Science topics: Projection

Science topic

# Projection - Science topic

A defense mechanism, operating unconsciously, whereby that which is emotionally unacceptable in the self is rejected and attributed (projected) to others.

Questions related to Projection

I want to reproject ERA5-Land data from orthogonal projection to 9km Ease-Grid v2 in MATLAB or R. Any leads are much appreciated. Thanks in advance.

I am trying to downscale GCM-(CanCM4) data to bhima basin catchment(finer scale) for projecting future scenarios. Further I have used following variables ta,ua,zg,va(all these @ 925,850,700,200 pressure levels) and pr,psl(total 6 variables). I am attaching image which I got from working on GCM, now considering mid point of these GCM grid points only 2 station lie on periphery(+ mark) for down scaling. Can I downscale these GCM points to the 0.5deg grid points? If yes, how to consider the weights?

Hello Maxent Community,

I have been generating SDM models and projections in Maxent. I have had some success with one set of variables (some Hydroshed variables mixed with World CLIM variables); however, I am having a bit of trouble with a model based solely on World CLIM variables.

I used a Peasron correlation matrix to select eight World CLIM variables, which model well with my species' occurrence data. The issue arises, when I ask Maxent to project on future World CLIM variables - the returned projection is almost entirely blue. There are a few specs of red and yellow (indicating prediction probabilities), but the vast majority of the projection is blue (indicating zero prediction probability). I tried rerunning the model with clamping turned off, but the problem persisted.

I am relatively novice with Maxent, so I was wondering if I am overlooking a setting that may help correct this issue. I am a bit confused, because when I ran the projection with the Hydroshed variables and some of the same World CLIM variables, the future projection looked just fine. Any advice or suggestions would be tremendously appreciated.

Thank you in advance for your help!

At the Monte Carlo casino, I saw an unusual advertising image. It rotated around a vertical axis and seemed suspended in the air; there was no screen behind it. The image was bright and colorful. Who knows how it works or has an idea how such a visual effect can be obtained?

Creating

**interpolating polynomial on the unit circle**by projecting the zeros of certain polynomials vertically on the unit circle as well as**reading their convergence behaviour**for the functions analytic inside the unit circle can how be seen for the**application part in the real life.**Where such type of interpolation can be applied to view a much wider perspective to such type of research problems.

What is MODIS's default Geographic Coordinate System and projected coordinate system? My study area is Bangladesh. I found the image distorted (the upper portion is bent towards left and lower portion is bent to the right. Pics attached). How can I project them to Geographic Coordinate System: WGS 1984 and projected coordinate system: WGS 1984 UTM Zone 45N ?

I have a WorldView 3 image with multispectral (only 8 bands, Coastal, Blue, Green, Yellow, Red, Rededge, NIR1, NIR2) and panchromatic band. I can open one of them (either multispectral or panchromatic) without any problem in ENVI 5.4. But when I open both images in a single ENVI window, both multispectral and panchromatic bands get distorted (looks like both are projected to a different coordinate system). Both images are in Geographic coordinate system- GCS_WGS_1984, Datum- D_WGS_1984. Can anyone help me to fix this problem?

I have programmed a method for solving quadratic optimization problems under linear constraints, this method is depends on the projection of a point onto a convex polyhedron in R^n, so I have programmed Dykstra's successive projection method and adapted it in my method, but Dykstra's successive projection algorithm dosen't work well, it's spend a lot of time even days to find a projection in 3 dimention real space!!!, I don't know if his algorithm is slow or I haven't programmed it properly!!! I have spend a lot of time on this method, so I'm very pluseur if somone can guide me to another projection method that I can get the algorithm code ready.

I am interested in commercially available liquid crystal displays for use as a spatial light modulator in a custom-designed video projector. Where can I buy such LCDs?

I want to convert projection of a co-ordinate from UTM to transverse mercator of specified location. I have false easting, false northing, central meridian, scale factor value of this location. How can I do this?

I am doing a hydraulic modelling in SMS software. I have downloaded a georeferenced satellite image from USGS earth explorer website. I opened both the satellite image and scatter data in SMS and made same projection for each. But, they were in different location with different co-ordinate as shown in picture below. How can I fix this issue?

How do you understand the entity known as the devil or satan in some religious traditions? Do you think the devil is a being with a life of its/his/her own, or a projection of the evil forces in the human psyche and in the universe, as some others have suggested? What do you think? What documented sources of support have you got? Thank you.

I am building a model to see how climate change can affect crop yield. climate and crop yield are connected through the Hargreaves evapotranspiration equation. How can I express this equation in continuous form? It is reasonable to express the variation in daily mean temperature as a percentage of the variation in global mean temperature?

There is a theorem stated as, Let k be a real closed field and D subset of k be a subring of k. Given a semi-algebraic set of k

^{n+1}defined over D, its projection to k^{n}is a semi-algebraic set defined over D. Can some one please explain what does this mean, and if possible how to visualize this geometricallyIe similar to the BioClim data kindly provided at WorldClim by Hijmans et al, 2005

I am doing some SDM modelling combining the BioClim dataset with N-deposition variables

-Ideally 30arcsec (~1km) resolution .tif's

-Ideally timepoint calculated around 2003 and projections for 2050 or 2070

I'm working with SMOS data to validate soil moisture at 70 stations, for an entire state.

To start the comparisons, I need to know how many stations are concentrated in each SMOS pixel, however, I'm having some trouble generating the Grid of the satellite in GIS software because of the image projection (EASE2).

Could someone indicate a way to do this?

Thanks for any help you can provide.

Researchers should have knowledge about mortality projections, risk or a wider scope of looking at mortality risk

A simpler way to get climate model projections or a resource that explains how to get data from CMIP5 in more detail would be much appreciated.

I want to know how one can make a infinite complex projective space into (naturally of course) a kahler manifold. in fact i'm trying yo undersand natural kahlerian structure of a arbitrary infinite complex projectice space/

For example, suppose in an image, there is a rectangular wall of which four of its corners are clearly visible, this would form a plane which looks like a trapezoid when viewed from an oblique angle.

From the image, the four points in 2-D space can be obtained with respect to the frame. How could you calculate the angle between the wall and the line of projection from the camera?

I take it that values (moral value, aesthetic value, etc.) are only knowable by ostensive definition (viz. by direct experience with the types of properties that any particular value is understood to be a token of). Now if true, I think that it is a mistake to suppose that they are merely the residue of the subjective states that they purport to be an experience of (a mere 'projection', as Mackie seems to suppose). Is it not equally true that 'objectivity' might be also coextensive with a property being "there in the world" even though the scale by which we measure it is a human construct? In other words, although the world does not come 'pre-sliced' into evaluative schemes, does it not still objectively 'answer' to the evaluative predicates we apply to it?

I am stuck with problem to determine the structure of quotient M/K , K is abelian Lie group, non-compact, M is R^n with removed finite numbers of linear subspaces, the question is wether or not M/K is smooth manifold. I think better is to use general theorem than trying to show directly that M/K is smooth manifold (maybe I am getting lazy), there is analogy with complex or projective spaces, these manifolds are quotients of Lie group action, we can prove directly they are manifolds, but instead of we can apply theorem, and I need such theorem, please help. Action of K is linear, so orbits are linear subspaces, but not all of them are of maximall dimension (and these have to be removed).

Galton (1868) famously described great variation between individuals regarding the vividness with which retrieved visual memories of scenes are typically re-visualized, ranging between "as vivid as perceptual experiencing" and no discernable imagery at all. Seashore's early research revealed similar individual variation regarding vividness of recalled (audiated) imagery for musical sounds, and moreover concluded that the characteristic level of vividness for an individual does not appear to be enhanceable by short-term or prolonged ear-training regimes. Vividness implies both fidelity or completeness of detailed feature-representation and intensity or loudness of recalled images. While I know from my own experimentation with various intensive ear-training tactics that the timbral richness and clarity of temporal onset of retrieved imagery can be much increased thereby, my findings bear out Seashore's regarding loudness - adult individuals have a permanently fixed personal ceiling level that is not affected by the content of the imagery.

The brain's encoding of imagery loudness seems to be poorly understood and not extensively researched. Neural firing-rate in primary auditory cortex (A1) has been shown to co-vary with loudness of sound-stimulus, and Penfield et al's famed experiments with electrical stimulation of exposed A1 surfaces in awake surgery-patients evoked auditory imagery at life-like levels. A1, however, does not appear to store encoded representations of loudness, and I would presume higher, cognitive levels to be accountable for that. However, there would also be factors concerning modulation of cortical activation/arousal-level by midbrain nuclei projections to consider - and I guess the question of what neural substrates

*actually*determine imagery-loudness boils down to identifying which of those modulatory projections cause the most impact on experienced loudness when artificially stimulated or suppressed. Would anyone reading be able to supply me with any researched information on that, please?On each closed manifold (orientable and nonorientable) there is non-trivial continuous involution. Is that true or not? Positive response is known for two-dimensional manifolds and real projective spaces.

Hi All,

I am working on structured light 3D camera. My main objective is to make Fast/Accurate structured light 3D camera by using commonly used projector.the main problem i usually faced during this process is to synchronize he camera with the projector. I want to expose the camera when pattern projection starts. This kind of synchronization is quite hard to achieve. How we can do this ?

I need the liquidus projection for the Ni-Ti-Zr system, and I could only find a partial liquidus projection in the composition region of 0 to 50 at.% Ni.

We consider M_{2}(C) as a 8 dimensional manifold with a natural Riemannian metric. The space of projections in M_{2}(C), all A with A=A*=A^{2}, is a two dimensional submanifold homeomorphic to S^{2}. We denote it by M. Is $M$ a round sphere, that is with constant curvature?(We restrict the metric of M_{2}(C) to M).

Problem of finding the minimum distance from a point to the complement of a convex set is a non-convex problem. Its solution set (the set of all projections) is not a singleton in general. There have been some algorithms for finding the projection onto a convex set. But in the reverse convex case, such as, projection onto the complement of an Ellipsoid, I don't know any algorithm to find a point in the set of projections.

we haven't a microscope projection to do it

As I read literature projecting contradictory views with profound theories and research, I am a bit perplexed and indecisive. One of the theories underscore the art of projection.

Looking forward to your views and suggestions.

The "piercing subspace" problem may be stated as follows:

There are given several subspaces in a projective space, rather non-intersecting.

Find an additional subspace of a prescribed dimension that has intersections of prescribed dimensions with each of the given subspaces. Determine how many solutions are there.

I believe this problem was popular many years ago. Can you suggest where to find the best results related to this problem?

The simplest example of this type of problems is the line piercing three skew lines in a 3d space. This problem does have infinite number of solutions. When the additional line is to intersect four lines then in real spaces there may be two, one double or none solutions while in complex spaces there are two solutions or one double. A theorem related to this is so called sixteen-point theorem.

Can you suggest some other theorems of this type and how to find them?

Investigating/Assessing the criminal tendencies in an Individual.

Does the projection of a vector $X$ in $\mathbb{R}^k$ onto a closed convex cone (positively homogenous set) $C$ in $\mathbb{R}^k$ with respect to a positve definite projection matrix $V$ always exist? i.e.

$\inf_{w\in C}\left(X-w\right)^TV\left(X-w\right)=\min_{w\in C}\left(X-w\right)^TV\left(X-w\right)$

Specifically I am interested in cone $C$ with linear constraints over its elements (of course these must not violate the properties of a cone).

Many thanks