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SENSOR FUSION LABORATORY Thad Roppel, Associate Professor AU Electrical and Computer Engineering Dept. EXAMPLES Infrared / Millimeter.

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Presentation on theme: "SENSOR FUSION LABORATORY Thad Roppel, Associate Professor AU Electrical and Computer Engineering Dept. EXAMPLES Infrared / Millimeter."— Presentation transcript:

1 SENSOR FUSION LABORATORY Thad Roppel, Associate Professor AU Electrical and Computer Engineering Dept. EXAMPLES Infrared / Millimeter wave radar for vehicle detection and identification Chemical sensor arrays – “artificial nose” Biomimetics – imitating animal sensorimotor behaviors Biomedical – using electrical and optical probes to study cardiac arrhythmias MISSION: Study the benefits of using simultaneous information from multiple sensors to probe the environment.

2 SENSOR FUSION LABORATORY Problem Complexity: Human vs. Machine HUMAN MACHINE EASY HARD EASY HARD Maximum Potential Benefit Object recognition Linguistics Extraction of Relevant Features from Sensor Arrays Arithmetic Logic Thresholding Tallying Judging

3 Personnel and Publications PERSONNEL Ting-To Lo (PhD): Molecular Switching in Biosensors Rama Narendran (PhD): Biomimetic Simulations of Organized Machine Behavior Jun Pan (PhD): Wireless Protocol for Electrical and Optical Cardiac Microprobes Aroldo Couto (MS): Flight Stabilization Using Adaptive Artificial Neural Networks Brian Wingfield (MS): Silicon Processing for Lateral Emission Fiber-Optic Sensors REPRESENTATIVE RECENT PUBLICATIONS D. M. Wilson, T. Roppel, and R. Kalim, "Aggregation of Sensory Input for Robust Performance in Chemical Sensing Microsystems," Sensors and Actuators B, 64(1–3), , June T. Roppel and D. M. Wilson, "Biologically-Inspired Pattern Recognition for Odor Detection," Pattern Recognition Letters, 21(3), 213–219, March D. M. Wilson, K. Dunman, T. Roppel, and R. Kalim, "Rank Extraction in Tin-Oxide Sensor Arrays," Sensors and Actuators B, 62(3), , April T. Roppel, R. Kalim, and D. Wilson, "Sensory Plane Analog-VLSI for Interfacing Sensor Arrays to Neural Networks, " Virtual Intelligence and Dynamic Neural Networks VI-DYNN '98, Stockholm, Sweden, June 22-26, 1998.

4 IR / MMW DATA FUSION Support: AFOSR Project Goal: Improved identification of military vehicles from aerial scenes. LANCE Missile Launcher T-62 Tank M-113 Armored Personnel Carrier (APC)

5 IR / MMW Fusion, cont’d APPROACH: IR SCENE PIXELS MMW RADAR DATA NEURAL NETWORK APC TANK LAUNCHER PERFORMANCE ASSESSMENT: ATL A+-- T-+- L--+ Multiple permutations Confusion matrix Average result OVERALL RESULT: 14 % improvement with sensor fusion

6 Chemical Sensor Arrays Support: DARPA PROJECT GOAL: Improved identification and detection of chemical plumes in non-laboratory conditions. VEHICLE SENSORS PLUME COMMAND STATION RF LINK ROAD WIND

7 Canine Training at IBDS Auburn is world-renowned for training of detection dogs at the Institute for Biological Detection Systems.

8 Chemical Sensor Arrays, cont’d Odor Sensor Array Timestep Sensor Voltage Sensor Outputs Sensor Array Dynamic Response

9 Chemical Sensor Arrays, cont’d Preprocessing

10 Chemical Sensor Arrays, cont’d ace Sample 1Sample Sample amm dal g87 g89 g93 oil pth Sensor # xyl Sensor # Sensor # 51015

11 Chemical Sensor Arrays, cont’d

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13 Chemical Sensor Arrays - Summary A recurrent neural network was trained to recognize 9 odors presented in an arbitrary time sequence. Response time is reduced by an order of magnitude by threshold preprocessing. Well-suited for use as a front-end for a hierarchical suite of NN’s in a portable, near-real time odor classification device.

14 BIOMIMETICS Support: Under discussion with AF Advanced Guidance Division, Munitions Directorate at Eglin AFB PROJECT GOAL: Learn sensor fusion from animals. Apply this to flying a drone to target using onboard video. Flies land accurately Bees find flowers Bats catch evading insects in flight

15 BIOMIMETICS, cont’d What do they “know” that we don’t? One possibility is that they use variations of optic flow. Represent sensory image field by motion vector field. Image SequenceOptic Flow Field

16 BIOMIMETICS, cont’d EXAMPLES A fly can land simply by maintaining constant optic flow. A dog can track by maintaining constant sensory flow across olfactory epithelium and following the gradient (using sniffing as a form of “chopper amplifier.” Questions to be answered: Can we guide a missile to target, orchestrate complex defense systems, identify faces in a crowd, or track contaminated food with similar approaches? END OF PRESENTATION


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