A Novel Quad-Polarization Agile Patch Antenna

In this communication we present a novel polarization-agile microstrip antenna design. To dynamically change the polarization state, the radiating patch is fed by a tunable quasi-lumped coupler. The whole structure can be dynamically altered to radiate electromagnetic waves with vertical linear, horizontal linear, right-handed circular or left-handed circular polarization simply by changing the operating mode of the quasi-lumped coupler. Due to its topology the coupler is simply reconfigured by switching the bias of two varactor diodes via a very simple DC bias circuitry: no additional capacitors or inductors are required. A prototype is fabricated with a 0.762-mm-thick upper layer substrate for the radiating element and a 0.130-mm-thick layer substrate for the coupler circuit, both with the same dielectric material relative permittivity of 2.22. The simulated and measured scattering parameters, the axial ratio in circular radiation-mode and the cross-polarization level in linear mode, the gain and the radiation patterns are presented. The agile polarization capabilities of this new antenna, as demonstrated in this communication, underscore its suitability for modern wireless communications in a multi-path propagation environment.

Dual-mode retrodirective/phased array

A method for producing a retrodirective (self-tracking) antenna, which can also be operated as a phased (selectively pointed) array through the addition of a simple switching circuit and DC bias offset adjustment, is presented. Phase adjustment to individual antenna elements is shown to be readily carried out by a simple frequency pushing technique, applied to a PLL circuit, thus replacing the requirement for additional phase shifters. Practical results when applied to a ten-element array operating at 2.4 GHz are shown for both modes of operation.

Plant-wide predictive control for a thermal power plant based on a physical plant model

A constrained non-linear, physical model-based, predictive control (NPMPC) strategy is developed for improved plant-wide control of a thermal power plant. The strategy makes use of successive linearisation and recursive state estimation using extended Kalman filtering to obtain a linear state-space model. The linear model and a quadratic programming routine are used to design a constrained long-range predictive controller One special feature is the careful selection of a specific set of plant model parameters for online estimation, to account for time-varying system characteristics resulting from major system disturbances and ageing. These parameters act as nonstationary stochastic states and help to provide sufficient degrees-of-freedom to obtain unbiased estimates of controlled outputs. A 14th order non-linear plant model, simulating the dominant characteristics of a 200 MW oil-fired pou er plant has been used to test the NPMPC algorithm. The control strategy gives impressive simulation results, during large system disturbances and extremely high rate of load changes, right across the operating range. These results compare favourably to those obtained with the state-space GPC method designed under similar conditions.

Disturbance attenuation in linear systems via dynamical compensators: A parametric eigenstructure assignment approach

A simple approach is proposed for disturbance attenuation in multivariable linear systems via dynamical output compensators based on complete parametric eigenstructure assignment. The basic idea is to minimise the H-2 norm of the disturbance-output transfer function using the design freedom provided by eigenstructure assignment. For robustness, the closed-loop system is restricted to be nondefective. Besides the design parameters, the closed-loop eigenvalues are also optimised within desired regions on the left-half complex plane to ensure both closed-loop stability and dynamical performance. With the proposed approach, additional closed-loop specifications can be easily achieved. As a demonstration, robust pole assignment, in the sense that the closed-loop eigenvalues are as insensitive as possible to open-loop system parameter perturbations, is treated. Application of the proposed approach to robust control of a magnetic bearing with a pair of opposing electromagnets and a rigid rotor is discussed.

Neural network based control for synchronous generators

In this paper, a Radial Basis Function neural network based AVR is proposed. A control strategy which generates local linear models from a global neural model on-line is used to derive controller feedback gains based on the Generalised Minimum Variance technique. Testing is carried out on a micromachine system which enables evaluation of practical implementation of the scheme. Constraints imposed by gathering training data, computational load, and memory requirements for the training algorithm are addressed.

SPECK-Based Lossless Multispectral Image Coding

This letter proposes an efficient extension of the set partitioning embedded block (SPECK) algorithm to lossless multispectral image coding. Such a wavelet-based coder is widely referred to in the literature, especially for lossless image coding, and is considered to be one of the most efficient techniques exhibiting very low computational complexity when compared with other state-of-the-art coders. The modification proposed in this letter is simple and provides significant improvement over the conventional SPECK. The key idea is to join each group of two consecutive wavelet-transformed spectral bands during the SPECK coding since they show high similarities with respect to insignificant sets at the same locations. Simulation results, carried out on a number of test images, demonstrate that this grouping procedure considerably saves on the bit budget for encoding the multispectral images.

Sampled-data gain scheduling of continuous LTV plants

This paper discusses the design of gain- scheduled sampled-data controllers for continuous-time polytopic linear parameter-varying systems. The scheduling variables are assumed to available only at the sampling instants, and a bound on the time-variation of the scheduling parameters is also assumed to be known. The resultant gain-scheduled controllers improve the maximum achieveable delay bound over previous constant-gain ones in the literature.

Intelligent Assembly Time Analysis Using a Digital Knowledge-Based Approach

The implementation of effective time analysis methods fast and accurately in the era of digital manufacturing has become a significant challenge for aerospace manufacturers hoping to build and maintain a competitive advantage. This paper proposes a structure oriented, knowledge-based approach for intelligent time analysis of aircraft assembly processes within a digital manufacturing framework. A knowledge system is developed so that the design knowledge can be intelligently retrieved for implementing assembly time analysis automatically. A time estimation method based on MOST, is reviewed and employed. Knowledge capture, transfer and storage within the digital manufacturing environment are extensively discussed. Configured plantypes, GUIs and functional modules are designed and developed for the automated time analysis. An exemplar study using an aircraft panel assembly from a regional jet is also presented. Although the method currently focuses on aircraft assembly, it can also be well utilized in other industry sectors, such as transportation, automobile and shipbuilding. The main contribution of the work is to present a methodology that facilitates the integration of time analysis with design and manufacturing using a digital manufacturing platform solution.

High-performance IQ modulator-based phase conjugator for modular retrodirective antenna array implementation

In this paper, we verify a new phase conjugating architecture suitable for deployment as (lie core building block in retrodirective antenna arrays, which can be scaled to any number of elements in a modular way without impacting on complexity. Our solution is based on a modified in-phase and quadrature modulator architecture, which completely resolves four major shortcomings of the conventional mixer-based approach currently used for the synthesis of phase conjugated energy derived from a sampled incoming wavefront. 1) The architecture presented removes the need for a local oscillator running at twice the RF signal frequency to be conjugated. 2) It maintains a constant transmit power even if receive power goes as low as -120 dBm. 3) All unwanted re-transmit signal products are suppressed by at least 40 dB. 4) The issue of poor RF-IF leakage prevalent in mixer-based phase-conjugation solutions is completely mitigated. The circuit has also been shown to have high conjugation accuracy (better than +/-1 degrees at -60-dBm input). Near theoretically perfect experimental monostatic and bistatic results are presented for a ten-element retrodirective array constructed using the new phase conjugation architecture.

Hybrid LMS-LMF algorithm for adaptive echo cancellation

The coefficients of an echo canceller with a near-end section and a far-end section are usually updated with the same updating scheme, such as the LMS algorithm. A novel scheme is proposed for echo cancellation that is based on the minimisation of two different cost functions, i.e. one for the near-end section and a different one for the far-end section. The approach considered leads to a substantial improvement in performance over the LMS algorithm when it is applied to both sections of the echo canceller. The convergence properties of the algorithm are derived. The proposed scheme is also shown to be robust to noise variations. Simulation results confirm the superior performance of the new algorithm.

Cost function adaptation: a stochastic gradient algorithm for data echo cancellation

A family of stochastic gradient algorithms and their behaviour in the data echo cancellation work platform are presented. The cost function adaptation algorithms use an error exponent update strategy based on an absolute error mapping, which is updated at every iteration. The quadratic and nonquadratic cost functions are special cases of the new family. Several possible realisations are introduced using these approaches. The noisy error problem is discussed and the digital recursive filter estimator is proposed. The simulation outcomes confirm the effectiveness of the proposed family of algorithms.

Equalisation of time variant multipath channels using amplitude banded LMS algorithms

For the purpose of equalisation of rapidly time variant multipath channels, we derive a novel adaptive algorithm, the amplitude banded LMS (ABLMS); which implements a nonlinear adaptation based on a coefficient matrix. Then we develop the: ABLMS algorithm as the adaptation procedure for a linear transversal equaliser (LTE) and a decision feedback equaliser (DFE) where a parallel adaptation scheme is deployed. Computer simulations demonstrate that with a small increase of computational complexity, the ABLMS based parallel equalisers provide a significant improvement related to the conventional LMS DFE and the LMS LTE in the case of a second order Markov communication channel model.