805 research outputs found

    A time-frequency based method for the detection and tracking of multiple non-linearly modulated components with births and deaths

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    International audienceThe estimation of the components which contain the characteristics of a signal attracts great attention in many real world applications. In this paper, we address the problem of the tracking of multiple signal components over discrete time series. We propose an algorithm to first detect the components from a given time-frequency distribution and then to track them automatically. In the first place, the peaks corresponding to the signal components are detected using the statistical properties of the spectral estimator. Then, an original classifier is proposed to automatically track the detected peaks in order to build components over time. This classifier is based on a total divergence matrix computed from a peak-component divergence matrix that takes account of both amplitude and frequency information. The peak-component pairs are matched automatically from this divergence matrix. We propose a stochastic discrimination rule to decide upon the acceptance of the peak-component pairs. In this way, the algorithm can estimate the number, the amplitude and frequency modulation functions, and the births and the deaths of the components without any limitation on the number of components. The performance of the proposed method, a post-processing of a time-frequency distribution is validated on simulated signals under different parameter sets. The method is also applied to 4 real-world signals as a proof of its applicability. Index Terms—Time-frequency domain, multicomponent, peak detection, component tracking, amplitude and frequency modulation , nonlinear, nonstationary, births and death

    Neural-Kalman Schemes for Non-Stationary Channel Tracking and Learning

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    This Thesis focuses on channel tracking in Orthogonal Frequency-Division Multiplexing (OFDM), a widely-used method of data transmission in wireless communications, when abrupt changes occur in the channel. In highly mobile applications, new dynamics appear that might make channel tracking non-stationary, e.g. channels might vary with location, and location rapidly varies with time. Simple examples might be the di erent channel dynamics a train receiver faces when it is close to a station vs. crossing a bridge vs. entering a tunnel, or a car receiver in a route that grows more tra c-dense. Some of these dynamics can be modelled as channel taps dying or being reborn, and so tap birth-death detection is of the essence. In order to improve the quality of communications, we delved into mathematical methods to detect such abrupt changes in the channel, such as the mathematical areas of Sequential Analysis/ Abrupt Change Detection and Random Set Theory (RST), as well as the engineering advances in Neural Network schemes. This knowledge helped us nd a solution to the problem of abrupt change detection by informing and inspiring the creation of low-complexity implementations for real-world channel tracking. In particular, two such novel trackers were created: the Simpli- ed Maximum A Posteriori (SMAP) and the Neural-Network-switched Kalman Filtering (NNKF) schemes. The SMAP is a computationally inexpensive, threshold-based abrupt-change detector. It applies the three following heuristics for tap birth-death detection: a) detect death if the tap gain jumps into approximately zero (memoryless detection); b) detect death if the tap gain has slowly converged into approximately zero (memory detection); c) detect birth if the tap gain is far from zero. The precise parameters for these three simple rules can be approximated with simple theoretical derivations and then ne-tuned through extensive simulations. The status detector for each tap using only these three computationally inexpensive threshold comparisons achieves an error reduction matching that of a close-to-perfect path death/birth detection, as shown in simulations. This estimator was shown to greatly reduce channel tracking error in the target Signal-to-Noise Ratio (SNR) range at a very small computational cost, thus outperforming previously known systems. The underlying RST framework for the SMAP was then extended to combined death/birth and SNR detection when SNR is dynamical and may drift. We analyzed how di erent quasi-ideal SNR detectors a ect the SMAP-enhanced Kalman tracker's performance. Simulations showed SMAP is robust to SNR drift in simulations, although it was also shown to bene t from an accurate SNR detection. The core idea behind the second novel tracker, NNKFs, is similar to the SMAP, but now the tap birth/death detection will be performed via an arti cial neuronal network (NN). Simulations show that the proposed NNKF estimator provides extremely good performance, practically identical to a detector with 100% accuracy. These proposed Neural-Kalman schemes can work as novel trackers for multipath channels, since they are robust to wide variations in the probabilities of tap birth and death. Such robustness suggests a single, low-complexity NNKF could be reusable over di erent tap indices and communication environments. Furthermore, a di erent kind of abrupt change was proposed and analyzed: energy shifts from one channel tap to adjacent taps (partial tap lateral hops). This Thesis also discusses how to model, detect and track such changes, providing a geometric justi cation for this and additional non-stationary dynamics in vehicular situations, such as road scenarios where re ections on trucks and vans are involved, or the visual appearance/disappearance of drone swarms. An extensive literature review of empirically-backed abrupt-change dynamics in channel modelling/measuring campaigns is included. For this generalized framework of abrupt channel changes that includes partial tap lateral hopping, a neural detector for lateral hops with large energy transfers is introduced. Simulation results suggest the proposed NN architecture might be a feasible lateral hop detector, suitable for integration in NNKF schemes. Finally, the newly found understanding of abrupt changes and the interactions between Kalman lters and neural networks is leveraged to analyze the neural consequences of abrupt changes and brie y sketch a novel, abrupt-change-derived stochastic model for neural intelligence, extract some neuro nancial consequences of unstereotyped abrupt dynamics, and propose a new portfolio-building mechanism in nance: Highly Leveraged Abrupt Bets Against Failing Experts (HLABAFEOs). Some communication-engineering-relevant topics, such as a Bayesian stochastic stereotyper for hopping Linear Gauss-Markov (LGM) models, are discussed in the process. The forecasting problem in the presence of expert disagreements is illustrated with a hopping LGM model and a novel structure for a Bayesian stereotyper is introduced that might eventually solve such problems through bio-inspired, neuroscienti cally-backed mechanisms, like dreaming and surprise (biological Neural-Kalman). A generalized framework for abrupt changes and expert disagreements was introduced with the novel concept of Neural-Kalman Phenomena. This Thesis suggests mathematical (Neural-Kalman Problem Category Conjecture), neuro-evolutionary and social reasons why Neural-Kalman Phenomena might exist and found signi cant evidence for their existence in the areas of neuroscience and nance. Apart from providing speci c examples, practical guidelines and historical (out)performance for some HLABAFEO investing portfolios, this multidisciplinary research suggests that a Neural- Kalman architecture for ever granular stereotyping providing a practical solution for continual learning in the presence of unstereotyped abrupt dynamics would be extremely useful in communications and other continual learning tasks.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Luis Castedo Ribas.- Secretaria: Ana García Armada.- Vocal: José Antonio Portilla Figuera

    Development and validation of a stand-alone DCS system for monitoring absolute cerebral blood flow.

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    Premature infants are at high risk of neonatal brain injury due to poor cerebrovascular regulation, leading to periods of dangerously low cerebral blood flow (CBF) and possible injury. However, there are currently no established bedside methods of monitoring CBF to alert the intensive care staff to cerebrovascular dysfunction. Diffuse correlation spectroscopy (DCS) is an emerging noninvasive optical technique for monitoring relative CBF. I developed a stand-alone DCS method of monitoring absolute CBF by incorporating a quantitative dynamic contrast-enhanced (DCE) technique. This required modifying a DCS system to capture multi-distance data to measure the tissue optical properties and to perform DCE experiments. The feasibility of the technique was assessed by measuring CBF in piglets under three flow condition. For validation, the tissue optical properties were compared to measurements from time-resolved NIRS. Across 7 animals, a strong linear correlation was observed between CBF values derived using the optical properties at baseline (R2=0.95), hypercapnia (R2=0.83) and hypocapnia (R2=0.88). These results demonstrate that the developed DCS system provides the unique ability to provide real-time monitoring of absolute CBF

    Complexity and Uncertainty in Human and Ecological Risk Assessment

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    Multiple interacting stressors in the environment present increasingly complex risks to human health. Too often, however, the data required for traditional risk assessment are either lacking or unavailable at the necessary spatial or temporal scale. In addition, assessment practices and management policies need to move away from single factor approaches in order to accommodate the reality of complex chemical mixtures and environmental stressors. Recent literature suggests that a paradigm shift is under way. This points to a need for the development of new techniques both for rapid data collection and flexible risk assessment strategies that can adapt to make use of readily available data. This dissertation presents two types of methods for improving the risk assessment process given these evolving challenges: predictive analytics and integrated effect-directed toxicity screening. The first technique addresses the characterization of environmental health using toxicological screening tools. Environmental influences on ecological and human health are often studied using indicators that represent important risk components such as chemical contamination, hazards, exposures, and biological stress. Unfortunately, studies are frequently constrained by the lack of calibrated indicators constructed from standardized metrics. The second technique is a novel method for population-level risk assessment that uses self-organizing feature maps (SOM) to generate multivariate clusters of cause-of-death and birth outcome metrics, in combination with the use of and supervised learning risk-propagation modelling to evaluate predictability of available indicators. I apply this method to identify exposure-outcome linkages at the county level for Wisconsin, USA and civil divisions in Dobrogea, Romania; thereby providing a dynamic visualization of public health risk relationships with behavioral risk factors (e.g. smoking, heavy drinking) and environmental factors (e.g. land cover, nitrates and faecal coliform in drinking water). These risk relationships do not demonstrate cause-effect, but provide guidance for targeted investigations and for risk-management prioritization. To investigate a unique way of measuring environmental health, a sediment contact assay using zebrafish (Danio rerio) embryos was adapted from Hollert et al. (2003) as an indicator of teratogenic stress within river sediments. Sediment samples were collected from Lake Michigan tributary watersheds. Sediment contact assay responses were then compared to prevalence of congenital heart disease (CHD) and vital statistic birth indicators aggregated from civil divisions associated with these same watersheds. Significant risk relationships were detected between variation in early life-stage (ELS) endpoints of zebrafish embryos 72 hour post-fertilization and the birth prevalence of human congenital heart disease and infant mortality. Examination of principal components of ELS endpoints suggests that variance related to zebrafish embryonic heart and circulatory malformations is most closely associated with human CHD prevalence. This study demonstrates a novel application of effect-based toxicity testing for ecological and human health risk assessments. These results support the hypothesis that bioassays normally used for ecological screening can be useful as indicators of environmental stress to humans so as to expand our understanding of environmental - human health linkages. Finally, next steps and new directions for these lines of thinking are discussed

    High-resolution sinusoidal analysis for resolving harmonic collisions in music audio signal processing

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    Many music signals can largely be considered an additive combination of multiple sources, such as musical instruments or voice. If the musical sources are pitched instruments, the spectra they produce are predominantly harmonic, and are thus well suited to an additive sinusoidal model. However, due to resolution limits inherent in time-frequency analyses, when the harmonics of multiple sources occupy equivalent time-frequency regions, their individual properties are additively combined in the time-frequency representation of the mixed signal. Any such time-frequency point in a mixture where multiple harmonics overlap produces a single observation from which the contributions owed to each of the individual harmonics cannot be trivially deduced. These overlaps are referred to as overlapping partials or harmonic collisions. If one wishes to infer some information about individual sources in music mixtures, the information carried in regions where collided harmonics exist becomes unreliable due to interference from other sources. This interference has ramifications in a variety of music signal processing applications such as multiple fundamental frequency estimation, source separation, and instrumentation identification. This thesis addresses harmonic collisions in music signal processing applications. As a solution to the harmonic collision problem, a class of signal subspace-based high-resolution sinusoidal parameter estimators is explored. Specifically, the direct matrix pencil method, or equivalently, the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) method, is used with the goal of producing estimates of the salient parameters of individual harmonics that occupy equivalent time-frequency regions. This estimation method is adapted here to be applicable to time-varying signals such as musical audio. While high-resolution methods have been previously explored in the context of music signal processing, previous work has not addressed whether or not such methods truly produce high-resolution sinusoidal parameter estimates in real-world music audio signals. Therefore, this thesis answers the question of whether high-resolution sinusoidal parameter estimators are really high-resolution for real music signals. This work directly explores the capabilities of this form of sinusoidal parameter estimation to resolve collided harmonics. The capabilities of this analysis method are also explored in the context of music signal processing applications. Potential benefits of high-resolution sinusoidal analysis are examined in experiments involving multiple fundamental frequency estimation and audio source separation. This work shows that there are indeed benefits to high-resolution sinusoidal analysis in music signal processing applications, especially when compared to methods that produce sinusoidal parameter estimates based on more traditional time-frequency representations. The benefits of this form of sinusoidal analysis are made most evident in multiple fundamental frequency estimation applications, where substantial performance gains are seen. High-resolution analysis in the context of computational auditory scene analysis-based source separation shows similar performance to existing comparable methods

    Environmental projects. Volume 15: Environmental assessment: Proposed 1-megawatt radar transmitter at the Mars site

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    The Goldstone Deep Space Communications Complex (GDSCC), located in the Mojave Desert about 64.5 km (40 mi) north of Barstow, California. and about 258 km (160 mi) northeast of Pasadena, California, is part of the National Aeronautics and Space Administration's (NASA's) Deep Space Network (DSN), one of the world's larger and more sensitive scientific telecommunications and radio navigation networks. The Goldstone Complex is managed, technically directed, and operated for NASA by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology in Pasadena, California. Activities at the GDSCC support the operation of six parabolic dish antennas located at five separate sites called Deep Space Stations (DSS's). Four sites, named Echo, Mars, Uranus, and Apollo, are operational for space missions, while the remaining Venus Site is devoted to research and development activities. The Mars Site at the GDSCC contains two antennas: the Uranus antenna (DSS 15, 34 m) and the Mars antenna (DSS 14, 70 m). This present volume deals solely with the DSS-14 Mars antenna. The Mars antenna not only can act as a sensitive receiver to detect signals from spacecraft, but it also can be used in radar astronomy as a powerful transmitter to send out signals to probe the solar system. At present, the Mars antenna operates as a continuous-wave microwave system at a frequency of 8.51 GHz at a power level of 0.5 MW. JPL has plans to upgrade the Mars antenna to a power level of 1 MW. Because of the anticipated increase in the ambient levels of radio frequency radiation (RFR), JPL retained Battelle Pacific Northwest Laboratories (BPNL), Richland, Washington, to conduct an environmental assessment with respect to this increased RFR. This present volume is a JPL-expanded version of the BPNL report titled Environmental Assessment of the Goldstone Solar System Radar, which was submitted to JPL in Nov. 1991. This BPNL report concluded that the operation of the upgraded Mars antenna at the GDSCC, with its increased potential electromagnetic radiation hazards and interferences, would have no significantly adverse biological, physical, or socioeconomic effects on the environment. Thus, a Finding of No Significant Impact (FONSI) is appropriate in accordance with local, State, Federal, and NASA environmental rules and regulations
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