• Journal of Biomedical Engineering Research
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• v.23 no.1
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• pp.49-60
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• 2002

Receive dynamic focusing with an array transducer can provide near optimum resolution only in the vicinity of transmit focal depth. A customary method to increase the depth of field is to combine several beams with different focal depths, with an accompanying decrease in the frame rate. In this Paper. we Present a simultaneous multiple transmit focusing method in which chirp signals focused at different depths are transmitted at the same time. These chirp signals are mutually orthogonal in a sense that the autocorrelation function of each signal has a narrow mainlobe width and low sidelobe levels. and the crossorelation function of any Pair of the signals has values smaller than the sidelobe levels of each autocorrelation function. This means that each chirp signal can be separated from the combined received signals and compressed into a short pulse. which is then individually focused on a separate receive beamformer. Next. the individually focused beams are combined to form a frame of image. Theoretically, any two chirp signals defined over two nonoverlapped frequency bands are mutually orthogonal In the present work. however, a tractional overlap of adjacent frequency bands is permitted to design more chirp signals within a given transducer bandwidth. The elevation of the rosscorrelation values due to the frequency overlap could be reduced by alternating the direction of frequency sweep of the adjacent chirp signals We also observe that the Proposed method provides better images when the low frequency chirp is focused at a near Point and the high frequency chirp at a far point along the depth. better lateral resolution is obtained at the far field with reasonable SNR due to the SNR gain in Pulse compression Imaging .

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.623-634
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• 2022

Due to river maintenance projects such as the creation of hydrophilic areas around rivers and the Four Rivers Project, the flow characteristics of rivers are continuously changing, and the risk of water quality accidents due to the inflow of various pollutants is increasing. In the event of a water quality accident, it is necessary to minimize the effect on the downstream side by predicting the concentration and arrival time of pollutants in consideration of the flow characteristics of the river. In order to track the behavior of these pollutants, it is necessary to calculate the diffusion coefficient and dispersion coefficient for each section of the river. Among them, the dispersion coefficient is used to analyze the diffusion range of soluble pollutants. Existing experimental research cases for tracking the behavior of pollutants require a lot of manpower and cost, and it is difficult to obtain spatially high-resolution data due to limited equipment operation. Recently, research on tracking contaminants using RGB drones has been conducted, but RGB images also have a limitation in that spectral information is limitedly collected. In this study, to supplement the limitations of existing studies, a hyperspectral sensor was mounted on a remote sensing platform using a drone to collect temporally and spatially higher-resolution data than conventional contact measurement. Using the collected spatio-temporal hyperspectral images, the tracer concentration was calculated and the transverse dispersion coefficient was derived. It is expected that by overcoming the limitations of the drone platform through future research and upgrading the dispersion coefficient calculation technology, it will be possible to detect various pollutants leaking into the water system, and to detect changes in various water quality items and river factors.