Search results for "computer.software_genre"
showing 10 items of 3858 documents
Causal inference in geosciences with kernel sensitivity maps
2020
Establishing causal relations between random variables from observational data is perhaps the most important challenge in today's Science. In remote sensing and geosciences this is of special relevance to better understand the Earth's system and the complex and elusive interactions between processes. In this paper we explore a framework to derive cause-effect relations from pairs of variables via regression and dependence estimation. We propose to focus on the sensitivity (curvature) of the dependence estimator to account for the asymmetry of the forward and inverse densities of approximation residuals. Results in a large collection of 28 geoscience causal inference problems demonstrate the…
Channel Gain Cartography via Mixture of Experts
2020
In order to estimate the channel gain (CG) between the locations of an arbitrary transceiver pair across a geographic area of interest, CG maps can be constructed from spatially distributed sensor measurements. Most approaches to build such spectrum maps are location-based, meaning that the input variable to the estimating function is a pair of spatial locations. The performance of such maps depends critically on the ability of the sensors to determine their positions, which may be drastically impaired if the positioning pilot signals are affected by multi-path channels. An alternative location-free approach was recently proposed for spectrum power maps, where the input variable to the maps…
Learning Automata Based Q-learning for Content Placement in Cooperative Caching
2019
An optimization problem of content placement in cooperative caching is formulated, with the aim of maximizing sum mean opinion score (MOS) of mobile users. Firstly, a supervised feed-forward back-propagation connectionist model based neural network (SFBC-NN) is invoked for user mobility and content popularity prediction. More particularly, practical data collected from GPS-tracker app on smartphones is tackled to test the accuracy of mobility prediction. Then, a learning automata-based Q-learning (LAQL) algorithm for cooperative caching is proposed, in which learning automata (LA) is invoked for Q-learning to obtain an optimal action selection in a random and stationary environment. It is p…
A probabilistic compressive sensing framework with applications to ultrasound signal processing
2019
Abstract The field of Compressive Sensing (CS) has provided algorithms to reconstruct signals from a much lower number of measurements than specified by the Nyquist-Shannon theorem. There are two fundamental concepts underpinning the field of CS. The first is the use of random transformations to project high-dimensional measurements onto a much lower-dimensional domain. The second is the use of sparse regression to reconstruct the original signal. This assumes that a sparse representation exists for this signal in some known domain, manifested by a dictionary. The original formulation for CS specifies the use of an l 1 penalised regression method, the Lasso. Whilst this has worked well in l…
Emergency Detection with Environment Sound Using Deep Convolutional Neural Networks
2020
In this paper, we propose a generic emergency detection system using only the sound produced in the environment. For this task, we employ multiple audio feature extraction techniques like the mel-frequency cepstral coefficients, gammatone frequency cepstral coefficients, constant Q-transform and chromagram. After feature extraction, a deep convolutional neural network (CNN) is used to classify an audio signal as a potential emergency situation or not. The entire model is based on our previous work that sets the new state of the art in the environment sound classification (ESC) task (Our paper is under review in the IEEE/ACM Transactions on Audio, Speech and Language Processing and also avai…
SIGNAL ANALYSIS AND PERFORMANCE EVALUATION OF A VEHICLE CRASH TEST WITH A FIXED SAFETY BARRIER BASED ON HAAR WAVELETS
2011
Author's version of an article published in the journal: International Journal of Wavelets, Multiresolution and Information Processing. Also available from the publisher at: http://dx.doi.org/10.1142/s0219691311003979 This paper deals with the wavelet-based performance analysis of the safety barrier for use in a full-scale test. The test involves a vehicle, a Ford Fiesta, which strikes the safety barrier at a prescribed angle and speed. The vehicle speed before the collision was measured. Vehicle accelerations in three directions at the center of gravity were measured during the collision. The yaw rate was measured with a gyro meter. Using normal speed and high-speed video cameras, the beha…
Adaptive Techniques for Microarray Image Analysis with Related Quality Assessment
2007
We propose novel techniques for microarray image analysis. In particular, we describe an overall pipeline able to solve the most common problems of microarray image analysis. We pro- pose the microarray image rotation algorithm (MIRA) and the statis- tical gridding pipeline (SGRIP) as two advanced modules devoted to restoring the original microarray grid orientation and to detecting, the correct geometrical information about each spot of input mi- croarray, respectively. Both solutions work by making use of statis- tical observations, obtaining adaptive and reliable information about each spot property. They improve the performance of the microarray image segmentation pipeline (MISP) we rec…
Online Fault Diagnosis System for Electric Powertrains Using Advanced Signal Processing and Machine Learning
2018
Online condition monitoring and fault diagnosis systems are necessary to prevent unexpected downtimes in critical electric powertrains. The machine learning algorithms provide a better way to diagnose faults in complex cases, such as mixed faults and/or in variable speed conditions. Most of studies focus on training phases of the machine learning algorithms, but the development of the trained machine learning algorithms for an online diagnosis system is not detailed. In this study, a complete procedure of training and implementation of an online fault diagnosis system is presented and discussed. Aspects of the development of an online fault diagnosis based on machine learning algorithms are…
Special Issue on Signal Processing and Machine Learning for Biomedical Data
2021
This Special Issue is focused on advanced techniques in signal processing, analysis, modelling, and classification, applied to a variety of medical diagnostic problems. Biomedical data play a fundamental role in many fields of research and clinical practice. Very often the complexity of these data and their large volume makes it necessary to develop advanced analysis techniques and systems. Furthermore, the introduction of new techniques and methodologies for diagnostic purposes, especially in the field of medical imaging, requires new signal processing and machine learning methods. The recent progress in machine learning techniques, and in particular deep learning, revolutionized various f…