This study considers H _∞ filter design for a class of linear networked control systems with quantisation. The effects of network-induced delay, data loss and signal quantisation on the system is considered, where the quantiser is selected to be logarithmic. Based on the modified delay-central-point method and using the convexity property of the matrix inequality, new criteria are derived for the H _∞ performance analysis of the filtering-error systems, which can lead to a less conservative analysis results and thus reduce the overdesign of the filter. Two examples are provided to show the less conservatism of the derived analysis results and also to illustrate the design procedures of the filters’ parameters.

Climate change and climate variability have become issues of global concern. Conventional Fourier analysis is the most common analysis method in the frequency domain. In this study, an evaluation model using limited terms of Fourier series is introduced to describe yearly air temperature movement by month and daily air temperature by hour. Then a forecast method based on the Fourier analysis in the least-square sense is proposed that incorporating with the least-square method, the Fourier-series model is extended to forecast that predict future temperature movements based on limited previous observation values. The forecast model is built by finding its optimum Fourier coefficients in the least-square sense based on the previous observed temperature movements. Experiments including yearly and daily air temperature evaluation and forecast at several observation stations in China, yield satisfied results agreeing well with actual observation values. Experimental results demonstrate that the Fourier evaluation model evaluates the annual and the daily air temperature movements most closely by about 5-term and 11-term Fourier series, respectively. The forecast model predicts both the annual and daily air temperature movements most fit with about 4-term Fourier series. Result analysis indicates workability and effectiveness of the proposed.

Over the past decades, extensive research has been carried out on various possible implementations of automatic speech recognition (ASR) systems. The most renowned algorithms in the field of ASR are the mel-frequency cepstral coefficients and the hidden Markov models. However, there are also other methods, such as wavelet-based transforms, artificial neural networks and support vector machines, which are becoming more popular. This review article presents a comparative study on different approaches that were proposed for the task of ASR, and which are widely used nowadays.

The authors propose two algorithms to solve sum mean-square error (MSE) minimisation and mixed quality of service (QoS) requirement problems for a multiuser multiple-input-multiple-output system with multiple linear transmit covariance constraints. These original problems in the downlink are complicated because of the coupled structure of the transmitter filters. To overcome this issue, MSE duality proposed in the literature is extended at different levels for a general linear transmit covariance constraint. Exploiting the general sum-MSE duality and subgradient method, the sum-MSE minimisation algorithm is proposed first for multiple linear transmit covariance constraints. Secondly, a novel algorithm is proposed to solve mixed QoS requirement problem, where multiple linear transmit covariance constraints are incorporated in the design of the receiver filters in the equivalent multiple access channel. This algorithm is developed based on stream-wise MSE duality and alternating optimisation framework . Simulation results have been provided to validate the convergence of the proposed algorithms. In addition, the proposed sum-MSE minimisation algorithm with per-antenna power constraints outperforms the existing algorithm in terms of achieved sum-MSE and power consumption at each transmit antenna.

In this study, the authors consider the problem of multiple antenna spectrum sensing by exploiting the prior information about unknown parameters. Under assumption that additional statistical side information is available about unknown parameters, a novel framework and some detectors are proposed, which are optimal for finite number of received samples. locally most powerful, average likelihood ratio and the novel generalised likelihood ratio detectors are calculated for the different practical conditions in wireless channels. In addition, the analytical performance evaluation for proposed detectors is provided whenever possible. The simulation and analytical results show that the a-priori information can be used for better spectrum sensing using finite number of samples.

In practice, the configuration of spaceborne multistatic synthetic aperture radar (MSAR) is generally three-dimensional (3D) stereo geometry, under which condition most current algorithms of motion parameter estimation are invalid. In this study, a joint space-time–frequency method is presented to estimate motion parameters of ground moving target for MSAR of 3D geometry. First, the array expression for azimuth echo named extended space-time model (ESTM) is derived; then on the basis of ESTM, a spatial time–frequency distribution method based on fractional Fourier transform algorithm is derived; finally the performance of the presented method is analysed, and the advantages comparing with velocity synthetic aperture radar are discussed via some computer simulations. The work provides a feasible method of multi-channel processing for the 3D MSAR.

The modulation system is an important part in all communication networks including wireline and wireless networks. Orthogonal frequency-division multiplexing/orthogonal frequency-division multiple access (OFDM/OFDMA) and single-carrier frequency-division multiple access (SCFDMA) are representatives of multicarrier and single-carrier modulations, which are widely used in different communication standards, like WiMAX and Long Term Evolution (LTE). However, all existed modulation systems use the fixed symbol block length or fixed subcarrier number. In the adaptive OFDM system the subcarrier number is still fixed, and just the symbol constellation mapping schemes are adaptively selected based on the subchannel quality. In this study, the author introduce a new concept, namely time-varying symbol block or time-varying subcarrier number into the modulation systems to improve the system performance. The author call the new modulation system time-varying OFDM (TV-OFDM), time-varying OFDMA (TV-OFDMA) for multicarrier modulation, and time-varying SCFDMA(TV-SCFDMA) for single-carrier modulation. Here, peak-to-average power ratio (PAPR) is used as criteria to change the symbol block length timely. The algorithm for determination of the symbol block length is presented. The simulation results show that the time-varying modulation schemes can clearly reduce the PAPR values for both multicarrier and single-carrier modulation systems.