Frequency Tracking and Parameter Estimation for Robust Quantum State-Estimation

In this paper we consider the problem of tracking the state of a quantum system via a continuous measurement. If the system Hamiltonian is known precisely, this merely requires integrating the appropriate stochastic master equation. However, even a small error in the assumed Hamiltonian can render this approach useless. The natural answer to this problem is to include the parameters of the Hamiltonian as part of the estimation problem, and the full Bayesian solution to this task provides a state-estimate that is robust against uncertainties. However, this approach requires considerable computational overhead. Here we consider a single qubit in which the Hamiltonian contains a single unknown parameter. We show that classical frequency estimation techniques greatly reduce the computational overhead associated with Bayesian estimation and provide accurate estimates for the qubit frequencyComment: 6 figures, 13 page

Correlating Heart Rate Variability with Mental Fatigue

The long duration and repetitive nature of watchstanding in the Navy triggers mental fatigue, which would cause a decline in working memory functions and response time. To minimize the damage caused by fatigue, it is necessary to understand how physiological parameters changes during the fatigue. According to recent researches, heart rate variability (HRV) can be calculated from photoplethysmogram (PPG) to indicate nervous system activity. N-back M-pitch, a cognitive working memory test was utilized to define the level of fatigue. An increase in fatigue was indicated by decrease in the accuracy of the test. In this project, HRV\u27s high frequency area and low frequency area have been found correlated to mental fatigue and change over time as hypothesized

Signal processing of EEG data and AI assisted classification of emotional responses based on visual stimuli

This report outlines the research conducted to explore on the topic of classification of human neurological data using machine learning models. The primary objective was to investigate alternative approaches for efficiently interpreting EEG data and test the possibilities for predicting human emotions. During the study, data was collected by recording the brain activity of volunteering respondents using electroencephalography. These participants were exposed to visual stimuli in the purpose of provoking specific neural activity as a result of emotional responses in the brain. The collected data underwent traditional signal preprocessing techniques typically employed in EEG data analysis. Subsequently, the filtered data was subjected to wavelet transformation, both with and without synchrosqueezing. Principal components analysis was used to perform dimensionality reduction on the resulting features extracted from the data. The final model achieved a prediction accuracy of 32% when classifying between eight different classes of emotional responses based on training data from three respondents

Narrowband signal processing techniques with applications to distortion product otoacoustic emissions.

by Ma Wing-Kin.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 121-124).Chapter 1 --- Introduction to Otoacoustic Emissions --- p.1Chapter 1.1 --- Introduction --- p.1Chapter 1.2 --- Clinical Significance of the OAEs --- p.2Chapter 1.3 --- Classes of OAEs --- p.3Chapter 1.4 --- The Distortion Product OAEs --- p.4Chapter 1.4.1 --- Measurement of DPOAEs --- p.5Chapter 1.4.2 --- Some Properties of DPOAEs --- p.8Chapter 1.4.3 --- Noise Reduction of DPOAEs --- p.8Chapter 1.5 --- Goal of this work and Organization of the Thesis --- p.9Chapter 2 --- Review to some Topics in Narrowband Signal Estimation --- p.11Chapter 2.1 --- Fourier Transforms --- p.12Chapter 2.2 --- Periodogram ´ؤ Classical Spectrum Estimation Method --- p.15Chapter 2.2.1 --- Signal-to-Noise Ratios and Equivalent Noise Bandwidth --- p.17Chapter 2.2.2 --- Scalloping --- p.18Chapter 2.3 --- Maximum Likelihood Estimation --- p.19Chapter 2.3.1 --- Finding of the ML Estimator --- p.19Chapter 2.3.2 --- Properties of the ML Estimator --- p.21Chapter 3 --- Review to Adaptive Notch/Bandpass Filter --- p.23Chapter 3.1 --- Introduction --- p.23Chapter 3.2 --- Filter Structure --- p.24Chapter 3.3 --- Adaptation Algorithms --- p.25Chapter 3.3.1 --- Least Squares Method --- p.25Chapter 3.3.2 --- Least-Mean-Squares Algorithm --- p.27Chapter 3.3.3 --- Recursive-Least-Squares Algorithm --- p.28Chapter 3.4 --- LMS ANBF Versus RLS ANBF --- p.31Chapter 3.5 --- the IIR filter Versus ANBF --- p.31Chapter 4 --- Fast RLS Adaptive Notch/Bandpass Filter --- p.33Chapter 4.1 --- Motivation --- p.33Chapter 4.2 --- Theoretical Analysis of Sample Autocorrelation Matrix --- p.34Chapter 4.2.1 --- Solution of Φ (n) --- p.34Chapter 4.2.2 --- Approximation of Φ (n) --- p.35Chapter 4.3 --- Fast RLS ANBF Algorithm --- p.37Chapter 4.4 --- Performance Study --- p.39Chapter 4.4.1 --- Relationship to LMS ANBF and Bandwidth Evaluation . --- p.39Chapter 4.4.2 --- Estimation Error of Tap Weights --- p.40Chapter 4.4.3 --- Residual Noise Power of Bandpass Output --- p.42Chapter 4.5 --- Simulation Examples --- p.43Chapter 4.5.1 --- Estimation of Single Sinusoid in Gaussian White Noise . --- p.43Chapter 4.5.2 --- Comparing the Performance of IIR Filter and ANBFs . . --- p.44Chapter 4.5.3 --- Harmonic Signal Enhancement --- p.45Chapter 4.5.4 --- Cancelling 50/60Hz Interference in ECG signal --- p.46Chapter 4.6 --- Simulation Results of Performance Study --- p.52Chapter 4.6.1 --- Bandwidth --- p.52Chapter 4.6.2 --- Estimation Errors --- p.53Chapter 4.7 --- Concluding Summary --- p.55Chapter 4.8 --- Appendix A: Derivation of Ts --- p.56Chapter 4.9 --- Appendix B: Derivation of XT(n)Λ(n)ΛT(n)X(n) --- p.56Chapter 5 --- Investigation of the Performance of two Conventional DPOAE Estimation Methods --- p.58Chapter 5.1 --- Motivation --- p.58Chapter 5.2 --- The DPOAE Signal Model --- p.59Chapter 5.3 --- Preliminaries to the Conventional Methods --- p.60Chapter 5.3.1 --- Conventional Method 1: Constrained Stimulus Generation --- p.60Chapter 5.3.2 --- Conventional Method 2: Windowing --- p.61Chapter 5.4 --- Performance Comparison --- p.63Chapter 5.4.1 --- Sidelobe Level Reduction --- p.63Chapter 5.4.2 --- Estimation Accuracy --- p.65Chapter 5.4.3 --- Noise Floor Level --- p.67Chapter 5.4.4 --- Additional Loss by Scalloping --- p.68Chapter 5.5 --- Simulation Study --- p.69Chapter 5.5.1 --- Sidelobe Suppressions of the Windows --- p.69Chapter 5.5.2 --- Mean Level Estimation --- p.70Chapter 5.5.3 --- Mean Squared Error Analysis --- p.71Chapter 5.6 --- Concluding Summary --- p.75Chapter 5.7 --- Discussion --- p.75Chapter 5.8 --- Appendix A: Cramer-Rao Bound of the DPOAE Level Estimation --- p.76Chapter 6 --- Theoretical Considerations of Maximum Likelihood Estimation for the DPOAEs --- p.77Chapter 6.1 --- Motivation --- p.77Chapter 6.2 --- Finding of the MLEs --- p.78Chapter 6.2.1 --- First Form: Joint Estimation of DPOAE and Artifact Pa- rameter --- p.79Chapter 6.2.2 --- Second Form: Artifact Cancellation --- p.80Chapter 6.3 --- Relationship of CM1 to MLE --- p.81Chapter 6.4 --- Approximating the MLE --- p.82Chapter 6.5 --- Concluding Summary --- p.84Chapter 6.6 --- Appendix A: Equivalent Forms for the Minimum Least Squares Error --- p.85Chapter 7 --- Optimum Estimator Structure and Artifact Cancellation Ap- proaches for the DPOAEs --- p.87Chapter 7.1 --- Motivation --- p.87Chapter 7.2 --- The Optimum Estimator Structure --- p.88Chapter 7.3 --- References and Frequency Offset Effect --- p.89Chapter 7.4 --- Artifact Canceling Algorithms --- p.92Chapter 7.4.1 --- Least-Squares Canceler --- p.93Chapter 7.4.2 --- Windowed-Fourier-Transform Canceler --- p.93Chapter 7.4.3 --- FRLS Adaptive Canceler --- p.95Chapter 7.5 --- Time-domain Noise Rejection --- p.97Chapter 7.6 --- Regional Periodogram --- p.98Chapter 7.7 --- Experimental Results --- p.99Chapter 7.7.1 --- Artifact Cancellation via External Reference --- p.99Chapter 7.7.2 --- Artifact Cancellation via Internal Reference --- p.99Chapter 7.7.3 --- Artifact Cancellation in presence of Transient Noise --- p.101Chapter 7.7.4 --- Illustrative Example: DPgrams --- p.102Chapter 7.8 --- Conclusion and Discussion --- p.111Chapter 7.9 --- Appendix A: Derivation of the Parabolic Interpolation Method . --- p.113Chapter 7.10 --- Appendix B: Derivation of Weighted-Least-Squares Canceler . . --- p.114Chapter 8 --- Conclusions and Future Research Directions --- p.118Chapter 8.1 --- Conclusions --- p.118Chapter 8.2 --- Future Research Directions --- p.119Bibliography --- p.12

A review of RFI mitigation techniques in microwave radiometry

Radio frequency interference (RFI) is a well-known problem in microwave radiometry (MWR). Any undesired signal overlapping the MWR protected frequency bands introduces a bias in the measurements, which can corrupt the retrieved geophysical parameters. This paper presents a literature review of RFI detection and mitigation techniques for microwave radiometry from space. The reviewed techniques are divided between real aperture and aperture synthesis. A discussion and assessment of the application of RFI mitigation techniques is presented for each type of radiometer.Peer ReviewedPostprint (published version

GA for feature selection of EEG heterogeneous data

The electroencephalographic (EEG) signals provide highly informative data on brain activities and functions. However, their heterogeneity and high dimensionality may represent an obstacle for their interpretation. The introduction of a priori knowledge seems the best option to mitigate high dimensionality problems, but could lose some information and patterns present in the data, while data heterogeneity remains an open issue that often makes generalization difficult. In this study, we propose a genetic algorithm (GA) for feature selection that can be used with a supervised or unsupervised approach. Our proposal considers three different fitness functions without relying on expert knowledge. Starting from two publicly available datasets on cognitive workload and motor movement/imagery, the EEG signals are processed, normalized and their features computed in the time, frequency and time-frequency domains. The feature vector selection is performed by applying our GA proposal and compared with two benchmarking techniques. The results show that different combinations of our proposal achieve better results in respect to the benchmark in terms of overall performance and feature reduction. Moreover, the proposed GA, based on a novel fitness function here presented, outperforms the benchmark when the two different datasets considered are merged together, showing the effectiveness of our proposal on heterogeneous data.Comment: submitted to Expert Systems with Application

A Study Of Equatorial Ionopsheric Variability Using Signal Processing Techniques

The dependence of equatorial ionosphere on solar irradiances and geomagnetic activity are studied in this dissertation using signal processing techniques. The statistical time series, digital signal processing and wavelet methods are applied to study the ionospheric variations. The ionospheric data used are the Total Electron Content (TEC) and the critical frequency of the F2 layer (foF2). Solar irradiance data are from recent satellites, the Student Nitric Oxide Explorer (SNOE) satellite and the Thermosphere Ionosphere Mesosphere Energetics Dynamics (TIMED) satellite. The Disturbance Storm-Time (Dst) index is used as a proxy of geomagnetic activity in the equatorial region. The results are summarized as follows. (1) In the short-term variations ≤ 27-days, the previous three days solar irradiances have significant correlation with the present day ionospheric data using TEC, which may contribute 18% of the total variations in the TEC. The 3-day delay between solar irradiances and TEC suggests the effects of neutral densities on the ionosphere. The correlations between solar irradiances and TEC are significantly higher than those using the F10.7 flux, a conventional proxy for short wavelength band of solar irradiances. (2) For variations ≤ 27 days, solar soft X-rays show similar or higher correlations with the ionosphere electron densities than the Extreme Ultraviolet (EUV). The correlations between solar irradiances and foF2 decrease from morning (0.5) to the afternoon (0.1). (3) Geomagnetic activity plays an important role in the ionosphere in short-term variations ≤ 10 days. The average correlation between TEC and Dst is 0.4 at 2-3, 3-5, 5-9 and 9-11 day scales, which is higher than those between foF2 and Dst. The correlations between TEC and Dst increase from morning to afternoon. The moderate/quiet geomagnetic activity plays a distinct role in these short-term variations of the ionosphere (~0.3 correlation)

Doppler radar-based non-contact health monitoring for obstructive sleep apnea diagnosis: A comprehensive review

Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage

Analysis of Dynamic Brain Imaging Data

Modern imaging techniques for probing brain function, including functional Magnetic Resonance Imaging, intrinsic and extrinsic contrast optical imaging, and magnetoencephalography, generate large data sets with complex content. In this paper we develop appropriate techniques of analysis and visualization of such imaging data, in order to separate the signal from the noise, as well as to characterize the signal. The techniques developed fall into the general category of multivariate time series analysis, and in particular we extensively use the multitaper framework of spectral analysis. We develop specific protocols for the analysis of fMRI, optical imaging and MEG data, and illustrate the techniques by applications to real data sets generated by these imaging modalities. In general, the analysis protocols involve two distinct stages: `noise' characterization and suppression, and `signal' characterization and visualization. An important general conclusion of our study is the utility of a frequency-based representation, with short, moving analysis windows to account for non-stationarity in the data. Of particular note are (a) the development of a decomposition technique (`space-frequency singular value decomposition') that is shown to be a useful means of characterizing the image data, and (b) the development of an algorithm, based on multitaper methods, for the removal of approximately periodic physiological artifacts arising from cardiac and respiratory sources.Comment: 40 pages; 26 figures with subparts including 3 figures as .gif files. Originally submitted to the neuro-sys archive which was never publicly announced (was 9804003