May be my question sounds very basic but i have a confusion. I want to represent matrix in the following form in the block diagram of the labVIEW, not front panel. How can i make such matrices using block diagram not by front panel??? Can someone help me?
cluster of matrices
These are the simple steps to achieve your request:
You can access any matrix using "unbundle by name"
Related
Is there a way to make a z-stack of 2-D images, at the isometric view in 3-D, of points in each 2-D image projecting downwards to the next slice of 2-D images? I am certain there is a technical term for this, but I just don't have the vocabulary to find the most pertinent answer. Would someone be able to point me in the right direction?
Below, I've drawn an "idea" of what this looks like. I'd love to know if this is possible without re-inventing wheels for matplotlib or other Python plotting libraries.
The original question was posed for doing so in Python. After many months of searching, I found a way to do so in TikZ. I cannot consider this my original work, it is largely based on Pascal Seppecher's interaction diagram found here.
To reconstitute my question above, one can use the above template to define:
Agents of different shapes, specify fills
The frame (plane)
which they reside in
Flows of directed edges that communicate
how agents interact with each other in each plane
Inter-plane
interaction flows
https://texample.net/tikz/examples/interaction-diagram/
I am attempting to mesh a complicated design (~80,000 faces) for a microchannel heat sink, as pictured, and I would appreciate some advice. I have tried a range of different mesh controls (especially face sizing and body sizing), mesh settings and element sizes, and all have failed to produce a working mesh. The most common errors are shown in the linked picture, in particular the one regarding "The following surfaces cannot be meshed with acceptable quality. Try using a different element size or virtual topology." However, I have already reduced the element size to 2x10^-6 m, which takes two days to resolve before failure.
Unfortunately I cannot alter the geometry significantly, as it is imported from generation in SolidWORKS as either a STEP or an x.t file. As such, any advice for how I can successfully mesh the geometry for CFD analysis in FLUENT would be greatly appreciated.
I can provide more details or the geometry file itself if required.
Thanks in advance.
Meshing Attempt
Probably your cad design is not clean at all. But it is impossible to notice from this image. If you don't have control over the geometry source it is trouble. Because you might ask somebody else about check and fix something. First check you can do with your model it's trying to reduce the number of elements until the minimum possible value. Then if the mesh runs properly you can relay in the surfaces of your cad model. After that, you can refine the mesh, but the refining process is something that you have to do following some error criteria. If you are also the designer why not try to simplify a bit the geometry if you consider it is really hard to mesh? Meshing properly is a hard task, you should go step-by-step until you reach some solution. Also, you must not allow the preprocessor mesh automatically, without giving some criteria. Probably the first thing you have to answer even before apply any mesh is, what is your Reynolds number? And what is the most valuable result in which you can base the goodness of your discretization?
Thank you for your suggestions. In the end I solved the issue by importing the original mesh generated by COMSOL into SpaceClaim, then employing both the "Smooth" and "Reduce Faces" tools in tandem to simplify the geometry, before finally using SolidWORKS to turn the smoothed mesh into a solid body. This body retained many of the same features as the original, but was much less complex, having two orders of magnitude fewer faces. In turn, this permitted both meshing and heat transfer analysis in FLUENT.
I want to calculate the surface area of a 3D body (binarized: 1=inside, 0=outside so it's "voxelated") using Paraview. I found the filter "integrate variable" that gives me a value and it's reasonable. But I want to know what's the algorithm implemented into Paraview to compute it! It's an open-source software so everything should be open but I cannot find the reference.
Any idea?
Pretty simple: this filter computes the area of each polygon and sums them up. There are quite a few types of polygons supported, so the details of computing the area of each vary. Please consult http://www.paraview.org/gitweb?p=ParaView.git;a=blob;f=ParaViewCore/VTKExtensions/Default/vtkIntegrateAttributes.cxx;h=352155009780b7a45d5b4c00a75178de0f724675;hb=HEAD for details.
I am trying to extract rotation matrix and translation matrix from essential matrix.
I took these answers as reference:
Correct way to extract Translation from Essential Matrix through SVD
Extract Translation and Rotation from Fundamental Matrix
Now I've done the above steps applying SVD to essential matrix, but here comes the problem. According to my understanding about this subject, both R and T has two answers, which leads to 4 possible solutions of [R|T]. However only one of the solutions would fit in the physical situation.
My question is how can I determine which one of the 4 solutions is the correct one?
I am just a beginner on studying camera position. So if possible, please make the answer be as clear (but simple) as possible. Any suggestion would be appreciated, thanks.
The simplest is testing a point 3D position using the possible solution, that is, a reconstructed point will be in front of both cameras in only one of the possible 4 solutions.
So assuming one camera matrix is P = [I|0], you have 4 options for the other camera, but only one of the pairs will place such point in front them.
More details in Hartley and Zisserman's multiple view geometry (page 259)
If you can use Opencv (version 3.0+), you count with a function called "recoverPose", this function will do that job for you.
Ref: OpenCV documentation, http://docs.opencv.org/trunk/modules/calib3d/doc/calib3d.html
I would like to generate visually appealing surface reconstruction from the the point clouds.
I am using point cloud library. I tried creating a mesh using poisson reconstruction method but later found that it gives a water tight reconstruction.
For example: In my case I have a point cloud of a room
Using the code at http://justpaste.it/code1 , I was able to get a reconstruction like this
(source: pcl-users.org)
The above picture has the surface which is covering the top view. This was visualized using MeshLab.
Then later on the MeshLab GUI when I press points, it looks like this.
(source: pcl-users.org)
But in the second picture there are points on its surface too(Not clearly visible in the attached picture).
Can you help in creating a model that has no points on the top and just has the inside structure ?
Any other suggestions to improve the reconstruction quality ?
The point cloud of the room and generated ply file can be downloaded from https://dl.dropboxusercontent.com/u/95042389/temp_pcd_ply_files.tar.bz2
One solution that works for me is obtaining a convex/concave hull of your point cloud. Then you can use this hull to filter/crop your mesh after Poisson reconstruction. If you use the PCL you can try ConvexHull or ConcaveHull together with CropHull and test the results. Hope this solves your issue, it did for me.
As far as my experience is concerned (meshing caves), meshing with Poisson will result in watertight model/mesh, which is why your model was covered entirely. I only deal with meshes using MeshLab but I am guessing it is the same thing. What I did try is using Ball-Pivoting meshing algorithm in MeshLab which result in non-watertight model. Maybe that is what you are looking for.