Search

The successful suppression of clutter arising from stationary or slowly moving tissue is one of the key issues in medical ultrasound color blood imaging. Remaining clutter may cause bias in the mean blood frequency estimation and results in a potentially misleading description of blood-flow. In this paper, based on the principle of general wall-filter, the design process of three classes of filters, infinitely impulse response with projection initialization (Prj-IIR), polynomials regression (Pol-Reg), and eigen-based filters are previewed and analyzed. The performance of the filters was assessed by calculating the bias and variance of a mean blood velocity using a standard autocorrelation estimator. Simulation results show that the performance of Pol-Reg filter is similar to Prj-IIR filters. Both of them can offer accurate estimation of mean blood flow speed under steady clutter conditions, and the clutter rejection ability can be enhanced by increasing the ensemble size of Doppler vector. Eigen-based filters can effectively remove the non-stationary clutter component, and further improve the estimation accuracy for low speed blood flow signals. There is also no significant increase in computation complexity for eigen-based filters when the ensemble size is less than 10.

Improvement of frame-rate is very important for high quality ultrasound imaging of fast-moving structures. It is also one of the key technologies of Three-Dimension (3-D) real-time medical imaging. In this paper, we have demonstrated a beamforming method which gives imaging frame-rate increment without sacrificing the quality of medical images. By using wider and fewer transmit beams in combination with four narrower parallel receive beams, potentially increasing the imaging frame-rate by a factor four. Through employing full transmit aperture, controlling the mainlobe width, and suppressing sidelobes of angular responses, the inherent gain loss of normal parallel beamfomer can be compensated in the maximal degree. The noise and interference signals also can be suppressed effectively. Finally, we show comparable lateral resolution and contrast of ultrasound images to normal single widow weighting beamformer on simulated phantoms of point targets, cyst and fetus of 12th week. As the computational cost is linear with the number of array elements and the same with Delay And Sum (DAS) beamformers, this method has great advantages of possibility for high frame-rate real-time applications.

Genetic Network Programming (GNP) is an evolutionary algorithm derived from GA and GP. Directed graph structure, reusability of nodes, and implicit memory function enable GNP to deal with complex problems in dynamic environments efficiently and effectively, as many paper demonstrated. This paper proposed a new method to optimize GNP by extracting and using rules. The basic idea of GNP with Rule Accumulation (GNP with RA) is to extract rules with higher fitness values from the elite individuals and store them in the pool every generation. A rule is defined as a sequence of successive judgment results and a processing node, which represent the good experiences of the past behavior. As a result, the rule pool serves as an experience set of GNP obtained in the evolutionary process. By extracting the rules during the evolutionary period and then matching them with the situations of the environment, we could, for example, guide agents' behavior properly and get better performance of the agents. In this paper, we apply GNP with RA to the problem of determining agents' behaviors in the Tile-world environment in order to evaluate its effectiveness. The simulation results demonstrate that GNP with RA could have better performances than the conventional GNP both in the average fitness value and its stability.

Traditional adaptive beamformers have robustness just for specific error condition. They suffer performance degradation in the presence of multiple errors such as sample covariance matrix estimation error and steering vector mismatch. In this article, a new robust adaptive beamforming algorithm based on jointly estimating the covariance matrix and steering vector mismatch is proposed to overcome both the problems of sample covariance errors and steering vector mismatch. The theoretical covariance matrix is estimated based on the shrinkage method. Subsequently, the difference between the actual and presumed steering vectors is estimated in the sense of that the output signal-to noise plus interference ratio (SINR) is maximized and then is used to obtain the actual steering vectors. The proposed algorithm is preferable to traditional ones in the condition of multiple errors. Both simulation results and performance analysis are presented that illustrated the effectiveness and superiority of the proposed method.

Contrast resolution and detail resolution are two key measures of ultrasound diagnostic image quality. Besides high imaging quality, the frame-rate is another important factor for research on tissue motion mechanism and diagnosis of pathological changes. In this paper, based on the principles of phased ultrasound beamforming, we introduce a new method which can not only improve frame-rate but also enhance image quality. Mainlobe width controlling and sidelobes suppressing are employed to improve image resolution, fewer and wider transmit beams combined with four parallel receive beams is used to increase the frame-rate by a factor of four. Simulation results demonstrate that the image quality using this method is much better than conventional beamformed image of wired targets, such as Hanning Window weighting and Rectangle Window weighting. This method also has the advantages of possibility of real time implementation.

As a new promising evolutionary computation method, Genetic Network Programming (GNP) is good at generating action rules for multi-agent control in dynamic environments. However, some unimportant nodes exist in the program of GNP. These nodes serve as some redundant information which decreases the performance of GNP and the quality of the generated rules. In order to prune these nodes, this paper proposes a novel method named Credit GNP, where a credit branch is added to each node. When the credit branch is visited, the node is neglected and its function is not executed, so that the unimportant nodes could be jumped. The probability of visiting this credit branch and to which node it is jumped is determined by both evolution and Sarsa-learning, therefore, the unimportant nodes could be pruned automatically. Simulation results on the Tile-world problem show that the proposed method could get better programs and generate better and more general rules.

Conventional evolutionary computation methods aim to find elite individuals as the optimal solutions. The rule accumulation method tries to find good experiences from individuals throughout the generations and store them as decision rules, which is regarded as solutions. Genetic Network Programming (GNP) is competent for dynamic environments because of its directed graph structure, reusability of nodes and partially observable processes. A GNP based rule accumulation method has been studied and applied to the stock trading problem. However, with the changing of dynamic environments, the old rules in the rule pool are incompetent for guiding agent's actions, thus updating these rules becomes necessary. This paper proposes a new method to update the accumulated rules in accordance with the environment changes. Sarsa-learning which is a good on-line learning policy is combined with off-line evolution to generate better individuals and update the rules in the rule pool. Tile-world problem which is an excellent benchmark for multi-agent systems is used as the simulation environment. Simulation results demonstrate the efficiency and effectiveness of the proposed method in dealing with the changing environments.

Many classical methods such as Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Strategies (ES), etc. have accomplished significant contribution to the study of evolutionary computation. And in the past decade, a new approach named Genetic Network Programming (GNP) has been proposed. It is designed for especially solving complex problems in dynamic environments. Generally speaking, GNP is based on the algorithms of existed classical evolutionary computation techniques and uses the data structure of directed graphs which becomes the unique feature of GNP. So far, many studies have indicated that GNP can solve the complex problems in the dynamic environments very efficiently and effectively. Focusing on GNP's distinguished expression ability of the graph structure, this paper proposes an enhanced architecture for the standard GNP in order to improve the performance of GNP by using the exploited information extensively during the evolution process of GNP. In the enhanced architecture, the important gene information of the elite individuals is extracted and accumulated during evolution. And among the accumulated information, some of them are selected and encapsulated in the Route Nodes which are used to guide the evolution process. In this paper, the proposed architecture has been applied to the tile-world which is an excellent bench mark for evaluating the evolutionary computation architecture. The performance of the GNP with Route Nodes (GNP-RN) is compared with the conventional GNP. The simulation results show some merits of the proposed method over the conventional GNPs demonstrating its superiority.

Genetic Network Programming(GNP) is a newly developed evolutionary computation method using a directed graph as its gene structure, which is its unique feature. It is competent for dealing with complex problems in dynamic environments and is now being well studied and applied to many real-world problems such as: elevator supervisory control, stock price prediction, traffic volume forecast and data mining, etc. This paper proposes a new method to accumulate evolutionary experiences and guide agent's actions by extracting and using generalized rules. Each generalized rule is a state-action chain which contains the past information and the current information. These generalized rules are accumulated and updated in the evolutionary period and stored in the rule pool which serves as an experience set for guiding new agent's actions. We designed a two-stage architecture for the proposed method and applied it to the Tile-world problem, which is an excellent benchmark for multi-agent systems. The simulation results demonstrated the efficiency and effectiveness of the proposed method in terms of both generalization ability and average fitness values and showed that the generalized rule accumulation method is especially remarkable when dealing with non-markov problems.

Genetic Network Programming (GNP) is a newly proposed evolutionary approach which can evolve itself and find the optimal solutions. It is a novel method based on the idea of Genetic Algorithm (GA) and uses the data structure of directed graphs. As GNP has been developed for dealing with problems in dynamic environments, many papers have demonstrated that GNP can be applied to many areas such as data mining, forecasting stock markets, elevator control systems, etc. Focusing on GNP's distinguished expression ability of the graph structure, this paper proposes a method named Genetic Network Programming with Reconstructed Individuals (GNP with RI). In the proposed method, the worst individuals are reconstructed and enhanced by the elite information before undergoing genetic operations (mutation and crossover). The enhancement of worst individuals mimics the maturing phe nomenon in nature, where bad individuals can become smarter after receiving good education. GNP with RI has been applied to the the-world which is an excellent benchmark for evaluating the proposed architecture. The performance of GNP with RI is compared with conventional GNP demonstrating its superiority.