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Hong Kong, China

The Hong Kong Polytechnic University is a public university located in Hung Hom, Hong Kong. The history of PolyU can be traced back to 1937, and it assumed full university status in 1994. It is one of the funded institutions of the territory's University Grants Committee .PolyU has an international faculty and student community and has developed a global network with more than 440 institutions in 47 countries and regions. PolyU offers 220 postgraduate, undergraduate and sub-degree programmes for more than 32,000 students every year. It is the largest UGC-funded tertiary institution in terms of number of students. Wikipedia.

Yuan X.,Hong Kong Polytechnic University
IEEE Transactions on Signal Processing | Year: 2012

This paper introduces a novel algorithm to estimate the direction-of-arrival (DOA) and the polarization of a completely-polarized polynomial-phase signal of an arbitrary degree. The algorithm utilizes a polarized vector-sensor, comprising a spatially collocated six-component electromagnetic vector-sensor, a dipole triad, or a loop triad. This ESPRIT-based algorithm is based on a time-invariant matrix-pencil pair, derived from the time-delayed data-sets collected by a single polarized vector-sensor. The high-order difference-function of the signal's phase constructs the invariant-factor used in the ESPRIT algorithm. The steering vector is estimated from the signal-subspace eigenvector of the data-correlation matrix, following which the closed-form DOA and polarization can be obtained. Given the degree of the polynomial-phase signal, this approach resolves the two-dimensional azimuth-elevation angle and the polarization of the source, and requires neither a priori knowledge of the polynomial-phase signal's coefficients nor a priori knowledge of the polynomial-phase signal's frequency-spectrum. The efficacy of the proposed algorithm is verified by Monte Carlo simulations. Estimation accuracies of the DOA and the polarization parameters are evaluated by the closed-form Cramér-Rao bounds, which are independent of the polynomial coefficients, the degree of the polynomial-phase signal, and the azimuth-angle of the source. © 2011 IEEE. Source

Fu T.-C.,Hong Kong Polytechnic University
Engineering Applications of Artificial Intelligence | Year: 2011

Time series is an important class of temporal data objects and it can be easily obtained from scientific and financial applications. A time series is a collection of observations made chronologically. The nature of time series data includes: large in data size, high dimensionality and necessary to update continuously. Moreover time series data, which is characterized by its numerical and continuous nature, is always considered as a whole instead of individual numerical field. The increasing use of time series data has initiated a great deal of research and development attempts in the field of data mining. The abundant research on time series data mining in the last decade could hamper the entry of interested researchers, due to its complexity. In this paper, a comprehensive revision on the existing time series data mining research is given. They are generally categorized into representation and indexing, similarity measure, segmentation, visualization and mining. Moreover state-of-the-art research issues are also highlighted. The primary objective of this paper is to serve as a glossary for interested researchers to have an overall picture on the current time series data mining development and identify their potential research direction to further investigation. © 2010 Elsevier Ltd. All rights reserved. Source

Ni M.,Hong Kong Polytechnic University
Journal of Power Sources | Year: 2012

Co-electrolysis of CO 2 and H 2O in a solid oxide electrolyzer cell (SOEC) offers a promising way for syngas production. In this study, an electrochemical model is developed to simulate the performance of an SOEC used for CO 2/H 2O co-electrolysis, considering the reversible water gas shift reaction (WGSR) in the cathode. The dusty gas model (DGM) is used to characterize the multi-component mass transport in the electrodes. The modeling results are compared with experimental data from the literature and good agreement is observed. Parametric simulations are performed to analyze the distributions of WGSR and gas composition in the electrode. A new method is proposed to quantify the contribution of WGSR to CO production by comparing the CO fluxes at the cathode-electrolyte interface and at the cathode surface. It is found that the reversible WGSR could contribute to CO production at a low operating potential but consume CO at a high operating potential at an operating temperature of 1073 K and inlet gas composition (molar fraction) of H 2O: 49.7%, CO 2: 25%, H 2: 25%, CO: 0.3%. In addition, the contribution of WGSR to CO production also depends on the operating temperature and inlet gas composition. © 2011 Elsevier B.V. All rights reserved. Source

Ni M.,Hong Kong Polytechnic University
Energy Conversion and Management | Year: 2013

A two-dimensional model is developed to simulate the performance of solid oxide fuel cells (SOFCs) fed with CO2 and CH4 mixture. The electrochemical oxidations of both CO and H2 are included. Important chemical reactions are considered in the model, including methane carbon dioxide reforming (MCDR), reversible water gas shift reaction (WGSR), and methane steam reforming (MSR). It's found that at a CH4/CO 2 molar ratio of 50/50, MCDR and reversible WGSR significantly influence the cell performance while MSR is negligibly small. The performance of SOFC fed with CO2/CH4 mixture is comparable to SOFC running on CH4/H2O mixtures. The electric output of SOFC can be enhanced by operating the cell at a low operating potential or at a high temperature. In addition, the development of anode catalyst with high activity towards CO electrochemical oxidation is important for SOFC performance enhancement. The model can serve as a useful tool for optimization of the SOFC system running on CH4/CO2 mixtures. © 2013 Elsevier Ltd. All rights reserved. Source

Huang K.,Hong Kong Polytechnic University
IEEE Transactions on Information Theory | Year: 2013

Designing mobiles to harvest ambient energy such as kinetic activities or electromagnetic radiation will enable wireless networks to be self-sustaining. In this paper, the spatial throughput of a mobile ad hoc network powered by energy harvesting is analyzed using a stochastic-geometry model. In this model, transmitters are distributed as a Poisson point process and energy arrives at each transmitter randomly with a uniform average rate called the energy arrival rate. Upon harvesting sufficient energy, each transmitter transmits with fixed power to an intended receiver under an outage-probability constraint for a target signal-to-interference-and-noise ratio. It is assumed that transmitters store energy in batteries with infinite capacity. By applying the random-walk theory, the probability that a transmitter transmits, called the transmission probability, is proved to be equal to the smaller of one and the ratio between the energy-arrival rate and transmission power. This result and tools from stochastic geometry are applied to maximize the network throughput for a given energy-arrival rate by optimizing transmission power. The maximum network throughput is shown to be proportional to the optimal transmission probability, which is equal to one if the transmitter density is below a derived function of the energy-arrival rate or otherwise is smaller than one and solves a given polynomial equation. Last, the limits of the maximum network throughput are obtained for the extreme cases of high energy-arrival rates and sparse/dense networks. © 1963-2012 IEEE. Source

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