Enhanced Pattern Recognition of target Chemicals and Bacteria with Cantilever Sensor Arrays

1. Enhanced Pattern Recognitionof target Chemicals and Bacteriawith Cantilever Sensor Arrays Asya Nikitina Robotics Research Laboratory Computer Science Department University of Nevada, Reno http://www.cse.unr.edu/~nikitina

2. The Problem • Goal: • Develop and test a reliable system for detection and differentiation of selected chemicals and bacteria in the unknown background mixture of gases using a microcantilever-based sensor array • Motivation: • Due to the high concern over chemical and biological weapons and significant interest from governmental, environmental and health-related agencies, there is a high need for miniaturized, portable, accurate, inexpensive, low power use chemical sensors capable of rapid, in situ detection of chemical signatures of bacteria and of chemical agents in the air

3. Approach • The ability to detect extremely small displacements makes the cantilever an ideal device for detection of extremely small forces and stresses • Adsorption of molecules on the surface of a cantilever or absorption of molecules by a coating material changes the total mass and, consequently, the resonance frequency of the cantilever • The resonance frequency shifts and bending of a cantilever can be measured with very high precision using different readout techniques and used as model’s features for the future classification

4. Results • During the current stage of our project, we are performing experiments in which the cantilever array is exposed to different concentration of some specified analytes (stage I - data collection) • Next stage will be training the cantilever array using different mixtures of the same analytes (stage II – training the cantilever sensor array) • The final stage of our project will be developing a kernel-based pattern recognition algorithm, which will be able not only to recognize the specified analytes in the gaseous mixture, but also to determine the relative concentrations of the analytes presented in the mixture (stage III – developing and implementing a kernel-based algorithm for pattern classification)

5. Intention prediction using Hidden Markov Model Jigar Patel Robotics Research Laboratory Computer Science Department University of Nevada, Reno http://www.jspatel.com

6. The Problem • Goal: • The main goal of my research is to predict robot’s intention in single or multi agent environment using trained Hidden Markov Model. • Make robot to predict other’s intention based on his own experience with environment like humans do. • Motivation: • When one person is going towards door another person can easily say he/she is going out of the door. This looks simple and obvious to us. But this is not true for robots. • Won’t be it interesting to see robot predicting other robot’s intention/behavior.

7. Approach • As required in my goal I need a robot having experience with environment before it starts predicting. • I am using Hidden Markov Model (HMM) to represent robot’s experience. • To train (or gain experience) I am letting robot achieve certain goal and train HMM for that behaviors related to it. Using the same technique train different HMM for more behaviors. • Now try to predict intention in single or multi agent environment based on training (or gained experience).

8. Results • So far I have trained HMM for interfere, follow, meet, seek behaviors. • In multi agent environment run more than one behaviors. Feed current status of environment to all HMM in parallel and get results. • I am also trying to derive behaviors that are not explicitly defined like Group Meet. This behavior may occur when more than one pair of robots try to meet. • I am getting overall 96% true prediction for simulated environment using Player/Stage as tool.