G. Abdrakhmanova, E. Grakhova, N. Knyazev, I. Meshkov, G. Voronkov, A. Sultanov
{"title":"Microstrip ultra-wideband antenna measurements","authors":"G. Abdrakhmanova, E. Grakhova, N. Knyazev, I. Meshkov, G. Voronkov, A. Sultanov","doi":"10.1109/USBEREIT.2018.8384610","DOIUrl":"https://doi.org/10.1109/USBEREIT.2018.8384610","url":null,"abstract":"New microstrip ultra-wideband antenna is presented and measured in this paper. The antenna was calculated to operate in 3.1–10.6 GHz ultra-wideband frequency band, even though the measurements revealed that it covers the band 3.4–12 GHz. It's manufactured on the basis of Rogers RO4350B, which is appropriate for this band. Antenna's total size with SMA connector 72970 Pomona is 39 mm × 19 mm. The main goal stated in this paper is to prove the antenna's characteristics, simulated before, by carrying out full-time measurements in anechoic chamber. Particularly the return loss, VSWR, transfer function, input impedance and radiation pattern were measured and analyzed, which finally showed similarity of simulation and measured results. It can be announced that the antenna has very wide radiation pattern (more than 180°) and well-matched to 50 Ohm in the entire frequency band. Since it's small, compact, low profile it can be applied in many modern high-capacity communication systems, including UWB, hybrid optical fiber-wireless networks, 3G and future 5G networks.","PeriodicalId":176222,"journal":{"name":"2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129218800","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":"Inverse problem of spectral reflection prediction by artificial neural networks: Preliminary results","authors":"D. Tarasov, O. Milder, A. Tyagunov","doi":"10.1109/USBEREIT.2018.8384572","DOIUrl":"https://doi.org/10.1109/USBEREIT.2018.8384572","url":null,"abstract":"Digital image processing requires substantial computations during characterization. Most of color prediction models require mathematical apparatus to predict spectral reflectance for a mixture of colorants that have been characterized by absorption and scattering during light propagation. However, few attempts were made to make a model for prediction of colorants values from an observing spectrum. This work devoted to application of artificial neural network approach for solving the inverse problem of spectral reflection prediction. Our attempt is based on the assumption that the prediction of the initial colorants from spectral data is possible by analogy with the work of the color perception system in humans. The model is built in Matlab and shows satisfactory accuracy of prediction.","PeriodicalId":176222,"journal":{"name":"2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126091187","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}