Matlab

## Overview

Matlab is a dataset for object detection tasks - it contains Face annotations for 220 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Matlab has a reputation for running slowly. Here are some pointers on how to speed computations, to an often unexpected degree. Subjects currently covered: Matrix Coding Implicit Multithreading on a Multicore Machine Sparse Matrices Sub-Block Computation to Avoid Memory Overflow Matrix Coding - 1 Matlab documentation notes that efficient computation depends on using the matrix facilities, and that mathematically identical algorithms can have very different runtimes, but they are a bit coy about just what these differences are. A simple but telling example: The following is the core of the GD-CLS algorithm of Berry et.al., copied from fig. 1 of Shahnaz et.al, 2006, "Document clustering using nonnegative matrix factorization': for jj = 1:maxiter A = W'*W + lambda*eye(k); for ii = 1:n b = W'*V(:,ii); H(:,ii) = A \ b; end H = H .* (H>0); W = W .* (V*H') ./ (W*(H*H') + 1e-9); end Replacing the columwise update of H with a matrix update gives: for jj = 1:maxiter A = W'*W + lambda*eye(k); B = W'*V; H = A \ B; H = H .* (H>0); W = W .* (V*H') ./ (W*(H*H') + 1e-9); end These were tested on an 8049 x 8660 sparse matrix bag of words V (.0083 non-zeros), with W of size 8049 x 50, H 50 x 8660, maxiter = 50, lambda = 0.1, and identical initial W. They were run consecutivly, multithreaded on an 8-processor Sun server, starting at ~7:30PM. Tic-toc timing was recorded. Runtimes were respectivly 6586.2 and 70.5 seconds, a 93:1 difference. The maximum absolute pairwise difference between W matrix values was 6.6e-14. Similar speedups have been consistantly observed in other cases. In one algorithm, combining matrix operations with efficient use of the sparse matrix facilities gave a 3600:1 speedup. For speed alone, C-style iterative programming should be avoided wherever possible. In addition, when a couple lines of matrix code can substitute for an entire C-style function, program clarity is much improved. Matrix Coding - 2 Applied to integration, the speed gains are not so great, largely due to the time taken to set up the and deal with the boundaries. The anyomous function setup time is neglegable. I demonstrate on a simple uniform step linearly interpolated 1-D integration of cos() from 0 to pi, which should yield zero: tic; step = .00001; fun = @cos; start = 0; endit = pi; enda = floor((endit - start)/step)step + start; delta = (endit - enda)/step; intF = fun(start)/2; intF = intF + fun(endit)delta/2; intF = intF + fun(enda)(delta+1)/2; for ii = start+step:step:enda-step intF = intF + fun(ii); end intF = intFstep toc; intF = -2.910164109692914e-14 Elapsed time is 4.091038 seconds. Replacing the inner summation loop with the matrix equivalent speeds things up a bit: tic; step = .00001; fun = @cos; start = 0; endit = pi; enda = floor((endit - start)/step)*step + start; delta = (endit - enda)/step; intF = fun(start)/2; intF = intF + fun(endit)*delta/2; intF = intF + fun(enda)*(delta+1)/2; intF = intF + sum(fun(start+step:step:enda-step)); intF = intF*step toc; intF = -2.868419946011613e-14 Elapsed time is 0.141564 seconds. The core computation take

This data set consists of Conductivity, Temperature, Depth (CTD) data in MATLAB Format from the 2002 Polar Star Mooring Cruise (AWS-02-I). These data are provided in a single mat-file (MATLAB) for the entire cruise.

Component 1 of sofware related to the paper: Teza, G., Pesci, A., Meschis, M., 2023. A MATLAB toolbox for computation of velocity and strain rate field from GNSS coordinate time series. Annals of Geophysics, Revision submitted.

Physiological waveforms - such as electrocardiograms (ECG), electroencephalograms (EEG), electromyograms (EMG) - are generated during the course of routine care. These signals contain information that can be used to understand underlying conditions of health. Effective processing and analysis of physiological data requires specialized software. The WaveForm DataBase (WFDB) Toolbox for MATLAB and Octave is a collection of over 30 functions and utilities that integrate PhysioNet's open-source applications and databases with the high-precision numerical computational and graphics environment of MATLAB and Octave.

Scripts and data acquired at the Mirror Lake Research Site, cited by the article submitted to Water Resources Research: Distributed Acoustic Sensing (DAS) as a Distributed Hydraulic Sensor in Fractured Bedrock M. W. Becker(1), T. I. Coleman(2), and C. C. Ciervo(1) 1 California State University, Long Beach, Geology Department, 1250 Bellflower Boulevard, Long Beach, California, 90840, USA. 2 Silixa LLC, 3102 W Broadway St, Suite A, Missoula MT 59808, USA. Corresponding author: Matthew W. Becker (matt.becker@csulb.edu).

There are many MATLAB users in the Hydrology space. These scientists work as researchers and educators in academia and in agencies and institutes. Many of these institutions partner with CUAHSI and use their resources to share data and research. For data analysis and visualization, HydroShare provides integrations with Jupyter notebooks and other tools, via an ‘open with’ affordance.

MATLAB Online provides access to MATLAB from any standard web browser wherever you have internet access. It is ideal for teaching, learning and convenient, lightweight access. With MATLAB Online, you can share your scripts, live scripts, and other MATLAB files with others directly. Additionally, you can publish your scripts and live scripts to the web as PDFs or HTML and share the URL with anyone.

This web application enables the interactive exploration of MATLAB artifacts (such as Live Scripts) through a similar ‘open with’ affordance. When working with Live Scripts, users are presented with the option to open these artifacts in the Live Editor environment.

These exercises are designed to introduce students to analyzing real-world datasets with Matlab (4 exercises) and ArcGIS (1 exercise). The activities all require students to watch a video and complete a task before class time, during which they would follow the guide to complete several different tasks. These tasks are specific to learning about Meadowbrook Creek, a first order urban stream in the Syracuse, NY area, but could easily be developed for other places and other types of datasets.

Class(matlab) Finalwork

## Overview

Class(matlab) Finalwork is a dataset for object detection tasks - it contains Fabric Defect annotations for 482 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Matlab code to simulate equilibrium geometry of selected cross-sections on the Lower American and Sacramento Rivers in California.

File List Duffy_et_al.m Metschfig.m MetschLLfunct.m simanneal.m UnsmoothedMetschDynamics.txt Description Duffy_et_al.m: main Matlab file; calls Metschfig.m, MetschLLfunct.m, and simanneal.m Together, these files provide the code for analyses of the full model given by Eq. 1. Matlab is produced by MathWorks (2007, MATLAB Version 7, MathWorks, Natick, Massachusetts, USA). UnsmoothedMetschDynamics.txt: data file called by Duffy_et_al.m; this file contains data on lake/epidemic identity (column 1), Julian day (column 2), infection prevalence (column 3), D. dentifera density (column 4), susceptible density (column 5), infected density (column 6), weighted temperature (column 7), and epilimnion temperature (column 8).

The .zip file contains data from Carreon A. et. al. "Simulating Radiative Heat Transfer...", stored in MATLAB figure files (.fig extension) with file names corresponding to the figures in the paper.

This repository contains the Matlab code and generated data for the manuscript "Data-driven geometric parameter optimization for PD-GMRES" which uses a quadtree approach to optimize parameters for the iterative solver PD-GMRES. It includes hardware specific data to allow for reproducibity of our results. Our calculations were performed using MATLAB R2019a and should be reproducible up to and including version R2022a. A change in version R2022b leads to different numerical behavior. However, the code does run on newer Matlab versions. Further information is contained in the README.

Matlab code to ratio images

The Matlab scripts will compute parametric maps from Bruker MR images as described in the JoVE paper published in 2017 Complete download (zip, 465.7 KiB)

These Matlab/Simulink Files were used to generate the plots for the submitted paper „Mathematical Modeling and Simulation of Thyroid Homeostasis: Implications for the Allan-Herndon-Dudley-Syndrome“.

To reproduce the plots, please execute the files in the following order: 1: MM_Parameters_Healthy.m 2: MM_Healthy.slx 3: MM_Parameters_ADHS.m 4: MM_AHDS.slx 5: MM_Plot_Results and analogously regarding the linear case.

This is a sample MATLAB code to estimate Logit-Mixed Logit Model in preference space. The example is provided for a model with 2 fixed parameters, 2 random parameters, and 3 alternatives. This code is an extension of the original code by Kenneth Train which considers all utility parameters to be random and the model is estimated in willingness-to-pay space.

This code uses a simuated data. data_generation.m generate this data and give test.csv and test_save.mat as outputs.

In the attached folder, main_test.m is the main file which takes test.csv and test_save.mat as inputs. The default setting is "no bootstrapping" because it would take some time to run. You can change WantBoot=1 to get the bootstrapped standard errors. You can increase the number of repetitions (NReps) to 50 to get stable standard error estimates.

To get the histogram of random coefficient 1, use bar(MidEst(1,:),FreqEst(1,:)).

Please cite the following papers if you use this code in any form:

Bansal, P., Daziano, R. A., & Achtnicht, M. (2018). Extending the logit-mixed logit model for a combination of random and fixed parameters. Journal of choice modelling, 27, 88-96.

Bansal, P., Daziano, R. A., & Achtnicht, M. (2018). Comparison of parametric and semiparametric representations of unobserved preference heterogeneity in logit models. Journal of choice modelling, 27, 97-113.

Exports required model results as text files, for reading into Matlab. Must be run from command line (after loading Abaqus module) thus: abaqus python ExportModelEndState02a.py Output databases... QUT Research Data Respository Dataset Resource available for download

This dataset contains MATLAB .m files for replication of images and visualization of data. See READ ME file. For image processing techniques used on the raw video data from the experiments, see https://github.com/ArcGriffin/Video-Data-Processing-CASPER-/tree/main.