1,721,029 research outputs found
Compressive Sensing with an Overcomplete Dictionary for High-Resolution DFT Analysis
Full text linkPublication in the conference proceedings of EUSIPCO, Lisbon, Portugal, 201
Characterization of a Compressive Sensing Preprocessor for Vector Signal Analysis
No full textIn spectrum sensing and wireless communications analysis, signals of interest typically occupy only a few among several possible bands and do so for short-time bursts within a given observation interval. The frequency and time location of these signals may be known only approximately a priori (for instance, the nominal frequency of a wireless channel) or, in general, not accurately enough to set up more detailed measurements. In this paper, a compressive sensing (CS) algorithm is employed to provide accurate preliminary information and suitably preprocessed data for a vector signal analysis algorithm. The CS paradigm exploits sparsity, a feature common to several signals of interest, to allow the design of efficient data acquisition schemes. It is shown that the application of the sensing method called modulated wideband converter allows one to successfully extract specific signal bursts from a record of samples covering a longer time interval and a broader bandwidth. The accuracy of the extraction process is analyzed and the results referring to vector analysis are presented. This provides blind spectrum sensing and signal extraction capabilities that can effectively simplify the time-consuming process of setting up a spectrum analyzer for vector signal analysis
Compressive Sensing Applications in Measurement: Theoretical issues, algorithm characterization and implementation
Full text linkAt its core, signal acquisition is concerned with efficient algorithms and protocols capable to capture and encode the signal information content. For over five decades, the indisputable theoretical benchmark has been represented by the wellknown Shannon’s sampling theorem, and the corresponding notion of information has been indissolubly related to signal spectral bandwidth.
The contemporary society is founded on almost instantaneous exchange of information, which is mainly conveyed in a digital format. Accordingly, modern communication devices are expected to cope with huge amounts of data, in a typical
sequence of steps which comprise acquisition, processing and storage. Despite the continual technological progress, the conventional acquisition protocol has come under mounting pressure and requires a computational effort not related to the actual signal information content.
In recent years, a novel sensing paradigm, also known as Compressive Sensing, briefly CS, is quickly spreading among several branches of Information Theory. It relies on two main principles: signal sparsity and incoherent sampling, and employs
them to acquire the signal directly in a condensed form. The sampling rate is related to signal information rate, rather than to signal spectral bandwidth. Given a sparse signal, its information content can be recovered even fromwhat could appear to be
an incomplete set of measurements, at the expense of a greater computational effort at reconstruction stage.
My Ph.D. thesis builds on the field of Compressive Sensing and illustrates how sparsity and incoherence properties can be exploited to design efficient sensing strategies, or to intimately understand the sources of uncertainty that affect measurements.
The research activity has dealtwith both theoretical and practical issues, inferred frommeasurement application contexts, ranging fromradio frequency communications to synchrophasor estimation and neurological activity investigation.
The thesis is organised in four chapters whose key contributions include:
• definition of a general mathematical model for sparse signal acquisition systems,
with particular focus on sparsity and incoherence implications;
• characterization of the main algorithmic families for recovering sparse signals
from reduced set of measurements, with particular focus on the impact of additive noise;
• implementation and experimental validation of a CS-based algorithmfor providing accurate preliminary information and suitably preprocessed data for a vector signal analyser or a cognitive radio application;
• design and characterization of a CS-based super-resolution technique for spectral analysis in the discrete Fourier transform(DFT) domain;
• definition of an overcomplete dictionary which explicitly account for spectral leakage effect;
• insight into the so-called off-the-grid estimation approach, by properly combining CS-based super-resolution and DFT coefficients polar interpolation;
• exploration and analysis of sparsity implications in quasi-stationary operative conditions, emphasizing the importance of time-varying sparse signal models;
• definition of an enhanced spectral content model for spectral analysis applications in dynamic conditions by means of Taylor-Fourier transform (TFT) approaches
EEG Gradient Artifact Removal by Compressive Sensing and Taylor-Fourier Transform
Full text linkSimultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) rep- resents a powerful tool for brain activity investigation. Unfortu- nately, EEG data collected during concurrent fMRI are affected by very large artifacts.
This paper focuses on the gradient artifact (GRA), related to the sawtooth profiles of magnetic flux inside the MRI scanner. A novel removal algorithm is proposed and validated on both sim- ulation and experimental data. A super-resolution method, based on compressive sensing, determines GRA harmonic frequencies. Amplitudes and phases of GRA components are estimated by means of the Taylor-Fourier transform (TFT), complying with dynamic operating conditions. Unlike averaging techniques, well- known in the literature, this allows computation of a specific template for each artifact occurrence, which is subtracted from the original data.
Experimental results show a significant reduction of spurious components in all the considered conditions. No significant distortions are introduced in spectral power distribution, allowing reliable clinical interpretation of the acquired trace
Comparative evaluation of on-line missing data regression techniques in intrapartum FHR measurements
No full textCardiotocography is a measurement technique used for fetal health status assessment in antepartum and intrapartum monitoring. A typical cardiotocogram recording consists of two simultaneously acquired signals, namely the fetal hearth rate (FHR) and the uterine activity (UA). Unfortunately, the FHR recordings suffer from frequent invalid or missing samples, due to artifacts or sensors misfunctions. In literature, this problem is typically solved by simplistic linear interpolations or sophisticated algorithms, whose computational complexity prevents from an on-line implementation. In this paper, we propose five regression techniques which rely exclusively on a reduced set of past samples, i.e. compliant with on-line implementation. We characterize their performances in terms of regression error, as well as specific clinical indices which account for the capability not to distort the original information content of the acquired signal
IEEE 802.15.6 compliant WBSN: A case study
No full textThe advances in wireless low-power sensors and the increasing demand for portable healthcare monitoring systems have inspired the realization of Wireless Body Sensor Networks (WBSNs). A WBSN is a collection of wearable and implantable sensors, associated to specific acquisition and control tasks, communicating with an aggregation central node, that processes the measurement data, extracts the clinical information, and if necessary forwards to a remote monitoring station. The IEEE Std. 802.15.6 defines the communication protocol for WBSN applications in terms of physical and medium access layers. In this context, the present paper focuses on the most suitable design of network configuration by considering how the occurrence of missing packets affects the resulting quality of service. Throughout numerical simulations in Matlab, we reproduce a plausible WBSN scenario for monitoring the cardiac activity during physical exercise. The effect of missing packets on the recovered electrocardiographic time-series at receiver side has been finally investigated, in terms of root-mean-square error and accuracy of the estimated heart rate, thus providing some criteria for choosing the most suitable network configuration
Measuring Cerebral Activation From fNIRS Signals: An Approach Based on Compressive Sensing and Taylor-Fourier Model
Full text linkFunctional near-infrared spectroscopy (fNIRS) is a noninvasive and portable neuroimaging technique that uses NIR light to monitor cerebral activity by the so-called haemodynamic responses (HRs). The measurement is challenging because of the presence of severe physiological noise, such as respiratory and vasomotor waves. In this paper, a novel technique for fNIRS signal denoising and HR estimation is described. The method relies on a joint application of compressed sensing theory principles and Taylor-Fourier modeling of nonstationary spectral components. It operates in the frequency domain and models physiological noise as a linear combination of sinusoidal tones, characterized in terms of frequency, amplitude, and initial phase. Algorithm performance is assessed over both synthetic and experimental data sets, and compared with that of two reference techniques from fNIRS literature
- …
