Analysis of the cyclic short-term variation of indoor power line channels

The purpose of this paper is to analyze the properties of indoor power line communications channels when they are used for broadband transmission. It will be shown that these channels exhibit a short-term variation, due to the fact that high-frequency parameters of electrical devices depend on the instantaneous amplitude of mains voltage. This phenomenon leads to a channel model proposal based on a linear periodically time-varying system and cyclostationary random noise. A measurement system, designed to capture these time variations in real power networks, and some of the most significant results obtained with it, are presented. In addition, a statistical analysis of the measurements performed in several indoor power line scenarios is included, so that the relevance of time variations in actual channels can be evaluated.     

Snow surface albedo estimation using terrestrial photography

A flexible and inexpensive remote sensing tool for albedo estimation using conventional terrestrial photography and its validation on an Alpine glacier are described. The proposed technique involves georeferencing oblique photographs to a digital elevation model (DEM), defining a mapping function between the information contained on a given pixel of the image and the corresponding cell of the DEM. This is attained by performing a perspective projection of the DEM after a viewing transformation into the camera coordinate system. Once the image is georeferenced, the reflectance values recorded by the film or digital camera are corrected for topographic and atmospheric influences and for the effect of the photographic process (lens-film-developing-scanning). Atmospheric transmittance is evaluated using the MODTRAN radiative transfer model. Diffuse and direct irradiation are estimated using a parametric solar irradiation model. The solar-ground geometry, anisotropy of reflected radiation, the effect of surrounding topography and the portion of visible sky are evaluated using terrain algorithms applied to the DEM. The response of the camera-film-scanner system is evaluated using an empirical approach. The result is a geographically correct map of normalized reflectance values. By comparing these to a surface of known albedo, the spatial distribution of albedos is calculated. Comparisons to in situ measurements on the Mer de Glace glacier, French Alps, show good agreement. Sources of error are identified and ways of improvement addressed. The georeferencing algorithm, implemented into the Interactive Data Language (IDL) is available from the author and at the user contributed IDL library at www.rsinc.com.     

OPERATING REGIME DECOMPOSITION USING NEURAL NETWORKS

This article addresses the problem of identification of a nonlinear process operating over a wide range of conditions. The global space is divided into multiple local regimes, a nonlinear model is developed for each regime, and a quadratic programming-based algorithm is used to ensure smooth transition between the regimes on-line. The use of nonlinear models as opposed to linear models reduces the number of local regimes needed. Neural networks are used to model these regimes because of their strong ability to capture nonlinearity, and their combination with the switching algorithm improves transient performance. The performance of the method is demonstrated on an exothermic CSTR and a pH neutralization process.     

Operating regime decomposition using neural networks

This article addresses the problem of identification of a nonlinear process operating over a wide range of conditions. The global space is divided into multiple local regimes, a nonlinear model is developed for each regime, and a quadratic programming-based algorithm is used to ensure smooth transition between the regimes on-line. The use of nonlinear models as opposed to linear models reduces the number of local regimes needed. Neural networks are used to model these regimes because of their strong ability to capture nonlinearity, and their combination with the switching algorithm improves transient performance. The performance of the method is demonstrated on an exothermic CSTR and a pH neutralization process.     

AN ALGORITHM FOR SUPERVISORY MULTIVARIABLE CONSTRAINED OPTIMIZATION OF INDUSTRIAL PROCESSES

An algorithm is presented for supervisory optimization of industrial processes that integrates the minimization of operating costs with process operating constraints. It is assumed that the supervisory algorithm manipulates the set points of a lower-level control system and that the set points are updated at long enough intervals of time so that the process reaches steady state between set point updates. This steady state assumption greatly simplifies the algorithm computations and, more importantly, significantly reduces the effort required for process identification. This article develops the algorithm and then presents results from its application to a simulated distillation train. The simulation parallels an application of the algorithm to an actual industrial train on a commercial distributed control system.     

Image source acquisition identification of mobile devices based on the use of features

Nowadays, forensic analysis of digital images is especially important, given the frequent use of digital cameras in mobile devices. The identification of the device type or the make and model of image source are two important branches of forensic analysis of digital images. In this paper we have addressed both of these, with an approach based on different types of image features and the classification using support vector machines. The study has mainly focused on images created with mobile devices and as a result, the techniques and features have been adapted or created for this purpose. There have been a total of 36 experiments classified into 5 sets, in order to test different configurations of the techniques. In the configuration of the experiments, the future use of the technique by the forensic analyst in real situations to create experiments with high technical requirements was taken into account, amongst other things.     

ON-LINE OPTIMIZATION OF DISTILLATION COLUMNS IN SERIES

This paper presents an on-line optimization algorithm for distillation columns which consists of a steepest descent technique based on a simple model of product recovery with on-line estimation of the critical model parameter. The algorithm is developed for a single column and then for a two-column train. A dynamic programming approach is used to reduce the optimization problem to a single parameter search for each column in the train. The resulting algorithm is simple and computer resource requirements are small. The algorithm has been successfully used in two industrial applications, one consisting of two columns in series and the other of three columns in series.     

Source identification for mobile devices,based on wavelet transforms combined with sensor imperfections

One of the most relevant applications of digital image forensics is to accurately identify the device used for taking a given set of images, a problem called source identification. This paper studies recent developments in the field and proposes the mixture of two techniques (Sensor Imperfections and Wavelet Transforms) to get better source identification of images generated with mobile devices. Our results show that Sensor Imperfections and Wavelet Transforms can jointly serve as good forensic features to help trace the source camera of images produced by mobile phones. Furthermore, the model proposed here can also determine with high precision both the brand and model of the device.     

On-line state and parameter estimation of EPDM polymerization reactors using a hierarchical extended Kalman filter

A hierarchical extended Kalman filter (EKF) design is proposed to estimate unmeasured state variables and key kinetic parameters in a first principles model of a continuous ethylene–propylene–diene polymer (EPDM) reactor. The estimator design is based on decomposing the dynamic model into two subsystems by exploiting the triangular model structure and the different sampling frequencies of on-line and laboratory measurements directly related to the state variables of each subsystem. The state variables of the first subsystem are reactant concentrations and zeroth-order moments of the molecular weight distribution (MWD). Unmeasured state variables and four kinetic parameters systematically chosen to reduce bias are estimated from frequent and undelayed on-line measurements of the ethylene, propylene, diene and total polymer concentrations. The state variables of the second subsystem are first-order moments of the MWD. Given state and parameters estimates from the first subsystem EKF, the first-order moments and three non-stationary parameters added to the model for bias reduction are estimated from infrequent and delayed laboratory measurements of the ethylene and diene contents and number average molecular weight of the polymer. Simulation tests show that the hierarchical EKF generates satisfactory estimates even in the presence of measurement noise and plant/model mismatch.