MATLAB App Research Articles

Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP

BackgroundDerivative profiling is a novel approach to identify differential signals from dynamic omics data sets. This approach applies variable step-size differentiation to time dynamic omics data. This work assumes that there is a general omics derivative that is a useful and descriptive feature of dynamic omics experiments. We assert that this omics derivative, or omics flux, is a valuable descriptor that can be used instead of, or with, fold change calculations.ResultsThe results of derivative profiling are compared to established methods such as Multivariate Adaptive Regression Splines, significance versus fold change analysis (Volcano), and an adjusted ratio over intensity (M/A) analysis to find that there is a statistically significant similarity between the results. This comparison is repeated for transcriptomic and phosphoproteomic expression profiles previously characterized in Aspergillus nidulans. This method has been packaged in an open-source, GUI-based MATLAB app, the Derivative Profiling omics Package (DPoP). Gene Ontology (GO) term enrichment has been included in the app so that a user can automatically/programmatically describe the over/under-represented GO terms in the derivative profiling results using domain specific knowledge found in their organism’s specific GO database file. The advantage of the DPoP analysis is that it is computationally inexpensive, it does not require fold change calculations, it describes both instantaneous as well as overall behavior, and it achieves statistical confidence with signal trajectories of a single bio-replicate over four or more points.ConclusionsWhile we apply this method to time dynamic transcriptomic and phosphoproteomic datasets, it is a numerically generalizable technique that can be applied to any organism and any field interested in time series data analysis. The app described in this work enables omics researchers with no computer science background to apply derivative profiling to their data sets, while also allowing multidisciplined users to build on the nascent idea of profiling derivatives in omics.

Influence of emissivity on infrared camouflage performance

The rapid development of infrared detection technology has generated an urgent demand for infrared camouflage, sparking widespread interest in low-emissivity materials. Novel material designs and advanced micro/nanofabrication technologies make it possible to realize materials with extremely low emissivity. However, a lower infrared emissivity does not always mean a better camouflage performance. There is a lack of sufficient discussion on how to determine an appropriate emissivity for a specific working condition to achieve effective infrared camouflage. Here, through outdoor experiments, we demonstrated that for a specific scenario, an appropriate emissivity always exists that can make the infrared characteristics of the target effectively blend into its background, and deviations from the emissivity result in deteriorated camouflage performance. Further, we established a heat transfer model to conduct quantitative analysis on the influence of emissivity on infrared camouflage performance in terms of surface temperature and radiative temperature in various conditions. In addition, we proposed a general method for determining the optimal emissivity of infrared camouflage, defined as the emissivity value at which the radiative temperatures of the target and the background are equal. To facilitate practical application of this method, we developed a user-friendly MATLAB app named “Optimal Emissivity Calculator” to calculate the optimal emissivity. It was found that for a vehicle’s engine compartment surface at approximately 340.0 K, the optimal emissivity is 0.4 with a background temperature of 300.0 K. This work highlights the significance of selecting appropriate emissivity for infrared camouflage and provides a reference for designing the emissivity of infrared camouflage materials.

Field-Programmable Gate Array Architecture for the Discrete Orthonormal Stockwell Transform (DOST) Hardware Implementation

Time–frequency analysis is critical in studying linear and non-linear signals that exhibit variations across both time and frequency domains. Such analysis not only facilitates the identification of transient events and extraction of key features but also aids in displaying signal properties and pattern recognition. Recently, the Discrete Orthonormal Stockwell Transform (DOST) has become increasingly utilized in many specialized signal processing tasks. Given its growing importance, this work proposes a reconfigurable field-programmable gate array (FPGA) architecture designed to efficiently implement the DOST algorithm on cost-effective FPGA chips. An accompanying MATLAB app enables the automatic configuration of the DOST method for varying sizes (64, 128, 256, 512, and 1024 points). For the implementation, a Cyclone V series FPGA device from Intel Altera, featuring the 5CSEMA5F31C6N chip, is used. To provide a complete hardware solution, the proposed DOST core has been integrated into a hybrid ARM-HPS (Advanced RISC Machine–Hard Processor System) control unit, which allows the control of different peripherals, such as communication protocols and VGA-based displays. Results show that less than 5% of the chip’s resources are occupied, indicating a low-cost architecture that can be easily integrated into other FPGA structures or hardware systems for diverse applications. Moreover, the accuracy of the proposed FPGA-based implementation is underscored by a root mean squared error of 6.0155 × 10−3 when compared with results from floating-point processors, highlighting its accuracy.

Determine Oil and Water Saturations in Preserved Source Rocks From 2D T1-T2 NMR

Two-dimensional (2D) T1-T2 NMR has been widely proposed as a method to determine the fluids present in unconventional source rocks. However, the assignment of the components in the 2D T1- T2 NMR spectra has so far been based on conceptual consideration and lacks rigorous experimental verification. Many assignment variations appeared in the literature and can be misleading. For example, large T1/T2 peaks in the 2D T1-T2 NMR were suggested to be fluid in small pores coupled with kerogen solid through dipolar relaxation. Our recent 500-MHz NMR relaxation experiments found that the dipolar relaxation between nanoconfined fluids and solid matrix is not big enough to support that large T1/T2 interpretation. The objective of this paper is to determine what quantitative data can be obtained using 2D T1-T2 NMR from source rocks with high confidence. We found the oil and water saturation can be accurately measured in preserved source rock plugs. Two-dimensional T1-T2 NMR data were collected on preserved core plugs from five wells of a source rock reservoir using an inversion-recovery Carr-Purcell-Meiboom-Gill (CMPG) pulse sequence on a 12-MHz NMR instrument. The acquired 2D data were inverted using an optimized inversion software, MUPen2D. We then obtain oil and water content in the plug from the 2D T1-T2 NMR using an in-house-developed NMR MATLAB app. The porosity of all the plugs was measured using a combined NMR and gas porosimetry (CNG) method. Oil and water saturation were also obtained using an industry-standard method developed by the Gas Research Institute (GRI) on the rock material close to where the plug was drilled. The oil and water saturations measured on the preserved plugs using 2D T1-T2 NMR and CNG are consistent with those from the GRI method using crushed rocks and an invasive cleaning procedure. The results show that oil and water saturations were accurately determined from 2D T1-T2 NMR on preserved source rock samples with high confidence. The NMR method is nondestructive and noninvasive and takes less than 4 hours on a preserved source rock plug, while GRI is destructive and invasive and can take several weeks for an accurate measurement on one sample. The measured results can be further used to determine the production potential of a source rock well in combination with log data.

Volatile budgets and evolution in porphyry-related magma systems, determined using apatite

Volatile-bearing minerals, such as apatite, Ca5(PO4)3(OH,F,Cl), can record changes in dissolved magmatic volatile species during differentiation and, unlike melt inclusions, are sensitive to the presence of an exsolved fluid phase. Populations of apatite crystals from an individual sample can therefore be used to define the progressive volatile evolution of melt ± fluid during magma differentiation. Despite the importance of fluid chemistry in mineralisation processes, this approach remains relatively underdeveloped for porphyry mineralisation scenarios. Here, we present a model, including a standalone MATLAB app, for melt + apatite ± fluid fractionation that incorporates non-ideal, temperature-dependent KDs for OH-Cl-F exchange and permits an analysis of uncertainty arising from non-unique parameter combinations. We apply the model to apatite from the Fe-Cu-Au Corrocohuayco porphyry-skarn system and analyse differences in volatile saturation state and fluid salinity between different units. We find that there is little difference in the overall fluid salinity, and thus the fluid copper loads, but that the more primitive unit (i.e. the gabbrodiorites) reached fluid saturation much later (after around 50% crystallisation) than the more evolved units, implying that the melt volatile concentration recorded by the apatites in the gabbrodiorites is not representative of the initial magma volatile budget. This work demonstrates that apatite can be a good alternative means of reconstructing the evolving magmatic fluid salinity within mineralising systems, and linking this to the trace metal content of the melt.

The impact of cloud technology and the MatLab app on the academic performance and cognitive load of further mathematics students

The impact of cloud technology and the MatLab app on the academic performance and cognitive load of further mathematics students

Non-Invasive Determination of the Mass Flow Rate for Particulate Solids Using Microwaves.

This paper presents a novel technique for the mass flow rate determination of particulate solids called the "Sliding Mass Technique". The mass flow rate is a measure of the mass of a substance that passes through a given cross-sectional area per unit time. Its calculation requires simultaneous detection of the concentration and velocity of the Material Under Test. A novel measurement technique is designed for determining the concentration of the mass flow without the necessity for density evaluation. The mass flow rate is determined by fusing the established concentration results with velocity results obtained from "Microwave Spatial Filtering Velocimetry". A new metamaterial-based mass flow sensor for particulate solids was designed, realized and measured in an industrial environment. A Software-Defined Radio (Ettus Research™'s USRP B210) was utilized as a sensor electronic system for DAQ purposes. A MATLAB app was developed to operate the SDR. Measurements were carried out on-site using a state-of-the-art wood pellet heating system with wood pellets with different moisture contents. The measurement results were found to be in very good agreement with the expected results, which strengthens the feasibility of this newly proposed measurement technique.

Low-Code Application and Practical Implications of Common Machine Learning Models for Predicting Punching Shear Strength of Concrete Reinforced Slabs

This paper investigates the effectiveness of machine learning (ML) models available in MATLAB Regression Learner app and MATLAB App Designer, both low-code applications, for accurately predicting punching shear strength (PSS) in reinforced concrete (RC) slabs. A database of 379 RC slab samples without transverse reinforcement was compiled from renowned publications. RandomSearch and Bayesian optimisation were employed for tuning hyperparameters. The performance of these models was compared with six empirical models, which included three current design codes, three equations from other researchers, and 227 finite-element simulations conducted by the authors. The ML models and finite-element method (FEM) demonstrated superior performance compared with the literature and practical codes. Furthermore, the results emphasised the exceptional performance of the Gaussian process regression (GPR) with optimised hyperparameters, exhibiting the best performance in validation, training, and testing datasets with R2 values of 0.95, 0.99, and 0.98, respectively. A user-friendly standalone application was developed, providing real-time predictions of the PSS using the two best-developed ML models, GPR and support vector machine (SVM), as well as six empirical models from the literature. This tool offers users flexibility in choosing the most appropriate model for their specific needs, delivering reliable, and accurate results for estimating the PSS of RC slabs.

Quantifying mesh textile and effective porosities: A straightforward image analysis procedure for morphological analysis of surgical meshes

Background and objectivesSurgical meshes have demonstrated greater reliability compared to suture repair for abdominal wall hernia treatment. However, questions remain regarding the properties of these devices and their influence on surgical outcomes. Morphological properties, including pore size and porosity, play a crucial role in mesh integration and encapsulation. In this study, we introduce a straightforward image analysis procedure for accurately calculating both textile porosity and effective porosity. The latter specifically considers pores that prevent bridging, providing valuable insights into mesh performance. MethodsA photographic setup was established to capture high-quality images of the meshes, accompanied by calibration images necessary for computing the effective porosity. The developed image analysis procedure comprises seven steps focused on improving the binarization process's quality, followed by the computation of textile and effective porosities. To facilitate usability, an app called "poreScanner" was designed using MATLAB app designer, guiding users through the algorithm described herein. The app was used to compute both porosities on 24 meshes sourced from various manufacturers, by averaging seven measurements obtained from as many images. The app's measurement stability was validated computing the coefficient of variation for both textile and effective porosity, for a total of 36 results (24 for the textile porosity and 12 for the effective one). Additionally, different operators independently tested one heavy and one light mesh, confirming the measurement's operator independence. ResultsThe results on the coefficient of variation indicated values below 5 % in 34 out of 36 cases, regardless of the mesh density. Similarly, the same parameter was computed to assess the independence of the procedure from different operators, yielding a maximum value of 1.84 %. These findings confirm the robustness and user-independence of the measurement procedure. ConclusionsThe procedure presented in this study is straightforward to replicate and yields dependable results. Its adoption has the potential to standardize the computation of surgical mesh porosity, enabling consistent determination of this crucial morphological parameter.

Simulation on human respiratory motion dynamics and platform construction

Bronchoscopy has a crucial role in the current treatment of lung diseases, and it is typical of interventional medical instruments led by manual intervention. The scientific study of bronchoscopy is now of primary importance in eliminating problems associated with manual intervention by scientific means. However, for its intervention environment, the trachea is often treated statically, without considering the effect of tracheal deformation on bronchoscopic intervention during respiratory motion. Therefore its findings can deviate from practical application. Thus, studying kinetic problems in respiratory motion is of great importance. This paper developed a mathematical model of mechanical properties of respiratory motion to express respiratory force from the perspective of dynamics of respiratory motion. The dynamical model was solved using MATLAB. Then, a finite element model of respiratory motion was built using Mimics, and the results of respiratory force solution were used as the load of model for dynamics simulation in ABAQUS. Then, a human–computer interaction platform was designed in MATLAB APP Designer to realize parametric calculation and fitting of respiratory force, and a personalized human respiratory motion dynamics simulation was completed in conjunction with ABAQUS. Finally, experimental validation of the interactive platform was performed using pulmonary function test data from three patients. Validation analysis by respiration striving solution, kinetic simulation and experiment found that Dynamical model and simulation results can be better adapted to the individualized study of human respiratory motion dynamics.

An application based on the analysis of thermograms to calculate the thermographic index for pain and swelling assessment.

As the world's population ages, the prevalence of rheumatic diseases is increasing. These diseases are often diagnosed and monitored using imaging techniques such as infrared thermography. In this study, we developed an application to calculate the thermographic index (TI) in a series of thermograms. Such index is used in rheumatology as a complementary diagnostic tool to assess pain and swelling. We designed the application in MATLAB App Designer; an environment for creating applications with a graphical user interface. To evaluate the performance of the developed application, we calculated the TI of ten trigger points (anterior shoulder, sternum, posterior shoulder, upper back, lower back, elbow, lateral shoulder, elbow pit, knee, and back of the knee) in a series of thermal images from 37 healthy participants. Results of the average temperature varied between 31.35 (knee for women) and 33.92 (lateral shoulder for men) in °C and TIs were lower than 2 and the results in the knee region are consistent with those reported in previous studies due to only the TI in the knee is known. This study contributes to the literature with reference values for nine trigger points other than the knee. This app is easy to use and may be useful in future applications for disease detection or monitoring of its progression.

Design of envelope analysis software for LNG unloading arm based on MATLAB App

According to investigate the current situation of the envelope of LNG unloading arm, this paper in view of the relationship between the structure and the envelope of LNG unloading arm, a complete set of calculation program is designed by using the app designer of MATLAB which can automatically generate the envelope according to the hydrological conditions. The program can be used to guide the emergency disengagement process of the unloading arm in case it can work safely on the LNG terminal in the actual project. This paper provides a design basis for promoting the domestic development of LNG unloading arm.

Design and Implementation of Real-time Fault Location Experimental System for Teaching and Training University Students

The fault location problem is one of the most important issues in power system operation and control. To obtain an experimental system for teaching and training electrical engineering students, this paper performed a study to design and implement a real-time fault location laboratory-scale model from practical hardware components. The impedance-based fault location method is embedded in the system to determine the distance to fault in the transmission line. Furthermore, the monitoring and controlling program is designed by the Matlab App Designer – A new professional app to create the graphical user interface and use the integrated editor quickly. Several fault types including three-phase fault, phase-to-phase fault, phase-to-ground fault are created to evaluate the performance of the fault location experimental system. The real-time measurement results which are acquired and observed on the user guide interface of the program confirm the effectiveness of the experimental system; therefore, the system can be considered a powerful tool for electrical engineering students.

Photovoltaic single diode model parameter extraction by dI/dV-assisted deterministic method

In this work, a state-of-the-art deterministic method is proposed for photovoltaic single diode model parameter extraction from experimental current–voltage (I-V) curves. This new method takes advantage of the numerical derivative (dI/dV) information, such that a high-quality solution can be obtained by linear least squares technique. The solution can be further improved through a two-step nonlinear optimization procedure using the trust-region-reflective or Levenberg-Marquardt algorithm. Compared with previous methods, the proposed method has achieved the best accuracy with the lowest computation cost up till now in the benchmark test of two commonly-used case studies. When comparing the computation costs of different works, it is pointed out for the first time that not only the number of iteration steps but also the single-step computation complexity should be taken into account if different deterministic optimization algorithms are implemented. Furthermore, the robustness of the proposed method is verified through a large-scale dataset containing more than one million I-V curves, showing that the method can be used for realtime monitoring of photovoltaic modules. Finally, a MATLAB app is developed and freely provided to the public.

Application Tool for Power System Analysis in Education and Research

AbstractApplication tools are increasingly being used in lectures to give computer simulation users a friendly and visual way to specify all input parameters with more configuration flexibility. The analysis and simulation of small to large‐scale electric power systems are done in this study using a software program called Application Tool for Power System (ATPS), which is based on MATLAB APP and Simulink. Anyone can look at the internal workings of ATPS, which is open‐source software, to learn how to code a power engineering problem. Due to its ease of use and adaptability, it is designed to be a simulation tool for researchers and educators. Power flow, stability, faults, economic dispatch, unit commitment, and time‐domain simulation analysis are all topics that are covered by ATPS methods. Additionally, classroom use has shown that the suggested software package helps to consolidate a better knowledge of power system problems.

Abstract 15470: Mathematical Modeling of the Determinants and Consequences of Mitral Regurgitation: Jacobian Sensitivity Analysis of a Lumped Parameter Model of the Cardiovascular System

Background: Mitral regurgitation (MR) results in complex changes to hemodynamics, which lumped parameter (LP) models of the cardiovascular (CV) system can model. A previously validated LP model using time varying elastance of the atria and ventricles models pressure, volume, and flow in the four chambers and valves of the heart and arterial and venous compartments. Here we model moderate MR and perform a Jacobian matrix sensitivity analysis to explore impact of CV parameters on MR variables. Methods: Using MATLAB app designer, we made an app to give input parameter and output variable specification for the LP model, displaying pressure, volume, and flow curves and specified output variables at equilibrium. Sensitivity analysis was done with a Jacobian matrix, showing the proportional change (Δ) in output variables ( y i ) to change in each input parameter ( x j ), [(Δy i /y i )/(Δx j /x j )]. We assessed the change in these output variables: LA peak pressure (LAP), Forward Stroke Volume (FSV), and MR Volume (MRVol); in response to changes in these input parameters: LV End Systolic Elastance (LVESE), MR Orifice Area (MROA), and peripheral vascular resistance (PVR) in the setting of moderate MR (MROA=0.3 cm 2 ). Results: Pressure curves for the left atrium and ventricle and aorta with and without moderate MR are shown, with the Jacobian sensitivity matrix in Figure 1. To use the Jacobian matrix, multiply a small proportional change in the input parameter (rows) with the value under the output variable (columns). For example, a 10% increase in MROA will cause a 3.7% increase in LAP, 0.8% fall in FSV, and 8.3% increase in MRVol. Conclusion: This analysis shows how small changes in many parameters impact multiple output variables. MRVol is most sensitive to changes in MROA, while the impact of LVESE (contractility) and PVR (afterload) are appropriately discordant. This model warrants further study to predict instantaneous hemodynamic changes in MR and a host of other entities.

N-GKLS: An NMPC problem generator and a test platform for NMPC solvers

Nonlinear Model Predictive Control (NMPC) solves a series of optimization problems online during the entire operational time of the controlled plant, and the performance of NMPC depends on the superiority of the solver embedded in NMPC. Traditionally, gradient-based deterministic methods are used as NMPC solvers, but metaheuristics are now becoming more and more popular for its ability to find the global optimal solution. In order to verify the superiorities of different algorithms to be NMPC solvers, a novel NMPC problem generator named NMPC-based GKLS generator (N-GKLS) is proposed. With the help of Optimal-Replace Method (ORM), a reliable comparison of the closed-loop performances result from receding horizon strategy in NMPC is guaranteed. In addition, comparison techniques named accumulated operational characteristic and accumulated operational zone are proposed to directly compare different optimization algorithms for solving various NMPC problems generated by N-GKLS. An auxiliary comparison table is also proposed to report numerical comparative results. In this way, N-GKLS is not only an NMPC problem generator, but also a test platform for verifying the superiorities of different algorithms as NMPC solvers. Finally, simulation results based on 8 metaheuristics and 3 deterministic methods are used to illustrate the effectiveness of the proposed N-GKLS. A corresponding MATLAB APP demo is designed and is available online: https://github.com/JiahongXu123/N-GKLS.

Genetic algorithm optimised Hadamard product method for inconsistency judgement matrix adjustment in AHP and automatic analysis system development

The analytic hierarchy process (AHP) is an important method to solve the multi-objective decision-making weight problem. However, due to the subjective judgement and selection preference of decision-makers, the consistency of the judgement matrix is inevitably poor. Hence, to improve the reliability of decision-making results, it is necessary to adjust the consistency of the judgement matrix. We propose a genetic algorithm optimised Hadamard product (GAOHP) to judge the consistency of the matrix. This method converts the original matrix consistency adjustment problem into an optimisation solution problem and uses a meta-heuristic algorithm to search the global optimisation quickly. Our method, which significantly improves the computing efficiency and realises that the judgement matrix after adjustment satisfies the basic consistency, has fully retained the judgement intention of the decision-makers. It offers the dual advantages of preserving the original intention of decision-makers and greater efficiency of operation. Finally, we developed an automatic analysis system based on MATLAB app designer to realise the rapid adjustment of the consistency of judgement matrix, which is conducive to providing an analysis system with simple and stable operation for AHP analysis.

A Novel Method for the Intelligent Recognition of Lattice Fringes in Coal HRTEM Images Based on Semantic Segmentation and Fuzzy Superpixels.

High-resolution transmission electron microscopy (HRTEM) can directly obtain the lattice fringes and structure parameters of coal. Aiming at present problems in extracting lattice fringes in HRTEM images, such as unlocated fringe regions, single-threshold segmentation, unclassified fuzzy superpixels, and tedious fringe pruning, an intelligent recognition method based on semantic segmentation, deep neural networks, fuzzy superpixels, and other algorithms is proposed. For unlocated fringe regions, the fringe regions are automatically localized with semantic segmentation. The whole semantic segmentation network adopts DeepLab V3+ based on ResNet to reduce unnecessary operations brought by non-fringe regions. For single-threshold segmentation of the image, the image is chunked before anything else. The genetic-optimized watershed algorithm is applied to divide the fringe base maps and non-fringe ones in order to avoid the distortion caused by different lights and shades of the image. For the fuzzy superpixels between the fringes and non-fringes, a similarity category judgment method based on neighboring pixels is proposed to solve the problem of unclassified fuzzy superpixels and to enrich and perfect the information of the lattice fringe base map. Eventually, as for lattice fringe overlap caused by coals piling together, a similarity judgment method based on the fringes’ characteristics is proposed to remove the bur portion of the lattice fringes and improve the pruning rate. Combining the above theories, a visualization tool based on MATLAB App Designer is designed, and the above four steps can be completed by this app to accurately display the results of coal aromatic lattice fringe identification in HRTEM images. Comparison with the lattice fringes drawn by leading experts shows that the fringes interpreted by this method are reliable. This method facilitates the extraction of lattice fringes in HRTEM, which lays the foundation for the labeling of HRTEM images in a variety of deep learning algorithms and facilitates the direct observation of coal structures by researchers.

A Nested Texture Inspired Novel Image Pattern Based Optical Camera Communication

In Optical Camera Communication, pattern recognition is a vital component of display-based communication. However, as the distance between the source and camera increases, the visibility of the pattern reduces. Decoding each pattern requires extensive computational resources. This paper proposes a nested texture-inspired novel binary hierarchical image pattern classification-based modulation for optical camera communication to provide efficient pattern classification and achieve longer communication distance. Custom 8x8 patterns are generated at the transmitter based on input data using nested outer and inner patterns. On the receiver side, multi-threaded pattern matching is performed simultaneously for inner and outer patterns for faster decoding. A simulator is built to test the performance of linear search and two variants of binary search for pattern matching. A hardware prototype and Matlab app are designed to perform experiments to test the performance of the proposed technique in a real-world environment. Experiments were conducted to select optimal camera parameter values for the best Signal to Noise Ratio(SNR) and to analyze the impact of pattern intensity profile on decoding at various distances. The proposed pattern communication technique showcased 48.6%, and 42.5% improvement in Bit Error Ratio(BER) compared to Data Matrix and QR Code Pattern-based communication techniques, respectively.