Samsung Lim

Indoor localization using FM radio signals: A fingerprinting approach

Indoor positioning has become highly important because of the failure of GPS in such areas. Many Wireless Local Area Networks (WLAN) indoor localization studies use the fingerprinting technique. In this study, a new positioning system is proposed based on broadcast FM as a signal of opportunity, with significant benefits for indoor positioning. This localization system uses FM signal strength fingerprinting. The deterministic approach of fingerprinting is considered, and several algorithms are compared. The results demonstrate a minimum mean distance error of 2.96m for the K-Weighted Nearest Neighbors (KWNN) algorithm with K=6. The comparison between using fingerprinting for FM and Wi-Fi is also discussed.

Accuracy assessment of a mobile terrestrial lidar survey at Padre Island National Seashore

The higher point density and mobility of terrestrial laser scanning (light detection and ranging (lidar)) is desired when extremely detailed elevation data are needed for mapping vertically orientated complex features such as levees, dunes, and cliffs, or when highly accurate data are needed for monitoring geomorphic changes. Mobile terrestrial lidar scanners have the capability for rapid data collection on a larger spatial scale compared with tripod-based terrestrial lidar, but few studies have examined the accuracy of this relatively new mapping technology. For this reason, we conducted a field test at Padre Island National Seashore of a mobile lidar scanner mounted on a sport utility vehicle and integrated with a position and orientation system. The purpose of the study was to assess the vertical and horizontal accuracy of data collected by the mobile terrestrial lidar system, which is georeferenced to the Universal Transverse Mercator coordinate system and the North American Vertical Datum of 1988. To accomplish the study objectives, independent elevation data were collected by conducting a high-accuracy global positioning system survey to establish the coordinates and elevations of 12 targets spaced throughout the 12 km transect. These independent ground control data were compared to the lidar scanner-derived elevations to quantify the accuracy of the mobile lidar system. The performance of the mobile lidar system was also tested at various vehicle speeds and scan density settings (e.g. field of view and linear point spacing) to estimate the optimal parameters for desired point density. After adjustment of the lever arm parameters, the final point cloud accuracy was 0.060 m (east), 0.095 m (north), and 0.053 m (height). The very high density of the resulting point cloud was sufficient to map fine-scale topographic features, such as the complex shape of the sand dunes.

Dynamic Block-Based Parameter Estimation for MRF Classification of High-Resolution Images

A Markov random field is a graphical model that is commonly used to combine spectral information and spatial context into image classification problems. The contributions of the spatial versus spectral energies are typically defined by using a smoothing parameter, which is often set empirically. We propose a new framework to estimate the smoothing parameter. For this purpose, we introduce the new concepts of dynamic blocks and class label cooccurrence matrices. The estimation is then based on the analysis of the balance of spatial and spectral energies computed using the spatial class co-occurrence distribution and dynamic blocks. Moreover, we construct a new spatially weighted parameter to preserve the edges, based on the Canny edge detector. We evaluate the performance of the proposed method on three data sets: a multispectral DigitalGlobe WorldView-2 and two hyperspectral images, recorded by the AVIRIS and the ROSIS sensors, respectively. The experimental results show that the proposed method succeeds in estimating the optimal smoothing parameter and yields higher classification accuracy values when compared with state-of-the-art methods.

Modelling spatial patterns of wildfire occurrence in South-Eastern Australia

ABSTRACT This paper describes the development and validation of spatial models for wildfire occurrence at a broad landscape scale. The hotspots databases from the Moderate Resolution Imaging Spectroradiometer (MODIS) and logistic regression models are investigated for the comprehensive understanding of environmental and socioeconomic determinants regulating the spatial distribution of wildfires over the 11-year period 2003–2013. The probability of occurrence of at least one fire on a 1 km2 grid cell in a 1,030,000 km2 region located in South-Eastern Australia is studied for the prediction of future fire occurrence. Our research shows that wildfires are most likely to occur in mountainous areas, forests, savannas and lands with high vegetation coverage, and are less likely to occur on grasslands and shrublands. Wildfires also tend to occur in areas near human infrastructures. Environmental variables are strong individual predictors of fire occurrence while socioeconomic variables contribute more to the final model. The influence of environmental and socioeconomic conditions on wildfire occurrence and the spatial patterns of wildfires identified in this study can assist fire managers in implementing appropriate management actions in South-Eastern Australia. This paper also demonstrates the potential of applying the MODIS active fire product in wildfire occurrence studies.

3D surface reconstruction of Terrestrial Laser Scanner data for forestry

Recently, Terrestrial Laser Scanners (TLS) have received considerable attention for their potential applications in forest management, archaeology, ecology as well as remote sensing and urban planning applications. Although TLS is limited in its use in small areas, it is feasible to be applied to forest inventory and deliver better sampling accuracy, objectivity, and can enhance or replace field surveys in forestry. This paper presents a framework for using TLS measurements taken by Leica HDS6000 TLS produce 3D point cloud data and to model individual trees. In particular, the quantitative and qualitative analysis of the 3D point cloud data for four different types of trees derived by TLS is discussed and the processing steps are presented. The Crust algorithm is used for the reconstruction of surfaces of arbitrary topology from the 3D point cloud data. The four individual tree models derived from the TLS system and their 3D surface reconstruction by the field survey of individual tree surfaces are possible with this technology.

Deriving Multi-Scale GEODATA from TOPO-250K Road Network Data

This paper presents a generalization methodology to derive multi‐scale GEODATA through an evaluation of ESRI ArcGIS™ software that was used as a testbed based on the principles of generalization. It focuses on integration and utilization of generalization operators in order to generalize a road network database and produce small scale maps at 1:500 000 and 1:1 000 000 from GEODATA TOPO‐250K Series 2 data. The derived maps are satisfactory when compared with the existing small‐scale road maps such as the Global Map at scale of 1:1000 000. It is suggested that a comprehensive evaluation of generalization systems and their performance is essential to marry the cartographic knowledge from experts and bring this into a generalization framework. Therefore, there is an opportunity to evaluate other generalization systems to derive a multi‐scale database from a master database in future investigations to enhance the generalization methodology.

SINGLE EPOCH ALGORITHM BASED ON TIKHONOV REGULARIZATION FOR DEFORMATION MONITORING USING SINGLE FREQUENCY GPS RECEIVERS

Abstract A new single epoch algorithm for deformation monitoring using single frequency GPS receivers, based on Tikhonov Regularization, is proposed in this paper. At first, the characteristic of the normal matrix for carrier phase measurements is analyzed. Since the normal matrix is rank-deficient, Tikhonov regularization theorem is applied to the least squares problem, and a regulariser is chosen to transform the matrix from the rank-deficient to the full rank. Then, the float ambiguity solutions and their Mean Squared Error Matrix (MSEM) are obtained. MSEM can be used to determine the search space for the integer ambiguities, combined with other integer ambiguity fixers such as LAMBDA. The proposed single epoch algorithm is tested using carrier phase measurements of two single frequency GPS receivers with a baseline over 3km and the results show that the success rate of the integer ambiguity fix is greater than 90%.

A biogeography-based optimisation algorithm for a realistic no-wait hybrid flow shop with unrelated parallel machines to minimise mean tardiness

This paper explores a no-wait hybrid flow shop scheduling problem (NWHFSSP) with realistic assumptions, including unrelated parallel machines at each stage, machine eligibility, sequence-dependent set-up times and different ready times, in order to minimise the mean tardiness. The largest position value rule is proposed to transmute continuous vectors of each solution into job permutations. Also, a novel biogeography-based optimisation (BBO) algorithm is developed to solve the aforementioned problem. To evaluate the effect of various parameters on the performance of the proposed BBO algorithm, response surface methodology (RSM) is employed. Production scenarios for small-scale and large-scale problems are created and tested for the validation purposes. Computational experiment results indicate that the proposed BBO outperforms all of the tested algorithms in terms of four measures, namely, mean relative percentage deviation (RPD), standard deviation of RPD, best RPD and worst RPD. It is shown that BBO produces the best solutions among the tested algorithms in terms of not only the four RPD measures but also computation time.

Mobile 3D indoor mapping using the Continuous Normal Distributions Transform

Existing approaches for indoor mapping are often either time-consuming or inaccurate. This paper presents the Continuous Normal Distributions Transform (C-NDT), an efficient approach to 3D indoor mapping that balances acquisition time, completeness and accuracy by registering scans acquired from a rotating LiDAR sensor mounted on a moving vehicle. C-NDT uses the robust Normal Distributions Transform (NDT) algorithm for scan registration, ensuring that the mapping is independent of the long-term quality of the odometry. We demonstrate that C-NDT produces more accurate maps than stand-alone dead-reckoning, achieves better map completeness than static scanning and is at least an order of magnitude faster than existing static scanning methods.

A framework of road extraction from airborne lidar data and aerial imagery

Abstract This paper presents a new framework of road extraction from airborne lidar data and aerial imagery, consisting of five main procedures: (1) data fusion of lidar data and aerial imagery, (2) creation of pseudo-scanlines from the fused lidar data, (3) initial extraction of road segments per pseudo-scanline, (4) refinement of road extraction to enhance the initial extraction results, and (5) final extraction of road surface and centrelines. A rule-based edge-clustering algorithm with various constraints is proposed to obtain smooth, elongated road segments per pseudo-scanline. Multiple refinement processes such as k-nearest-neighbours clustering, rule-based identification of intersection nodes and spatial interpolation are implemented to eliminate false positives and to connect the misclosures caused by the occlusion from trees, buildings and vehicles. Finally, curve fitting is employed to obtain accurate road centrelines. The quantified completeness and correctness of five test results are 82.6 and 87.4 percent, 89.2 and 91.2 percent, 80.7 and 87.6 percent, 84.2 and 90.4 percent and 79.5 and 89.5 percent, respectively.