Capacity of the Class of MIMO Channels With Incomplete CDI—Properties of Mutual Information for a Class of Channels

This paper is concerned with multiple-input multiple-output (MIMO) wireless channel capacity, when the probability distribution of the channel matrix p(H) is not completely known to the transmitter and the receiver. The partial knowledge of a true probability distribution of the channel matrix p(H) is modelled by a relative entropy D(middot||middot) such that D(p||p_nom) les d, d ges 0, where d is the distance from the so-called nominal channel matrix distribution p_nom(H). The capacity of this compound channel is equal to the maximin of the mutual information, where the minimum is with respect to the channel matrix distribution, and the maximum is with respect to the covariance matrix of a transmitted signal. The existence of a minimizing probability distribution is proved, and the explicit formula for the minimizing distribution is derived in terms of the nominal distribution p_nom(H) and parameter d. A number of properties of the mutual information, minimized over the set of channel distributions, are derived. Specifically, upper and lower bounds are derived for the minimized mutual information, while its convexity with respect to d is shown. In the case of the Rayleigh fading, an explicit formula for the capacity and the optimal transmit covariance matrix are derived.     

Finite-dimensional nonlinear output feedback dynamic games andbounds for sector nonlinearities

In general, nonlinear output feedback dynamic games are infinite dimensional. The paper treats a class of minimax games when the nonlinearities enter the dynamics of the unobservable states. An information state approach is introduced to recast these games as one of full information in infinite dimensions. Explicit solutions of the first-order partial differential information state equation are derived in terms of a finite-number of sufficient statistics. When the nonlinearities are sector bounded, suboptimal finite-dimensional strategies are derived     

Assessing the urban water balance: the Urban Water Flow Model and its application in Cyprus

Modelling the urban water balance enables the understanding of the interactions of water within an urban area and allows for better management of water resources. However, few models today provide a comprehensive overview of all water sources and uses. The objective of the current paper was to develop a user-friendly tool that quantifies and visualizes all water flows, losses and inefficiencies in urban environments. The Urban Water Flow Model was implemented in a spreadsheet and includes a water-savings application that computes the contributions of user-selected saving options to the overall water balance. The model was applied to the coastal town of Limassol, Cyprus, for the hydrologic years 2003/04-2008/09. Data were collected from the different authorities and hydrologic equations and estimations were added to complete the balance. Average precipitation was 363 mm/yr, amounting to 25.4 × 10(6)m(3)/yr, more than double the annual potable water supply to the town. Surface runoff constituted 29.6% of all outflows, while evapotranspiration from impervious areas was 21.6%. Possible potable water savings for 2008/09 were estimated at 5.3 × 10(3) m(3), which is 50% of the total potable water provided to the area. This saving would also result in a 6% reduction of surface runoff.     

Minimax design of recursive digital filters

The application of two new algorithms for minimax optimization due to Charalambous and Bandler is investigated. The application is to the problem of finding the coefficients of a recursive digital filter to meet arbitrary specifications of the magnitude or the group delay characteristics. Unlike the original minimax algorithm due to Bandler and Charalambous in which a sequence of least $\text{p}\text{th}$ optimizations as $\text{p}$ tends to infinity is taken, the two new algorithms do not require the value of $\text{p}$ to do this. Instead, a sequence of least $\text{p}\text{th}$ optimization problems is constructed with finite values of $\text{p}$ in the range $\text{1 < p < ∞}$. A criterion is given under which the order of the filter can be increased by growing filter sections. A general computer program has been developed, based on the ideas presented.     

Performance evaluation of TCP extensions on ATM over high bandwidthdelay product networks

Practical experiments in a satellite network environment assist in the design and understanding of future global networks. This article describes the practical experiences gained from TCP/IP on ATM networks over a high-speed satellite link and presents performance comparison studies of such networks with the same host/traffic configurations over local area and wide area networks. These comparison studies on the LAN, WAN, and satellite environments increase our understanding of the behavior of high-bandwidth networks. NASA's Advanced Communications Technology Satellite (ACTS), with its special characteristics and high data rate satellite channels, and the ACTS ATM Internetwork (AAI) were used in these experiments to deliver broadband traffic. Network performance tests were carried out using application-level software (Netspec) on SONET OC-3 (155.52 Mb/s) satellite links. Finally, we experimentally study the performance, efficiency, fairness, and aggressiveness of TCP Reno, TCP New Reno, and TCP SACK end hosts on ATM networks over high BDP networks     

The application of a glow discharge mass spectrometer in the steel industry

With the VG 9000 glow discharge mass spectrometer (GDMS), major, minor, and trace elemental constituents in conducting and semiconducting materials can be measured directly. The instrument has been developed by a combination of two well founded and proven techniques; namely, a glow discharge cell for the production of ions and a double focussing mass spectrometer designed for high resolution and high sensitivity. The glow discharge cell offers the advantage that the sputtering action and the subsequent ionisation which occur, lead to minimal matrix dependency and a narrow range of sensitivity factors across the entire periodic table. The VG 9000 GDMS has been sucessfully employed to analyse a wide range of materials including steel. For most elements the detection limits are in the ppb range. Carbon, nitrogen and oxygen have been analysed successfully to ppm concentration levels. The glow discharge mass spectrometry has considerable potential for the steel industry, offering a high level of precision together with high speed of operation.     

Wind turbine gearbox failure and remaining useful life prediction using machine learning techniques

This research investigates the prediction of failure and remaining useful life (RUL) of gearboxes for modern multi‐megawatt wind turbines. Failure and RUL are predicted through the use of machine learning techniques and large amounts of labelled wind turbine supervisory control and data acquisition (SCADA) and vibration data. The novelty of this work stems from unprecedented access to one of the world's largest wind turbine operational and reliability databases, containing thousands of turbine gearbox failure examples and complete SCADA and vibration data in the build up to those failures. Through access to that data, this paper is unique in having enough failure examples and data to draw the conclusions detailed in the remainder of this abstract. This paper shows that artificial neural networks provide the most accurate failure and RUL prediction out of three machine learning techniques trialled. This work also demonstrates that SCADA data can be used to predict failure up to a month before it occurs, and high frequency vibration data can be used to extend that accurate prediction capability to 5 to 6 months before failure. This paper demonstrates that two class neural networks can correctly predict gearbox failures between 72.5% and 75% of the time depending on the failure mode when trained with SCADA data and 100% of the time when trained with vibration data. Data trends in the build up to failure and weighting of the SCADA data inputs are also provided. Lastly, this work shows how multi‐class neural networks demonstrate more potential in predicting gearbox failure when trained with vibration data as opposed to training with SCADA data.