Focusing on the state estimation problem of a Wireless Sensor Network (WSN) without a fusion center, a Quantized Distributed Kalman Filtering (QDKF) algorithm was proposed. Firstly, based on the weighting criterion of node estimation accuracy, a weight matrix was dynamically chosen in the Distributed Kalman Filtering (DKF) algorithm to minimize the global estimation Error Covariance Matrix (ECM). And then, considering the bandwidth constraint of the network, a uniform quantizer was added into the DKF algorithm. The requirement of the network bandwidth was reduced by using the quantized information during the communication. Simulations were conducted by using the proposed QDKF algorithm with an 8-bit quantizer. In the comparison experiments with the Metropolis weighting and the maximum degree weighting, the estimation Root Mean Square Error (RMSE) of the mentioned dynamic weighting method decreased by 25% and 27.33% respectively. The simulation results show that the QDKF algorithm using dynamic weighting can improve the estimation accuracy and reduce the requirement of network bandwidth, and it is suitable for network communications limited applications.

During the automatic segmentation of cardiac structures in echocardiographic sequences within a cardiac cycle, the contour with weak edges can not be extracted effectively. A new approach combining Speeded Up Robust Feature (SURF) and Chan-Vese model was proposed to resolve this problem. Firstly, the weak boundary of heart chamber in the first frame was marked manually. Then, the SURF points around the boundary were extracted to build Delaunay triangulation. The positions of weak boundaries of subsequent frames were predicted using feature points matching between adjacent frames. The coarse contour was extracted using Chan-Vese model, and the fine contour of object could be acquired by region growing algorithm. The experiment proves that the proposed algorithm can effectively extract the contour of heart chamber with weak edges, and the result is similar to that by manual segmentation.

To solve the low efficiency of scheduling in injection molding workshop, an improved job-shop scheduling method was proposed based on clustering mold. The production time was reduced by merging jobs with the same tool list, and the energy consumption was reduced through small model injection machine preferred scheduling. The theoretical analysis and the experimental results show that the proposed mehtod can improve productivity and reduce power consumption more than 50%, making injection molding shop job scheduling be more efficient.