A genetic-algorithms based evolutionary computational neural network for modelling spatial interaction data

Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this contribution. Up to now, this issue has been either completely neglected in spatial application domains, or tackled by search heuristics (see Fischer and Gopal 1994). With the view of modelling interactions over geographic space, this paper considers this problem as a global optimization problem and proposes a novel approach that embeds backpropagation learning into the evolutionary paradigm of genetic algorithms. This is accomplished by interweaving a genetic search for finding an optimal CNN topology with gradient-based backpropagation learning for determining the network parameters. Thus, the model builder will be relieved of the burden of identifying appropriate CNN-topologies that will allow a problem to be solved with simple, but powerful learning mechanisms, such as backpropagation of gradient descent errors. The approach has been applied to the family of three inputs, single hidden layer, single output feedforward CNN models using interregional telecommunication traffic data for Austria, to illustrate its performance and to evaluate its robustness.

Measurement Errors and their Propagation in the Registration of Remote Sensing Images (?)

Reference control points (RCPs) used in establishing the regression model in the registration or geometric correction of remote sensing images are generally assumed to be ?perfect?. That is, the RCPs, as explanatory variables in the regression equation, are accurate and the coordinates of their locations have no errors. Thus ordinary least squares (OLS) estimator has been applied extensively to the registration or geometric correction of remotely sensed data. However, this assumption is often invalid in practice because RCPs always contain errors. Moreover, the errors are actually one of the main sources which lower the accuracy of geometric correction of an uncorrected image. Under this situation, the OLS estimator is biased. It cannot handle explanatory variables with errors and cannot propagate appropriately errors from the RCPs to the corrected image. Therefore, it is essential to develop new feasible methods to overcome such a problem. In this paper, we introduce the consistent adjusted least squares (CALS) estimator and propose a relaxed consistent adjusted least squares (RCALS) method, with the latter being more general and flexible, for geometric correction or registration. These estimators have good capability in correcting errors contained in the RCPs, and in propagating appropriately errors of the RCPs to the corrected image with and without prior information. The objective of the CALS and our proposed RCALS estimators is to improve the accuracy of measurement value by weakening the measurement errors. The validity of the CALS and RCALS estimators are first demonstrated by applying them to perform geometric corrections of controlled simulated images. The conceptual arguments are further substantiated by a real-life example. Compared to the OLS estimator, the CALS and RCALS estimators give a superior overall performances in estimating the regression coefficients and variance of measurement errors. Keywords: error propagation, geometric correction, ordinary least squares, registration, relaxed consistent adjusted least squares, remote sensing images.

Development of Realistic Stimuli for the Evaluation of Listening Effort using Auditory Evoked Potentials

Purpose – Listeners often report difficulty perceiving speech in background noise, such as when listening in a restaurant. A common complaint of difficulty perceiving speech in noisy restaurants leads to the development of the present study, where audio recordings of connected discourse mixed with restaurant noise at different signal-to-noise ratios were made to determine the effect of restaurant noise on listening effort. Listening effort has previously been examined with psychophysiological measures, a dual-task paradigm, and qualitative measures using a variety of auditory stimuli ranging from simple tonal stimuli to complex speech stimuli, such as consonant-vowel syllables, words, and full sentences, but never in the context of a conversation. Real-life restaurant noise has also never been used in research study. The central goal is to develop realistic stimuli using real-life conversations that can potentially be used for an electrophysiologic study to determine the effect of background noise on listening effort. Three different conversations with each focusing on a particular topic (food, animals, and locations) were developed. Each conversation contains 25 high- and 25 low-probability target words. The incorporation of high- and low-probability target words in the connected discourse allows the exploration of the effect of predictability in conversations on psychophysiological recordings (P3 and N4). A framework of a potential study utilizing the realistic stimuli with a dual-task paradigm and measurement of auditory evoked potentials (P3 and N4) for the evaluation of the effect of background noise on listening effort is also proposed and pilot data applying this framework to one research subject is presented. The use of real-life conversations in varying restaurant noise for the evaluation of listening effort is a novel approach and has potential to inform clinical practice by providing an ecologically-valid means to assess the difficulties experienced in difficult, but realistic listening situations

Another integrable case in the Lorenz model

A scaling invariance in the Lorenz model allows one to consider the usually discarded case sigma=0. We integrate it with the third Painlev\'e function.Comment: 3 pages, no figure, to appear in J. Phys.

Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition

Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, and many others. Such complex noise could degrade the quality of the acquired HSIs, limiting the precision of the subsequent processing. In this paper, we present a novel tensor-based HSI restoration approach by fully identifying the intrinsic structures of the clean HSI part and the mixed noise part respectively. Specifically, for the clean HSI part, we use tensor Tucker decomposition to describe the global correlation among all bands, and an anisotropic spatial-spectral total variation (SSTV) regularization to characterize the piecewise smooth structure in both spatial and spectral domains. For the mixed noise part, we adopt the $\ell_1$ norm regularization to detect the sparse noise, including stripes, impulse noise, and dead pixels. Despite that TV regulariztion has the ability of removing Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian noise for some real-world scenarios. Then, we develop an efficient algorithm for solving the resulting optimization problem by using the augmented Lagrange multiplier (ALM) method. Finally, extensive experiments on simulated and real-world noise HSIs are carried out to demonstrate the superiority of the proposed method over the existing state-of-the-art ones.Comment: 15 pages, 20 figure