{"title":"GMP-based Iterative Equalization in MIMO ISI Channels","authors":"Fu Songying, Liu Lizhe, Shen Binsong","doi":"10.1109/ICCC47050.2019.9064157","DOIUrl":"https://doi.org/10.1109/ICCC47050.2019.9064157","url":null,"abstract":"In iterative equalization, the main problem to be solved is to gain the input information according to posterior probability density. To solve this problem, a generalized message passing algorithm is adopted, which can effectively perform channel estimation and obtain channel response information required for equalization. In MIMO receiving system, by designing an improved STBC frame structure, we can utilize the diversity gain brought by the system, and the receiving signal structure is convenient for calculation, and the performance of iterative reception can be improved. The detector includes a soft equalizer and a channel estimator, whose principle is similar to that of a soft equalizer and a soft decoder. It exchanges external information block by block according to an iterative algorithm, thereby improving the performance of the equalizer. By analyzing the performance of the algorithm, it is proposed to increase the semi-adaptive damping coefficient to ensure the independence of the equalizer and the interleaver. The results show that the performance of the improved algorithm is better than that of the receiver system without the improved STBC frame structure and without the GMP channel estimation algorithm in the MIMO receiving system.","PeriodicalId":6739,"journal":{"name":"2019 IEEE 5th International Conference on Computer and Communications (ICCC)","volume":"26 1","pages":"784-791"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82861083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yong Chen, T. Zhang, Yueming Cai, Yongxiang Liu, Weiwei Yang
{"title":"Secure Transmission of Cognitive Wiretap Networks Based on Differential Spatial Modulation","authors":"Yong Chen, T. Zhang, Yueming Cai, Yongxiang Liu, Weiwei Yang","doi":"10.1109/ICCC47050.2019.9064057","DOIUrl":"https://doi.org/10.1109/ICCC47050.2019.9064057","url":null,"abstract":"This paper studies the secrecy performance of cognitive wiretap networks, in which the secrecy secondary transmitter (Alice) transmits information to multiple secondary receivers (Bobs) in the presence of an eavesdropper (Eve). Specifically, to reduce the power consumption and complexity of the secondary users, this paper put forward a novel transmission scheme based on the differential spatial modulation (DSM), which can reduce the processing complexity at Alice and Bobs. Moreover, the exact and asymptotic closed-form expressions, for secrecy outage probability and effective secrecy throughput, are derived to assess the performance of the proposed DSM scheme. From the analysis, we demonstrate that a) the secrecy diversity is determined by the number of the Alice’s antennas and the number of Bobs, b) the secrecy performance of the considered cognitive wiretap networks based on DSM outperforms the same networks without DSM.","PeriodicalId":6739,"journal":{"name":"2019 IEEE 5th International Conference on Computer and Communications (ICCC)","volume":"99 1","pages":"1559-1564"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86576270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jianpeng Li, Chuanjiang Guan, Guozhen Shi, Yang Li
{"title":"An Improved Real-time Scheduling Algorithm Based on Deadline Constraint","authors":"Jianpeng Li, Chuanjiang Guan, Guozhen Shi, Yang Li","doi":"10.1109/ICCC47050.2019.9064437","DOIUrl":"https://doi.org/10.1109/ICCC47050.2019.9064437","url":null,"abstract":"With multi-core processors becoming standard hardware platforms for various computing systems, the integration of real-time systems on multi-core hardware has become an inevitable trend. Designing real-time scheduling technology for multi-processor systems is a key issue for system designers. According to the urgent degree of tasks in different processing stages, an improved real-time scheduling algorithm based on deadline constraints (IDCSA) is proposed, including the task insertion feasible condition in the node queues and the task preemption condition of the node resources. While prioritizing urgent tasks, it avoids hanging tasks and waiting tasks missing their deadlines. The simulation results show that the algorithm has good load balancing performance and resource utilization. Compared with other two real-time scheduling algorithms, the success rate of task scheduling in IDCSA increases by more than 10% and 8% respectively.","PeriodicalId":6739,"journal":{"name":"2019 IEEE 5th International Conference on Computer and Communications (ICCC)","volume":"47 1","pages":"23-28"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91525109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Pedestrian Gender Detection Based on Mask R-CNN","authors":"Xinyue Li, Samuel Cheng","doi":"10.1109/ICCC47050.2019.9064348","DOIUrl":"https://doi.org/10.1109/ICCC47050.2019.9064348","url":null,"abstract":"Based on Mask R-CNN, one of the most significant models for instance segmentation, we present our pedestrian gender detection algorithm in this paper. First of all, we verify the effectiveness of Mask R-CNN in pedestrian detection. On this basis, we design our network by merging Mask R-CNN with a gender recognition branch. And due to the lack of current datasets, we not only present a method to train our model, but also build a shopping mall dataset to test our model. The application of our detection model to our small dataset is of great significance to the business planning and the security maintenance of the entertainment center.","PeriodicalId":6739,"journal":{"name":"2019 IEEE 5th International Conference on Computer and Communications (ICCC)","volume":"103 3","pages":"2082-2086"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91553242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Structure-from-Motion Retrieval from VideoSAR Sequences: Toward a Unified Framework","authors":"Ying Zhang, Daiyin Zhu, Gong Zhang, H. Leung","doi":"10.1109/ICCC47050.2019.9064214","DOIUrl":"https://doi.org/10.1109/ICCC47050.2019.9064214","url":null,"abstract":"Video synthetic aperture radar (VideoSAR) has attracted significant attention in the computer vision-based information retrieval field. In this paper, a unified three-dimensional reconstruction framework is proposed for both the straight and circular VideoSAR modes, and is suitable for height retrieval with/without geometric prior knowledge. First, two VideoSAR imaging modes are presented to explain the formation of dynamic observation. Then, structure-from-motion retrieval method with/without geometric prior knowledge is proposed by exploiting shadow information. Finally, experimental results based on VideoSAR fragments measured by the airborne and MiniSAR systems demonstrate the effectiveness of the unified retrieval method with a higher accuracy compared with that of a single SAR imagery.","PeriodicalId":6739,"journal":{"name":"2019 IEEE 5th International Conference on Computer and Communications (ICCC)","volume":" 13","pages":"736-740"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91411679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Study on the Safety Situation Prediction of Virtual Network Loads","authors":"Dong Liu, Jing Chang","doi":"10.1109/ICCC47050.2019.9064403","DOIUrl":"https://doi.org/10.1109/ICCC47050.2019.9064403","url":null,"abstract":"The prediction of virtual network loads is an important part of the planning in a virtual network load system, which also lays the foundation for the system’s production and operation. Therefore, sound prediction of virtual network loads is essential for the planning and the operation of virtual network load systems. Based on related literature concerning the applications of the gray neural network model in the prediction of virtual network loads, the study on the safety situation prediction of virtual network loads was provided from the perspective of the gray neural network model in this paper. The experiments showed that the prediction performance of complex virtual network loads based on the gray neural network model optimization was excellent.","PeriodicalId":6739,"journal":{"name":"2019 IEEE 5th International Conference on Computer and Communications (ICCC)","volume":"68 1","pages":"1192-1196"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83045117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Design of a THz Microstrip Fast Wave Antenna","authors":"Zheng Liu, Xiaodian Cheng, Qi Zhu","doi":"10.1109/ICCC47050.2019.9064225","DOIUrl":"https://doi.org/10.1109/ICCC47050.2019.9064225","url":null,"abstract":"A novel terahertz(THz) microstrip fast wave antenna working at 200 GHz is proposed in this paper. Firstly an odd-mode feeding method using CPS is employed to excite fast wave in the THz microstrip structure. Then the THz slot ring antenna is fed by the odd-mode feed network structure based on fast wave theory, which can enlarge the size of antenna for reducing design redundancy of THz antenna, manufacturing difficulty and decreasing fabrication cost. Furthermore, compared with an equally sized ring fed by a common microstrip, simulation results verify that the radiation pattern of our proposed fast wave antenna does not have a deep depression in the zenith direction with a enlarged size. It meets the requirements of many applications.","PeriodicalId":6739,"journal":{"name":"2019 IEEE 5th International Conference on Computer and Communications (ICCC)","volume":"61 1","pages":"696-700"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83151891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Classification of Precipitation Particles Types Using Images from Precipitation Microphysical Characteristics Sensor","authors":"Xichuan Liu, Binsheng He, Kang Pu, Yuntao Hu","doi":"10.1109/ICCC47050.2019.9064276","DOIUrl":"https://doi.org/10.1109/ICCC47050.2019.9064276","url":null,"abstract":"To make full use of the images data from a newly designed precipitation micro-physical characteristics sensor (CPMS), a classification method of precipitation particles based on support vector machine (SVM) techniques is presented in this paper. Firstly, a set of descriptors including fall velocity, size, shape, and pixel parameters of precipitation particles is calculated. Secondly, the descriptors of one-minute sample are calculated by the mean values of 16 feature descriptors from all particles in one minute. Thirdly, the proposed classification model identifies the following five types of precipitation particles: small crystal snowflakes, dendric snowflakes, columnar snowflakes, aggregated snowflakes, and raindrops. More than 4,000 images of precipitation particles are divided into a training set with 94 samples and a testing set with 117 samples with 1-min resolution. The results show that the SVM classification model have good performance, the OA and K are 94% and 0.92 respectively, and the OA values of each type are more than 85%. Above results demonstrate the PMCS’s capability to classify the types of precipitation particles, which can be used as an automatic observation system for present weather, water monitoring, etc.","PeriodicalId":6739,"journal":{"name":"2019 IEEE 5th International Conference on Computer and Communications (ICCC)","volume":"37 1","pages":"576-580"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79109266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Distributed Source Coding Based on Denoising Theory","authors":"Zhiyuan He, Jianhua Chen, Jingjian Li","doi":"10.1109/ICCC47050.2019.9064272","DOIUrl":"https://doi.org/10.1109/ICCC47050.2019.9064272","url":null,"abstract":"Aiming at the error propagation problem and other shortcomings of existing distributed source coding (DSC) schemes, we propose a DSC scheme based on the denoising theory (DSCBDT). First, the amount of data that need to be transmitted is reduced by sampling the source output sequence. The un-sampled sequence is compressed by conventional arithmetic coder. Finally, the receiver recovers the sampled symbols by making full use of the correlation between the side-information (SI) sequence and the un-sampled sequence. The experiment results show that compared with the low-density parity-check (LDPC) code-based DSC scheme, the proposed scheme shows higher compression ratios and lower error rate when the correlation between the sources is weak. Specifically, the error propagation problem is effectively avoided. The DSCBDT scheme is highly efficient, practical and easy to implement.","PeriodicalId":6739,"journal":{"name":"2019 IEEE 5th International Conference on Computer and Communications (ICCC)","volume":"60 1","pages":"1537-1541"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79110741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lufeng Yuan, Jun Wang, Shifeng Fan, Yingying Bian, Binming Yang, Yueyue Wang, Xiaobin Wang
{"title":"Automatic Legal Judgment Prediction via Large Amounts of Criminal Cases","authors":"Lufeng Yuan, Jun Wang, Shifeng Fan, Yingying Bian, Binming Yang, Yueyue Wang, Xiaobin Wang","doi":"10.1109/ICCC47050.2019.9064408","DOIUrl":"https://doi.org/10.1109/ICCC47050.2019.9064408","url":null,"abstract":"We research how automatically predict the charges and relevant law articles of criminal cases in our work. At first, the distributions of charges, relevant law articles and fact description of criminal cases are analyzed based on CAIL2018. CAIL2018, the first large-scale dataset for legal judgment prediction in China, contains large amounts of criminal cases collected from the Supreme People’s Court of China. By our analysis, we find the distribution of criminal cases is typical 8020 distribution. Then we present our framework to predict criminal cases automatically. In our framework, data enhancement, oversampling, key word extraction are used to optimize data quality, and deep learning is employed to predict charges and relevant articles. In the prediction, single deep learning model is tested firstly, then ensemble of different deep learning models are compared to achieve better performance than that of single model. In our work, we find data enhancement and ensemble strategy can improve the performance of judgment prediction. More differences of joint models and data, better performance of ensemble strategy.","PeriodicalId":6739,"journal":{"name":"2019 IEEE 5th International Conference on Computer and Communications (ICCC)","volume":"396 1","pages":"2087-2091"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83474815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}