In order to solve the problems that the low-level features of the backbone are not fully utilized， and the effective features are lost due to large-times upsampling in DeepLabV3+ semantic segmentation， a Cumulative Distribution Channel Attention DeepLabV3+ （CDCA-DLV3+） model was proposed. Firstly， a Cumulative Distribution Channel Attention （CDCA） was proposed based on the cumulative distribution function and channel attention. Then， the cumulative distribution channel attention was used to obtain the effective low-level features of the backbone part. Finally， the Feature Pyramid Network （FPN） was adopted for feature fusion and gradual upsampling to avoid the feature loss caused by large-times upsampling. On validation set Pascal Visual Object Classes （VOC）2012 and dataset Cityscapes， the mean Intersection over Union （mIoU） of CDCA-DLV3+ model was 80.09% and 80.11% respectively， which was 1.24 percentage points and 1.02 percentage points higher than that of DeepLabV3+ model. Experimental results show that the proposed model has more accurate segmentation results.

The existing fall detection works mainly focus on indoor scenes， and most of them only model people’s body posture features， ignoring background information of the scene and the interaction information between people and the ground. Aiming at the problem， from the perspective of practical application of elevator scene， a fall detection algorithm based on scene prior and attention guidance was proposed. Firstly， elevator historical data was used to automatically learn the scene prior information from people’s trajectories by Gaussian probability distribution modelling. Then， the scene information was taken as a spatial attention mask and fused with the global features of the neural network to focus on local information of the ground area. After that， the fused local and global features were further aggregated using adaptive weighting method to improve the robustness and discriminative ability of the generated features. Finally， the features were fed into a classifier module consisting of a global average pooling layer and a fully connected layer to perform the fall prediction and classification. Experimental results show that the detection accuracy of the proposed algorithm on the self-built elevator scene dataset Elevator Fall Detection Dataset and the public UR Fall Detection Dataset reached 95.36% and 99.01% respectively， which is increased by 3.52 percentage points and 0.61 percentage points respectively compared with that of ResNet50 with complicated network structure. It can be seen that proposed attention mechanism with Gaussian scene prior guidance can make the network focus on information of the ground area， which is more conducive to detect fall events. By using it， the detection model has high accuracy， and the algorithm meets the real-time application requirements.

With the rapid development of the Internet of Things (IoT), security of constrained devices suffer a serious challenge. LightWeight Cryptography (LWC) as the main security measure of constrained devices is getting more and more attention of researchers. The recent advance in issues of lightweight cryptography such as design strategy, security and performance were reviewed. Firstly, design strategies and the key issues during the design were elaborated, and many aspects such as principle and implementation mechanisms of some typical and common lightweight cryptography were analyzed and discussed. Then not only the commonly used cryptanalysis methods were summarized but also the threat of side channel attacks and the issues should be noted when adding resistant mechanism were emphasized. Furthermore, detailed comparison and analysis of the existing lightweight cryptography from the perspective of the important indicators of the performance of lightweight cryptography were made, and the suitable environments of hardware-oriented and software-oriented lightweight cryptography were given. Finally, some unresolved difficult issues in the current and possible development direction in the future of lightweight cryptography research were pointed out. Considering characteristics of lightweight cryptography and its application environment, comprehensive assessment of security and performance will be the issues which worth depth researching in the future.

Lexical stress is an important prosodic feature, especially for stress-timed language such as English. To overcome the defects of fixed frame-length features, pitch synchronization feature analysis method was proposed while Pitch Synchronization Energy (PSE) and Pitch Synchronization Peak (PSP) features were defined and extracted. Their contributions, along with traditional features and their combinations, to English lexical stress detection were evaluated with ISLE database. Experimental results show that the combination of new feature and traditional features demonstrates a 6.65% error rate reduction compared with using traditional ones.