Oscar Ibarra-Manzano

Optimal Synchronization of Local Clocks by GPS 1PPS Signals Using Predictive FIR Filters

In this paper, we discuss optimal synchronization of local clocks using Global Positioning System (GPS) one-pulse-per-second (1PPS) timing signals. To eliminate sawtooth errors that are peculiar to the 1PPS signals and optimally steer the clock errors each M seconds, we propose the use of a ramp-predictive finite-impulse-response (FIR) filter that is known to be optimal for clock models on large averaging horizons. A low-pass filter is used to smooth the hold filter output between the optimally predicted points. A GPS-locked crystal clock has been investigated in detail in terms of the time interval error, Allan deviation, and precision time protocol (PTP) variance. A high-efficiency implementation of the proposed synchronization algorithm is experimentally demonstrated.

Optimal FIR filtering of the clock time errors

This paper discusses optimal finite impulse response (FIR) filtering of the clock first state known as the time interval error. An exact optimal FIR filter gain is derived assuming arbitrary, zero-mean and jointly independent sources of noise in the clock and in the measurement. The gain is simplified for large averaging horizons associated with clock models that change slowly with time. It is shown that the simplified gain is invariant to the initial state errors, does not depend on noise components in the model and in the measurement and turns out to be equal to the earlier studied unbiased FIR filter gain. A polynomial representation of the gain is also discussed.

Noise Power Gain for Discrete-Time FIR Estimators

The noise power gain (NPG) matrix is specialized in state space for transversal finite impulse response (FIR) estimators intended for filtering, prediction, and smoothing of discrete time-variant K-state models with M states measured. A computationally efficient iterative algorithm for NPG associated with unbiased estimation is provided along. Based on a numerical example, we show that the estimates are well bounded with the error bound (EB) specified in the three-sigma sense by the main components of the NPG matrix and measurement noise variance. In turn, the cross-components in the NPG matrix represent interactions in the estimator channels. It is concluded that EB can serve as an efficient measure of errors in optimal and suboptimal FIR and Kalman structures.

Approximate estimates of limiting errors of passive wireless SAW sensing with DPM

This paper discusses the approximate statistical estimates of limiting errors associated with single differential phase measurement of a time delay (phase difference) between two reflectors of the passive surface acoustic wave (SAW) sensor. The remote wireless measurement is provided at the ideal coherent receiver using the maximum likelihood function approach. Approximate estimates of the mean error, mean square error, estimate variance, and Cramer-Rao bound are derived along with the error probability to exceed a threshold in a wide range of signal-to-noise ratio (SNR) values. The von Mises/Tikhonov distribution is used as an approximation for the phase difference and differential phase diversity. Simulation of the random phase difference and limiting errors also is applied.

Synthesis and nonlinear optical behavior of Ag nanoparticles in PMMA

In this work we have synthesized silver nanoparticles in Poly (methyl methacrylate) (PMMA). This was achieved by polymerizing the mixture of monomer and corresponding metal compound, followed by post-heating treatment. The linear absorption coefficient of the samples was measured using a spectrophotometer, where an absorption peak at 420nm was observed. This peak grows up and shifts as a function of the concentration of the radical initiator. The linear refractive index was measured using the Fresnel equations and agrees with previous reported results. The nonlinear properties were obtained using the single lens Z-scan method, where the nonlinear absorption coefficient (Δα) was found between 5.5975514 and 17.9483493cm-1. The nonlinear refractive index coefficient (Δη) was found to be negative and its value oscillates between 12.9099 E-06 and 22.4276 E-06. Finally, the third-order coefficient (χ(3)) was calculated in the range of 233-787 E-9 esu.

Moving Average Hybrid FIR Filter in Ultrasound Image Processing

In this work, we present a novel technique for ultrasound image processing (2D signals). The moving average hybrid FIR filter (MAH-FIR) which it work with two different FIR filter, the moving average (MA) and median hybrid (FMH) filters. We experimented with continuous linear regression function and Lagrange multiplier to obtain the approximation of the MA-FIR and FMH-FIR filters, respectively. Furthermore, we showed both filters composed into a block diagram. Finally, we present evaluations qualitative and quantitative of ultrasound image processing.

Investigation of an Optimum Sampling Interval for a Local Clock TIE Model with an Unbiased FIR Filtering Algorithm

An investigation of the optimal time step (sampling interval) is provided for the time interval error (TIE) model of a local crystal clock in GPS-based timekeeping. For the sawtooth noise of a receiver, the local clock states are estimated employing an unbiased finite impulse response (FIR) filtering algorithm. We exploit the local crystal clock imbedded to the Stanford Frequency Counter SR620. The measurements are provided using the SynPaQ III GPS Sensor as a timing receiver and rubidium clock (SR625) as a reference source of time for the crystal clock

Enhancing Ultrasound Images Using Hybrid FIR Structures

The problem of saving a sharp edge with a simultaneous enhancing in the image is typical for ultrasound applications. Ultrasound imaging is a technique that is widely used in a variety of clinical applications, such as cardiology (Najarian & Splinter, 2007), obstetrics and gynecology (Jan, 2006), and others. Due to the blur and typically non Gaussian noise, an origin ultrasound image has a poor resolution. That forces researches to create image processing algorithms having a contradictive ability of cleaning the image of noise but saving its sharp edge. An overall panorama of nonlinear filtering following the median strategy has been presented by Pitas and Venetsanopoulos (Pitas & Venetsanopoulos, 1990) along with important modifications for a large class of nonlinear filters employing the order statistics. The algorithm issues for the filter design have been discussed in (Kalouptsidis & Theodoridis, 1993). In (Astola & Kuosmanen, 1997), the finite impulse response (FIR) median hybrid filters (MHF) strategy has been proposed with applications to image processing. An important step ahead has been made in (Heinonen & Neuvo, 1987; 1988), where the FIR MHF structures have been designed. In the sequel, the MHF structures have extensively been investigated, developed, and used by many authors. Basically, hybrid FIR structures can be designed using different types of estimators. Among possible solutions, the polynomial estimators occupy a special place, since the polynomial models often well formalize a priori knowledge about different processes. Relevant signals are typically represented with degree polynomials to fit a variety of practical needs. Examples of applications of polynomial structures can be found in signal processing (Dumitrescu, 2007; Mathews & Sicuranza, 2001), timescales and clock synchronization (Shmaliy, 2006), image processing (Bose, 2004), speech processing (Heinonen & Neuvo, 1988), etc. The polynomial estimators suitable for such structures can be obtained from the generic form of the p-step predictive unbiased FIR filter proposed in (Shmaliy, 2006; 2009). Such estimators usually process data on finite horizons of N points that typically obtain a nice restoration. In this Chapter, we first give the theory of the p-step smoothing unbiased FIR estimator of polynomial signals viewing an image as a multistate space model. We then use the polynomial solutions in the design of FIR MHF structures and justify optimal steps p from the standpoint of minimum produced errors. We show advantages of the approach employing the three generic ramp FIR solutions. Namely, we exploit the 1-step predictive

A stochastic analysis of an anharmonic sensor phase response

The probability density function (pdf) of a modulo 2/spl pi/ phase response slope of an intrinsic anharmonic sensor of a crystal oscillator is studied in detail. It is noted that without an external drive, the sensor is excited by the oscillator noise floor with a signal-to-noise ratio (SNR) of around unity. The slope pdf is provided both in the rigorous integral form and in the T-distribution-based approximation. It is shown that the slope mean value is estimated to be zero with SNR =0. It then gradually tends toward actual value as SNR rises so that with SNR >2 the bias of slope estimates is almost negligible. With 0/spl les/ SNR <0.7, the slope variance stays at a maximum and then asymptotically diminishes toward zero as the SNR rises. The importance of these studies resides in a shown fact that, practically, having SNR <2 in anharmonic sensors may result in substantial bias and variance for phase response slope mod 2/spl pi/ estimates.

Drift Errors in Passive Remote Wireless SAW Sensing With Multiple DPM

We address a probabilistic analysis of drift errors in passive remote wireless surface acoustic wave sensing with multiple differential phase measurement. The rigorous probability density of the differential phase difference is derived and its particular functions, all having no closed forms, are given for different signal-to-noise ratios in the received radio frequency pulses. Employing the von Mises/Tikhonov distribution, an efficient approximation is found via the modified Bessel functions of the first kind and zeroth order. Engineering features and small errors of the approximation are demonstrated. An analysis is given of the phase difference drift rate and error probability for the drift rate to exceed a threshold.