Parametric Power Spectral Density Analysis of Noise from Instrumentation in MALDI TOF Mass Spectrometry

Noise in mass spectrometry can interfere with identification of the biochemical substances in the sample. For example, the electric motors and circuits inside the mass spectrometer or in nearby equipment generate random noise that may distort the true shape of mass spectra. This paper presents a stochastic signal processing approach to analyzing noise from electrical noise sources (i.e., noise from instrumentation) in MALDI TOF mass spectrometry. Noise from instrumentation was hypothesized to be a mixture of thermal noise, 1/f noise, and electric or magnetic interference in the instrument. Parametric power spectral density estimation was conducted to derive the power distribution of noise from instrumentation with respect to frequencies. As expected, the experimental results show that noise from instrumentation contains 1/f noise and prominent periodic components in addition to thermal noise. These periodic components imply that the mass spectrometers used in this study may not be completely shielded from the internal or external electrical noise sources. However, according to a simulation study of human plasma mass spectra, noise from instrumentation does not seem to affect mass spectra significantly. In conclusion, analysis of noise from instrumentation using stochastic signal processing here provides an intuitive perspective on how to quantify noise in mass spectrometry through spectral modeling

Reliable and Repeatable Power Measurements in DVB-T Systems

Non

Data adaptive velocity/depth spectra estimation in seismic wide angle reflection analysis

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Woods Hole Oceanographic Institution and the Massachusetts Institute of Technology July 1977In studying the earth with reflection seismics, one of the major unknowns is the velocity structure of the medium. Techniques used to determine the velocity structure commonly involve multi-channel arrays which measure the spatial as well as the time structure of the returning signals. The application of a data adaptive technique, the Maximum Likelihood Method, to the problem of estimating seismic velocities is described. The peculiar problems of this application are identified and investigated. The windowing of short duration signals is shown to be an important consideration, and the statistics of the MLM estimator for a single observation of the data set are presented. The adaptive estimator is applied to an ideal covariance matrix, to simulated data, and to field data. The results show the MLM velocity/depth estimator to be a valuable tool in seismic analysis, and the windowing and statistical results should have general applications in a variety of fields.This study was supported in part by NSF-IDOE Grant GX-4094, NOAA Contract 04-6-158-44081, ONR Contract N00014-77-6-0266, by a fellowship from the Research Laboratory of Electronics at MIT, and by the MIT/WHOI Joint program in Ocean Engineering

Electrohysterogram signal component cataloging with spectral and time-frequency methods

The Electrohysterogram (EHG) is a new instrument for pregnancy monitoring. It measures the uterine muscle electrical signal, which is closely related with uterine contractions. The EHG is described as a viable alternative and a more precise instrument than the currently most widely used method for the description of uterine contractions: the external tocogram. The EHG has also been indicated as a promising tool in the assessment of preterm delivery risk. This work intends to contribute towards the EHG characterization through the inventory of its components which are: • Contractions; • Labor contractions; • Alvarez waves; • Fetal movements; • Long Duration Low Frequency Waves; The instruments used for cataloging were: Spectral Analysis, parametric and non-parametric, energy estimators, time-frequency methods and the tocogram annotated by expert physicians. The EHG and respective tocograms were obtained from the Icelandic 16-electrode Electrohysterogram Database. 288 components were classified. There is not a component database of this type available for consultation. The spectral analysis module and power estimation was added to Uterine Explorer, an EHG analysis software developed in FCT-UNL. The importance of this component database is related to the need to improve the understanding of the EHG which is a relatively complex signal, as well as contributing towards the detection of preterm birth. Preterm birth accounts for 10% of all births and is one of the most relevant obstetric conditions. Despite the technological and scientific advances in perinatal medicine, in developed countries, prematurity is the major cause of neonatal death. Although various risk factors such as previous preterm births, infection, uterine malformations, multiple gestation and short uterine cervix in second trimester, have been associated with this condition, its etiology remains unknown [1][2][3]

Time-varying autoregressive (TVAR) models for multiple radar observations

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.We consider the adaptive radar problem where the properties of the (nonstationary) clutter signals can be estimated using multiple observations of radar returns from a number of sufficiently homogeneous range/azimuth resolution cells. We derive a method for approximating an arbitrary Hermitian covariance matrix by a time-varying autoregressive model of order m, TVAR(m), that is based on the Dym-Gohberg band-matrix extension technique which gives the unique TVAR(m) model for any nondegenerate covariance matrix. We demonstrate that the Dym-Gohberg transformation of the sample covariance matrix gives the maximum-likelihood (ML) estimate of the TVAR(m) covariance matrix. We introduce an example of TVAR(m) clutter modeling for high-frequency over-the-horizon radar that demonstrates its practical importanceYuri I. Abramovich, Nicholas K. Spencer, and Michael D. E. Turle

Stock Markets and Business Cycle Comovement in Germany before World War I: Evidence from Spectral Analysis

This paper examines the comovement of the stock market and of real activity in Germany before World War I under the effcient market hypothesis. We employ multivariate spectral analysis to compare rivaling national product estimates to stock market behavior in the frequency domain. Close comovement of one series with the stock market enables us to decide between various rivaling business cycle chronologies. We find that business cycle dates obtained from deflated national product series are severely distorted by interference with the implicit price deflator. Among the nominal series, the income estimate of Hoffmann (1965) correlates best with the stock market, while the tax based estimate of Hoffmann and Müller (1959) is too smooth especially before 1890. We find impressive comovement between the stock market and nominal wages, a sub-series of Hoffmann's income estimate. We can show that a substantial part of this nominal wage series is driven by data on real investment activity. Our findings confirm the traditional business cycle chronology for Germany of Burns and Mitchell (1946) and Spiethoff (1955), and lead us to discard later, rivaling business cycle chronologies.Business Cycle Chronology, Imperial Germany, Spectral Analysis, Effcient Market Hypothesis

A study of event-related electrocortical oscillatory dynamics associated with cued motor-response inhibition during performance of the Go/NoGo task within a sample of prenatally alcohol-exposed children and age-matched controls

Fetal alcohol spectrum disorders (FASDs) are a spectrum of disorders that occur due to prenatal alcohol exposure (PAE). Response inhibition refers to the ability to inhibit/suppress a prepotent behavioural tendency set in motion during an experimental task. Our research explored neocortical processing in heavy-exposed children from Cape Town, South Africa, performing the Go/NoGo response inhibition task. We utilised event-related electroencephalographic methodologies to examine event-related potentials (ERP) and eventrelated changes in induced oscillatory power - event-related desynchronisation (ERD)/eventrelated synchronisation (ERS). Across visual and auditory Go/NoGo tasks, we observed equivalent levels of inhibitory control between heavy-exposed (HE) participants and normally-developing controls; however, HEs demonstrated significantly slower reaction times relative to the control group. In an auditory ERP study, we observed a number of alcohol-related changes in ERP waveform morphology, such as decreased P2 amplitude, reduced P3 amplitude, and longer P3 peak latency. In addition, within the HE group, late in the trials, a slow-wave component was observed in both experimental conditions. A significant difference in N2 amplitude across conditions that has consistently been observed in normally-developing samples was not observed in the HE group. We extended previous research findings in the visual domain by analysing induced oscillatory responses. We observed within the normally-developing sample: (1) in both experimental conditions, a frontal induced beta-band ERS related to decision-making; and (2) in the NoGo-condition, a frontal gamma-band ERS related to cognitive-control. Within the HE group, the beta-ERS was not observed in either of the experimental conditions, neither was the gamma-ERS observed in the NoGo-condition. Frontal induced beta-power was predictive of performance accuracy in the HE group, but not in the control group. The observed alcohol-related effects were not explained and/or mediated by IQ (WISC-IQ), socio-economic circumstances, comorbid ADHD, or teratogenic effects related to postnatal lead exposure and prenatal cigarette-smoke exposure. Our results point to alterations in scalp-measured event-related neocortical oscillatory dynamics and slower processing of task demands due to heavy PAE. These alcohol-related effects are observable on ERP component measures, primarily related to conflict-monitoring and attention-based processing. PAE also affects induced classes of neocortical oscillatory dynamics related to decision-making and cognitive-control processes required to inhibit a prepotent motor-response