Search results for "data"

Habituating Students to IPR Questions During Creative Project Work

2017

Methods for estimating forest stem volumes by tree species using digital surface model and CIR images taken from light UAS

2012

In this paper we consider methods for estimating forest tree stem volumes by species using images taken from light unmanned aircraft systems (UAS). Instead of using LiDAR and additional multiband imagery a color infrared camera mounted to a light UAS is used to acquire both imagery and the DSM of target area. The goal of this study is to accurately estimate tree stem volumes in three classes. The status of the ongoing work is described and an initial method for delineating and classifying treetops is presented.

An Efficient Network Log Anomaly Detection System Using Random Projection Dimensionality Reduction

2014

Network traffic is increasing all the time and network services are becoming more complex and vulnerable. To protect these networks, intrusion detection systems are used. Signature-based intrusion detection cannot find previously unknown attacks, which is why anomaly detection is needed. However, many new systems are slow and complicated. We propose a log anomaly detection framework which aims to facilitate quick anomaly detection and also provide visualizations of the network traffic structure. The system preprocesses network logs into a numerical data matrix, reduces the dimensionality of this matrix using random projection and uses Mahalanobis distance to find outliers and calculate an a…

Data mining framework for random access failure detection in LTE networks

2014

Sleeping cell problem is a particular type of cell degradation. There are various software and hardware reasons that might cause such kind of cell outage. In this study a cell becomes sleeping because of Random Access Channel (RACH) failure. This kind of network problem can appear due to misconfiguration, excessive load or software/firmware problem at the Base Station (BS). In practice such failure might cause network performance degradation, which is hardly traceable by an operator. In this paper we present a data mining based framework for the detection of problematic cells. In its core is the analysis of event sequences reported by a User Equipment (UE) to a serving BS. The choice of N i…

Lattice Boltzmann Simulations at Petascale on Multi-GPU Systems with Asynchronous Data Transfer and Strictly Enforced Memory Read Alignment

2015

The lattice Boltzmann method is a well-established numerical approach for complex fluid flow simulations. Recently general-purpose graphics processing units have become accessible as high-performance computing resources at large-scale. We report on implementing a lattice Boltzmann solver for multi-GPU systems that achieves 0.69 PFLOPS performance on 16384 GPUs. In addition to optimizing the data layout on the GPUs and eliminating the halo sites, we make use of the possibility to overlap data transfer between the host CPU and the device GPU with computing on the GPU. We simulate flow in porous media and measure both strong and weak scaling performance with the emphasis being on a large scale…

Scalable implementation of dependence clustering in Apache Spark

2017

This article proposes a scalable version of the Dependence Clustering algorithm which belongs to the class of spectral clustering methods. The method is implemented in Apache Spark using GraphX API primitives. Moreover, a fast approximate diffusion procedure that enables algorithms of spectral clustering type in Spark environment is introduced. In addition, the proposed algorithm is benchmarked against Spectral clustering. Results of applying the method to real-life data allow concluding that the implementation scales well, yet demonstrating good performance for densely connected graphs. peerReviewed

The influence of dataset size on the performance of cell outage detection approach in LTE-A networks

2015

The configuration and maintenance of constantly evolving mobile cellular networks are getting more and more complex and hence expensive. Self-Organizing Networks (SON) concept is an umbrella term for the set of automated solutions for network operations proposed by 3rd Generation Partnership Project (3GPP) group. Automated cell outage detection is one of the components of SON functionality. In early studies our research group developed data-driven approach for the detection of malfunctioning cells. In this paper we investigate the performance of the proposed solution as a function of the density of active users and the size of observation interval. The evaluation is conducted in Long Term E…

Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm

2019

Tensor decomposition is a powerful tool for analyzing multiway data. Nowadays, with the fast development of multisensor technology, more and more data appear in higherorder (order > 4) and nonnegative form. However, the decomposition of higher-order nonnegative tensor suffers from poor convergence and low speed. In this study, we propose a new nonnegative CANDECOM/PARAFAC (NCP) model using proximal algorithm. The block principal pivoting method in alternating nonnegative least squares (ANLS) framework is employed to minimize the objective function. Our method can guarantee the convergence and accelerate the computation. The results of experiments on both synthetic and real data demonstrate …

Coupling high-resolution satellite imagery with ALS-based canopy height model and digital elevation model in object-based boreal forest habitat type …

2014

We developed a classification workflow for boreal forest habitat type mapping. In object-based image analysis framework, Fractal Net Evolution Approach segmentation was combined with random forest classification. High-resolution WorldView-2 imagery was coupled with ALS based canopy height model and digital terrain model. We calculated several features (e.g. spectral, textural and topographic) per image object from the used datasets. We tested different feature set alternatives; a classification accuracy of 78.0 % was obtained when all features were used. The highest classification accuracy (79.1 %) was obtained when the amount of features was reduced from the initial 328 to the 100 most imp…

Genome size evolution in macroparasites.

2014

Reduction in genome size has been associated not only with a parasitic lifestyle in intracellular microparasites but also in some macroparasitic insects and nematodes. We collected the available data on genome size for flatworms, annelids, nematodes and arthropods, compared those with available data for the phylogenetically closest free-living taxa and found evidence of smaller genome sizes for parasites in six of nine comparisons. Our results suggest that despite great differences in evolutionary history and life cycles, parasitism as a lifestyle promotes convergent genome size reduction in macroparasites. We discuss factors that could be associated with small genome size in parasites whic…