In order to solve the problem of low quality of results returned by spatial keyword bichromatic reverse k nearest neighbor query, a bichromatic reverse k nearest neighbor query method based on distance-keyword similarity constraint was proposed. Firstly, a threshold was set to filter out the low-quality users in the query results, so that the existence of users with relatively long spatial distance in the query results was avoided and the quality of the query results was ensured. Then, in order to support this query, an index of Keyword Multiresolution Grid rectangle-tree (KMG-tree) was proposed to manage the data. Finally, the Six-region-optimize algorithm based on Six-region algorithm was proposed to improve the query processing efficiency. The query efficiency of the Six-region-optimize algorithm was about 85.71% and 23.45% on average higher than those of the baseline and Six-region algorithms respectively. Experimental test and analysis were carried out based on real spatio-temporal data. The experimental results verify the effectiveness and high efficiency of the Six-region-optimize algorithm.

Tracking of the person crossing the cameras is a technical challenge for smart city and intelligent security. And person re-identification is the most important technology for cross-camera person tracking. Due to the domain bias, applying person re-identification algorithms for cross-scenario application leads to the dramatic accuracy reduction. To address this challenge, a method based on Generative Adversarial Network (GAN) Uniting with Spatial-Temporal pattern (STUGAN) was proposed. First, training samples of the target scenario generated by the GAN were introduced to enhance the stability of the recognition model. Second, the spatio-temporal features were used to construct the spatio-temporal pattern of the target scenario, so as to screen low-probability matching samples. Finally, the recognition model and the spatio-temporal pattern were combined to realize the person re-identification task. On classic datasets of this field named Market-1501 and DukeMTMC-reID, the proposed method was compared with BoW (Bag-of-Words), PUL (Progressive Unsupervised Learning), UMDL (Unsupervised Multi-task Dictionary Learning) and other advanced unsupervised algorithms. The experimental results show that the proposed method achieves 66.4%, 78.9% and 84.7% recognition accuracy for rank-1, rank-5 and rank-10 indicators on the Market-1501 dataset respectively, which are 5.7, 5.0 and 4.4 percentage points higher than the best results of the comparison algorithm, respectively; and the mean Average Precision (mAP) higher than the comparison algorithms except Similarity Preserving cycle-consistent Generative Adversarial Network (SPGAN).

To solve the problem that the reconstructed 3D model of a single image has high uncertainty, a network model based on depth image estimation, spherical projection mapping and 3D generative adversarial network was proposed. Firstly, the depth image of the input image was obtained by the depth estimator, which was helpful for the further analysis of the image. Secondly, the obtained depth image was converted into a 3D model by spherical projection mapping. Finally, 3D generative adversarial network was utilized to judge the authenticity of the reconstructed 3D model, so as to obtain 3D model closer to reality. In the comparison experiments with LVP algorithm which learning view priors for 3D reconstruction, the proposed model has the Intersection-over-Union (IoU) increased by 20.1% and the Charmfer Distance (CD) decreased by 13.2%. Theoretical analysis and simulation results show that the proposed model has good generalization ability in the 3D model reconstruction of a single image.

Aiming at the limitations of traditional noise reduction methods for acoustic signals in low Signal-to-Noise Ratio (SNR) environment, a real-time noise reduction algorithm was proposed by combining adaptive threshold Voice Activity Detection (VAD) algorithm and Minimum Mean-Square Error Log-Spectral Amplitude estimation (MMSE-LSA). Firstly, the background noise was estimated in VAD algorithm by probability statistics based on the maximum value of the energy probability, and the obtained background noise was updated in real time and saved. Then, the background noise updated in real time was used as the reference noise of MMSE-LSA, and the noise amplitude spectrum was updated adaptively. Finally, the noise reduction processing was performed. The experimental results on four kinds of acoustic signals in real scenes show that the proposed algorithm can guarantee the real-time processing of low SNR acoustic signals; and compared with the traditional MMSE-LSA algorithm, it has the SNR of the noise reduction signal increased by 10-15 dB without over-subtraction. It can be applied to practical engineering.

Aiming at the problem of the present mainstream adversarial attack algorithm that the attack invisibility is reduced by disturbing the global image features, an untargeted attack algorithm named PS-MIFGSM (Perceptual-Sensitive Momentum Iterative Fast Gradient Sign Method) was proposed. Firstly, the areas of the image focused by Convolutional Neural Network (CNN) in the classification task were captured by using Grad-CAM algorithm. Then, MI-FGSM (Momentum Iterative Fast Gradient Sign Method) was used to attack the classification network to generate the adversarial disturbance, and the disturbance was applied to the focus areas of the image with the non-focus areas of the image unchanged, thereby, a new adversarial sample was generated. In the experiment, based on three image classification models Inception_v1, Resnet_v1 and Vgg_16, the effects of PS-MIFGSM and MI-FGSM on single model attack and set model attack were compared. The results show that PS-MIFGSM can effectively reduce the difference between the real sample and the adversarial sample with the attack success rate unchanged.

Because of complexity of background replacement, the traditional method is inefficient and the accuracy is difficult to improve. To solve these problems, a fast image background replacement method based on dilated convolution, called FABRNet, was proposed. First of all, the first three parts of VGG (Visual Geometry Group network) model were used for convolution and pooling operations of input images. Secondly, the combination of multiple sets of dilated convolutions were embedded into convolution neural network to make the network have a large and fine enough receptive field; meanwhile, the residual network structure was used to ensure the accuracy of the information distribution in the convolution process. Finally, the image was scaled to the original size and output by bilinear interpolation algorithm. Compared with three classical methods such as KNN (K-Nearest Neighbors) matting, Portrait matting and Deep matting, the experimental results show that FABRNet can effectively complete the background automatic replacement, and has advantages in running speed.

Existing measures to detect spam reviews mainly focus on designing features from the perspective of linguistic and psychological clues, which hardly reveal the latent semantic information of the reviews. A Hierarchical Attention-based Neural Network (HANN) model was proposed to mine latent semantic information. The model mainly consisted of the following two layers:the Word2Sent layer, which used a Convolutional Neural Network (CNN) to produce continuous sentence representations on the basis of word embedding, and the Sent2Doc layer, which utilized an attention pooling-based neural network to generate document representations on the basis of sentence representations. The generated document representations were directly employed as features to identify spam reviews. The proposed hierarchical attention mechanism enables our model to preserve position and intensity information completely. Thus, the comprehensive information, history, future, and local context of any position in a document can be extracted. The experimental results show that our method can achieve higher accuracy, compared with neural network-based methods only, the accuracy is increased by 5% on average, and the classification effect is improved significantly.

Concerning ranging difficulties with 60GHz signals in Non Line of Sight (NLOS) conditions, a new positioning algorithm based on beamforming in Wireless Local Area Network (WLAN) was proposed. Firstly, the beamforming technology was applied to search the strongest path by adjusting receiving antennas along the channel path with the maximum power.The searching robustness was enhanced and the location coverage was expanded. Secondly, the time delay bias in NLOS conditions was modeled as a Gaussian random variable to reconstruct the NLOS measurements. Finally, to further improve the positioning accuracy, the outlier detection mechanism was introduced by setting a reasonable detection threshold. The localization simulation experiments were conducted on Matlab using STAs-STAs (STAtions-STAtions) channel model, the Time of Arrival (TOA) localization algorithm based on traditional coherent estimation method achieved the average positioning error at about 2m, and the probability of 1m localization accuracy was just 0.5% under NLOS conditions, while the proposed algorithm achieved the average positioning error at 1.02cm, and the probability of 1m localization accuracy reached 94%. Simulation results show that the beamforming technology is an effective solution to 60GHz localization in NLOS conditions, and the localization accuracy and the probability of successful localization are effectively improved.

Aiming at the problem of low efficiency and low computational efficiency of local window K-distribution detection algorithm, a fast ship target detection algorithm based on local window K-distribution was proposed. Firstly, the original Synthetic Aperture Radar (SAR) image was selected by the iterative segmentation algorithm, and the potential target pixels in the original SAR image were removed according to the pre-selection. Then two-order and four-order integral images were calculated by using the sliding window at each pixel in the background images. Two-order and four-order moments of the K-distribution were calculated in the integral image in order to estimate the parameters of K-distribution. Secondly, the detection threshold was determined by solving the probability density function and the regions of interest were obtained according to the threshold. Finally, the false alarm target was detected by the method of fuzzy difference. The detection experiment using the real SAR image show that the running time of the algorithm is reduced by 50% compared with the local window K-distribution algorithm, and the quality factor is improved from 44.4% to 100%. The proposed algorithm not only ensures the real-time performance of the algorithm, but also improves the detection accuracy, and it has a certain application value in the automatic detection of SAR ship.

For the bottleneck relief of urban rail transit station infrastructure is lack of quantitative system analysis and its cost is uncertain, a quantitative analysis model for station bottleneck was put forward, and based on which a new optimization strategy was advanced. First of all, passenger train flow diagram was established, and based on the series-parallel hybrid queuing network system optimization model was constructed; secondly, on the basis of the existing optimization strategy, a new control optimization strategy was put forward: change sequence, namely exchange machine physical sequence of security check device and automatic ticket gate; lastly, in Shanghai Xinzhuang subway station according to the two kinds of optimization strategy, specific optimization schemes were advanced and then simulated. Three optimization schemes effectively reduce the passenger queuing time, but the total cost difference is very big. As for a certain arrival rate, compared with no optimization scheme, optimization scheme one with adding one security device reduced the waiting time by 92.5%, increased the total cost by 3.2%; optimization scheme two with exchanging the sequence of security check device and brake machine, reduced the waiting time by 80.3%, decreased the total cost by 50.4%; and optimization scheme three, the composition of scheme one and two, almost completely eliminated the queue waiting time, but increased the total cost by 29.6%. The result analyses show that the proposed model can well simulate the bottleneck relief cost, and the new strategy is superior to the traditional strategy in reducing the total cost.

In order to solve the problems of having nonlinear observation equations and being susceptible to initial value of filtering in bearings-only target tracking, a range-parameterized hybrid coordinates Square Root Cubature Kalman Filter (SRCKF) algorithm was proposed. Firstly,it applied the SRCKF to hybrid coordinates,obtained better tracking effect than the SRCKF under Cartesian coordinates. And then it combined the range parameterization strategy with the SRCKF under hybrid coordinates, and eliminated the impact of unobservable range. The simulation results show that the proposed algorithm can significantly improve the accuracy and robustness although the computational complexity increases slightly.

UWB (UltraWideBand) technology is considered to be the most suitable for indoor wireless location and IEEE802.15.4a is the first radio ranging and positioning physical layer IEEE standard. In order to let the sender know the quality of the ranging, FoM (Figure of Merit) is added in this protocol, but how to produce FoM is not given. On the basis of analyzing the statistical characteristics of the received signal energy block, a method based on the joint parameters of skewness and the maximum slope was proposed to estimate the FoM in UWB radio channel. The simulation finds that this method can provide reference for accurate ranging and positioning, and can improve the ranging accuracy about 30% in the CM1(Channel Model) channel.

This paper introduced a low-cost platform for locating bluetooth cell phones and releasing position information. The authors improved the scheme of measuring Received Signal Strength Indication (RSSI) of bluetooth devices to locate multiple cell phones simultaneously, developed cell phone program to receive position information via Wi-Fi and display it on map. The experimental results show that the system is accurate in positioning and easy to use, and it provides a platform support for applications of Internet of Things (IOT) under current hardware condition.

To optimize processing industry’s production,the large history data were analyzed by fuzzy association rules.Firstly,the RFCM algorithm and Apriori algorithm were expounded.The production parameters were fuzzed by fuzzy clustering algorithm.Depending on Fuzzy_ClustApriori algorithm,the fuzzy association rules were studied and implemented in processing industry.Then the valuable fuzzy rules were got.Based on the rules,the production of processing industry can be improved.

Time-interval sequential pattern mining was used to discover frequent subsequences as patterns from sequence database of flowing industry. Firstly, the large history database were analyzed by fuzzy theory and the exceptional equipment parameters were found. After scattering exceptional parameters by Time-window approach, a new time-interval sequential database was got by dealing with time intervals. In order to find time-interval sequential pattern, TimeSeq_PrefixSpan algorithm is developed from the conventional PrefixSpan algorithm and implemented in flowing industry's production. Then the models can monitor faults when the equipments circulating.

Development of WLAN and its security protocols were presented. Then researches were made on some of the most important security standards formed in the development. Finally, based on the characteristics of these protocols the implementation was displayed.