Real-time control of wave energy converters requires knowledge of future incident wave elevation in order to approach optimal efficiency of wave energy extraction. We present an approach where the wave elevation is treated as a time series and it is predicted only from its past history. A comparison of a range of forecasting methodologies on real wave observations from two different locations shows how the relatively simple linear autoregressive model, which implicitly models the cyclical behavior of waves, can offer very accurate predictions of swell waves for up to two wave periods into the future.

/pdf/short-term-wave-forecasting-for-real-time-control-of-wave-1etxagdbx4.pdf

Short-Term Wave Forecasting for Real-Time Control of Wave Energy Converters

https://www.dtc.umn.edu/s/resources/gg-se.pdf

A bibliography on nonlinear system identification

The entropy is a concept which may serve to define quantities such as the conditional entropy and the mutual information. Using a novel algorithm for the estimation of the mutual information from data, we analyse several financial time series and demonstrate the usefulness of this new approach. The issues of long-range dependence and non-stationarity are discussed.

The entropy as a tool for analysing statistical dependences in financial time series

Social media analytics is showing promise for the prediction of financial markets. However, the true value of such data for trading is unclear due to a lack of consensus on which instruments can be predicted and how. Current approaches are based on the evaluation of message volumes and are typically assessed via retrospective (ex-post facto) evaluation of trading strategy returns. In this paper, we present instead a sentiment analysis methodology to quantify and statistically validate which assets could qualify for trading from social media analytics in an ex-ante configuration. We use sentiment analysis techniques and Information Theory measures to demonstrate that social media message sentiment can contain statistically-significant ex-ante information on the future prices of the S&P500 index and a limited set of stocks, in excess of what is achievable using solely message volumes.

/pdf/when-can-social-media-lead-financial-markets-27q8xy9nsz.pdf

When can social media lead financial markets

We present an information-theoretic measure for the amount of randomness or stochasticity that exists in a signal. This measure is formulated in terms of the rate of growth of multi-information for every new signal sample of the signal that is observed over time. In case of a Gaussian statistics it is shown that this measure is equivalent to the well-known spectral flatness measure that is commonly used in audio processing. For nonGaussian linear processes a generalized spectral flatness measure is developed, which estimates the excessive structure that is present in the signal due to the nonGaussianity of the innovation process. An estimator for this measure is developed using Negentropy approximation to the non-Gaussian signal and the innovation process statistics. Applications of this new measure are demonstrated for the problem of voiced/unvoiced determination, showing improved performance.

Generalization of spectral flatness measure for non-Gaussian linear processes

The time-sensitive network paradigm envisions the integration of operation technology and information technology in the same network. One of the requirements for building time-sensitive networks is sharing a global time along the network. This requirement is especially critical in wireless systems, where there are few robust methods to perform accurate time transfer. In this article, the problem of time transfer over realistic wireless channels is studied, and a time distribution scheme is proposed. The time distribution scheme has three components: Precision time protocol, a novel timestamping method (enhanced timestamps) and an algorithm to implement the enhanced timestamps. The performance of the proposed scheme has been evaluated in MATLAB using the IEEE 802.11n Standard under several standard wireless local area network channel models. The results show that the system can reach subnanosecond time transfer accuracy under non-line-of-sight and time-variant conditions, but its performance greatly depends on the signal-to-noise ratio and on the channel variation rate.

/pdf/enhanced-timestamping-method-for-subnanosecond-time-tvxip4iur5.pdf

Enhanced Timestamping Method for Subnanosecond Time Synchronization in IEEE 802.11 Over WLAN Standard Conditions

One of the striking questions in prediction theory is this: is there a chance to predict future values of a given signal? Usually, we design a predictor for a special signal or problem and then measure the resulting prediction quality. If there is no a priori knowledge on the optimal predictor, the achieved prediction gain will depend strongly of the prediction model used. To cope with this lack of knowledge, a theorem on the maximum achievable prediction gain of stationary signals is presented. This theorem provides the foundation for estimating a quality goal for the predictor design, independent of a special predictor implementation (linear or nonlinear). As usual, the prediction gain is based on the mean square error (MSE) of the predicted signal. The achievable maximum of the prediction gain is calculated using an information theoretic quantity known as the mutual information. In order to obtain the gain, we use a nonparametric approach to estimate the maximum prediction gain based on the observation of one specific signal. We illustrate this by means of well-known example signals and show an application to load forecasting. An estimation algorithm for the prediction gain has been implemented and used in the experimental part of the paper.

A tight upper bound on the gain of linear and nonlinear predictors for stationary stochastic processes

We present the design of a suite of protocols for wireless sensor networks (WSNs) with respect to a complete life cycle of a WSN node from warehouse to the end of operation. While there are numerous publications on various, usually isolated, aspects of WSNs, the whole life cycle of a node from registration in an automation system via warehouse, calibration, mounting, performing measurements to finally unmounting, has not yet been sufficiently addressed as compound survey. Our application example is a WSN to be used in automotive test beds in which a large amount of testing with many different sensors is performed in controlled environments. While there is published work on WSNs for performing the measurements focusing on node hardware and MAC protocol, we now extend this work by accounting for the whole life cycle of operation of such a WSN and its nodes. This is mainly achieved by introducing optimized MAC protocols for wireless communication in all life cycle phases. Right from beginning of the life cycle the nodes are synchronized with a base node. Even during long offline periods nodes stay synchronized. The life cycle is modeled via a set of states, instantiated in state machines, which control operation in the base station and the nodes. Besides, considering the whole life cycle of the sensor nodes, our design minimizes energy consumption, largely avoids collisions due to suitable multiple access protocols, and allows tight synchronization even during long sleep periods. A demonstrator concludes the presentation and shows functionality and benefits of the concept.

Life cycle of wireless sensor nodes in industrial environments

This paper presents an in-depth study of nonlinear long-term prediction of speech signals. While previous studies of nonlinear prediction focused on short-term prediction (with only moderate performance advantage over adaptive linear prediction in most cases), successful long-term prediction strongly depends on the nonlinear oscillator framework for speech modeling. This hypothesis has been confirmed in a series of experiments run on a voiced speech database. We provide results for the prediction gain as a function of the prediction delay using two methods. One is based on an extended form of radial basis function networks and is intended to show what performance can be reached using a nonlinear predictor. The other relies on calculating the mutual information between multiple signal samples. We explain the role of this mutual information function as the upper bound on the achievable prediction gain. We show that with matching memory and dimension, the two methods yield nearly the same value for the achievable prediction gain. We try to make a fair comparison of these values against those obtained using optimized linear predictors of various orders. It turns out that the nonlinear predictor's gain is significantly higher than that for a linear predictor using the same parameters.

Nonlinear long-term prediction of speech signals

In this paper a novel synchronization method for wireless sensor networks with star topology is presented. We address timing synchronization using low frequency real-time clocks in all nodes. A beacon-driven TDMA-protocol for bidirectional node/base communication is used. Between the beacons, which are sent by the base station, lie the superframe time intervals to handle data transmission from node to base. We discuss the protocol and its energy saving advantages including the challenges of synchronization. We reduce the required communication for synchronization based on long term synchronicity of the node to save energy. Due to the individual node clock, the accurate superframe time interval usually will consist of a rational number of clock ticks. We propose to use a ΔΣ-converter to generate a sequence of superframes with different time durations, but each consisting of integer multiples of clock ticks, which - on average - achieve the accurate superframe duration for any rational number of clock ticks. We show by theory and measurements that our novel approach leads to a variance of the synchronization error which is constant at a value of 0.25 clock cycles. The variance is independent of the rate at which the nodes listen to the beacon of the base station.

Hans-Peter Bernhard

Papers

Enhanced Timestamping Method for Subnanosecond Time Synchronization in IEEE 802.11 Over WLAN Standard Conditions

A tight upper bound on the gain of linear and nonlinear predictors for stationary stochastic processes

Life cycle of wireless sensor nodes in industrial environments

Nonlinear long-term prediction of speech signals

Timing synchronization of low power wireless sensor nodes with largely differing clock frequencies and variable synchronization intervals