If I get the cell id
Will it work to get the exact location of the mobile devices through j2me programming?
I got to know how to get cell id by this link:
Can I fetch position of mobile device in j2me having no GPS service using information from cell tower
But whether to get location accurate is still a question?
One Single cell is not enough to get you the accurate location. you need at least 3 different cell to get a location as accurate as GPS
What the cell information can do for you is give you a good information of the area and measurements
See API Information http://www.opencellid.org/api
How To
Get a cell ID and save the distance
When the user cell changes .. get the distance using same API
As soon as you are able to get an additional cell information start triangulation
Triangulation http://en.wikipedia.org/wiki/Triangulation
Calculate distance between P1 and P2.
Calculate the distance between P1 and the center point of the new n-sphere
Calculate the ratio new n-sphere/d. (Used to find per-dimension distances between P1 and the centerpoint of the new n-sphere.
Calculate square of radius
Calculate center point of new n-sphere
Calculate distance between P1 and P2 on this dimension
Calculate distance between P1 and the center point of the new n-sphere on this dimension
Calculate the coordinate of the center point of the new n-sphere on this dimension.
Have only implemented the calculation on client side PHP not j2me ...
Thanks
:)
In short, no. To get an accurate location you need a mobile device with built-in GPS.
The Cell ID is a number identifying the cellphone tower that the mobile device is connected to. Therefore at best you can get the position of the cellphone tower, and use that as an estimate of the user position.
Also note that API's such as http://www.opencellid.org/api only give an estimate of the cell tower position based on data from other users, so it might not even be an accurate position for the cell tower.
In my experience this will work reasonably well in densely populated cities - you can get an accuracy of about 200-500m (still not close to what you'd get with a GPS). However, in more rural areas the accuracy could be as low as 10 or 20 km.
As pointed out by Baba, triangulation can give you a more accurate position. However, for this you need the distances to at least 3 towers, while in J2ME you can only get the ID of the closest tower (not even the distance). Some network operators do triangulation themselves, as they have all the data available to them. However, you'll have to talk to the specific network operators if you want access to this data.
Related
I have an problem I need some help with. I have 2 sensors (a weather sensor and a camera) mounted under a large balloon (basically). The weather sensor records pitch, roll, yaw, altitude, heading (0-360), lon and lat. I also have x,y,z values that represent the offset distance that the weather sensor is from the camera. The camera does not have it's own INS so the values from the weather sensor are sent to the camera. However, since those values are coming from the weather sensor that is not in the same position as the camera, the values are not accurate. I need to perform calculations on the values before sending them to the camera and that is where I need help.
For the record:
Both devices are facing the front of the craft
The X is the Front to Back axis
The Y is the Top to Bottom axis
The Z is the Left to Right axis
coordinate planes diagram
I know the formula to get coordinates for a target point given a starting point, bearing, and distance. I can get the distance by using the Pythagorean Theorem (using the measured X and Z values). Those are 10.58055" and 17.53322" respectively. We already have the starting point (it comes from the weather sensor).
First, am I on the right track here?
Second, how do I appropriately calculate bearing? I can use trig to get the angle that the weather sensor is offset from the camera, which I think is required. I also think I need to account for the orientation of the sensor to the camera (i.e. if the weather sensor is in front of the camera, it needs to "turn around" to get to the camera). This would mean that if the X value is negative and the Z value is positive, I would subtract my angle (let's call it theta) from 180. However, that would only work if I was heading north so I believe you then need to add in the heading (that came from the weather sensor).
I think I am close on this. I need some smarter people letting me know if I am approaching this correctly and then possibly little things like the appropriate way to handle the bearing measurement going above 360 (which I believe is to just subtract 360).
I work in the oil & gas industry and I'm seeking advice about how to calculate the minimum distance between a set of wells (the wells are drawn as straight lines on a map). My goal is for each individual well to have a unique "spacing" value (measured in feet) which is basically the straight-line horizontal distance to the closest wellbore on a map. Below is a simple example of what I'm trying to accomplish (assume the pipe | symbol is a wellbore and the dashes are the distance between the wells)
|--|---|-|
In the drawing above we have 4 wells. The 1st well (starting from the far left) would have a spacing value of 2 (since there are 2 dashes to the closest well), the 2nd well would also have a value of 2 (since the closest well is the one to the far left which is two spaces away), the 3rd well would have a value of 1, and the 4th well would have a value of 1.
Now imagine that I have hundreds of these wells (each with latitude/longitude points that describe the start & end points of each well) and I have them all mapped in TIBCO Spotfire (scattered across Texas). Do you guys know if it would even be possible to automate a calculation like the above? I would also like to build in a rule that says the max distance between wells is 2640 ft (half of a mile).
Any ideas are appreciated!
I think you should be able to do this without any R or iron python.
Within Spotfire, you can calculate the distance in miles between 2 points using the formula below (substitute 6371 for 3958.756 to get the answer in kilometres).
GreatCircleDistance([Lat 1],[Lon 1],[Lat 2],[Lon 2]) * 3958.756
For your use case, you could cross join your table of locations, so that you have a row for every possible location combination, then calculate the distance between them using the formula above. After that, it should be pretty straight forward to find each wells closest pair.
I'm trying to determine where, in a set of measurement data, the data takes a dive...
... so I can plot a vertical line and
... plot a horizontal line in the graph.
I have no problem doing the 2nd and 3rd bullet points above on my own, so that's taken care of.
The problem I need help with is the first bullet point - determining WHERE the data takes a dive - WHERE the data crosses a threshold that basically says, "Whatever-it-is you're measuring, is no longer performing as it is expected to.".
Here's what I'm doing:
I am taking measurements using a measuring device and that device is logging the measurements in its internal memory and allowing me to download that measurement data to my computer into a csv when the test session is complete.
I pull that csv into an xls and plot the data on a graph. (see attached image)
Here's what I want to do:
If you look at the attached image I would like to find the value where the data DEFINITELY crosses BELOW the horizontal line so I can say, "Here is where the device being tested 'gave up the ghost' and was no longer able to perform as desired."
What the data roughly looks like:
Each measurement set will have the rough look and feel of the attached image but slightly different each time. (because each object I am testing will have roughly the same performance characteristics but they all have their own manufacturing defects and variations.)
The data set for the attached image is a data set of 7000 measurements.
I never really know where the horizontal line will be.
Examples of the data sets I have gotten in the past several tests look like this:
(394 to 0)
(390000 to 0)
(3.88 to 0)
(375000 to 0)
(39.55 to 0)
(59200 to 0)
and each data set will have about 1,000 to 7,000 measurements each.
Here's how I was trying to solve this issue:
I was using SLOPE() and trying to latch onto where the slop of the line took a dive / started to work its way to a zero slope (which is a vertical line) so when it starts approaching a really small slope then it MUST be taking a dive. That didn't really work.
I was looking at using STDEV.P() in Excel and feeding it the entire data set. Then I was looking at doing the same thing but feeding it only the first 10, 30, 60 measurements but then I thought - we never really know just how many measurements will come through. Then I thought I would use the first 10% of the measurements and feed that to STDEV.P().
Please let me know what you think of this and please let me know of any ideas you may have.
Thanks.
H
Something like this should work to flag when the decay rate increases.
To find what 'direction' your data is going in you need the derivative.
Excel doesn't have a derivative formula but you can set it up pretty easily by using the (change in y)/(change in x) as demonstrated here:
http://faculty.educ.ubc.ca/sanderson/lab/CLFbiom/demo/diff.htm
I would then check a formula which counts how many datarows you have (=COUNTA(A:A) or similar)
Then uses that to get a step of 10% of your data
Then check the value of the derivative in a cell against a cell 10% further down. If it's still a negative (to account for the slight downhill at first) then you'll know
The right way to go about this is to model the data with an unknown discontinuity, something like "if time < break_time then (some constant plus noise) else (decaying exponential)". A maximum likelihood estimation for that model might require iteration or other operations which are clumsy in Excel -- maybe you should consider VB or Python or some other programming language. I.e. choose the tool to fit the problem and not the other way around.
See Seber and Wild, "Nonlinear Regression", for an extensive discussion of models with discontinuities.
If your data can be generally characterized as having:
(A) a more or less flat plateau region, followed by
(B) a downward trending region
then a basic strategy could be to start at then end of the data and march towards the beginning one point at a time, checking to see that the values are increasing. Once they stop increasing, you've found the break point.
The strategy assumes (unwisely?) that the downward trending region is smooth/noiseless. To make the solution more robust to noise, you could compare values that are 5 apart, or 10 apart, or whatever interval works to filter out the noise. Or you could use a moving average.
This strategy could potentially be made more efficient by starting the search somewhere in the middle of the data but still in downward trending portion. If you know (based on experience) that any value that is (say) 0.5X the maximum is in the downward trending portion, you could start the search there.
Hope that helps.
It appears as though you want to detect when the slope changes from something near zero to something negative. One way to detect this is to calculate the 2nd derivative of the values (calculate the slope of the slope). The 2nd derivative should be near zero in the flat portion of the data AND in the downward trending portion of the data. It should go negative at the break point. So finding the minimum (most negative) value of the 2nd should locate the break point.
To implement this, you probably will need to filter noise. So calculate the first derivative (slope) over some suitable window of data:
=SLOPE(moving window of say 25 raw values)
Then calculate the second derivative (slope of slope):
=SLOPE(moving window of say 25 slope values)
Then look for the minimum.
Hope that helps.
I'm currently drawing up a mock database schema with two tables: Booking and Waypoint.
Booking stores the taxi booking information.
Waypoint stores the pickup and drop off points during the journey, along with the lat lon position. Each sequence is a stop in the journey.
How would I calculate the distance between the different stops in each journey (using the lat/lon data) in Excel?
Is there a way to programmatically define this in Excel, i.e. so that a formula can be placed into the mileage column (Booking table), lookup the matching sequence (via bookingId) for that journey in the Waypoint table and return a result?
Example 1:
A journey with 2 stops:
1 1 1 MK4 4FL, 2, Levens Hall Drive, Westcroft, Milton Keynes 52.002529 -0.797623
2 1 2 MK2 2RD, 55, Westfield Road, Bletchley, Milton Keynes 51.992571 -0.72753
4.1 miles according to Google, entry made in mileage column in Booking table where id = 1
Example 2:
A journey with 3 stops:
6 3 1 MK7 7DT, 2, Spearmint Close, Walnut Tree, Milton Keynes 52.017486 -0.690113
7 3 2 MK18 1JL, H S B C, Market Hill, Buckingham 52.000674 -0.987062
8 3 1 MK17 0FE, 1, Maids Close, Mursley, Milton Keynes 52.040622 -0.759417
27.7 miles according to Google, entry made in mileage column in Booking table where id = 3
If you want to find the distance between two points just use this formula and you will get the result in Km, just convert to miles if needed.
Point A: LAT1, LONG1
Point B: LAT2, LONG2
ACOS(COS(RADIANS(90-Lat1)) *COS(RADIANS(90-Lat2)) +SIN(RADIANS(90-Lat1)) *SIN(RADIANS(90-lat2)) *COS(RADIANS(long1-long2)))*6371
Regards
Until quite recently, accurate maps were constructed by triangulation, which in essence is the application of Pythagoras’s Theorem. For the distance between any pair of co-ordinates take the square root of the sum of the square of the difference in x co-ordinates and the square of the difference in y co-ordinates. The x and y co-ordinates must however be in the same units (eg miles) which involves factoring the latitude and longitude values. This can be complicated because the factor for longitude depends upon latitude (walking all round the North Pole is less far than walking around the Equator) but in your case a factor for 52o North should serve. From this the results (which might be checked here) are around 20% different from the examples you give (in the second case, with pairing IDs 6 and 7 and adding that result to the result from pairing IDs 7 and 8).
Since you say accuracy is not important, and assuming distances are small (say less than 1000 miles) you can use the loxodromic distance.
For this, compute the difference of latitutes (dlat) and difference of longitudes (dlon). If there were any chance (unlikely) that you're crossing meridian 180º, take modulo 360º to ensure the difference of longitudes is between -180º and 180º. Also compute average latitude (alat).
Then compute:
distance= 60*sqrt(dlat^2 + (dlon*cos(alat))^2)
This distance is in nautical miles. Apply conversions as needed.
EXPLANATION: This takes advantage of the fact that one nautical mile is, by definition, always equal to one minute-arc of latitude. The cosine corresponds to the fact that meridians get closer to each other as they approach the poles. The rest is just application of Pythagoras theorem -- which requires that the relevant portion of the globe be flat, which is of course only a good approximation for small distances.
It all depends on what the distance is and what accuracy you require. Calculations based on "Earth locally flat" model will not provide great results for long distances but for short distance they may be ok. Models assuming Earth is a perfect sphere (e.g. Haversine formula) give better accuracy but they still do not produce geodesic grade results.
See Geodesics on an ellipsoid for more details.
One of the high accuracy (fraction of a mm) solutions is known as Vincenty's formulae. For my Excel VBA implementation look here https://github.com/tdjastrzebski/Vincenty-Excel
for one of our clients we are providing a system for retrieving the closest N landmarks from the users zipcode location.
We have a database of all the available zipcodes (650,000+) with the coresponding coordinates (latitude and longitude) and also all of 400+ landmarks in the country.
For now we are using the following process from finding closest N landmarks
Retrieve the lat and lng of the selected zipcode
Get the coordinates of all the landmarks
Order them by using a geographic distance formula
Take the closest N+2 landmarks and get the real distance to them using the following process
check if the distance between coordinates is stored in the distance cache table
if not it goes to a map engine, retrieved the distance and stores it in the cache
Reorder the list and return first N closest landmarks
The problem is we need to optimize this both from database access point of view and 3rd party access also.
We have tried to cache for all zipcodes the distance to closest M landmarks but the table would gain an additional 6Gb of data and it would take around 250 days to fill since a request takes aprox 30 sec.
We were thinking on partitioning the data and grouping close postcodes together but that will void the exact distance.
What optimising solutions you see in this situation.
Thank you.
You could try an repetitive approach.
Pick a value to use as your "radius"
Go through all results and pick only ones +- radius horizontally and vertically (according to geolocation
if not enough rows returned, increase "radius" and start again
Now perform distance calculation and use a PriorityQueue to minimise the number of calculations used in this sort and select the required items
This should be done on database- level. You should use a database with an geographic extension as SQL Server 2008 R2, or the excellent open source choise PostGre SQL with PostGIS extension. With those you store Geographical BLOBs instead of coordinates, and there are many built in functions to calculate geography that will take care of step 2 to 5 for you.
I suggest you start here:
http://postgis.refractions.net/
Regards