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Peijian Zhang,Aili Qu,Gengxin Sun,Fengjing Shao,Xing Yang,Daoben Li 보안공학연구지원센터 2015 International Journal of Future Generation Communi Vol.8 No.6
A new CDMA system which uses fractional chip shift expanded generalized complementary orthogonal code groups as spectrum spreading code sequences and offer high spectral efficiency is introduced in the paper. This new CDMA system is totally different with conventional CDMA system in access codes designing and spectrum spreading process. In this CDMA system, the access codes of different cells keep orthogonality, which eliminates inter cells interferences (ACI) by aperiodic cross-correlation function (CCF). The spreading process of new CDMA system introduces inter symbols interference like overlap time division multiplexing (OVTDM) deliberately so as to improve spectral efficiency. The construction of expanded generalized complementary orthogonal code groups, shift method, principle of new CDMA system are explained respectively. Analysis and simulation results verify the validity of this new CDMA system.
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Junrui Xue,Yutan Wang,Aili Qu,Yingpeng Dai 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.6
Semantic segmentation is an effective means for autonomous robots to understand the surrounding scenes. For autonomous robot, it requires the balance of accuracy and speed. Moreover, it is necessary to correctly extract environmental information in complex environments such as occlusion, poor illumination, and shadows condition. To solve above problems, a novel image-based Multi-scale Feature Extraction Network (MFENet) is designed for real-time semantic segmentation task. This network preserves different level features in the encoder and fuses those features to accurately segment each object. In addition, to enhance the representation ability, fusion module is introduced for information exchange between feature maps with different spatial resolution. Moreover, standard convolution is replaced by Multiscale Feature Extraction (MFE) module in intermediate layers, which could strengthen the feature extraction ability. On the Cityscapes dataset, MFENet achieves 72.4% Mean Intersection over Union (MIoU) with 8.0 million parameters at the speed of 30.5 FPS on a single GTX 1070Ti card. Finally, MFENet is deployed on an autonomous robot and tested in the real world. It produces good semantic segmentation results at the speed of 65.5 FPS. The results reveals the proposed MFENet could work well in real-world applications.
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