Ivan-cs488-paper - Revision history

Babic91: /* Software functionality */

2012-12-10T16:45:01Z

Software functionality

← Older revision		Revision as of 16:45, 10 December 2012
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			Final algorithm is shown in the following figure:

	[[File:Filemoze.png\|1000px\|thumb\|center\|alt=Algorithm No1\|Algorithm No1]]		[[File:Filemoze.png\|1000px\|thumb\|center\|alt=Algorithm No1\|Algorithm No1]]


	As we can see on the algorithm diagram, everything starts with the button Collect which will take three reading of latitude and longitude, average them up and store one final value in csv file column number 1 and 2. Once this process is complete, three reading of altitude will be taken and average will be stored in the csv file in column number 3. Furthermore, column number 4 will be populated with magnetic field data. The date and time will be stored in the column number 5 and 6. This part is going to happen every time one press the collect button regardless of which sensor probe is plugged to the monitoring platform.		As we can see on the algorithm diagram, everything starts with the button Collect which will take three reading of latitude and longitude, average them up and store one final value in csv file column number 1 and 2. Once this process is complete, three reading of altitude will be taken and average will be stored in the csv file in column number 3. Furthermore, column number 4 will be populated with magnetic field data. The date and time will be stored in the column number 5 and 6. This part is going to happen every time one press the collect button regardless of which sensor probe is plugged to the monitoring platform. The column number 7 will have a unique key which will correspond to the person who is currently using the monitoring platform. The cvs column number 8 will contain name and the ID of the device (water, air, soil, temperature...) and after column 8 other instances of data will contain specific parameters generated by different probes. In the case of Yocto-Meteo, the output will look like this.

	The column number 7 will have a unique key which will correspond to the person who is currently using the monitoring platform. The cvs column number 8 will contain name and the ID of the device (water, air, soil, temperature...) and after column 8 other instances of data will contain specific parameters generated by different probes. In the case of Yocto-Meteo, the output will look like this.

	After pressing Collect, this output should be stored to memory:		After pressing Collect, this output should be stored to memory:

Babic91: /* Software functionality */

2012-12-10T16:39:48Z

Software functionality

← Older revision		Revision as of 16:39, 10 December 2012
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	[[File:Filemoze.png\|1000px\|thumb\|center\|alt=Algorithm No1\|Algorithm No1]]		[[File:Filemoze.png\|1000px\|thumb\|center\|alt=Algorithm No1\|Algorithm No1]]


			As we can see on the algorithm diagram, everything starts with the button Collect which will take three reading of latitude and longitude, average them up and store one final value in csv file column number 1 and 2. Once this process is complete, three reading of altitude will be taken and average will be stored in the csv file in column number 3. Furthermore, column number 4 will be populated with magnetic field data. The date and time will be stored in the column number 5 and 6. This part is going to happen every time one press the collect button regardless of which sensor probe is plugged to the monitoring platform.

	The column number 7 will have a unique key which will correspond to the person who is currently using the monitoring platform. The cvs column number 8 will contain name and the ID of the device (water, air, soil, temperature...) and after column 8 other instances of data will contain specific parameters generated by different probes. In the case of Yocto-Meteo, the output will look like this.		The column number 7 will have a unique key which will correspond to the person who is currently using the monitoring platform. The cvs column number 8 will contain name and the ID of the device (water, air, soil, temperature...) and after column 8 other instances of data will contain specific parameters generated by different probes. In the case of Yocto-Meteo, the output will look like this.

Babic91: /* Validating of sensors precision and accuracy */

2012-12-10T09:32:18Z

Validating of sensors precision and accuracy

← Older revision		Revision as of 09:32, 10 December 2012
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	Purple line shows average of all termometers other than Arduino (because of its deflection from all other devices) and Yocto-Meteo RED and BLUE. This graph is a prof that Yocto-Meteor devices are vert accurate because their line is almost identical to average of other 6 thermometers.		Purple line shows average of all termometers other than Arduino (because of its deflection from all other devices) and Yocto-Meteo RED and BLUE. This graph is a prof that Yocto-Meteor devices are vert accurate because their line is almost identical to average of other 6 thermometers. The 2.5$^\circ$C difference between average and Ypcto-Meteo in the CCG Machine Room can be explained with shorter period of sensors adaptation in the CCH Machine Room setup.

	[[File:average.png\|900px\|thumb\|center\|alt=A cartoon centipede reads books and types on a laptop.\|The Wikipede edits]]		[[File:average.png\|900px\|thumb\|center\|alt=A cartoon centipede reads books and types on a laptop.\|The Wikipede edits]]

Babic91: /* Validating of sensors precision and accuracy */

2012-12-10T09:25:18Z

Validating of sensors precision and accuracy

← Older revision		Revision as of 09:25, 10 December 2012
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	This graph shows all eight termometers and we can see that values are very close, they are all in the window of ~~5C maximum~~. ~~The biggest difference in~~ the ~~graph~~ is by Arduino ~~set up~~, one of the reasons for this could be a low end thermometer.		This graph shows all eight termometers and we can see that values are very close, they are all in the window of 5$^\circ$C difference. From the Figure, it is clear that Arduino setup has the biggest variations and difference when compared to all other sensors, one of the reasons for this could be a low end thermometer.

	[[File:accuracy_1.png\|900px\|thumb\|center\|alt=A cartoon centipede reads books and types on a laptop.\|The Wikipede edits]]		[[File:accuracy_1.png\|900px\|thumb\|center\|alt=A cartoon centipede reads books and types on a laptop.\|The Wikipede edits]]

Babic91: /* Base sensor */

2012-12-10T09:21:46Z

Base sensor

← Older revision		Revision as of 09:21, 10 December 2012
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	= Base sensor =		= Base sensor =

	My sample sensor will be {\bf Yocto-Meteo}, a device developed in Switzerland capable of measuring via USB the current humidity, pressure, and temperature. Designer of this product claims that device has accuracy of 0.3$^\circ$ ~~Celsius~~ for temperature, about 4\% for the humidity rate, and 1mbar for the atmospheric pressure (my personal test of precision and accuracy of Yocto-Meteo will be documented in 5.1). Furthermore, Yocto-Meteo has integrated data logger capable of recording data on its internal flash for later retrieval when connected again by USB (this function wont be used for my preliminary device test stage).		My sample sensor will be {\bf Yocto-Meteo}, a device developed in Switzerland capable of measuring via USB the current humidity, pressure, and temperature. Designer of this product claims that device has accuracy of 0.3$^\circ$C for temperature, about 4\% for the humidity rate, and 1mbar for the atmospheric pressure (my personal test of precision and accuracy of Yocto-Meteo will be documented in 5.1). Furthermore, Yocto-Meteo has integrated data logger capable of recording data on its internal flash for later retrieval when connected again by USB (this function wont be used for my preliminary device test stage).

Babic91: /* Validating of sensors precision and accuracy */

2012-12-10T09:21:13Z

Validating of sensors precision and accuracy

← Older revision		Revision as of 09:21, 10 December 2012
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	This graph shows all 8 termometers and we can see that values are very close. The biggest difference in the graph is by Arduino set up, one of the reasons for this could be a low end thermometer.		This graph shows all eight termometers and we can see that values are very close, they are all in the window of 5C maximum. The biggest difference in the graph is by Arduino set up, one of the reasons for this could be a low end thermometer.

	[[File:accuracy_1.png\|900px\|thumb\|center\|alt=A cartoon centipede reads books and types on a laptop.\|The Wikipede edits]]		[[File:accuracy_1.png\|900px\|thumb\|center\|alt=A cartoon centipede reads books and types on a laptop.\|The Wikipede edits]]

Babic91: /* Validating of sensors precision and accuracy */

2012-12-10T09:16:35Z

Validating of sensors precision and accuracy

← Older revision		Revision as of 09:16, 10 December 2012
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	One of the ways to check ~~'''~~accuracy~~'''~~ is to use multiple different thermometers, place them in the same environment, take readings at the same time and compare them to Yocto-Meteo readings.		One of the ways to check {\bf accuracy} is to use multiple different thermometers, place them in the same environment, take readings at the same time and compare them to Yocto-Meteo readings.
	For the purpose of this experiment, I have used 8 individual termometers and record their values at the same time. The following picture demonstrates my set up of all 8 devices.		For the purpose of this experiment, I have used eight individual termometers and record their values at the same time. I waited few minutes before taking the temperature measurements because of sensor adaptation to extreme temperature change (from swimming poll to the Earlham Heart) The following picture demonstrates my set up of all eight devices.

	[[File:setup_4.png\|900px\|thumb\|center\|alt=A cartoon centipede reads books and types on a laptop.\|The Wikipede edits]]		[[File:setup_4.png\|900px\|thumb\|center\|alt=A cartoon centipede reads books and types on a laptop.\|The Wikipede edits]]

Babic91: /* Validating of sensors precision and accuracy */

2012-12-10T09:13:04Z

Validating of sensors precision and accuracy

← Older revision		Revision as of 09:13, 10 December 2012
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	[[File:precision.png\|800px\|thumb\|center\|alt=Estimating Precision of Yocto-Meteo device\|Estimating Precision of Yocto-Meteo device]]		[[File:precision.png\|800px\|thumb\|center\|alt=Estimating Precision of Yocto-Meteo device\|Estimating Precision of Yocto-Meteo device]]

	Based on this graph, we can conclude that Yocto-Meter are very precise because 2 of them have almost the same measurements.		Based on this graph, we can conclude that Yocto-Meter are very precise because two of them have almost the same measurements.

Babic91: /* Base sensor */

2012-12-10T09:07:06Z

Base sensor

← Older revision		Revision as of 09:07, 10 December 2012
Line 54:		Line 54:
	= Base sensor =		= Base sensor =

	My sample sensor will be ~~'''~~Yocto-Meteo~~'''~~, a device developed in Switzerland capable of measuring via USB the current humidity, pressure, and temperature. Designer of this product claims that device has accuracy of 0. for temperature, about 4% for the humidity rate, and 1mbar for the atmospheric pressure (my personal test of precision and accuracy of Yocto-Meteo will be documented in 5.1). Furthermore, Yocto-Meteo has integrated data logger capable of recording data on its internal flash for later retrieval when connected again by USB (this function wont be used for my preliminary device test stage).		My sample sensor will be {\bf Yocto-Meteo}, a device developed in Switzerland capable of measuring via USB the current humidity, pressure, and temperature. Designer of this product claims that device has accuracy of 0.3$^\circ$ Celsius for temperature, about 4\% for the humidity rate, and 1mbar for the atmospheric pressure (my personal test of precision and accuracy of Yocto-Meteo will be documented in 5.1). Furthermore, Yocto-Meteo has integrated data logger capable of recording data on its internal flash for later retrieval when connected again by USB (this function wont be used for my preliminary device test stage).

Babic91: /* Geocoded data */

2012-12-10T08:30:16Z

Geocoded data

← Older revision		Revision as of 08:30, 10 December 2012
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	=Geocoded data =		=Geocoded data =

	Some data sets that I came across in the last few years were geocoded. This process is not specifically ~~de?ned~~ neither correct definition exists. It is usually in the form of a ZIP Code or a mailing address. To place it on a map accurately one must know its coordinates. Generally it means recording the Latitude (the angular distance, in degrees, minutes, and seconds of a point north or south of the Equator) and Longitude (the angular distance, in degrees, minutes, and seconds, of a point east or west of the Prime (Greenwich) Meridian) of the place where measurement was taken. Geocoding could also be seen as metadata of reading. Some research papers that I came across, in addition to latitude and longitude had another parameters such as altitude, temperature, date and time in their metadata. Surprisingly, none had all of them.		Some data sets that I came across in the last few years were geocoded. This process is not specifically defned neither correct definition exists. It is usually in the form of a ZIP Code or a mailing address. To place it on a map accurately one must know its coordinates. Generally it means recording the Latitude (the angular distance, in degrees, minutes, and seconds of a point north or south of the Equator) and Longitude (the angular distance, in degrees, minutes, and seconds, of a point east or west of the Prime (Greenwich) Meridian) of the place where measurement was taken. Geocoding could also be seen as metadata of reading. Some research papers that I came across, in addition to latitude and longitude had another parameters such as altitude, temperature, date and time in their metadata. Surprisingly, none had all of them.

	I have learned from the people I work with and later confirmed by my personal experience that we are great at collecting huge quantities of data but at the same time we are not good at making sense of those data and taking the full advantage of them. One way of taking better advantage of collected date would be to visualize them. Having automatically geocoded date would make the process of visualization much easier, more precise and more interesting.		I have learned from the people I work with and later confirmed by my personal experience that we are great at collecting huge quantities of data but at the same time we are not good at making sense of those data and taking the full advantage of them. One way of taking better advantage of collected date would be to visualize them. Having automatically geocoded date would make the process of visualization much easier, more precise and more interesting.

	Enriching data with another dimension (latitude, longitude and altitude) is crucial if one wants to have very useful and good quality readings. Therefore I came up with special algorithm for data geocoding on our sensor monitoring platform (Nexus 7).		Enriching data with another dimension (latitude, longitude and altitude) is crucial if one wants to have very useful and good quality readings. Therefore I came up with special algorithm, which can be see on the Figure 3, for data geocoding on our sensor monitoring platform (Nexus 7).


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	As we can see on the algorithm diagram, everything starts with the button Collect which will take three reading of latitude and longitude, average them up and store one final value in csv file column number 1 and 2. Once this process is complete, three reading of altitude will be taken and average will be stored in the csv file in column number 3. Furthermore, column number 4 will be populated with magnetic field data. The date and time will be stored in the column number 5 and 6. This part is going to happen every time one press the collect button regardless of which sensor probe is plugged to the monitoring platform.		As we can see on the algorithm diagram, everything starts with the button Collect which will take three reading of latitude and longitude, average them up and store one final value in csv file column number 1 and 2. Once this process is complete, three reading of altitude will be taken and average will be stored in the csv file in column number 3. Furthermore, column number 4 will be populated with magnetic field data. The date and time will be stored in the column number 5 and 6. This part is going to happen every time one press the collect button regardless of which sensor probe is plugged to the monitoring platform.

	= Base sensor =		= Base sensor =