Earticle

Orthogonal frequency division multiplexing (OFDM) can accomplish high use effectiveness. In this paper, the authors consolidate OFDM with wavelength division multiplexing (WDM) to examine the optical fiber transmission characteristics of a WDM-OFDM hybrid system that can accomplish high usage effectiveness even when the quantity of channels is high. The outline and simulative examination of the Integrated WDM-COOFDM framework is done at different estimations of the channel spacing. Hence all the info channels have been put directly at different values like 50GHz, 100 GHz and 150 GHz. The most extreme transmission separation accomplished is 600km. Simulation results reveal that with the increase in the channel spacing, the interference decreases and thus the performance is improved. The enhanced Q element and BER acquired with the 150 GHz channel dispersing is 33.4db and 9E-12.

Ant colony algorithm since it has a better convergence and parallelism, widely used in the data retrieval, however, is not high, due to the characteristics of retrieval object use seriously affect the accuracy of retrieval, and according to this problem, this paper proposed an improved ant colony algorithm, this algorithm will retrieve objects comprehensive characteristics into the ant colony algorithm, and solve the convergence speed and computational complexity of the algorithm, obtained good results in image retrieval.

To make sure human face can be more accurately identified in various poses, an identification method based on the characteristics human face image have been proposed in the thesis. First, it is a design for the optimal sampling matrix to acquire compressed measurement data; then it adopts iterative method based on the close loops of l2 and l1 in normal form to get an estimate on human face images, since the method based on norm l2 can measure the relevancy of human face images in the space and time of the continuous time point, and the method based on norm l1 mainly uses the modified total variation method and basis pursuit noise-reduction method. The simulation design has suggested that the method adopted in the thesis can achieve a higher human face identification rate and a remarkable promotion effect.

In this study, electroencephalography signals recorded while participants were doing verbal and quantitative tasks, are classified. A dataset containing 1044 records obtained from 18 participants are used for subject-dependent classifications. Features are derived from phase locking values calculated between all channel pairs. Features are reduced before the classification process by using both analysis of variance and correlation based feature selection methods. Instances in the dataset are classified by using the nearest neighbor algorithm. An average classification accuracy of 92.35% is achieved over 18 participants. It is shown that phase locking value is distinctive especially when it is calculated on delta and gamma frequency bands measured between frontal and occipital regions.

Under mild conditions, it can be induced from the Karush–Kuhn–Tucker condition that the Pareto set, in the decision space, of a continuous Multiobjective Optimization Problems(MOPs) is a piecewise continuous (m 1)  D manifold(where m is the number of objectives). One hand, the traditional Multiobjective Optimization Algorithms(EMOAs) cannot utilize this regularity property; on the other hand, the Regular Model-Based Multiobjective Estimation of Distribution Algorithm(RM-MEDA) only able to build the linear model of decision space using linear modelling algorithm, such as: the local principal component analysis algorithm(Local PCA).Aim at the shortcomings of EMOAs and RM-MEDA, the Manifold-Learning-Based Multiobjective Evolutionary Algorithm Via Self-Organizing Maps(ML-MOEA/SOM) is proposed for continuous multiobjective optimization problems. At each generation, first, via Self-Organizing Maps, the proposed algorithm learns such a nonlinear manifold in the decision space; then, new trial solutions is built through expanding the neurons of SOM with random noise; at the end, a nondominated sorting-based selection is used for choosing solutions for the next generation. Systematic experiments have shown that, overall, ML-MOEA/SOM outperforms NSGA-II, and is competitive with RM-MEDA in terms of convergence and diversity, on a set of test instances with variable linkages. We have demonstrated that, compared with NSGA-II and RM-MEDA, via self-Organizing maps, ML-MOEA/SOM can dig nonlinear manifold hidden in the decision space of multiobjective optimization problems.

In cooperative relay networks with multiple users and multiple potential relays, which relay nodes are selected has great impact on the system performance. It is an optimization problem for selecting suitable relay nodes. The exhaustive search can solve this problem but the complexity will increase factorially with the network size, i.e., the number of users and the number of relays in the network. In this paper, we formulate both single-objective and multi-objective relay selection problems. For single-objective relay selection problem, only one system objective is considered. A novel quantum bee colony optimization (QBCO) based relay selection scheme is proposed. For multi-objective relay selection problem, two contradictive objectives are considered simultaneously. A novel non-dominated sorting quantum bee colony optimization (NSQBCO) based relay selection scheme is proposed. Simulation results show that the proposed relay selection schemes have the ability to find global optimal solution but have less computational complexity compared with exhaustive search scheme.