Search results for "vector"
showing 10 items of 2660 documents
Structured Output SVM for Remote Sensing Image Classification
2011
Traditional kernel classifiers assume independence among the classification outputs. As a consequence, each misclassification receives the same weight in the loss function. Moreover, the kernel function only takes into account the similarity between input values and ignores possible relationships between the classes to be predicted. These assumptions are not consistent for most of real-life problems. In the particular case of remote sensing data, this is not a good assumption either. Segmentation of images acquired by airborne or satellite sensors is a very active field of research in which one tries to classify a pixel into a predefined set of classes of interest (e.g. water, grass, trees,…
Application of Model Predictive Control in Discrete Displacement Cylinders to Drive a Knuckle Boom Crane
2018
In this paper, two Discrete Displacement Cylinders (DDCs) are used to drive the boom of a knuckle boom crane. DDCs operate by connecting one of several available pressure levels to each chamber in order to produce different forces. A trade-off exists with such systems, between the accuracy of tracking and energy dissipation due to switching. A popular way to approach this problem is a Force Shifting Algorithm (FSA). However, in this paper, the trade-off is managed by use of a Model Predictive Control (MPC) algorithm. The tracking accuracy and energy efficiency of the MPC and FSA strategies for DDCs are compared to a PID strategy for standard cylinders. The comparison is obtained by use of a…
Localization Based on Parallel Robots Kinematics as an Alternative to Trilateration
2022
In this article, a new scheme for range-based localization is proposed. The main goal is to estimate the position of a mobile point based on distance measurements from fixed devices, called anchors, and on inertial measurements. Due to the nonlinear nature of the problem, an analytic relation to compute the position starting from these measurements does not exist, and often trilateration methods are used, generally based on least-square algorithms. The proposed scheme is based on the modeling of the localization process as a parallel robot, thereby methodologies and control algorithms used in the robotic area can be exploited. In particular, a closed-loop control system is designed for trac…
Clinically-Driven Virtual Patient Cohorts Generation: An Application to Aorta
2021
The combination of machine learning methods together with computational modeling and simulation of the cardiovascular system brings the possibility of obtaining very valuable information about new therapies or clinical devices through in-silico experiments. However, the application of machine learning methods demands access to large cohorts of patients. As an alternative to medical data acquisition and processing, which often requires some degree of manual intervention, the generation of virtual cohorts made of synthetic patients can be automated. However, the generation of a synthetic sample can still be computationally demanding to guarantee that it is clinically meaningful and that it re…
Integrating Environmental Temperature Conditions into the SIR Model for Vector-Borne Diseases
2020
International audience; Nowadays, Complex networks are used to model and analyze various problems of real-life e.g. information diffusion in social networks, epidemic spreading in human population etc. Various epidemic spreading models are proposed for analyzing and understanding the spreading of infectious diseases in human contact networks. In classical epidemiological models, a susceptible person becomes infected after getting in contact with an infected person among the human population only. However, in vector-borne diseases, a human can be infected also by a living organism called a vector. The vector population that also help in spreading diseases is very sensitive to environmental f…
Emulation of 2D Hydrodynamic Flood Simulations at Catchment Scale Using ANN and SVR
2021
Two-dimensional (2D) hydrodynamic models are one of the most widely used tools for flood modeling practices and risk estimation. The 2D models provide accurate results
Deep Convolutional Neural Network for HEp-2 fluorescence intensity classification
2019
Indirect ImmunoFluorescence (IIF) assays are recommended as the gold standard method for detection of antinuclear antibodies (ANAs), which are of considerable importance in the diagnosis of autoimmune diseases. Fluorescence intensity analysis is very often complex, and depending on the capabilities of the operator, the association with incorrect classes is statistically easy. In this paper, we present a Convolutional Neural Network (CNN) system to classify positive/negative fluorescence intensity of HEp-2 IIF images, which is important for autoimmune diseases diagnosis. The method uses the best known pre-trained CNNs to extract features and a support vector machine (SVM) classifier for the …
A multi-process system for HEp-2 cells classification based on SVM
2016
An automatic system for pre-segmented IIF images analysis was developed.A non-standard pipeline for supervised image classification was adopted.The system uses a two-level pyramid to retain some spatial information.From each cell image 216 features are extracted.15 SVM classifiers one-against-one have been implemented. This study addresses the classification problem of the HEp-2 cells using indirect immunofluorescence (IIF) image analysis, which can indicate the presence of autoimmune diseases by finding antibodies in the patient serum. Recently, studies have shown that it is possible to identify the cell patterns using IIF image analysis and machine learning techniques. In this paper we de…
Automated detection of microaneurysms using robust blob descriptors
2013
International audience; Microaneurysms (MAs) are among the first signs of diabetic retinopathy (DR) that can be seen as round dark-red structures in digital color fundus photographs of retina. In recent years, automated computer-aided detection and diagnosis (CAD) of MAs has attracted many researchers due to its low-cost and versatile nature. In this paper, the MA detection problem is modeled as finding interest points from a given image and several interest point descriptors are introduced and integrated with machine learning techniques to detect MAs. The proposed approach starts by applying a novel fundus image contrast enhancement technique using Singular Value Decomposition (SVD) of fun…
An Automatic HEp-2 Specimen Analysis System Based on an Active Contours Model and an SVM Classification
2019
The antinuclear antibody (ANA) test is widely used for screening, diagnosing, and monitoring of autoimmune diseases. The most common methods to determine ANA are indirect immunofluorescence (IIF), performed by human epithelial type 2 (HEp-2) cells, as substrate antigen. The evaluation of ANA consist an analysis of fluorescence intensity and staining patterns. This paper presents a complete and fully automatic system able to characterize IIF images. The fluorescence intensity classification was obtained by performing an image preprocessing phase and implementing a Support Vector Machines (SVM) classifier. The cells identification problem has been addressed by developing a flexible segmentati…