Sreeraman Rajan

On the Cyclostationarity of OFDM and Single Carrier Linearly Digitally Modulated Signals in Time Dispersive Channels: Theoretical Developments and Application

Previous studies on the cyclostationarity aspect of orthogonal frequency division multiplexing (OFDM) and single carrier linearly digitally modulated (SCLD) signals assumed simplified signal and channel models or considered only second-order cyclostationarity. This paper presents new results concerning the cyclostationarity of these signals under more general conditions, including time dispersive channels, additive Gaussian noise, and carrier phase, frequency, and timing offsets. Analytical closed-form expressions are derived for time- and frequency-domain parameters of the cyclostationarity of OFDM and SCLD signals. In addition, a condition to eliminate aliasing in the cycle and spectral frequency domains is derived. Based on these results, an algorithm is developed for recognizing OFDM versus SCLD signals. This algorithm obviates the need for commonly required signal preprocessing tasks, such as signal and noise power estimation and the recovery of symbol timing and carrier information.

Feature-Based Neural Network Approach for Oscillometric Blood Pressure Estimation

In this paper, we present a novel feature-based neural network (NN) approach for estimation of blood pressure (BP) from wrist oscillometric measurements. Unlike previous methods that use the raw oscillometric waveform envelope (OMWE) as input to the NN, in this paper, we propose to use features extracted from the envelope. The OMWE is mathematically modeled as a sum of two Gaussian functions. The optimum parameters of this model are found by minimizing the least squares error between the model and the OMWE using the Levenberg-Marquardt algorithm and are used as features. Two separate feed-forward NNs (FFNNs) are then designed to estimate the systolic and diastolic BPs using these features. The FFNNs are trained using the resilient backpropagation learning algorithm and tested on a data set of BP measurements recorded from 85 subjects. The performance is then compared with that of the conventional maximum amplitude algorithm, adaptive neuro-fuzzy inference system, and already published NN-based methods. It is found that the proposed approach achieves lower values of mean absolute error and standard deviation of error in the estimation of BP. In addition, the proposed approach has the following advantages: lower complexity with respect to the design parameters, smaller training data set, and lower computational load.

Cyclostationarity-Based Robust Algorithms for QAM Signal Identification

This letter proposes two novel algorithms for the identification of quadrature amplitude modulation (QAM) signals. The cyclostationarity-based features used by these algorithms are robust with respect to timing, phase, and frequency offsets, and phase noise. Based on theoretical analysis and simulations, the identification performance of the proposed algorithms compares favorably with that of alternative approaches.

Measurement of Heart Rate Variability Using an Oscillometric Blood Pressure Monitor

We apply the maximal overlap discrete wavelet transform (MODWT)-based spectral density estimation method to measure heart rate variability (HRV) from short-duration pulse wave signals produced by an automated oscillometric blood pressure (BP) monitor during routine measurements. To test the accuracy of this wavelet HRV metric, we study the linear correlations that it achieves with chronological age and BP in a healthy population of 85 subjects. We define accuracy as the quality of the linear regression of HRV with age and BP. Results are compared with a number of traditional HRV metrics and earlier published work. The MODWT HRV metric achieves higher (and more significant) correlations with age and BP compared to other metrics. Moreover, these correlations are in agreement with earlier published work on correlations of HRV (measured from much longer duration electrocardiogram signals) with age and BP. As a further enhancement, we combine the MODWT HRV metric with other HRV metrics inside a multiple-linear-regression model and show an improvement in the correlations between the predicted and actual ages and the predicted and actual BP. Our work thus indicates the suitability of the MODWT metric either as a stand alone or in combination with other metrics for characterizing HRV from short-duration oscillometric pulse wave signals. Based on our results, we conclude that oscillometric BP monitors can be used to measure HRV in addition to measuring BP.

Direction of Arrival Estimation Using Directive Antennas in Uniform Circular Arrays

The effect of directional antenna elements in uniform circular arrays (UCAs) for direction of arrival (DOA) estimation is studied in this paper. While the vast majority of previous work assumes isotropic antenna elements or omnidirectional dipoles, this work demonstrates that improved DOA estimation accuracy and increased bandwidth is achievable with appropriately-designed directional antennas. The Cramer-Rao Lower Bound (CRLB) is derived for UCAs with directional antennas and is compared to isotropic antennas for 4- and 8-element arrays using a theoretical radiation pattern. The directivity that minimizes the CRLB is identified and microstrip patch antennas approximating the optimal theoretical gain pattern are designed to compare the resulting DOA estimation accuracy with a UCA using dipole antenna elements. Simulation results show improved DOA estimation accuracy and robustness using microstrip patch antennas as opposed to conventional dipoles. Additionally, it is shown that the bandwidth of a UCA for DOA estimation is limited only by the broadband characteristics of the directional antenna elements and not by the electrical size of the array as is the case with omnidirectional antennas.

Efficient approximations for the arctangent function

1053-5888/06/

Cyclostationarity-based Algorithm for Blind Recognition of OFDM and Single Carrier Linear Digital Modulations

20.00©2006IEEE T his article provides several efficient approximations for the arctangent function using Lagrange interpolation and minimax optimization techniques. These approximations are particularly useful when processing power, memory, and power consumption are important issues. In addition to comparing the errors and the computational workload of these approximations, we also extend them to all four quadrants.

Confidence Interval Estimation for Oscillometric Blood Pressure Measurements Using Bootstrap Approaches

The paper studies the cyclostationarity of an orthogonal frequency division multiplexing (OFDM) with a view to recognizing OFDM against single carrier linear digital (SCLD) modulations. The analytical expressions for the nth-order cyclic cumulants (CCs) and cycle frequencies of an OFDM signal embedded in additive white Gaussian noise and subject to phase, frequency and timing offsets are derived An algorithm based on a second-order CC is proposed to recognize OFDM against SCLD modulations. The recognition algorithm of the authors obviates the need for preprocessing tasks, such as symbol timing estimation, carrier and waveform recovery, and signal and noise power estimation. The results of simulation experiments confirm the theoretical analysis.

Oscillometric Blood Pressure Estimation: Past, Present, and Future

Although estimation of average blood pressure is commonly done with oscillometric measurements, confidence intervals (CIs) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) are not usually estimated. This paper adopts bootstrap methodologies to build CI from a small sample set of measurements, which is a situation commonly encountered in practice. Three bootstrap methodologies, namely, nonparametric percentile bootstrap, standard bootstrap, and bias-corrected and accelerated bootstrap are investigated. A two-step methodology is proposed based on pseudomeasurements using bootstrap principles to first derive the pseudomaximum amplitudes and then the pseudoenvelopes (PEs). The SBP and DBP are estimated using the new relationships between mean cuff pressure and PE and then the CIs for such estimates are obtained. In order to reduce the amount of processing, a single-step methodology that directly derives PE using bootstrap principles is also presented. Application of the proposed methodology on an experimental data set of 85 patients with five sets of measurements for each patient has yielded a narrower CI than the currently available conventional methods such as Students t-distribution method.

Joint signal detection and classification based on first-order cyclostationarity for cognitive radios

The use of automated blood pressure (BP) monitoring is growing as it does not require much expertise and can be performed by patients several times a day at home. Oscillometry is one of the most common measurement methods used in automated BP monitors. A review of the literature shows that a large variety of oscillometric algorithms have been developed for accurate estimation of BP but these algorithms are scattered in many different publications or patents. Moreover, considering that oscillometric devices dominate the home BP monitoring market, little effort has been made to survey the underlying algorithms that are used to estimate BP. In this review, a comprehensive survey of the existing oscillometric BP estimation algorithms is presented. The survey covers a broad spectrum of algorithms including the conventional maximum amplitude and derivative oscillometry as well as the recently proposed learning algorithms, model-based algorithms, and algorithms that are based on analysis of pulse morphology and pulse transit time. The aim is to classify the diverse underlying algorithms, describe each algorithm briefly, and discuss their advantages and disadvantages. This paper will also review the artifact removal techniques in oscillometry and the current standards for the automated BP monitors.