Advanced Topographic Laser Altimeter System (ATLAS) is a new micropulse photon-counting laser system that offers unprecedented options for the observation of forest ecosystems. However, the ATLAS system is sensitive to solar background noise, which poses a tremendous challenge to the photon cloud noise filtering for various observation time scenes in a forest environment. This article presents a multilevel adaptive photon cloud noise filtering algorithm (MLAPCNF) for different observation time scenes that integrate the improved density-based spatial clustering of applications with noise (DBSCAN) algorithm and the improved localized statistics algorithm. The MLAPCNF algorithm was tested at different observation time scenarios, laser intensities, and forest coverage using the ATLAS dataset for forests located in nine study areas in the United States. The results showed that the MLAPCNF algorithm was effective in identifying noise photons and preserving signal photons in the raw ATLAS data with an$R$value of 0.99 and$F$value of 0.79 which produced marginally superior results than the other existing filtering methods. The$F$values of the MLAPCNF algorithm under daytime observation conditions were 0.01–0.03 higher than those under nighttime observation conditions, indicating that the algorithm performed better under daytime observation conditions. Results demonstrated that the proposed method can eliminate the impact of observation time differences in forest environments. Overall, the MLAPCNF algorithm outperforms the other existing filtering techniques at the given test site and is capable of delivering accurate data for estimating forest structural parameters.

The Global Ecosystem Dynamics Investigation (GEDI) contains a full waveform, multi-beam laser altimeter, which provides vertical vegetation structure information. However, few studies have assessed the accuracy of using GEDI LiDAR data in retrieving ground topography from dense forest environments. There has been no in-depth study assessing the accuracy of GEDI data with different kinds of elevation index in footprint samples from densely forested areas (canopy cover >70%). To address this limitation, this study aims to assess the accuracy of ground topography estimates from dense forested terrain through the combination of airborne LiDAR data. The results show that under dense forest conditions, the mean elevation in the GEDI footprint performed more accurate than the max elevation, the min elevation and the median elevation in the GEDI footprint. GEDI provides reasonable estimates of mean terrain height, with R2of 0.99, root mean squared error (RMSE) of 6.08 m and mean absolute error (MAE) of 3.92 m. In addition, dense forest ecosystems also reduce the accuracy of the terrain height estimates. Overall, GEDI with its full waveform technology represents promising spaceborne LiDAR for terrain height retrieval in dense forest environments.

This paper proposes a practical synchronization waveform that is resilient to frequency error for machine-type communications with applications in massive Internet of Things (IoT). Mathematical properties of the waveform are derived, which are keys to addressing the practical issues. In particular, it is shown that this type of waveform is asymptotically optimal in the presence of a frequency error, in the sense that its asymptotic performance is the same as the optimal matched-filter detector that is free of frequency error. This asymptotic property enables optimization of the waveform under the constraints imposed by an application. It is also shown that such optimized waveform comes in pairs, which facilitates the formation of a new waveform capable of frequency error estimation and timing refinement at the receiver. Detailed comparisons with the LTE narrowband IoT primary synchronization signal are provided.

We formulate an integrated design and evaluation mechanism applying techniques in the field of flexible engineering design and real options analysis to build a liquefied natural gas production and fueling system for a Singapore oil & gas company. The proposed methodology consists of four main steps: 1) design and evaluation under deterministic demand, 2) uncertainty characterization and performance re-evaluation under different uncertainty realization scenarios, 3) optimization under uncertainty, 4) post-optimality sensitivity analysis. Among the highlights of this work are applications of a metaheuristic method to more efficiently search for optimal flexible designs as well as extensive one-way and two-way sensitivity analyses to generate valuable insights. The final results show strong value in adding flexibility in this production and fueling system.

This poster presents GuideLoc, a highly efficient aerial wireless localization system that uses directional antennas mounted on a mini Multi-rotor Unmanned Aerial Vehicle (UAV), to enable detecting and positioning of targets. Taking advantage of angle and signal strength information of frames transmitted from targets, GuideLoc can directly fly towards the targets with the minimum flight route and time. We implement a prototype of GuideLoc using ArduCopter and evaluate the performance by simulations and experiments. Experimental results show that GuideLoc achieves an average location accuracy of 2.7 meters and reduces flight distance more than 50% compared with other known wireless localization approaches using UAV.