Search results for "Data processing"
showing 10 items of 175 documents
Context-related data processing in artificial neural networks for higher reliability of telerehabilitation systems
2015
Classification is a data processing technique of a great significance both for native eHealth systems and web telemedicine solutions. In this sense, artificial neural networks have been widely applied in telerehabilitation as powerful tools to process information and acquire a new medical knowledge. But effective analysis of multidimensional heterogeneous medical data, still poses considerable difficulties. It was shown that processing too many data features simultaneously is costly and has some adverse effects on the resulting models classification properties. Therefore, there is a strong need to develop new techniques for selecting features from the very large data sets that include many …
Regression diagnostics applied in kinetic data processing: Outlier recognition and robust weighting procedures
2010
An efficient protocol, based on advanced statistical diagnostics and robust fitting techniques applied to the least-squares processing of kinetic data of chemical reactions, is presented and discussed. The procedure, which is aimed at obtaining highly accurate estimation of the fitting parameters, consists of the identification of the outliers that remarkably impair the fitting by means of the so-called “leverage analysis” and some related diagnostics. This approach allows the elimination of the actually aberrant observations from the data set and/or their robust weighting to inhibit the negative effects induced on the data fitting, with consequent reduction of the bias introduced into the …
A hybrid multi-criteria approach to GPR image mining applied to water supply system maintenance
2018
[EN] Data processing techniques for Ground Penetrating Radar (GPR) image mining provide essential information to optimize maintenance management of Water Supply Systems (WSSs). These techniques aim to elaborate on radargrams in order to produce meaningful graphical representations of critical buried components of WSSs. These representations are helpful non-destructive evaluation tools to prevent possible failures in WSSs by keeping them adequately monitored. This paper proposes an integrated multi-criteria decision making (MCDM) approach to prioritize various data processing techniques by means of ranking their outputs, namely their resulting GPR image representations. The Fuzzy Analytic Hi…
Industrial Environment Mapping Using Distributed Static 3D Sensor Nodes
2018
This paper presents a system architecture for mapping and real-time monitoring of a relatively large industrial robotic environment of size 10 m × 15 m × 5 m. Six sensor nodes with embedded computing power and local processing of the 3D point clouds are placed close to the ceiling. The system architecture and data processing is based on the Robot Operating System (ROS) and the Point Cloud Library (PCL). The 3D sensors used are the Microsoft Kinect for Xbox One and point cloud data is collected at 20 Hz. A new manual calibration procedure is developed using reflective planes. The specified range of the used sensor is 0.8 m to 4.2 m, while depth data up to 9 m is used in this paper. Despite t…
Real-Time 3D Face Acquisition Using Reconfigurable Hybrid Architecture
2007
Acquiring 3D data of human face is a general problem which can be applied in face recognition, virtual reality, and many other applications. It can be solved using stereovision. This technique consists in acquiring data in three dimensions from two cameras. The aim is to implement an algorithmic chain which makes it possible to obtain a three-dimensional space from two two-dimensional spaces: two images coming from the two cameras. Several implementations have already been considered. We propose a new simple real-time implementation based on a hybrid architecture (FPGA-DSP), allowing to consider an embedded and reconfigurable processing. Then we show our method which provides depth map of …
An alternative simple approach to estimate atmospheric correction in multitemporal studies
1989
Abstract Studies that use multitemporal images require the conversion of original digital data into the corresponding physical magnitudes. Atmospheric correction is one of the most important steps in this process, which is usually undertaken using atmospheric radiative transfer models. The main difficulty in these models is the need of atmospheric input data which are not usually available. An alternative approach to atmospheric correction is proposed in this Letter. It is based on the idea that the atmospheric effects over two or more dates can be determined in a relative way, by using the apparent reflectance values of surfaces whose ground reflectance can be considered unchangeable with …
Event-based encoding from digital magnetic compass and ultrasonic distance sensor for navigation in mobile systems
2016
Event-based encoding reduces the amount of generated data while keeping relevant information in the measured magnitude. While this encoding is mostly associated with spiking neuromorphic systems, it can be used in a broad spectrum of tasks. The extension of event-based data representation to other sensors would provide advantages related to bandwidth reduction, lower computing requirements, increased processing speed and data processing. This work describes two event-based encoding procedures (magnitude-event and rate-event) for two sensors widely used in industry, especially for navigation in mobile systems: digital magnetic compass and ultrasonic distance sensor. Encoded data meet Address…
Machine learning in remote sensing data processing
2009
Remote sensing data processing deals with real-life applications with great societal values. For instance urban monitoring, fire detection or flood prediction from remotely sensed multispectral or radar images have a great impact on economical and environmental issues. To treat efficiently the acquired data and provide accurate products, remote sensing has evolved into a multidisciplinary field, where machine learning and signal processing algorithms play an important role nowadays. This paper serves as a survey of methods and applications, and reviews the latest methodological advances in machine learning for remote sensing data analysis.
Dimensionality Reduction Techniques: An Operational Comparison On Multispectral Satellite Images Using Unsupervised Clustering
2006
Multispectral satellite imagery provides us with useful but redundant datasets. Using Dimensionality Reduction (DR) algorithms, these datasets can be made easier to explore and to use. We present in this study an objective comparison of five DR methods, by evaluating their capacity to provide a usable input to the K-means clustering algorithm. We also suggest a method to automatically find a suitable number of classes K, using objective "cluster validity indexes" over a range of values for K. Ten Landsat images have been processed, yielding a classification rate in the 70-80% range. Our results also show that classical linear methods, though slightly outperformed by more recent nonlinear al…
A new fast and fault-tolerant identification algorithm for spectral databases
1995
A new method for an automatic, computer and database driven identification of UV/VIS spectra is described. It is shown that an identification algorithm must consider the spectral differences as well as their common features. The described identification method allows identifications, even if the spectra are distorted or shifted.