FinalProposal
Robot Chemotaxi
Project Proposal
Artificial Intelliscents
Mathew Davison
Bobby Harkreader
David Mackey
Dhivya Padmanabhan
Department of Computer Science and Engineering
Texas A&M University
February 11, 2009
TAMU CPSC 483 Project Proposal 1/15
Table of Contents
TAMU CPSC 483 Project Proposal 2/15
1 Executive summary
The executive summary is a brief description of the project. The purpose is to give a quick overview of
(1) need, goal and objectives, (2) design and implementation, and (3) expected results and benefits of the project. The intended audience of the executive summary is a program director, someone who makes decisions about which projects will receive funding. Since the executive summary is a summary, it should be written last.
2 Introduction
The Robot Chemotaxi project aims to build a robot with a sensing unit that can detect chemical spills.
This involves the area of robotics (artificial intelligence) as we attempt to build an intelligent robot that can navigate through terrains, travel around curves and obstacles and reach the required destination within a predetermined response time. We also have a chemical sensor that actively reads for the presence of a chemical spill and sends analog feedback pertaining to the location of the contaminant to the robot. The robot then interprets the feedback and navigates to the appropriate location using the principles of chemotaxis.
2.1 Need Statement
In several laboratories, factories and other working units where there is a high possibility of chemical spills, there are inadequate detection systems in place. A large amount of time is spent contacting external units trained to detect and handle chemical spills. This can lead to escalation of the dangers of the situation. Also, humans are inadequately equipped to detect odors and resolve to use sniffer dogs or other animals to locate spills. Training and raising these animals is an expensive process. Further, continued exposure to potentially harmful chemicals could be harmful for these animals hence raising a moral issue.
2.2 Goal and Objective
The goal of this project is to create an autonomous system that can provide an effective, low cost, and speedy detection of chemical spills.
Our objectives include the following:
Build an autonomous robot that can navigate though flat and slightly curved surfaces with obstacles
A chemical sensor will be mounted on the robot and direct the robot towards the location of the spill
The response time of the Robot Chemotaxi would be 5 minutes from a distance of 3 meters, on average.
It will navigate within 1 foot of the spill location successfully 50% of the time when the Robot
Chemotaxi is placed within 3 meters of the chemical spill.
The sensor will take an average of 10 seconds to detect the chemical concentration in its vicinity and 50 seconds for performing a circular sweep of the robot
After successfully navigating to within 1 foot of the source of the spill, an alert system to inform the user of the spill location will be activated
A beeper on the robot that will sound when the alert has been activated
LEDs on the robot will blink when the alert has been activated
The system must perform well in a laboratory environment
The system will not cause any harm to the users or operating environment
The system should have the ability to be quickly deployed, defined as 1 minute
2.3 Design Constraints and Feasibility
The main constraint for our project is the budget. High quality sensors and robotic platforms are rather expensive. Another constraint we need to work with is the response time of the robot. Since the distance between the robot and the chemical spill and the number of obstacles in its path are variable, it is difficult to predetermine a response time to evaluate the performance of the robot. Also, the environmental conditions could affect the performance of our robot. The robot could be diverted drastically off course in the presence of wind as the chemical's effervescence would travel in the direction of the wind. Also, high humidity and moisture might have an impact on the chemical sensor and reduce the dependability of the readings.
3 Literature and technical survey
---insert doc here
4 Proposed work
4.1
Evaluation of alternative solutions
A brief description of our alternative solutions is: a.
Using alternative sensors with our iRobot platform b.
Using an alternative robotic platform with our chemical sensors c.
Using multiple sensors on our robot base to improve the performance of the Robot Chemotaxi d.
Using a semi autonomous system e.
Enabling the sensor to be rotated around the circumference of the robot as opposed to keeping it stationary and rotating the robot.
Alternative Sensors
A potential alternative to our PID based chemical detecting robot would be to utilize a different type of sensor for use in the robot. One alternative sensor would have been an infrared spectrophotometer. Using one would have increased the range of gases to include some that can’t be detected by any other method.
However, those devices are expensive and would be too large to mount on a robot. Another sensor we could have utilized was an electrochemical sensor. These sensors are cheaper which would allow for the use of more sensors. However, the range of chemicals is limited which would decrease the usability of the robot. A catalytic sensor would have had the advantage of having a long life decreasing the maintenance on the robot. However, such a sensor would be limited in detectable gases and would not be able to work in high concentrations.
Alternative Robot Base
Another potential alternative to the design would be to use a different robot base as the basis for the design. Several robots were considered for this design. A couple of the platforms, like the Garcia, were well beyond the budget available. Others such as the Qwerk, which were considered, were quickly discarded as possibilities as they were discovered to be unavailable anymore. Several others, such as the
NI Elvis Speedy 33 and the Arudino, were determined to be too difficult to program due to the necessity of programming even the most basic actions and the need to develop an entire platform around the board.
The iRobot was found to be the best of all categories as it was affordable, available, and allowed for optimal code reuse. Additionally, it readily had all the required capabilities.
Multiple Sensors
Potentially, the current design could be upgraded by adding additional sensors to the robot. This would drastically alter the design of the robot’s AI. By doing this, the robot would not be required to update its heading every time it detects a drop in concentration. Instead it would be able to alter its course by comparing the readings being received by the sensors to determine the current best direction to head in.
This would result in an increase in both speed and accuracy. However, due to the cost of an individual sensor, this option is far beyond our budget and must be relegated to a potential future improvement.
Semi-Autonomous System
An alternative solution which was discussed for the robot was to make it be only semi-autonomous. In this design, the robot would have simply been the carrier for the sensor. In this design there would have to be a human operator nearby who would be controlling the robot by remote control. The robot would give feedback to the operator by the volume or frequency of the beeps it would make, which would be determined by the concentration of the chemical it would be detecting. That is to say, the higher the concentration being read, the louder or more frequent the beeps would be. By removing the AI, the time it would have taken the robot to find the source would be decreased as the operator would presumably be able to process the data more intelligently. However, this solution would have required a person to remain in the area of the chemical, potentially putting them in danger, which would run counter to the purpose of this robot.
Rotating Sensor
Another alteration to the design which was discussed was to have the sensor on top of the robot to rotate as the robot moved around. This would have theoretically allowed for improved direction detection.
However, this design was ultimately discarded due to physical limitations of the sensor. The sensor has a delay on achieving the voltage for the concentration level that it should be detecting. The delay on this can be as large as 20 seconds. This delay would cause the sensor data to potentially be unreliable or require a large delay to be added in order to allow the sensor to adjust. This delay would have removed any benefits made by the design change.
4.2
Design specifications
Following is a block diagram describing our design specifications and process.
piD-TECH photoionization sensor
Sonar Obstacle avoidance system iRobot Create
Alert System
ATMega 168
Microcontroller
Robot AI
The design of the automated robotic chemical location device combines a robotic platform and an extremely sensitive chemical sensor. A user will be able to deploy the device in an environment where a chemical spill has occurred, but the source of the spill is unknown. The device will then intelligently navigate to the source of the contaminant and proceed to alert the operator of its location so appropriate action can be taken in containing the spill.
Image 1 : IRobot Create
The robot has many design parameters that need to be addressed. It must be able to be rapidly deployed, be able to easily navigate its environment, be easy to maintain, and be relatively low cost so that it can be disposed of if it becomes overly contaminated. In addition, ease of development and ability to modify it to fit the design specifications if necessary are desired features. After a survey of available robotic platforms, of which there are many, we narrowed down our selection to the IRobot Create and a custom design based on the Arudino microcontroller . A sampling of the other options we considered included the
NI Elvis Speedy33 , Trossen Robotics Stringer , and Acroname Brainstem . The Arudino platform has the advantages of lower cost and higher opportunities for customization, but has added design time and development time. The IRobot Create is the most comprehensive platform and is our primary choice. The microcontroller, an ATMega168, provides a 10bit ADC and the eight analog inputs provide the needed capabilities to interface with the various components. The eight inputs are connected to the ADC via a mux and the values of each of the inputs are stored in a register value. Once the register has been read from, it is possible to poll the analog device again. This can happen at near real-time and will not hinder our system design. Its low profile and ability to rotate 360° makes it ideal for navigating in a lab environment. The rechargeable battery provides power for 3.5 hours of operation which is adequate time to allow the robot to operate multiple times between charges. It is preassembled and provides four mounting points for extensibility. In addition, it supports the Microsoft Robotics Development platform which allows for better code reusability, scalability, and extensibility. In addition, it allows for development in any .NET language using the .NET debugging environment, which will aid in reducing stress on the team.
Image 2 : Photoionization Sensor
Ideally, the robot will be deployed from a safe distance from the contaminating agent, so the chemical sensor must be able to accurately detect very low concentrations of such chemicals in the air. After analyzing various methods for chemical detection, we narrowed down the types of devices to photoionization detectors, conductive polymers, and metal oxide semiconductor sensors. While many research projects have success with conductive polymers, they used complex chemical processes to create their own sensors and there are very few viable commercial options. Metal oxide semiconductor-based sensors are extremely affordable, with companies like Figaro and E2V offering components for under
$15. However, they are not able to reach the level of sensitivity we desire. Photoionization detectors allow for detection of contaminates in the parts per billion range, but are not able to distinguish between chemicals and the prices are considerably more expensive. The two components we considered were
Baseline-MOCON’s piD-TECH , and AlphaSense’s PID . AlphaSense’s offering has a linear dynamic range of 100-ppb to 6,000-ppm for $475. However, it is shipping from the UK which in increase the cost significantly and will take four weeks to acquire. Baseline-MOCON’s piD-TECH costs $540 and detects a range of 50-ppb to 2000-ppm.
AlphaSense’s PID has a better initial response time, and has a wider detection range. However, because they are based out of the United Kingdom, the time to acquire the sensor is much longer than we would like and requires an electronic bank transfer or establishment of a line of credit with the company. In addition, shipping costs are far greater from the UK than the United States. Baseline-MOCON’s piD-
TECH is adequate, meets our needs and is much easier to acquire. For that reason, we have elected to purchase the piD-TECH sensor.
The cost of the sensors does not make them ideal. In order to reduce cost, we are basing our design around the use of a single sensor. This single sensor will be mounted to the top of the robot and interface directly with the 10bit ADC of the microcontroller. Its power demands of 5V at 20mA will be supplied by the robot’s battery.
Many living creatures navigate their environment based on chemotaxis and many of our algorithms are based on the behaviors of these animals. The simplest of algorithms is based on the E. coli microorganism and uses a zigzag pattern to locate its source. It judges whether or not it is on a good path and if so it moves farther in that direction than if it is on a bad path then adjusts its angle of travel and repeats.
Another combines the chemical sensor with wind detection and follows the behavior of the dung beetle. A third models itself on the mating behaviors of the silkworm and involves quick jolts in various directions.
Many of the most effective algorithms use a gradient based approach by measuring the difference between two sensors and adjusting appropriately. However, these gradient based algorithms require the use of 2 chemical sensors. Due to budget constraints, using two sensors is not viable. Therefore, we have elected to base our design on the E. coli. However, if further testing proves this is ineffective, we are prepared for the potential need to implement the dung beetle algorithm that incorporates wind detection.
The straightforward implementation of any of the chemical detection algorithms will need be modified to account for our need for obstacle detection. We plan to allow the need for immediate obstacle avoidance to preempt the directional input from the chemical sensing algorithm, using that data to judge which direction would be a better choice to attempt to circumvent the obstacles. When no chemical concentrations are detected, the robot will fall back to a random walk type algorithm with some intelligence to ensure that it does not continue in a incorrect direction for too long of a time. In addition, we would like to store as much chemical concentration data as possible on the Flash EEPROM the microcontroller provides to allow for later analysis.
The algorithm will make use of the Microsoft Robotics Development platform for which the IRobot
Create has been designed to work well with. The platform provides a wide array of libraries that will aid in development of the robot and has a simulation framework that will allow us to begin development on the algorithm before the other materials have been acquired. In addition, this framework is free of charge, which removes many of the concerns we had when first considering adopting this software package.
Image 3 : Parallax Ping))) Sonar Proximity Sensor
The robot needs additional hardware to detect obstacles effectively. For this purpose we have decided on a sonar proximity sensor. After researching available sensors, we narrowed our selection down to the
Parallax Ping))) and the Maxbotix EZ Range Finder. They both run off a 5V at 30mA power supply,
which the robot is capable of providing. There is one signal pin which will plug into one of the remaining six analog to digital input pins the microcontroller provides. We decided on the Parallax Ping))) because its reading interval is much shorter, 20ms verses 50ms, and provides multiple ways to interface with it, specifically TTL, making development easier. The Maxbotix requires RS232 communication. The one disadvantage of the Ping))) verses the Maxbotix is the maximum range. The Ping))) detects to about 3m while the Maxbotix can reach up to 6m. However, we do not see the need to detect obstacles at such a long distance allowing us to use the Ping))) sensor. On board, the iRobot provides bumper detection and weak IR detection on the sides of the robot, which will be used as a fall-back in situations where the sonar operates in an unexpected fashion.
In addition, the robot must provide feedback to the user when the source of the contaminant has been located. Many options were evaluated, from something as simple as a blinking LED to communicating over wireless 802.11, Bluetooth or GSM. After analyzing cost and development time, we decided on alerting the operator via an audible single. The iRobot Create has a speaker built into the front of the robot that is capable of a wide range of audible sounds. Using this speaker requires no additional hardware purchases and minimizes integration time. Interfacing with the speaker uses the same methods as interfacing with the wheels or other input devices on the robot. Once the algorithm determines that it has located the source, it will trigger the audible alert.
4.3
Approach for design validation
We will be employing multiple stages of testing in the development process and attempt to be contingent in discovering potential issues. This will help ensure that the project is on track and defects are detected early in the development cycle. This will help us better use our time, money and other available sources more adeptly. Any design alterations incorporated will also be tested immediately. Essentially, we will first ensure that the robot is programmed to move in the appropriate direction based on the feedback from the sensor. We will also test the sensor’s output to confirm that the output voltage increases with higher chemical concentrations. We will test the obstacle detection hardware to ensure that it is capable of preemptively detecting any hazards in order to ensure a safer work environment and to ensure the safety of the robot.
The main criteria our various testing scenarios will be looking to cover are success rates, response times and alert systems. We will measure the percentage of successful navigation by the Robot Chemotaxi to the chemical spill for each of the test cases. Each test case will be run a minimum of 5 times. The total response time taken by the sensors to read the chemical concentrations and the robot to navigate to the source of the spill will also be measured. The alert mechanisms we expect to see are, a beeper being sounded and the LEDs blinking when the robot is within 1 foot of the spill.
Our basic functional case testing would be to position the robot approximately 3 meters away from a chemical spill with no obstacles in its path and measure the success rate, response time and alert system deployed. We will then proceed to test the Robot Chemotaxi by placing 1 obstacle in its path to test the obstacle avoidance component of the robot. The third test scenario is to place multiple obstacles along the direct path of the robot forcing it to take a more curved path to reach the chemical spill. Here, we will be testing if the robot will be able to successfully read the chemical spill while navigating obstacles. In the next test case, the obstacles will be placed in a manner such that the robot will not be able to continually detect stronger concentrations of the chemical. The expected behavior would be that the robot proceeds to move around the obstacle despite the contradictory chemical concentration reading and then continue to move towards the chemical spill.
We will then proceed to test the Robot Chemotaxi more intensively to observe the robot’s navigation algorithms. The robot will be placed at a position from where the chemical sensors will be unable to obtain a reading from the chemical spill. The expected behavior here is that the robot moves in zig-zag
movements until it detects a chemical spill and then proceeds towards it. In the next test case, the robot will be placed extremely close to the spill. The expected behavior here is that once the robot gets a strong reading, it makes slight movements around the spill to determine that the chemical concentration only weakens in all other directions and then activates the alert system.
Figure: Testing Scenario with obstacle in path
During the testing process, we will actively look for a few potential problems that may arise. The presence of any wind or turbulence could divert the chemical effervescence in the direction of the air movements. This could alter the sensor readings and divert the Robot Chemotaxi in the direction of the wind as opposed to the location of the spill. We will artificially generate air currents in our testing environment and observe the behavior of our robot. We propose to use an anemometer to relay feedback of the wind direction and speed to the Robot Chemotaxi and create modifications in our existing program.
The algorithm will adjust the path due to the wind readings and successfully navigate to the chemical.
However, this test case will be dependent on the cost of the anemometer and will be included only if the cost works within our financial constraints.
5 Engineering standards
5.1
Project management
Each team member will assume responsibility over a certain division of the project but we will all contribute equally to the different areas. The division leader is expected to coordinate and delegate the work required to the other team members. For instance, the individual responsible for the software development of the Robot Chemotaxi will delegate the different programming segments to the other members and then integrate it. We will be meeting regularly during the lab and class hours to brainstorm
for new ideas and discuss the individual and group progress made. Every member of the team will actively contribute to the project and be aware of the progress made by the other members. We also hope to promote a healthy working environment where the members can bring their different opinions and skills to the table and work cohesively to achieve our goals.
Bobby Harkreader has been the treasurer of the KTE Chapter at Texas A&M University for the past year.
His duties included depositing income, reimbursing expenditures, paying chapter dues and verifying financial statements for the organization. This experience qualifies him to handle the finances and purchases for the project. The financial responsibilities include maintaining a bill of materials and acquiring funds for the project. He has had computer science classes at Texas A&M University including a class on algorithms and data structures. Also, he has had software engineering internships with IBM involving implementation and testing of algorithms in the C language. This technical knowledge qualifies him to work on the software design portion of the project. His duties will be developing the chemical source finding algorithm and the obstacle avoidance algorithm.
Dhivya Padmanabhan is responsible for the hardware design of the project. Her experiences with prior classes such as CPSC 462 and internships at Microsoft and University of California, Santa Barbara enables her to work with the hardware section of the project. As part of the CPSC 462 course, she helped design the Mag-Chute which involved designing a high power circuit to charge an inductor coil to project permanent magnets through a 6 foot chute and a sensing circuit to detect the path of the magnet as it travels through the chute. The sensing circuit also relayed feedback to a micro controller that would in turn fire LEDs to display the path of the magnet in real time. Her responsibilities will include designing and understanding the hardware of the iRobot, PID- Tech Sensors and installing the sensor on the robot.
She will also be involved in developing the obstacle navigation and alert systems of the robot.
Matthew Davison will be in charge of managing the integration between the sensor and the robot’s programming. Matthew is qualified for this position as he has taken multiple classes relating to the principles utilized in this process on both the hardware and software side. During these classes, he programmed the interface between a microcontroller and a VGA monitor, thus showing his capabilities in this area.
5.2
Schedule of tasks, Pert and Gantt charts
Please see attached Gantt chart.
5.3
Economic analysis
Economical viability
There are several different types of organizations that would have reason to purchase a Robot Chemotaxi, making our product marketable. Chemical research labs may store chemicals or have chemicals piped through the lab. These pipes may break or crack and the stored chemicals may leak, which would cost the company money and create a hazard. In areas where workers will be present, OSHA restricts the levels of hazardous chemicals that are allowed to be in the air. Also, chemical plants and chemical distributors would have similar problems. Robot Chemotaxis would help companies show compliance to OSHA standards, save the companies money and most importantly, protect the companies' employees. The significant costs of the robotic chemotaxi are the robot platform and the photoionization detector. The prototype will use a more expensive robot platform which will make programming and testing easier. The cost of this robotic platform will drop immensely with high volume production, since we will be able to design a more stripped down, low cost robot platform. The miniature photoionization detector will still have a large cost during volume production. However, miniature photoionization detectors are relatively new products and as more and more companies begin to produce them, the cost will decrease.
Sustainability
Robot platforms are highly available from many vendors. There may be small maintenance for the robot platform such as cleaning and small repairs. The photoionization detector is only available from a limited number of companies. Since it is a relatively new product, this problem is expected to subside as more companies begin to produce photoionization detectors. The detectors themselves need to be periodically maintained by replacing the air filter that they use.
Manufacturability
The accuracy of concentration levels given by the sensor is affected by humidity levels in the air.
However, since our algorithm will compare relative concentrations, the accuracy is not important. Strong wind currents generated by air conditioning or small fans will significantly reduce the accuracy of the
Robot Chemotaxi. In the worst case, even small, common place air drafts will cause the Robot Chemotaxi to report the source of the chemical spill meters away from where the actual chemical spill is located. A high production yield is expected, since high quality parts will be used for the robot platform and more importantly, the chemical sensor. In the case of wireless communication being used to report chemical spills, FCC compliant IEEE standards would be used.
5.4
Societal, safety and environmental analysis
Robot Chemotaxis benefit society by protecting workers in laboratories and chemical storage facilities.
Also, by finding leaking chemical containers quickly, the robot chemotaxi will be able to save companies money, therefore increasing efficiency. Since a photoionization detector will be used to detect the chemicals, the robot chemotaxi may be used to find environmentally harmful chemical leaks as well as the harmless chemicals we will use for testing. These Robot Chemotaxis help companies to comply with various OSHA standards such as:
1910.1048(j)
Housekeeping. For operations involving formaldehyde liquids or gas, the employer shall conduct a program to detect leaks and spills, including regular visual inspections.
1910.1048(j)(1)
Preventative maintenance of equipment, including surveys for leaks, shall be undertaken at regular intervals.
These Robot Chemotaxis find chemical spills quickly, without putting anyone at risk. Although the robot platform will be small, it will be driving around blind to humans that may have to work beside the robots.
This can lead to a few minor safety issues, such as stepping on the robot and causing damage to the robot or harm to the worker who may fall. Also, the robot may run into the feet of workers while trying to locate a chemical spill. These sorts of accidents may be prevented by making the robot more noticeable with a bright light or bright color.
The impact of our Robot Chemotaxi design on the environment will be negligible. The robot is meant to be reusable, so garbage will not be an issue. The robot will be battery powered, and periodically the battery may have to be replaced. If customers simply throw the old batteries out, it will be detrimental to the environment. To minimize this occurrence, warnings that encourage recycling of the batteries will be added to the manuals and to the battery itself.
5.5
Itemized budget
Item Name iRobot Create® Premium
Development Package piD-TECH plus Black Label,
Plug-In PID Sensor
0.05ppm to 2,000ppm - w/10.6ev Lamp
Vendor iRobot.com [Microsoft]
Item Number: 4412
Baseline-Mocon Inc
Part No. ZPP6018001
Need
The iRobot will serve as the robot platform for the chemotaxis
The piD-TECH sensor will be mounted on the robot to detect and relay feedback on the location of the chemical to be sensed
Cost ($)
229
540
Rubbing Alcohol 16 fl oz Wal-Mart The chemical that will be detected by the chemotaxis.
Multiple bottles will be purchased for testing needs during project development
3.05*3
Ultra-Sonic Range Finder Radio Shack
Model: 28015
Catalog #: 276-031
It will be used in obstacle navigation by the robot
Total Cost
32.99
811.14
6 References
R. Andrew Russell, Alireza Bab-Hadiashar , Rod L. Shepherd , Gordon G. Wallace : A comparison of reactive robot chemotaxis algorithms. Robotics and Autonomous Systems 45 (2): 83-97 (2003)
R.A. Russell, S. Kennedy, A novel air flow sensor for miniature mobile robots, in: Mechatronics, vol. 10,
No. 8, Elsevier, Amsterdam, 2000, pp. 935–942. http://www.osha.gov/pls/oshaweb/owadisp.show_document?p_table=STANDARDS&p_id=10075
Various Papers by Dr. Andrew Russel, Monash Univ http://www.ecse.monash.edu.au/staff/rar/
7 Appendices
7.1
Product datasheets
Include product datasheets that may be particularly relevant to your proposed work. Say you want to use a certain type of microcontroller, because it has just the right combination features (e.g., types of I/O ports, or power consumption, etc). You would then attach datasheets for this product.
NOTE: including a data sheet does not replace the need for explaining how the component works and
how it will be integrated in the system (section 0).
7.2
Bios and CVs
Matthew Davison is a Computer Engineering undergraduate student at Texas A&M University.
Originally from San Antonio, he attended the Design and Technology Academy, a magnet school of Roosevelt High School, where he gained a love of programming. He came to Texas
A&M University in the fall of 2005.
Over the course of his undergraduate degree, he developed a love of logic circuit design resulting in him programming a pipelined MIPS processor in Verilog utilizing only the gate functions for
CPSC 321. He continued his study of logic circuit design, eventually programming a VGA driver for FPGA based circuits. Upon his expected graduation in May 2009, he intends to go to
Franklin Pierce Law Center to pursue his JD. He intends to specialize in Intellectual Property, potentially getting a joint LLM.
Robert Chandler Harkreader is a computer engineering student at Texas A&M University.
His focus is on computer science and he will graduate in May of 2009. As a student, he took mostly computer science courses and a few electrical engineering courses.
Mr. Harkreader participated in a co-op with IBM from May – December 2007. During this time, he worked in the AIX TCP/IP department and collaborated with a team of interns and full time employees to develop a performance enhancing feature for use with virtual machines on the AIX
VIOS operating system. He also participated in an internship with IBM from May –August 2008.
During this internship, he led a team of interns on a project to develop a device driver for a psuedo-device for the AIX operating system.
After graduation in May, Mr. Harkreader will continue his education at Texas A&M University in the graduate computer science department.
Dhivya Padmanabhan is a senior in Texas A&M University hoping to obtain a Bachelors
Degree in Computer Engineering (Computer Science track) in May 2009. Having been interested in computer science since high school, she has been able to expand her purview beyond high level programming languages and gain knowledge in the different fields of computer science through the different courses and internship/ research opportunities offered in her undergraduate career.
Ms. Padmanabhan has contributed to the field of wireless networking by conducting an experimental study of audio transmission and performance over wireless mesh network under the guidance of Dr. Belding of the Mobility Management and Networking (MOMENT) laboratory in
University of California, Santa Barbara in the summer of 2007. She has also worked in the field of Sketch Recognition with Dr. Tracy Hammond and her PhD students in the Sketch Recognition
Lab at Texas A&M University from fall 2007 through fall 2008. Here she built several user studies for the SRL website and implemented algorithms that were being researched by the PhD students. She has been a recipient of an honorable mention for the Outstanding Undergraduate award by the computing research association (CRA) for the year 2008 and 2009 for her efforts in research.
Dhivya has also gained valuable industry experience through her internship at Microsoft
Corporation in the summer of 2008. She worked as a software developer in Test in the Windows
Sharepoint Services team in the Microsoft Office division. She worked on testing the content
management feature in Share point. She wrote several test scripts in C# to test the feature and also found several bugs through manual testing.
Upon graduating in May 2009, Dhivya intends to join Microsoft Corporation as a full time employee and continue to expand her knowledge in computer engineering.
