Drift in a Popular Metal Oxide Sensor Dataset Reveals Limitations for Gas Classification Benchmarks

Funding Information: We thank A. J. Lilienthal, M. Psarrou and S. Sutton for fruitful discussions on multiple occasions, which led to valuable insights. MS was funded by the NSF/CIHR/DFG/FRQ/UKRI-MRC Next Generation Networks for Neuroscience Program (NSF award no. 2014217 , MRC award no. MR/T046759/1 ), and the EU Flagship Human Brain Project SGA3 (H2020 award no. 945539 ). JF acknowledges the Spanish Ministry of Economy and Competitiveness DPI2017-89827-R , Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine, initiatives of Instituto de Investigación Carlos III, Share4Rare Project (Grant agreement 780262 ), and ACCIÓ (Innotec A CE014/20/000018 ). JF also acknowledges the CERCA Programme/Generalitat de Catalunya and the Serra Húnter Program . B2SLab is certified as 2017 SGR 952. Funding Information: We thank A. J. Lilienthal, M. Psarrou and S. Sutton for fruitful discussions on multiple occasions, which led to valuable insights. MS was funded by the NSF/CIHR/DFG/FRQ/UKRI-MRC Next Generation Networks for Neuroscience Program (NSF award no. 2014217, MRC award no. MR/T046759/1), and the EU Flagship Human Brain Project SGA3 (H2020 award no. 945539). JF acknowledges the Spanish Ministry of Economy and Competitiveness DPI2017-89827-R, Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine, initiatives of Instituto de Investigaci?n Carlos III, Share4Rare Project (Grant agreement 780262), and ACCI? (Innotec ACE014/20/000018). JF also acknowledges the CERCA Programme/Generalitat de Catalunya and the Serra H?nter Program. B2SLab is certified as 2017 SGR 952. Publisher Copyright: © 2022Metal oxide (MOx) gas sensors are a popular choice for many applications, due to their tunable sensitivity, space efficiency and low cost. Publicly available sensor datasets are particularly valuable for the research community as they accelerate the development and evaluation of novel algorithms for gas sensor data analysis. A dataset published in 2013 by Vergara and colleagues contains recordings from MOx gas sensor arrays in a wind tunnel. It has since become a standard benchmark in the field. Here we report a latent property of this dataset that limits its suitability for gas classification studies. Measurement timestamps show that gases were recorded in separate, temporally clustered batches. Sensor baseline response before gas exposure were strongly correlated with the recording batch, to the extent that baseline response was largely sufficient to infer the gas used in a given trial. Zero-offset baseline compensation did not resolve the issue, since residual short-term drift still contained enough information for gas/trial identification using a machine learning classifier. A subset of the data recorded within a short period of time was minimally affected by drift and suitable for gas classification benchmarking after offset-compensation, but with much reduced classification performance compared to the full dataset. We found 18 publications where this dataset was used without precautions against the circumstances we describe, thus potentially overestimating the accuracy of gas classification algorithms. These observations highlight potential pitfalls in using previously recorded gas sensor data, which may have distorted widely reported results.Peer reviewe

An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification

Abstract: Increasing the number of sensors in a gas identification system generally improves its performance as this will add extra features for analysis. However, this affects the computational complexity, especially if the identification algorithm is to be implemented on a hardware platform. Therefore, feature reduction is required to extract the most important information from the sensors for processing. In this paper, linear discriminant analysis (LDA) and principal component analysis (PCA)-based feature reduction algorithms have been analyzed using the data obtained from two different types of gas sensors, i.e., seven commercial Figaro sensors and in-house fabricated 4×4 tin-oxide gas array sensor. A decision tree-based classifier is used to examine the performance of both the PCA and LDA approaches. The software implementation is carried out in MATLAB and the hardware implementation is performed using the Zynq system-on-chip (SoC) platform. It has been found that with the 4×4 array sensor, two discriminant functions (DF) of LDA provide 3.3% better classification than five PCA components, while for the seven Figaro sensors, two principal components and one DF show the same performances. The hardware implementation results on the programmable logic of the Zynq SoC shows that LDA outperforms PCA by using 50% less resources as well as by being 11% faster with a maximum running frequency of 122 MHz

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Recent Advances in Industrial and Applied Mathematics

This open access book contains review papers authored by thirteen plenary invited speakers to the 9th International Congress on Industrial and Applied Mathematics (Valencia, July 15-19, 2019). Written by top-level scientists recognized worldwide, the scientific contributions cover a wide range of cutting-edge topics of industrial and applied mathematics: mathematical modeling, industrial and environmental mathematics, mathematical biology and medicine, reduced-order modeling and cryptography. The book also includes an introductory chapter summarizing the main features of the congress. This is the first volume of a thematic series dedicated to research results presented at ICIAM 2019-Valencia Congress

Recent Advances in Industrial and Applied Mathematics

This open access book contains review papers authored by thirteen plenary invited speakers to the 9th International Congress on Industrial and Applied Mathematics (Valencia, July 15-19, 2019). Written by top-level scientists recognized worldwide, the scientific contributions cover a wide range of cutting-edge topics of industrial and applied mathematics: mathematical modeling, industrial and environmental mathematics, mathematical biology and medicine, reduced-order modeling and cryptography. The book also includes an introductory chapter summarizing the main features of the congress. This is the first volume of a thematic series dedicated to research results presented at ICIAM 2019-Valencia Congress

Modern Approaches To Quality Control

Rapid advance have been made in the last decade in the quality control procedures and techniques, most of the existing books try to cover specific techniques with all of their details. The aim of this book is to demonstrate quality control processes in a variety of areas, ranging from pharmaceutical and medical fields to construction engineering and data quality. A wide range of techniques and procedures have been covered

11th International Coral Reef Symposium Proceedings

A defining theme of the 11th International Coral Reef Symposium was that the news for coral reef ecosystems are far from encouraging. Climate change happens now much faster than in an ice-age transition, and coral reefs continue to suffer fever-high temperatures as well as sour ocean conditions. Corals may be falling behind, and there appears to be no special silver bullet remedy. Nevertheless, there are hopeful signs that we should not despair. Reef ecosystems respond vigorously to protective measures and alleviation of stress. For concerned scientists, managers, conservationists, stakeholders, students, and citizens, there is a great role to play in continuing to report on the extreme threat that climate change represents to earth’s natural systems. Urgent action is needed to reduce CO2 emissions. In the interim, we can and must buy time for coral reefs through increased protection from sewage, sediment, pollutants, overfishing, development, and other stressors, all of which we know can damage coral health. The time to act is now. The canary in the coral-coal mine is dead, but we still have time to save the miners. We need effective management rooted in solid interdisciplinary science and coupled with stakeholder buy in, working at local, regional, and international scales alongside global efforts to give reefs a chance.https://nsuworks.nova.edu/occ_icrs/1000/thumbnail.jp