: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

/pdf/nonlinear-time-series-analysis-1j3a35n4y3.pdf

Nonlinear Time Series Analysis.

This article describes research and development on car noise and vibration by the German car manufacturer Opel. Opel now has one of the world's largest anechoic chambers, 22m long by 16m wide. It can measure 'pass-by' noise in any weather, and has a powertrain test rig, which allows the whole powertrain and exhaust system to be examined away from the bodywork. By making vibrations visible, holography is helping to reduce the development time of a new car model by up to two months; it uses the interference principle to analyse structures without actual contact. Using holographic patterns, acoustic experts can work with design engineers on modifications to eliminate vibration. Psychoacoustics is perhaps the most exciting area of research. It applies the theory that sound can counter behaviour as well as influence it. If a driver is being aggressive or driving too fast, sound inside the car can be used to alter his driving style. By 'learning' a driver's personality, a car could take appropriate action to ensure safety and comfort by producing pleasant sounds in the car. However, these methods are still in the experimental stage. Opel is also conducting research on the stressful effects of noise and vibrations in cars.

The science of sound

Next Generation Wireless Lans 802 11n And 802 11ac

This paper presents computationally efficient implementations for several recent algorithms based on the iterative adaptive approach (IAA) for uniformly sampled one- and two-dimensional data sets, considering both the complete data case, and the cases when the data sets are missing samples, either lacking arbitrary locations, or having gaps or periodically reoccurring gaps. By exploiting the method's inherent low displacement rank, together with the development of suitable Gohberg-Semencul representations, and the use of data dependent trigonometric polynomials, the proposed implementations are shown to offer a reduction of the necessary computational complexity by at least one order of magnitude. Numerical simulations together with theoretical complexity measures illustrate the achieved performance gain.

Efficient Implementation of Iterative Adaptive Approach Spectral Estimation Techniques

This paper is a summary of the research development in the rational total nonlinear dynamic modelling over the last two decades. Total nonlinear dynamic systems are defined as those where the model parameters and input controller outputs are subject to nonlinear to the output. Previously, this class of models has been known as rational models, which is a model that can be considered to belong to the nonlinear autoregressive moving average with exogenous input NARMAX model subset and is an extension of the well-known polynomial NARMAX model. The justification for using the rational model is that it provides a very concise and parsimonious representation for highly complex nonlinear dynamic systems and has excellent interpolatory and extrapolatory properties. However, model identification and controller design are much more challenging compared to the polynomial models. This has been a new and fascinating research trend in the area of mathematical modelling, control, and applications, but still within a limited research community. This paper brings several representative algorithms together, developed by the authors and their colleagues, to form an easily referenced archive for promotion of the awareness, tutorial, applications, and even further research expansion.

/pdf/review-of-rational-total-nonlinear-dynamic-system-modelling-1h8lyb5m93.pdf

Review of rational total nonlinear dynamic system modelling, identification, and control

Nuclear quadrupole resonance (NQR) is a solid-state radio frequency (RF) spectroscopic technique, allowing the detection of compounds containing quadrupolar nuclei, a requirement fulfilled by many high explosives and narcotics. The practical use of NQR is restricted by the inherently low signal-to-noise ratio (SNR) of the observed signals, a problem that is further exacerbated by the presence of strong RF interference (RFI). The current literature focuses on the use of conventional, multiple-pulsed NQR (cNQR) to obtain signals. Here, we investigate an alternative method called stochastic NQR (sNQR), having many advantages over cNQR, one of which is the availability of signal-of-interest free samples. In this paper, we exploit these samples forming a matched subspace-type detector and a detector employing a prewhitening approach, both of which are able to efficiently reduce the influence of RFI. Further, many of the ideas already developed for cNQR, including providing robustness to uncertainties in the assumed complex amplitudes and exploiting the temperature dependencies of the NQR spectral components, are recast for sNQR. The presented detectors are evaluated on both simulated and measured trinitro-toluene (TNT) data.

Robust Detection of Stochastic Nuclear Quadrupole Resonance Signals

Nuclear quadrupole resonance (NQR) is a solid-state radio frequency (RF) spectroscopic technique that allows for the detection of many narcotics and highly explosive substances. Unfortunately, the practical use of NQR is often restricted by the presence of strong RF interference (RFI). In this letter, extending our recent work on stochastic NQR (sNQR), we propose acquiring signal-of-interest free samples, containing only corrupting signals, and exploiting them to reduce the effects of RFI on conventional NQR (cNQR) measurements. Similar to the sNQR case, the presented detectors are able to substantially outperform previous cNQR detectors when RFI is present.

Countering Radio Frequency Interference in Single-Sensor Quadrupole Resonance

Magnitude squared coherence (MSC) is a useful bivariate spectral measure that finds application in a wide variety of fields. In this paper, we develop a nonparametric Capon-based MSC estimator that utilizes a segmented reformulation of the recently introduced iterative adaptive approach (IAA) to provide high resolution MSC spectrum estimates. The proposed estimator, termed segmented-IAA-MSC (or SIAA-MSC, for short), allows for unevenly sampled data as well as for sequences with arbitrarily missing samples. The estimator first uses segmented-IAA to find accurate estimates of the auto- and cross-covariance matrices of the given sequences. These estimates are then used in a Capon-based MSC estimator reformulated to allow for nonuniformly sampled sequences. To achieve higher statistical accuracy, the estimation problem is formulated so as to allow for overlapped segmentation of the available data. The proposed SIAA-MSC estimator is found to yield improved estimates as compared to the more commonly used least squares Fourier transform (LSFT) based MSC estimator.

Coherence Spectrum Estimation From Nonuniformly Sampled Sequences

The use of intelligent control schemes in nonlinear model based control (NMBC) has gained widespread popularity. Neural networks, in particular, have been used extensively to model the dynamics of nonlinear plants. However, in most cases, these models do not lend themselves to easy maneuvering for controller design. Therefore, a common need is being felt to develop intelligent control strategies that lead to computationally simple control laws. To address this issue, we recently proposed a U-model based controller utilizing nonlinear adaptive filters. The present work extends that concept further to include higher-order neural networks (HONN) for better approximation. The main feature of the proposed structure is its ability to capture higher-order nonlinear properties of the input pattern space while allowing the synthesis of a simple control law. The effectiveness of the proposed scheme is demonstrated through application to various nonlinear models and a comparison with the Backstepping controller is presented.

Utilizing higher-order neural networks in U-model based controllers for stable nonlinear plants

Nuclear quadrupole resonance (NQR) is a solid-state radio frequency spectroscopic technique that can be used to detect the presence of quadrupolar nuclei, that are prevalent in many narcotics, drugs, and explosive materials. Similar to other modern spectroscopic techniques, such as nuclear magnetic resonance, and Raman spectroscopy, NQR also relies heavily on statistical signal processing systems for decision making and information extraction. This chapter provides an overview of the current state-of-the-art algorithms for detection, estimation, and classification of NQR signals. More specifically, the problem of NQR-based detection of illicit materials is considered in detail. Several single- and multi-sensor algorithms are reviewed that possess many features of practical importance, including (a) robustness to uncertainties in the assumed spectral amplitudes, (b) exploitation of the polymorphous nature of relevant compounds to improve detection, (c) ability to quantify mixtures, and (d) efficient estimation and cancellation of background noise and radio frequency interference.

/pdf/an-overview-of-nqr-signal-detection-algorithms-2ek575nas7.pdf

Naveed Butt

Papers

Robust Detection of Stochastic Nuclear Quadrupole Resonance Signals

Countering Radio Frequency Interference in Single-Sensor Quadrupole Resonance

Coherence Spectrum Estimation From Nonuniformly Sampled Sequences

Utilizing higher-order neural networks in U-model based controllers for stable nonlinear plants

An Overview of NQR Signal Detection Algorithms