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The effects of high pressure (100–500 MPa) pretreatment on the properties of reduced salt (1.5% of NaCl, w/w) surimi (RSS) containing 0.8% (w/w) κ‐carrageenan (RSS‐K) gels were investigated. Results showed that the gel strength and water‐holding capacity of RSS‐K gels obtained the maximum value at 300 MPa. The low‐field nuclear magnetic resonance revealed that pretreatment at 300 MPa shortened the T2 spin–spin relaxation time of RSS‐K gels and increased the proportion of immobilized water in gels. Scanning electron microscopy imaging suggested that the 3D network structure of RSS‐K gels became smoother, more continuous and uniform, and denser at 300 MPa. These results indicated that pretreatment at 300 MPa could improve the gel properties by changing the water state and microstructure of RSS‐K gels. Practical applications Consumers' growing awareness of the reduction of NaCl intake has led the aquatic product processing industry to develop healthier surimi‐based products with lower NaCl. However, the gel‐forming ability of surimi is reduced by less NaCl, so effective technologies are required to facilitate gelation for the improvement of the gel properties. Adding κ‐carrageenan (dietary fiber) and high pressure processing can be a potential method to improve the gel quality of reduced NaCl surimi gel.

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As road traffic sign recognition is a crucial component for automatic driver assistance systems, it is a key problem in computer vision as well. Therefore, in this paper, we study on the problem of road traffic sign recognition utilising the computer vision technology. The main innovation of this paper is to propose an improved convolutional neural network, and then use it to tackle the road traffic sign recognition problem. Convolutional neural network can learn features from training data set, and a convolutional network contains alternating layers of convolution and pooling. Particularly, RGB traffic images are transformed to grey scale images, and then grey scale images are input to the improved convolutional neural network. Furthermore, the fixed layers are utilised to discover region of interests, and the learnable layers are used to extract features. In general, output information of the proposed two learnable layers are input to the classifier separately, and parameters of learnable layers and the classifier are trained at the same time. Finally, GTSDB data set is chosen to make performance evaluation, among which 600 images and 300 images are regarded as training and testing data set respectively. Experimental results demonstrate that the improved CNN-based traffic sign recognition performs better than the traditional CNN.

 Abstract-Nowadays, Dedicated Short Range Communication (DSRC) system's application is increasing fast, and has been applied to electronic toll collection, parking management, traffic control and other fields. Meanwhile, the complexity and quantity of data is also increasing, thus making the implementation and security requirements more strict. Compared with Turbo codes, Low Density Parity Check (LDPC) codes can achieve a better safety and performance, which have been widely studied. Based on this, we try to apply LDPC codes into DSRC system. In this paper, we first introduce the basic content of DRSC standard, and then analyze LDPC codes in DSRC system. At the end, by simulations, we do comparisons between LDPC codes and conventional Turbo codes in DSRC system when using different decoding algorithms and in different channels.