Search results for "Machine"
showing 10 items of 2592 documents
SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points
2022
In this article we present SHARP, an original approach for obtaining human activity recognition (HAR) through the use of commercial IEEE 802.11 (Wi-Fi) devices. SHARP grants the possibility to discern the activities of different persons, across different time-spans and environments. To achieve this, we devise a new technique to clean and process the channel frequency response (CFR) phase of the Wi-Fi channel, obtaining an estimate of the Doppler shift at a radio monitor device. The Doppler shift reveals the presence of moving scatterers in the environment, while not being affected by (environment-specific) static objects. SHARP is trained on data collected as a person performs seven differe…
Aerial Spectrum Surveying: Radio Map Estimation with Autonomous UAVs
2020
Radio maps are emerging as a popular means to endow next-generation wireless communications with situational awareness. In particular, radio maps are expected to play a central role in unmanned aerial vehicle (UAV) communications since they can be used to determine interference or channel gain at a spatial location where a UAV has not been before. Existing methods for radio map estimation utilize measurements collected by sensors whose locations cannot be controlled. In contrast, this paper proposes a scheme in which a UAV collects measurements along a trajectory. This trajectory is designed to obtain accurate estimates of the target radio map in a short time operation. The route planning a…
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…
An Automatic System for the Analysis and Classification of Human Atrial Fibrillation Patterns from Intracardiac Electrograms
2008
This paper presents an automatic system for the analysis and classification of atrial fibrillation (AF) patterns from bipolar intracardiac signals. The system is made up of: 1) a feature- extraction module that defines and extracts a set of measures potentially useful for characterizing AF types on the basis of their degree of organization; 2) a feature-selection module (based on the Jeffries-Matusita distance and a branch and bound search algorithm) identifying the best subset of features for discriminating different AF types; and 3) a support vector machine technique-based classification module that automatically discriminates the AF types according to the Wells' criteria. The automatic s…
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…
A Review on Approaches for Condition Based Maintenance in Applications with Induction Machines located Offshore
2012
Published version of an article in the journal: Modeling, Identification and Control. Also available from the publisher at: http://dx.doi.org/10.4173/mic.2012.2.4 Open access This paper presents a review of different approaches for Condition Based Maintenance (CBM) of induction machines and drive trains in offshore applications. The paper contains an overview of common failure modes, monitoring techniques, approaches for diagnostics, and an overview of typical maintenance actions. Although many papers have been written in this area before, this paper puts an emphasis on recent developments and limits the scope to induction machines and drive trains applied in applications located offshore.
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…
Support Vector Machines Framework for Linear Signal Processing
2005
This paper presents a support vector machines (SVM) framework to deal with linear signal processing (LSP) problems. The approach relies on three basic steps for model building: (1) identifying the suitable base of the Hilbert signal space in the model, (2) using a robust cost function, and (3) minimizing a constrained, regularized functional by means of the method of Lagrange multipliers. Recently, autoregressive moving average (ARMA) system identification and non-parametric spectral analysis have been formulated under this framework. The generalized, yet simple, formulation of SVM LSP problems is particularized here for three different issues: parametric spectral estimation, stability of I…
Nonlinear morphological correlation: optoelectronic implementation
2008
An optoelectronic implementation of the nonlinear morphological correlation by use of a threshold-decomposition technique and a joint transform correlator architecture is presented. This nonlinear morphological correlation provides improved image detection compared with standard linear optical pattern-recognition correlation methods. It also offers a more robust detection of low-intensity images in the presence of high-intensity patterns to be rejected.
From Signal Processing to Machine Learning
2018
This chapter reviews the main landmarks of signal processing in the 20th century from the perspective of algorithmic developments. It focuses on cross‐fertilization with the field of statistical (machine) learning in the last decades. In the 21st century, model and data assumptions as well as algorithmic constraints are no longer valid, and the field of machine‐learning signal processing has erupted, with many successful stories to tell. The chapter also focuses on digital signal processing (DSP), which deals with the analysis of digitized and discrete sampled signals. Machine learning is a branch of computer science and artificial intelligence that enables computers to learn from data. Mac…