Concerning insufficient and blind use of the Received Signal Strength Indicator (RSSI) information among unknown nodes, a new RSSI collaborative location algorithm of selecting preference accuracy for Wireless Sensor Network (WSN) was proposed. Firstly, the nodes with high locating accuracy were selected from coarsely located unknown nodes based on the RSSI thresholds. Secondly, subset judgment method was used to seek out the unknown nodes which were less affected by the environment as the second collaboration backbone nodes. Then, based on the positioning errors of the anchor nodes, anchor node replacement criterion was used to further extract the high-precision node from the secondary selected cooperative nodes as the optimal cooperative backbone nodes. Finally, the collaborative backbone nodes were used as the cooperative objects, and the unknown nodes were modified according to the precision priorities. In the simulation experiments, the average localization accuracy of the proposed algorithm was within 1.127 m in 100 m*100 m grids. In terms of locating accuracy, the average locating accuracy of the proposed algorithm is improved by 15% compared with the improved WSN locating algorithm using RSSI model. In terms of time efficiency, compared with the traditional RSSI collaborative location algorithm, the proposed algorithm improves the time efficiency by 20% under the same condition. It can be seen that the proposed algorithm can effectively enhance the locating accuracy, reduce computational complexity and improve time efficiency.

The existing query analysis methods usually treat the entity resolution as an offline preprocessing process to clean the whole data set. However, with the continuous increasing of data size, such offline cleaning mode with high computing complexity has been difficult to meet the needs of real-time analysis in most applications. In order to solve the problem of aggregation query on duplicate charged records, a new method integrating entity resolution with approximate aggregation query processing was proposed. Firstly, a block-based sampling strategy was adopted to collect samples. Then, an entity recognition method was used to identify the duplicate entities on the sampled samples. Finally, the unbiased estimation of aggregated results was reconstructed according to the results of entity recognition. The proposed method avoids the time cost of identifying all entities, and returns the query results that satisfy user needs by identifying only a small number of sample data. The experimental results on both real dataset and synthetic dataset demonstrate the efficiency and reliability of the proposed method.

Aiming at problems of inconvenient operations, large noise interference of same frequency band in heart rate, and great influence by environmental temperature in existing non-contact measurement of heart rate, a non-contact measurement method of heart rate based on Eulerian video magnification technology was proposed. Firstly, a tiny beating of radial artery of wrist was magnified by Eulerian video magnification technology. Secondly, a statistics of luminance variance for pixels in an enlarged video frame was performed in time domain. Meanwhile, skin area in YCrCb color space was segmented. Thirdly, the pulsing region of radial artery in a video was extracted by luminance variance statistics and skin segmentation incorporating with image morphological processing. Finally, the non-contact measurement of heart rate was implemented by time-frequency analysis through Fourier transform of luminance signal of radial artery extracted in time domain. The experimental results show that Root Mean Square Error (RMSE) is reduced by 50.5% and 32.6%, respectively compared to Independent Component Analysis (ICA) and pulse alternating current signal analysis; Mean Absolute Difference (MAD) is 12% lower than wavelet filtering method. In this paper, the proposed approach for non-contact measurement of heart rate has a good consistency with pulse oximeter measurement, which is satisfied to pharmaceutical industry standards. It also can be used in daily family health care and telemedicine to monitor heart rate.

Concerning the problem of high energy consumption caused by data collision and cluster selection process in dynamic clustering target tracking of Wireless Sensor Network (WSN), a dynamic clustering method based on energy optimization for WSN was proposed. Firstly, a time division election transmission model was proposed, which avoided data collision actively to reduce energy consumption of nodes in a dynamic cluster. Secondly, based on energy information and tracking quality, the energy-balanced farthest node scheduling strategy was proposed, which optimized custer head node scheduling. Finally, according to the weighted centroid localization algorithm, the target tracking task was completed. Under the environment of random deployment of nodes, the experimental results show that, the average tracking accuracy of the proposed method for non-linear moving objects was 0.65 m, which is equivalent to that of Dynamic Cluster Member Selection method for multi-target tracking (DCMS), and improved by 45.8% compared to Distributed Event Localization and Tracking Algorithm (DELTA). Compared with DCMS and DELTA, the proposed algorithm can effectively reduce energy consumption of the dynamic tracking clusters by 61.1% and prolong the network lifetime.

According to characters of marine ecology domain knowledge, a marine ecology knowledge organization model was proposed. Referring to engineering field literature and the device-function knowledge representation theory that the "function" concept was used to describe marine ecology functional process; a viewpoint of device-function was fixed, a domain upper ontology for marine ecosystem was presented, and then marine ecological conceptual model and marine ecology OWL ontology were constructed. By extending OWL-DL, OWL-Process model oriented function-process was proposed, and then marine ecology function-process ontology instance was constructed. Based on constructed marine ecology ontology repository, marine ecological knowledge management system was developed. The ontology application system provides marine ecology knowledge query and crisis early warning functions; and it also verifies the validity, rationality and feasibility of constructed marine ecology ontology.

OOFDM (Orthogonal Frequency Division Multiplexing) is a focus in research of broadband communications now. The combination of OFDM with ROF (Radio Over Fiber) system can overcome optical dispersion and improve the performance of ROF system。 A generation and transmission system of optical millimeter-wave carrying OFDM signal based on phase modulator was proposed. In the center station, Double SideBand with Suppressed Carrier (DSB-SC) optical millimeter wave signal was generated utilizing phase modulator along with optical filter, while OFDM signal was modulated only onto one of the two optical carriers and transported along optical fiber. The paper set a model about optical carrier OFDM generation and analyzed fiber dispersion influence on phase, and proved the feasibility of the system through the simulation test. The result show the system's eye chart is very clear over 95km optical fiber.

To address the drawback that the Least Squares Support Vector Machines (LSSVM) is sensitive to noises or outliers, a LSSVM model incorporating with prior knowledge on data was proposed based on noise distribution and sample affinity. Information of noise distribution for samples was introduced in the training process. A strategy based on the sample affinity was presented to discriminate data with noises. A fuzzy membership was automatically generated and assigned to each corresponding data point in the sample set by using the strategy and the noise model. The ability of FLSSVM was improved to resist noises. The flexibility was increased to treat data points with noises or outliers. The proposed method was applied to fault diagnosis for the lubricating oil refining process. The experimental result shows that the proposed method has better robustness.