such kind integration we continue to improve our previously proposed ontology-driven methods and tools for seamless integration of software systems with neural interfaces in a unified way. The presented notable improvements include the introduction of a new data processing pipeline that utilizes a portable device to validate the effectiveness of the system and the development of an intuitive graphical user interface enabling real-time data visualization facilities that provides an easy and understandable feedback.

Development of Graphical User Interface for Link Budget Analysis of Point-to-Point Communications at 5 GHz and 11 GHz

It is well known that simulation tools are essential for the design and optimization of wireless communication systems. This paper proposes a Python script that can be used for planning and predicting a connection link budget by analyzing its basic parameters. Our proposal consists of an application that calculates the connection budget for point-to-point links operating at 5 GHz and 11 GHz, taking into account all the necessary microwave parameters. For validating the efficiency of the proposed tool, this paper presents comprehensive simulation results derived from comparing our tool to a couple of other simulation tools by means of calculating the same parameters.

Development of Model and Graphical User Interface for Leak Localization in Crude Oil Pipeline

Pipelines provide the most efficient and cost-effective means for fluid transport but the challenge posed by leaks has substantially increased the risks and hazards in pipeline fluid transportation. The development of efficient leak detection system pays off in quicker leak detection and localization, leading to quicker responses and remediation works by the pipeline emergency response team. This would ultimately bring about less severity of the pipeline leak in terms of financial losses, human and environmental consequences. Steady state modeling of crude oil pipeline flow with attempt to determine and localize leak has been achieved in this study. Mathematical models have been developed to detect and localize leaks in crude oil pipeline during leak occurrence. Leak detection was modeled using the conservation of mass equation while leak localization was modeled by modifying the Darcy-Weisbach pipeline liquid flow equations and utilizing the Swamee-Jain friction factor correlation. Equations for flowrate and pressure drop along the pipeline were developed for two cases: a case where there is no leak in the pipeline and a case where there is leak in the pipeline. Leak localization equation was determined by equating the fluid flow equation when there is no leak and when there is leak. The model was structured and simulated in Matlab software with the model tested for four field cases. The results from the simulation revealed swift leak detection, accurate localization of the leak, and determination of the pressure at the point of leak. Experimentally determined results from actual field measurements of leak incidences were used to validate the results. The result determined from the model developed proved accurate with only an average error of 0.216 miles.

Developing MATLAB graphical user interface for acquiring single star SCIDAR data

To enhance operational efficiency and meet experimental demands, we have developed a graphical user interface (GUI) using MATLAB for Acquiring Single Star SCIDAR Data, leveraging the software’s integrated GUI Development Environment (GUIDE) tool. This interface streamlines the preprocessing and numerical computation of the power spectrum of atmospheric speckles while providing real-time graphical representations of atmospheric parameters, including the vertical profile of the refractive index structure function Cn2(h). It also incorporates parameters related to adaptive optics and high angular resolution, such as seeing, enabling immediate and instantaneous visual assessment of observational conditions. Furthermore, the novelty of this GUI lies in the ease of acquiring and processing data from various atmospheric parameters. The Single Star SCIDAR (Scintillation Detection and Ranging) method relies on analyzing the scintillation of light from single stars to assess the turbulent characteristics of the atmosphere. This assessment is based on the description provided by Cn2(h) derived from minimizing an objective function determined using the power spectrum of atmospheric speckles from single stars. For this purpose, a minimization algorithm called active-set is used.

Directed self-assembly software for single cell deposition.

Laser direct-write (LDW) bioprinting methods offer a diverse set of tools to design experiments, fabricate tissue constructs and to cellular microenvironments all in a CAD/CAM manner. To date, we have just scratched the surface of the system’s potential and for LDW to be utilized to its fullest, there are many distinct hardware and software components that must be integrated and communicate seamlessly. In this perspective article, we detail the development of novel graphical user interface (GUI) software to improve LDW capability and functionality. The main modules in the control software correspond to cell transfer, microbead fabrication, and micromachining. The modules make the control of each of these features, and the management of printing programs that utilize one or more features, to be facile. The software also addresses problems related to construct scale-up, print speed, experimental conditions, and management of sensor data. The control software and possibilities for integrated sensor data are presented.

Speech Emotion Recognition using Machine Learning

This research paper presents a Speech Emotion Recognition (SER) system utilizing a Multilayer Perceptron (MLP) classifier and real-time audio analysis. The system records audio samples, extracts relevant features, and employs machine learning techniques to predict emotions in spoken language. The study focuses on the development of an intuitive Graphical User Interface (GUI) using the Kivy framework, providing a user-friendly platform for real-time emotion analysis. In shortly In this project, we attempt to detect underlying emotions such as (sad, happy, neutral, angry, disgust, surprised, fearful and calm) in recorded speech by analysing the acoustic features of the audio data of recordings and Created an application to implement the same on user input.

A new propulsion system GUI based control amenable model development for high‐power rockets

SummaryThis paper proposes a new algorithm to model and characterize an autonomous high‐power rocket using an indigenously developed graphical user interface (GUI) platform. This platform features a newly devised app, termed as THIEC Rocketry App which embeds the simulation based analysis to determine the design parameters of the rocket, required for a vertical flight. A solid propellant using potassium nitrate and sucrose, also known as rocket‐candy, is considered for the GUI development. The GUI facilitates the designer to specify the desired flight parameters for the rocket propulsion system. Various characteristic plots for visualization and analysis are made available in GUI. The obtained parameters from the GUI are then utilized in computer‐aided designing (CAD) for further identification of geometrical parameters like inertia tensor, center of gravity (CG) and center of pressure (CP). The mathematical control amenable model of the rocket is then developed using first principles so as to achieve an altitude up to 3 km. The overall system represents a complex nonlinear multi‐input multi‐output (MIMO) dynamics, having six degrees of freedom. The Newton‐Euler formulation is employed to develop the equations of motion. The attitude control using canards is analyzed via simulations for the complete flight path ‐ the boost and coast flights. Finally, the developed GUI based model is validated by practically manufacturing the components of the propulsion system for the small‐scale high‐power rocket. The proposed model will create the pathway for the development of some robust model‐based control schemes for such autonomous rockets in future.

Development of graphical user interface for design of experiments via Gaussian process regression and its case study

Development of graphical user interface for design of experiments via Gaussian process regression and its case study

A Detailed Study on IIR-FIR Filters and Design of a Graphical User Interface for Simulation of EEG Signals

Electroencephalography (EEG) plays a pivotal role in accepting brain activity and identifying neurological disorders. The precise analysis and explanation of EEG signals require the solicitation of digital signal processing techniques, such as Infinite Impulse Response (IIR) as well as Finite IJmpulse Response (FIR) filters. This paper investigates into a complete exploration of IIR-FIR filters and their application in EEG signal processing. The first part of this research paper involves a detailed examination of IIR and FIR filter types, their mathematical foundations, pros, and cons. Various filter design methods, such as Chebyshev, Butterworth and Elliptic filters, are discussed as well as compared through literature survey. Speculative aspects, including filter design, transfer functions, and frequency responses, are presented in a clear and much accessible manner. The second phase of the study introduces the design of a Graphical User Interface (GUI) on mathematical modelling tool aimed at enabling EEG signal simulation and analysis. This GUI is designed to enable users, including researchers and clinicians, to generate synthetic EEG signals with controllable parameters, apply IIR-FIR filters in real-time, and thereby visualize the filtered signals. The interface offers user-friendly controls for customizing filter characteristics, such as filter order, cutoff frequencies, and filter type. To validate the efficiency of the designed GUI and the selected IIR-FIR filters, general simulations are conducted using EEG datasets. The results showcase the GUI's efficacy in real-time EEG signal processing, demonstrating its prospective in research, clinical diagnostics, and educational settings and many areas. In summary, this paper presents a full investigation into IIR-FIR filters, proposing insights into their theory and practical application for EEG signal processing. The development of a intelligible GUI for EEG signal simulation and study further enhances the approachability of these mathematical modelling tools to a wider audience, eventually contributing to progressions in the field of neuroscience and brain signal analysis.

Diagnosis and Study of Mechanical Vibrations in Cargo Vehicles Using ISO 2631-1:1997.

This study presents the design and implementation of an electronic system aimed at capturing vibrations produced during truck operation. The system employs a graphical interface to display vibration levels, ensuring the necessary comfort and offering indicators as a solution to mitigate the damage caused by these vibrations. Additionally, the system alerts the driver when a mechanical vibration that could potentially impact their health is detected. The field of health is rigorously regulated by various international standards and guidelines. The case of mechanical vibrations, particularly those transmitted to the entire body of a seated individual, is no exception. Internationally, ISO 2631-1:1997/Amd 1:2010 oversees this study. The system was designed and implemented using a blend of hardware and software. The hardware components comprise a vibration sensor, a data acquisition card, and a graphical user interface (GUI). The software components consist of a data acquisition and processing library, along with a GUI development framework. The system underwent testing in a controlled environment and demonstrated stability and robustness. The GUI proved to be intuitive and could be integrated into modern vehicles with built-in displays. The findings of this study suggest that the proposed system is a viable and effective method for capturing vibrations in trucks and informing drivers about vibration levels. This system has the potential to enhance the comfort and safety of truck drivers.

Development of graphical user interface for boost converter employing visual studio

In power electronics, the boost converter is a typical circuit that raises the voltage from the input to the desired output voltage. For learning how a boost converter works, you need an easy-to-use GUI that lets you change the input voltage, output voltage, and load resistance. In this study, we look at how to use Visual Studio to make a boost converter’s simple user interface (GUI). Details are given about how the software was programmed and how the interface was made, as well as how the graphical user interface was made. This circuit has a total of four devices, two for current and two for voltage. Voltage sensors can measure both the input and output voltage, while current sensors can measure both the input and output current. The Arduino Uno microcontroller is used as a processor for getting data and as a link between sensor devices and personal computers. The results show that the GUI we made works well for analyzing the features of the boost converter circuit.

Development of Graphical User Interface (GUI) for Surgery Assistance Application

Telemedicine is a technology that promotes patient engagement and makes healthcare more affordable and accessible. Telehealth technologies are used in healthcare settings such as teleconferencing, patient data transfer, and shared remote learning. However, current teleconferencing has limited ability to perform annotation on real-time videos. The proposed surgery assistance system would revolutionize the medical surgery industries where surgery can be monitored remotely by a surgical specialist with minimal assistance. The supervising surgeon can provide assistance to his subordinates in the operating room by annotating on videos of body parts transmitted live then the annotation will be projected directly onto the patient’s body as physician’s guidance. A comprehensive surgery assistance system is proposed as there are not many open-source systems that can support this technology. The system is capable of detecting the reference points placed on the body parts that will be operated on. Any movement of the reference points should be detected by this system, which would also alert the user with a message when movement is detected. This process is critical to ensure that the video images delivered to the remote monitor are identical to the video images captured in the operating room. Python version 3.9.4 was used, along with its associated libraries, to develop the proposed system. Socket communication using Transport Control Protocol (TCP) is used to make video transmission more secure. The proposed graphical user interface was accessed experimentally using the functional and reliability tests with the average percentage of 92% and 95% respectively to ensure that it was well implemented and both of the proposed goals were achieved.

Speech Emotion Recognition using Machine Learning With Real-time Audio Analysis

This research paper presents a Speech Emotion Recognition (SER) system utilizing a Multilayer Perceptron (MLP) classifier and real-time audio analysis. The system records audio samples, extracts relevant features, and employs machine learning techniques to predict emotions in spoken language. The study focuses on the development of an intuitive Graphical User Interface (GUI) using the Kivy framework, providing a user-friendly platform for real-time emotion analysis. In shortly In this project, we attempt to detect underlying emotions such as (sad, happy, neutral, angry, disgust, surprised, fearful and calm) in recorded speech by analysing the acoustic features of the audio data of recordings and Created an application to implement the same on user input

A Study of Engineering Student IQ and EQ Based on Analyzing of Electroencephalogram (EEG) Signals

This paper elaborates about the analyzing of Intelligent Quotient (IQ) and Emotional Quotient (EQ) of an engineering student based on Electroencephalogram (EEG) or brain signals using a computing tool such as MATLAB. The influence of individual’s IQ and EQ on the development of innovative thinking is significant. An IQ alone is not adequate to measure the success of a person. An EQ somehow is found to have a substantial correlation with individual’s performance which shows that both IQ and EQ are important in determining the character and manner of individual’s thinking skills. The main objective of this project is to analyze the output of EEG signals produced by an engineering student based on their IQ and EQ level. A comparison of which quotient influences the student’s thinking skill is also observed in this study. In this project, a single channel EEG amplifier is used to record the EEG signals from 6 different subjects with three different conditions: while not doing anything, during an IQ test and during an EQ test. The data is recorded using the EEG sensors with Unicorn Recorder software that is connected to the amplifier while recording is run. The result of the experiment also explained in terms of the mean value and standard deviation of the EEG bands for each subject and condition. The results of the study conclude that the IQ level of an individual is affected by the Beta band of the EEG signals. Meanwhile, Alpha band of the EEG signals is more dominant in the determination of EQ level of an individual. When Alpha band is higher than Beta band, it shows that the subject is dominant to an EQ dominant person. If Beta band is higher than Alpha band, then IQ is the dominant quotient for the subject. The highest k-NN classification accuracy is obtained at 80:20 classification with 81.84% accuracy using Mean features and 79.53% accuracy using Standard Deviaton features. This study also included the development of Graphical User Interface (GUI) in MATLAB. The GUI is used for the classification of the chosen EEG features. Theta, Alpha, and Beta power band are observed from the EEG signals to identify brain activity. In addition, IQ and EQ analyzer is constructed using Arduino microcontroller once the analysis of the EEG signals are done through GUI.

Import Substitution of Information Systems based on the С# Language and Network

The purpose of the article is to study methods of migration to other environments of information systems previously created in Microsoft Windows in the C# language. These environments either belong to the open class, or were developed directly in Russia. One of the main migration tools is the separation of systems into components. In this case, the transformation tasks of each component are solved separately. Methods of transformation of information systems functioning in an environment based on operating systems, database management tools, web tools, frameworks and libraries are considered. The main directions of preparation of the executive environment, the components of which are included in the register of Russian software, or provide open source codes, are highlighted. Operating systems that meet the given criteria are selected. Basically, these systems are founded on Linux kernel. Methods for configuring the Apache 2 web server and creating a virtual host are defined. A brief assessment of database migration methods was carried out. The issues of correcting the C# source code when changing the operating environment and methods of converting the source text when changing the ways of working with the graphical user interface are considered. The main problems during the transition to the new system and ways to solve them are shown. Taking into account the mandatory code correction required during the migration process and the development of a new graphical user interface, the transition from Windows to Linux is possible at real time-consuming costs. Using existing methods of migrating databases, web services and client applications allows you to complete the migration of information systems based on the C# language into environments that meet the criteria for import substitution.

Identification and Prediction of Metastatic Tumor in Breast Using Deep Learning Techniques

Abstract: Our work aims to develop a system for breast cancer detection using Image Processing Techniques. It addresses the limitations of mammography, such as painful procedures, by introducing thermal image analysis. For mammogram analysis, morphology techniques are applied to enhance the images before feature extraction. The preprocessed image used to classify mammograms into threecategories: Normal Fatty Breast, Abnormal Fatty Breast, and Glandular Breast. In the case of thermal images, the system analyses the distribution of heat around the breast. Variations in heat levels are used to identify potential cancerous areas. Additionally, the project includes the development of a user-friendly Graphical User Interface (GUI) to enhance accessibility for users. The proposed system provides option on architectures namely CNN, VGG16 and Densenet121 using which the result is obtained.

Graphical User Interface Development for a Hospital-Based Predictive Risk Tool: Protocol for a Co-Design Study.

This co-design research method details the iterative process developed to identify health professional recommendations for the graphical user interface (GUI) of an artificial intelligence (AI)-enabled risk prediction tool. Driving the decision to include a co-design process is the belief that choices regarding the aesthetic and functionality of an intervention are best made by its intended users and that engaging these users in its design will promote the tool's adoption and use. The aim of this research is to identify health professional design and uptake recommendations for the GUI of an AI-enabled predictive risk tool. We will hold 3 research phases, each consisting of 2 workshops with health professionals, between mid-2023 and mid-2024. A total of 6 health professionals will be sought per workshop, resulting in a total enrollment of 36 health professionals at the conclusion of the research. A total of 7 workshop activities have been scheduled across the 3 workshops; these include context of use, notifiers, format, AI survey-Likert, prototype, AI survey-written, and testing. The first 6 of these activities will be repeated in each workshop to enable the iterative development and refinement of GUI. The last activity (testing) will be performed in the final workshop to examine health professionals' thoughts on the final GUI iteration. Qualitative and quantitative results data will be produced from tasks in each research activity. Qualitative data will be examined through inductive thematic analysis or deductive thematic analysis in accordance with the Nonadoption, Abandonment, and Challenges to the Scale-up, Spread, and Sustainability (NASSS) framework; visual data will be examined in accordance with "framework of interactivity;" and quantitative data will be examined using descriptive statistics. Project registration with the Australia and New Zealand Clinical Trial Registry has been requested (#384098). Finalized design recommendations are expected in early to mid-2024, with a results manuscript to be submitted in mid-2024. This research method has human research ethics approval from the South Australian Department of Health and Wellbeing (#2022/HRE00131) as well as from the Human Research Ethics Committee of the University of South Australia (application ID#204143). Understanding whether an intervention is needed in a particular situation is just the start; designing an intervention so that it is used within that situation is paramount. This co-design process engages end users to create a GUI that includes the aesthetic and functional details they need in a manner that aligns with their existing work practices. Indeed, interventions that fail to do this may be disliked, and at worst, they may be dangerous. PRR1-10.2196/47717.

A Semi-Autonomous Multi-Vehicle Architecture for Agricultural Applications

The ageing population, climate change, and labour shortages in the agricultural sector are driving the need to reevaluate current farming practices. To address these challenges, the deployment of robot systems can help reduce environmental footprints and increase productivity. However, convincing farmers to adopt new technologies poses difficulties, considering economic viability and ease of use. In this paper, we introduce a management system based on the Robot Operating System (ROS) that integrates heterogeneous vehicles (conventional tractors and mobile robots). The goal of the proposed work is to ease the adoption of mobile robots in an agricultural context by providing to the farmer the initial tools needed to include them alongside the conventional machinery. We provide a comprehensive overview of the system’s architecture, the control laws implemented for fleet navigation within the field, the development of a user-friendly Graphical User Interface, and the charging infrastructure for the deployed vehicles. Additionally, field tests are conducted to demonstrate the effectiveness of the proposed framework.

Machine-learning based characteristic estimation method in printed circuit board production lines

In this study, software and hardware that supported automatic optical inspection (AOI) for printed circuit board production line was proposed and demonstrated. The proposed method showed an effective solution that predicts off-line electromagnetic (EM) characteristic of manufactured components through in-line pattern integrity. A spiral antenna that represented complex patterns was used as the evaluation target with imitated production variations. Numerical evaluation on EM properties, batch fabrication, hardware setup and optimization, algorithm and graphical user interface development, machine learning and artificial intelligence modeling, and data verification and analysis were thoroughly conducted in this study. Results indicated that when the antenna showed pattern distortion, its passive capacitance, active intensity, and active frequency increased, decreased, and decreased, respectively. These results proved that the developed system and method overcame the inability of in-line EM measurement in conventional setup. The results also showed high estimation accuracy that was not yet achieved in the past. Compared to existing or similar AOI ideas, the proposed method supports analyses on complex pattern, provides solutions on target design, and efficient algorithm generation. This work also proved active and passive EM signals with evidences, and exhibited outstanding confidence levels for characteristic estimations. The proposed system and method indicated their potential in smart manufacturing.