I am trying to calculate the "greenspace access" per capita at an 800m radius for each pixel in a LULC map.
I have a 4m resolution raster with greenspace pixels labeled as 0/1 and a 4m raster with population density. In theory I wanted to do something like a buffer analysis for each pixel, but the buffer tool can only be done on polygon features. Does anyone have an idea for a way around this?
Related
I made a software to create and optimize a racing line in a racetrack.
Now I want to integrate it using real data recorded from GPS, so I need to obtain the g-g diagram, where g is the acceleration. The real g-g diagram is a set of points, in a scatter graph. I need to obtain the contour of that scatter plot, to use it as boundary of limits accelerations.
To obtain data to work on it I recorded myself on two different racetrack.
The code I wrote translate the x-y coordinate to polar R-theta.
Then I divide the circle in a definite number of sector (say, 20).
I calculate the histogram of all R's values in each sector, then from histogram I take the last value with an acceptable number of samples.
Then I draw these lines, and this is the result:
It's not bad, but this boundary is a little inside from the real data, real acceleration is a little bit bigger. I cannot take only the max value, because in this way I take in consideration the absurd values (like 3g in right corner, for sure an error). Moreover, the limit change if I change the number of bins on the histogram, but I cannot find a way to choose the right number of bins.
How can I determine the "true" convex hull, ignoring the outliers?
I've noticed that there is a limit to how fast one of my box2d bodies can move (2 meters per tick), is there a way I could increase this cap?
You can use World.setVelocityThreshold(1000000.0f); to increase the the limit, but this can lead to other problems.
A better alternative is to reduce the size of the world, so the limit of 2 meters per calculation step is sufficient.
This answer describes the problems when increasing the velocity threshold and describes other possible solutions to this kind of problem.
I am able to get pretty good results from batch gradient descent(batch size 37000) but when i try out mini-batch gradient descent i get very poor results (even with adam and dropout).
In batch gd i'm able to get 100% train and 97% dev/cv accuracy.
Whereas in mini-batch of size 128 i'm getting only around 88% accuracy in both.
The train loss seems to revolve around 1.6 and doesn't decrease with any further iteration but slowly decreases when i increase the batch size(hence improving accuracy).And eventually i arrive at batch size of 37000 for max accuracy.
I tried tweaking alpha but still same accuracy.
I'm training the mnist digits dataset.
What could be the reason? please help
In Batch Gradient Descent, all the training data is taken into consideration to take a single step. In mini batch gradient descent you consider some of data before taking a single step so the model update frequency is higher than batch gradient descent.
But mini-batch gradient descent comes with a cost:
Firstly, mini-batch makes some learning problems from technically untackleable to be tackleable due to the reduced computation demand with smaller batch size.
Secondly, reduced batch size does not necessarily mean reduced gradient accuracy. The training samples many have lots of noises or outliers or biases.
I believe that because of the oscillations in mini-batch you might fell into a local minima. Try to increase the learning rate with mini-batch it may solve the problem. also try to normalize the pictures it may help too.
I found the solution
The lmbda value i used for batch gd (i.e 10) seems to to be too big for mini batch gd.
And by decreasing it to 0.1 , i fixed the problem.
Extracting screenshots from RAM dumps
Some classical security / hacking challenges include having to analyze the dump of the physical RAM of a system. volatility does a great job at extracting useful information, including wire-view of the windows displayed at the time (using the command screenshot). But I would like to go further and find the actual content of the windows.
So I'd like to reformulate the problem as finding raw images (think matrix of pixels) in a large file. If I can do this, I hope to find the content of the windows, at least partially.
My idea was to rely on the fact that a row of pixels is similar to the next one. If I find a large enough number of lines of the same size, then I let the user fiddle around with an interactive tool and see if it decodes to something interesting.
For this, I would compute a kind of spectrogram. More precise a heatmap where the shade show how likely it is for the block of data #x to be part of an image of width y bytes, with x and y the axis of the spectrogram. Then I'd just have to look for horizontal lines in it. (See the examples below.)
The problem I have right now is to find a method to compute that "kind of spectrogram" accurately and quickly. As an order of magnitude, I would like to be able to find images of width 2048 in RGBA (8192 bytes per row) in a 4GB file in a few minutes. That means processing a few tens of MB per second.
I tried using FFT and autocorrelation, but they do not show the kind of accuracy I'm after.
The problem with FFT
Since finding the length of a mostly repeating pattern looks like finding a frequency, I tried to use a Fourier transform with 1 byte = 1 sample and plot the absolute value of the spectrum.
But the main problem is the period resolution. Since I'm interested in finding the period of the signal (the byte length of a row of pixels), I want to plot the spectrogram with period length on the y axis, not the frequency. But the way the discrete Fourier transform work is that it computes the frequencies multiple of 1/n (for n data points). Which gives me a very low resolution for large periods and a higher-than-needed resolution for short periods.
Here is a spectrogram computed with this method on a 144x90 RGB BMP file. We expect a peak at an offset 432. The window size for the FFT was 4320 bytes.
And the segment plot of the first block of data.
I calculated that if I need to distinguish between periods k and k+1, then I need a window size of roughly k². So for 8192 bytes, that makes the FFT window about 16MB. Which would be way too slow.
So the FFT computes too much information I don't need and not enough information I would need. But given a reasonable window size, it usually show a sharp peak at about the right period.
The problem with autocorrelation
The other method I tried is to use a kind of discrete autocorrelation to plot the spectrogram.
More exactly, what I compute is the cross-correlation between a block of data and half of it. And only compute it for the offsets where the small block is fully inside the large block. The size of the large block has to be twice larger than the max period to plot.
Here is an example of spectrogram computed with this method on the same image as before.
And the segment plot of the autocorrelation of the first block of data.
Altough it produces just the right amount of data, the value of the autocorrelation change slowly, thus not making a sharp peak for the right period.
Question
Is there a way to get both a sharp peak and around the correct period and enough precision around the large periods? Either by tweaking the afformentioned algorithms or by using a completely different one.
I can't judge much about the FFT part. From the title ("Finding images in RAM dump") it seems you are trying to solve a bigger problem and FFT is only a part of it, so let me answer on those parts where I have some knowledge.
analyze the RAM dump of a system
This sounds much like physical RAM. If an application takes a screenshot, that screenshot is in virtual RAM. This results in two problems:
a) the screenshot may be incomplete, because parts of it are paged out to disk
b) you need to perform a physical address to virtual address mapping in order to bring the bytes of the screenshot into correct order
find raw images from the memory dump
To me, the definition of what a raw image is is unclear. Any application storing an image will use an image file format. Storing only the data makes the screenshot useless.
In order to perform an FFT on the data, you should probably know whether it uses 24 bit per pixel or 32 bit per pixel.
I hope to find a screenshot or the content of the current windows
This would require an application that takes screenshots. You can of course hope. I can't judge about the likeliness of that.
rely on the fact that a row of pixels is similar to the next one
You probably hope to find some black on white text. For that, the assumption may be ok. If the user is viewing his holiday pictures, this may be different.
Also note that many values in a PC are 32 bit (Integer, Float) and 0x00000000 is a very common value. Your algorithm may detect this.
images of width 2048
Is this just a guess? Or would you finally brute-force all common screen sizes?
in RGBA
Why RGBA? A screenshot typically does not have transparency.
With all of the above, I wonder whether it wouldn't be more efficient to search for image signatures like JPEG, BMP or PNG headers in the dump and then analyze those headers and simply get the picture from the metadata.
Note that this has been done before, e.g. WinDbg has some commands in the ext debugger extension which is loaded by default
!findgifs
!findjpegs
!findjpgs
!findpngs
I have an application where 96% of the time is spent in 3D texture memory interpolation reads (red points in diagram).
My kernels are designed to do 1000~ memory reads on a line that crosses the texture memory arbitrarily, a thread per line (blue lines). This lines are densely packed, very close to each other, travelling in almost parallel directions.
The image shows the concept of what I am talking about. Imagine the image is a single "slice" from the 3D texture memory, e.g. z=24. The image is repeated for all z.
At the moment, I am executing threads just one line after the other, but I realized that I might be able to benefit from texture memory locality if I call adjacent lines in the same block, reducing the time for memory reads.
My questions are
if I have 3D texture with linear interpolation, how could I benefit most from the data locality? By running adjacent lines in the same block in 2D or adjacent lines in 3D (3D neighbors or just neighbors per slice)?
How "big" is the cache (or how can I check this in the specs)? Does it load e.g. the asked voxel and +-50 around it in every direction? This will directly relate with the amount of neighboring lines I'd put in each block!
How does the interpolation applies to texture memory cache? Is the interpolation also performed in the cache, or the fact that its interpolated will reduce the memory latency because it needs to be done in the text memory itself?
Working on a NVIDIA TESLA K40, CUDA 7.5, if it helps.
As this question is getting old, and no answers seem to exist to some of the questions I asked, I will give a benchmark answer, based on my research building the TIGRE toolbox. You can get the source code in the Github repo.
As the answer is based in the specific application of the toolbox, computed tomography, it means that my results are not necessarily true for all applications using texture memory. Additionally, my GPU (see above) its quite a decent one, so your mileage may vary in different hardware.
The specifics
It is important to note: this is a Cone Beam Computed Tomography applications. This means that:
The lines are more or less uniformily distributed along the image, covering most of it
The lines are more or less parallel with adjacent lines, and will predominantly be always in a single plane. E.g. They always are more or less horizontal, never vertical.
The sample rate on top of the lines is the same, meaning that adjacent lines will always sample the next point very close to each other.
All this information is important for memory locality.
Additionally, as said in the question, 96% of the time of the kernel is memory reading, so its safe to assume that the variation of the kernel times reported are due to changes in speed of memory reading.
The questions
If I have 3D texture with linear interpolation, how could I benefit most from the data locality? By running adjacent lines in the same block in 2D or adjacent lines in 3D (3D neighbors or just neighbors per slice)?
Once one gets a bit more experienced with the texture memory sees that the straightforward answer is: run as many as possible adjacent lines together. The closer to each other the memory reads are in image index, the better.
This effectively for tomography means running square detector pixel blocks. Packing rays (blue lines in the original image) together.
How "big" is the cache (or how can I check this in the specs)? Does it load e.g. the asked voxel and +-50 around it in every direction? This will directly relate with the amount of neighboring lines I'd put in each block!
While impossible to say, empirically I found that running smaller blocks is better. My results show that for a 512^3 image, with 512^2 rays, with a sample rate of ~2 samples/voxel, the block size:
32x32 -> [18~25] ms
16x16 -> [14~18] ms
8x8 -> [11~14] ms
4x4 -> [25~29] ms
The block sizes are effectively the size of a square adjacent rays that are computed together. E.g. 32x32 means that 1024 Xrays will be computed in parallel, adjacent to each other in a square 32x32 block. As the exact same operations are performed in each line, this means that the samples are taken about a 32x32 plane on the image, covering approximately 32x32x1 indexes.
It is predictable that at some point when reducing the size of the blocks the speed would get slow again, but this is at (at least for me) surprisingly low value. I think this hints that the memory cache loads relatively small chunks of data from the image.
This results shows an additional information not asked in the original question: what happens with out of bounds samples regarding speed. As adding any if condition to the kernel would significantly slow it down, the way I programmed the kernel is by starting sampling in a point in the line that is ensured to be out of the image, and stop in a similar case. This has been done by creating a fictional "sphere" around the image, and always sampling the same amount, independent of the angle between the image and the lines themselves.
If you see the times for each kernel that I have shown, you'd notice all of them are [t ~sqrt(2)*t], and I have checked that indeed the longer times are from when the angle between the lines and the image is multiples of 45 degrees, where more samples fall inside the image (texture).
This means that sampling out of the image index (tex3d(tex, -5,-5,-5)) is computationally free. No time is spend in reading out of bounds. Its better to read a lot of out of bounds points than to check if the points fall inside the image, as the if condition will slow the kernel and sampling out of bounds has zero cost.
How does the interpolation applies to texture memory cache? Is the interpolation also performed in the cache, or the fact that its interpolated will reduce the memory latency because it needs to be done in the text memory itself?
To test this, I ran the same code but with linear interpolation (cudaFilterModeLinear)and nearest neighbor interpolation (cudaFilterModePoint). As expected, improvement of speed is present when nearest neighbor interpolation is added. For 8x8 blocks with the previously mentioned image sizes, in my pc:
Linear -> [11~14] ms
Nearest -> [ 9~10] ms
The speedup is not massive but its significant. This hints, as expected, that the time that the cache takes in interpolating the data is measurable, so one needs to be aware of it when designing applications.