Frequency Estimation

Doppler ultrasound is widely used in medical applications to extract the blood Doppler flow velocity in the arteries via spectral analysis. The spectral analysis of non-stationary signals and particularly Doppler signals requires adequate tools that should present both good time and frequency resolutions. It is well-known that the most commonly used time-windowed Fourier transform, which provides a time-frequency representation, is limited by the intrinsic trade-off between time and frequency resolutions. Parametric methods have then been introduced as an alternative to overcome this resolution problem. However, the performances of those methods deteriorate when high non-stationarities are present in the Doppler signal. For the purpose of accurately estimating the Doppler frequency shift, even when the temporal flow velocity is rapid (high non-stationarity), we propose to combine the use of the time-varying auto-regressive method and the (dominant) pole frequency. This proposed method performs well in the context where non-stationarities are very high. A comparative evaluation has been made between classical (FFT based) and auto-regressive (both block and recursive) algorithms. Among recursive algorithms we test an adaptive recursive method as well as a time-varying recursive method. Finally, the superiority of the time-varying parametric approach in terms of frequencies tracking and of delay on the frequency estimate is illustrated on both simulated and in vivo Doppler signals.

Hydrologic designs require accurate estimation of quartiles of extreme floods. But in many developing regions, records of flood data are seldom available. A model framework using the dimensionless index flood for the transfer of Flood Frequency Curve (FFC) among stream gauging sites in a hydrologically homogeneous region is proposed. Key elements of the model framework include: (1) confirmation of the homogeneity of the region; (2) estimation of index flood-basin area relation; (3) derivation of the regional flood frequency curve (RFFC) and deduction of FFC of an ungauged catchment as a product of index flood and dimensionless RFFC. As an application, 1983 to 2004 annual extreme flood from six selected gauging sites located in Anambra-Imo River basin of southeast Nigeria, were used to demonstrate that the developed index flood model: 𝑄 𝑚 = 0.495𝐴 0.6676 , overestimated flood quartiles in an ungauged site of the basin. It is recommended that, for wider application, the model results can be improved by the availability and use of over 100 years length of flood data spatially distributed at critical locations of the watershed.

This study investigates the application of empirical mode decomposition to signals from very low frequency transmitters in Europe that were received in Thessaloniki, Greece, to provide a method for depicting seismic-ionospheric precursor phenomena that occur prior to an earthquake. The basis for ionosphere interactions with seismic phenomena has been well documented in past studies, and the depiction of disturbances applied from the earthionosphere waveguide on the received signals was the purpose of this study. Empirical mode decomposition is a method for processing of nonlinear and nonstationary signals, to decompose them into their functional components, known as intrinsic mode functions. This method can provide high pass filtering to signals, thus depicting a clearer image of any abnormal disturbances in the signals that are not part of the normal noise content.

The frequency and rate of change of frequency (ROCOF) signals acquired from phasor measurement units (PMUs) are instrumental in the security monitoring and feedback control of power grids, necessitating minimal response time and high precision. Existing algorithms rarely achieve a simultaneous balance between superior noise filtering performance and rapid response time when measuring frequency and ROCOF. To address this problem, this article introduces an innovative algorithm that integrates αβγ series filters. The proposed algorithm leverages an ααβ (aab) filter, which combines an α filter and an αβ filter, and the reference P-class PMU algorithm from the IEC/IEEE 60255.118.1-2018 standard file. By providing an original mathematical modeling approach to aab parameter acquisition, this filter effectively amalgamates the advantages of the two foundational filters. Extensive experimental simulations demonstrate that our proposed algorithm has exceptional performance across various metrics of frequency and ROCOF measurement, achieving results that compare favorably with other advanced algorithms.

The ovary atresia charactrristics of sardine, Sardina pilchardus, 011 the Iberiari Peninsula uiere descnbed using the same nomenclature and criteria adopted for thr northern anchovy, Engraulis mordax)., by Hunter and Maceuncz (1985a). The data used uiere based on sardine biologicai information obtained from laboratory expenmrnts aud Daily Egg Production Method (DEPM) suroey samples. 111the laboratory, the occurrrnce of atresia was low, and most atresia stages correspended to a-stage. An in crease in the number of femalPs in more advanced degeneration stagrs was noted as staroation time increased. In the natural population, atresia stages mostly appeared after the complete degeneration or postovulatory [ollicles. The complete yolked oocyte atresia process took no more than 6 days.

The k-PHD method, a generalization of the well-known Pisarenko harmonic decomposition (PHD) method, is considered for frequency estimation of a single real random-phased sinusoid in noise. With the use of a simple variance analysis technique, an exact expression of the k-PHD frequency variance is derived. An approximate k-PHD variance formula for sufficiently large data lengths and signal-to-noise ratios is also given. Computer simulations are included to validate the theoretical development.

This work presents an analysis of 3.5 million calls made to a mental health and well-being helpline, seeking to answer the question, what different groups of callers can be characterised by specific usage patterns? Calls were extracted from a telephony informatics system. Each call was logged with a date, time, duration and a unique identifier allowing for repeat caller analysis. We utilized data mining techniques to reveal new insights into help-seeking behaviours. Analysis was carried out using unsupervised machine learning (K-means clustering) to discover the types of callers, and Fourier transform was used to ascertain periodicity in calls. Callers can be clustered into five or six caller groups that offer a meaningful interpretation. Cluster groups are stable and re-emerge regardless of which year is considered. The volume of calls exhibits strong repetitive intra-day and intra-week patterns. Intra-month repetitions are absent. This work provides new data-driven findings to mod...

Inter-area oscillations are inherent to power systems which can be caused due to oscillation of two coherent groups of generators against each other with low or negative damping. Poorly damped oscillations can pose various problems such as limiting transfer capabilities and in more severe cases can lead to uncontrolled islanding causing system blackout. In this paper, for damping inter-area oscillation a new method based on the wide area measurement system (WAMS) and using generation rescheduling (GR) is proposed. The proposed algorithm consists of three tasks; identification of inter-area mode, determination the most effective generators for rescheduling and enhancing damping of inter-area mode by GR. In this approach, first by means of Independent Component Analysis (ICA) and modified Random Decrement (RD) techniques and using on-line power and rotor angle oscillation data provided by WAMS, the frequency and damping of the inter-area mode is identified. In the case of low damping of oscillation, by using mode shape of the inter area mode, generation rescheduling is applied for improvement of damping. In this paper, a non-model based method is adopted for estimating mode shape from the measured data like generator rotor speeds. The proposed approach is applied on a two area small system and IEEE 39-bus test system and the results demonstrate effectiveness of the method for enhancing oscillation damping.

This paper presents a feasibility study on how to implement power quality (PQ) metrics in a low-cost smart metering platform. The study is aimed at verifying the possibility of implementing PQ monitoring in distribution networks without replacing existing smart metering devices or adding new modules for PQ measurements, thus zeroing the installation costs. To this aim, an electronic board, currently used for remote energy metering, was chosen as a case study, specifically the STCOMET platform. Starting from the specifications of this device, the possibility of implementing power quality metrics is investigated in order to verify if compliance with standard requirements for PQ instruments can be obtained. Issues related to device features constraints are discussed; possible solutions and correction algorithms are presented and experimentally verified for different PQ metrics with a particular focus on harmonic analysis. The feasibility study takes into account both the use of on-boar...

In this paper a criterion for the choice of the optimal window for the amplitude estimation of a sine-wave corrupted by a stationary white noise using the energy-based method (EBM) is proposed. The window is chosen by ensuring that the spectral leakage effect on the ...

The first part of this paper describes an algorithm for estimating the fundamental frequency F0 of a speech signal, using an autocorrelation algorithm. After that, it was shown that, due to the discrete structure of the autocorrelation function, the accuracy of the fundamental frequency estimate largely depends on the sampling period TS. Then, in order to increase the accuracy of the estimation, an interpolation of the correlation function is performed. Interpolation is performed using a one parameter (1P) Keys interpolation kernel. The second part of the paper presents an experiment in which the optimization of the 1P Keys kernel parameter was performed. The experiment was performed on test Sine and Speech signals, in the conditions of ambient disturbances (N8 Babble noise, SNR = 5 to -10 dB). MSE was used as a measure of the accuracy of the fundamental frequency estimate. Kernel parameter optimization was performed on the basis of the MSE minimum. The results are presented graphic...

The frequency estimation of a single tone corrupted by additive white Gaussian noise has received significant attention over the last decades due to its wide applicability in signal processing. In this paper, we propose a computationally fast and statistically improved hybrid single tone estimator which outperforms other recently proposed approaches, lowering the signal-to-noise ratio at which the Cramér-Rao lower bound is closely followed. Numerical simulations indicate that, in contrast to many other techniques, the performance of the hybrid estimator is essentially independent of the underlying frequency component.

This paper presents new results improving by a factor of 10 the accuracy of an ODFT-based frequency estimation algorithm. These results are shown to be robust to the influence of additive noise and compare favorably to other non-iterative frequency domain estimation algorithms. A perspective is given on possible application areas, namely those involving real-time constraints.

In this paper, the problem of frequency estimation using adaptive filters is addressed based on the augmented complex normalized least mean squares (ACNLMS) technique. In other words, motivated from ACLMS technique, a new version called ACNLMS that outperforms the previous techniques is proposed. The proposed method makes use of two weight coefficients, like ACLMS, together with normalized time variant step size, thus its promising in increasing and enhancing both convergence rate and accuracy. Then, using derived weight coefficient, the frequency is estimated at each step. The performance and convergence analysis of the proposed method along with simulation results comparing with two existing techniques, CLMS and ACLMS, are provided in critical cases such as various unbalanced conditions and presence of harmonic distortion. Simulation results indicate that the proposed technique achieves a better performance in terms of convergence rate and frequency estimation accuracy as compared...

Abstract: In this paper, an original idea of designing new materials is investigated. The property of Helmholtz equation to be invariant under geometric transformations is exploited to obtain new materials with inhomogeneous and anisotropic distribution of elastic ...

This paper derives a general expression for the mean square error in estimating the fundamental frequency of a multiharmonic signal from a nite sequence of noisy measurements. The distinguishing feature of this expression is that it is applicable at values of signal-to-noise-ratio (SNR) within the threshold region, i n c o n trast to earlier expressions (the Cramer Rao bounds) that are valid only at high SNR's. Theoretical performance curves are thereby calculated (mean square error versus SNR) that establish the existence of a threshold e ect. Until now, the existence of a threshold e ect was demonstrable only by s i m ulation. Examples are given comparing various multiharmonic estimation scenarios to the single tone case under comparable conditions. The theoretical performance curves in these examples are corroborated by M o n te-Carlo simulation.

The analysis and modeling of extreme areal precipitation is carried out focusing the attention on extreme areal precipitation events rather than extreme point precipitation. The data is daily precipitation values from southern Norway. By investigating the spatial coverage of the fractional area (fraction of the considered catchment) where it rains over a certain intensity threshold, it was found, for a range of rain intensities, that the ratio between two fractional areas of consecutive thresholds is independent of the actual value of the rain intensity and the size of the considered catchment. The mean areal precipitation is seen as the sum of the fractional thresholded rainy areas for all intensity thresholds. By the properties of independence of the consecutive area ratios, it is possible to simulate fractional thresholded rainy areas, and thus compute the mean areal rainfall. The mean and the standard deviation of simulated series of extreme areal precipitation for five catchments of different size (from 48000 to 468 km 2) are in very good agreement with those of the observed. This is also the case for the observed and simulated values of the fractional thresholded rainy areas. The mean values of the spatial coverage of thresholded areas for mesoscale convective complexes in the United States are found to be well modeled by the proposed method. 1.

Frequency estimation plays a critical role in many applications involving personal and private categorical data. Such data are often collected sequentially over time, making it valuable to estimate their distribution online while preserving privacy. We propose AdOBEst-LDP, a new algorithm for adaptive, online Bayesian estimation of categorical distributions under local differential privacy (LDP). The key idea behind AdOBEst-LDP is to enhance the utility of future privatized categorical data by leveraging inference from previously collected privatized data. To achieve this, AdOBEst-LDP uses a new adaptive LDP mechanism to collect privatized data. This LDP mechanism constrains its output to a subset of categories that "predicts" the next user's data. By adapting the subset selection process to the past privatized data via Bayesian estimation, the algorithm improves the utility of future privatized data. To quantify utility, we explore various well-known information metrics, including (but not limited to) the Fisher information matrix, total variation distance, and information entropy. For Bayesian estimation, we utilize posterior sampling through stochastic gradient Langevin dynamics, a computationally efficient approximate Markov chain Monte Carlo (MCMC) method. We provide a theoretical analysis showing that (i) the posterior distribution of the category probabilities targeted with Bayesian estimation converges to the true probabilities even for approximate posterior sampling, and (ii) AdOBEst-LDP eventually selects the optimal subset for its LDP mechanism with high probability if posterior sampling is performed exactly. We also present numerical results to validate the estimation accuracy of AdOBEst-LDP. Our comparisons show its superior performance against non-adaptive and semi-adaptive competitors across different privacy levels and distributional parameters. CCS Concepts: • Computing methodologies → Online learning settings; • Security and privacy → Privacy-preserving protocols; • Mathematics of computing → Bayesian computation; Markov-chain Monte Carlo methods;

This paper introduces a novel eigenstructure-based algorithm uni-vector-sensor ESPRIT that yields closed-form direction-of-arrival (DOA) estimates and polarization estimates using one electromagnetic vector sensor. A vector sensor is composed of six spatially co-located nonisotropic polarizationsensitive antennas, measuring all six electromagnetic field components of the incident wave field. Uni-vector-sensor ESPRIT is based on a matrix-pencil pair of temporally displaced data sets collected from a single electromagnetic vector sensor. The closed-form parameter estimates are obtained through a vector cross-product operation on each decoupled signalsubspace eigenvector of the data correlation matrix. This method exploits the electromagnetic sources' polarization diversity in addition to their spatial diversity, requires no a priori knowledge of signal frequencies, suffers no frequency-DOA ambiguity, pairs automatically the x-axis direction cosines with the y-axis direction cosines, eliminates array interelement calibration, can resolve up to five completely polarized uncorrelated monochromatic sources from near field or far field. It impressively out-performs an array of spatially displaced identically polarized antennas of comparable array-manifold size and computational load.

The aim of this study was to estimate the frequency of postoperative complications after mandibular third molar (M3) surgery and identify the risk indicators. Patients and Methods: This was a prospective cohort study of a sample of subjects having at least 1 mandibular M3 surgically extracted at a teaching hospital in Jordan. The predictor variables were categorized as patient, anatomic, and operative specific. The outcome variables were postoperative complications recorded as present or absent. Bivariate analyses were computed, then a multivariate logistic regression model was used to identify independent predictors for the common postoperative complications. Results: The study sample was comprised of 149 patients who had 245 extractions. The mean age was 21.6 Ϯ 3.32 years; 64.9% were females. In the multivariate logistic regression model, age (P ϭ .033, odds ratio [OR] ϭ 1.178), M3 side in relation to the handedness of the operator (P ϭ .048, OR ϭ 4.078), and lingual retraction (P ϭ .001, OR ϭ 11.293) were the variables found as independent predictors for alveolar osteitis. The level of impaction had a significant association with trismus, and operation time acted as an independent predictor for pain (P Ͻ .001, OR ϭ 1.085). Conclusion: Postoperative morbidity increases with older age, deeper impaction, M3 side differing from the handedness of the operator, and longer procedures.

This paper compares two popular methods for time-frequency analysis: the Fast Fourier Transform (FFT) and the Continuous Wavelet Transform (CWT) for a synthetic chirp signal wave. We apply these techniques to a chirp signal, which is a non-stationary signal with a frequency that changes over time. The results demonstrate the strengths and limitations of each method, highlighting the FFT's ability to provide global frequency information and the CWT's superior time-frequency localization. FFT renders the representation of signal only in the frequency domain. The CWT, using the morse wavelet, on other hand provides a more compact visualisation of the signal in both time and frequency domain visualising or representing the frequency domain of the signal simultaneously. The performance of these techniques is compared visually and computationally.

SUMMARY It is imperative to carry out comprehensive seismic hazard assessment for big cities in order to reduce the potential damage from earthquakes. One of the fundamental steps in seismic hazard assessment is microzonation of cities, which provides a basis for ...

The hydro-meteorological variables of extreme rainfall are not easy to explain due to unexpected changes in climate and varied usage of water with growing population. Regional rainfall frequency analysis is the one such method that is useful for the requirement of more accurate estimates of rainfall yearly or desineally for the regions having lack of fresh water resources. The series of Annual Maximum Monthly Rainfall Totals (AMMRT) has been used for the seven sites of northern Punjab, Pakistan using L-moments. The results of different test, the run test, lag-1 correlation and Mann-Whitney U test illustrate that the data series of the seven sites of northern Punjab were found random and independently and identically distributed and have no serial correlation. Heterogeneity measure exposed that the region is homogeneous and discordancy measure gives the evidence that no site is discordant among the seven. The result of goodness of fit test including L-moment Ratio diagrams, Z DIST st...

BACKGROUND: Haplotype based linkage disequilibrium (LD) mapping has become a powerful and cost-effective method for performing genetic association studies, particularly in the search for genetic markers in linkage disequilibrium with complex disease loci. Various methods (e.g. Monte-Carlo (Gibbs sampling); EM (expectation maximization); and Clark's method) have been used to estimate haplotype frequencies from routine genotyping data. RESULTS: These algorithms can

The development of molecular typing techniques applied to the study of population genetic diversity originates data with increasing precision but at the cost of some ambiguities. As distinct techniques may produce distinct kinds of ambiguities, a crucial issue is to assess the differences between frequency distributions estimated from data produced by alternative techniques for the same sample. To that aim, we developed a resampling scheme that allows evaluating, by statistical means, the significance of the difference between two frequency distributions. The same approach is then shown to be applicable to test selective neutrality when only sample frequencies are known. The use of these original methods is presented here through an application to the genetic study of a Munda human population sample, where three different HLA loci were typed using two different molecular methods (reverse PCR-SSO typing on microbeads arrays based on Luminex technology and PCR-SSP typing), as described in details in the companion article by Riccio et al. [The Austroasiatic Munda population from India and its enigmatic origin: An HLA diversity study. Hum. Biol. 38:405-435 (2011)]. The differences between the frequency estimates of the two typing techniques were found to be smaller than those resulting from sampling. Overall, we show that using a resampling scheme in validating frequency estimates is effective when alternative frequency estimates are available. Moreover, resampling appears to be the unique way to test selective neutrality when only frequency data are available to describe the genetic structure of populations.

The occurrence of a time span effect and illusory superiority in self reports of health behaviors was tested. It was found that people report proportionally lower frequencies of both healthy and unhealthy behaviors if they give frequency estimations over a longer as compared to a shorter time span (time span effect). In addition, they report lower frequencies of unhealthy behaviors, and higher frequencies of healthy behaviors for themselves than for the average other (illusory superiority). For healthy but not for unhealthy behaviors a stronger illusory superiority effect was obtained in frequency reports for behavioral expectations as compared to past behaviors. Potential explanations and implications for subjective measurements of health behavior are discussed.

The occurrence of a time span effect and illusory superiority in self reports of health behaviors was tested. It was found that people report proportionally lower frequencies of both healthy and unhealthy behaviors if they give frequency estimations over a longer as compared to a shorter time span (time span effect). In addition, they report lower frequencies of unhealthy behaviors, and higher frequencies of healthy behaviors for themselves than for the average other (illusory superiority). For healthy but not for unhealthy behaviors a stronger illusory superiority effect was obtained in frequency reports for behavioral expectations as compared to past behaviors. Potential explanations and implications for subjective measurements of health behavior are discussed.

The singular value decomposition (SVD) is an important tool for subspace estimation. In adaptive signal processing, we are especially interested in tracking the SVD of a recursively updated data matrix. This paper introduces a new tracking technique that is designed for rectangular sliding window data matrices. This approach, which is derived from the classical bi-orthogonal iteration SVD algorithm, shows excellent performance in the context of frequency estimation. It proves to be very robust to abrupt signal changes, due to the use of a sliding window. Finally, an ultra-fast tracking algorithm with comparable performance is proposed.

The paper presents the natural frequencies shift of a Warren truss with seven elements in the case that members of the structure are weakened by considering that the modulus of elasticity with a lower value. Taking into consideration that the slenderness ratio has a constant value and the transverse section of the truss bars is changed, the relative frequency shift for the first seven vibration mode were calculated by using the values obtained from numerical modal analysis. The conclusions reveal the fact from the dynamic behavior of the structure it can be obtained templates to identify the weakened truss bar of the structure.

This paper proposes an analytic relation for the deflection of a cantilever beam with a transverse crack subjected to dead load. The mathematical relation is deduced involving the decreased capacity of the beam to store energy, which is in direct relation with the crack position and depth. Eventually, the validity of the relation is proved by means of the finite element method.

Abstract:-This paper presents the researches made by the authors in order to develop an algorithm and method to localize damages in beams using vibration measurement. The chosen type of beam was the cantilever one, for which a database containing the first ten natural frequencies in undamaged state was realized. Afterwards, eight points relevant for the characterization of the beam's dynamic behavior were determined by analytical calculus. The database was completed with the first ten natural frequencies of the beam damaged ...

In this paper, a new class of Bayesian lower bounds is proposed. Derivation of the proposed class is performed via projection of each entry of the vector-function to be estimated on a closed Hilbert subspace of L 2 . This Hilbert subspace contains linear transformations of elements in the domain of an integral transform, applied on functions used for computation of bounds in the Weiss-Weinstein class. The integral transform generalizes the traditional derivative and sampling operators, used for computation of existing performance lower bounds, such as the Bayesian Cramér-Rao, Bayesian Bhattacharyya and Weiss-Weinstein bounds. It is shown that some well known Bayesian lower bounds can be derived from the proposed class by specific choice of the integral transform kernel. A new lower bound is derived from the proposed class using the Fourier transform kernel. The proposed bound is compared with other existing bounds in term of signal-to-noise ratio (SNR) threshold region prediction, in the problem of frequency estimation. The bound is shown to be computationally manageable and provides better prediction of the SNR threshold region, exhibited by the maximum a-posteriori probability (MAP) and minimum-mean-square-error (MMSE) estimators.

We investigate the performance of main frequency estimation methods for a single-component complex sinusoid under complex additive white Gaussian noise (AWGN) as well as phase noise (PN). Two methods are under test: Maximum Likelihood (ML) method using Fast Fourier Transform (FFT), and the autocorrelation method (Corr). Simulation results showed that FFT-method has superior performance as compared to the Corr-method in the presence of additive white Gaussian noise (affecting the amplitude) and phase noise, with almost 20dB difference.

We consider the problem of output regulation for LTI systems in the presence of unknown exosystems. The only a priori knowledge about the exosystem consists in the fact that it generates multi-frequency signals, while the number and the value of the frequencies are not known. The control scheme relies on two main components: an estimation algorithm, to reconstruct the signal generated by the exosystem, and a controller, to enforce the output regulation property to the closed-loop system. To tackle the first task, we propose a hybrid observer for the estimation of the (possibly piece-wise continuous) number and values of the frequencies contained in the exogenous signal. The hybrid observer is particularly appealing for numerical implementations and it is combined with a self-tuning algorithm of the free parameters (gains), thus improving its performance even in case of noisy measurements. Semi-global exponential convergence of the estimation error is provided. As far as the second task is concerned, a robust hybrid regulator is designed for practical rejection of the multi-frequency disturbance signal acting on the plant. The result is achieved by exploiting the frequencies estimated by the hybrid observer. The effectiveness of the proposed control scheme is shown by means of numerical simulations.

ostatistical Kriging is performed on hydrologic model parameters in a twodimensional region—different from the geographical space—as a hydrospace. The x-axis in percent is a relative difference of soil characteristics between an embedded 12 watersheds in reference to a large one related to the Niger River in West Africa; noted var_WHC, it stands for Water Holding Capacity. The y-axis in percent, var_Nash, is a hydrologic model’s efficiency in two contexts: (a) calibrated model parameters on the reference watershed are injected in modelling on each sub-watershed in validation phase to produce a series of Nash values as references, (b) a second series of Nash values is produced in calibrations. SimulHyd which stands for Simulation of Hydrological Systems is applied along with a French hydrological model—Genie Rural with 2 parameters at Monthly time step. The built Nash-WHC hydrospace and its two variants, or hybrids, permit the krige of both hydrologic model’s parameters. The relative variation of upper module absolute ranges from 0.1% to 15.68%—
the developed hydro-geostatistics practice is considered in reference to hydrological calibration. Accepted as hydrogeostatistics practice, it is applicable to ungauged watersheds to estimate hydrologic models’ parameters

We investigate dynamic voltage and frequency scaling (DVFS) as a mechanism for dynamic reliability management (DRM) of chip multiprocessors (CMPs). The proposed DRM scheme operates as a control technique whose objective is to drive the operation of the CMP such that reliability changes towards a desired target. While the chip multiprocessor is continuously monitored and reliability is estimated in real time, the voltage and frequency of different cores in the CMP are dynamically adjusted such that reliability converges towards the target. When the temperature of cores increases and thus reliability degrades, the proposed DRM scheme throttles selectively the frequency of the cores with the highest temperature. This is turn, leads to a lower power dissipation in those cores whose temperature decreases, thereby improving reliability. We leverage existing simulation and estimation tools to develop the proposed DRM scheme. Simulations results show that the proposed DRM scheme provides an effective way to tradeoff reliability and performance.

We investigate dynamic voltage and frequency scaling (DVFS) as a mechanism for dynamic reliability management (DRM) of chip multiprocessors (CMPs). The proposed DRM scheme operates as a control technique whose objective is to drive the operation of the CMP such that reliability changes towards a desired target. While the chip multiprocessor is continuously monitored and reliability is estimated in real time, the voltage and frequency of different cores in the CMP are dynamically adjusted such that reliability converges towards the target. When the temperature of cores increases and thus reliability degrades, the proposed DRM scheme throttles selectively the frequency of the cores with the highest temperature. This is turn, leads to a lower power dissipation in those cores whose temperature decreases, thereby improving reliability. We leverage existing simulation and estimation tools to develop the proposed DRM scheme. Simulations results show that the proposed DRM scheme provides an effective way to tradeoff reliability and performance.

An in situ optical oyster heart rate sensor generates signals requiring frequency estimation with properties different to human ECG and speech signals. We discuss the method of signal generation and highlight a number of these signal properties. An optimal heart rate estimation approach was identified by application of a variety of frequency estimation techniques and comparing results to manually acquired values. Although a machine learning approach achieved the best performance, accurately estimating 96.8% of the heart rates correctly, a median filtered autocorrelation approach achieved 93.7% with significantly less computational requirement. A method for estimating heart rate variation is also presented.

In this paper, we study the autoregressive and moving average (ARMA) filter for lidar signal processing. After a short presentation of the atmospheric laser Doppler instrument project (ALADIN), we introduce the objective of this paper, which is to extract the Doppler frequency and to retrieve the spectral width of a noised lidar signal. A general presentation of ARMA filters and parametric adaptive algorithms (PAA's) is provided. Then we present results about the choice of the model, the Doppler frequency estimate, and the spectral width estimate. Finally, we study the possible estimate of SNR, which is biased by the first estimates (Doppler frequency and spectral width).

Across 5 experimental studies, the authors explore selective processing biases for physically attractive others. The findings suggest that (a) both male and female observers selectively attend to physically attractive female targets, (b) limiting the attentional capacity of either gender results in biased frequency estimates of attractive females, (c) although females selectively attend to attractive males, limiting females' attentional capacity does not lead to biased estimates of attractive males, (d) observers of both genders exhibit enhanced recognition memory for attractive females but attenuated recognition for attractive males. Results suggest that different mating-related motives may guide the selective processing of attractive men and women.

We present a Web-accessible database (ALFRED) that allows public access to gene frequency data for a diverse set of population samples and genetic systems. The data in ALFRED are modeled based on the experience and needs of a single laboratory, but with the expectation that the database will meet the needs of a much broader scientific community that needs population-specific gene frequency estimates. Our database currently contains data on more than 40 populations representing most major regions of the world and data on more than 150 genetic systems including SNPs, STRPs, and insertion-deletion polymorphisms. While data are not available for all population-genetic system combinations, over 2000 allele frequency tables already exist. In this paper, we enumerate the broad needs in the scientific domain, describe their significance, and describe how we have designed the database to meet those needs. We compare our database with dbSNP, the NCBI database that has a broader but overlapping purpose.

Data detection in burst mode systems requires that an accurate frequency estimate of oscillator instabilities and doppler frequency be formed using the preamble. Current techniques manipulate the preamble's autocorrelation samples to form the frequency estimate. In this paper we present a new estimator which uses the average of the product of adjacent autocorrelation samples. The algorithm has a feedforward structure which is easy to implement and a wide frequency acquisition range. The frequency estimate is unbiased and asymptotically reaches the Cramer Raó bound (CRB). From the derivation of the variance we calculate optimum weights using constrained optimisation. Implementation issues suggest it is possible to use a sub-optimal number of autocorrelation samples. Simulation results show that the corresponding sub-optimum weights have little effect on performance. Finally, the estimator's performance is compared to alternative techniques in the literature.

Identifying microsatellite instability (MSI) by partitioning DNA into multiple small pools containing only single genome amounts of DNA results in trapping both progenitor and low-frequency mutant alleles into pools where they can be identified and counted following PCR. Statistical approaches determining both the frequencies and the significant differences between frequencies of these Poisson-distributed alleles are presented. Results indicate a level of sensitivity and quantification not possible by standard PCR methods. Using material from colon cancer patients with high levels of MSI in their tumors, we also present the molecular and robotic methods for carrying out such studies. Validation experiments indicated mutants detectable at frequencies >0.03 above background. Frequencies obtained in tumor tissue (>0.25) met the expectations of the approach. Significant levels of MSI were detected in the constitutive tissue of the patient carrying a germ-line mutation for mismatch repair, suggesting both mechanistic and clinical applications of the procedure.