Hongmei Zhao, Huikun Xu, Jielei Zhao, Xuebin Li, Kunfeng Shi
{"title":"Modeling and Analyzing for Human Body Blockage in Millimeter Wave at 28 GHz in Crowded Indoor Environment","authors":"Hongmei Zhao, Huikun Xu, Jielei Zhao, Xuebin Li, Kunfeng Shi","doi":"10.5220/0008097800780083","DOIUrl":"https://doi.org/10.5220/0008097800780083","url":null,"abstract":"Millimeter wave (mm-wave) communication is a promising way of wireless communication in the future. This paper analyses mm-wave propagation characteristics, including path loss, shadow fading and delay spread at 28GHz in a crowded meeting room scenario. In order to obtain more realistic simulation results, we consider the electromagnetic parameters of human skin and clothes. At the same time, we set different transmitter (TX) heights and compare the influence of different TX heights on channel parameters. The result shows that path loss and delay spread would be increased due to the existence of human body blockages. With the increase of TX heights, the path loss factor n would be decreased. All routes shadow fading obey the normal distribution with a mean of 0. This paper provides a theoretical guiding for the design of wireless communication system in a crowded indoor environment.","PeriodicalId":415288,"journal":{"name":"Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications","volume":"32 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114111420","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":"Saliency Guided Depth Prediction from a Single Image","authors":"Yu Wang, Lizhuang Ma","doi":"10.5220/0008099101530159","DOIUrl":"https://doi.org/10.5220/0008099101530159","url":null,"abstract":"With the recent surge of deep neural networks, depth prediction from a single image has seen substantial progress. Deep regression networks are typically learned from large data without much constraints about the scene structure, thus often leading to uncertainties at discontinuous regions. In this paper, we propose a structure-aware depth prediction method based on two observations: depth is relatively smooth within the same objects, and it is usually easier to model relative depth than model the absolute depth from scratch. Our network first predicts an initial depth map and takes an object saliency map as input, which helps to teach the network to learn depth refinement. Specifically, a stable anchor depth is first estimated from the detected salient objects, and the learning objective is to penalize the difference in relative depth versus the estimated anchor. We show such saliency-guided relative depth constraint unveils helpful scene structures, leading to significant gains on the RGB-D saliency dataset NLPR and depth prediction dataset NYU V2. Furthermore, our method is appealing in that it is pluggable to any depth network and is trained end-to-end with no overhead of time during testing.","PeriodicalId":415288,"journal":{"name":"Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130267113","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":"Community-based Message Forwarding in Mobile Social Networks","authors":"Zhiming Chen, Yang-Zhao Xiang","doi":"10.5220/0008097401970202","DOIUrl":"https://doi.org/10.5220/0008097401970202","url":null,"abstract":"With the popularity of various smart devices and the application of sensor network technology, message transmission using mobile devices is becoming widespread. This paper focuses on the forwarding in mobile social network (MSN). The MSN is a special Delay Tolerant Network (DTN) consisting of mobile nodes. In MSN, nodes move and share information with each other through carried short-range wireless communication devices. Mobile nodes in the MSN typically access some building areas more frequently, such as schools, companies, or apartments, while visiting other areas, such as the roads between buildings, less frequently. The building areas that nodes frequently visit are called communities. To increase delivery ratio and reduce transmission time in MSN, this paper proposes a novel zero-knowledge multi-copy routing algorithm, Mixed Message Forwarding (MMF) which exploits and improves the metric, namely centrality. Centrality reflects the importance of a node in the network. MMF improves copy diffusion by using different directions of node movement as well. Special facilities called boundary boxes are added to the network scenario. Boundary boxes are special throw boxes. Throw boxes are relays with large storage space and fixed position. MMF is designed and evaluated, which utilizes the aforementioned boundary boxes to reduce transmission delay. The simulation results show that the MMF can improve the delivery ratio and reduce the transmission delay, compared with other algorithms.","PeriodicalId":415288,"journal":{"name":"Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129289948","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":"A Blockchain-based Housing Rental System","authors":"Qi-Long Chen, Rong-hua Ye, Fei-Long Lin","doi":"10.5220/0008097201840190","DOIUrl":"https://doi.org/10.5220/0008097201840190","url":null,"abstract":"This paper proposes a blockchain-based solution for housing rental system to achieve peer-to-peer sharing of listings information in an intermediary-free manner. The new solution programmatically converts traditional lease agreements into smart lease contracts, improve the efficiency of the leasing process, and store transaction records for the leasing process to provide legal protection for tenants and landlords. The emerging technologies, including smart contract and IPFS (Inter Planetary File System) are exploited in the new system. IPFS is used to store listings information. The Ethereum’s Smart Contract is used to manage the hash of IPFS listings information, and a smart lease contract is written to make the housing rental process traceable and","PeriodicalId":415288,"journal":{"name":"Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122343958","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":"DNN Based Cooperative Calibration for Master-Slave Multi-Camera Network","authors":"Congcong Hua, Shaozhuo Zhai, Yuehu Liu","doi":"10.5220/0008096401710177","DOIUrl":"https://doi.org/10.5220/0008096401710177","url":null,"abstract":"Master-slave camera systems, consisting of wide-field and pan-tilt-zoom (PTZ) camera, are widely applied in surveillance. They can monitoring the wide scene, and the high-resolution details of interesting target can be captured by PTZ camera. In order to achieve this function, the accurate cooperative calibration for these system is a prerequisite. However, the nonlinear changing PTZ parameters (e.g. intrinsic and extrinsic) with pan, tilt and zoom lead to inaccurate calibration by existing methods. What's more, the process of traditional step-by-step calibration method makes accumulative error. In this paper, we provide a new end-to-end deep neural network for cooperative calibration. This network establishes a mapping relationship between pix coordinate in wide-field camera and control parameters of the PTZ camera. By this model, the control parameters of the PTZ camera can be acquired without any complex camera calibration operation. Experiments show that the proposed neural network has little calibration errors as compared to the ground","PeriodicalId":415288,"journal":{"name":"Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125131167","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":"Product Identification Based on Unsupervised Detection Keypoint Alignment and Convolutional Neural Networks","authors":"K. Le","doi":"10.5220/0008099201600165","DOIUrl":"https://doi.org/10.5220/0008099201600165","url":null,"abstract":"Traditional shelf auditing is a manual audit. With the development of computer vision and deep learning technology, it has become possible to use machine automatic image recognition instead of manual auditing. Existing product identification is based on the use of two-dimensional code recognition and radio frequency identification (RFID), which relies on hardware and is relatively expensive. The training data of product identification is difficult to collect. This paper proposes a product identification method based on convolutional neural network, and explores how to effectively obtain the product data sets. At the same time, it introduces the unsupervised keypoint detection alignment method for the product detection part, and proves that it can improve the correct rate of product identification.","PeriodicalId":415288,"journal":{"name":"Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122068936","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":"3D Bounding Box Generative Adversarial Nets","authors":"Ping Kuang, Haoshuang Wang","doi":"10.5220/0008096801170121","DOIUrl":"https://doi.org/10.5220/0008096801170121","url":null,"abstract":"Recently, Generative Adversarial Networks (GANs) gradually applied to the generation of 3D objects and has achieved remarkable success, but at the same time, it also faces some problems, such as the training instability, low-quality samples and mode collapse. We propose a novel framework, namely 3D Bounding Box Generative Adversarial Network (3D-BBGAN), which can reduce the probability space of generation by adding conditional information. According this way, we can get 3D objects with more detailed geometries.","PeriodicalId":415288,"journal":{"name":"Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications","volume":"262 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123241552","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":"TOF Indoor Location Algorithm based on RBF Neural Network","authors":"Hongmei Zhao, Jielei Zhao","doi":"10.5220/0008097901280134","DOIUrl":"https://doi.org/10.5220/0008097901280134","url":null,"abstract":"In the UWB positioning system, due to the existence of multipath effects, NLOS and other factors, a certain degree of measurement error will result. In particular, the NLOS error has become a key factor affecting the positioning accuracy. Large NLOS errors often lead to a sharp decline in the positioning performance of UWB Indoor Positioning System. In this paper, a large number of data with NLOS error is used as a sample, and it is trained by RBF artificial neural network algorithm. In this way, the influence of the NLOS error can be eliminated at the source, and the positioning accuracy of the TOF can be improved.","PeriodicalId":415288,"journal":{"name":"Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications","volume":"137 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128666262","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}