scholarly journals The Independent Variable Interpolation Technique for Nonuniformly Sampled Shallow-Angle Lidar Data

2011 ◽  
Vol 28 (12) ◽  
pp. 1672-1678 ◽  
Author(s):  
M. R. Belmont ◽  
P. Ashwin
Keyword(s):  
Computational Cost ◽  
Lidar Data ◽  
Distributed Data ◽  
Sampled Data ◽  
Sea Waves ◽  
Energy Capture ◽  
New Class ◽  
Lidar Measurements ◽  
Practical Engineering ◽  
Independent Variable

Abstract Shallow-angle lidar offers an attractive approach to acquiring spatial profiles of sea waves, which are of value in both oceanographic research and practical engineering applications, such as in the control of wave energy capture devices and for a variety of vessel operations. However, the wave elevation values produced by shallow-angle lidar are inevitably nonuniformly distributed in space and, given that most processing algorithms require uniformly sampled data, an equivalent set of uniformly distributed data must be derived from the lidar measurements. A new class of algorithm is introduced to achieve this goal and applied to experimental shallow-angle lidar data. Compared to traditional methods the new approach has advantages in terms of both computational cost and the degree of nonuniformity that can be accommodated.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals Aerosol optical and microphysical retrievals from a hybrid multiwavelength lidar data set – DISCOVER-AQ 2011

2014 ◽  
Vol 7 (9) ◽  
pp. 3095-3112 ◽  
Author(s):  
P. Sawamura ◽  
D. Müller ◽  
R. M. Hoff ◽  
C. A. Hostetler ◽  
R. A. Ferrare ◽  
...  
Keyword(s):  
Remote Sensing ◽  
In Situ Measurements ◽  
Index Of Refraction ◽  
High Spectral Resolution ◽  
Lidar Data ◽  
Data Set ◽  
Microphysical Properties ◽  
Lidar Measurements ◽  
Multiwavelength Lidar

Abstract. Retrievals of aerosol microphysical properties (effective radius, volume and surface-area concentrations) and aerosol optical properties (complex index of refraction and single-scattering albedo) were obtained from a hybrid multiwavelength lidar data set for the first time. In July 2011, in the Baltimore–Washington DC region, synergistic profiling of optical and microphysical properties of aerosols with both airborne (in situ and remote sensing) and ground-based remote sensing systems was performed during the first deployment of DISCOVER-AQ. The hybrid multiwavelength lidar data set combines ground-based elastic backscatter lidar measurements at 355 nm with airborne High-Spectral-Resolution Lidar (HSRL) measurements at 532 nm and elastic backscatter lidar measurements at 1064 nm that were obtained less than 5 km apart from each other. This was the first study in which optical and microphysical retrievals from lidar were obtained during the day and directly compared to AERONET and in situ measurements for 11 cases. Good agreement was observed between lidar and AERONET retrievals. Larger discrepancies were observed between lidar retrievals and in situ measurements obtained by the aircraft and aerosol hygroscopic effects are believed to be the main factor in such discrepancies.

scholarly journals CIST: An Improved ISAR Imaging Method Using Convolution Neural Network

2020 ◽  
Vol 12 (16) ◽  
pp. 2641
Author(s):  
Shunjun Wei ◽  
Jiadian Liang ◽  
Mou Wang ◽  
Xiangfeng Zeng ◽  
Jun Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Computational Cost ◽  
Convolution Neural Network ◽  
Imaging Method ◽  
Sampled Data ◽  
High Quality ◽  
Imaging Methods ◽  
Adaptive Parameter ◽  
Imaging Results ◽  
Isar Imaging

Compressive sensing (CS) has been widely utilized in inverse synthetic aperture radar (ISAR) imaging, since ISAR measured data are generally non-completed in cross-range direction, and CS-based imaging methods can obtain high-quality imaging results using under-sampled data. However, the traditional CS-based methods need to pre-define parameters and sparse transforms, which are tough to be hand-crafted. Besides, these methods usually require heavy computational cost with large matrices operation. In this paper, inspired by the adaptive parameter learning and rapidly reconstruction of convolution neural network (CNN), a novel imaging method, called convolution iterative shrinkage-thresholding (CIST) network, is proposed for ISAR efficient sparse imaging. CIST is capable of learning optimal parameters and sparse transforms throughout the CNN training process, instead of being manually defined. Specifically, CIST replaces the linear sparse transform with non-linear convolution operations. This new transform and essential parameters are learnable end-to-end across the iterations, which increases the flexibility and robustness of CIST. When compared with the traditional state-of-the-art CS imaging methods, both simulation and experimental results demonstrate that the proposed CIST-based ISAR imaging method can obtain imaging results of high quality, while maintaining high computational efficiency. CIST-based ISAR imaging is tens of times faster than other methods.

scholarly journals Spectral correction of turbulent energy damping on wind lidar measurements due to spatial averaging

2021 ◽  
Vol 14 (2) ◽  
pp. 1457-1474
Author(s):  
Matteo Puccioni ◽  
Giacomo Valerio Iungo
Keyword(s):  
Turbulent Energy ◽  
Lidar Data ◽  
Spatial Averaging ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Probe Length ◽  
Lidar Measurements ◽  
Low Pass ◽  
Sampling Height ◽  
Wind Lidar

Abstract. Continuous advancements in pulsed wind lidar technology have enabled compelling wind turbulence measurements within the atmospheric boundary layer with probe lengths shorter than 20 m and sampling frequency on the order of 10 Hz. However, estimates of the radial velocity from the back-scattered lidar signal are inevitably affected by an averaging process within each probe volume, generally modeled as a convolution between the true velocity projected along the lidar line-of-sight and an unknown weighting function representing the energy distribution of the laser pulse along the probe length. As a result, the spectral energy of the turbulent velocity fluctuations is damped within the inertial subrange, thus not allowing one to take advantage of the achieved spatio-temporal resolution of the lidar technology. We propose to correct the turbulent energy damping on the lidar measurements by reversing the effect of a low-pass filter, which can be estimated directly from the power spectral density of the along-beam velocity component. Lidar data acquired from three different field campaigns are analyzed to describe the proposed technique, investigate the variability of the filter parameters and, for one dataset, assess the corrected velocity variance against sonic anemometer data. It is found that the order of the low-pass filter used for modeling the energy damping on the lidar velocity measurements has negligible effects on the correction of the second-order statistics of the wind velocity. In contrast, the cutoff wavenumber plays a significant role in spectral correction encompassing the smoothing effects connected with the lidar probe length. Furthermore, the variability of the spatial averaging on wind lidar measurements is investigated for different wind speed, turbulence intensity, and sampling height. The results confirm that the effects of spatial averaging are enhanced with decreasing wind speed, smaller integral length scale and, thus, for smaller sampling height. The method proposed for the correction of the second-order turbulent statistics of wind-velocity lidar data is a compelling alternative to existing methods because it does not require any input related to the technical specifications of the used lidar system, such as the energy distribution over the laser pulse and lidar probe length. On the other hand, the proposed method assumes that surface-layer similarity holds.

scholarly journals Perilaku Pengurangan Sampah: Potensi Pengungkapan dan Pelaporan Keberlanjutan di Perguruan Tinggi

2020 ◽  
Vol 5 (1) ◽  
pp. 151
Author(s):  
Aditya Pandu Wicaksono ◽  
Riana Mahfuroh ◽  
Arya Lintang Risang Bagus
Keyword(s):  
Higher Education ◽  
Undergraduate Students ◽  
Active Role ◽  
Waste Reduction ◽  
Distributed Data ◽  
Reduction Behavior ◽  
Waste Production ◽  
Sustainability Issue ◽  
Independent Variable ◽  
Financial Aspect

Not only does accounting focus on financial aspect, but also placing attention on non-financial aspect such as sustainability issue that covers management of waste. Higher education institutions are required to take an active role in order to mitigate waste production although there is no regulation and guidance on sustainability. This research is conducted to discover predictor variables that influence waste reduction behavior in higher education. Samples are 185 accounting undergraduate students in Universitas Islam Indonesia. The questionnaire is electronically distributed. Data are analyzed using statistic tool namely smart PLS version 3.0. This research finds there are no significant influences from TPB’s independent variables to intention. Convenience and knowledge do not significantly influence intention reducing waste production. On the other hand, moral obligation is the only independent variable that significantly influences intention. Intention, additionally, is significant predictor for performing waste reduction behavior.

scholarly journals Stability of Sampled-Data Control for Lurie Systems with Slope-Restricted Nonlinearities

2020 ◽  
Author(s):  
Mathias Giordani Titton ◽  
João Manoel Gomes da Silva Jr. ◽  
Giórgio Valmórbida
Keyword(s):  
Continuous Time ◽  
Optimization Problems ◽  
Linear Matrix ◽  
Sampled Data ◽  
Closed Loop System ◽  
Integral Term ◽  
New Class ◽  
Data Control ◽  
The Stability ◽  
Continuous Time System

This paper deals with the stability analysis of aperiodic sampled-data Lurie systems, where the nonlinearity is assumed to be both sector and slope restricted. The proposed method is based on the use of a new class of looped-functionals whose derivative is negative along the trajectories of the continuous-time system. In addition, it contains a generalized Lurie-type function that is quadratic on both the states and the nonlinearity and has a Lurie-Postnikov integral term, which provides some advantages in comparison to simpler candidate functions. On this basis, stability conditions in the form of linear matrix inequalities (LMIs) are formulated. It is shown that the proposed conditions guarantee that the Lurie function is strictly decreasing at the sampling instants, which also implies that the continuous-time trajectories converge asymptotically to the origin. We then formulate some optimization problems for computing themaximal intersampling interval or the maximal sector bounds for which the stability of the sampled-data closed-loop system is guaranteed. A numerical example to illustrate the results is provided.

scholarly journals Rosenbrock Type Methods for Solving Non-Linear Second-Order in Time Problems

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2225
Author(s):  
Maria Jesus Moreta
Keyword(s):  
Computational Cost ◽  
Intermediate Stage ◽  
Second Order ◽  
First Order ◽  
New Class ◽  
Nyström Methods ◽  
Non Linear ◽  
Second Order In Time ◽  
Linear Problems ◽  
Runge Kutta

In this work, we develop a new class of methods which have been created in order to numerically solve non-linear second-order in time problems in an efficient way. These methods are of the Rosenbrock type, and they can be seen as a generalization of these methods when they are applied to second-order in time problems which have been previously transformed into first-order in time problems. As they also follow the ideas of Runge–Kutta–Nyström methods when solving second-order in time problems, we have called them Rosenbrock–Nyström methods. When solving non-linear problems, Rosenbrock–Nyström methods present less computational cost than implicit Runge–Kutta–Nyström ones, as the non-linear systems which arise at every intermediate stage when Runge–Kutta–Nyström methods are used are replaced with sequences of linear ones.

scholarly journals Doppler Lidar Estimation of Mixing Height Using Turbulence, Shear, and Aerosol Profiles

2009 ◽  
Vol 26 (4) ◽  
pp. 673-688 ◽  
Author(s):  
Sara C. Tucker ◽  
Christoph J. Senff ◽  
Ann M. Weickmann ◽  
W. Alan Brewer ◽  
Robert M. Banta ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Air Quality ◽  
Potential Temperature ◽  
Lidar Data ◽  
Doppler Lidar ◽  
Mixing Height ◽  
Coherent Doppler Lidar ◽  
Lidar Measurements ◽  
Comparison Of The Results ◽  
Humidity Profiles

Abstract The concept of boundary layer mixing height for meteorology and air quality applications using lidar data is reviewed, and new algorithms for estimation of mixing heights from various types of lower-tropospheric coherent Doppler lidar measurements are presented. Velocity variance profiles derived from Doppler lidar data demonstrate direct application to mixing height estimation, while other types of lidar profiles demonstrate relationships to the variance profiles and thus may also be used in the mixing height estimate. The algorithms are applied to ship-based, high-resolution Doppler lidar (HRDL) velocity and backscattered-signal measurements acquired on the R/V Ronald H. Brown during Texas Air Quality Study (TexAQS) 2006 to demonstrate the method and to produce mixing height estimates for that experiment. These combinations of Doppler lidar–derived velocity measurements have not previously been applied to analysis of boundary layer mixing height—over the water or elsewhere. A comparison of the results to those derived from ship-launched, balloon-radiosonde potential temperature and relative humidity profiles is presented.

scholarly journals A review of turbulence measurements using ground-based wind lidars

2013 ◽  
Vol 6 (11) ◽  
pp. 3147-3167 ◽  
Author(s):  
A. Sathe ◽  
J. Mann
Keyword(s):  
Turbulence Model ◽  
Isotropic Turbulence ◽  
Lidar Data ◽  
Turbulence Statistics ◽  
Probe Volume ◽  
Turbulence Measurements ◽  
Lidar Measurements ◽  
Processing Algorithms ◽  
New Algorithms ◽  
Standard Techniques

Abstract. A review of turbulence measurements using ground-based wind lidars is carried out. Works performed in the last 30 yr, i.e., from 1972–2012 are analyzed. More than 80% of the work has been carried out in the last 15 yr, i.e., from 1997–2012. New algorithms to process the raw lidar data were pioneered in the first 15 yr, i.e., from 1972–1997, when standard techniques could not be used to measure turbulence. Obtaining unfiltered turbulence statistics from the large probe volume of the lidars has been and still remains the most challenging aspect. Until now, most of the processing algorithms that have been developed have shown that by combining an isotropic turbulence model with raw lidar measurements, we can obtain unfiltered statistics. We believe that an anisotropic turbulence model will provide a more realistic measure of turbulence statistics. Future development in algorithms will depend on whether the unfiltered statistics can be obtained without the aid of any turbulence model. With the tremendous growth of the wind energy sector, we expect that lidars will be used for turbulence measurements much more than ever before.

scholarly journals Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu

2020 ◽  
Vol 12 (4) ◽  
pp. 609
Author(s):  
Wanwan Liang ◽  
Mongi Abidi ◽  
Luis Carrasco ◽  
Jack McNelis ◽  
Liem Tran ◽  
...  
Keyword(s):  
High Resolution ◽  
Computational Cost ◽  
Vegetation Mapping ◽  
Second Step ◽  
Support Vector ◽  
Lidar Data ◽  
Sampling Effort ◽  
Large Spatial Scale ◽  
Knox County ◽  
Vegetation Species

Mapping vegetation species is critical to facilitate related quantitative assessment, and mapping invasive plants is important to enhance monitoring and management activities. Integrating high-resolution multispectral remote-sensing (RS) images and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple sources of high-resolution RS data for vegetation mapping on a large spatial scale can be both computationally and sampling intensive. Here, we designed a two-step classification workflow to potentially decrease computational cost and sampling effort and to increase classification accuracy by integrating multispectral and lidar data in order to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1362 km2) of Tennessee (U.S.). Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive, coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the producer’s accuracy, user’s accuracy, and Kappa for the SVM model on kudzu were 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions as well as map other vegetation species.

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