3,627 research outputs found
Inducing Features of Random Fields
We present a technique for constructing random fields from a set of training
samples. The learning paradigm builds increasingly complex fields by allowing
potential functions, or features, that are supported by increasingly large
subgraphs. Each feature has a weight that is trained by minimizing the
Kullback-Leibler divergence between the model and the empirical distribution of
the training data. A greedy algorithm determines how features are incrementally
added to the field and an iterative scaling algorithm is used to estimate the
optimal values of the weights.
The statistical modeling techniques introduced in this paper differ from
those common to much of the natural language processing literature since there
is no probabilistic finite state or push-down automaton on which the model is
built. Our approach also differs from the techniques common to the computer
vision literature in that the underlying random fields are non-Markovian and
have a large number of parameters that must be estimated. Relations to other
learning approaches including decision trees and Boltzmann machines are given.
As a demonstration of the method, we describe its application to the problem of
automatic word classification in natural language processing.
Key words: random field, Kullback-Leibler divergence, iterative scaling,
divergence geometry, maximum entropy, EM algorithm, statistical learning,
clustering, word morphology, natural language processingComment: 34 pages, compressed postscrip
THE USE OF OPEN SOURCE SOFTWARE FOR MONITORING BEE DIVERSITY IN NATURAL SYSTEMS: THE BEEMS PROJECT
Abstract. This work wants to highlight the results obtained during the BEEMS (Monitoring Bee Diversity in Natural System) project, which the main goal was to answer the following question: Which biotic and abiotic indicators of floral and nesting resources best reflect the diversity of bee species and community composition in the Israeli natural environment? The research was oriented towards the cost-effectiveness analysis of new aerial geomatics techniques and classical ground-based methods for collecting the indicators described above, based only on open-source software for data analysis. Two complementary study systems in central Israel have been considered: the Alexander Stream National Park, an area undergoing an ecological restoration project in a sandy ecosystem, and the Judean foothills area, to the South of Tel Aviv. In each study system, different surveys of bees, flowers, nesting substrates and soil, using classical field measurement methods have been conducted. Simultaneously, an integrated aero photogrammetric survey, acquiring different spectral responses of the land surface by means of Uncrewed Aerial Vehicle (UAV) imaging systems have been performed. The multispectral sensors have provided surface spectral response out of the visible spectrum, while the photogrammetric reconstruction has provided three-dimensional information. Thanks to Artificial Intelligence algorithms and the richness of the data acquired, a methodology for Land Cover Classification has been developed. The results obtained by ground surveys and advanced geomatics tools have been compared and overlapped. The results are promising and show a good fit between the two approaches, and high performance of the geomatics tools in providing valuable ecological data
Single-Baseline RTK Positioning Using Dual-Frequency GNSS Receivers Inside Smartphones
Global Navigation Satellite System (GNSS) positioning is currently a common practice thanks to the development of mobile devices such as smartphones and tablets. The possibility to obtain raw GNSS measurements, such as pseudoranges and carrier-phase, from these instruments has opened new windows towards precise positioning using smart devices. This work aims to demonstrate the positioning performances in the case of a typical single-base Real-Time Kinematic (RTK) positioning while considering two different kinds of multi-frequency and multi-constellation master stations: a typical geodetic receiver and a smartphone device. The results have shown impressive performances in terms of precision in both cases: with a geodetic receiver as the master station, the reachable precisions are several mm for all 3D components while if a smartphone is used as the master station, the best results can be obtained considering the GPS+Galileo constellations, with a precision of about 2 cm both for 2D and Up components in the case of L1+L5 frequencies, or 3 cm for 2D components and 2 cm for the Up, in the case of an L1 frequency. Moreover, it has been demonstrated that it is not feasible to reach the phase ambiguities fixing: despite this, the precisions are still good and also the obtained 3D accuracies of positioning solutions are less than 1 m. So, it is possible to affirm that these results are very promising in the direction of cooperative positioning using smartphone devices
LAYING THE FOUNDATION FOR AN ARTIFICIAL NEURAL NETWORK FOR PHOTOGRAMMETRIC RIVERINE BATHYMETRY
Abstract. This work aims to test the effectiveness of artificial intelligence for correcting water refraction in shallow inland water using very high-resolution images collected by Unmanned Aerial Systems (UAS) and processed through a total FOSS workflow. The tests focus on using synthetic information extracted from the visible component of the electromagnetic spectrum. An artificial neural network is created using data of three morphologically similar alpine rivers. The RGB information, the SfM depth and seven radiometric indices are calculated and stacked in an 11-bands raster (input dataset). The depths are calculated as the difference between the Up component of the bathymetry cross-sections and the water surface quotas and constitute the dependent variable of the regression. The dataset is then scaled. The observations of one of the analyzed case studies are used as the unseen dataset to test the generalization capability of the model. The remaining observations are divided into test (20%) and training (80%) datasets. The generated NN is a 3-layer MLP model with one hidden layer and the Rectified Linear Unit (ReLU) and sigmoid activation functions. The weights are initialized to small Gaussian random values, and kernel regularizers, L1 and L2, are added to reduce the overfitting. Weights are updated with the Adam search technique, and the mean squared error is the loss function. The importance and significance of 11 variables are assessed. The model has a 0.70 r-squared score on the test dataset and 0.77 on the training dataset. The MAE is 0.06 and the RMSE 0.08, similar results obtained from the unseen dataset. Although the good metrics, the model shows some difficulties generalizing swallow depths
CHARACTERIZATION OF A MOBILE MAPPING SYSTEM FOR SEAMLESS NAVIGATION
Abstract. Mobile Mapping Systems (MMS) are multi-sensor technologies based on SLAM procedure, which provides accurate 3D measurement and mapping of the environment as also trajectory estimation for autonomous navigation. The major limits of these algorithms are the navigation and mapping inconsistence over the time and the georeferencing of the products. These issues are particularly relevant for pose estimation regardless the environment like in seamless navigation. This paper is a preliminary analysis on a proposed multi-sensor platform integrated for indoor/outdoor seamless positioning system. In particular the work is devoted to analyze the performances of the MMS in term of positioning accuracy and to evaluate its improvement with the integration of GNSS and UWB technology. The results show that, if the GNSS and UWB signal are not degraded, using the correct weight to their observations in the Stencil estimation algorithm, is possible to obtain an improvement in the accuracy of the MMS navigation solution as also in the global consistency of the final point cloud. This improvement is measured in about 7 cm for planimetric coordinate and 34 cm along the elevation with respect to the use of the Stencil system alone
GNSS positioning using mobile devices with the android operating system
The access and the use of the global navigation satellite system (GNSS) pseudo-range and carrier-phase measurements mobile devices as smartphones and tablets with an Android operating system has transformed the concept of accurate positioning with mobile devices. In this work, the comparison of positioning performances obtained with a smartphone and an external mass-market GNSS receiver both in real-time and post-processing is made. Particular attention is also paid to accuracy and precision of positioning results, also analyzing the possibility of estimating the phase ambiguities as integer values (fixed positioning) that it is still challenging for mass-market devices. The precisions and accuracies obtained with the mass-market receiver were about 5 cm and 1 cm both for real-time and post-processing solutions, respectively, while those obtained with a smartphone were slightly worse (few meters in some cases) due to the noise of its measurements
Monocular Visual Odometry with Unmanned Underwater Vehicle Using Low Cost Sensors
The positioning in underwater environments is today a strong necessity for many purposes, such as construction, communication, localization, and environmental monitoring. The use of underwater rover allows to perform visual inspections, maintenance and repair of many infrastructures, like dams, pipes, tunnels, structures as well as the analyses of the underwater environments in lakes, rivers and seas. This work deals with interesting results about monocular visual odometry for unmanned underwater navigation systems. Interesting results have been obtained considering low-cost sensors simulating the real-time, challenging operational conditions, applied for a dedicated archaeological situation. Particular algorithms for navigation procedures and outliers' rejections have been written and will presented in this paper: these aspects have a great importance especially for autonomous navigation solutions in underwater complex environments, such as for archaeological applications
GNSS Positioning using Android Smartphone
The possibility to manage pseudorange and carrier-phase measurements from the Global Navigation Satellite System (GNSS) chipset installed on smartphones and tablets with an Android operating system has changed the concept of precise positioning with portable devices. The goal of this work is to compare the positioning performances obtained with a smartphone and an external mass-market GNSS receiver both in real-time and post-processing. The attention is also focused not only on the accuracy and precision, but also on the possibility to determine the phase ambiguity values as integer (fixed positioning) that it is still a challenging aspect for mass-market devices: if the mass-market receiver provides good results under all points of view both for real-time and post-processing solutions (with precisions and accuracies of about 5 cm and 1 cm, respectively), the smartphone has a bad behaviour (order of magnitude of some meters) due to the noise of its measurements
Seamless Navigation using UWB-based Multisensor System
This work presents an Ultra-wideband-based (UWB) approach to seamless positioning and navigation applied in a real test-bed. It deploys two different solutions for positioning estimation in function of the operational environment. Outdoors, a classical hybridization between Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) is applied while indoors, an UWB/INS integration is performed relying on a low-cost commercial platform which integrates both UWB unit and IMU. The implementation of this procedure will be presented with more details in the paper. The aim of the work is to validate the performances in term of accuracy, precision and seamlessness behavior of the low-cost UWB technology available today. The results shown an overall accuracy of about 60 cm considering the entire path walked, both outdoor and indoors
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