Special Issue "Sensor Algorithms"

Guest Editor
Dr. Costas Busch
Department of Computer Science, Louisiana State University, 296 Coates Hall, Baton Rouge, LA 70803, USA
Website: http://www.csc.lsu.edu/~busch
E-mail:

Related Special Issue: The Special Issue Sensor Algorithms in the Journal Sensors

Submission

All papers should be submitted to algorithms@mdpi.org. To be published continuously until the deadline and papers will be listed together at the special issue website.

Submitted papers should not have been published nor be under consideration for publication elsewhere. All papers are refereed through a peer-review process. A guide for authors is available on the Instructions for Authors page. Algorithms is an international peer-reviewed quarterly journal published by Molecular Diversity Preservation International.

Open Access publication fees are 300 CHF per paper. English correction fees and/or formatting fees (250 CHF) will be added in certain cases (550 CHF per paper for those papers that require extensive additional formatting and/or English corrections.).

Article Processing Charges (APC)

Article Processing Charges (APC) will be waived for well prepared manuscripts of invited papers. For the first two volumes of this new journal the APC are of 300 CHF (or 550 CHF per paper for those papers that require extensive additional formatting and/or English corrections).

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Title: Retrieval algorithms for estimating soil moisture and vegetation biomass from satellite microwave images
Authors: S.Paloscia, P.Pampaloni, S.Pettinato, E.Santi
Abstract: to be added soon

Title: Energy-Efficient Adaptive Sensor Scheduling for Target Tracking in Wireless Sensor Networks.
Authors: Wendong Xiao, Lihua Xie, Sen Zhang, Frank L. Lewis
Affiliations: Institute for Infocomm Research, Singapore; Nanyang Technological University, Singapore; National University of Singapore; University of Texas at Arlington, USA
Abstract: to be added soon

Title: Deriving phytoplankton light absorption of the Arctic Ocean using ocean color: implications for phytoplanktonic response to ongoing global warming
Authors: Atsushi Matsuoka and Victoria J. Hill
Abstract: to be added soon

Title: Satellite remote sensing algorithms for cirrus cloud detection and retrieval
Author: Steve Ou
Abstract: to be added soon

Title: Semi-empirical algorithm for the retrieval of ecology-relevant water constituents in various aquatic environments
Author: Dmitry Pozdnyakov
Abstract: to be added soon

Title: Variations on the Kalman filter for aircraft engine health monitoring
Authors: Olivier Léonard and Sébastien Borguet
Affiliation: University of Liège, Belgium
Abstract: The concept of condition-based maintenance is widely recognised in the aircraft engine community as a means to improve engine safety and operability as well as to reduce its life cycle costs. In this perspective, generating a reliable information about the health condition of the engine is a requisite. Module performance analysis aims at assessing the changes in the performance of the engine components, described by so-called health parameters, on the basis of measurements collected along the gas path of the engine. Among numerous possibilities, the evaluation of the health parameters can be cast as a parameter estimation problem. The celebrated Kalman filter has enjoyed a reasonable success in estimating the health parameters. This algorithm can be seen as a minimum mean-squared-error (variance) estimator within a recursive framework. With respect to the specificity of engine performance monitoring, the Kalman filter possesses a number of appealing features. Indeed, it takes into account the noisy nature of the data, it allows a prior knowledge to be set on the parameters, it is recursive and it has a moderate computational burden. Modifications of the generic algorithm can however be considered in order to further enhance its capability to track accurately the health condition of an engine. Three directions investigated by the authors are reported in the subsequent paragraphs. The first area for possible enhancement is the robustness of the Kalman filter with respect to sensor faults. In the derivation of the Kalman filter, the measurement noise is modelled as a zero-mean, white, gaussian random variable. Obviously, this assumption is not verified when instrumentation faults occur. As a result, the identified health parameters tend to diverge from their actual values which strongly deteriorates the relevance of the diagnosis. Robustness against outliers can efficiently be blended in the algorithm by replacing the Gaussian distribution for the measurement noise by the Huber distribution, leading to a Quadratic Programming problem for which efficient solvers are available. The second area for improvement is linked to the temporal evolution of the engine health. The Kalman filter has proven its capability to track gradual deterioration with a good accuracy, but its response to an abrupt fault is a long delay in recognising the fault, and/or a spread of the estimated fault on several components whereas it generally impacts only a reduced number of them. The main reason of this deficiency lies in the transition model of the parameters that is blended in the Kalman filter and assumes a smooth evolution of the engine condition. The tracking of abrupt faults is enhanced by combining the Kalman filter and a secondary system that monitors the residuals in order to detect and estimate the abrupt fault event.
The third area for innovation is related to the nature of the data. Most of the applications reported so far make use of measurements collected during steady-state operation of the engine. However, processing measurements obtained during transient operation of the engine has been shown to significantly improve the diagnosis procedure. The level of information about the condition of the engine is indeed much richer during a transient sequence due to the large number of operating points traversed. During a transient analysis, two tasks must be performed simultaneously : the estimation of the state variables associated to the dynamic behaviour of the engine and the identification of the health parameters. This is carried out by running two Kalman filters in parallel, each dedicated to one of both tasks.
The present paper reviews the aforementioned modifications to the Kalman filter at the theoretical level and illustrates the expected improvements through a number of scenarios simulated with a generic commercial turbofan model.

Title: Kernel Extrapolation Methods for Probabilistic Gas Distribution Modelling
Author: Achim Lilienthal
Affiliation: Örebro University, Department of Technology, AASS Learning Systems Lab, 70182 Örebro, Sweden
Abstract: Gas distribution modelling is the task of deriving a truthful representation of the observed gas distribution from a set of spatially and temporally distributed measurements. It is a very challenging task mainly because in many realistic scenarios gas is dispersed by turbulent advection. Instead of trying to solve the fluid dynamics equations, we propose to create a probabilistic model of the observed gas distribution, treating gas sensor measurements as random variables. To create a probabilistic gas distribution model, we propose and discuss Kernel extrapolation algorithms, which model the observed variance in addition to the distribution mean. We derive the algorithm and present a method for learning the hyper-parameters. Based on real world data collected with a mobile robot that was periodically stopped at pre-defined points to carry out the measurements, we demonstrate the consistency of maps obtained from stationary and mobile sensors and present a quantitative comparison with alternative approaches.

Title: An investigation of DMSP Special Sensor Microwave Imager (SSM/I) nonlinearity
Authors: Banghua Yan and Fuzhong Weng
Abstract: will be added soon

Title: Sensor Algorithms for Damage Detection with Active Sensing Technology
Author: Lingyu (Lucy) Yu and Victor Giurgiutiu
Abstract: will be added soon

Title: Methodology, Algorithms, and Emerging Tool for Automated Design of
Intelligent Integrated Multisensor Systems
Authors: Kuncup Iswandy and Andreas König
Abstract: The emerging of novel sensing elements, computing nodes, wireless communication and integration technology provides unprecedented possibilities for intelligent systems design and application. Each new application system must be designed from scratch employing sophisticated methods from conventional signal processing to computational intelligence. Currently, a significant part of this overall algorithmic chain still has to be assembled manually in a time and labor consuming process by experienced designers. In this research work, the challenge is picked up and a methodology and algorithms for automated design of intelligent integrated and resource-aware multisensor systems is introduced, employing multi-objective evolutionary computation. A benchmark gas sensor data of gas recognition example is investigated in our experiments. The experimental results prove the effectiveness of our proposed methodology and tool, where competitive results in terms of classification accuracy and flexibility can be achieved.

Title: Designing, Control and in-situ Visualization of Gas Nitriding Process
Authors: Jerzy Ratajski¹, Roman Olik¹, Tomasz Suszko¹, Jerzy Dobrodziej², Jerzy Michalski³
Affiliations: ¹Institute of Mechatronics, Nanotechnology and Vacuum Technique, Koszalin University of Technology
²Institute for Sustainable Technology, Radom
³Institute of Precision Mechanics, Warsaw
Abstract: The article presents a complex system of the designing, in-situ visualization and control of the commonly used process of surface treatment, the gas nitriding process. In the conception of computer designing, artificial intelligence methods were used taking into consideration difficulties in an analytical or numerical solution of complex thermodynamic problems concerning phase transformations among others. As a result, possibilities were obtained of the poly-optimization and poly-parametric simulations of the course of the process combined with a visualization of the changes of the values of parameters in the function of time, as well as possibilities of prediction of the properties of nitrided layers. For in-situ visualization of the growth of the nitrided layer, computer procedures were developed which make use of the results of the correlations of direct and differential voltage and time runs of the process result sensor (magnetic sensor), with the proper layer growth stage. Computer procedures make it possible to combine, in the duration of the process, registered voltage and time runs with the theoretical and experimental model of the process.