70,521 research outputs found
Using a laser measurement system for monitoring morphological changes on the Strug rock fall, Slovenia
A medium-ranged high performance handheld reflectorless laser measurement system, was used for a morphological survey on the Strug rock fall in W Slovenia in the period from August 2003 to August 2004. The purpose was to evaluate its potential for monitoring ground surface changes in rock fall source areas and to help evaluating morphological changes by measuring distance from fixed points. In the area, 21 fixed geodetic points have been established. Altogether, seven measurement sets with more than 5500 points have been gathered in the rock fall area. Choosing a point cloud with a density of less than 1 point per 10 m(2) on a very rough rock fall surface failed to be a good solution. The changes on larger areas were shown by displacements of selected significantly large-sized rock blocks with a volume of several m(3). Because only smaller changes were observed between the single field series, the rock fall surface generally remained unchanged. Local surface changes of the order of 1 m or more, were clearly shown by measurements in the selected referenced cross sections. The usage of these cross sections gave a possibility to evaluate volumetric changes on the surface. The laser measurement system provided a good replacement for the classical terrestrial geodetic survey equipment, especially when performing remote monitoring of morphological changes in rock fall hazard zones, however, the case is different when fixed points are to be measured precisely
Single-tree detection in high-density LiDAR data from UAV-based survey
UAV-based LiDAR survey provides very-high-density point clouds, which involve very rich information about forest detailed structure, allowing for detection of individual trees, as well as demanding high computational load. Single-tree detection is of great interest for forest management and ecology purposes, and the task is relatively well solved for forests made of single or largely dominant species, and trees having a very evident pointed shape in the upper part of the canopy (in particular conifers). Most authors proposed methods based totally or partially on search of local maxima in the canopy, which has poor performance for species that have flat or irregular upper canopy, and for mixed forests, especially where taller trees hide smaller ones. Such considerations apply in particular to Mediterranean hardwood forests. In such context, it is imperative to use the whole volume of the point cloud, however keeping computational load tractable. The authors propose the use of a methodology based on modelling the 3D-shape of the tree, which improves performance w.r.t to maxima-based models. A case study, performed on a hazel grove, is provided to document performance improvement on a relatively simple, but significant, case
Survey on security issues in file management in cloud computing environment
Cloud computing has pervaded through every aspect of Information technology
in past decade. It has become easier to process plethora of data, generated by
various devices in real time, with the advent of cloud networks. The privacy of
users data is maintained by data centers around the world and hence it has
become feasible to operate on that data from lightweight portable devices. But
with ease of processing comes the security aspect of the data. One such
security aspect is secure file transfer either internally within cloud or
externally from one cloud network to another. File management is central to
cloud computing and it is paramount to address the security concerns which
arise out of it. This survey paper aims to elucidate the various protocols
which can be used for secure file transfer and analyze the ramifications of
using each protocol.Comment: 5 pages, 1 tabl
Throughput Maximization in Cloud Radio Access Networks using Network Coding
This paper is interested in maximizing the total throughput of cloud radio
access networks (CRANs) in which multiple radio remote heads (RRHs) are
connected to a central computing unit known as the cloud. The transmit frame of
each RRH consists of multiple radio resources blocks (RRBs), and the cloud is
responsible for synchronizing these RRBS and scheduling them to users. Unlike
previous works that consider allocating each RRB to only a single user at each
time instance, this paper proposes to mix the flows of multiple users in each
RRB using instantly decodable network coding (IDNC). The proposed scheme is
thus designed to jointly schedule the users to different RRBs, choose the
encoded file sent in each of them, and the rate at which each of them is
transmitted. Hence, the paper maximizes the throughput which is defined as the
number of correctly received bits. To jointly fulfill this objective, we design
a graph in which each vertex represents a possible user-RRB association,
encoded file, and transmission rate. By appropriately choosing the weights of
vertices, the scheduling problem is shown to be equivalent to a maximum weight
clique problem over the newly introduced graph. Simulation results illustrate
the significant gains of the proposed scheme compared to classical coding and
uncoded solutions.Comment: 7 pages, 7 figure
Automated Classification of Airborne Laser Scanning Point Clouds
Making sense of the physical world has always been at the core of mapping. Up
until recently, this has always dependent on using the human eye. Using
airborne lasers, it has become possible to quickly "see" more of the world in
many more dimensions. The resulting enormous point clouds serve as data sources
for applications far beyond the original mapping purposes ranging from flooding
protection and forestry to threat mitigation. In order to process these large
quantities of data, novel methods are required. In this contribution, we
develop models to automatically classify ground cover and soil types. Using the
logic of machine learning, we critically review the advantages of supervised
and unsupervised methods. Focusing on decision trees, we improve accuracy by
including beam vector components and using a genetic algorithm. We find that
our approach delivers consistently high quality classifications, surpassing
classical methods
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