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Piecewise smooth signal denoising is cast as a non-linear optimization problem in terms of transition boundaries and a parametric smooth signal family. Optimal transition boundaries for a given number of transitions are obtained by using particle swarm optimization. The piece-wise smooth section parameters are obtained as the maximum likelihood estimates conditioned on the optimal transition boundaries...
Wireless source localization has found a number of applications in wireless sensor networks. In this work, we investigate robust and low complexity solutions to the problem of source localization based on the time-difference of arrivals (TDOA) measurement model. By adopting a min-max approximation to the maximum likelihood source location estimation, we develop two low complexity algorithms that can...
Orthogonal frequency division multiplexing (OFDM) systems require the knowledge of signal-to-noise ratio (SNR) at the receiver in order to maximize the system performance. In general, the noise variance is required by the SNR estimator and the knowledge of noise variance also improves the performances of carrier frequency offset (CFO) and channel state estimations. This paper first investigates the...
The objective of this work is to investigate the effect of both multilayer perceptron based and Gaussian maximum likelihood event positioning algorithms on the spatial resolution of a positron emission mammography (PEM) dectector, which use a continuous LSO crystal and a multianode photomultiplier tube, using Monte Carlo simulations. Moreover, the effect of crystal thickness on spatial resolution...
Due to their light weight, low power, and practically unlimited identification capacity, radio frequency identification (RFID) tags and associated devices offer distinctive advantages and are widely recognized for their promising potential in context-aware computing; by tagging objects with RFID tags, the environment can be sensed in a cost- and energy-efficient means. However, a prerequisite to fully...
Automatic modulation classification with a single receiver has been intensively studied for two decades. Enhancing the successful classification probability is a bottleneck in this research field especially with weak signals in a non-cooperative communication environment. A sensor network with distributed classification techniques is expected to achieve technology breakthrough in providing spatial...
Pulsed Arterial Spin Labeling (PASL) techniques potentially allow the absolute, non-invasive quantification of brain perfusion using Magnetic Resonance Imaging (MRI). This can be achieved by fitting a kinetic model to the data acquired at a number of inversion times (TI). Some model parameters such as the arterial transit time need to be estimated together with perfusion, while others are usually...
The maximum a posteriori penalty function (MAP-PF) approach is applied to tracking the bearing and bearing rate of multiple wideband sources in unknown colored noise. The track estimation problem is formulated directly from the array data using the maximum a posteriori (MAP) estimation criterion. The penalty function (PF) method of nonlinear programming is used to obtain a tractable solution. A sequential...
We address the problem of estimating a covariance matrix R using K samples zk whose covariance matrices are τkR, where τk are random variables. This problem naturally arises in radar applications in the case of compound-Gaussian clutter. In contrast to the conventional approach which consists in considering R as a deterministic quantity, a knowledge-aided (KA) approach is advocated here, where R is...
In this paper, we develop algorithms for robust linear regression by leveraging the connection between the problems of robust regression and sparse signal recovery. We explicitly model the measurement noise as a combination of two terms; the first term accounts for regular measurement noise modeled as zero mean Gaussian noise, and the second term captures the impact of outliers. The fact that the...
In this paper we present a novel approach for estimating feature-space maximum likelihood linear regression (fMLLR) transforms for full-covariance Gaussian models by directly maximizing the likelihood function by repeated line search in the direction of the gradient. We do this in a pre-transformed parameter space such that an approximation to the expected Hessian is proportional to the unit matrix...
A discretetized version of a continuous optimization problem is considered for the case where data is obtained from a set of dispersed sensor nodes and the overall metric is a sum of individual metrics computed at each sensor. An example of such a problem is maximum likelihood estimation based on statistically independent sensor observations. By ordering transmissions from the sensor nodes, a method...
This paper presents a sound source (talker) localization method using only a single microphone, where a HMM (Hidden Markov Model) of clean speech is introduced to estimate the acoustic transfer function from a user's position. The new method is able to carry out this estimation without measuring impulse responses. The frame sequence of the acoustic transfer function is estimated by maximizing the...
We prove that discrete Fourier basis channel model is optimal for least squares estimation of channel coefficients used for low-complexity equalization of OFDM transmission over doubly selective channels. We show that regardless of channel statistics, the channel model order is determined by the number of coefficients used in the equalization. Our theoretical findings are numerically validated by...
In this paper, we propose a novel algorithm for the detection of faulty measurements in Global Positioning System (GPS) navigation. In this context, satellite failures result in measurement biases which greatly impair positioning accuracy. Among the different algorithms developed to solve the navigation problem while detecting the biased measurements, the generalized likelihood ratio (GLR) offers...
A novel method of direction-of-arrival (DOA) estimation based on subarray beamforming for uniform circular arrays is proposed. In this method, the beamform manifold of uniform circular arrays is transformed via virtual structure, and then the virtual arrays are divided into two subarrrays. The target DOA is estimated from the phase shift between the reference signal and its phase-shifted version by...
This paper focuses on the estimation of the direction-of-arrival (DOA) of signals impinging on a linear sensor array. In contrast to conventional arrays, where the number of channels equals the number of sensors, we use tapered subarray structures. For this type of array, each channel consists of several sensor elements with different amplitude tapering. By this means, a pre-focussing can be achieved...
In communication systems which transmit a continuous stream of data, the coded data symbols are typically transmitted in frames. Frames contain one or multiple blocks of coded data. The Start Of a Frame (SOF) is especially required to synchronize the decoder, especially in systems which use modern codes such as Turbo codes. This process is known as frame synchronization. We examine the general problem...
Inferring causal relationships of a genetic regulatory network is one of the fundamental problems in system biology. In this paper, an identification algorithm is developed that can be effectively applied to causal interaction identification of a large scale genetic regulatory network from noisy steady-state experiment data. A distinguished feature of the algorithm is that power law distribution has...
Pulsed Arterial Spin Labeling (PASL) techniques potentially allow the absolute, non-invasive quantification of brain perfusion and arterial transit time. This can be achieved by fitting a kinetic model to the data acquired at a number of inversion time points (TI). The intrinsically low SNR of PASL data, together with the uncertainty in the model parameters, can hinder the estimation of the parameters...
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