MATLAB-based Graphical User Interface Research Articles

Voxeloc: A time-saving graphical user interface for localizing and visualizing stereo-EEG electrodes

BackgroundThanks to its unrivalled spatial and temporal resolutions and signal-to-noise ratio, intracranial EEG (iEEG) is becoming a valuable tool in neuroscience research. To attribute functional properties to cortical tissue, it is paramount to be able to determine precisely the localization of each electrode with respect to a patient’s brain anatomy. Several software packages or pipelines offer the possibility to localize manually or semi-automatically iEEG electrodes. However, their reliability and ease of use may leave to be desired. New methodVoxeloc (voxel electrode locator) is a Matlab-based graphical user interface to localize and visualize stereo-EEG electrodes. Voxeloc adopts a semi-automated approach to determine the coordinates of each electrode contact, the user only needing to indicate the deep-most contact of each electrode shaft and another point more proximally. ResultsWith a deliberately streamlined functionality and intuitive graphical user interface, the main advantages of Voxeloc are ease of use and inter-user reliability. Additionally, oblique slices along the shaft of each electrode can be generated to facilitate the precise localization of each contact. Voxeloc is open-source software and is compatible with the open iEEG-BIDS (Brain Imaging Data Structure) format. Comparison with existing methodsLocalizing full patients’ iEEG implants was faster using Voxeloc than two comparable software packages, and the inter-user agreement was better. ConclusionsVoxeloc offers an easy-to-use and reliable tool to localize and visualize stereo-EEG electrodes. This will contribute to democratizing neuroscience research using iEEG.

Matlab based graphical user interface for the monitoring and early detection of keratoconus

The rapid and extensive growth in medical imaging modalities and their applications is creating a pressing need for computers and computing in image processing, visualization, archival, and analysis. In this article, a Matlab-based graphical user interface (GUI) program is proposed for the monitoring and early detection of keratoconus. The findings show the efficiency of the proposed to detect the early stage of keratoconus. The proposed neural network model produces accuracy, ranging from 96% to 92%. It considers, respectively, 2 classes (normal cornea and keratoconus) and 3 classes (keratoconus, suspected keratoconus or normal) which will increase to 99% with respect to the 2 classes of keratoconus and 94% to the 3 classes when combining topography parameters with OCT image corneal pachymetry measurements and clinical judgments. 
 The compatibility of the graphical interface components with common medical data and image analysis tools facilitates the involvement of the ophthalmologist in the digitization of the medical records, the image processing and the conception of multimodal artificial intelligence applications for medical imaging.

PAHPhysRAD: A Digital Imaging and Communications in Medicine Research Tool for Segmentation and Radiomic Feature Extraction.

Segmentation and analysis of organs at risks (OARs) and tumor volumes are integral concepts in the development of radiotherapy treatment plans and prediction of patients' treatment outcomes. To develop a research tool, PAHPhysRAD, that can be used to semi- and fully automate segmentation of OARs. In addition, the proposed software seeks to extract 3214 radiomic features from tumor volumes and user-specified dose-volume parameters. Developed within MATLAB, PAHPhysRAD provides a comprehensive suite of segmentation tools, including manual, semi-automatic, and automatic options. For semi-autosegmentation, meta AI's Segment Anything Model was incorporated using the bounding box methods. Autosegmentation of OARs and tumor volume are implemented through a module that enables the addition of models in Open Neural Network Exchange format. To validate the radiomic feature extraction module in PAHPhysRAD, radiomic features extracted from gross tumor volume of 15 non-small cell lung carcinoma patients were compared against the features extracted from 3D Slicer™. The dose-volume parameters extraction module was validated using the dose volume data extracted from 28 tangential field-based breast treatment planning datasets. The volume receiving ≥20 Gy (V20) for ipsilateral lung and the mean doses received by the heart and ipsilateral lung, were compared against the parameters extracted from Eclipse. The Wilcoxon signed-rank test revealed no significant difference between the majority of the radiomic features derived from PAHPhysRAD and 3D Slicer. The average mean lung and heart doses calculated in Eclipse were 5.51 ± 2.28 Gy and 1.64 ± 1.98 Gy, respectively. Similarly, the average mean lung and heart doses calculated in PAHPhysRAD were 5.45 ± 2.89 Gy and 1.67 ± 2.08 Gy, respectively. The MATLAB-based graphical user interface, PAHPhysRAD, offers a user-friendly platform for viewing and analyzing medical scans with options to extract radiomic features and dose-volume parameters. Its versatility, compatibility, and potential for further development make it an asset in medical image analysis.

A study on the prediction model of water column sound absorption coefficients considering the characteristics of the East Sea of South Korea

East Sea of Korea has the deepest water depth among Korean seas, and the water column changes are distinct in space and time, resulting in a wide range of water column sound absorption coefficients. In this study, we calculate and analysis the spatiotemporal variation of water column sound absorption coefficient in the East Sea of Korea, which has unique oceanographic characteristics. Using extensive CTD data from the Korea Oceanographic Data Centre (KODC) from 1991 to 2021, we calculate the sound absorption coefficient using the widely applied Francois–Garrison model and reveal the unique absorption characteristics of the East Sea. The derived coefficients are then used to calibrate and improve past multibeam echosounder system (MBES) data, which suffered from incomplete water column information and arbitrary mean values. To facilitate the calculation and development of sound absorption coefficient, a MATLAB-based graphical user interface (GUI) was developed. The results of this study contribute to the calculation and inference of sound absorption coefficients along spatial and temporal dimensions of the water column in the East Sea and to the extraction of more accurate seafloor information from MBES data. This study developed the way for future research focusing on algorithmic advances and new methodologies for the calculation of sound absorption coefficients in the West and South Seas of Korea.

A simulation model for an acoustic effect generator with graphical user interface

Acoustic effects are usually created with the assistance of acoustic setup units. Although there are numerous advanced technologies in the realm of electronics, generating changeable auditory effects with low-cost electronic equipment is a difficult task. Acoustic effects are commonly employed in biomedical research on sound localization, sound detection, and sound perception, as well as in numerous applications of perception by human subjects, localization in the median/vertical plane, and sound localization. It has been shown that in order to generate varied acoustic effects using a hardware system, the operator must make regular adjustments to the acoustic setup, which necessitates technical expertise as well as a significant amount of time spent rewiring the electrical equipment’s. In this research, a MATLAB-based Graphical user interface is built and developed to assist users in creating a variety of acoustic effects such as isochronic sound, sine wave sound, low and high frequency sound signals. The output of the generated acoustic effects can be seen on the speakers/headsets. The results reveal that this GUI-based tool can create a wide range of acoustic effects.

A graphical user interface (GUI) for model-based radiation-induced acoustic computed tomography

Radiation-induced acoustic computed tomography (RACT) is an evolving biomedical imaging modality that aims to reconstruct the radiation energy deposition in tissues. Traditional back-projection (BP) reconstructions carry noisy and limited-view artifacts. Model-based algorithms have been demonstrated to overcome the drawbacks of BPs. However, model-based algorithms are relatively more complex to develop and computationally demanding. Furthermore, while a plethora of novel algorithms has been developed over the past decade, most of these algorithms are either not accessible, readily available, or hard to implement for researchers who are not well versed in programming. We developed a user-friendly MATLAB-based graphical user interface (GUI; RACT2D) that facilitates back-projection and model-based image reconstructions for two-dimensional RACT problems. We included numerical and experimental X-ray-induced acoustic datasets to demonstrate the capabilities of the GUI. The developed algorithms support parallel computing for evaluating reconstructions using the cores of the computer, thus further accelerating the reconstruction speed. We also share the MATLAB-based codes for evaluating RACT reconstructions, which users with MATLAB programming expertise can further modify to suit their needs. The shared GUI and codes can be of interest to researchers across the globe and assist them in efficient evaluation of improved RACT reconstructions.

Optical Microscope Based Universal Parameter for Identifying Layer Number in Two-Dimensional Materials.

Optical contrast is the most common preliminary method to identify layer number of two-dimensional (2D) materials, but it is seldom used as a confirmatory technique. We explain the reason for variation of optical contrast between imaging systems, motivating system-independent measurement of optical contrast as a critical need. We describe a universal method to quantify the layer number using the RGB (red-green-blue) and RAW optical images. For RGB images, the slope of 2D flake (MoS2, WSe2, graphene) intensity vs substrate intensity is extracted from optical images with varying lamp power. The intensity slope identifies layer number and is system independent. For RAW images, intensity slopes and intensity ratios are completely system and intensity independent. Intensity slope (for RGB) and intensity ratio (for RAW) are thus universal parameters for identifying layer number. The RAW format is not present in all imaging systems, but it can confirm layer number using a single optical image, making it a rapid and system-independent universal method. A Fresnel-reflectance-based optical model provides an excellent match with experiments. Furthermore, we have created a MATLAB-based graphical user interface that can identify layer number rapidly. This technique is expected to accelerate the preparation of heterostructures and to fulfill a prolonged need for universal optical contrast method.

Experimental Analysis of Training Parameters Combination of ANN Backpropagation for Climate Classification

Artificial Neural Networks are widely used in prediction activities and classification processes. However, the implementation on average only uses a network architecture with one hidden layer, while the development of architectures with two or three hidden layers has not been done much. This article discusses the process of developing ANN Backpropagation using a Matlab-based graphical user interface with three hidden layers combined with non-linear activation functions (logsig, tansig, tanh) and training functions (trainrp and trainlm) based on learning rate and momentum. Architecture was created to study climate change in the area around Lombok International Airport station by training on hydrological data (rainfall) from January 2012 to December 2021 with a data type of 10-day interval (36 data every year). The number of neurons in the first hidden layer was determined using the Hecht-Nielsen model, while the second and third hidden layers used the Lawrence-Fredrickson model. Simulation results with architecture 36-73-37-19-1, a learning rate of 0.1, and momentum of 0.9 showed that variations in the activation function logsig-logsig-logsig-purelin and trainlm function demonstrated the best result with epoch of 7, MSE of 0.00090, and RMSE of 0.03011 in the training process and epoch of 5, MSE of 0.003758, and RMSE of 0.0613 in the data testing process. Furthermore, the prediction results demonstrated that a Q-value of 0.222 based on the Schmidt-Ferguson criteria obtained higher rainfall intensity information than previous years with climate category B (wet). Therefore, the government must be careful in determining policies related to field activities especially in agriculture because of climatic conditions with high rainfall.

Design and Development of task light simulator for Quality Lighting Factor and Simulation of LED tubebased Illumination system for Laboratory

Daylight is only the natural light available surrounding us which is mostly useful in outdoor use. Artificial lighting is used to a greater extent in the world for a variety of indoor as well as outdoor applications. The fundamental requirement of light is to see the objects in a dark time. Nowadays powerful light sources are developed to fulfill the fundamental requirement and the quality illumination. Quality lighting provides uniform distribution in space, good color rendering, and adequate light. The requirement of every task is different and the quality of lighting depends on a number of factors, so there must be a database available that will provide recommended values for a given task. Though there are several simulation software available for analysis of illumination, they are able to provide only information of illumination pattern based on user inputs. There is a need fora user-friendly GUI (Graphical User Interface) which provides necessary parameter values for good lighting design and spectral power distribution of different LED (Light Emitting Diode) sources. The main aim of this work is to model and simulation of spectral characteristics of monochromatic and white LED. Develop a MATLAB-based GUI to select recommended values for different tasks according to Illuminating Engineering Society (IES) standards which helps lighting designers for selection of proper light sources. Uniform illumination is obtained and energy is saved by the integration of daylight with artificial light. A case study of LED tube-based glare-free illumination for the laboratory is also reported

Optimizing Source Wavelets Extracted from the Chirp Sub-Bottom Profiler Using an Adaptive Filter with Machine Learning

In this study, we extracted three source wavelets of a Chirp sub-bottom profiler (SBP), which is widely used for high-resolution marine seismic exploration, using a MATLAB-based graphical user interface tool for computational processing. To extract the source wavelet for general seismic exploration data processing techniques, including that for Chirp SBPs, we first evaluated source wavelet extraction techniques using an adaptive machine learning filter. Subsequently, we performed deterministic deconvolution by extracting the optimal source wavelet from the raw data of Chirp SBP. This source wavelet was generated by applying an adaptive filter. Various methods have been studied to solve the multivariate optimization problem of minimizing the error; in this study, a least-mean-square algorithm was selected owing to its suitability for application to geophysical time-series data. Extracting the source wavelet is a crucial part of high-resolution marine seismic wave exploration data processing. Our results highlight the effectiveness of performing deterministic deconvolution by extracting source wavelets using adaptive filters, and we believe that our method is useful for marine seismic exploration data processing.

Quantification of a Facility Radiological Security Risk Index With a Graphical User Interface Tool.

Healthcare facilities around the world routinely use radioactive sources to diagnose and treat illness. To effectively manage the security of radioactive sources, these facilities need to understand the risk, which is comprised of threat, vulnerability, and consequences. The threat component of risk requires knowledge of potential adversaries and understanding their capabilities and intentions. To help articulate the multiple layers of threat and support better informed decisions, the research developed a risk-based methodology to evaluate radiological security at the facility level. The methodology is applied to a radiological dispersal device (RDD) incident from three radionuclides of concern: 137Cs, 60Co, and 192Ir. The results of the research have led to the creation of a potential facility risk index (PFRI). The PFRI is mathematically represented as the exponential product of the maximum expected utility among the threat groups, the sum of geographic vulnerability and cultural vulnerability, and net consequences. The PFRI is a novel risk index that quantifies the facility risk on a scale of 1 to 10, 1 being "very low risk" and 10 being "very high risk." A MATLAB-based graphical user interface (GUI) tool was also developed to enable the radiological facility (i.e., healthcare facility) staff to conduct self-assessments and manage their most valuable assets. The PFRI methodology is a useful starting point for any healthcare facility risk assessment and is a valuable input for decision makers considering potential investments in security upgrades.

Prediction of the load-shortening curve of CFST columns using ANN-based models

Artificial neural network (ANN) as a machine learning (ML) technique has been successfully applied in engineering applications such as structural dynamics and structural design. It has also received considerable attention for the design of concrete-filled steel tube (CFST) columns. However, the application of ANN method to CFST columns is mainly restricted to the prediction of the ultimate strength. In this paper, a novel approach to predict and plot the complete axial load-shortening curve of concentrically loaded rectangular and circular CFST columns using ANN method is presented. To train the networks, a database including 392 test results of rectangular and circular CFST columns with their corresponding data points of the load-deflection curves is compiled. In addition, 1152 finite element models (FEMs) are generated and analysed using ABAQUS to expand the training data and address data gaps in the experimental database. The validity of the developed FEMs is verified by experimental data. Based on the trained ANN models, a MATLAB-based graphical user interface (GUI) is also developed to provide a convenient tool for users to predict and plot the axial load-shortening response of rectangular and circular CFST columns. Using the developed GUI, a parametric study is also conducted to verify the accuracy of the ANN models in predicting the behaviour of CFST columns fabricated with different geometric and material properties. The results show that the developed ANN models can accurately predict the load-deflection response of CFST columns.

HFOApp: A MATLAB Graphical User Interface for High-Frequency Oscillation Marking.

Epilepsy affects 3.4 million people in the United States, and, despite the availability of numerous antiepileptic drugs, 36% of patients have uncontrollable seizures, which severely impact quality of life. High-frequency oscillations (HFOs) are a potential biomarker of epileptogenic tissue that could be useful in surgical planning. As a result, research into the efficacy of HFOs as a clinical tool has increased over the last 2 decades. However, detection and identification of these transient rhythms in intracranial electroencephalographic recordings remain time-consuming and challenging. Although automated detection algorithms have been developed, their results are widely inconsistent, reducing reliability. Thus, manual marking of HFOs remains the gold standard, and manual review of automated results is required. However, manual marking and review are time consuming and can still produce variable results because of their subjective nature and the limitations in functionality of existing open-source software. Our goal was to develop a new software with broad application that improves on existing open-source HFO detection applications in usability, speed, and accuracy. Here, we present HFOApp: a free, open-source, easy-to-use MATLAB-based graphical user interface for HFO marking. This toolbox offers a high degree of intuitive and ergonomic usability and integrates interactive automation-assist options with manual marking, significantly reducing the time needed for review and manual marking of recordings, while increasing inter-rater reliability. The toolbox also features simultaneous multichannel detection and marking. HFOApp was designed as an easy-to-use toolbox for clinicians and researchers to quickly and accurately mark, quantify, and characterize HFOs within electrophysiological datasets.

FastGAPP – A MATLABBased Program Supports Earth Scientists Interpreting Geochemical, Petrological and Sedimentological Data

FastGAPP (Fast Geoscientific Analyses Plotting Program) is a MATLAB-based graphical user interface. It is developed to quickly explore and interpret geochemical major and trace element analyses of igneous rocks in over 100 published classification and discrimination plots, variation diagrams and normalised rare-earth element and multi-elements plots. All plots can be modified (fonts, lines, frame etc.) to produce publication-ready and high-quality raster or vector graphics. Among geochemical analyses of magmatic rocks, several other sub-programs are integrated to display petrographic analyses of igneous rocks, composition and grain size analyses of sedimentary rocks, and grain size analyses of soils. FastGAPP also provides several tools to support data handling, to extend the integrated geochemical databases and/or even create new sub-programs for different fields of geosciences. FastGAPP v2.0 is available on GitHub and Zenodo.

A MATLAB-BASED GUI FOR REMOTE ELECTROOCULOGRAPHY VISUAL EXAMINATION

In this work, a MATLAB-based graphical user interface is proposed for the visual examination of several eye movements. The proposed solution is algorithm-based, which localizes the area of the eye movement, removes artifacts, and calculates the view trajectory in terms of direction and orb deviation. To compute the algorithm, a five-electrode configuration is needed. The goodness of the proposed MATLAB-based graphical user interface has been validated, at the Clinic of Child Neurology of University Hospital of Ostrava, through the EEG Wave Program, which was considered as “gold standard” test. The proposed solution can help physicians on studying cerebral diseases, or to be used for the development of human-machine interfaces useful for the improvement of the digital era that surrounds us today.

Development of Cognitive and Psychomotor Task for EEG Application with Matlab-based GUI

Conventionally, the evaluation of cognitive and psychomotor ability performance is typically based on self-writing reports. However, such assessment was unable to measure the actual performance during the task execution. Furthermore, the performance results perceived by the instructor is mainly subjective. For an alternative solution, a new approach is proposed to acquire brainwave signals (i.e. electroencephalogram (EEG)) during task performance. The application of EEG offers a promising approach in evaluating skill abilities at physiological level. Hence, a task related to the assessed skill needs to properly design to ensure the induced EEG signal is reliable for performance evaluation. With the aid of Graphical User Interface (GUI), the time frame of each response can be capture and recorded. Therefore, this paper presents the development of task assessment in evaluating the performance of cognitive and psychomotor abilities based on EEG signal.

EDDIDAT: a graphical user interface for the analysis of energy-dispersive diffraction data.

EDDIDAT is a MATLAB-based graphical user interface for the convenient and versatile analysis of energy-dispersive diffraction data obtained at laboratory and synchrotron sources. The main focus of EDDIDAT up to now has been on the analysis of residual stresses, but it can also be used to prepare measurement data for subsequent phase analysis or analysis of preferred orientation. The program provides access to the depth-resolved analysis of residual stresses at different levels of approximation. Furthermore, the graphic representation of the results also serves for the consideration of microstructural and texture-related properties. The included material database allows for the quick analysis of the most common materials and is easily extendable. The plots and results produced with EDDIDAT can be exported to graphics and text files. EDDIDAT is designed to analyze diffraction data from various energy-dispersive X-ray sources. Hence it is possible to add new sources and implement the device-specific properties into EDDIDAT. The program is freely available to academic users.

DetEdit: A graphical user interface for annotating and editing events detected in long-term acoustic monitoring data.

Passive acoustic monitoring has become an important data collection method, yielding massive datasets replete with biological, environmental and anthropogenic information. Automated signal detectors and classifiers are needed to identify events within these datasets, such as the presence of species-specific sounds or anthropogenic noise. These automated methods, however, are rarely a complete substitute for expert analyst review. The ability to visualize and annotate acoustic events efficiently can enhance scientific insights from large, previously intractable datasets. A MATLAB-based graphical user interface, called DetEdit, was developed to accelerate the editing and annotating of automated detections from extensive acoustic datasets. This tool is highly-configurable and multipurpose, with uses ranging from annotation and classification of individual signals or signal-clusters and evaluation of signal properties, to identification of false detections and false positive rate estimation. DetEdit allows users to step through acoustic events, displaying a range of signal features, including time series of received levels, long-term spectral averages, time intervals between detections, and scatter plots of peak frequency, RMS, and peak-to-peak received levels. Additionally, it displays either individual, or averaged sound pressure waveforms, and power spectra within each acoustic event. These views simultaneously provide analysts with signal-level detail and encounter-level context. DetEdit creates datasets of signal labels for further analyses, such as training classifiers and quantifying occurrence, abundances, or trends. Although designed for evaluating underwater-recorded odontocete echolocation click detections, DetEdit can be adapted to almost any stereotyped impulsive signal. Our software package complements available tools for the bioacoustic community and is provided open source at https://github.com/MarineBioAcousticsRC/DetEdit.

LandMOD ET mapper: A new matlab-based graphical user interface (GUI) for automated implementation of SEBAL and METRIC models in thermal imagery

Remote sensing based surface energy balance (SEB) models, such as SEB Algorithm for Land (SEBAL) and Mapping evapotranspiration (ET) at high Resolution with Internalized Calibration (METRIC) are widely used to monitor ET. However, their operational use is challenged by model complexity and the tedious process of selecting hot and cold pixels. We developed a new Matlab-based ET mapping toolbox named “LandMOD ET Mapper” for automated implementation of these models to facilitate their widespread applications among new users. The toolbox is demonstrated using one Landsat and one Moderate Resolution Imaging Spectroradiometer (MODIS) Terra image covering three AmeriFlux sites in Nebraska, U.S. Daily ET estimates were comparable with the observed ET for five days when near cloud-free Landsat and MODIS Terra images were available in the 2006 maize growing season. The toolbox provides a viable ET mapping option to new and inexperienced users with reduced time and labor demands.

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data.

With the advent of flow cytometers capable of measuring an increasing number of parameters, scientists continue to develop larger panels to phenotypically explore characteristics of their cellular samples. However, these technological advancements yield high-dimensional data sets that have become increasingly difficult to analyze objectively within traditional manual-based gating programs. In order to better analyze and present data, scientists partner with bioinformaticians with expertise in analyzing high-dimensional data to parse their flow cytometry data. While these methods have been shown to be highly valuable in studying flow cytometry, they have yet to be incorporated in a straightforward and easy-to-use package for scientists who lack computational or programming expertise. To address this need, we have developed ExCYT, a MATLAB-based Graphical User Interface (GUI) that streamlines the analysis of high-dimensional flow cytometry data by implementing commonly employed analytical techniques for high-dimensional data including dimensionality reduction by t-SNE, a variety of automated and manual clustering methods, heatmaps, and novel high-dimensional flow plots. Additionally, ExCYT provides traditional gating options of select populations of interest for further t-SNE and clustering analysis as well as the ability to apply gates directly on t-SNE plots. The software provides the additional advantage of working with either compensated or uncompensated FCS files. In the event that post-acquisition compensation is required, the user can choose to provide the program a directory of single stains and an unstained sample. The program detects positive events in all channels and uses this select data to more objectively calculate the compensation matrix. In summary, ExCYT provides a comprehensive analysis pipeline to take flow cytometry data in the form of FCS files and allow any individual, regardless of computational training, to use the latest algorithmic approaches in understanding their data.