Search results for "Preprocess"

Vibration signature analysis for rotor broken bar diagnosis in double cage induction motor drives

2013

The paper investigates the diagnosis of rotor broken bars in field oriented controlled (FOC) double cage induction motor drives, using current and vibration signature analysis techniques. The Impact of the closed loop control system cannot be neglected when the detection of asymmetries in the machine are based on the signature analysis of electrical variables. The proposed diagnosis approach is based on optimized use of wavelet analysis by a pre-processing of phase current or axial/radial vibration signals. Thus, the time evolution of the tracked rotor fault components can be effectively analyzed. This paper shows also the relevance of the fault components computed from axial vibration sign…

DEVELOPMENT AND IMPLEMENTATION OF MACHINE LEARNING METHODS FOR THE IIF IMAGES ANALYSIS

2021

Inducing the Lyndon Array

2019

In this paper we propose a variant of the induced suffix sorting algorithm by Nong (TOIS, 2013) that computes simultaneously the Lyndon array and the suffix array of a text in $O(n)$ time using $\sigma + O(1)$ words of working space, where $n$ is the length of the text and $\sigma$ is the alphabet size. Our result improves the previous best space requirement for linear time computation of the Lyndon array. In fact, all the known linear algorithms for Lyndon array computation use suffix sorting as a preprocessing step and use $O(n)$ words of working space in addition to the Lyndon array and suffix array. Experimental results with real and synthetic datasets show that our algorithm is not onl…

Textureless macula swelling detection with multiple retinal fundus images

2011

Retinal fundus images acquired with nonmydriatic digital fundus cameras are versatile tools for the diagnosis of various retinal diseases. Because of the ease of use of newer camera models and their relatively low cost, these cameras can be employed by operators with limited training for telemedicine or point-of-care (PoC) applications. We propose a novel technique that uses uncalibrated multiple-view fundus images to analyze the swelling of the macula. This innovation enables the detection and quantitative measurement of swollen areas by remote ophthalmologists. This capability is not available with a single image and prone to error with stereo fundus cameras. We also present automatic alg…

Automatic Monitoring System for the Evolution of the Hemangiomas

2019

In this paper we describe an automatic monitoring system for the evolution of infantile hemangiomas using a fuzzy logic system based on two parameters: area and redness. To follow the evolution, we have used for each subject pairs of images at different moments of time. The starting points of the algorithm are the rectangular regions of interest (ROI), manually selected for each of the two images, and automatically segmented using Otsu’s method in combination with different preprocessing methods. Using the results of segmentation, we could compute the evolution of the area and the evolution of the redness of hemangioma. These two parameters were used as input for the fuzzy logic system, obt…

MetaCache-GPU: Ultra-Fast Metagenomic Classification

2021

The cost of DNA sequencing has dropped exponentially over the past decade, making genomic data accessible to a growing number of scientists. In bioinformatics, localization of short DNA sequences (reads) within large genomic sequences is commonly facilitated by constructing index data structures which allow for efficient querying of substrings. Recent metagenomic classification pipelines annotate reads with taxonomic labels by analyzing their $k$-mer histograms with respect to a reference genome database. CPU-based index construction is often performed in a preprocessing phase due to the relatively high cost of building irregular data structures such as hash maps. However, the rapidly growi…

Incremental Gaussian Discriminant Analysis based on Graybill and Deal weighted combination of estimators for brain tumour diagnosis

2011

In the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally…

The ATLAS Level-1 Calorimeter Trigger: PreProcessor implementation and performance

2012

The PreProcessor system of the ATLAS Level-1 Calorimeter Trigger (L1Calo) receives about 7200 analogue signals from the electromagnetic and hadronic components of the calorimetric detector system. Lateral division results in cells which are pre-summed to so-called Trigger Towers of size 0.1 × 0.1 along azimuth (phi) and pseudorapidity (η). The received calorimeter signals represent deposits of transverse energy. The system consists of 124 individual PreProcessor modules that digitise the input signals for each LHC collision, and provide energy and timing information to the digital processors of the L1Calo system, which identify physics objects forming much of the basis for the full ATLAS fi…

A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks

2019

In this paper, we apply a new promising tool for pattern classification, namely, the Tsetlin Machine (TM), to the field of disease forecasting. The TM is interpretable because it is based on manipulating expressions in propositional logic, leveraging a large team of Tsetlin Automata (TA). Apart from being interpretable, this approach is attractive due to its low computational cost and its capacity to handle noise. To attack the problem of forecasting, we introduce a preprocessing method that extends the TM so that it can handle continuous input. Briefly stated, we convert continuous input into a binary representation based on thresholding. The resulting extended TM is evaluated and analyzed…

Un procedimiento de fuerte reducción de las dimensiones del RCPS/π

2009

Recently, in the field of project scheduling problems the concept of partially renewable resources has been introduced. Theoretically, it is a generalization of both renewable and non-renewable resources. From an applied point of view, partially renewable resources allow us to model a large variety of situations that do not fit into classical models, but can be found in real problems in timetabling and labour scheduling. When modelling real problems, the problem of project scheduling with partially renewable resources, as many other combinatorial problems, gets such large dimensions that it is quite difficult to apply solution procedures. In this paper, we describe some powerful preprocessi…