Search results for "component"
showing 10 items of 1682 documents
A new methodology for Functional Principal Component Analysis from scarce data. Application to stroke rehabilitation.
2015
Functional Principal Component Analysis (FPCA) is an increasingly used methodology for analysis of biomedical data. This methodology aims to obtain Functional Principal Components (FPCs) from Functional Data (time dependent functions). However, in biomedical data, the most common scenario of this analysis is from discrete time values. Standard procedures for FPCA require obtaining the functional data from these discrete values before extracting the FPCs. The problem appears when there are missing values in a non-negligible sample of subjects, especially at the beginning or the end of the study, because this approach can compromise the analysis due to the need to extrapolate or dismiss subje…
Partial Methylation at Am100 in 18S rRNA of Baker's Yeast Reveals Ribosome Heterogeneity on the Level of Eukaryotic rRNA Modification
2014
Ribosome heterogeneity is of increasing biological significance and several examples have been described for multicellular and single cells organisms. In here we show for the first time a variation in ribose methylation within the 18S rRNA of Saccharomyces cerevisiae. Using RNA-cleaving DNAzymes, we could specifically demonstrate that a significant amount of S. cerevisiae ribosomes are not methylated at 2'-O-ribose of A100 residue in the 18S rRNA. Furthermore, using LC-UV-MS/MS of a respective 18S rRNA fragment, we could not only corroborate the partial methylation at A100, but could also quantify the methylated versus non-methylated A100 residue. Here, we exhibit that only 68% of A100 in t…
Using SOM and PCA for analysing and interpreting data from a P-removal SBR
2008
This paper focuses on the application of Kohonen self-organizing maps (SOM) and principal component analysis (PCA) to thoroughly analyse and interpret multidimensional data from a biological process. The process is aimed at enhanced biological phosphorus removal (EBPR) from wastewater. In this work, SOM and PCA are firstly applied to the data set in order to identify and analyse the relationships among the variables in the process. Afterwards, K-means algorithm is used to find out how the observations can be grouped, on the basis of their similarity, in different classes. Finally, the information obtained using these intelligent tools is used for process interpretation and diagnosis. In the…
PROLISEAN: A New Security Protocol for Programmable Matter
2021
The vision for programmable matter is to create a material that can be reprogrammed to have different shapes and to change its physical properties on demand. They are autonomous systems composed of a huge number of independent connected elements called particles. The connections to one another form the overall shape of the system. These particles are capable of interacting with each other and take decisions based on their environment. Beyond sensing, processing, and communication capabilities, programmable matter includes actuation and motion capabilities. It could be deployed in different domains and will constitute an intelligent component of the IoT. A lot of applications can derive fro…
A voltammetric e-tongue tool for the emulation of the sensorial analysis and the discrimination of vegetal milks
2018
[EN] The relevance of plant-based food alternatives to dairy products, such as vegetable milks, has been growing in recent decades, and the development of systems capable of classifying and predicting the sensorial profile of such products is interesting. In this context, a methodology to perform the sensorial analysis of vegetable milks (oat, soya, rice, almond and tiger nut), based on 12 parameters, was validated. An electronic tongue based on the combination of eight metals with pulse voltammetry was also tested. The current intensity profiles are characteristic for each non-dairy milk type. Data were processed with qualitative (PCA, dendrogram) and quantitative (PLS) tools. The PCA stat…
Classification of Sequences with Deep Artificial Neural Networks: Representation and Architectural Issues
2021
DNA sequences are the basic data type that is processed to perform a generic study of biological data analysis. One key component of the biological analysis is represented by sequence classification, a methodology that is widely used to analyze sequential data of different nature. However, its application to DNA sequences requires a proper representation of such sequences, which is still an open research problem. Machine Learning (ML) methodologies have given a fundamental contribution to the solution of the problem. Among them, recently, also Deep Neural Network (DNN) models have shown strongly encouraging results. In this chapter, we deal with specific classification problems related to t…
Extraction of ERP from EEG data
2007
In this article, a simple but novel technique for extracting a linear subspace related to event related potentials (ERPs) from ElectroEncephaloGraphy (EEG) data is introduced. The technique consists of a sequence of basic linear operations applied to multidimensional EEG data in a problem-specific manner. The derivation of the proposed technique is given and results with real data are described together with overall conclusions.
Healthcare trajectory mining by combining multidimensional component and itemsets
2012
Sequential pattern mining is aimed at extracting correlations among temporal data. Many different methods were proposed to either enumerate sequences of set valued data (i.e., itemsets) or sequences containing multidimensional items. However, in real-world scenarios, data sequences are described as events of both multidimensional items and set valued information. These rich heterogeneous descriptions cannot be exploited by traditional approaches. For example, in healthcare domain, hospitalizations are defined as sequences of multi-dimensional attributes (e.g. Hospital or Diagnosis) associated with two sets, set of medical procedures (e.g. $ \lbrace $ Radiography, Appendectomy $\rbrace$) and…
MAGICPL: A Generic Process Description Language for Distributed Pseudonymization Scenarios
2021
Abstract Objectives Pseudonymization is an important aspect of projects dealing with sensitive patient data. Most projects build their own specialized, hard-coded, solutions. However, these overlap in many aspects of their functionality. As any re-implementation binds resources, we would like to propose a solution that facilitates and encourages the reuse of existing components. Methods We analyzed already-established data protection concepts to gain an insight into their common features and the ways in which their components were linked together. We found that we could represent these pseudonymization processes with a simple descriptive language, which we have called MAGICPL, plus a relati…
A speech recognition approach for an industrial training station
2021
This paper presents a speech recognition service used in the context of commanding and guiding the activities around an industrial training station. The entire concept is built on a decentralized microservice architecture and one of the many hardware and software components is the speech recognition engine. This engine grants users the possibility to interact seamlessly with other components in order to ensure a gradual and productive learning process. By working with different API’s for both English and Romanian languages, the presented approach manages to obtain good speech recognition for defining task phrases aiding the training procedure and to reduce the recognition required time by a…