This resource contains the experimental data that was included in tecplot input files but in matlab files. dba1_cp has all the results is dimensioned (7,2) first dimension is 1-7 for each span station 2nd dimension is 1 for upper surface, 2 for lower surface. dba1_cp(ispan,isurf).x are the x/c locations at span station (ispan) and upper(isurf=1) or lower(isurf=2) dba1_cp(ispan,isurf).y are the eta locations at span station (ispan) and upper(isurf=1) or lower(isurf=2) dba1_cp(ispan,isurf).cp are the pressures at span station (ispan) and upper(isurf=1) or lower(isurf=2) Unsteady CP is dimensioned with 4 columns 1st column, real 2nd column, imaginary 3rd column, magnitude 4th column, phase, deg M,Re and other pertinent variables are included as variables and also included in casedata.M, etc

Dataset

This dataset was created by mtech saurabh

Released under Apache 2.0

Contents

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

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).

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.

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.

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

Two folders are provided. The MATLAB folder contains MATLAB codes for running Kernel PLS cross-validation, calibration and prediction of properties in various data sets: corn, yerba, wheat and meat. The R folder contains the same data with R codes for the same activities.

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).

Dataset

This dataset was created by HAYDER .

Released under Database: Open Database, Contents: Database Contents

Contents

There are the data and code for paper "A new concept model for seed germination and seedling tillering of winter wheat in the field" submitted to Royal Society Open Science.

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).

Routine to reproduce Figs 1D–1F, 2B, 2C, 3, 4 and 5 and S1–S6, panel B in S7, and S8 Figs. (ZIP)

In this appendix

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.

Abnormal Event Detection at 150 FPS in MATLAB dataset

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)

MATLAB SMT Res Augmented Dataset

## Overview

MATLAB SMT Res Augmented Dataset is a dataset for object detection tasks - it contains Defects annotations for 1,631 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).

Download all the Matlab source code, ANSYS APDL code, and ANSYS Workbench archive project files that accompany the book in one zip file.