A photonic implementation of a practical broadband RF Hilbert transformer is demonstrated by using a four-tap transversal system. An almost ideal 90 degrees phase shift with less than 3 dB of amplitude ripple has been achieved from 2.4 to 17.6 GHz. An efficient method to realize both transformed (quadrature-phase) and reference (in-phase) signal has been achieved by using a coarse wavelength division multiplexing coupler. Extension of the transformer bandwidth and further improvements of its implementation are discussed.
A broadband photonic instantaneous frequency measurement system utilizing four-wave mixing in highly nonlinear fiber is demonstrated. This new approach is highly stable and does not require any high-speed electronics or photodetectors. A first principles model accurately predicts the system response. Frequency measurement responses from 1 to 40 GHz are demonstrated and simple reconfiguration allows the system to operate over multiple bands.
This paper compares the performance of anti-noise methods, particularly probabilistic and re-sampling methods, using NSGA2. It then proposes a computationally less expensive approach to counteracting noise using re-sampling and fitness inheritance. Six problems with different difficulties are used to test the methods. The results indicate that the probabilistic approach has better convergence to the Pareto optimal front, but it looses diversity quickly. However, methods based on re-sampling are more robust against noise but they are computationally very expensive to use. The proposed fitness inheritance approach is very competitive to re-sampling methods with much lower computational cost.
Abstract-Many random events usually are associated with executions of operational plans at various companies and organizations. For example, some tasks might be delayed and/or executed earlier. Some operational constraints can be introduced due to new regulations or business rules. In some cases, there might be a shift in the relative importance of objectives associated with these plans. All these potential modifications create a huge pressure on planning staff for generating plans that can adapt quickly to changes in environment during execution. In this paper we address adaptation in dynamic environments.Many researchers in evolutionary community addressed the problem of optimization in dynamic environments. Through an overview on applying evolutionary algorithms for solving dynamic optimization problems, we classify the work into two main categories: (1) finding/tracking optima and (2) adaptation and we discuss their relevance for solving planning problems. Based on this discussion, we propose a computational approach to adaptation within the context of planning. This approach models the dynamic planning problem as a multi-objective optimization problem and an evolutionary mechanism is incorporated, this adapts the current solution to new situations when a change occurs.As the multi-objective model is used, the proposed approach produces a set of non-dominated solutions after each planning cycle. This set of solutions can be perceived as an informationrich data set which can be used to support the adaptation process against the effect of changes. The main question is how to exploit this set efficiently? In this paper we propose a method based on the concept of centroids over a number of changing-time steps, at each step we obtain a set of non-dominated solutions.We carried out a case study on this proposed approach. Mission planning was used for our experiments and experimental analysis. We selected mission planning as our test environment because battlefields are always highly dynamic and uncertain and can be conveniently used to demonstrate different types of changes, especially time-varying constraints. The obtained results support the significance of our centroid-based approach.
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