Abstract
In this paper, we present an analytical approach to accurately assessing the performance of subcarrier multiplexed optical systems for the delivery of future mobile radio broadband services, The existence of a wide dynamic range of power levels in the return link, together with laser nonlinear distortion, represents a major problem and makes performance evaluation difficult. By modeling laser nonlinear distortion using Volterra series analysis, we show that the performance assessment of these systems becomes not only tractable, but also that they can be can be optimized based on the worst case condition, This analysis is performed here for the case of continuous phase frequency shift keying (CPFSK) modulation, for which we calculate the power spectral density (PSD) of the intermodulation products, The overall system performance is then investigated, and its dependence on laser bias and RIN is examined, As an illustrative example we consider a specific 50 channel CPFSK system occupying the bandwidth of 2 to 2.1 GHz.

Abstract
This work describes a video coder based on a hybrid DPCM-vector quantization algorithm that is suited for bit rates ranging from 8-16 kb/s, The proposed approach involves segmenting difference images into variable-size and variable-shape blocks and performing segmentation and motion compensation simultaneously, The purpose of obtaining motion vectors for variable-size and variable-shape blocks is to improve the quality of motion estimation, particularly in those areas where the edges of moving objects are situated, For the larger blocks, decimation takes place in order to simplify vector quantization, For very active blocks, which are always of small dimension, a specific vector quantizer has been applied, the fuzzy classified vector quantizer (FCVQ), The coding algorithm described displays good performance in the compression of test sequences at the rates of 8 and 16 kb/s; the signal-to-noise ratios obtained are good in both cases, The complexity of the coder implementation is comparable to that of conventional hybrid coders, while the decoder is much simpler in this proposal.

Abstract
Artificial co-evolutionary techniques are proposed in a new and novel paradigm to solve the problem of designing a robust fixed PID controller for a plant with prescribed plant uncertainties. The co-evolutionary scheme used, involves generating two separate populations, one representing the controller and the other the plant. Two separate cost functions are then used in the co-evolutionary scheme to reflect the different goals of the two populations. The two populations are then co-evolved such that the population of plants, with the prescribed uncertainties, contains the set of difficult plants to control and a population of controllers emerges, which can control all these difficult plants effectively. The resulting paradigm not only results in a robust controller design but also produces a set of worst case plants. This co-evolutionary approach is illustrated through co-evolving a PID controller for a linear plant which has a set of prescribed uncertainties.

Abstract
In this article, we describe the analysis, design and implementation of a control architecture for a mobile platform to autonomously carry out transportation, surveillance and inspection tasks in semi-structured industrial environments. Based on a hierarchical structure composed by the Organization, Coordination and Functional levels organized linguistically and structured according to the Principle of Increasing Precision with Decreasing Intelligence, this control architecture is permits the real-time parallel execution of tasks.

Abstract
This paper presents an artificial neural network (ANN) based approach for the definition of preventive control strategies of autonomous power systems with a large renewable power penetration. For a given operating point, a fast dynamic security evaluation for a specified wind perturbation is performed using an ANN. If insecurity is detected, new alternative stable operating points are suggested, using a hybrid ANN-optimization approach that checks several feasible possibilities, resulting from changes in power produced by diesel and wind generators, and other combinations of diesel units in operation, Results obtained from computer simulations of the real power system of Lemnos (Greece) support the validity of the developed approach.

Abstract
This paper presents a Monte-Carlo algorithm considering loads defined by fuzzy numbers. In this methodology states are sampled according to the probabilistic models governing the life cycle of system components while fuzzy concepts are used to model uncertainty related to future load behavior. This model can be used to evaluate generation/transmission power system reliability for long term planning studies as one uses the more adequate uncertainty models for each type of data. For each sampled state a Fuzzy Optimal Power Flow is run so that one builds its power not supplied membership function. The paper proposes new indices reflecting the integration of probabilistic models and fuzzy concepts and discusses the application of variance reduction techniques if loads are defined by fuzzy numbers. A case-study based on the IEEE 30 bus system illustrates this methodology.