At present， most pooling methods mainly extract aggregated feature information from the 1-order pooling layer or the 2-order pooling layer， ignoring the comprehensive representation capability of multiple pooling strategies for scenes， which affects the scene recognition performance. To address the above problems， a joint model with first- and second-order pooling networks learning for remote sensing scene classification was proposed. Firstly， the convolutional layers of residual network ResNet-50 were utilized to extract the initial features of the input images. Then， a second-order pooling approach based on the similarity of feature vectors was proposed， where the information distribution of feature values was modulated by deriving their weight coefficients from the similarity between feature vectors， and the efficient second-order feature information was calculated. Meanwhile， an approximate solving method for calculating square root of covariance matrix was introduced to obtain the second-order feature representation with higher semantic information. Finally， the entire network was trained with the combination loss function composed of cross-entropy and class-distance weighting. As a result， a discriminative classification model was achieved. The proposed method was tested on AID （50% training proportion）， NWPU-RESISC45 （20% training proportion）， CIFAR-10 and CIFAR-100 datasets and achieved classification accuracies of 96.32%， 93.38%， 96.51% and 83.30% respectively， which were increased by 1.09 percentage points， 0.55 percentage points， 1.05 percentage points and 1.57 percentage points respectively， compared with iterative matrix SQuare RooT normalization of COVariance pooling （iSQRT-COV）. Experimental results show that the proposed method effectively improves the performance of remote sensing scene classification.

Aiming at the lack of built-in security mechanism in Message Queue Telemetry Transport (MQTT) protocol to protect communication information between the Internet of Things (IoT) devices, as well as the problem that the credibility of MQTT broker is questioned in the new concept of zero trust security, a new solution based on proxy re-encryption for implementing secure end-to-end data transmission between publisher and subscriber in MQTT communication was proposed. Firstly, the Advanced Encryption Standard (AES) was used to symmetrically encrypt the transmitted data for ensuring the confidentiality of the data during the transmission process. Secondly, the proxy re-encryption algorithm that defines the MQTT broker as a semi-honest participant was adopted to encrypt the session key used by the AES symmetric encryption, so as to eliminate the implicit trust of the MQTT broker. Thirdly, the computation of re-encryption key generation was transferred from clients to a trusted third party for the applicability of the proposed scheme in resource-constrained IoT devices. Finally, Schnorr signature algorithm was employed to digitally sign the messages for the authenticity, integrity and non-repudiation of the data source. Compared with the existing MQTT security schemes, the proposed scheme acquires the end-to-end security features of MQTT communication at the expense of the computation and communication overhead equivalent to that of the lightweight security scheme without end-to-end security.

Concerning the insufficient consideration of the cumulative error and offset which online Global Positioning System (GPS) trajectory data compression based on motion state change and the insufficient key point evaluation of online GPS trajectory data compression based on the offset calculation, an online compression of GPS trajectory data based on motion state change, named Synchronous Euclidean Distance (SED) Limited Thresholds Algorithm (SLTA), was proposed. This algorithm used steering angle and speed change to evaluate information of trajectory point. At the same time, SLTA introduced the SED to limit offset of trajectory point. So SLTA could reach better information retention. The experimental results show that the trajectory compression ratio can reach about 50%. Compared with Thresholds Algorithm (TA), the average SED error (less than 5 m) of SLTA can be negligible. For other trajectory data compression algorithms, SLTA's average angel error is the lowest (1.5°-2.3°) and run time is the most stable. SLTA can stably and effectively do online GPS trajectory data compression.

This paper proposed a new, real-time and robust local feature and descriptor, which can be applied to computer vision field with high demands in real-time. Since CenSurE has extremely efficient computation,it has got wide attention. Due to its linear scales, the filter response signal is very sparse and cannot acquire high repeatability. Therefore, this paper modified the detector using logarithmic scale sampling, and obtained better performance. The new rapid descriptor was based on gradient of the summed image patch, called GSIP. The GSIP descriptor has superior performance. An extensive experimental evaluation was performed to show that the GSIP descriptor increases the distinctiveness of local image descriptors for image region matching and object recognition compared with the state-of-the-art SURF descriptor. Furthermore, compared with SURF, GSIP achieves a two-fold speed increase.

The traditional training methods of Gaussian Mixture Model (GMM) are sensitive to the initial model parameters, which often leads to a local optimal parameter in practice. To resolve this problem, a new GMM optimization method was proposed based on Particle Swarm Optimization (PSO). It utilized Maximum Likelihood (ML) algorithm in the PSO iteration and provided a new architecture of hybrid algorithm. Because of the global optimization characteristic of the particle swarm optimizer method and the strong local searching capacity of ML, it can obtain model parameters with high precision. Experiment for text-independent speaker identification shows that this method can obtain more optimum GMM parameters and better results than the traditional method.

Because the heterogeneous information is relatively loose and independent in seamless transportation, data grid is adopted to manage the distributed heterogeneous information and realize resources share and information integration. The paper presents a architecture of seamless transportation information grid(STIG),the architecture integrates Information integration, semantic query, transportation planning, etc key technologies. At last, take combined rail and sea transport for example, the process of multimodal transportation based on STIG was described.