I'm thinking to stick for a particular framework to work for my academic course but only based on results I should prove. I want to plot the graph for all the three frameworks where No.of Vertices is one axis and FPS (threshold is 60) is on other axis. Will that be good enough to take single predefined model in formats like obj, collada, json etc and load it in three frameworks? Then log the frame rate and number of vertices to some external file and thereafter use the data for plotting a graph to report the best framework among three based on Performance parameter. But I'm looking for some boilerplate codes for all these frameworks to load different models (can be used for number of vertices dimension in my graph) and log the frame rates for every second to external file. This is the approach I've been thinking. But couldn't find much help on this on internet. I wish someone could help me?
You can get FPS histogram data using stats.js library which is bundled with all Three.js examples
https://github.com/mrdoob/stats.js
Exporting the collected data to a file can be done using HTML5 File System API.
http://www.html5rocks.com/en/tutorials/file/filesystem/
Related
I have data sets of Heart Rate Variability data and i am trying to determine if the data collected each day is similar or related for data collected the next day. Please how can i go about this. I am looking to see if Dynamic time warping or cross correlation can do this but i am confused as to how to go about it. I am open to suggestions. I am hoping to write my code using either Matlab or Python
I have tried using Dynamic time warping and Cross correlation to compare the signals
Goal
I am making a flight game on Roblox that requires real-world map data to generate terrain.
Problems
I have absolutely no idea how to make this kind of program, and I have been unable to find any terrain generators that meet my requirements and have only found one terrain generator for Roblox.
Requests
The terrain needs to be generated fast enough so that commercial planes, which travel at speeds of around 500 knots, will not fly out of generated terrain. Also, accurate airports need to be generated with taxiways and runways, as well as the airport building. In addition, I also need the taxiway and runway location data, as well as the location of taxiway markings so that planes can pathfind along taxiways and runways, as well as do an ILS approach. Finally, data that is used for terrain should be acquired live so that I don't have to create an enormous map and use up too much storage.
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I have started working on a program to accomplish this. If finished, the project will be linked here.
It's not going to be high-quality terrain, but you can download map data from openstreetmap.org and create a mesh for the ground. Then use the building information to display buildings as basic shapes. Airports should also be easy to extract. I suggest creating one mesh per chunk, then stream the chunks required to the client, assuming that this works properly in Roblox. I'm not sure how detailed you want the meshes to be, but especially with two or more levels of detail, it should be no problem for the server.
As I am fairly new to RapidMiner, I have a Historical Financial Data Set (with attributes Date, Open, Close, High, Low, Volume Traded) from Yahoo Finance and I am trying to find a way to segment it such as in the image below:
I am also planning on performing this segmentation on more than one of such Data Sets and then comparing between each segmentation (i.e. Segment 1 for Data Set A against Segment 1 for Data Set B), so I would preferably require an equal number of segments each.
I am aware that certain extensions are available within the RapidMiner Marketplace, however I do not believe that any of them have what I am looking for. Your assistance is much appreciated.
Edit: I am currently trying to replicate the Voting-Based Outlier Mining for Multiple Time Series (V-BOMM) with multiple data sets. So far, I am able to perform the operation by recording and comparing common dates against each other.
However, I would like to enhance the process to compare Segments rather than simply dates. I have gone through the existing functionalities of RapidMiner, and thus far I don't believe any fit my requirements.
I have also considered Dynamic Time Warping, but I can't seem to find an available functionality in RapidMiner.
Ultimate question: Can someone guide me to functionalities that can help replicate the segmentation in the attached image such that the segments can be compared between Historic Data Sets in RapidMiner? Also, can someone guide me on how to implement Dynamic Time Warping using RapidMiner?
I would use the new version of the Time Series extension, using the windowing features to segment the time series into whatever parts you want. There is a nice explanation of the new tools in the blog section of the community.
I'm collecting data from different sensors and write them to a Cassandra database.
The Sensor-ID accts as a partition key, the timestamp of the sensors data as clustering column. Additionally a value of the sensor is stored.
Each sensor collects something about 30000 to 60000 values a day.
The simplest thing I wane do is draw a graph showing this data. This is not a problem for a few hours but when showing a week or even a longer range, all the data has to be loaded into the backend (a rails application) for further processing. This isn't really fast with my test dataset and won't be faster in production I think.
So my question is, how to speed this up. I thought about pre-processing the data directly in the database but it seems, that Cassandra isn't able to do such things.
For a graph with a width of 1000px it isn't interesting to draw ten thousands of points - so it would be interesting to gather only relevant, pre-aggregated data from the database.
For example, when showing the data for a whole day in a graph with a width of 1000px, it would be enough to take 1000 average values (this would be an average clustered by 86seconds - 60*60*24 / 1000).
Is this a good approach? Or are there other techniques fasten this up? How would I handle this with database? Create a second Table and store some average values? But the resolution of the graph may change...
Other approaches would be drawing mean values by day, week, month and so on. Maybe vor this a second table could do a good job!
Cassandra is all about letting you write and read your data quickly. Think of it as just a data store. It can't (really) do any processing on that data.
If you want to do operations on it, then you are going to need to put the data into something else. Storm is quite popular for building computation clusters for processing data from Cassandra, but without knowing exactly the scale you need to operate at, then that may be overkill.
Another option which might suit you is to aggregate data on the way in, or perhaps in nightly jobs. This is how OLAP is often done with other technologies. This can work if you know in advance what you need to aggregate. You could build your sets into hourly, daily, whatever, then pull a smaller amount of data into Rails for graphing (and possibly aggregate it even further to exactly meet the desired graph requirements).
For the purposes of storing, aggregating, and graphing your sensor data, you might consider RRDtool which does basically everything you describe. Its main limitation is it does not store raw data, but instead stores aggregated, interpolated values. (If you need the raw data, you can still use Cassandra for that.)
AndySavage is onto something here when it comes to precomputing aggregate values. This does require you to understand in advance the sorts of metrics you'd like to see from the sensor values generally.
You correctly identify the limitation of a graph in informing the viewer. Questions you need to ask really fall into areas such as:
When you aggregate are you interested in the mean, median, spread of the values?
What's the biggest aggregation that you're interested in?
What's the goal of the data visualisation - is it really necessary to be looking at a whole year of data?
Are outliers the important part of the dataset?
Each of these questions will lead you down a different path with visualisation and the application itself too.
Once you know what you're wanting to do, an ETL process harnessing some form of analytical processing will be needed. This is where the Hadoop world would be useful investigating.
Regarding your decision to use Cassandra as your timeseries historian, how is that working for you? I'm looking at technical solutions for a similar requirement at the moment and it's one of the options on the table.
I am programming a face recognition program using OpenCV.
When generating the eigenfaces:
do I need to use a big database of unknown faces ?
do I need to use only photos of the people I want my system to recognize ?
do I need to use both ?
I am talking about the eigenfaces generation, this is the "learning" step.
And how many photos do I need to use to have decent accuracy ? More like 20, or 2000 ?
Thanks
Eigenfaces works by projecting the faces into a particular "face basis" using principal component analysis or PCA. The basis does not have to include photos of people you want to recognize.
Instead, I would encourage you to train based upon a big database (at least 10k faces) that is well registered (eigenfaces doesn't work well with images that are shifted). The original paper by Turk and Pentland was remarkable partly due to the large pin registered face database they released. I would also say that try to have the lighting normalized to the same between the database and your test inputs.
In terms of testing, first 20 components should be sufficient to reconstruct a human recognizable face and first 100 components should be enough to discriminate between any two face for essentially arbitrarily large dataset.
You don't need too many random faces to compose a human face; somewhere close to 20 should give good results, maybe go with more if you can. They should all be lined up as much as possible to one another, front facing, and photos in grayscale under the same lighting conditions.