Innovation Rate Sampling of Pulse Streams with Application to Ultrasound Imaging

Signals comprised of a stream of short pulses appear in many applications including bio-imaging and radar. The recent finite rate of innovation framework, has paved the way to low rate sampling of such pulses by noticing that only a small number of parameters per unit time are needed to fully describe these signals. Unfortunately, for high rates of innovation, existing sampling schemes are numerically unstable. In this paper we propose a general sampling approach which leads to stable recovery even in the presence of many pulses. We begin by deriving a condition on the sampling kernel which allows perfect reconstruction of periodic streams from the minimal number of samples. We then design a compactly supported class of filters, satisfying this condition. The periodic solution is extended to finite and infinite streams, and is shown to be numerically stable even for a large number of pulses. High noise robustness is also demonstrated when the delays are sufficiently separated. Finally, we process ultrasound imaging data using our techniques, and show that substantial rate reduction with respect to traditional ultrasound sampling schemes can be achieved.Comment: 14 pages, 13 figure

Search for gravitational-wave bursts in LIGO data from the fourth science run

The fourth science run of the LIGO and GEO 600 gravitational-wave detectors, carried out in early 2005, collected data with significantly lower noise than previous science runs. We report on a search for short-duration gravitational-wave bursts with arbitrary waveform in the 64-1600 Hz frequency range appearing in all three LIGO interferometers. Signal consistency tests, data quality cuts, and auxiliary-channel vetoes are applied to reduce the rate of spurious triggers. No gravitational-wave signals are detected in 15.5 days of live observation time; we set a frequentist upper limit of 0.15 per day (at 90% confidence level) on the rate of bursts with large enough amplitudes to be detected reliably. The amplitude sensitivity of the search, characterized using Monte Carlo simulations, is several times better than that of previous searches. We also provide rough estimates of the distances at which representative supernova and binary black hole merger signals could be detected with 50% efficiency by this analysis.Comment: Corrected amplitude sensitivities (7% change on average); 30 pages, submitted to Classical and Quantum Gravit

Coherent network analysis technique for discriminating gravitational-wave bursts from instrumental noise

Existing coherent network analysis techniques for detecting gravitational-wave bursts simultaneously test data from multiple observatories for consistency with the expected properties of the signals. These techniques assume the output of the detector network to be the sum of a stationary Gaussian noise process and a gravitational-wave signal, and they may fail in the presence of transient non-stationarities, which are common in real detectors. In order to address this problem we introduce a consistency test that is robust against noise non-stationarities and allows one to distinguish between gravitational-wave bursts and noise transients. This technique does not require any a priori knowledge of the putative burst waveform.Comment: 18 pages, 11 figures; corrected corrupted figur

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions