Yaqin Zhao

Ant colony optimization algorithm with mutation mechanism and its applications

Mutated ant colony optimization (MACO) algorithm is proposed by introducing the mutation mechanism to the ACO algorithm, and is applied to the traveling salesman problem (TSP) and multiuser detection in this paper. Ant colony optimization (ACO) algorithms have already successfully been used in combinatorial optimization, however, as the pheromone accumulates, we may not get a global optimum because it can get stuck in a local minimum resulting in a bad steady state. The presented MACO algorithm can enlarge searching range and avoid local minima by randomly changing one or more elements of the local best solution, which is the mutation operation in genetic algorithm. As the mutation operation is simple to implement, the performance of MACO is superior with almost the same computational complexity. MACO is applied to TSP and multiuser detection, and via computer simulations it is shown that MACO has much better performance in solving these two problems than ACO algorithms.

A robust, coarse-to-fine traffic sign detection method

We present a traffic sign detection method which has won the first place for the prohibitory and mandatory signs and the third place for the danger signs in the GTSDB competition. The method uses the histogram of oriented gradient (HOG) and a coarse-to-fine sliding window scheme. Candidate ROIs are first roughly detected within a small-sized window, and then further verified within a large-sized window for higher accuracy. Experimental results show that the proposed method achieves high recall and precision ratios, and is robust to various adverse situations including bad lighting condition, partial occlusion, low quality and small projective deformation.

A hierarchical method for traffic sign classification with support vector machines

Traffic sign classification is an important function for driver assistance systems. In this paper, we propose a hierarchical method for traffic sign classification. There are two hierarchies in the method: the first one classifies traffic signs into several super classes, while the second one further classifies the signs within their super classes and provides the final results. Two perspective adjustment methods are proposed and performed before the second hierarchy, which significantly improves the classification accuracy. Experimental results show that the proposed method gets an accuracy of 99.52% on the German Traffic Sign Recognition Benchmark (GTSRB), which outperforms the state-of-the-art method. In addition, it takes about 40 ms to process one image, making it suitable for realtime applications.

A population declining mutated ant colony optimization multiuser detector for MC-CDMA

In this letter, a population declining mutated ant colony optimization (PDMACO) multiuser detector for MC-CDMA is proposed. The PDMACO algorithm is a hybrid ACO algorithm introducing population declining mechanism and mutation mechanism, which can achieve excellent performance by enlarging the searching range and avoiding local minima with almost the same computational complexity. Simulations show that its performance in reducing the bit-error rate and near-far effect is much better than that of the ACO multiuser detectors and close to that of the optimal multiuser detector.

A fast and robust ellipse-detection method based on sorted merging.

A fast and robust ellipse-detection method based on sorted merging is proposed in this paper. This method first represents the edge bitmap approximately with a set of line segments and then gradually merges the line segments into elliptical arcs and ellipses. To achieve high accuracy, a sorted merging strategy is proposed: the merging degrees of line segments/elliptical arcs are estimated, and line segments/elliptical arcs are merged in descending order of the merging degrees, which significantly improves the merging accuracy. During the merging process, multiple properties of ellipses are utilized to filter line segment/elliptical arc pairs, making the method very efficient. In addition, an ellipse-fitting method is proposed that restricts the maximum ratio of the semimajor axis and the semiminor axis, further improving the merging accuracy. Experimental results indicate that the proposed method is robust to outliers, noise, and partial occlusion and is fast enough for real-time applications.

A novel joint spatial-code clustered interference alignment scheme for large-scale wireless sensor networks.

Interference alignment (IA) has been put forward as a promising technique which can mitigate interference and effectively increase the throughput of wireless sensor networks (WSNs). However, the number of users is strictly restricted by the IA feasibility condition, and the interference leakage will become so strong that the quality of service will degrade significantly when there are more users than that IA can support. In this paper, a novel joint spatial-code clustered (JSCC)-IA scheme is proposed to solve this problem. In the proposed scheme, the users are clustered into several groups so that feasible IA can be achieved within each group. In addition, each group is assigned a pseudo noise (PN) code in order to suppress the inter-group interference via the code dimension. The analytical bit error rate (BER) expressions of the proposed JSCC-IA scheme are formulated for the systems with identical and different propagation delays, respectively. To further improve the performance of the JSCC-IA scheme in asymmetric networks, a random grouping selection (RGS) algorithm is developed to search for better grouping combinations. Numerical results demonstrate that the proposed JSCC-IA scheme is capable of accommodating many more users to communicate simultaneously in the same frequency band with better performance.

Population Declining Ant Colony Optimization Multiuser Detection in Asynchronous CDMA Communications

In this paper we present a population declining ant colony optimization (PDACO) multiuser detector for asynchronous CDMA communications. Ant colony optimization (ACO) algorithms have already been used in multiuser detection in CDMA systems; however, as the pheromone accumulates, we may not get a global optimum because it stops searching early. PDACO can enlarge searching range through increasing the initial population of the ant colony, and the population declines in successive iterations. So, the performance of PDACO is superior with the same computational complexity. PDACO is applied to multiuser detection in asynchronous CDMA systems over slowly multipath Rayleigh-fading channels in this paper. Via computer simulations it is shown that the performance of PDACO detector is much better in bit-error rate and near-far effect resistance than conventional detector, ACO detector and genetic algorithm detector, and is close the optimal multiuser detector.

Stochastic Cellular Neural Network for CDMA Multiuser Detection

A novel method for the multiuser detection in CDMA communication systems based on a stochastic cellular neural network (SCNN) is proposed in this paper. The cellular neural network (CNN) can be used in multiuser detection, but it may get stuck in a local minimum resulting in a bad steady state. The annealing CNN detector has been proposed to avoid local minima; however, the near-far effect resistant performance of it is poor. So, the SCNN detector is proposed here through adding a stochastic term in a CNN. The performance of the proposed SCNN detector is evaluated via computer simulations and compared to that of the conventional detector, the stochastic Hopfield network detector, and the Annealing CNN detector. It is shown that the SCNN detector can avoid local minima and has a much better performance in reducing the near-far effect than these detectors, as well as a superior performance in bit-error rate.

A multilevel quantifying spread spectrum PN sequence based on chaos of cellular neural network

A novel multilevel quantifying spread spectrum PN sequence based on the chaos of Cellular Neural Network (CNN) is proposed in this paper. The chaotic sequences are created from a CNN with three cells and multilevel quantified to be the PN sequences for the spread spectrum communication systems (SSCS). And then better sequences are filtered out in terms of the equilibria points, self-correlation and mutual-correlation of the chaotic PN sequences. These PN sequences can provide more enhanced multiple access capacity and robustness to the noises than conventional m- and Gold sequences because of the sensitivity to the initial conditions of the chaotic sequences and the good dynamical performance of the CNN. The filter processing helps the SSCS to resist on the rake declination and the interference from other users. The experiment results show that the proposed chaotic PN sequences are much better than the conventional sequences for SSCS.

Successive elimination motion estimation algorithm based on multi-resolution

This paper proposes a new motion estimation method based on the adaptive break successive elimination algorithm and multi-resolution in a digital image stabilization (DIS) system. A computational scheme that facilitates the estimation of the local motion vectors is developed. First, the images are transferred to layers with different resolutions by the linear or wavelet transition and the performances of the translations are compared in the later experiments. With multi-resolution, the size of the sub-image and the search area were broadened and little moving objects can be eliminated. Then, positions which were calculated with lower possibility to be the destination by successive elimination algorithm were removed before they are matched. Finally, also with the adoption of the principle of adaptive break, the threshold which decides the times of a block matching is set adaptively according to the errors during the match while the accuracy remaines. Experimental results show that with similar performance as that of TSS, the computational load of the proposed motion estimation algorithm is 3% of the full-search matching and 34% of TSS..