MATLAB - Science topic

% Define your SEIR model equations with time delay

function dydt = seir_delay(t, y, Z, beta, gamma, delta, tau)

% Extract variables

S = y(1);

E = y(2);

I = y(3);

R = y(4);

% Evaluate delayed variables

ZE = Z(:,1);

ZI = Z(:,2);

% Define model equations

dSdt = -beta * S * I - beta * delta * S * ZE(end);

dEdt = beta * S * I + beta * delta * S * ZE(end) - gamma * E;

dIdt = gamma * E - tau * I - gamma * I * ZI(end);

dRdt = tau * I;

% Return the derivatives

dydt = [dSdt; dEdt; dIdt; dRdt];

end

% Set up parameter ranges

beta_range = linspace(0, 1, 100); % Range for beta

gamma = 0.1; % Recovery rate

delta = 0.1; % Transmission rate with delay

tau = 0.1; % Recovery time

% Initial conditions

S0 = 0.8;

E0 = 0.1;

I0 = 0.1;

R0 = 0;

% Initialize arrays to store steady-state solutions

steady_states = zeros(length(beta_range), 4);

% Loop through each parameter value and solve the DDE

for i = 1:length(beta_range)

beta = beta_range(i);

% Define the delay function

delay_fun = @(t) [interp1(t_data, E_data, t-tau, 'linear', 'extrap');

interp1(t_data, I_data, t-tau, 'linear', 'extrap')];

% Solve the DDE

sol = dde23(@seir_delay, [tau, 20], @delay_fun, [S0; E0; I0; R0]);

% Store the steady-state solution

steady_states(i, :) = sol.y(:, end)';

end

% Plot the bifurcation diagram

figure;

plot(beta_range, steady_states(:, 3), 'b');

xlabel('Transmission rate (\beta)');

ylabel('Steady-state infected');

title('Bifurcation diagram of SEIR model with time delay');

While the exact specifications of the iPhone 15 Pro aren't officially available yet (as of May 7, 2024), it's likely that its 6GB RAM will be sufficient to run MATLAB's animated or dynamic 2D plots. Here's why:

However, there are some caveats to consider:

Overall, for basic animated or dynamic 2D plots, the iPhone 15 Pro's 6GB RAM should be sufficient. But for more demanding tasks, consider using the desktop version of MATLAB on a computer.

Here are some additional points to keep in mind:

I recommend checking the official documentation or contacting MathWorks (MATLAB's developer) for specific information on MATLAB Mobile's capabilities and compatibility with the iPhone 15 Pro.

import numpy as np # Replace with your file pathfilename = "path/to/uavsar.grd" # Read data based on file format (e.g., data type, number of elements) with open(filename, "rb") as f: data = np.fromfile(f, dtype=np.float32) # Adjust data type as needed # Reshape data based on number of rows, columns, and polarization channels # (Information on these might be in a separate file or require manual parsing)data = data.reshape((rows, columns, num_channels)) # Access specific polarization channel (e.g., VV)vv_channel = data[:, :, 0] # Assuming VV is the first channel (adjust indexing) # Further processing and analysis of the data

The total pressure drop in a Stirling engine is calculated by summing the pressure drops at the hot and cold ends of the engine. The pressure drop at the hot end occurs as the working fluid expands due to heat absorption, while at the cold end, it happens as the fluid contracts and cools. Maintaining an appropriate pressure difference between the hot and cold ends is crucial for efficient engine operation. Calculating the total pressure drop requires considering factors such as temperature, volume of the working fluid, and engine design specifics.

It´s can be solved by two principal ways. Firstly, you can use many functions by commands that can be mixed in scripts if goal are more complex. Depending of problem of calculus, you can use a symbolic enviroment for calculus, or use numerical approaches if problems no needs analitic exactity.

In second way, you can use algebra-toolboxes with integration of many methods dependind of general purposes.

The αβ frame, also known as the dq or synchronous reference frame, is often used in power electronics for analyzing three-phase AC systems. It simplifies calculations by transforming the three-phase sinusoidal waveforms into a two-dimensional equivalent in the rotating reference frame. This allows for easier control design and analysis.

An LCL filter consists of an inductor (L), capacitor (C), and another inductor (L) connected in series between the converter and the grid. Here's how to model it in the αβ frame:

Here are some resources that delve deeper into this topic:

FUZZYZ-ERROR CONTROL METHOD

An efficient approach to processing BrainVision files with ECG data in EEGLAB involves the following steps: Download and set up EEGLAB along with the necessary plugins: Make sure that you have MATLAB installed and the EEGLAB toolbox. Furthermore, in order to properly use EEGLAB, you must install the BIOSIG plugin, which can handle several electrophysiological data types, such as BrainVision files. To do this, use the "File" menu and choose "Manage EEGLAB Extensions." Then, proceed to install the BIOSIG plugin. Import the BrainVision file: BrainVision data files have three components: .vhdr (header), .vmrk (markers), and .eeg (binary data). To import these files into EEGLAB: Launch the EEGLAB software from the MATLAB command line using the " eeglab " command. Navigate to the "File" menu and choose "Import data." From the dropdown menu, choose "From other formats using BIOSIG." Choose the .vhdr file that corresponds to your dataset. Perform ECG data identification and preprocessing. After being loaded, the EEG dataset will be shown in EEGLAB. You focus on working with ECG data, be sure you locate the appropriate channel(s). While the specific naming practices may differ, ECG channels are often designated. If needed, the ECG data may be preprocessed using filters, detrending, or other preprocessing techniques. For example, powerline noise may be eliminated by using a notch filter, or the signal can be smoothed using a low-pass filter. Analysis and treatment of artifacts: ECG signals may include several types of noise and artifacts. To manage artifacts, it is advisable to use the built-in tools or extra plugins in EEGLAB. One may use independent component analysis (ICA) to distinguish and isolate noise or artifacts from the ECG signal. To perform heart rate variability analysis or identify certain ECG characteristics, such as R-peaks, it may be necessary to develop bespoke scripts or use specialist toolboxes. Exporting or doing more analysis: After the data has been cleaned and preprocessed, it may be exported for further analysis or visualization. To facilitate its use in other toolboxes or programming environments, you may export it as a MATLAB matrix using commands such as eeg_getdatact().

This paper may help

https://se.mathworks.com/matlabcentral/fileexchange/72293-optimal-power-flow-opf-problem-version-1.

To calculate the propagation length of surface plasmons numerically, you can use methods like finite element analysis (FEA) or finite difference time domain (FDTD) simulations. These techniques solve Maxwell's equations to model the behavior of electromagnetic waves interacting with materials at the nanoscale. By simulating the interaction of light with metal-dielectric interfaces, you can determine the distance over which surface plasmons propagate before being absorbed or attenuated. Additionally, you may need to consider factors such as the material properties of the metal and dielectric, wavelength of incident light, and surface roughness.

Multiple powergui blocks can be used. However, the step size must be wisely chosen in different subsystems. The auto mode can take care the minimum sampling time of solver. Likewise, a proper identification of plant with higher sampling rate may be beneficial.

Note that particle physics is different from wave mechanics because they both have a different physical nature.

The nature of particles is described or modeled as being small and confined to a point in space, while a particle wave is something that propagates and extends almost into infinite free space. Additionally, particles collide and scatter while matter waves refract, diffract, and interfere in a process called superposition. They add or cancel each other in superpositions.

The question arises how can Bmatrix chains describe the seemingly opposite nature via the same matrix?

The answer lies in the specific definition of the boundary conditions in both cases.

Assuming that the boundary conditions in Bmatrix chains are Dirichlet or similar, then they describe a microscopic or macroscopic particle. On the other hand, assuming that the boundary condition extends to infinity, the chains of the B matrix describe the mechanics of wave particles.

Thank you guys

Hi Sangeetha,

I also have the same issue with MSP files,

Could you solve this? I appreciate your suggestion if you can,

Bests,

Assessing the global behavior of a model and obtaining equilibrium points while analyzing their stability through simulation series typically involves the following steps:

Hi Hossein,

You seem to be asking two questions here. The first is

1. Hi. Can anyone help me with writing a thesis on resilience?

and the second,

2. Does anyone know how hurricane are modeled in MATLAB? And how do I use its outputs to show the impact on the distribution network?

The former requires more context on what "thesis on resilience" means.

Therefore, I shall attempt to answer the first half of the second question.

The Tropical Cyclone Report on Hurricane Sandy, created by the National Hurricane Center (NHC), meticulously outlines the storm's trajectory, strength, effects, and meteorological features. These reports, often released following noteworthy tropical cyclone occurrences, provide crucial insights for researchers, emergency planners, and the general public, enhancing comprehension of the storm's behaviour and repercussions. You can find this report attached or at https://www.nhc.noaa.gov/data/tcr/AL182012_Sandy.pdf. This report may serve as a theoretical beginning point for what you want to understand.

For the MATLAB implementation using data from this same file, please visit MATLAB central at https://se.mathworks.com/matlabcentral/fileexchange/50575-hurricane-sandy-fluid-mechanics-simulation-animated-gifs-linked-in-updates for a maybe useful MATLAB script file by Yussef Rikli on this. Cheers.

Thank you Junaid Ahad and Josnier Ramos Guardarrama for your valuable inputs

Automatic Modulation Classification (AMC) is a technique used in wireless communication systems to identify the type of modulation being used in a received signal. This is important because different modulation schemes encode information in different ways, and a receiver needs to know the modulation type to properly demodulate the signal and extract the data.

Here's a breakdown of AMC:

Here are some resources for further reading:

You have not provided the equations or any matlab code, so I'm just guessing. Happy to help out directly if you can post your actual problem in equations or Matlab code.

There's a really good Matlab toolbox called Symbolic Toolbox. It will solve systems of equations either symbolically, or directly. It's easy to use and understand.

Yes, detecting faults in HVDC systems using travelling waves and analysing them with wavelet transforms in MATLAB is a valid approach. However, you're right, the transient nature of DC faults can make it challenging. Here's why and some alternative techniques to consider:

The following paper has used Matlab/Simulink:

You may try to talk to the authors. More clues can be found at

The Fuzzy Logic Toolbox in MATLAB enables the implementation of Takagi-Sugeno (TS) fuzzy systems for approximation tasks. While there are no predefined functions for direct function or parameter approximation using TS fuzzy logic, users can construct such systems using toolbox functions. The general approach involves defining the fuzzy system by specifying input and output variables, selecting appropriate membership functions, and defining fuzzy rules. Training data, consisting of input-output pairs, is prepared to train the fuzzy system, followed by the tuning of parameters like membership function parameters and rule strengths through fuzzy inference. Trained fuzzy systems are validated using separate validation data, and adjustments are made as needed. Once validated, the fuzzy system can be utilized to approximate functions or parameters by inputting values and obtaining output. A provided example demonstrates this approach, showcasing the construction of a TS fuzzy system for function approximation in MATLAB. Adjustments to the system's structure, membership functions, and training parameters may be necessary based on specific requirements and datasets.

See this video explaining the Matlab code for plotting the bifurcation diagram, with code available in the description https://www.youtube.com/watch?v=O506G8DtmQk

thank you so much sir.... I know that wavelet Decomposition breaks the signal like this that' s why I decided to opt for level 6 decomposition in order to get the delta frequency too, but the detail coefficients I am getting have very different dimensions so I just wat to sure ..that Are they OK

To efficiently collect the dataset associated with an IEC microgrid, including fault and non-fault scenarios, you can use MATLAB along with Simulink for simulation purposes. Here's a general outline of how you can do it:

Here's a simplified MATLAB code outline demonstrating these steps:

matlabCopy code% Step 1: Load Simulink model model = 'IEC_microgrid_model'; open_system(model); % Step 2: Configure simulation parameters fault_types = {'Short Circuit', 'Voltage Sag', 'Line Fault'}; % Define fault types fault_locations = {'Bus 1', 'Line 2-3', 'Transformer 1'}; % Define fault locations % Step 3: Set up data logging simOut = sim(model); % Step 4: Simulation execution (loop over fault scenarios) for i = 1:numel(fault_types) for j = 1:numel(fault_locations) % Inject fault inject_fault(model, fault_types{i}, fault_locations{j}); % Run simulation simOut = sim(model); % Step 5: Data processing (extract features and labels) % Extract data from simOut and process as needed % Step 6: Dataset generation % Organize processed data into dataset format % Save dataset to file or memory end end

In this code:

You'll need to customize and expand upon this outline based on the specifics of your IEC microgrid model and the data you want to collect. Additionally, ensure that your Simulink model is appropriately configured to accurately represent the behavior of the microgrid under different operating conditions and fault scenarios.

I am not sure if Simulink has such resistor models already built in it but below is a simple Matlab script that can be used to model such a resistor whose resistance value varies with time. I have used the sine function to model the transient variation. You can define anything as per your needs.

% Define time vector

t = linspace(0, 10, 1000); % Adjust time range and resolution as needed

% Define a function for temperature variation with time (customize as needed)

temperature = 25 + 10 * sin(2 * pi * 0.05 * t);

% Define a function for resistance variation with time and temperature (customize as needed)

resistance = 10 + 5 * sin(2 * pi * 0.1 * t) + 0.1 * (temperature - 25);

figure;

plot(t, temperature, 'LineWidth', 2);

xlabel('Time (s)');

ylabel('Temperature (°C)');

title('Resistor Temperature vs Time');

figure;

plot(t, resistance, 'LineWidth', 2);

xlabel('Time (s)');

ylabel('Resistance (\Omega)');

title('Variable Resistor: Resistance vs Time');

We have uploaded several codes in Matlab central, feel free to browse through them, there are a couple on chaotic image encryption, and many more on chaotic random bit generators https://uk.mathworks.com/matlabcentral/fileexchange/?q=profileid:28825692

Been years since I used Matlab but as far as I know you can just program it as a new function, it is not a big deal. These might help you do that:

To represent an electric arc in MATLAB and measure its parameters such as voltage, current, and temperature, you can simulate the circuit using MATLAB's Simulink toolbox. First, create a circuit model that includes components like resistors, capacitors, and inductors to represent the electrical properties of the arc. Then, use appropriate sensors to measure voltage and current across the arc, and incorporate a temperature sensor to measure the arc temperature. Finally, use MATLAB functions to analyze the data collected from the sensors and calculate the arc parameters.

with my pleasure

Thank you @Adnan Majeed

See this solution:

% Define the differential equation

function dxdt = myODE(t, x, a)

% Define the parameters

alpha = a(1);

beta = a(2);

% Define the dynamical system

dxdt = [alpha * x(1) - beta * x(1)^3];

end

% Define parameter range and initial condition

alpha_values = linspace(-2, 2, 100); % Range of alpha values

beta = 1; % Fixed beta value

x0 = 0.1; % Initial condition

% Iterate over parameter values and solve ODEs

for i = 1:length(alpha_values)

% Define current parameter set

a = [alpha_values(i), beta];

% Solve ODE

[t, x] = ode45(@(t,x) myODE(t,x,a), [0, 20], x0);

% Plot phase portrait

plot(x(:,1), x(:,2));

hold on;

end

% Labeling and formatting

xlabel('x');

ylabel('dx/dt');

title('Phase Portrait');

grid on;

hold off;

##In this code:

Dear Homayoun,

You can use Tustin’s method for time discretization. (To convert the Laplace form to the z form). Then you could use this hint that (z^-1)*x(n) is equal to x(n-1). I have written the method to implement discrete form of PR controller on the Simulink for you in the attached PDF. Please recheck it. Moreover, the link (https://imperix.com/doc/implementation/proportional-resonant-controller) is also helpful.

Best Regards

Thank You very much,Bechir Hbibi .I will try this method.

Implementing a Photovoltaic Thermal (PVT) hybrid system in MATLAB involves several steps, including modeling the solar radiation, simulating the photovoltaic (PV) module, and modeling the thermal system.

Hi Ruaa Shallal Abbas Anooz

To effectively use QuadRiGA for generating data, start by downloading and installing the MATLAB-based tool and thoroughly understanding its documentation. Identify your specific requirements, such as simulating channel impulse responses for wireless communication scenarios, and explore the relevant functions within QuadRiGA. Adjust parameters within these functions to tailor the simulations to your needs, considering factors like carrier frequency, antenna configuration, and environment type. Run simulations, validate results against real-world measurements if necessary, and iterate on your setup as needed for accuracy. Seek support from documentation or online communities for any challenges encountered during the process. Through these steps, you can utilize QuadRiGA to generate the desired data effectively.

Please I have a question. Can someone help me ? I want to connect NetSim with Python, that's the error message: ConnectionResetError: [WinError 10054] An existing connection was forcibly closed by the remote host

Lazaros Moysis thank you for your response and needful behaviour.

Using greedy algorithm we can find dominating sets for a graph

Hello Songcai,

I am facing the same problem, did you manage to find a way to use vectors with the function or did you just end up using a loop?

Thanks,

Caio

If you have mathematical model, then you can.

Matlab

function [partial_corr, conf_interval] = partialcorr(data, x, y, z) % This function calculates the partial correlation coefficient and its confidence interval. % Calculate linear regression coefficients beta_xy = regress(data(:, y), [ones(size(data,1),1) data(:, x)]); beta_yz = regress(data(:, z), [ones(size(data,1),1) data(:, y)]); % Calculate residuals res_x = data(:, x) - beta_xy(2)*data(:, y) - beta_xy(1); res_y = data(:, y) - beta_yz(2)*data(:, z) - beta_yz(1); % Calculate partial correlation coefficient partial_corr = corr(res_x, res_y); % Calculate standard error n = size(data, 1); se = sqrt((1 - partial_corr^2) / (n - 3)); % Calculate custom confidence interval (e.g., 95%) alpha = 0.05; t_stat = partial_corr / se; df = n - 3; conf_interval = tinv(1 - alpha/2, df) * se + partial_corr; end

Use code with caution. Learn more

content_copy

Matlab

% Sample data data = [1 2 3; 4 5 6; 7 8 9; 10 11 12]; % Calculate partial correlation between x and y, controlling for z [partial_corr, conf_interval] = partialcorr(data, 1, 2, 3); % Display results fprintf('Partial correlation: %.4f\n', partial_corr); fprintf('Confidence interval: [%.4f, %.4f]\n', conf_interval(1), conf_interval(2));

Use code with caution. Learn more

content_copy

Simbiology is indeed closely intertwined with MATLAB, offering several ways to leverage its capabilities for biological modeling and simulation. Here's a breakdown of how they interact:

Note: Simbiology is not the only way to use MATLAB for biological modeling. Alternative toolboxes and libraries exist, each with its own strengths and weaknesses. Choosing the right approach depends on your specific needs and expertise.

The stiffness coefficient for a shell element is determined based on the shell's geometry, such as its thickness, and the material properties, such as the Young's modulus and Poisson's ratio. Unlike beam elements, shell elements have additional degrees of freedom and exhibit different deformation characteristics, which impact the stiffness coefficient calculation.

Dear esteemed colleague,

I hope this message finds you well and making significant progress in your research endeavors. Your inquiry about the fitting of Isothermal Titration Calorimetry (ITC) data into a model is a critical aspect of interpreting the thermodynamic parameters of molecular interactions, an area that is of paramount importance in biochemistry and molecular biology. ITC is a powerful technique used to study the binding affinity, stoichiometry, enthalpy (ΔH), and entropy (ΔS) changes associated with the interaction between two molecules. The process of fitting ITC data into a model to extract these parameters involves several key steps, which I outline below:

In conclusion, fitting ITC data into a model is a systematic process that requires careful attention to experimental design, data preprocessing, model selection, and rigorous analysis. The derived thermodynamic parameters provide invaluable insights into the molecular mechanisms underlying the interaction, contributing to our understanding of biological processes and the development of therapeutic agents.

Should you require further assistance or wish to delve deeper into any aspect of ITC data analysis, please do not hesitate to reach out.

Warm regards,

Perhaps this protocol list can give us more information to help solve the problem.

Solving partial differential equations (PDEs) with three independent variables in MATLAB can be a complex task depending on the specific form of the PDE. MATLAB provides several functions for solving PDEs, and the choice of method depends on the nature of your problem.

Here's a general outline of the steps you might take:

Here's a simple example using pdepe for a one-dimensional problem:

matlabCopy codefunction pdex1 m = 0; x = linspace(0,1,100); t = linspace(0,1,10); sol = pdepe(m, @pdex1pde, @pdex1ic, @pdex1bc, x, t); u = sol(:,:,1); surf(x,t,u) title('Numerical solution computed with 20 mesh points'); xlabel('Distance x'); ylabel('Time t'); zlabel('u(x,t)'); end function [c,f,s] = pdex1pde(x,t,u,DuDx) c = 1; f = DuDx; s = 0; end function u0 = pdex1ic(x) u0 = sin(pi*x); end function [pl,ql,pr,qr] = pdex1bc(xl,ul,xr,ur,t) pl = ul; ql = 0; pr = ur - 1; qr = 0; end

Can you help me plot the bifurcation of my model with dde_biftool?

Solving time fractional differential equations with delay using the Crank-Nicolson method involves discretizing the equation in both time and space. The Crank-Nicolson method is a numerical scheme that combines implicit and explicit methods to approximate the solution. Below is an example MATLAB code to solve a time fractional differential equation with delay using the Crank-Nicolson method. Note that this is a basic template, and you may need to adapt it to your specific equation and boundary/initial conditions.

% Parameters alpha = 0.5; % Fractional order (0 < alpha < 1) T = 1; % Total simulation time dt = 0.01; % Time step N = T / dt; % Number of time steps % Space domain L = 10; % Length of the spatial domain dx = 0.1; % Spatial step x = 0:dx:L; % Spatial grid % Initial condition u0 = sin(pi * x / L); % Crank-Nicolson method A = sparse(N + 1, N + 1); rhs = zeros(N + 1, 1); for n = 1:N % Discretized fractional derivative with delay if n > 1 u_delayed = u(:, n - 1); else u_delayed = u0; end D_alpha = fracderiv(x, u(:, n), alpha) + fracderiv(x, u_delayed, alpha); % Crank-Nicolson discretization A(n, n) = 1; A(n, n + 1) = -0.25 * dt * D_alpha; A(n + 1, n) = 0.25 * dt * D_alpha; A(n + 1, n + 1) = 1; % Right-hand side rhs(n) = u(:, n); rhs(n + 1) = u(:, n + 1); % Solve the linear system u(:, n + 1) = A \ rhs; end % Plot the results figure; surf(x, (1:N + 1) * dt, u'); xlabel('Space (x)'); ylabel('Time (t)'); zlabel('u(x, t)'); title('Numerical Solution using Crank-Nicolson Method'); % Fractional derivative function function D_alpha = fracderiv(x, u, alpha) N = length(x); h = x(2) - x(1); D_alpha = zeros(N, 1); for i = 2:N-1 D_alpha(i) = (u(i+1) - u(i-1)) / (2 * h) + ... alpha / (h * gamma(2 - alpha)) * (u(i+1) - 2 * u(i) + u(i-1)); end % Forward and backward differences at boundaries D_alpha(1) = (u(2) - u(1)) / h; D_alpha(N) = (u(N) - u(N-1)) / h; end

Have you tried the edfreader?

You can find it online or I can send it to you.

best

Dear Renjith Vijayakumar Selvarani and Dear Qamar Ul Islam,

Many thanks for your notice.

Please make sure that a supported version of Matlab is installed and that Matlab's "bin/win32" folder is on Windows

Differents authors, such as Torma et al., 2015 recommended used fir reggriding of precipitation inverse of distance weithing (idw). If you use Climate Data Operator (CDO) you can use its command:

cdo remapdis,gridfile infile.nc outfile.nc

Please, you read CDO User Manual.

Best regards,

Axel

see this

https://arxiv.org/pdf/2007.11127.pdf

Dear Demet Yeşilbaş

Commonly you could import.mat, .xlsx, .CSV and so on in most of MATLAB Toolbox so you don not have any limitation with file format and you can easily import it to MATLAB using mentioned Command. Also if you want to import some special columns and rows ( not all of them ) , you could define them in above-mentioned command.

Usually appropriate file format is .mat for importing time series data and their values but because your data are not very big and it has less dimension. So, it is okay to use excel file but if you want I can change it to .mat file which is readable for all of MATLAB Toolboxes.

Warm Regards,

Hossein

Teşekkür ederim!

Matpower has a couple of m-files to read *.raw data so as to convert it to matpower format.

I guess you can use volshow() method and then save

Dimitrios, thanks a lot for the detailed answer!

Greetings, everyone! I'm currently utilizing OSLO, an optical design software. Unfortunately, I haven't found any details regarding the magnification of the objective lens. Additionally, I have uncertainties about the enclosed area (red dott), which I've included in an image. If anyone has any insights, please share them with me.

The function is actually undefined; however, to make other mathematical theorems make any sense it is taken as 1 otherwise these theorems will fall apart and become undefined. Some branches of mathematics do however consider it to be undefined.

A simple approach to apply IA on adsorption research is through estimation of results of adsorption (means % of adsorption) based upon the several variables that can partake in a adsorption process (means, pH, shaking time, content of adsorbent, content of pollutant, temperature, among others). You can use in a easy way the Matlab neural network tool (named prediction) to be able to "predict" the results of an adsorption process after having created an artificial neural network that can predict a %adsorption on the basis of several parameters.

If you have analytical calculations, post it and i will try to model it in MATLAB.

Hi Anna I think it ıs possible

but I prefer to use Mathematica

Best regards

Hey there Rakesh Narthu! So, you're diving into the world of ILS glideslope receiver board design – that's no small feat! Now, to tackle the frequency detection using the Goertzel algorithm in MATLAB, here's a sleek approach for you Rakesh Narthu:

```matlab

function amplitude = goertzel_algorithm(signal, target_frequency, sampling_rate)

N = length(signal);

k = round(target_frequency * N / sampling_rate);

omega = (2 * pi * k) / N;

coeff = 2 * cos(omega);

Q1 = 0;

Q2 = 0;

for n = 1:N

Q0 = signal(n) + coeff * Q1 - Q2;

Q2 = Q1;

Q1 = Q0;

end

real_part = Q1 - Q2 * cos(omega);

imag_part = Q2 * sin(omega);

amplitude = sqrt(real_part^2 + imag_part^2) / (N / 2);

end

% Usage example

sampling_rate = 1000; % replace this with your actual sampling rate

target_frequencies = [90, 150]; % frequencies you want to detect

% Your signal (replace this with your actual signal)

t = 0:1/sampling_rate:1; % adjust the time range

signal = cos(2 * pi * 90 * t) + cos(2 * pi * 150 * t);

for freq = target_frequencies

amplitude = goertzel_algorithm(signal, freq, sampling_rate);

disp(['Amplitude at ' num2str(freq) ' Hz: ' num2str(amplitude)]);

end

```

This MATLAB code defines a function `goertzel_algorithm` to perform frequency detection using the Goertzel algorithm. It then demonstrates how to use it with your specified target frequencies.

Feel free to adapt this code to your specific needs, and let me know if you need any further guidance or if I can assist you with anything else!

Attached steps to install python, after that install the Visual Studio Code, the environment is very similar with Matlab.

Regards Francisco

Thanks Professor Filippo Maria Denaro

Hey there Zeynab Molaei! Sure thing, my friend Zeynab Molaei. Calculating Coulombic efficiency can be a bit tricky, but I've got you Zeynab Molaei covered. Here's a MATLAB code snippet to help you out:

```matlab

function coulombic_efficiency = calculate_coulombic_efficiency(charge, discharge)

% Assuming charge and discharge are vectors of current values over time

% Calculate the total charge and discharge capacities

total_charge = trapz(charge);

total_discharge = trapz(discharge);

% Coulombic efficiency formula: (Discharge capacity / Charge capacity) * 100

coulombic_efficiency = (total_discharge / total_charge) * 100;

end

```

Make sure to replace `charge` and `discharge` with your actual current data. This function will give you Zeynab Molaei the Coulombic efficiency. If you Zeynab Molaei encounter any issues or need further clarification, feel free to ask. I'm here to help!

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