A Simplified Min-Sum Decoding Algorithm for Non-Binary LDPC Codes

Non-binary low-density parity-check codes are robust to various channel impairments. However, based on the existing decoding algorithms, the decoder implementations are expensive because of their excessive computational complexity and memory usage. Based on the combinatorial optimization, we present an approximation method for the check node processing. The simulation results demonstrate that our scheme has small performance loss over the additive white Gaussian noise channel and independent Rayleigh fading channel. Furthermore, the proposed reduced-complexity realization provides significant savings on hardware, so it yields a good performance-complexity tradeoff and can be efficiently implemented.Comment: Partially presented in ICNC 2012, International Conference on Computing, Networking and Communications. Accepted by IEEE Transactions on Communication

Investigation on dynamics of a three-directional coupled vehicle-road system

When a vehicle is braking or turning, the longitudinal or lateral tire forces increase greatly and it is necessary to consider the effects of vertical, longitudinal and lateral tire forces on vehicle and road dynamics. This work aims to propose a three-directional coupled vehicle-road system for revealing the properties of three-directional (3D) interaction between vehicle and road. A 23-DOF full-body heavy vehicle model considering the nonlinearity of suspension damping and tire stiffness is built, and a double-layer rectangular thin plate on viscoelastic foundation with four simply supported boundaries is employed to model the road. The equations of motion of vehicle and road, and the 3D tire forces connecting the vehicle and road are formulated. The responses of 3D coupled, vertical coupled and uncoupled vehicle-road model are compared in four maneuver conditions and the effects of parameters on 3D vehicle-road interaction are discussed. It is found that both the 3D coupled model and the vertical coupled model are good enough to predict vehicle responses accurately, but the 3D coupled model is the most suitable for calculating road responses accurately. During the maneuver of sharp steering or emergency braking, or when a vehicle runs on a road with small surface roughness and big adhesion coefficient, the role of 3D vehicle-road interaction becomes too important to be neglected

GFF: Gated Fully Fusion for Semantic Segmentation

Semantic segmentation generates comprehensive understanding of scenes through densely predicting the category for each pixel. High-level features from Deep Convolutional Neural Networks already demonstrate their effectiveness in semantic segmentation tasks, however the coarse resolution of high-level features often leads to inferior results for small/thin objects where detailed information is important. It is natural to consider importing low level features to compensate for the lost detailed information in high-level features.Unfortunately, simply combining multi-level features suffers from the semantic gap among them. In this paper, we propose a new architecture, named Gated Fully Fusion (GFF), to selectively fuse features from multiple levels using gates in a fully connected way. Specifically, features at each level are enhanced by higher-level features with stronger semantics and lower-level features with more details, and gates are used to control the propagation of useful information which significantly reduces the noises during fusion. We achieve the state of the art results on four challenging scene parsing datasets including Cityscapes, Pascal Context, COCO-stuff and ADE20K.Comment: accepted by AAAI-2020(oral

Herding Effect based Attention for Personalized Time-Sync Video Recommendation

Time-sync comment (TSC) is a new form of user-interaction review associated with real-time video contents, which contains a user's preferences for videos and therefore well suited as the data source for video recommendations. However, existing review-based recommendation methods ignore the context-dependent (generated by user-interaction), real-time, and time-sensitive properties of TSC data. To bridge the above gaps, in this paper, we use video images and users' TSCs to design an Image-Text Fusion model with a novel Herding Effect Attention mechanism (called ITF-HEA), which can predict users' favorite videos with model-based collaborative filtering. Specifically, in the HEA mechanism, we weight the context information based on the semantic similarities and time intervals between each TSC and its context, thereby considering influences of the herding effect in the model. Experiments show that ITF-HEA is on average 3.78\% higher than the state-of-the-art method upon F1-score in baselines.Comment: ACCEPTED for ORAL presentation at IEEE ICME 201