Multichannel Sensor Systems Research Articles

Optimization of sensors based on surface waves in flat-layered structures

A method for increasing the sensitivity of surface-wave sensors in multifilm structures is proposed based on the results of a theoretical study of the reflection of a plane electromagnetic wave from a flat-layered structure containing homogeneous films. A method for optimizing the parameters of the films forming the surface structure is developed and tested. The devices proposed are characterized by high field amplification in a surface layer of multifilm structure and a higher sensitivity to variations in the optical properties of the thin near-surface layer. Application of the proposed multifilm structures is expected to increase significantly the sensitivity of the existing sensors based on surface-plasmon polaritons, which are applied in modern optical multichannel biological, chemical, and physical sensor systems.

A robust functional-data-analysis method for data recovery in multichannel sensor systems.

Multichannel sensor systems are widely used in condition monitoring for effective failure prevention of critical equipment or processes. However, loss of sensor readings due to malfunctions of sensors and/or communication has long been a hurdle to reliable operations of such integrated systems. Moreover, asynchronous data sampling and/or limited data transmission are usually seen in multiple sensor channels. To reliably perform fault diagnosis and prognosis in such operating environments, a data recovery method based on functional principal component analysis (FPCA) can be utilized. However, traditional FPCA methods are not robust to outliers and their capabilities are limited in recovering signals with strongly skewed distributions (i.e., lack of symmetry). This paper provides a robust data-recovery method based on functional data analysis to enhance the reliability of multichannel sensor systems. The method not only considers the possibly skewed distribution of each channel of signal trajectories, but is also capable of recovering missing data for both individual and correlated sensor channels with asynchronous data that may be sparse as well. In particular, grand median functions, rather than classical grand mean functions, are utilized for robust smoothing of sensor signals. Furthermore, the relationship between the functional scores of two correlated signals is modeled using multivariate functional regression to enhance the overall data-recovery capability. An experimental flow-control loop that mimics the operation of coolant-flow loop in a multimodular integral pressurized water reactor is used to demonstrate the effectiveness and adaptability of the proposed data-recovery method. The computational results illustrate that the proposed method is robust to outliers and more capable than the existing FPCA-based method in terms of the accuracy in recovering strongly skewed signals. In addition, turbofan engine data are also analyzed to verify the capability of the proposed method in recovering non-skewed signals.

Detection of intrusions in information systems by sequential change-point methods

Sequential multi-chart detection procedures for detecting changes in multichannel sensor systems are developed. In the case of complete information on pre-change and post-change distributions, the detection algorithm represents a likelihood ratio-based multichannel generalization of Page’s cumulative sum (CUSUM) test that is applied to general stochastic models that may include correlated and nonstationary observations. There are many potential application areas where it is necessary to consider multichannel generalizations and general statistical models. In this paper our main motivation for doing so is network security: rapid anomaly detection for an early detection of attacks in computer networks that lead to changes in network traffic. Moreover, this kind of application encourages the development of a nonparametric multichannel detection test that does not use exact pre-change (legitimate) and post-change (attack) traffic models. The proposed nonparametric method can be effectively applied to detect a wide variety of attacks such as denial-of-service attacks, worm-based attacks, port-scanning, and man-in-the-middle attacks. In addition, we propose a multichannel CUSUM procedure that is based on binary quantized data; this procedure turns out to be more efficient than the previous two algorithms in certain scenarios. All proposed detection algorithms are based on the change-point detection theory. They utilize the thresholding of test statistics to achieve a fixed rate of false alarms, while allowing changes in statistical models to be detected “as soon as possible”. Theoretical frameworks for the performance analysis of detection procedures, as well as results of Monte Carlo simulations for a Poisson example and results of detecting real flooding attacks, are presented.

A discussion on ‘Detection of intrusions in information systems by sequential change-point methods’ by Tartakovsky, Rozovskii, Blažek, and Kim

The discussion focuses on issues arising in practical applications of the CUSUM procedures developed by Tartakovsky et al. to detect changes in multichannel sensor systems. These issues are illustrated with a data set from the MIT Lincoln Laboratory that is used to estimate the parameters of procedures for detecting denial-of-service attacks.

On-line signal quality estimation of multichannel surface electromyograms

When multichannel surface-electromyography (MCSEMG) systems are used, there is a risk of recording low-quality signals. Such signals can be confusing for analysis and interpretation and can be caused by power-line interference, motion artifacts or poor electrode-skin contact. Usually, the electrode-skin impedance is measured to estimate the quality of the contact between the electrodes and the skin. However, this is not always practical, and the contact can change over short time-scales. A fast method is described to estimate the quality of individual signals of monopolar MCSEMG recordings based on volume conduction of myo-electric signals. The characteristics of the signals were described using two descriptor variables. Outliers (extreme data points) were detected in the two-dimensional distributions of the descriptor variables using a non-parametric technique, and the quality of the signals was estimated by their outlier probabilities. The method's performance was evaluated using 1 s long signals visually classified as very poor (G 1), poor (G2) or good quality (G3). Recordings from different subjects, contraction levels and muscles were used. An optimum threshold at 0.05 outlier probability was proposed and resulted in classification accuracies of 100% and > 70% for G I and G2 signals, respectively, whereas <5% of the G3 signals were classified as poor. In conclusion, the proposed method estimated MCSEMG signal quality with high accuracy, compared with visual assessment, and is suitable for on-line implementation. The method could be applied to other multichannel sensor systems, with an arbitrary number of descriptor variables, when their distributions can be assumed to lie within a certain range.