Search results for "pre-processing"

2014

This paper considers the parameter estimation for linear time-invariant (LTI) systems in an input-output setting with output error (OE) time-delay model structure. The problem of missing data is commonly experienced in industry due to irregular sampling, sensor failure, data deletion in data preprocessing, network transmission fault, and so forth; to deal with the identification of LTI systems with time-delay in incomplete-data problem, the generalized expectation-maximization (GEM) algorithm is adopted to estimate the model parameters and the time-delay simultaneously. Numerical examples are provided to demonstrate the effectiveness of the proposed method.

Using NASA'S Long Term Data Record version 3 for the monitoring of land surface vegetation

2011

Numerous datasets have been made available for the observation of our planet from space. The aim of this work is the observation of changes in vegetation, through the use of a recent remote sensing dataset, NASA's Long Term Data Record (LTDR). Several authors have pointed out that vegetation monitoring benefits of the simultaneous use of Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST). Therefore, this work presents the procedure developed to monitor vegetation with the LTDR dataset, using both NDVI and LST parameters. This procedure includes data preprocessing (estimation of NDVI and LST, orbital drift correction, atmospherically contaminated data reconstruc…

A statistical calibration model for Affymetrix probe level data

2009

Gene expression microarrays allow a researcher to measure the simultaneous response of thousands of genes to external conditions. Affymetrix GeneChip{ $Ⓡ$} expression array technology has become a standard tool in medical research. Anyway, a preprocessing step is usually necessary in order to obtain a gene expression measure. Aim of this paper is to propose a calibration method to estimate the nominal concentration based on a nonlinear mixed model. This method is an enhancement of a method proposed in Mineo et al. (2006). The relationship between raw intensities and concentration is obtained by using the Langmuir isotherm theory.

Optical techniques for river flow monitoring: an automatic procedure for the identification of the best video sequence to process by LS-PIV technique

2022

Technological advances over last decades gave an innovative impulse to the development of new streamflow measurements techniques, especially regarding remote flow monitoring methods that allow for non-intrusive measurements. The most widely used image-based techniques are the optical ones, such as Large-Scale Particle Image Velocimetry (LS-PIV) and Large-Scale Particle Tracking Velocimetry (LS-PTV). The dynamic movement of tracers floating on the water surface of a river is recorded, and the resulting videos are processed by applying a statistical cross-correlation analysis to detect the most probable frame-by-frame displacement, finally deriving the surface velocity field. To obtain river …

Normalization of T2W-MRI Prostate Images using Rician a priori

2016

International audience; Prostate cancer is reported to be the second most frequently diagnosed cancer of men in the world. In practise, diagnosis can be affected by multiple factors which reduces the chance to detect the potential lesions. In the last decades, new imaging techniques mainly based on MRI are developed in conjunction with Computer-Aided Diagnosis (CAD) systems to help radiologists for such diagnosis. CAD systems are usually designed as a sequential process consisting of four stages: pre-processing, segmentation, registration and classification. As a pre-processing, image normalization is a critical and important step of the chain in order to design a robust classifier and over…

Optical techniques: non-intrusive river monitoring approach

2022

<p>Technological advances over last decades gave an innovative impulse to development of new streamflow measurements techniques, making possible to implement remote flow monitoring methods that allow for non-intrusive measurements. Here, we focus on image-based techniques that involve the use of digital camera, either installed on a bridge or equipped by a drone (UAVs – Unmanned Aerial Vehicles). The most widely known and used optical techniques are the Large-Scale Particle Image Velocimetry and the Large-Scale Particle Tracking Velocimetry. Optical techniques are based on four main steps: (i) seeding and recording, (ii) images pre-processing, (iii) images proces…

Pre-production validation of the ATLAS level-1 calorimeter trigger system

2006

The Level-1 Calorimeter Trigger is a major part of the first stage of event selection for the ATLAS experiment at the LHC. It is a digital, pipelined system with several stages of processing, largely based on FPGAs, which perform programmable algorithms in parallel with a fixed latency to process about 300 Gbyte/s of input data. The real-time output consists of counts of different types of trigger objects and energy sums. Prototypes of all module types have been undergoing intensive testing before final production during 2005. Verification of their correct operation has been performed stand-alone and in the ATLAS test-beam at CERN. Results from these investigations will be presented, along …

Rule-guided identification of cosmic-ray patterns in PLASTEX

1992

Some techniques devised in the computer science fields of pattern recognition and expert systems are being applied to the interpretation of EAS responses in the PLASTEX experiment. An attempt is made to codity in a set of rules the expertise of trained researchers who are able to recognize and classify different hit patterns even in the presence of noisy background, and in spite of imperfections in the detector response. The patterns expected to be useful include, but are not limited to, track patterns. The software described here, as a progress report, automatically finds patterns corresponding to isolated tracks, and patterns composed of tracks that connect with each other in a layer of d…

Modeling recurrent distributions in streams using possible worlds

2015

Discovering changes in the data distribution of streams and discovering recurrent data distributions are challenging problems in data mining and machine learning. Both have received a lot of attention in the context of classification. With the ever increasing growth of data, however, there is a high demand of compact and universal representations of data streams that enable the user to analyze current as well as historic data without having access to the raw data. To make a first step towards this direction, we propose a condensed representation that captures the various — possibly recurrent — data distributions of the stream by extending the notion of possible worlds. The representation en…

RPPanalyzer Toolbox: An improved R package for analysis of reverse phase protein array data

2014

Analysis of large-scale proteomic data sets requires specialized software tools, tailored toward the requirements of individual approaches. Here we introduce an extension of an open-source software solution for analyzing reverse phase protein array (RPPA) data. The R package RPPanalyzer was designed for data preprocessing followed by basic statistical analyses and proteomic data visualization. In this update, we merged relevant data preprocessing steps into a single user-friendly function and included a new method for background noise correction as well as new methods for noise estimation and averaging of replicates to transform data in such a way that they can be used as input for a new t…