I am looking for an answer on how to acquire data for the vehicle rain sensor and automatic wiper data from any vehicle.
For example I know that with Carberry (a raspberry pi) it provides a lot of OBD features, however, I cannot seem to find anything on acquiring data for rain sensor, precipitation, wiper frequency, wiper speed...
Can anyone help me in the right direction?
Many thanks for any help that is given.
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I am currently trying to run the maxent modelling in R on a dataset of moon jellyfish in the north east Atlantic waters. I have environmental variables datasets from Copernicus ocean monitoring service. Since much of my presence points have a basis in citizen science observations they are mostly in coastal areas. My problem is that the environmental variables doesn’t always reach all the way in to the coast, and especially fjords and bay areas. Therefor some of my analyses give the error: “more than half of the presence points have NA predictor values”. Is there any way to join these presence points that fall outside to the closest raster? I have tried a few commands like approxNA, st_nearest_feature and focal but non of these seem valid in this matter.
Any help in this matter or other tips on working with coastal data in maxent would be highly appreciated!
I am working on a project about time-series forecasting using LSTMs layers. The dataset used for training and testing the model was collected among 443 persons which worn a sensor that samples a physical variable ( 1 variable/measure) every 5 minutes, for each patient there are around 5000 records/readings.
Although, I can train and test my model under different scenarios, I am troubled finding information about how to apply transfer learning in such an architecture. I mean, I understand I can use inductive transfer-learning by copying the matrix-weights from the general model onto a secondary model (unknown person), then after I can re-train this model with specific data and evaluate the result.
But I would like to know if somebody knows other ways to apply transfer-learning on this type of architecture or where to find information about it since there aren't many scientific papers talking about it, mostly they talk about NLP and other type of application but time series?
Cheers X )
I'm working on an archaeological excavation point cloud dataset with over 2.5 Billion points. This points come from a trench, a cube 10 x 10 x 3 m. Each point cloud is a layer, the gaps between are the excavated volumes. There are 444 volumes from this trench, 700 individual point clouds.
Can anyone give me some direction to any algorithms which can mesh these empty spaces? I'm already doing this semi-automatically using Open3D and other python libraries, but if we could train the program to assess all the point clouds and deduce the volumes it would save us a lot of time and hopefully get better results.
I'd like to calculate the matrix of travel times between US zipcodes. There are about 30k visible zipcodes, so this is 900 million calculations (or 450 million assuming travel time is the same in both directions).
I haven't used graphhopper before but it seems suited to the task. My question are:
What's the best way of doing it?
Will this overload the graphhopper servers?
How long will it take?
I can supply latitude and longitude for each pair of zip codes.
Thanks - Steve
I've not tested GraphHopper yet for these large amount of points, but it should be possible.
What's the best way of doing it?
It would be probably faster if you avoid the HTTP overhead and use the Java lib directly like in this example. Be sure to assign enough RAM as the matrix itself is already 2g if you only use a short value for the distance or time. See also this question.
Will this overload the graphhopper servers?
The API is not allowed to be used without an API key which you can grab here. Or set up your own GraphHopper server.
How long will it take?
Will take probably some days though.
Warning - enterprisy note: we provide support to setup your servers or for your usecase. And also we sell a matrix add-on which makes those calculations at least 10 times faster.
I'm writing a tool to collect information about power consumption of notebooks. I need to measure the current power consumption, and I use Perfmon to do so. But I found a strange bug.
Here is the typical graph of power consumption (this is "Power Meter" - "Power" - "_Total"):
Measurements are updated about once every 10-15 seconds.
But if the run Everest (or AIDA64) Power Management tab will be updating this more often, the results are more accurate:
Measurements are updated about once every 1-2 seconds.
I do not understand what happens when we run Everest. I really need to get accurate data.
Do you have any ideas?
I would really appreciate any suggestions in this regard.