Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Ismail Rakip Karas"'
Autor:
Tarik Adnan Almohamad, Mohd Fadzli Mohd Salleh, Mohd Nazri Mahmud, Ismail Rakip Karas, Nor Shahida Mohd Shah, Samir Ahmed Al-Gailani
Publikováno v:
IEEE Access, Vol 9, Pp 25843-25857 (2021)
In order to pursue rapid development of the new generation of wireless communication systems and elevate their security and efficiency, this paper proposes a novel scheme for automatic dual determination of modulation types and signal to noise ratios
Externí odkaz:
https://doaj.org/article/93f07f43925e4eeea7c6aba60303c4bb
Publikováno v:
Mehran University Research Journal of Engineering and Technology, Vol 30, Iss 4, Pp 673-680 (2011)
The landslide susceptibility models require the appropriate and reliable terrain analytical based study of the landslides prone areas using SRTM (Shuttle Radar Topography Mission) data, based on certain GIS (Geographical Information Systems) and remo
Externí odkaz:
https://doaj.org/article/ba60e6bd8e9a4eb4949c19357f7f36a0
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 7, Iss 6, p 223 (2018)
We propose SmartEscape, a real-time, dynamic, intelligent and user-specific evacuation system with a mobile interface for emergency cases such as fire. Unlike past work, we explore dynamically changing conditions and calculate a personal route for an
Externí odkaz:
https://doaj.org/article/9c80dbf504b24d27aecd743cf55370f6
Publikováno v:
Sensors, Vol 8, Iss 4, Pp 2673-2694 (2008)
This paper presents a new model, MUSCLE (Multidirectional Scanning for Line Extraction), for automatic vectorization of raster images with straight lines. The algorithm of the model implements the line thinning and the simple neighborhood methods to
Externí odkaz:
https://doaj.org/article/bb249c724fd3414eb48bf8293703aac0
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIV-4-W3-2020, Pp 81-84 (2020)
The massive disasters that arise by nature and humanity are significantly leads to several losses in lives and infrastructures. Disasters such as chemical explosions, flash floods and volcanoes. The high level of preparedness from the governments and
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIV-4-W3-2020, Pp 351-354 (2020)
Exploratory Spatial Analysis Techniques (ESDA) have become popular to identify the spatial association of different variables in many fields of natural and social sciences. The application of Global Moran’s I statistics enables us to provide visual
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIV-4-W3-2020, Pp 375-378 (2020)
The users can contribute to geographic information through platforms such as Wikimapia and OpenStreetMap. They can also generate data by themselves with their applications in cyber worlds like Google Earth. This study is primarily designed to be a gu
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-4-W19, Pp 9-15 (2019)
Around the world, vegetation cover functioning as shelter to wildlife, clean water, food security as well as treat large part of air pollution problem. Accurate predictive data early warn and provide knowledge for decision makers to reduce the effect
In this article, a new method of vehicles detecting and tracking is presented: A thresholding followed by a mathematical morphology treatment are used. The tracking phase uses the information about a vehicle. An original labeling is proposed in this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57d9353c55d4d12043fa63b3033cf892
https://zenodo.org/record/5717647
https://zenodo.org/record/5717647
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-4-W16, Pp 39-46 (2019)
Remote sensing satellite images plays a significant role in mapping land use/land cover LULC. Machine learning ML provide robust functions for satellite image classification. The objective of this paper is to extend the capability of GIS specialists