We present a d-dimensional exponential analysis algorithm that offers a range of advantages compared to other methods. The technique does not suffer the curse of dimensionality and only needs O((d + 1)n) samples for the analysis of an n-sparse expression. It does not require a prior estimate of the sparsity n of the d-variate exponential sum. The method can work with sub-Nyquist sampled data and offers a validation step, which is very useful in low SNR conditions. A favourable computation cost results from the fact that d independent smaller systems are solved instead of one large system incorporating all measurements simultaneously. So the method also lends itself easily to a parallel execution. Our motivation to develop the technique comes from 2D and 3D radar imaging and is therefore illustrated on such examples.
Aim at local optimal problem in the path planning of mobile robot by artificial immune algorithm, it is proposed that the improved artificial immune algorithm of mobile robot path planning. Based on artificial immunity algorithm, the potential function method of an artificial potential field is used in this algorithm, improving randomness of the initial population of the artificial immune algorithm, then the algorithm make initial population turn to evolutionary operation through crossover, variance and selection operator to get optimum antibody. The simulation results showed that this algorithm is easy to get the optimal path, at the same time, increasing the speed of the path planning, and the length of the optimal path planning is less 28.5% compare with the traditional immune algorithm.
Aim at search precocity of particle swarm algorithm and slow convergence speed problem for ant colony algorithm, in the automatic guided vehicle path optimization a path optimization algorithm is proposed, which is fused by particle swarm algorithm and ant colony algorithm. Firstly, robot motion space model of the algorithm is created using link figure. After got fixed circulation rapid global, search to get more optimal path by means of improved fastest convergence ant system, then using a particle ants information communication method to update pheromone, finally, optimal path is drew. The simulation experiment shows that, even in the complex environment, this algorithm can also has the advantage of ant colony algorithm to optimize the result accurately and particle swarm algorithm local optimization accurately and rapidly, and a global security obstacle avoidance of optimal path is plot, the route is shorten 8% compare than the general ant colony algorithm.
Decomposition is integral to most image processing algorithms and often required in texture analysis. We present a new approach using a recent 2-dimensional exponential analysis technique. Exponential analysis offers the advantage of sparsity in the model and continuity in the parameters. This results in a much more compact representation of textures when compared to traditional Fourier or wavelet transform techniques. Our experiments include synthetic as well as real texture images from standard benchmark datasets. The results outperform FFT in representing texture patterns with significantly fewer terms while retaining RMSE values after reconstruction. The underlying periodic complex exponential model works best for texture patterns that are homogeneous. We demonstrate the usefulness of the method in two common vision processing application examples, namely texture classification and defect detection.
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