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Analysis of electroencephalographic recordings (EEG) often requires the solution of a forward problem, where the scalp potentials created by a known source are determined. Most current solution methods are based on the Geselowitz formulation that solves a boundary integral equation for the potential. We show that the problem can also be reformulated as an integral equation for the accumulated charge...
Biological and biomedical signals, when adequately analyzed and processed, can be used to impart quantitative diagnosis during primary health care consultation to improve patient adherence to recommended treatments. For example, analyzing neural recordings from neurostimulators implanted in patients with neurological disorders can be used by a physician to adjust detrimental stimulation parameters...
Neural tracking using electroencephalography (EEG) recordings suffers from physiologic and extraphysiologic artifacts. We propose an integrated method to adaptively track multiple neural sources while reducing the effects of artifacts. Time-frequency features are first extracted from EEG recordings without pre-processing to suppress artifacts. Unsupervised clustering using Gaussian mixture modeling...
We provide an overview of recent work on distributed and agile sensing algorithms and their implementation. Modern sensor systems with embedded processing can allow for distributed sensing to continuously infer intelligent information as well as for agile sensing to configure systems in order to maintain a desirable performance level. We examine distributed inference techniques for detection and estimation...
We propose adaptive learning methods for identifying different behavioral tasks of patients with Parkinson's disease (PD). The methods use local field potential (LFP) signals that were collected during Deep Brain Stimulation (DBS) implantation surgeries. Using time-frequency signal processing methods, features are first extracted and then clustered in the feature space using two different methods...
Probability hypothesis density (PHD) filtering, implemented using particle filters, is a Bayesian technique used to non-linearly track multiple objects. In this paper, we propose a new approach based on PHD particle filters (PHD-PF) to automatically track the number of magnetoencephalography (MEG) neural dipole sources and their unknown states. In particular, by separating the MEG measurements using...
We consider the problem of tracking a maneuvering target in urban terrain with high clutter. Although multipath has been previously exploited to improve target tracking in complex urban environments, when the clutter is high, multipath returns can suffer from large losses in signal-to-noise ratio (SNR), reducing probability of detection (PD). Maneuvering, a common motion in urban terrain, can also...
We investigate the problem of tracking a moving target in urban terrain using a multiple-input and multiple-output (MIMO) radar system. Our proposed method aims to maximize the target information using an optimal configuration of MIMO widely-separated radar sensors while exploiting multipath returns from all the sensors. Furthermore, we adaptively configure the parameters of the transmit waveforms...
Parameter estimation of biological signals such as the electrocardiogram (ECG) is of key clinical significance and can be used to monitor cardiac health and diagnose heart diseases. However, statistical ECG models with unknown parameters depend upon a priori parameters such as mean cardiac frequency and user-specified parameters such as the number of harmonics in the ECG model. These parameters can...
We investigate the target tracking problem of adapting asymmetric multi-modal sensing operation platforms using radio frequency (RF) radar and electro-optical (EO) sensors. Although the multi-modality framework allows for the integration of complementary information, there are many challenges to overcome, including targets with different energy returns, and information loss due to low signal-to-noise...
We investigate the use of the particle filtering sequential Bayesian estimation technique and its hardware implementation for tracking neural activity. We propose using the multiple particle filter (MPF) approach in order to reduce the computational intensity incurred due to the large number of sensors required to observe the noninvasive magnetoencephalography (MEG) measurements needed to estimate...
Sequential Monte Carlo particle filters (PFs) are useful for estimating nonlinear non-Gaussian dynamic system parameters. As these algorithms are recursive, their real-time implementation can be computationally complex. In this paper, we analyze the bottlenecks in existing parallel PF algorithms, and we propose a new approach that integrates parallel PFs with independent Metropolis-Hastings (PPF-IMH)...
Two new space-time-frequency direction of arrival estimation algorithms are presented that decrease the estimation variance under specific conditions of narrow differential direction of arrival when multiple signals impinge upon the sensor array. The first algorithm, called Wide-Lane, provides a modest extension of existing space-time-frequency (STF) techniques by integrating a wide path along the...
We investigate two methods for estimating the matched signal transformations caused by time-varying underwater acoustic channels in orthogonal frequency division multiplexing (OFDM) communication systems. The underwater acoustic channel for this 12-20 kHz medium frequency range OFDM system is best modeled using multipath and wideband Doppler scale changes on the transmitted signal. As a result, our...
We investigate the use of time-frequency (TF) methods to query biological sequences in search of regions of similarity or critical relationships among the sequences. Existing querying approaches are insensitive to repeats, especially in low-complexity regions, and do not provide much support for efficiently querying sub-sequences with inserts and deletes (or gaps). Our approach uses highly-localized...
We integrate multipath exploitation with adaptive waveform design in order to increase the tracking performance of a vehicle moving in urban terrain. Mitigation of both clutter and strong multipath returns can result in increased target detection. However, exploiting multiple bounces from obstacles such as buildings can be shown to increase radar coverage and scene visibility, especially in the absence...
A key challenge in real-world structural health monitoring (SHM) is diversity of damage phenomena and variability in environmental and operational conditions. Conventional learning techniques, while adequate for moderately complex inference tasks, can be limiting in highly complex and rapidly changing environments, especially when insufficient data is available. We present an adaptive learning methodology...
This NSF Phase 3 CCLI multidisciplinary project consists of a collaborative implementation and national dissemination effort that involves Arizona State University (ASU), Johns Hopkins University (JHU), Prairie View A&M University, University of Washington-Bothell (UWB), Rose-Hulman Institute of Technology, University of New Mexico (UNM), and the University of Cyprus (UCY). The project involves...
We propose an agile sensing algorithm to optimally select the transmission waveform of a multiple-input, multiple-output (MIMO) radar system in order to improve target localization. Specifically, we first derive the Cramer-Rao lower bound (CRLB) for the joint estimation of the antenna reflection coefficients and the range and direction-of-arrival of a stationary target using MIMO radar with colocated...
We derive the Cramer-Rao lower bound (CRLB) on the covariance of the joint estimates of the parameters of moving targets using measurements from multiple-input, multiple-output (MIMO) radars. We first derive the CRLB for MIMO radars with colocated antennas for estimating the target's direction of arrival, range and range-rate. We then demonstrate that the CRLB for phased array radars is a special...
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