Algorithm-aided performance enhancement of a trace explosives sensor

•Waveforms comprising sensor’s response to injected materials are exploited through a systematic study.•A machine-learning based approach is applied to examine the sensor data.•Results demonstrate much improved sensor performance by implementing an appropriate signal processing algorithm.•Exploitati...

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Bibliographic Details
Published in:Sensors and actuators. B, Chemical Chemical, 2018-04, Vol.259, p.935-944
Main Authors: Necioğlu, Burhan F., Su, Wansheng, Rhodes, Jefferson, O’Donnell, Sarah, Taczak, Mark, Guharay, Samar K.
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Experimentation
Explosives
Explosives detection
False alarms
Identification
Machine learning
Performance enhancement
Performance improvement
Sensor
Sensors
Signal processing
Target recognition
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Summary:•Waveforms comprising sensor’s response to injected materials are exploited through a systematic study.•A machine-learning based approach is applied to examine the sensor data.•Results demonstrate much improved sensor performance by implementing an appropriate signal processing algorithm.•Exploitation of sensor data by advanced signal processing can augment sensor performance without changing the hardware. This article describes the exploitation of data obtained from a mature trace explosives detection device with the goal of enhancing the device’s performance via signal processing and no hardware modification. This is achieved by implementing a machine-learning algorithm based on nearest-neighbor binary classification. This study aims to determine the parameters of the algorithm defining key feature vectors for both explosive and non-explosive materials via systematic experimentation and measurements. Receiver operating characteristic (ROC) curves are estimated showing the trade-off between detection/false alarm rates, and the results demonstrate the merit of this approach for advancing the performance of this technology. Furthermore, the algorithm is shown to enable not just improved detection, but also a capability for target or materials identification.
ISSN:0925-4005
1873-3077
DOI:10.1016/j.snb.2017.12.055