Detection of cell-cyclic elements in mis-sampled gene expression data using a robust Capon estimator

We present a method for the estimation of possible cell cyclic elements in mis-sampled microarray data. Accurate assessment of the frequency content of microarray data gives insight into genes which could be cell-cycle regulated. Cell cycle regulation is one component of the complex network of genetic regulatory processes and is especially relevant to the study of cancer. As cDNA microarray experiments involve human sampling of cell populations, slight variations in the sampling times invariably occur. Here, we propose estimating the frequency content of microarray data using the recent robust Capon estimator, and formulate a suitable uncertainty region to minimize over. The estimator is shown to yield robust estimates with real microarray data and to identify cell-cyclic genes that elude both the traditional Periodogram and the Capon spectral estimator

On the efficient estimation of blood velocities

Pulsed wave (PW) Doppler ultrasound systems are commonly used to examine blood flow dynamics and the technique plays a very important role in numerous diagnostic applications. Commonly, narrow-band PW systems estimate the blood velocity using an autocorrelation-based estimator. Herein, we examine a recently proposed hybrid frequency estimator, and via extensive numerical simulations using simulated blood scatterers show the achievable performance gain of this method as compared to the traditional approach

A hybrid phase-based single frequency estimator

The topic of low computational complexity frequency estimation of a single complex sinusoid corrupted by additive white Gaussian noise has received significant attention over the last decades due to the wide applicability of such estimators in a variety of fields. In this letter, we propose a computationally fast and statistically improved hybrid phase-based estimator that outperforms other recently proposed approaches, lowering the signal-to-noise ratio at which the Cramér–Rao lower bound is closely followed

Parameter estimation and equalization techniques for communication channels with multipath and multiple frequency offsets

We consider estimation of frequency offset (FO) and equalization of a wireless communication channel, within a general framework which allows for different frequency offsets for various multipaths. Such a scenario may arise due to different Doppler shifts associated with various multipaths, or in situations where multiple basestations are used to transmit identical information. For this general framework, we propose an approximative maximum-likelihood estimator exploiting the correlation property of the transmitted pilot signal. We further show that the conventional minimum mean-square error equalizer is computationally cumbersome, as the effective channel-convolution matrix changes deterministically between symbols, due to the multiple FOs. Exploiting the structural property of these variations, we propose a computationally efficient recursive algorithm for the equalizer design. Simulation results show that the proposed estimator is statistically efficient, as the mean-square estimation error attains the Crame´r-Rao lower bound. Further, we show via extensive simulations that our proposed scheme significantly outperforms equalizers not employing FO estimation

On the Efficient Estimation of Blood Velocities

Pulsed wave (PW) Doppler ultrasound systems are commonly used to examine blood flow dynamics and the technique plays a very important role in numerous diagnostic applications. Commonly, narrow-band PW systems estimate the blood velocity using an autocorrelation-based estimator. Herein, we examine a recently proposed hybrid frequency estimator, and via extensive numerical simulations using simulated blood scatterers show the achievable performance gain of this method as compared to the traditional approach