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IoT and Remote Sensing
Published in Lavanya Sharma, Mukesh Carpenter, Computer Vision and Internet of Things, 2022
The most important property of any remote sensing system is spatial resolution. Spatial resolution is used in the determination of the capability of any system of remote sensing to record the details of the data spatially. In analog photography, the extent of the sharpness of an image is considered to be the spatial resolution of an image. There are many factors on which the spatial resolution depends such as the image motion during exposure, resolving power of the camera lens or the film, and the atmospheric environments during image exposure. The power of the lens as well as that of a film can be quantifiable. “The Resolving Power of the camera lens can be determined by means of a resolution test pattern, which is made up of a number of sets of varying thickness parallel black lines, which are being separated by the white spaces of same thickness, known as line pair” [10–15].
Head loss of inclined fish screen at turbine intake
Published in Bjørn Honningsvåg, Grethe Holm Midttømme, Kjell Repp, Kjetil Arne Vaskinn, Trond Westeren, Hydropower in the New Millennium, 2020
C. Reuter, K. Rettemeier, J. Köngeter
For the numerical model calculation a sample run of the river power plant in Germany was used. The three dimensional finite element grid of the intake cove comprises one third of the weir, the separation pier and the four intake chambers of the power plant including the machine casing of the generator. The discretization is limited to the front of the wicket gates. The spatial resolution of the grid must be adapted to the structure of the expected solution of the flow field. Regions with high velocity gradients require small element sizes. Therefor the discretization nearby and inside the intake chambers is finer than in the upstream water bay and in front of the weir. However, a high geometric resolution leads to high node and element numbers. Because of limited computer capacities, it is necessary to develop a compromise between spatial resolution and computational efficiency. This optimization process leads to a grid, which counts 40105 nodes and 34584 elements (Rettemeier et al., 1999). Figure 1 shows an inside view of the grid. The flow direction is towards the intake bellmouth (left hand side). The separation pier with the buffle in front of it is located in the background.
The Scaling Laws of HyperSurfaces
Published in Christos Liaskos, The Internet of Materials, 2020
Hamidreza Taghvaee, Sergi Abadal, Eduard Alarcon, Albert Cabellos-Aparicio, Taqwa Saeed, Andreas Pitsillides, Odysseas Tsilipakos, Christos Liaskos, Anna Tasolamprou, Maria Kafesaki, Alex Pitilakis, Nikolaos Kantartzis, Vassos Soteriou, Marios Lestas
Spatial resolution is a significant quality factor of scanning devices as it determines the minimum distance of two points in angular space that can be discriminated by the scanner. The resolution is inversely proportional to the beam width and HPBW is a well-known parameter for beam width estimation. Figure 8.9 plots HPBW as function of the dimensional parameters and the number of states. We basically observe that, as expected, the HSF size is the main determinant of the beam width. The main reason is that the aperture of the device increases. We achieve a steady beam width below 15$^{o}$ for Dm ≥ 6λ approximately. The lowest value achieved within the bounds of our exploration is around 5$^{o}$. The impact of the discretization and quantization error, given by the unit cell size and the number of states, is generally irrelevant in this case.
Next generation of GIS: must be easy
Published in Annals of GIS, 2021
A-Xing Zhu, Fang-He Zhao, Peng Liang, Cheng-Zhi Qin
The second challenge is the efficiency of computation. On one hand, the spatial extent (scope) for geospatial analyses are becoming larger and larger. It is not unusual to see a geographic analysis to be performed over a regional to continental spatial extent. At the same time, researches have been conducted at much finer spatial details in order to better understand the geographic phenomenon for better decision support. Spatial analyses conducted at higher spatial resolution and over larger spatial extent will certainly make geographic analysis data-intensive and computation-intensive. On the other hand, the computation process is also getting more and more complicated. As our understanding of geographic phenomena grows deeper, geographic algorithms or models are becoming more and more complex (Neitsch et al. 2011; Zhu et al. 2019). The complexity of models or algorithms adds to the computation intensity which already overwhelms the computation power of the existing GIS software platforms due to the increase over spatial extent at a fine spatial detail requested by current geographic analyses. As a result, many of geographic analyses become difficult and even unachievable for existing GIS software to complete.
Increasing the detail of European land use/cover data by combining heterogeneous data sets
Published in International Journal of Digital Earth, 2020
Konštantín Rosina, Filipe Batista e Silva, Pilar Vizcaino, Mario Marín Herrera, Sérgio Freire, Marcello Schiavina
As a suggestion for future research, we think that a more detailed classification of economic activities would be useful for addressing several policy relevant topics. Given the limitations of the presented approach, we propose two avenues. The first avenue would continue in the attempt to exhaustively classify the land, even when the activities are mixed (i.e. the dominant prevails). This would ideally require finer target units (individual cells or polygons segmented using all intersecting roads and railways), but some degree of class mix is inevitable (even single building can sometimes host multiple sectors). Using high-performance computing might facilitate advancement of the spatial resolution (e.g. to 50 m), which would reduce ambiguity and harvest more information from detailed input datasets (UA and potentially OSM polygons). The other avenue would be to acknowledge the intrinsic mix-up of some economic activities by allowing for a Sector mix class or by assessing the share of each sector in the polygon, instead of a categorical classification. In such case, an alternative data model would need to be considered. It is not practical to attach multiple attributes to the target entities via a single raster dataset.
Distributed fiber optics sensors for civil engineering infrastructure sensing
Published in Journal of Structural Integrity and Maintenance, 2018
According to COST Action 299 (Thévenaz, 2009), spatial resolution is defined as the “minimum distance over which a system is able to indicate the value of the measurands”, or the smallest distance that quantities can be measured with full accuracy. Full accuracy is the real value of an event with the tolerance of measurands’ resolution. In some applications, minimum detection distance covers the value within 10% of the measurands’ transition amplitude (Thévenaz, 2009). As discussed later, in time domain modulation techniques (i.e. OTDR), the spatial resolution is a function of pulse width, while in frequency domain modulation techniques (i.e. OFDR) the spatial resolution is a function of the speed of frequency sweeping rate.