Search results for "Vector"
showing 10 items of 2660 documents
Dynamic laser speckle analyzed considering inhomogeneities in the biological sample
2017
Dynamic laser speckle phenomenon allows a contactless and nondestructive way to monitor biological changes that are quantified by second-order statistics applied in the images in time using a secondary matrix known as time history of the speckle pattern (THSP). To avoid being time consuming, the traditional way to build the THSP restricts the data to a line or column. Our hypothesis is that the spatial restriction of the information could compromise the results, particularly when undesirable and unexpected optical inhomogeneities occur, such as in cell culture media. It tested a spatial random approach to collect the points to form a THSP. Cells in a culture medium and in drying paint, repr…
The problem of spatial homogeneity in an LCoS projector
2019
Abstract Video projectors allow interesting applications in vision sciences since they provide a large projection area. We have colorimetrically characterized a LCoS projector using a mathematical model requiring additivity and constancy of the primaries -a sigmoid function in our case. Significant differences in chromaticity in the CIELAB space, but not in lightness, were found between the center and the corners of the screen. The lack of spatial homogeneity led us to estimate the parameters of the model as a function of spatial position, using different strategies. The best result was obtained by interpolating the values of the parameters of the model determined from experimental measurem…
Learning vector quantization with alternative distance criteria
2003
An adaptive algorithm for training of a nearest neighbour (NN) classifier is developed in this paper. This learning rule has some similarity to the well-known LVQ method, but uses the nearest centroid neighbourhood concept to estimate optimal locations of the codebook vectors. The aim of this approach is to improve the performance of the standard LVQ algorithms when using a very small codebook. The behaviour of the learning technique proposed here is experimentally compared to those of the plain k-NN decision rule and the LVQ algorithms.
Fast Decentralized Linear Functions via Successive Graph Shift Operators
2019
Decentralized signal processing performs learning tasks on data distributed over a multi-node network which can be represented by a graph. Implementing linear transformations emerges as a key task in a number of applications of decentralized signal processing. Recently, some decentralized methods have been proposed to accomplish that task by leveraging the notion of graph shift operator, which captures the local structure of the graph. However, existing approaches have some drawbacks such as considering special instances of linear transformations, or reducing the family of transformations by assuming that a shift matrix is given such that a subset of its eigenvectors spans the subspace of i…
Highly efficient liposome-mediated gene transfer of inducible nitric oxide synthase in vivo and in vitro in vascular smooth muscle cells.
2000
Objective: The efficient introduction of regulatory genes into vascular smooth muscle cells (SMCs) is one of the most promising options for gene therapy of cardiovascular diseases. Cationic liposome-mediated gene transfer may become a favorable transfection technique with regard to patient’s safety for in vivo administration. However, this method until now has its limitation in a low transfection efficiency. Therefore, the present study was designed to improve cationic liposome-mediated transfection of rabbit vascular SMCs in vitro and in vivo, in order to enhance transfection efficiency and present an optimized system which may offer a potential therapeutic benefit for in vivo application.…
Simulating term structure of interest rates with arbitrary marginals
2011
Decision models under uncertainty rely their analysis on scenarios of the economic factors. A key economic factor is the term structure of interest rates (yields). Simulation models of the yield curve usually assume that the conjugate distribution of the interest rates is lognormal. Dynamic models, like vector auto-regression, implicitly postulate that the logarithm of the interest rates is normally distributed. Statistical analyses have, however, shown that stationary transformations (yield changes) of the interest rates are substantially leptokurtic, thus posing serious doubts on the reliability of the available models. We propose in this paper a VARTA model (Biller and Nelson, 2003) to s…
Experimental Comparison of Efficiency Enhancement Algorithms for Three-Phase Induction Motors
2019
This paper presents an experimental comparison of two efficiency improvement algorithms for three-phase induction motors by adopting Loss Model Algorithms (LMAs). The efficiency enhancement is evaluated starting from several simulations for different conditions of load and speed and, then, validated by comparing the experimental results obtained by applying the two control strategies and the traditional Field Oriented Control (FOC). Significant results in terms of efficiency enhancement are presented and discussed.
Automatic segmentation and 3D reconstruction of intravascular ultrasound images for a fast preliminar evaluation of vessel pathologies.
2007
Abstract Intravascular ultrasound (IVUS) imaging is used along with X-ray coronary angiography to detect vessel pathologies. Manual analysis of IVUS images is slow and time-consuming and it is not feasible for clinical purposes. A semi-automated method is proposed to generate 3D reconstructions from IVUS video sequences, so that a fast diagnose can be easily done, quantifying plaque length and severity as well as plaque volume of the vessels under study. The methodology described in this work has four steps: a pre-processing of IVUS images, a segmentation of media–adventitia contour, a detection of intima and plaque and a 3D reconstruction of the vessel. Preprocessing is intended to remove …
Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples
2021
Abstract Predictive models based on radiomics and machine-learning (ML) need large and annotated datasets for training, often difficult to collect. We designed an operative pipeline for model training to exploit data already available to the scientific community. The aim of this work was to explore the capability of radiomic features in predicting tumor histology and stage in patients with non-small cell lung cancer (NSCLC). We analyzed the radiotherapy planning thoracic CT scans of a proprietary sample of 47 subjects (L-RT) and integrated this dataset with a publicly available set of 130 patients from the MAASTRO NSCLC collection (Lung1). We implemented intra- and inter-sample cross-valida…
RGD motifs on the surface of baculovirus enhance transduction of human lung carcinoma cells.
2006
Baculovirus vectors have been shown to enter a variety of mammalian cell lines and gene transfer with wild-type baculovirus (WT) has been demonstrated both in vitro and in vivo. Different protein motifs have been displayed on the viral surface to serve as ligands for cell-specific receptor molecules. We have generated recombinant baculovirus vectors displaying an RGD-motif, recognized by alphaV integrin, on the viral surface. The RGD motifs within the C-terminus of coxsackie virus A9 and human parechovirus 1 VP1 proteins were fused to the N-terminus of the major envelope glycoprotein, gp64, of Autographa californica multiple nucleopolyhedrovirus. The recombinant RGD-presenting viruses bound…