In the indoor robot localization and 3D dense mapping, the existing methods can not satisfy the requirements of high-precision localization, large-scale and rapid mapping. The ORB-SLAM (Oriented FAST and Rotated BRIEF-Simultaneous Localization And Mapping) algorithm, which has three parallel threads including tracking, map building and relocation, was used to estimate the three-dimensional (3D) pose of the robot. And then 3D dense point cloud was obtained by using the depth camera KINECT. The key frame extraction method in spatial domain was introduced to eliminate redundant frames, and the sub-map method was proposed to reduce the cost of mapping, thereby the whole speed of the algorithm was improved. The experiment results show that the proposed method can locate the robot position accurately in a large range. In the range of 50 meters, the root-mean-square error of the robot is 1.04 m, namely the error is 2%, the overall speed is 11 frame/s, and the localization speed is up to 17 frame/s. The proposed method can meet the requirements of indoor robot localization and 3D dense mapping with high precision, large-scale and rapidity.

Concerning the low efficiency of present methods of IP lookup, a new data lookup algorithm based on Multi-Bit Priority Tries (MBPT) was proposed in this paper. By storing the prefixes with higher priority in dummy nodes of multi-bit tries in proper order and storing the prefixes for being extended in an auxiliary storage structure，this algorithm tried to make the structure find the longest matching prefix in the internal node instead of the leaf node. Meanwhile, the algorithm avoided the reconstruction of router-table when it needed to be updated. The simulation results show that the proposed algorithm can effectively minimize the number of memory accesses for dynamic router-table operations, including lookup, insertion and deletion, which significantly improves the speed of router-table lookup as well as update.

As it is known that the underwater environment is quite complicated and changeable, as a result, targets and pseudo targets always have a high degree of mixing, and one single segmentation method usually could not abstract ideal target regions. Therefore, this paper proposed a new segmentation method based on Blob analysis and Bayesian design-making. Firstly, the optimistic thresholds were calculated by the improved OTSU algorithm, and then the image was segmented according to this threshold. Through analyzing the connectivity characters, closed contours of regions were achieved. Secondly, the connected regions were described using 7 dimensions of Blob operators and pseudo-target regions were eliminated based on Bayesian decision-making rules. Finally, burrs and disturbances were wiped off through the usage of mathematical morphology operators and ideal target regions were achieved. Through dealing with the images grabbed during the pool experiments using the above method, accuracy and efficiency of the method were verified and the real target regions were acquired.

To overcome the shortcomings of conventional algal bloom forecast system in acquiring data, this study applied the Internet of Things (IoT) technology to establish a data transmission network with three-layer structure, and thus secured data continuity. With improved retrieval approach of water quality parameters, technology of Wireless Sensor Network (WSN) and forecast model of algal bloom, the blue-green algal bloom forecast platform was developed. The evaluation demonstrates that the platform achieves an overall accuracy of 80% in forecasting blue-green blooms in Taihu Lake in next three days.