Graphical Interface Research Articles

Performance evaluation of low energy adaptive clustering hierarchy-based cluster routing protocols in wireless sensor networks using a new graphical user interface

Wireless sensor network (WSN) is widely used for field data acquisition and monitoring in different domains. To make this type of network functional, efficient routing protocols must be implemented. Nevertheless, WSNs have an energy constraint due to limited batteries. Many clustered protocols are proposed to overcome it. However, the implementation of these protocols would be difficult to understand without a simulation tool, as some problems may arise during their development. Testing real-world applications requires a lot of effort and cost because they often use many nodes in large networks. Therefore, the simulation tool is the most relative way to evaluate these protocols. This paper presents graphical-based cluster protocols simulation interface for WSN(GCPS-WSN), a new interface to simulate some clustered protocols in WSNs. GCPS-WSN allows the user to evaluate the performance of certain low energy adaptive clustering hierarchy (LEACH) enhanced protocols to choose the most appropriate one for his system. The user can simulate protocols without any knowledge of software programming.

Medication dispenser with touch screen and consumption authenticator for monitoring adherence to medication in the elderly

The elderly population continues to grow, this population has many chronic conditions, which is why they take multiple pills at the same time, this situation makes it difficult for them to take their medications at the appropriate time, which can cause health complications and even death. death. The objective of this research is to improve medication adherence using a medication dispenser that allows authenticating medication consumption accurately with a high level of usability. To do this, we implemented a dispenser with an alarm system that can be configured from a graphical interface, with internet of things (IoT) to remotely monitor the intake of pills and authenticate their consumption through artificial vision that will use the instant messaging system to inform the caregiver about the situation and finally measure the dispenser usability.

Four Quadrant Operation of DC Motor Controlled by Android Application

Abstract: The project is designed to develop a four quadrant speed control system for a DC motor. The motor is operated in four quadrants i.e. clockwise; counter clock-wise, forward brake and reverse brake. It also has a feature of speed control. The four quadrant operation of the dc motor is best suited for industries where motors are used and as per requirement as they can rotate in clockwise, counter-clockwise and also apply brakes immediately in both the directions. In case of a specific operation in industrial environment, the motor needs to be stopped immediately. In such scenario, this proposed system is very apt as forward brake and reverse brake are its integral features. Instantaneous brake in both the directions happens as a result of applying a reverse voltage across the running motor for a brief period and the speed control of the motor can be achieved with the PWM pulses generated by the microcontroller. The microcontroller used in this project is from 8051 family. Remote operation is achieved by any smart-phone/Tablet etc., with Android OS, upon a GUI (Graphical User Interface) based touch screen operation. Bluetooth device is provided to connect with android application device for the operation of the motor which are interfaced to the microcontroller that provides an input signal to it and in turn controls the speed of the motor through a motor driver IC. This project can be enhanced by using higher power electronic devices to operate high capacity DC motors. Regenerative braking for optimizing the power consumption can also be incorporated

Handwritten Digit Recognition

Abstract: This paper presents a powerful Handwritten Digit Recognition System that combines a Graphical User Interface (GUI) based on Tkinter with Convolutional Neural Networks (CNNs). Our approach, which makes use of the MNIST dataset, includes careful data pretreatment to facilitate efficient CNN model training. Convolutional and pooling layers are included in the design of the model, and they are optimized with the Adam optimizer for higher learning rates. The evaluation's findings demonstrate excellent memory, accuracy, and precision. Furthermore, real-time digit drawing is made possible via an intuitive Tkinter GUI, which confirms the model's applicability. By showing the effectiveness of CNNs and offering an interactive platform for natural user interaction, the research provides a holistic solution to handwritten digit recognition. This method is promising for various uses in digit recognition scenarios, highlighting its flexibility and usefulness in real-world situations.

Unified Steganography for Concealing and Extracting Secret Messages

Abstract: With the increasing significance of data security in the digital age, steganography has emerged as a crucial technique for concealing information within various forms of media. This paper presents a unified steganographic framework designed to embed and extract secret messages in images, audio, and text files. Utilizing the Least_Significant_Bit (LSB) method, our approach ensures minimal distortion of the host media while maintaining high security and robustness of the hidden messages. The system is implemented in Python, featuring an intuitive graphical user interface (GUI) to facilitate user interaction. Comprehensive experiments and analyses demonstrate the effectiveness of our method in maintaining the imperceptibility and integrity of the steganographic media. This unified solution offers a versatile and efficient tool for secure communication, digital watermarking, and protection of intellectual property. Our findings underline the potential of integrating steganographic techniques across different media formats to enhance data security and privacy.

Handwritten Digit Recognition System using Machine Learning

Abstract: Handwritten digit recognition remains a crucial area of research in pattern recognition and machine learning. In this paper, we present a novel approach to enhance handwritten digit recognition systems by incorporating deep learning techniques and an interactive graphical user interface (GUI). Our system employs convolutional neural networks (CNNs) for feature extraction and classification, allowing for improved accuracy and robustness in digit recognition tasks.

Real Time Monitoring of Temperature And Humidity Using LabVIEW and ML

Abstract: This project presents a Real-Time Temperature and Humidity Monitoring System built on LabVIEW, featuring ML predictive analytics. It facilitates real-time data collection from diverse sensors, ensuring precise environmental monitoring. The ML algorithms within the system forecast future temperature and humidity trends, aiding proactive decision-making. Users enjoy an intuitive graphical interface for visualizing data, analysing historical trends, and receiving alerts for threshold variations. Additionally, the system provides remote monitoring, data logging, and reporting, enhancing its usability and accessibility. This integrated solution caters to the crucial requirement for accurate environmental monitoring, with applications spanning industries like manufacturing, agriculture, and climate control, ultimately enhancing efficiency and decision-making in these sectors.

Theoretical Investigation and Design of Bioactive Quinoline Derivatives as Inhibitors of DNA Gyrase of Salmonella typhi

The ever-increasing rate of resistance to existing antibiotics by Salmonella typhi has made the search for novel drug candidates a necessity. In this study, Molecular docking technique was used to screen 18 bioactive quinoline derivatives against DNA gyrase of Salmonella typhi using PyRx graphical user interface of AutoDock Vina software. Ligand with the best binding affinity against the target macromolecule was used as prototype to design novel analogues with enhanced potencies. With the aid of Swiss ADME online server and Osiris DataWarrior v5.5.0 programme, the absorption, distribution, metabolism, excretion and toxicity (ADMET) profiles of the ligands were evaluated and their electronic properties were computed using density functional theory (DFT) method of Spartan 14.0 software. Ligand 3 (L3) with the best binding affinity (TG) value of −10.5 kcal/mol was chosen as a prototype to design La and Lb with TG value of −10.7 kcal/mol and −10.8 kcal/mol; binding constant (ka) of 7.04 × 107 and 8.35 × 107 ; and dissociation constant (kd) of 1.42 x 10−8 and 1.2 × 10−8, respectively. When compared with ciprofloxacin (TG = −7.7 kcal/mol), the ligands could be more potent. Likewise, the ligands were found to possess excellent oral bioavailability and pharmacokinetic profiles. Furthermore, DFT calculations on La and Lb revealed that they possess highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) energy gap of 3.46 eV and 3.45 eV; and global electrophilicity index of 3.83 eV and 3.96 eV, respectively. The designed ligands tend to be consistent with all the validation protocols deployed in this study and as such could be recommended as novel drug candidates for treatment of Salmonella typhi induced salmonellosis.

Ensemble Learning Models for Prediction of Punching Shear Strength in RC Slab-Column Connections

In reinforced concrete (RC) structures, accurate prediction of the punching shear strength (PSS) of slab-column connections is imperative for ensuring safety. The existing equations in the literature show variability in defining parameters influencing PSS. They neglect potential variable interactions and rely on a limited dataset. This study aims to develop an accurate and reliable model to predict the PSS of slab-column connections. An extensive dataset, including 616 experimental results, was collected from earlier studies. Six robust ensemble machine learning techniques—random forest, gradient boosting, extreme gradient boosting, adaptive boosting, gradient boosting with categorical feature support, and light gradient boosting machines—are employed to predict the PSS. The findings indicate that gradient boosting stands out as the most accurate method compared to other prediction models and existing equations in the literature, achieving a coefficient of determination of 0.986. Moreover, this study utilizes techniques to explain machine learning predictions. A feature importance analysis is conducted, wherein it is observed that the reinforcement ratio and compressive strength of concrete demonstrate the highest influence on the PSS output. SHapley Additive exPlanation is conducted to represent the influence of variables on PSS. A graphical user interface for PSS prediction was developed for users’ convenience. Doi: 10.28991/CEJ-SP2024-010-01 Full Text: PDF

Revolutionizing Precision Agriculture with Drone-Based Imaging and Fuzzy Intelligent Algorithms

This paper proposes a clear understanding of drone based agricultural image processing techniques using fuzzy intelligent algorithm. This procedure is used for drone-based agricultural monitoring through intelligent calculations by various images acquired and trained from the field. The objective is to upgrade the exactness and proficiency of crop yield and the accuracy in agriculture. The proposed methods use high quality images caught by drones to remove crop-related infections and evaluate their wellbeing status. The fuzzy based calculation is applied to characterize the harvest wellbeing status and produce a choice to guide and assist the farmers of various regions to concentrate the area of infection based on the images captured from drones. The projected approach is assessed utilizing the information and the consideration and appropriate recommendation can be made after careful evaluation of infected region in the field. Graphical User Interface (GUI) is developed using fuzzy based algorithm which can segment the infectious leaves and classify the type of diseases and check the healthiness of the leaves. Results show that the proposed approach accomplishes higher exactness and improved effectiveness in distinguishing crop wellbeing status in comparable with existing the procedures. This study shows the capability of integrating drone-based agriculture with fuzzy intelligent image processing tool which makes efficient for farmers to improve the quality of crops, attain greater yield and to develop the nation with drone-based agribusiness.

Feasibility of continuous non-invasive delivery of oxygen monitoring in cardiac surgical patients: a proof-of-concept preliminary study

PurposeOxygen delivery (DO2) and its monitoring are highlighted to aid postoperative goal directed therapy (GDT) to improve perioperative outcomes such as acute kidney injury (AKI) after high-risk cardiac surgeries associated with multiple morbidities and mortality. However, DO2 monitoring is neither routine nor done postoperatively, and current methods are invasive and only produce intermittent DO2 trends. Hence, we proposed a novel algorithm that simultaneously integrates cardiac output (CO), hemoglobin (Hb) and oxygen saturation (SpO2) from the Edwards Life Sciences ClearSight System® and Masimo SET Pulse CO-Oximetry® to produce a continuous, real-time DO2 trend.MethodsOur algorithm was built systematically with 4 components – machine interface to draw data with PuTTY, data extraction with parsing, data synchronization, and real-time DO2 presentation using a graphic-user interface. Hb readings were validated.ResultsOur algorithm was implemented successfully in 93% (n = 57 out of 61) of our recruited cardiac surgical patients. DO2 trends and AKI were studied.ConclusionWe demonstrated a novel proof-of-concept and feasibility of continuous, real-time, non-invasive DO2 monitoring, with each patient serving as their own control. Our study also lays the foundation for future investigations aimed at identifying personalized critical DO2 thresholds and optimizing DO2 as an integral part of GDT to enhance outcomes in perioperative cardiac surgery.

Development of a MATLAB‐based graphical user interface to illustrate Coulomb's law and Gauss's law

AbstractCoulomb's law and Gauss's law are two fundamental laws in electrostatics that involve electric charges and electric fields. These concepts that are not visible physically in real life can be challenging for students to understand, especially when it involves complex charge distributions. This paper presents a MATLAB‐based graphical user interface (GUI) to visualise the concept of Coulomb's law and Gauss's law as well as a serious snake game to reinforce users' knowledge. This MATLAB GUI features a diverse range of 3‐dimensional (3D) visualisations illustrating electric field attributes such as electric force, charge distribution and electric flux. These visualisations dynamically adapt to user‐defined parameters, offering a rich and varied exploration of the electric field's characteristics without confining the scope to a specific count of models. In Coulomb's law section, the GUI plots the electric forces in 3‐D for dipole, three‐point charges, and unlimited charge. The Gauss's law section consists of windows for illustration of the fundamentals of Gauss's law for the electric field, and the illustration for the application of Gauss's law in finding the electric field for a point charge, infinite line charge and infinite plane charge. The serious snake game developed in this work allows students to engage in quizzes related to Coulomb's law and Gauss's law to serve as a tool for reinforcing their learning outcomes. The MATLAB‐based GUI was chosen as the platform due to its excellent visualisation capabilities, ease of programming and capability to act as a powerful tool to enhance the learning of Coulomb's law and Gauss's law for students.

Enhancing OpenEP: atrial conduction velocity and conduction velocity heterogeneity quantification through EP Workbench

Abstract Background Alterations in atrial conduction velocity have been implicated in the pathogenesis of atrial arrhythmias, with conduction velocity thought to be slower and more heterogeneous in areas promoting atrial fibrillation. However, clinical studies of conduction velocity are challenging since there are no available platforms that provide researchers and clinicians with the ability to quantify conduction velocity and analyse conduction heterogeneity from electroanatomic mapping data. Purpose In this paper we sought to address these limitations by enhancing the OpenEP Python library by (1) automating calculations using previously-published conduction velocity estimation algorithms; (2) providing an interactive graphical representing of conduction velocity and conduction heterogeneity and (3) implementing a histogram analysis tool to quantify conduction velocity and enable the identification of slow conduction regions. Method Two well-known and previously published methods for calculating cardiac conduction velocity, Triangulation and polynomial surface fitting, were implemented. Conduction velocity estimation was improved further by excluding regions with wave collisions and focal discharges. These regions were identified using the divergence of conduction velocity vector fields, with positive divergence representing focal sources and negative divergence representing areas of collision. Finally, calculated conduction velocities using electrogram data were interpolated over the surface mesh via Radial Basis Function (RBF) interpolation method. All algorithms were implemented in the OpenEP Python library (openep-py), with visualisation tools provided in the complementary graphical interface, EP Workbench. Results An extensible "Analysis" feature was created in openep-py, to provide conduction velocity and divergence calculations which are fully customisable through the graphical interface based on user preferences. The EP Workbench desktop application displays resulting cardiac surface maps to depict calculated conduction velocity and divergence fields, together with conventional activation and voltage maps (Figure 1). The histogram analysis tool can be used to identify regions of slow conduction velocity from electroanatomic mapping data. Using a threshold of <0.3m/s, two slow conducting channels are visualised on the posterior wall of a test case (Figure 2). The tool is not specific to conduction velocity and may also be used to quantify other data types in EP Workbench. Finally, computed conduction velocity and divergence values and surface maps are stored in the standard OpenEP data structure, allowing further analysis following data export. Conclusion We have extended OpenEP to provide a comprehensive set of tools for conduction velocity calculation and analysis which will facilitate investigation of the structural and functional basis of conduction velocity alterations in disease states.

A new ring fixator system for automated bone fixation.

In the field of orthopaedics, external fixators are commonly employed for treating extremity fractures and deformities. Computer-assisted systems offer a promising and less error-prone treatment alternative to manual fixation by utilising a software to plan treatments based on radiological and clinical data. Nevertheless, existing computer-assisted systems have limitations and constraints. This work represents the culmination of a project aimed at developing a new automatised fixation system and a corresponding software to minimise human intervention and associated errors, and the developed system incorporates enhanced functionalities and has fewer constraints compared to existing systems. The automatised fixation system and its graphical user interface (GUI) demonstrate promising results in terms of accuracy, efficiency, and reliability. The developed fixation system and its accompanying GUI represent an improvement in computer-assisted fixation systems. Future research may focus on further refining the system and conducting clinical trials.

Proto.IoT: supporting IoT prototype ideation for non-experts in the classroom

ABSTRACT This paper introduces Proto.IoT, an IoT prototyping toolkit designed to complement teaching in Higher Education (HE) by enabling non-expert users to learn IoT concepts through making within time-constrained lecture settings. The significance of Proto.IoT lies in its abstraction of hardware and software implementations, facilitating accessibility for non-experts through a visual rule-based programming environment and plug-and-play approaches. A pilot study, involving 25 IoT developers from the UK and Malaysia, evaluated the IoT back-end system architecture, revealing that Proto.IoT was perceived as user-friendly and effective in reducing development time. The main evaluation focused on the toolkit's front-end rule-based graphical user interface (GUI), engaging 41 undergraduate students. Results indicated that the toolkit supported prototype ideation and was intuitive to use. These findings illustrate the potential of IoT prototyping toolkits to complement traditional lectures, fostering rapid prototyping and ideation, thus facilitating maker education within formal classroom settings.

FungiRegEx: A Tool for Pattern Identification in Fungal Proteomic Sequences Using Regular Expressions

In the context of proteomic-scale research, it is imperative to automatically analyze numerous species and subspecies to discern distinctive characteristics present in multiple species of the fungi kingdom that contain sequences of interest that could fulfill a specific biological function. To achieve this, complex sequences must be recognized within an organism’s entire set of proteomes. Our study presents FungiRegEx, a piece of software that facilitates the identification of regular expressions of proteomes of fungal organisms and uses real-time data retrieval of the different species from the JGI Mycocosm database without the need to download any file. Integrating a graphical user interface that makes it easy to use, the tool offers regular expression searches on 2402 fungal species from the JGI Mycocosm portal. The tool was validated with the AXSXG sequence and the RXRL effector, demonstrating the effectiveness of FungiRegEx in identifying user-defined patterns in the recovered sequences. This tool allows customization and filtering, and it can save results if required, combining speed, adaptability, and ease of use. It provides an experience without a console and programming, displaying the results in a GUI and making them easier to read. Its architecture guarantees optimized use of resources, time consumption, and implementation flexibility, allowing the customization of specific software parameters for resource management. The tool’s potential for future research and exploration is emphasized, providing a nuanced perspective on its practical use within the fungal genomics community. The tools are available at the addresses mentioned in the text.

Enhancing Friction Stir Welding: Quality Machine Learning Based Friction Stir Welding Tool Condition Monitoring

Ensuring the quality and optimizing the tool in Friction Stir Welding (FSW) process is quite complex and the solution relies on implementing Condition Monitoring. The major impact of this process yields good quality welds and cuts down the non-operational timing and cost. Condition Monitoring is the key to find a solution to the challenging problem of ensuring quality and optimizing the tool in the FSW process. The creation of a graphical user interface (GUI) and the development and comparison of several models, including Decision Tree (DT), Random Forest (RF), Light Gradient Boosted Machine (LGBM), and Extreme Gradient Boosting (XGBoost), are the main objectives of this study. By offering an uniform interface for tracking and evaluating tool condition data, GUI can make it easier for operators and the maintenance crew to collaborate. Vibration analysis is the first step in tool condition monitoring. Al5083 and AZ31B are used as the workpiece and H13 as the tool in this investigation. The signals are obtained from the experimental setup via DAQ, and LabView processes them. A Python script converts the raw signals into statistical data. Following that, the data was loaded into ML models and optimized using Optuna. TKinter has been used to create the GUI. For prediction, the best models were included in the GUI. By the deployed models, LGBM generates 96% for 1000 rpm, 96.55% for 1200 rpm, and 95.90% for 1400 rpm for Al5083 93.22% for 1000 rpm, 99.29% for 1200 rpm, and 91.50% for 1000 rpm for AZ31B. For real-time prediction, these models are thus connected to a graphical user interface. In each case, the LGBM classifier topped the others. This work served as an initial basis for the creation of a semi-onboard diagnostic approach that requires minimal human input.

Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience.

The study of complex behaviors is often challenging when using manual annotation due to the absence of quantifiable behavioral definitions and the subjective nature of behavioral annotation. Integration of supervised machine learning approaches mitigates some of these issues through the inclusion of accessible and explainable model interpretation. To decrease barriers to access, and with an emphasis on accessible model explainability, we developed the open-source Simple Behavioral Analysis (SimBA) platform for behavioral neuroscientists. SimBA introduces several machine learning interpretability tools, including SHapley Additive exPlanation (SHAP) scores, that aid in creating explainable and transparent behavioral classifiers. Here we show how the addition of explainability metrics allows for quantifiable comparisons of aggressive social behavior across research groups and species, reconceptualizing behavior as a sharable reagent and providing an open-source framework. We provide an open-source, graphical user interface (GUI)-driven, well-documented package to facilitate the movement toward improved automation and sharing of behavioral classification tools across laboratories.

A novel method for developing contrast-detail curves from clinical patient images based on statistical low-contrast detectability

Purpose. To develop a method to extract statistical low-contrast detectability (LCD) and contrast-detail (C-D) curves from clinical patient images. Method. We used the region of air surrounding the patient as an alternative for a homogeneous region within a patient. A simple graphical user interface (GUI) was created to set the initial configuration for region of interest (ROI), ROI size, and minimum detectable contrast (MDC). The process was started by segmenting the air surrounding the patient with a threshold between −980 HU (Hounsfield units) and −1024 HU to get an air mask. The mask was trimmed using the patient center coordinates to avoid distortion from the patient table. It was used to automatically place square ROIs of a predetermined size. The mean pixel values in HU within each ROI were calculated, and the standard deviation (SD) from all the means was obtained. The MDC for a particular target size was generated by multiplying the SD by 3.29. A C-D curve was obtained by iterating this process for the other ROI sizes. This method was applied to the homogeneous area from the uniformity module of an ACR CT phantom to find the correlation between the parameters inside and outside the phantom, for 30 thoracic, 26 abdominal, and 23 head images. Results. The phantom images showed a significant linear correlation between the LCDs obtained from outside and inside the phantom, with R2 values of 0.67 and 0.99 for variations in tube currents and tube voltages. This indicated that the air region outside the phantom can act as a surrogate for the homogenous region inside the phantom to obtain the LCD and C-D curves. Conclusion. The C-D curves obtained from outside the ACR CT phantom show a strong linear correlation with those from inside the phantom. The proposed method can also be used to extract the LCD from patient images by using the region of air outside as a surrogate for a region inside the patient.

Design of Fast Radio Burst Signal Recognition System based on Deep Learning

In order to identify fast radio burst signals from the original observation data of FAST radio telescopes, this paper designs a fast radio burst signal recognition system based on deep learning object detection algorithm. The system uses the incoherent achromatization algorithm and the YOLO series target recognition algorithm to realize the recognition of fast radio burst signals, and provides users with a friendly graphical system interface. In view of the different performance of users' computers, the system has the function of selecting different algorithm models. Experiments have proved that the system achieves 86% recall and 83% accuracy in the FRB20201124A real-world data test set.