Search Results (37)

As a popular linear filter, the mean filter is widely used in different applications as a basic tool for image enhancement. Its main purpose is to reduce the noise in an image and thus to prepare the picture for other image-processing operations depending on the current task. In the last decade, the amount of data, particularly images, that has to be processed in a variety of applications has increased significantly, and thus the usage of effective and fast filtering algorithms has become crucial. The aim of the present research is to identify what type of software (MATLAB, Scilab, or MPI-based) is preferred for reducing the filtering time and consequently save energy. Thus, the aim of the present research corresponds to actual trends in information processing and corresponds to green computing concepts. A set of experimental images divided into two groups—one for small images and a second one for big images—is used for performance evaluation of the recursive mean filter. This type of linear filter was chosen due to its very good denoising characteristics. The filter is implemented in MATLAB and Scilab environments using their specific commands and it is also implemented using the C language with the MPI library to provide the opportunity for parallel execution. Two mobile computer systems are used for experimental performance evaluation and the results indicate that the slowest filtering execution is registered when Scilab is used and the fastest execution is achieved when MPI is used with the C implementation. Depending on the amount and size of the images that have to be filtered, this study formulates advice for achieving effective performance throughout the whole process of working with images. Full article

Exosomes distributed by extracellular vesicles carry various information highly consistent with cells, becoming a new type of biomarker for tumor screening. However, although conventional characterization technologies can quantify size and morphology for exosomes, they are limited in related fields such as function tracing, protein quantification at unit point, and microstructural information. In this paper, firstly, different exosome characterization methods are systematically reviewed, such as dynamic light scattering, nanoparticle tracking analysis, flow cytometry, electron microscope, and emerging super-resolution imaging technologies. Then, advances in applications are described one by one. Last but not least, we compare the features of different technologies for exosomes and propose that super-resolution imaging technology can not only take into account the advantages of conventional characterization techniques but also provide accurate, real-time, and super-resolution quantitative analysis for exosomes. It provides a fine guide for exosome-related biomedical research, as well as application in liquid biopsy and analysis techniques. Full article

An attractive application of crude glycerol is in the generation of biomethane by means of anaerobic co-digestion. Thus, the objective of this work was to evaluate the potential of neural networks and fuzzy logic to predict the production of biomethane from the anaerobic co-digestion of glycerol and/or sugarcane molasses. Firstly, a reactor model was implemented using Scilab (v. 6.1.1), considering the Monod two-substrate with an intermediate (M2SI) kinetic model proposed by Rakmak et al. (Rakmak, N.; Noynoo, L.; Jijai, S.; Siripatana, C. Lecture Notes in Applied Mathematics and Applied Science in Engineering. Melaka, Malaysia, p. 11–20, 2019), to generate a database for subsequent fitting and evaluation of neural and fuzzy models. The neural network package of Matlab was used. Fuzzy modeling was applied using the Takagi–Sugeno approach available in the ANFIS package of Matlab. The biomethane production data simulated using Scilab were considered in neural network modeling and validation, firstly employing a “generic” network applicable to all eight scenarios, providing a very good fit (R² > 0.99). Excellent performance was also observed for specific artificial neural networks (one for each condition, again by using validation data generated by the M2SI model). The parameters of the M2SI model for the eight different conditions were also mapped using a neural network, as a function of the organic material composition, providing a fit with R² > 0.99 when using 25 neurons. In the case of fuzzy logic, an RMSE (Root Mean Squared Error) of 18.88 mL of methane was obtained with 216 rules, which was a value lower than 0.5% of the order of magnitude of the accumulated methane. It could be concluded from the results that fuzzy logic and artificial neural networks offer excellent ability to predict methane production, as well as to parameterize the M2SI kinetic model (using neural networks). Full article

The purpose of the study was to identify and predict the optimized parameters for phosphoric acid production. This involved modeling the crystal reactor, UCEGO filter (as a detailed model of the filter is not available in Aspen Plus or other simulation software), and acid separator using Sci-Lab to develop Cape-Open models. The simulation was conducted using Aspen Plus and involved analyzing 10 different phosphates with varying qualities and fractions of P₂O₅ and other minerals. After a successful simulation, a sensitivity analysis was conducted by varying parameters such as capacity, filter speed, vacuum, particle size, water temperature for washing the filtration cake, flow of recycled acid and strong acid from the separator below the filter, flow of slurry to reactor 1, temperature in reactors, and flow of H₂SO₄, resulting in nearly one million combinations. To create an algorithm for predicting process parameters and the maximal extent of recovering H₃PO₄ from slurry, ANN models were developed with a determination coefficient of 99%. Multi-objective optimization was then performed using a genetic algorithm to find the most suitable parameters that would lead to a higher reaction degree (96–97%) and quantity of separated H₃PO₄ and lower losses of gypsum. The results indicated that it is possible to predict the influence of process parameters on the quality of produced acid and minimize losses during production. The developed model was confirmed to be viable when compared to results found in the literature. Full article

In max-plus algebra, some algorithms for determining the eigenvector of irreducible matrices are the power algorithm and the Kleene star algorithm. In this research, a modified Kleene star algorithm will be discussed to compensate for the disadvantages of the Kleene star algorithm. The Kleene star algorithm’s time complexity is $O\left(n\left(n!\right)\right)$, and the new Kleene star algorithm’s time complexity is $O\left(n^4\right)$, while the power algorithm’s time complexity cannot be calculated. This research also applies max-plus algebra in a railroad network scheduling problem, constructing a graphical user interface to perform schedule calculations quickly and easily. Full article

This paper addresses the modeling and the control of an autonomous bicycle robot where the reference point is the center of gravity. The controls are based on the wheel heading’s angular velocity and the steering’s angular velocity. They have been developed to drive the autonomous bicycle robot from a given initial state to a final state, so that the total running cost is minimized. To solve the problem, the following approach was used: after having computed the control system Hamiltonian, Pontryagin’s Minimum Principle was applied to derive the feasible controls and the costate system of ordinary differential equations. The feasible controls, derived as functions of the state and costate variables, were substituted into the combined nonlinear state–costate system of ordinary differential equations and yielded a control-free, state–costate system of ordinary differential equations. Such a system was judiciously vectorized to easily enable the application of any computer program written in Matlab, Octave or Scilab. A Matlab computer program, set as the main program, was developed to call a Runge–Kutta function coded into Matlab to solve the combined control-free, state–costate system of ordinary differential equations coded into a Matlab function. After running the program, the following results were obtained: seven feasible state functions from which the feasible trajectory of the robot is derived, seven feasible costate functions, and two feasible control functions. Computational simulations were developed and provided in order to persuade the readers of the effectiveness and the reliability of the approach. Full article

Slightly off-axis digital holographic microscopy (DHM) is the extension of digital holography imaging technology toward high-throughput modern optical imaging technology. However, it is difficult for the method based on the conventional linear Fourier domain filtering to solve the imaging artifacts caused by the spectral aliasing problem. In this article, we propose a novel high-accuracy, artifacts-free, single-frame, digital holographic phase demodulation scheme for low-carrier-frequency holograms, which incorporates the physical model into a conventional deep neural network (DNN) without training beforehand based on a massive dataset. Although the conventional end-to-end deep learning (DL) method can achieve high-accuracy phase recovery directly from a single-frame hologram, the massive datasets and ground truth collection can be prohibitively laborious and time-consuming. Our method recognizes such a low-carrier frequency fringe demodulation process as a nonlinear optimization problem, which can reconstruct the artifact-free phase details gradually from a single-frame hologram. The phase resolution target and simulation experiment results quantitatively demonstrate that the proposed method possesses better artifact suppression and high-resolution imaging capabilities than the physical methods. In addition, the live-cell experiment also indicates the practicality of the technique in biological research. Full article

The paper presents a modified algorithm for determining the accuracy parameter of the system for differential corrections and monitoring (SDCM) navigation solution in air navigation. For this purpose, a solution to determine the resultant accuracy parameter was proposed by using two on-board global navigation satellite system (GNSS) receivers. The mathematical algorithm takes into account the calculation of a single point positioning accuracy for a given GNSS receiver and a weighting factor combining the position error values. The weighting factor was determined as a function of the number of tracked GNSS satellites used in the SDCM single point positioning solution. The resultant accuracy parameter was expressed in ellipsoidal coordinates BLh (B—latitude, L—longitude, h—ellipsoidal height). The study used GNSS kinematic data recorded by two on-board receivers: Trimble Alloy and Septentrio AsterRx2i, located in a Diamond DA 20-C1 aircraft. The test flight was performed near the city of Olsztyn in north-eastern Poland. Calculations and analyses were performed using RTKLIB software and the Scilab environment. On the basis of the performed tests, it was found that the proposed algorithm for SDCM system allows for improvement in the determination of the resultant accuracy value by 56–80% in relation to the results of position errors from a single GNSS receiver. Additionally, the proposed algorithm was tested for the European Geostationary Navigation Overlay Service (EGNOS) system, and in this case, the improvement in the accuracy parameter was even better and was in the range of 69–89%. The resulting SDCM and EGNOS positioning accuracy met the International Civil Aviation Organization (ICAO) certification requirements for SBAS systems in air navigation. The mathematical algorithm developed in this work was tested positively and can be implemented within the SBAS augmentation system in air navigation. Full article

Currently, transitioning from fossil fuels to renewable sources of energy is needed, considering the impact of climate change on the globe. From this point of view, there is a need for development in several stages such as storage, transmission, and conversion of power. In this paper, we demonstrate a simulation of a hybrid energy storage system consisting of a battery and fuel cell in parallel operation. The novelty in the proposed system is the inclusion of an electrolyser along with a switching algorithm. The electrolyser consumes electricity to intrinsically produce hydrogen and store it in a tank. This implies that the system consumes electricity as input energy as opposed to hydrogen being the input fuel. The hydrogen produced by the electrolyser and stored in the tank is later utilised by the fuel cell to produce electricity to power the load when needed. Energy is, therefore, stored in the form of hydrogen. A battery of lower capacity is coupled with the fuel cell to handle transient loads. A parallel control algorithm is developed to switch on/off the charging and discharging cycle of the fuel cell and battery depending upon the connected load. Electrically equivalent circuits of a polymer electrolyte membrane electrolyser, polymer electrolyte membrane fuel cell, necessary hydrogen, oxygen, water tanks, and switching controller for the parallel operation were modelled with their respective mathematical equations in MATLAB^® Simulink^®. In this paper, we mainly focus on the modelling and simulation of the proposed system. The results showcase the simulated system’s mentioned advantages and compare its ability to handle loads to a battery-only system. Full article

Photonic hook (PH) is a new type of non-evanescent light beam with subwavelength curved structures. It has shown promising applications in super-resolution imaging and has the potential to be used in micromachining, optical trapping, etc. PHs are generally produced by illuminating mesoscale asymmetric particles with optical plane waves. In this work, we used the finite-difference time-domain (FDTD) method to investigate the PH phenomenon under point-source illumination. We found that the PHs can be effectively generated from point-source illuminated patchy particles. By changing the background refractive index, particle diameters and the position and coverage ratio of Ag patches, the characteristics of the PHs can be effectively tuned. Moreover, the structure of the intensity distribution of the light field generated from small and large particles can have an opposite bending direction due to the near-field light-matter interaction. Full article

Fringe projection profilometry (FPP) is widely applied to 3D measurements, owing to its advantages of high accuracy, non-contact, and full-field scanning. Compared with most FPP systems that project visible patterns, invisible fringe patterns in the spectra of near-infrared demonstrate fewer impacts on human eyes or on scenes where bright illumination may be avoided. However, the invisible patterns, which are generated by a near-infrared laser, are usually captured with severe speckle noise, resulting in 3D reconstructions of limited quality. To cope with this issue, we propose a deep learning-based framework that can remove the effect of the speckle noise and improve the precision of the 3D reconstruction. The framework consists of two deep neural networks where one learns to produce a clean fringe pattern and the other to obtain an accurate phase from the pattern. Compared with traditional denoising methods that depend on complex physical models, the proposed learning-based method is much faster. The experimental results show that the measurement accuracy can be increased effectively by the presented method. Full article

The mathematical model proposed by Chapman and Antano (Electrochimica Acta, 56 (2010), 128–132) for the catalytic electrochemical–chemical (EC’) processes in an irreversible second-order homogeneous reaction in a microelectrode is discussed. The mass-transfer boundary layer neighbouring an electrode can contribute to the electrode’s measured AC impedance. This model can be used to analyse membrane-transport studies and other instances of ionic transport in semiconductors and other materials. Two efficient and easily accessible analytical techniques, AGM and DTM, were used to solve the steady-state non-linear diffusion equation’s infinite layers. Herein, we present the generalized approximate analytical solution for the solute, product, and reactant concentrations and current for the small experimental values of kinetic and diffusion parameters. Using the Matlab/Scilab program, we also derive the numerical solution to this problem. The comparison of the analytical and numerical/computational results reveals a satisfactory level of agreement. Full article

The present paper discusses a new approach for the experimental determination of modal parameters (resonant frequencies, modal shapes and damping coefficients) based on measured displacement values, using the non-contact optical method of digital image correlation (DIC). The output is a newly developed application module that, based on a three-dimensional displacement matrix from the experimental measurement results, can construct a frequency response function (FRF) for the purpose of experimental and operational modal analysis. From this frequency response function, the modal parameters of interest are able to be determined. The application module has been designed for practical use in Scilab 6.1.0, and its code interfaces directly with the ISTRA4D high-speed camera software. The module was built on measurements of a steel plate excited by an impact hammer to simulate experimental modal analysis. Verification of the correctness of the computational algorithm or the obtained modal parameters of the excited sheet metal plate was performed by simulation in the numerical software Abaqus, whose modal shapes and resonant frequencies showed high agreement with the results of the newly developed application. Full article

This paper presents a Python-based simulation technique that can be used to predict the behavior of switch-mode non-isolated (SMNI) DC-DC converters operating in closed loop. The proposed technique can be implemented in an open-source numerical computation software, such as Scilab, Octave or Python, which makes it versatile and portable. The software that will be used to implement the proposed technique is Python, since it is an open-source programming language, unlike MATLAB, which is one of most-used programming and numeric computing platforms to simulate this type of system. The proposed technique requires the discretization of the equations that govern the open-loop operation of the converter, as well as the discretization of the transfer function of the controller. To simplify the implementation of the simulation technique, the code must be subdivided into different modules, which together form a package. The converter under analysis will be a buck converter operating in CCM. The proposed technique can be extended to any other SMNI DC-DC converter. The validation of the proposed technique will be carried out by comparing it with the results obtained in LTspice. Full article