MATLAB-based Method Research Articles

The Gompertz model for biohydrogen production kinetics: Origin, application and solving methods

The Gompertz model has been extensively employed in describing biohydrogen kinetics. In this study, the Gompertz model, including its origins, modifications, application for describing biohydrogen kinetics, and solving methods, was explored in detail. The Gompertz model has undergone modifications to enhance its applicability in biological processes. The Lay-modified Gompertz model, tailored explicitly for modeling bioproduction, demonstrates versatility in predicting crucial factors, contributing to a deeper understanding of production efficiency. The applications of this model in biohydrogen production were summarized. Additionally, the solving methods for the Gompertz model were presented for different software platforms, in which Microsoft Excel, Origin, SigmaPlot, and GraphPad Prism offer user-friendly interfaces. The MATLAB-based method is more versatile, allowing users to solve the Gompertz model without the need for relatively accurate initial parameter values. The Excel sheet and MATLAB file are provided in Supplementary Material. This study enhances the understanding and practical application of the Gompertz model in describing biohydrogen production kinetics.

Detecting Shadows in Computer Vision: A MATLAB-Based Approach

The abstract outlines a novel MATLAB-based technique for enhancing shadow detection accuracy in computer vision applications. This MATLAB-based method uses thresholding and color analysis to improve the accuracy of shadow identification. Luminance (L*) is distinguished from chromaticity (a* and b*) in digital photographs by converting them into the Lab color space. This makes it easier to identify possible shadowed areas. The luminance channel's thresholding makes it possible to distinguish between regions that are well-lit and those that are shadowed. The computation of chromaticity distance also helps in detecting modest color changes that are suggestive of shadows. The culmination of the suggested technique is the creation of a binary shadow mask, which isolates the image's shaded regions. The research addresses limitations of existing approaches by maintaining robustness in diverse lighting conditions and complex scenarios, thereby enhancing image comprehension and the precision of shadow detection. The proposed technique holds promise for improving object recognition and scene analysis in computer vision applications, offering a robust and reliable solution for identifying and isolating shadows in digital images.

Calculation of the Shading Factors for Solar Modules with MATLAB

Shadows severely affect the performance of solar photovoltaic (PV) systems. A proper description of this effect is useful for sizing and simulating PV systems when shadows cannot be avoided. Shading factors represent the basis for simulating the effect of shadows on solar modules. These factors can be used to estimate shading losses, calculate their I-V and P-V curves under shading conditions, or develop new maximum power point tracking (MPPT) techniques. Open-source libraries focused on solar energy have gained popularity in recent years. One of the currently most popular ones is the PV_LIB toolbox initially developed by Sandia Laboratories. PV_LIB significantly facilitates solar energy calculations. However, it currently lacks functions for taking into account shaded conditions. In this paper, a detailed Matlab-based method for calculating the shading factors is provided. The method has been used for elaborating a toolbox for shading calculations. The current work could help extend the functionalities of the PV_LIB toolbox. The results were compared against other currently popular computer programs, namely the System Advisor Model (SAM) and PVsyst. With this method, it is also possible to calculate shading factors with smaller time steps than possible with the mentioned programs. This work also shows the importance of using small time steps and how this can affect the accuracy of the calculated shading factors. The contribution of this work is providing a way of quantifying shadow losses in PV systems with Matlab, allowing for better accuracy, flexibility, and transparency during the calculation. The functions developed in this work can be accessed by contacting the authors.

Semi-automated segmentation of the lateral periventricular regions using diffusion magnetic resonance imaging

The lateral ventricular perimeter (LVP) of the brain is a critical region because in addition to housing neural stem cells required for brain development, it facilitates cerebrospinal fluid (CSF) bulk flow and functions as a blood-CSF barrier to protect periventricular white matter (PVWM) and other adjacent regions from injurious toxins. LVP injury is common, particularly among preterm infants who sustain intraventricular hemorrhage or post hemorrhagic hydrocephalus and has been associated with poor neurological outcomes. Assessment of the LVP with diffusion MRI has been challenging, primarily due to issues with partial volume artifacts since the LVP region is in close proximity to CSF and other structures of varying signal intensities that may be inadvertently included in LVP segmentation.This research method presents:•A novel MATLAB-based method to segment a homogenous LVP layer using high spatial resolution parameters (voxel size 1.2 × 1.2 × 1.2 mm3) to only capture the innermost layer of the LVP.•The segmented LVP is averaged from three contiguous axial slices to increase signal to noise ratio and reduce the effect of any residual volume averaging effect and eliminates manual and inter/intrarater-related errors.

Automated sleep scoring in rats and mice using the naive Bayes classifier

We describe a new simple MATLAB-based method for automated scoring of rat and mouse sleep using the naive Bayes classifier. This method is highly sensitive resulting in overall auto-rater agreement of 93%, comparable to an inter-rater agreement between two human scorers (92%), with high sensitivity and specificity values for wake (94% and 96%), NREM sleep (94% and 97%) and REM sleep (89% and 97%) states. In addition to baseline sleep–wake conditions, the performance of the naive Bayes classifier was assessed in sleep deprivation and drug infusion experiments, as well as in aged and transgenic animals using multiple EEG derivations. 24-h recordings from 30 different animals were used, with approximately 5% of the data manually scored as training data for the classification algorithm.

Processing of short transient signals in multi-element analysis using an ICP-OES instrument equipped with a CCD-based detection system in Paschen-Runge mount

A general protocol for the processing of short transient signals in multi-element analysis using an ICP-OES instrument equipped with a CCD detector system was developed. The performance of the optical system was optimized and the line positions set while using pneumatic nebulization for sample introduction, and the resulting settings were afterwards applied for the acquisition of transient signals, generated via electrothermal vaporization (ETV). The influence of the most important data acquisition parameters, sampling rate and integration time was evaluated for a wide spectral range. Subsequently, a MATLAB-based method was used for off-line generation of the 3D analyte spectrum, off-line BG correction and integration of the transient signals. The applicability of the approach developed was demonstrated via the determination of Al in two certified reference materials, one with low and another with high Ca concentration, using ETV-ICP-OES. Accurate results were obtained for Al, despite the significant overlap of the Al analytical line and the ‘wing’ of the matrix-related Ca line.

HingeInflex: a MATLAB-based method for precise selection of the hinge and the inflection points in folds

AbstractSubjectivity in visual selection of hinge and inflection points leads to significant errors in analyses of fold shapes in profile sections. This article gives a method for precise determination of these points. The method: (1) imports a fold image into Matlab, (2) digitizes points on the image, (3) increases the number of available points by using an interpolation algorithm, (4) fits a polynomial curve to the points, and (5) searches for the hinge and the inflection points mathematically. Tests on several folds confirm that the ‘HingeInflex’ is a rapid, robust and user-friendly method for precise selection of the hinge and inflection points.