Chemical Sensors SP 2011

Weeks 1-4:

We accomplished the following tasks:

- Added to the Matlab data processing algorithm to match peaks between sensor pairs. - Ordered 3 new pairs of sensors, so that each sensor within a pair does not collect data in a biased manner based on age.

Additionally, we made a game plan for the rest of the semester:

- Create new sensor array with new sensors and collect new unbiased data from the updated array.

- Email Dr. Raman about our research and a possible collaboration.

- Debug Matlab data processing algorithm.

- Ensure that Labview and Matlab data processing algorithms allows updated sensor array to reliably detect the location of the isopropyl alcohol source.

- Interface the data processing algorithm and sensor array with a robot, and develop a robot motion algorithm.

- Investigate the effects of using a shorter span of data on the accuracy of the signal processing algorithm.

Week 5:

- Created new sensor array.

- Fixed the matlab brute force algorithm

- Collected data from new sensor array, 5 trials for each distance away from the array for each side. This gave a total of 10 trials per distance, or 500 trials.

plan for this week:

- make presentation for next week (power point)

- 2 at each distance at each side

- drawing of sensor configuration

Week 6:

- Made more corrections to brute force algorithm, and processed trials from week 5. Unfortunately, the results still indicate that the sensor array detects the isopropyl alcohol source on one side of the array, no matter where the alcohol is actually located.

- Realized that the resistances that were in series with the sensors were chosen specifically for the sensors in the old array. Therefore, altered resistances in series with sensors in the circuit so that voltages produced by each sensor in a pair were consistent. I.e., sensors 1 and 2 should produce similar voltages when smelling alcohol, as should sensors 3 and 4, and 5 and 6. Used this array to collect a second batch of data (2 trials per side per distance). However, this still gave bad results, i.e., results that were similar to those from week 5's data.

- Next, we tried putting each sensor was in series with the same resistance, regardless of the disparities in voltage. We also spaced the sensors farther apart (1 inch), to improve the discrimination between sides. With this modified circuit configuration, we took 3 trials per side per distance. Processed this data, and the brute-force algorithm STILL yielded bad results. The dynamic-time-warp algorithm looks more promising, but is still pretty bad. The results of DTW suggest that sensor pair 2 is always biased towards one side. Therefore, attempted to run the DTW algorithm on only the other 2 pairs, but still working on the coding for this.

- When we used the dynamic time warp m.files and removed the transient response, we observed more promising data. For the next meeting, we will work on fine tuning the resistances within the circuit (most likely lowering all the resistances) and modify the data acquisition algorithm to get bumpier signals. -- that is, if there are no peaks by the end of the data collection, we will not continue onto the cooling period, but continue data collection until we see a peak.

Week 7:

- This week was hard in terms of making times to meet. We have created the sinage slide for the lobby and have created SEVERAL meeting times for next week.

- We plan to carry out the goals of week six next week and collect more data.

Week 8:

- After much fiddling, we set the resistances in the circuit as follows:

 Sensors 1 and 2:  9.1k
 Sensors 3 and 4:  3.4k
 Sensors 5 and 6:  9.1k
 We did this in an attempt to make our voltage signals bumpier.  The signals did improve, although a significant number were still smooth even with the lower resistances.  This means that lowering the resistances seems to be effective only to a certain point.

- We collected data, 3 trials per distance, and 5 distances per side, using the modified circuit. Analysis of the DTW results is confusing, and appears to be biasing one side, or to be too smooth to interpret at all. Currently investigating the morphology of the signals to determine which of them are flat.

- Goals for after Spring Break:

 1. Create algorithm to accept only bumpy signals.

Week 9:

Week 10:

Week 11: