Huaxiang Lu

Automatic Morphological Measurement of the Quantum Dots Based on Marker-Controlled Watershed Algorithm

In the field of material growth, the quantum dot (QD) image analysis is a fundamental tool. The main challenge is that such study is used to be made by nonautomatic procedures which are time consuming and subjective. We aim to implement an algorithm of automatic analysis of the QDs images. In this frame, efficient QDs segmentation is prerequisite. In this paper, a fast and robust method for the visual segmentation of QDs image based on marker-controlled watershed transform is proposed. According to the foreground markers and the boundary of the coarse partition, the watershed transform is utilized to accurately separate QDs. A next process is then implemented to filter the possible attached substrates based on the area-height distribution of the extracted QDs. Finally, almost all the QDs can be accurately and robustly extracted and thus their properties can be measured. The experimental results show that the proposed approach gives a good tradeoff between the easy usability and efficiency, execution time, and segmentation quality.

SOC Dynamic Power Management Using Artificial Neural Network

Dynamic power management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article, we try to introduce back propagation network and radial basis network into the research of the system-level power management policies. We proposed two PM policies-back propagation power management (BPPM) and radial basis function power management (RBFPM) which are based on artificial neural networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional power management (PM) techniques - BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79 -1.45-1.18-competitive separately for traditional timeout PM-adaptive predictive PM and stochastic PM

Automatic segmentation of clustered quantum dots based on improved watershed transformation

Abstract Automated analysis of the quantum dots (QDs) images is very important in the field of material science. In this frame, efficient QDs segmentation is prerequisite. In this paper, we propose an algorithm of automatic detection and segmentation of the QDs, especially the clustered ones. We depend on fuzzy c-means (FCM) method for initial segmentation of the QDs from the substrate background. Then we present a modified watershed algorithm with markers and a novel marking function. The markers are extracted by adaptive H-minima transformation. Then a marking function based on Quasi-Euclidean distance transform is introduced to accurately and rapidly separate the clustered QDs. We demonstrate the comparisons of our method with the existing approaches. The experimental results show that the proposed method is efficient and accurate with very little running time and has a high quality on QDs segmentation.

Single-electron tunneling depressing synapse for cellular neural networks

In this paper, a cellular neural network with depressing synapses for contrast-invariant pattern classification and synchrony detection is presented, starting from the impulse model of the single-electron tunneling junction. The results of the impulse model and the network are simulated using simulation program with integrated circuit emphasis (SPICE). It is demonstrated that depressing synapses should be an important candidate of robust systems since they exhibit a rapid depression of excitatory postsynaptic potentials for successive presynaptic spikes.

Application of ICA in on-line verification of the phase difference of the current sensor

The performance of the current sensor in power equipment may become worse affected by the environment. In this paper, based on ICA, we propose a method for on-line verification of the phase difference of the current sensor. However, not all source components are mutually independent in our application. In order to get an exact result, we have proposed a relative likelihood index to choose an optimal result from different runs. The index is based on the maximum likelihood evaluation theory and the independent subspace analysis. The feasibility of our method has been confirmed by experimental results.

SOC dynamic power management using artificial neural network

Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level power management policies. We proposed two PM policies-Back propagation Power Management (BPPM) and Radial Basis Function Power Management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques — BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79, 1.45, 1.18-competitive separately for traditional timeout PM, adaptive predictive PM and stochastic PM.

Application of FastICA in on-line calibration of the current sensor's phase difference

The performance of the current sensor in power equipment may become worse affected by the environment. In this paper, a phase difference measurement method based on FastICA is proposed to calibrate the phase difference of the current sensor on-line. However, not all source components are mutually independent in our application. Additionally, due to limitations of the algorithm, the phase errors of the separated results are different in different test runs. In order to get an exact result, we have proposed an evaluation index to choose an optimal result from different runs. The index is based on the relationship of mapping components to observed signals. Simulation results indicate that the method presented in this paper is effective and feasible.

Parallel particle swarm optimization with genetic communication strategy and its implementation on GPU

Taking into account the advantage of high computation to communication ratio of coarse-grained parallel model, we implement coarse-grained parallel particle swarm optimization (PPSO) on Graphic Processing Unit (GPU), which is very popular for parallel computing nowadays. Meanwhile, a heuristic communication strategy called genetic migration is proposed in this paper. Numerical experimental results show that PPSO with genetic migration (PPSO_GM) can greatly improve the convergence property of particle swarm optimization (PSO), compared with PPSO with traditional unidirectional ring migration (PPSO_URM); and two orders of magnitude more speedups are achieved by PPSO_GM against serial PSO (SPSO) for all ten 100-dimensional benchmark test functions.

DBFNN based adaptive excitation controller of a power system using backstepping design

DBFNN (Direction Basis Neural Network) proposed recently had many novel properties. In this paper, a DBFNN based direct adaptive controller was designed for SISO strict-feedback system by using backstepping method. A virtual controller was designed in every step of backstepping and the real controller was acquired in the last step. The tuning law of NN weights was derived from a selected integral Lyapunov function. So the stability of the closed loop and convergence of weights were guaranteed. The proposed scheme was applied to design an excitation controller for a power system. The simulation demonstrates good tracking performance and robustness of the designed controller.