Search results for "ENCODE"
showing 10 items of 91 documents
Adapting hierarchical bidirectional inter prediction on a GPU-based platform for 2D and 3D H.264 video coding
2013
The H.264/AVC video coding standard introduces some improved tools in order to increase compression efficiency. Moreover, the multi-view extension of H.264/AVC, called H.264/MVC, adopts many of them. Among the new features, variable block-size motion estimation is one which contributes to high coding efficiency. Furthermore, it defines a different prediction structure that includes hierarchical bidirectional pictures, outperforming traditional Group of Pictures patterns in both scenarios: single-view and multi-view. However, these video coding techniques have high computational complexity. Several techniques have been proposed in the literature over the last few years which are aimed at acc…
Different mechanisms underlie implicit visual statistical learning in honey bees and humans
2020
International audience; The ability of developing complex internal representations of the environment is considered a crucial antecedent to the emergence of humans’ higher cognitive functions. Yet it is an open question whether there is any fundamental difference in how humans and other good visual learner species naturally encode aspects of novel visual scenes. Using the same modified visual statistical learning paradigm and multielement stimuli, we investigated how human adults and honey bees ( Apis mellifera ) encode spontaneously, without dedicated training, various statistical properties of novel visual scenes. We found that, similarly to humans, honey bees automatically develop a comp…
Interpretable machine learning models for single-cell ChIP-seq imputation
2019
AbstractMotivationSingle-cell ChIP-seq (scChIP-seq) analysis is challenging due to data sparsity. High degree of data sparsity in biological high-throughput single-cell data is generally handled with imputation methods that complete the data, but specific methods for scChIP-seq are lacking. We present SIMPA, a scChIP-seq data imputation method leveraging predictive information within bulk data from ENCODE to impute missing protein-DNA interacting regions of target histone marks or transcription factors.ResultsImputations using machine learning models trained for each single cell, each target, and each genomic region accurately preserve cell type clustering and improve pathway-related gene i…
A 3D Network Based Shape Prior for Automatic Myocardial Disease Segmentation in Delayed-Enhancement MRI
2021
Abstract Objectives: In this work, a new deep learning model for relevant myocardial infarction segmentation from Late Gadolinium Enhancement (LGE)-MRI is proposed. Moreover, our novel segmentation method aims to detect microvascular-obstructed regions accurately. Material and methods: We first segment the anatomical structures, i.e., the left ventricular cavity and the myocardium, to achieve a preliminary segmentation. Then, a shape prior based framework that fuses the 3D U-Net architecture with 3D Autoencoder segmentation framework to constrain the segmentation process of pathological tissues is applied. Results: The proposed network reached outstanding myocardial segmentation compared wi…
Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
2019
In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of …
Residual Stresses Induced by Cold Expansion of Adjacent and Cut-Out Holes
2012
Fatigue life of fastener holes can be enhanced via a cold-expansion process to introduce a compressive residual stress field around the hole edge and to reduce crack growth propagation. Considering that aerospace components contain multiple rows of holes, the present investigation focuses on the evaluation of the three-dimensional residual stress distribution in adjacent cold-expanded (CE) holes. The redistribution of residual stresses caused by a cut introduced between two adjacent holes was also investigated. Finite element (FE) analysis and experimental technique were used to assess the residual stress distribution in a 6082-T6 aluminum plate with two adjacent holes expanded sequentially…
Vector representation of non-standard spellings using dynamic time warping and a denoising autoencoder
2017
The presence of non-standard spellings in Twitter causes challenges for many natural language processing tasks. Traditional approaches mainly regard the problem as a translation, spell checking, or speech recognition problem. This paper proposes a method that represents the stochastic relationship between words and their non-standard versions in real vectors. The method uses dynamic time warping to preprocess the non-standard spellings and autoencoder to derive the vector representation. The derived vectors encode word patterns and the Euclidean distance between the vectors represents a distance in the word space that challenges the prevailing edit distance. After training the autoencoder o…
Autoencoders and Recurrent Neural Networks Based Algorithm for Prognosis of Bearing Life
2018
Bearings are one of the most critical components in electric motors, gearboxes and wind turbines. Therefore, bearing fault detection and prognosis of remaining useful life are important to prevent productivity losses. In this study, a novel method is proposed for prognosis of bearing life using an autoencoder and recurrent neural networks-based prediction algorithm. Promising results have been obtained from the experimental data. A monotonic upward trend of the produced health indicator is obtained for all test cases, being one of critical indicators of a proper prognosis. The remaining useful life estimation is moderately accurate under a limited data.
PMSM Drives Sensorless Position Control with Signal Injection and Neural Filtering
2009
Vector Field Oriented Control (FOC) is one of the best control methods for high-dynamic electrical drives. To avoid the adoption of the speed/position sensor (resolver/encoder), a sensorless technique should be used. Among the various sensorless methods in literature, those based on machine saliency detection by signal injection seem to be most useful for thier giving the possibility of closing the position control loop. This paper proposes a method for enhancing both rotating and pulsating voltage carrier injection methods by a neural adaptive band filter. Results show the goodness of the proposed solution.
Flat panel displays for medical monitoring systems
2005
Flat panel displays are ideal for demanding hospital and clinic applications like vital sign monitoring and bedside administration. They take up much less space than conventional monitors (CRT based) and can be wall-mounted, cart-mounted or used on a desk stand. User friendly interface demands easy to use input device, rotary position encoders and touch screen panels offers simplicity replacing bulky keyboards. This article will present an overview about today's medical monitoring systems tendencies, describing as an example, a custom medical monitoring module developed to be integrated in an assisted ventilation system.