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The International Electrotechnical Commission (IEC) flicker standard 61000-4-15, which was originally proposed for 230 V/50 Hz systems in Europe and further revised with an amendment to include the necessary modifications for 120 V/60 Hz systems in North America, is becoming more internationally adopted for evaluating the flicker severity. Since the IEC flicker standard is restricted to work on systems with 230 V/50 Hz or 120 V/60 Hz, however, the IEC standard cannot be directly applied to the other power systems beyond its coverage (e.g. 220 V/60 Hz systems in South Korea). A new weighting filter derived from lamp tests, new reference points and a new performance test specification to accommodate 220 V/60 Hz systems in South Korea, complying with the IEC standard, are proposed. The proposed weighting filter, reference points and performance test specification provide a practical guide to develop flicker meters for 220 V/60 Hz systems in South Korea.

An optimisation-based iterative design of fixed-structure controllers with frequency-domain criteria is presented. An interactive multiobjective optimisation procedure is presented, which is based on a direct shaping of the frequency responses, without the need to construct explicit weighting filters. At each iteration, a constrained optimisation problem is formulated in such a way that successive improvements of the design to meet the designer's specifications are achieved. For computational efficiency, time-domain expressions consisting of linear matrix equations are used to characterise the frequency responses.

For multi-target tracking (MTT) in the presence of clutters, both issues of state estimation and data association are crucial. This study tackles them jointly by Sequential Monte Carlo methods, a.k.a. particle filters. A number of novel particle algorithms are devised. The first one, which we term Monte-Carlo data association (MCDA), is a direct extension of the classical sequential importance resampling (SIR) algorithm. The second one is called maximum predictive particle filter (MPPF), in which the measurement combination with the maximum predictive likelihood is used to update the estimate of the multi-target's posterior. The third, called proportionally weighting particle filter (PWPF), weights all feasible measurement combinations according to their predictive likelihoods, and uses them proportionally in the importance sampling framework. We demonstrate the efficiency and superiority of our methods over conventional approaches through simulations.

Fast schemes to reduce additive white Gaussian noise (AWGN) and speckle in videos are presented. The proposed schemes use a change detection technique to measure the interframe motion and carry out estimations in both the spatial and temporal directions of the video. In the case of AWGN reduction, the well-known edge adaptive Wiener filter is used to perform the spatial estimation. Two different filters to carry out temporal estimation are presented based on novel weighted scalar Kalman and weighted running average filters, respectively. These temporal estimators are applied on the spatial estimate to obtain the spatiotemporal estimate. A new method is then used to appropriately combine the spatial and spatiotemporal estimates in order to obtain the final estimate of the uncorrupted signal. To achieve speckle reduction, we use an unbiased homomorphic system that comprises an edge adaptive filter for spatial estimation and the weighted running average filter for temporal estimation. The effectiveness of the various proposed algorithms is demonstrated and compared with that of some of the existing schemes through extensive simulations. It is found that the use of a change detection technique, instead of the popularly used complex motion estimation and compensation technique, to measure the interframe motion results in a considerable reduction of processing time. The proposed schemes perform equally well or better than the existing schemes in reducing the noise in videos.

The prescanned minmax centre-weighted (PMCW) filter, which is capable of restoring images severely corrupted by impulsive noise, is presented. Before filtering, the input image is scanned by a running window; the maximum and minimum of each ranked set in the running window are grouped as the first subset, and the rest as the second subset. Then, the two subsets are filtered in sequence by a centered weight filter. It is shown that filtering results in the first subset provide the extension property by which the effective smoothing region for the subsequent filtering in the second subset is extended. The detail-control property of PMCW is characterised, and some relationships between PMCW and ranked-order based filters are derived. Quantitative comparisons demonstrate that the PMCW filter offers a more desirable combination of noise suppression and detail preservation properties than can other median-type filters.