H. Haggag, M. Hossny, D. Filippidis, D. Creighton, S. Nahavandi, V. Puri
{"title":"Measuring depth accuracy in RGBD cameras","authors":"H. Haggag, M. Hossny, D. Filippidis, D. Creighton, S. Nahavandi, V. Puri","doi":"10.1109/ICSPCS.2013.6723971","DOIUrl":"https://doi.org/10.1109/ICSPCS.2013.6723971","url":null,"abstract":"This paper presents the comparison between the Microsoft Kinect depth sensor and the Asus Xtion for computer vision applications. Depth sensors, known as RGBD cameras, project an infrared pattern and calculate the depth from the reflected light using an infrared sensitive camera. In this research, we compare the depth sensing capabilities of the two sensors under various conditions. The purpose is to give the reader a background to whether use the Microsoft Kinect or Asus Xtion sensor to solve a specific computer vision problem. The properties of the two depth sensors were investigated by conducting a series of experiments evaluating the accuracy of the sensors under various conditions, which shows the advantages and disadvantages of both Microsoft Kinect and Asus Xtion sensors.","PeriodicalId":294442,"journal":{"name":"2013, 7th International Conference on Signal Processing and Communication Systems (ICSPCS)","volume":"139 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":"126014223","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":"Iterative interference alignment techniques for broadband wireless systems with limited feedback","authors":"S. Teodoro, Adão Silva, R. Dinis, A. Gameiro","doi":"10.1109/ICSPCS.2013.6723902","DOIUrl":"https://doi.org/10.1109/ICSPCS.2013.6723902","url":null,"abstract":"Interference alignment (IA) is a promising technique that allows high capacity gains in interfering channels. In this paper we consider iterative IA techniques for the downlink of OFDM-based (Orthogonal Frequency Division Multiplexing) broadband wireless systems with limited feedback. A quantized version of the channel state information (CSI) associated to the different links between base station (BS) and user terminal (UT) is feedback from the UT to the BS, which sends it to the other BSs through a limited-capacity backhaul network. This information is employed by each BS to perform the overall IA design. Our channel quantization method requires much less complexity than random vector quantization based techniques and requires the quantization of a fraction of the channel frequency response samples. The results have shown that a small number of quantization bits per multipath component is enough to allow performance close to one obtained with perfect channel knowledge.","PeriodicalId":294442,"journal":{"name":"2013, 7th International Conference on Signal Processing and Communication Systems (ICSPCS)","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":"130902973","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}