Group 28 Final Paper (1)

THERMAL HOME MONITORING

By

Jayanth Alangar

Gene Shiue

Tam Tran

Final Report for ECE 445, Senior Design, Spring 2015

TA: Ankit Jain

7 May 2015

Project No. 28

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Abstract

The Thermal Home Monitoring project provides homeowners with a surveillance system that will involve a thermal live feed, human counting and an Android application for use. Home owners will be able to view (on their Android phone) how many people are in each room, what the room will look like on a thermal camera and will only need one outlet to power the system. In this project, we find that this thermal home monitoring project can count up to two people within a room at a time, and the data and camera live feed can be viewed on an Android application.

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Table of Contents

1. Introduction ............................................................................................................................................. 1

1.1 System Block Diagram .................................................................................................................... 1

1.2 Block Descriptions .......................................................................................................................... 2

1.2.1 Power Supply .......................................................................................................................... 2

1.2.2 Thermal Camera ...................................................................................................................... 2

1.2.3 myRIO ...................................................................................................................................... 2

1.2.4 Raspberry Pi ............................................................................................................................ 2

1.2.5 Android Application ................................................................................................................ 2

2 Design ................................................................................................................................................... 3

2.1 Power Supply ................................................................................................................................. 3

2.1.1 Schematics .............................................................................................................................. 3

2.1.2 Calculations ............................................................................................................................. 4

2.1.3 Simulations .............................................................................................................................. 5

2.2 Thermal Camera ............................................................................................................................. 6

2.3 DSP ................................................................................................................................................. 6

2.3.1 Training the Classifier .............................................................................................................. 6

2.3.2 Alternative Method for Human Detection .............................................................................. 6

2.3.3 Detection Implementation ...................................................................................................... 6

2.4 Raspberry Pi ................................................................................................................................... 7

2.5 Android App ................................................................................................................................... 8

3. Design Verification ............................................................................................................................ 11

3.1 Power Supply ............................................................................................................................... 11

3.2 myRIO ........................................................................................................................................... 11

3.3 Raspberry Pi ................................................................................................................................. 12

3.4 Android App ................................................................................................................................. 12

4. Costs .................................................................................................................................................. 13

4.1 Parts ............................................................................................................................................. 13

4.2 Labor ............................................................................................................................................ 13

4.3 Total Cost ..................................................................................................................................... 14

5. Conclusion ......................................................................................................................................... 15

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5.1 Accomplishments ........................................................................................................................ 15

5.2 Uncertainties ............................................................................................................................... 15

5.3 Future work ................................................................................................................................. 15

5.4 Ethical Considerations .................................................................................................................. 15

References ................................................................................................................................................. 17

Appendix A Requirement and Verification Table .................................................................................. 18

Appendix B LabVIEW Front Panel and Block Diagram .......................................................................... 19

Appendix C Image and Text Acquisition Code ....................................................................................... 20

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1. Introduction Current home monitoring systems lack capabilities of providing meaningful data to the user

other than them just looking at a camera feed. With the use of a thermal camera in a home monitoring system along with an application of human detection algorithms, a user can get information that they find useful, such as, where their children are and what they are doing. The way the user can access this data via the Internet is through an Android developed phone application.

1.1 System Block Diagram

Figure 1: Block Diagram of Implemented System

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1.2 Block Descriptions The project consists of several modules, with each of these modules integrated together to form the final product. This section describes each of the individual modules.

1.2.1 Power Supply The power circuit consists of AC/DC and DC/DC converters to provide a steady voltage source at different levels for the thermal camera. Isolation was included as a protection mechanism.

1.2.2 Thermal Camera This module serves as the image capture for the project. It’s responsible for capturing thermal images of a room. The camera will capture an image and send it to the Raspberry Pi, which will be sent to the myRIO for processing. The camera will be powered by the power supply.

1.2.3 myRIO This block serves to perform the human detection algorithms. The myRIO’s tasks are to receive an image from the Raspberry Pi, process the image to detect any humans, and then to output a text file stating how many humans are in the image.

1.2.4 Raspberry Pi This module works as a server of the project. It is responsible for transferring images taken from

the thermal camera to our Android app and myRIO, storing computed data from the myRIO, keeping track of user settings specified from the Android app, and sending out email notifications to users if needed.

1.2.5 Android Application This block is the user interface of the project. It allows our users to add and remove rooms, set

thresholds for different rooms, specify users’ email addresses to receive notifications, and most importantly view information about users’ rooms. The users will be able to use these functionalities over the Internet.

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2 Design This section will discuss the design procedures that were taken throughout the project. Once again, the sections will be split into each module that was described in the block diagram.

2.1 Power Supply An integral part of the power supply design is converting the 120 Vac from a wall outlet to the

different DC voltage levels that the camera needs. This section will discuss the mechanism by which this was solved.

2.1.1 Schematics In order to begin producing the necessary voltage levels, a wall adapter was used to convert the

120 Vac to 5 Vdc to remove current spikes [1]. From this 5 Vdc level, a buck converter was used to take the voltage down to the necessary level of 3.3 V [2]. The buck converter is shown in Figure 2.

Figure 2: Buck Converter for Camera Input Power

Figure 2 shows the generic components for the passives within the buck converter. We will discuss the true values of these components in the following section. Also in Figure 2, the MOSFET is treated as a switch, used to connect 5 V to rest of the circuit. We were able to control the MOSFET through the signal that was sent to the gate. Figure 3 shows the circuit we used to provide this control.

Figure 3: Gate Driver Circuit

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The gate signal was produced with a pulse-‐width modulation (PWM) IC. In order to produce the signal, an isolated 12 V input source was needed. Not only does this circuit provide a square wave output signal, designated as the “Gate Signal” in Figure 3, but provided us external control over the frequency and duty ratio of the square wave. This can be seen in the top left corner of Figure 3. The components labeled as “Frequency” and “Duty Ratio” are potentiometers that allowed us to adjust in order to provide the correct output voltage.

2.1.2 Calculations In order to ensure that the voltage and current specifications of the camera were met, we had to perform calculations for individual passive components. Sample calculations are performed here for the 5 V to 3.3V DC/DC converter. As seen in the schematic, the buck converter that was used has an inductor and capacitor. These values control the variability of the current and voltage. Overall, there were four parameters we had to control for. These parameters were frequency, duty ratio, inductance, and capacitance. There were three equations that we used to evaluate each of the four parameters so one had to be fixed. For the purposes of our power supply, the value we chose to fix was the frequency. We chose a value of 50 kHz (a value greater than the human auditory threshold of 20 kHz) for our circuit, primarily because this value is typically used in power applications.

The first parameter that needed to be set was the duty ratio of the MOSFET that was used. For a buck converter, Equation 1 sets the duty ratio.

𝐷 = !!"#!!"

( 1 )

𝐷 = 3.35= 0.66

Using the duty ratio, the passives can be evaluated. The first is the capacitor of the system. The

capacitor controls the output voltage. Equation 2 shows the relationship of capacitance to voltage. To evaluate for the capacitance needed, constants were taken from the specifications and a frequency of 50 kHz were assumed. The current through the capacitor is evaluated as the output current.

𝑖! = 𝐶 ∗ !"!"

( 2 )

0.093 = 𝐶 ∗ (2 ∗ 0.150) ∗ 𝑓

𝐷

𝐶 = (0.093 ∗ 0.66)(0.30 ∗ 50,000)

= 4.1 𝜇𝐹

The final passive component is the inductor. The inductor provides a means of controlling the variability of the current. Once again, all constants are evaluated through the specifications and with a frequency of 50 kHz. The voltage across the inductor is calculated to be the difference between the input and output voltages. Equation 3 shows this relationship.

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𝑉! = 𝐿 ∗ !"

!" ( 3 )

(5 − 3.3) = 𝐿 ∗ (2 ∗ 0.017) ∗ 𝑓

𝐷

𝐿 = (1.7 ∗ 0.66)

(0.034 ∗ 50,000)= 660 𝜇𝐻

2.1.3 Simulations Using values greater than what we obtained with the calculations for the buck converter, a Simulink model was created. This Simulink model was used to provide us a baseline for our future design and to prove that is a viable means to produce the 3.3 V needed for the FLiR thermal camera. In addition, we added parasitic inductances, capacitances, and resistances (obtained from datasheets) in order to provide a more accurate Simulink model. Figure 4 shows this output voltage. The output at 100 ms is marked on the plot and is shown to be 3.298 V, well within the specifications we need for the thermal camera input. One potential issue that arose was the overshoot seen at approximately 7 ms. However, because this was a momentary voltage, protection was built into the thermal camera circuitry to handle this event. Because the voltage ripple was nearly zero, and the current ripple is simply a resistive load, we were able to meet the specifications provided for current as well. Because the simulations were successful, the buck converter topology was used for the final circuit.

Figure 4: Simulink Simulations of Camera Input Power

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2.2 Thermal Camera A FLiR development kit thermal camera module is used here due to its active technical support and low cost. PureEngineering LLC. provided the protocol for communication used. [3]

2.3 DSP A DSP will be necessary for performing human detection. It will need a file I/O system to store a trained classifier model and to store the camera live feed from the Raspberry Pi for processing. The DSP will be handled by the myRIO, where the Vision Development Module will be used. The myRIO runs on Linux, so file I/O can be easily handled by SSH and SFTP protocols. Communication with the Raspberry PI will be handled via SFTP since the myRIO has Wi-‐Fi capability. For human detection, we used NI’s particle classification trainer and the Vision Development Module VI’s.

2.3.1 Training the Classifier For developing a classifier, we used sets of 86 images with zero people in a room, 85 images of

one individual in different positions, postures, places in a room, and 17 images of two individuals. We trained the classifier to have three class labels of 0, 1 and 2. The classifier performs feature extraction by analyzing a shape of bright pixels that denote a high temperature entity. This shape of pixels will be used as feature vector for the classifier. NI describes the feature vectors in [4].

The NI Particle Classification Training program outputs a trained classifier model file that we send to the myRIO via an SFTP “put” command. Once the myRIO has the file on its directory, we created a VI to load the classifier and to test images against it for human detection.

2.3.2 Alternative Method for Human Detection Previously, we used HOG feature extraction VI that was developed to perform the human detection. However, a third party Linear SVM VI [5] that was needed for classification was not compatible with the myRIO because the necessary software libraries were not on the myRIO.

The HOG algorithm does an edge detection of truncated smaller pictures (cells) of the original picture. After edge detection, the gradient of the edges will be calculated. A histogram of these edge directions will be computed for these cells and then by using all the histograms generated by the cells of the image, a descriptor can be made to describe what is human or not. The HOG feature extraction that was implemented is shown in Figure 5.

2.3.3 Detection Implementation Once a classifier model is trained, images are sent from the Raspberry Pi using an SFTP “put”

command, to the myRIO where the myRIO will load the image, test it against the classifier and the classifier testing VI will output the class label for that image. For our purposes, the classifier will output 0, 1 or 2, where the integers denote the number of people that the myRIO can see. This output is stored onto a text file called “info.txt”.

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Figure 5: Approach for HOG Feature Extraction, taken from [6]

2.4 Raspberry Pi The Raspberry Pi needs to be able to connect to Wi-‐Fi to do useful work. However, the module

itself does not have built-‐in Wi-‐Fi. RaspberryPiHQ suggested getting an Edimax EW-‐7811UN, an USB Wi-‐Fi adapter, since it is cheap, small, and compatible [7]. However, the same USB Wi-‐Fi adapter was already with the Raspberry Pi when we checked it out from the lab, so we didn’t need to buy one. From RaspberryPiHQ’s tutorial, we were able to configure the Raspberry Pi to connect to wireless Internet.

For the Raspberry Pi to act as a server, it needs a static IP so it can communicate with other

modules through the same procedure every time. We used Wicd-‐curses, a tool that helps connecting to wireless networks with specific configurations, to set a static IP with the help of [8].

The final step to set up a web server is to install MySQL and PHP. This will allow the Raspberry Pi

to store data and communicate with the Android app with json objects as parameters. [9] explains how to install MySQL and PHP on the Raspberry Pi and make those two parts interact with each other. That website also covers how to make PHP pages on Raspberry Pi public in order to allow other subsystems like the Android app to communicate information across the internet.

The flowchart of the module is shown in Figure 6. There are two branches, receiving a query

from the Android app or acquiring data from myRIO. When receiving a query from the Android app, it executes the query and returns the query results. After acquiring data from myRIO, it updates the database and checks if email notification needs to be performed as well.

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Figure 6: Flowchart of the Raspberry Pi as a server

To output human detection data from the myRIO, the Raspberry Pi will initiate an SFTP protocol with the myRIO using a Python script with a Paramiko library. Once this SFTP protocol successfully connects, the Raspberry Pi will continuously send an image and retrieve a text file from the myRIO in a while loop until a break command is sent as shown in Figure 7. The retrieving process will involve a “get” command for the “info.txt” file where the Raspberry Pi will read the text file to see the output of myRIO.

Figure 7: File I/O Flowchart

For the email notification, we used a Python script with SMTP. [10] shows the exact code that we needed to make the protocol work.

2.5 Android App The main purpose of this Android application is to allow the user to interact with the other subsystems without knowing many technical details. The flowchart is shown below in Figure 8. The app sends a request to the server (Raspberry Pi), waits for the response, and displays the response.

The main functionalities of the app include viewing rooms, adding/removing rooms, setting rooms’ thresholds, and setting user’s email address. Each of the mentioned functionalities above has a unique tab in the application. The menu of the application is shown in Figure 9 below.

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Figure 8: Flowchart of the Android App

Figure 9: Android Application Menu

The first functionality is viewing rooms. For each room that the user added, there is a page like shown in Figure 10. The page shows the name of the room, buttons to go to the next/previous room (only available for more than one room, hence not shown in the figure), the number of people in the current room, and a button to show/hide live feed. The number of people in the room and the live feed automatically refresh themselves once every second.

Figure 10: Example Page of a Room

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The second functionality is adding/removing rooms. The user interface for this functionality is shown in Figure 11. This requires the user to enter a room name and the IP address of the Raspberry Pi that corresponds to the room. We have implemented an IP address check. The check will make sure the user has entered in a valid IP address after the user hits the ‘Add’ button. There is also a check that the room name should not exceed 30 characters.

Figure 11: Adding/Removing Rooms

The next functionality is setting rooms’ thresholds. Figure 12 is an example of how the tab of this functionality looks like. This functionality is very simple; it asks for a number for each room as the threshold for the room.

Figure 12: Setting Room Thresholds

The final functionality is setting user’s email address. Figure 13 shows what this tab looks like.

We implemented an email format check for this tab to make sure the user entered a valid email address.

Figure 13: Setting User's Email Address

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3. Design Verification

3.1 Power Supply Requirements Verification Results (Y/N)

Supply 3.3 V with a tolerance of 5%

Use digital multimeter to measure proper output of 3.3 V with 35 Ω load

Y The output of the power supply had an average voltage of 3.3 V with a ripple of 20 mV.

3.2 myRIO

Requirements Verification Results (Y/N)

Can receive and send signals to Raspberry PI

Using an SFTP protocol between the myRIO and the Raspberry PI, the myRIO will be sending a text file to the RPI for reading and the RPI will be sending an image to the myRIO for viewing.

Y We confirmed the result by going into the directories via SSH and seeing the files were there. We opened up the files in both systems.

Receives camera live feed from Raspberry PI within 3 seconds with a 5% tolerance.

View the camera live feed on both the Raspberry PI and the myRIO, and see that both live feeds are similar. Live feed will be checked by viewing the file on the Linux directory

Y We confirmed the live updates by viewing new images on the on the Front Panel display of the myRIO code.

Has a missed rate human detection of less than 30%

Run human detection algorithm on a set of thermal images and do statistical analyses of the false positives and the passes.

Y We took a set of 15 images with 0,1, or 2 people and the myRIO was correct on 12 images for a missed rate human detection of 20%.

Outputs human detection data within a 5 second timeframe with 10% tolerance.

Use a timing program to record how much time the myRIO will take to output data, given an input.

Y We timed the runthrough of taking a picture to receiving an info.txt file to be 3 seconds on average.

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3.3 Raspberry Pi


Send data through the Internet

Ping Google and get a response to show that the module is connected to the Internet

Y We used a ping program to ping google.com and received packets back to verify the connection.

3.4 Android App


Receive and display data with an error of no more than ±1 person

Make a dummy program on the Raspberry Pi to output 3 people in the room, and receive/display 3 (±1) people on the Android app

Y We created a dummy Python script to output 3 people in a room. The stats page of the room showed 3 people in the room after one second.

Send e-‐mail notification if number of persons detected in room exceeds set threshold

Make a dummy program on the Raspberry Pi to output 3 people in the room and set the threshold to 2 people. Check e-‐mail to see if e-‐mail is sent.

Y We set the e-‐mail address to our e-‐mail and set the threshold to 2 people. After using the same dummy program as before to output 3, we received an e-‐mail saying the threshold was exceeded.

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4. Costs

4.1 Parts Table 1: Parts Cost

Item Manufacturer Vendor Quantity Cost

myRIO National

Instruments National

Instruments 1 $200

FLiR Dev Kit (Thermal Camera Module) FLiR Sparkfun 1 $349.95

Raspberry Pi Raspberry Pi Foundation Amazon 1 $30

Wifi USB Adapter Edimax Amazon 1 $9

Inductor: 39 mH API Delevan Mouser 1 $6.50

Inductor: 2.61 mH TDK Mouser 1 $1.76

Capacitor: 47 uF Samsung Digikey 2 $1.54

Schottky Diode MCC Digikey 2 $0.56

FETs Rohm Digikey 2 $0.80

Wall Adapter Phihong USA Digikey 1 $6.38

Total 14 $606.49

4.2 Labor Table 2: Labor Costs

Name Hourly Rate Total Hours Invested

Total = Hourly Rate x 2.5 x Total Hours Invested

Tam Tran $27.50 225 $15,468.75

Jayanth Alangar $27.50 225 $15,468.75

Gene Shiue $8.25 500 $10,312.50

Total 950 $41,250

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4.3 Total Cost Table 3: Total Cost for Labor and Parts

Section Total

Labor $41,250

Parts $821.49

Grand Total $42,071.49

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5. Conclusion

5.1 Accomplishments For our project, we were able to achieve all of our goals. We managed to have a missed rate

detection of below the 30% that we were aiming for. We developed an Android application that can interface with the thermal home monitoring system, where the user can see how many people are in a room, set a threshold and emailing system and can even view a live feed of a room. We were also able to produce a power supply that can be powered by a home outlet, which will power the thermal camera, Raspberry Pi, and the myRIO.

5.2 Uncertainties At this time, we were unable to fully integrate all of the different modules for our project. We found that our project was easy to access by intruders since we lack encryption for the data that we have. Another uncertainty is the limit of human detection, since we were unable to procure a large training set with many different numbers of people, we can only count up to 2 people. This meant that the myRIO would just guess at the number if the image has 3 or more people. Furthermore, upon testing the power supply on a PCB, we had a blown part and we had to go back to the breadboard design.

5.3 Future work The communication between Android app and Raspberry Pi would be improved by having some

sort of an authentication system. This would make sure that the homeowner’s data are secure. Also, texting notification can be added on top of email notification to allow the user to have more choices on the notification method based on their preference.

For the human detection, the missed rate percentage can be decreased by having a larger

training set for each class and as well as having a large training set for 3 or more people. Other hardware platforms besides the myRIO should be tested for human detection since different software platforms besides LabView can be used that are primarily used for signal processing for example, using a BeagleBone Black with C++.

The power supply can be improved by transferring the circuit onto a PCB. This would provide an overall cleaner product that is less bulky. In addition, we would be able to have a product that is easier to adjust if adjustments need to be made.

5.4 Ethical Considerations Our project followed the IEEE Code Of Ethics, taken from [11], because it is the law that electrical

engineers follow. Below we state the relevant clauses of the IEEE code of ethics that relate to our project.

1. To accept responsibility in making decisions consistent with the safety, health, and welfare of the public, and to disclose promptly factors that might endanger the public or the environment; o We will test the power supply of our circuit to make sure it is functioning within spec and we

will put warnings where necessary. 2. To be honest and realistic in stating claims or estimates based on available data;

o Any claims made by this project will be verified through statistical analysis and/or following the requirements and verification table.

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3. To reject bribery in all its forms; o We are working on this project out of pure interests without any desire to individually

benefit ourselves financially. We will ensure that we will not cut corners under the intention of making money.

4. To improve the understanding of technology; its appropriate application, and potential consequences; o The purpose of this project is to build on top of preexisting home monitoring systems, but to

include the use of computer vision algorithms and to make use of smart phones to add a sense of security to the user.

5. To maintain and improve our technical competence and to undertake technological tasks for others only if qualified by training or experience, or after full disclosure of pertinent limitations; o Everyone in this project has taken relevant coursework for their role in this project. If there

is a gap in knowledge for this project, research will be done. 6. To seek, accept, and offer honest criticism of technical work, to acknowledge and correct errors,

and to credit properly the contributions of others; o This project already goes through design reviews that will involve the criticisms of the

course instructor, TAs, and peers. This feedback system will help improve the quality of this project.

7. To avoid injuring others, their property, reputation, or employment by false or malicious action; o This project is not intended to harm individuals in anyway but to give them a sense of

security. Any part of the project that seems potentially dangerous will be marked with explicit safety warnings.

8. To assist colleagues and co-‐workers in their professional development and to support them in following this code of ethics.

o From participating in peer reviews and giving suggestions in the senior design lab, we as a team can help support others professionally and spread the IEEE code of ethics in this way.

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References [1] P. Krein, Elements of Power Electronics. New York: Oxford University Press, 2015. [2] Sparkfun, 'FLIR LEPTON Long Wave Infrared (LWIR) Datasheet', 2014. [Online]. Available:

https://cdn.sparkfun.com/datasheets/Sensors/Infrared/FLIR_ Lepton_Data_Brief.pdf. [Accessed: 12-‐ Feb-‐ 2015].

[3] GitHub, 'PureEngineering/LeptonModule', 2015. [Online]. Available:

https://github.com/PureEngineering/LeptonModule. [Accessed: 09-‐ Apr-‐ 2015].

[4] Zone.ni.com, 'Binary Particle Classification -‐ NI Vision 2011 Concepts Help -‐ National

Instruments', 2015. [Online]. Available: http://zone.ni.com/reference/en-‐XX/help/372916L-‐

01/nivisionconcepts/binary_particle_classification/. [Accessed: 26-‐ Mar-‐ 2015].

[5] Decibel.ni.com, 'Community: LabVIEW Machine Learning Toolkit -‐ National Instruments', 2015.

[Online]. Available: https://decibel.ni.com/content/docs/DOC-‐19328. [Accessed: 30-‐ Mar-‐ 2015].

[6] N. Dalal, Histogram of Oriented Gradients for Object Detection, 1st ed. Columbia: University of Missouri, 2015.

[7] How-‐To: Add WiFi to the Raspberry Pi. Available at: http://www. http://raspberrypihq.com/how-‐to-‐add-‐wifi-‐to-‐the-‐raspberry-‐pi/. Accessed May 2015.

[8] Wi-‐fi on Raspberry Pi, a simple guide. Available at: http://www.raspyfi.com/wi-‐fi-‐on-‐raspberry-‐pi-‐a-‐simple-‐guide/. Accessed May 2015.

[9] Get MySql and PHP to Work Together in Raspberry Pi. Available at: http://www.php5dp.com/get-‐mysql-‐and-‐php-‐to-‐work-‐together-‐in-‐raspberry-‐pi/. Accessed May 2015.

[10] Send Mail Through Gmail with Python. Available at: http://www.nixtutor.com/linux/send-‐mail-‐through-‐gmail-‐with-‐python/. Accessed April 2015.

[11] IEEE.org, 'IEEE Code of Ethics', 2015. [Online]. Available: http://www.ieee.org/about/corporate/governance/p7-‐8.html. [Accessed: 22-‐ Feb-‐ 2015].

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Appendix A Requirement and Verification Table Module Requirements Verification Procedure (Y/N)

myRIO Can receive and send signals to Raspberry PI

Using an SFTP protocol between the myRIO and the Raspberry PI, the myRIO will be sending a text file to the RPI for reading and the RPI will be sending an image to the myRIO for viewing.

Y

Receives camera live feed from Raspberry PI within 3 seconds with a 5% tolerance.

View the camera live feed on both the Raspberry PI and the myRIO, and see that both live feeds are similar. Live feed will be checked by viewing the file on the Linux directory

Y

Has a missed rate human detection of less than 30%

Run human detection algorithm on a set of thermal images and do statistical analyses of the false positives and the passes.

Y

Outputs human detection data within a 5 second timeframe with 10% tolerance.

Use a timing program to record how much time the myRIO will take to output data, given an input.

Y

Power Supply Circuit

Supply 3.3 V with a tolerance of 5%

Use digital multimeter to measure proper output of 3.3 V with 35 Ω load

Y

Raspberry Pi Send data through the Internet Ping Google and get a response to show that the module is connected to the Internet

Y

Android App Receive and display data with an error of no more than ±1 person

Make a dummy program on the Raspberry Pi to output 3 people in the room, and receive/display 3 (±1) people on the Android app

Y

Send e-‐mail notification if number of persons detected in room exceeds set threshold

Make a dummy program on the Raspberry Pi to output 3 people in the room and set the threshold to 2 people. Check e-‐mail to see if e-‐mail is sent.

Y

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Appendix B LabVIEW Front Panel and Block Diagram

Figure 14: Front Panel for Human Detection

Figure 15: Code Block Diagram

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Appendix C Image and Text Acquisition Code import paramiko import cmd import sys import os import time t0 = time.time() paramiko.util.log_to_file('/tmp/paramiko.log') # Open a transport host = '192.168.1.164' transport = paramiko.Transport(host) # Auth password = '123' username = 'admin' transport.connect(username = username, password = password) # Go! sftp = paramiko.SFTPClient.from_transport(transport) var = 1 while var <= 100: os.system("./leptgraypng >temp.png ; convert temp.png -‐normalize -‐contrast -‐sharpen 5 sample.bmp") # Download filepath = '../lvuser/info.txt' localpath = '/home/pi/info.txt' sftp.get(filepath, localpath) # Upload localpath = '/home/pi/Downloads/LeptonModule-‐master/flirpi/sample.bmp' filepath = '../lvuser/image.bmp' sftp.put(localpath, filepath) # Break while loop after 100 iterations var = var +1 #Close connection sftp.close() transport.close() #Record timing of code t1 = time.time() total = t1 -‐ t0 print "Total Time Is: %s seconds" % str(total)

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Group 28 Final Paper (1)