Search results for "REDUCTION"

Can improving a biscuit's nutritional characteristics be compatible with maintaining it sensory quality?

2013

Poster (1 page) ; http://www.pangborn2013.com/; International audience; Authorities encourage people to reduce fat and sugar consumption in public campaigns such as the National Nutritional Health Program in France. French producers are also encouraged to improve the nutritional composition of well-known commercial products by reducing fat and/or sugar contents. The objective of our study was to determine whether it was possible to do so while maintaining the sensory quality of the reformulated products. The study dealt with the impact of fat and sugar reduction on liking and sensory perception of 6 types of French commercial biscuits and cakes. For each type of product, one example of the …

On GPU-accelerated fast direct solvers and their applications in image denoising

2015

Fast 3D Ray Tracing for Indoor Coverage Solutions

2016

Optimal wireless indoor network planning requires huge number of iterations and evaluations of indoor coverage for each antenna location until an optimal solution is reached. Consequently, accurate and scalable calculation of power strength for indoor scenarios becomes necessary. The contribution in this paper is to reduce the complexity of 3D ray tracing for deterministic indoor power prediction. In order to achieve that while preserving the accuracy, image theory with feasible reflection volume as preprocessing approach is introduced. This proposed algorithm stores the image, its feasible reflection volume and valid area of receiving points. Significant complexity reduction is achieved by…

Visible-NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit

2015

Abstract The development of systems for automatically detecting decay in citrus fruit during quality control is still a challenge for the citrus industry. The feasibility of reflectance spectroscopy in the visible and near infrared (NIR) regions was evaluated for the automatic detection of the early symptoms of decay caused by Penicillium digitatum fungus in citrus fruit. Reflectance spectra of sound and decaying surface parts of mandarins cv. ‘Clemenvilla’ were acquired in two different spectral regions, from 650 nm to 1050 nm (visible–NIR) and from 1000 nm to 1700 nm (NIR), pointing to significant differences in spectra between sound and decaying skin for both spectral ranges. Three diffe…

Nonlinear data description with Principal Polynomial Analysis

2012

Principal Component Analysis (PCA) has been widely used for manifold description and dimensionality reduction. Performance of PCA is however hampered when data exhibits nonlinear feature relations. In this work, we propose a new framework for manifold learning based on the use of a sequence of Principal Polynomials that capture the eventually nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) is shown to generalize PCA. Unlike recently proposed nonlinear methods (e.g. spectral/kernel methods and projection pursuit techniques, neural networks), PPA features are easily interpretable and the method leads to a fully invertible transform, which is a desirable property…

Modeling user preferences in content-based image retrieval: A novel attempt to bridge the semantic gap

2015

This paper is concerned with content-based image retrieval from a stochastic point of view. The semantic gap problem is addressed in two ways. First, a dimensional reduction is applied using the (pre-calculated) distances among images. The dimension of the reduced vector is the number of preferences that we allow the user to choose from, in this case, three levels. Second, the conditional probability distribution of the random user preference, given this reduced feature vector, is modeled using a proportional odds model. A new model is fitted at each iteration. The score used to rank the image database is based on the estimated probability function of the random preference. Additionally, so…

Embedded Real-Time Surveillance Using Multimodal Mean Background Modeling

2008

Automated video surveillance applications require accurate separation of foreground and background image content. Cost-sensitive embedded platforms place real-time performance and efficiency demands on techniques to accomplish this task. In this chapter, we evaluate pixel-level foreground extraction techniques for a low-cost integrated surveillance system. We introduce a new adaptive background modeling technique, multimodal mean (MM), which balances accuracy, performance, and efficiency to meet embedded system requirements. Our evaluation compares several pixel-level foreground extraction techniques in terms of their computation and storage requirements, and functional accuracy for three r…

Local dimensionality reduction within natural clusters for medical data analysis

2005

Inductive learning systems have been successfully applied in a number of medical domains. Nevertheless, the effective use of these systems requires data preprocessing before applying a learning algorithm. Especially it is important for multidimensional heterogeneous data, presented by a large number of features of different types. Dimensionality reduction is one commonly applied approach. The goal of this paper is to study the impact of natural clustering on dimensionality reduction for classification. We compare several data mining strategies that apply dimensionality reduction by means of feature extraction or feature selection for subsequent classification. We show experimentally on micr…

A novel method for network intrusion detection based on nonlinear SNE and SVM

2017

In the case of network intrusion detection data, pre-processing techniques have been extensively used to enhance the accuracy of the model. An ideal intrusion detection system (IDS) is one that has appreciable detection capability overall the group of attacks. An open research problem of this area is the lower detection rate for less frequent attacks, which result from the curse of dimensionality and imbalanced class distribution of the benchmark datasets. This work attempts to minimise the effects of imbalanced class distribution by applying random under-sampling of the majority classes and SMOTE-based oversampling of minority classes. In order to alleviate the issue arising from the curse…

Semisupervised kernel orthonormalized partial least squares

2012

This paper presents a semisupervised kernel orthonormalized partial least squares (SS-KOPLS) algorithm for non-linear feature extraction. The proposed method finds projections that minimize the least squares regression error in Hilbert spaces and incorporates the wealth of unlabeled information to deal with small size labeled datasets. The method relies on combining a standard RBF kernel using labeled information, and a generative kernel learned by clustering all available data. The positive definiteness of the kernels is proven, and the structure and information content of the derived kernels is studied. The effectiveness of the proposed method is successfully illustrated in standard UCI d…