A committee machine gas identification system based on dynamically reconfigurable FPGA

This paper proposes a gas identification system based on the committee machine (CM) classifier, which combines various gas identification algorithms, to obtain a unified decision with improved accuracy. The CM combines five different classifiers: K nearest neighbors (KNNs), multilayer perceptron (MLP), radial basis function (RBF), Gaussian mixture model (GMM), and probabilistic principal component analysis (PPCA). Experiments on real sensors' data proved the effectiveness of our system with an improved accuracy over individual classifiers. Due to the computationally intensive nature of CM, its implementation requires significant hardware resources. In order to overcome this problem, we propose a novel time multiplexing hardware implementation using a dynamically reconfigurable field programmable gate array (FPGA) platform. The processing is divided into three stages: sampling and preprocessing, pattern recognition, and decision stage. Dynamically reconfigurable FPGA technique is used to implement the system in a sequential manner, thus using limited hardware resources of the FPGA chip. The system is successfully tested for combustible gas identification application using our in-house tin-oxide gas sensors

An Efficient Approach for Preprocessing Data from aLarge-Scale Chemical Sensor Array

In this paper, an artificial olfactory system (Electronic Nose) that mimics the biological olfactory system is introduced. The device consists of a Large-Scale Chemical Sensor Array (16, 384 sensors, made of 24 different kinds of conducting polymer materials) that supplies data to software modules, which perform advanced data processing. In particular, the paper concentrates on the software components consisting, at first, of a crucial step that normalizes the heterogeneous sensor data and reduces their inherent noise. Cleaned data are then supplied as input to a data reduction procedure that extracts the most informative and discriminant directions in order to get an efficient representation in a lower dimensional space where it is possible to more easily find a robust mapping between the observed outputs and the characteristics of the odors in input to the device. Experimental qualitative proofs of the validity of the procedure are given by analyzing data acquired for two different pure analytes and their binary mixtures. Moreover, a classification task is performed in order to explore the possibility of automatically recognizing pure compounds and to predict binary mixture concentrations

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.Comment: 14 Pages, 9 Figure

Accelerated High-Resolution Photoacoustic Tomography via Compressed Sensing

Current 3D photoacoustic tomography (PAT) systems offer either high image quality or high frame rates but are not able to deliver high spatial and temporal resolution simultaneously, which limits their ability to image dynamic processes in living tissue. A particular example is the planar Fabry-Perot (FP) scanner, which yields high-resolution images but takes several minutes to sequentially map the photoacoustic field on the sensor plane, point-by-point. However, as the spatio-temporal complexity of many absorbing tissue structures is rather low, the data recorded in such a conventional, regularly sampled fashion is often highly redundant. We demonstrate that combining variational image reconstruction methods using spatial sparsity constraints with the development of novel PAT acquisition systems capable of sub-sampling the acoustic wave field can dramatically increase the acquisition speed while maintaining a good spatial resolution: First, we describe and model two general spatial sub-sampling schemes. Then, we discuss how to implement them using the FP scanner and demonstrate the potential of these novel compressed sensing PAT devices through simulated data from a realistic numerical phantom and through measured data from a dynamic experimental phantom as well as from in-vivo experiments. Our results show that images with good spatial resolution and contrast can be obtained from highly sub-sampled PAT data if variational image reconstruction methods that describe the tissues structures with suitable sparsity-constraints are used. In particular, we examine the use of total variation regularization enhanced by Bregman iterations. These novel reconstruction strategies offer new opportunities to dramatically increase the acquisition speed of PAT scanners that employ point-by-point sequential scanning as well as reducing the channel count of parallelized schemes that use detector arrays.Comment: submitted to "Physics in Medicine and Biology

On-The-Fly Processing of continuous high-dimensional data streams

[EN] A novel method and software system for rational handling of time series of multi-channel measurements is presented. This quantitative learning tool, the On-The-Fly Processing (OTFP), develops reduced-rank bilinear subspace models that summarise massive streams of multivariate responses, capturing the evolving covariation patterns among the many input variables over time and space. Thereby, a considerable data compression can be achieved without significant loss of useful systematic information. The underlying proprietary OTFP methodology is relatively fast and simple it is linear/bilinear and does not require a lot of raw data or huge cross-correlation matrices to be kept in memory. Unlike conventional compression methods, the approach allows the high-dimensional data stream to be graphically interpreted and quantitatively utilised in its compressed state. Unlike adaptive moving-window methods, it allows all past and recent time points to be reconstructed and displayed simultaneously. This new approach is applied to four different case-studies: (i) multi-channel Vis-NIR spectroscopy of the Belousov Zhabotinsky reaction, a complex, ill understood chemical process; (ii) quality control of oranges by hyperspectral imaging; (iii) environmental monitoring by airborne hyperspectral imaging; (iv) multi-sensor process analysis in the petrochemical industry. These examples demonstrate that the OTFP can automatically develop high-fidelity subspace data models, which simplify the storage/transmission and the interpretation of more or less continuous time series of high-dimensional measurements to the extent there are covariations among the measured variables.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2014-55276-C5-1R, Shell Global Solutions International B.V. (Amsterdam, The Netherlands), Idletechs AS (Trondheim, Norway), the Norwegian Research Council (Grant 223254) through the Centre of Autonomous Marine Operations and Systems (AMOS) at the Norwegian University of Science and Technology (Trondheim, Norway) and the Ministry of Education, Youth and Sports of the Czech Republic (CENAKVA project CZ.1.05/2.1.00/01.0024 and CENAKVA II project L01205 under the NPU I program). The authors want to acknowledge Prof. Bjorn Alsberg for providing the Vis-NIR equipment and the Laboratorio de Sistemas e Tecnologia Subaquatica of the University of Porto, the Hydrographic Institute of the Portuguese Navy and the University of the Azores for carrying out the REP15 exercise, during which the hyperspectral push broom image was collected.Vitale, R.; Zhyrova, A.; Fortuna, JF.; De Noord, OE.; Ferrer, A.; Martens, H. (2017). On-The-Fly Processing of continuous high-dimensional data streams. Chemometrics and Intelligent Laboratory Systems. 161:118-129. doi:10.1016/j.chemolab.2016.11.003S11812916

Improving the performance of gas sensor systems with advanced data evaluation, operation, and calibration methods

In order to facilitate the widespread use of gas sensors, some challenges must still be overcome. Many of those are related to the reliable quantification of ultra-low concentrations of specific compounds in a background of other gases. This thesis focuses on three important items in the measurement chain: sensor material and operating modes, evaluation of the resulting data, and test gas generation for efficient sensor calibration. New operating modes and materials for gas-sensitive field-effect transistors have been investigated. Tungsten trioxide as gate oxide can improve the selectivity to hazardous volatile organic compounds like naphthalene even in a strong and variable ethanol background. The influence of gate bias and ultraviolet light has been studied with respect to the transport of oxygen anions on the sensor surface and was used to improve classification and quantification of different gases. DAV3E, an internationally recognized MATLAB-based toolbox for the evaluation of cyclic sensor data, has been developed and published as opensource. It provides a user-friendly graphical interface and specially tailored algorithms from multivariate statistics. The laboratory tests conducted during this project have been extended with an interlaboratory study and a field test, both yielding valuable insights for future, more complex sensor calibration. A novel, efficient calibration approach has been proposed and evaluated with ten different gas sensor systems.Vor der weitverbreiteten Nutzung von Gassensoren stehen noch einige Herausforderungen, insbesondere die zuverlässige Messung ultrakleiner Konzentrationen bestimmter Substanzen vor einem Hintergrund anderer Gase. Diese Arbeit konzentriert sich auf drei wichtige Glieder der erforderlichen Messkette: Material und Betriebsweise von Sensoren, Auswertung der anfallenden Daten sowie Generierung von Testgasen zur effizienten Kalibrierung. Neue Betriebsmodi und Materialien für gassensitive Feldeffekttransistoren wurden getestet. Wolframtrioxid kann als Gateoxid die Selektivität für flüchtige organische Verbindungen wie Naphthalin in einem variierenden Ethanolhintergrund verbessern. Der Einfluss von Gate-Bias und ultravioletter Strahlung auf die Bewegung von Sauerstoffionen auf der Oberfläche wurde untersucht und genutzt, um die Klassifizierung und Quantifizierung von Gasen zu verbessern. Eine international anerkannte MATLAB-Toolbox zur Auswertung zyklischer Sensordaten, DAV3E, wurde entwickelt und als open source veröffentlicht. Sie stellt eine nutzerfreundliche Oberfläche und speziell angepasste Algorithmen der multivariaten Statistik zur Verfügung. Die Laborexperimente wurden ergänzt durch vergleichende Messungen in zwei unabhängigen Laboren und einen Feldtest, womit wertvolle Erkenntnisse für die künftig notwendige, komplexe Kalibrierung von Sensoren gewonnen wurden. Ein neuartiger, effizienter Kalibrieransatz wurde vorgestellt und mit zehn unterschiedlichen Sensorsystemen evaluiert