scholarly journals Comparison of data-processing algorithms for the lidar detection of mackerel in the Norwegian Sea

2009 ◽  
Vol 66 (6) ◽  
pp. 1023-1028 ◽  
Author(s):  
James H. Churnside ◽  
Eirik Tenningen ◽  
James J. Wilson

Abstract Churnside, J. H., Tenningen, E., and Wilson, J. J. 2009. Comparison of data-processing algorithms for the lidar detection of mackerel in the Norwegian Sea. – ICES Journal of Marine Science, 66: 1023–1028. A broad-scale lidar survey was conducted in the Norwegian Sea in summer 2002. Since then, various data-processing techniques have been developed, including manual identification of fish schools, multiscale median filtering, and curve fitting of the lidar profiles. In the automated techniques, applying a threshold to the data, as carrried out already to eliminate plankton scattering, has been demonstrated previously to improve the correlation between lidar and acoustic data. We applied these techniques to the lidar data of the 2002 survey and compared the results with those of a mackerel (Scomber scombrus) survey done by FV “Endre Dyrøy” and FV “Trønderbas” during the same period. Despite a high level of variability in both lidar and trawl data, the broad-scale distribution of fish inferred from the lidar agreed with that of mackerel caught by the FV “Endre Dyrøy”. This agreement was obtained using both manual and automated processing of the lidar data. This work is the first comparison of concurrent lidar and trawl surveys, and it demonstrates the utility of airborne lidar for mackerel studies.

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3433 ◽  
Author(s):  
Jianqing Wu ◽  
Hao Xu ◽  
Yuan Tian ◽  
Rendong Pi ◽  
Rui Yue

Roadside light detection and ranging (LiDAR) is an emerging traffic data collection device and has recently been deployed in different transportation areas. The current data processing algorithms for roadside LiDAR are usually developed assuming normal weather conditions. Adverse weather conditions, such as windy and snowy conditions, could be challenges for data processing. This paper examines the performance of the state-of-the-art data processing algorithms developed for roadside LiDAR under adverse weather and then composed an improved background filtering and object clustering method in order to process the roadside LiDAR data, which was proven to perform better under windy and snowy weather. The testing results showed that the accuracy of the background filtering and point clustering was greatly improved compared to the state-of-the-art methods. With this new approach, vehicles can be identified with relatively high accuracy under windy and snowy weather.


Author(s):  
Л.Д. Егорова ◽  
Л.А. Казаковцев

В статье обсуждается применение методов фрактального анализа для решения задачи автоматической фильтрации сигнала ЭЭГ от артефактов различной природы. Изучается возможность использования показателя Херста в качестве информативного признака для алгоритмов интеллектуальной обработки данных. The article discusses the possibility of using fractal analysis to solve the problem of automatic filtering of the EEG signal from artifacts of various nature. The possibility of using the Hurst exponent as an informative feature for intelligent data processing algorithms is investigated


Sign in / Sign up

Export Citation Format

Share Document