Search results for "Pattern recognition"
showing 10 items of 2301 documents
On the classification of dynamical data streams using novel “Anti-Bayesian” techniques
2018
Abstract The classification of dynamical data streams is among the most complex problems encountered in classification. This is, firstly, because the distribution of the data streams is non-stationary, and it changes without any prior “warning”. Secondly, the manner in which it changes is also unknown. Thirdly, and more interestingly, the model operates with the assumption that the correct classes of previously-classified patterns become available at a juncture after their appearance. This paper pioneers the use of unreported novel schemes that can classify such dynamical data streams by invoking the recently-introduced “Anti-Bayesian” (AB) techniques. Contrary to the Bayesian paradigm, tha…
Automated analysis of images for molecular quantification in immunohistochemistry.
2018
The quantification of the expression of different molecules is a key question in both basic and applied sciences. While protein quantification through molecular techniques leads to the loss of spatial information and resolution, immunohistochemistry is usually associated with time-consuming image analysis and human bias. In addition, the scarce automatic software analysis is often proprietary and expensive and relies on a fixed threshold binarization. Here we describe and share a set of macros ready for automated fluorescence analysis of large batches of fixed tissue samples using FIJI/ImageJ. The quantification of the molecules of interest are based on an automatic threshold analysis of im…
Life cycle assessment of roads: Material and process related energy savings
2018
The need for climate change mitigation calls for significant actions to match the sustainable development goals and, in this context, road construction and management play a relevant role (cf. EU Green Public Procurement Criteria for Road Design, Construction and Maintenance and Environmental Product Declarations - EPD). In such a context, the role of the Life Cycle Assessment (LCA) methodology is broadly recognized as a tool to quantify sustainability of processes and systems. This study aims at calculating the life-cycle energy and carbon footprint of a typical Italian urban road, including materials production, transportation, construction, maintenance, and rehabilitation. The LCA approa…
CONSTRUCTING, BOOTSTRAPPING, AND COMPARING MORPHOMETRIC AND PHYLOGENETIC TREES: A CASE STUDY OF NEW WORLD MONKEYS (PLATYRRHINI, PRIMATES)
2005
Morphometric data sets are often phenetically analyzed by using various kinds of spatial, metric, or nonmetric multivariate analyses. Such methods produce results that are difficult to compare directly with molecular or morphological phylogenetic hypotheses, which are usually expressed by using nonspatial tree representations. Therefore, it is useful in a comparative approach to analyze, and above all to visualize, morphometric pairwise relationships as tree structures. For this purpose, several additive or ultrametric methods exist, which often return different topologies for the same data set. Objective criteria are thus needed to identify the tree-building algorithm (or algorithm family)…
Eigenexpressions: Emotion Recognition Using Multiple Eigenspaces
2013
This paper presents an appearance-based holistic method for expression recognition. A two stage supervised learning approach is used. At the first stage, training images are used to compute one subspace per expression. At the second stage, the same images are used to train a classifier. In this step, Euclidean distances from each image to each particular subspace are used as the input to the classifier. The resulting system significantly outperforms the baseline eigenfaces method on the Cohn-Kanade data set, with performance gains in the range 10%-20%.
CNN based Gearbox Fault Diagnosis and Interpretation of Learning Features
2021
Machine learning based fault diagnosis schemes have been intensively proposed to deal with faults diagnosis of rotating machineries such as gearboxes, bearings, and electric motors. However, most of the machine learning algorithms used in fault diagnosis are pattern recognition tools, which can classify given data into two or more classes. The underlined physical phenomena in fault diagnosis are not directly interpretable in machine learning schemes, thus it is usually called black/gray box models. In this study, convolutional neural networks (CNN) machine learning algorithm is proposed to classify gearbox faults, and the learning features of the CNN filters are visualized to understand the…
Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction
2018
Due the fact that the required therapy to treat Ventricular Fibrillation (V F) is aggressive (electric shock), the lack of a proper detection and recovering therapy could cause serious injuries to the patient or trigger a ventricular fibrillation, or even death. This work describes the development of an automatic diagnostic system for the detection of the occurrence of V F in real time by means of the time-frequency representation (T F R) image of the ECG. The main novelties are the use of the T F R image as input for a classification process, as well as the use of combined classifiers. The feature extraction stage is eliminated and, together with the use of specialized binary classifiers, …
Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation
2020
The epileptogenic focus is a brain area that may be surgically removed to control of epileptic seizures. Locating it is an essential and crucial step prior to the surgical treatment. However, given the difficulty of determining the localization of this brain region responsible of the initial seizure discharge, many works have proposed machine learning methods for the automatic classification of focal and non-focal electroencephalographic (EEG) signals. These works use automatic classification as an analysis tool for helping neurosurgeons to identify focal areas off-line, out of surgery, during the processing of the huge amount of information collected during several days of patient monitori…
Computation of the field diffracted by a local surface defect: application to tip–sample interaction in the photon scanning tunneling microscope
1996
We use a method based on the Fourier transform of the electromagnetic field to compute the field diffracted by a local deformation of a plane boundary surface. We give a complete development of each step of the technique. To show the interaction that exists between the probe of a near-field optical microscope and the observed sample, we use the model of a truncated cone-shaped tip above a rectangular surface defect. We compute the electrical intensity along a line located between the tip and the local surface defect. We show the influence of the polarization of the incident wave and the effect of the position of the tip with respect to the position of the surface defect.
On the repeatability of the EMI for the monitoring of bonded joints
2015
We study the feasibility and the repeatability of the electromechanical impedance (EMI) method for the health monitoring of lightweight bonded joints. The EMI technique exploits the coupling between the displacement field and the potential field of a piezoelectric material, by attaching or embedding a piezoelectric transducer to the structure to be monitored. The sensor is excited by an external voltage and the electrical admittance which is the ratio between the electric current and the applied voltage is measured as it depends on the mechanical coupling between the transducer and the host structure. Owing to this interaction, the admittance may represent a signature for the health of the …