Spurious Peaks Research Articles

Effect of Bulk Acoustic Wave in Piezo Driven Nanomechanical Motion

Piezo actuation of mechanical resonators is widely adapted because of its simplicity and versatility. Piezo-driven atomic force microscopy cantilevers in air or liquid have a substantial drawback in that they produce spurious resonances that conceal the cantilever resonance peak. Bulk acoustic wave propagation via the piezo-shaker and device substrate causes these undesired peaks. Such restrictions of piezo actuation are rarely reported in nanomechanical resonant sensing. Because most NEMS (nanoelectromechanical systems) experiments are carried out at low pressure to achieve a higher quality factor ) and hence increased sensitivity, spurious resonances are frequently overlooked due to their insignificance. However, this piezo-driven issue may affect NEMS responses at higher pressures (lower Q) and must be addressed carefully. This study reveals spurious resonances from high vacuum to the atmosphere while investigating piezo-driven nanoscale doubly clamped beam responses. At all pressures, spurious peaks with a characteristic frequency span independent of air damping exist, and at higher pressures, they squeeze the mechanical peak. Such squeezing provides a larger derived from the driven phase responses by order of magnitude than the mechanical computed from the measured thermal noise spectra. Interestingly, the characteristic frequency span, not air damping, is revealed to dominate driven .

Analysis of Spurious Peaks Due to Ohmic Losses in Solidly Mounted Resonators by Combined Bvd-Mason Modelling

Analysis of Spurious Peaks Due to Ohmic Losses in Solidly Mounted Resonators by Combined Bvd-Mason Modelling

MmWave V2V Localization in MU-MIMO Hybrid Beamforming

Recent trends for vehicular localization in millimetre-wave (mmWave) channels include employing a combination of parameters such as angle of arrival (AOA), angle of departure (AOD), and time of arrival (TOA) of the transmitted/received signals. These parameters are challenging to estimate, which along with the scattering and random nature of mmWave channels, and vehicle mobility lead to errors in localization. To circumvent these challenges, this paper proposes mmWave vehicular localization employing difference of arrival for time and frequency, with multiuser (MU) multiple-input-multiple-output (MIMO) hybrid beamforming; rather than relying on AOD/AOA/TOA estimates. The vehicular localization can exploit the number of vehicles present, as an increase in the number of vehicles reduces the Cramér-Rao bound (CRB) of error estimation. At 10 dB signal-to-noise ratio (SNR) both spatial multiplexing and beamforming result in comparable localization errors. At lower SNR values, spatial multiplexing leads to larger errors compared to beamforming due to formation of spurious peaks in the cross ambiguity function. Accuracy of the estimated parameters is improved by employing an extended Kalman filter leading to a root mean square (RMS) localization error of approximately 6.3 meters.

Frequency-domain analysis of dual-loop optoelectronic oscillators.

Optoelectronic oscillators (OEOs) are hybrid systems consisting of optical and radio-frequency (RF) parts that are used to produce ultralow phase noise RF oscillations. Dual-loop OEOs can overcome some problems incorporated with single-loop OEOs such as the mode-hopping phenomenon and the large spurious peaks in the phase noise. Therefore, they are usually considered the practical implementation of many OEOs. Here, a frequency-domain steady-state and phase noise analysis approach of these systems is presented, based on the conversion matrix approach. Compared with the existing time-domain analysis approaches, it requires much smaller run times. Compared with the other frequency-domain modeling approaches, such as the linear-time-invariant phase transmission models, it can take all noise-transferring phenomena between various sidebands and all amplitude-noise to phase-noise conversions and vice versa into account. Therefore, it can be regarded as a comprehensive analysis approach to dual-loop OEOs. The validity of the new approach is verified by comparing its results with those of the previously published formulations in the literature.

CryoSat-2 interferometric mode calibration and validation: A case study from the Austfonna ice cap, Svalbard

Satellite radar altimetry is widely used to measure glacier and ice sheet elevation changes, but can suffer from uncertainties related to geolocation and signal penetration. The unique capabilities of ESA's CryoSat-2 allow for accurate geolocation but impacts from signal penetration persist. This study uses surface elevations from Global Navigation Satellite System and airborne laser transects over the Austfonna ice cap, Svalbard, to measure the elevation bias of CryoSat-2 Point-of-closest-approach (POCA) and swath points, and to provide validation for dhdt estimates derived through the application of a least-squares plane-fit algorithm to these data. The mean elevation bias of swath points varies between 1 and 1.5m of penetration, which is close to observed winter snow depths. Histograms of POCA elevation bias for the applied leading-edge retracker peak near the surface, with a distribution skewed towards the sub-surface. At the onset of surface melt, surface scattering dominates backscatter, and penetration reduces. This results in spurious peaks in derived elevation and mass change time series. In spite of this seasonal variability in elevation bias, the validation dhdt dataset demonstrates that the CryoSat-2 dhdt estimates are robust on multi-year timescales. The transition from volume to surface scattering suggests the potential to estimate yearly snowpack thickness.

Accurate quantification of phosphorus intergranular segregation in iron by STEM-EDX

This study describes a method to quantify phosphorus grain boundary segregation by Energy Dispersive X-ray Spectroscopy in Scanning Transmission Electron Microscope (STEM-EDX). A “box-type method” is employed, removing the long-discussed problems of interaction volume and the beam broadening effect. The proposed methodology also introduces a novel way of subtracting the spectrum background to remove the influence of coherent Bremsstrahlung and spurious peaks. A Fe-P model alloy was used to compare the box method to the quantification results previously obtained by atom probe tomography on two high angle grain boundaries. The results are specifically reported in surface concentration (atom/nm2) to avoid additional hypotheses and allow the results between the two techniques to be directly compared. The measurements show that the box-type method can accurately measure phosphorus intergranular segregation in iron.

A simple and effective attachment to prevent dome diffraction reaching 2D or 1D detectors in x-ray diffractometers.

An attachment has been developed for x-ray diffractometer systems equipped with a domed stage when using a 2D or 1D detector. It consists of a single screen in front of the detector positioned such that it blocks diffraction from the dome. This results in measured data free of disturbing spurious peaks and background, thereby greatly facilitating further data analysis. Its working principle is universally applicable and allows for all specimen orientation movements needed for x-ray diffraction measurements, including texture, stress, and mapping.

High-fidelity approximation of grid- and shell-based sampling schemes from undersampled DSI using compressed sensing: Post mortem validation

While many useful microstructural indices, as well as orientation distribution functions, can be obtained from multi-shell dMRI data, there is growing interest in exploring the richer set of microstructural features that can be extracted from the full ensemble average propagator (EAP). The EAP can be readily computed from diffusion spectrum imaging (DSI) data, at the cost of a very lengthy acquisition. Compressed sensing (CS) has been used to make DSI more practical by reducing its acquisition time. CS applied to DSI (CS-DSI) attempts to reconstruct the EAP from significantly undersampled q-space data. We present a post mortem validation study where we evaluate the ability of CS-DSI to approximate not only fully sampled DSI but also multi-shell acquisitions with high fidelity. Human brain samples are imaged with high-resolution DSI at 9.4T and with polarization-sensitive optical coherence tomography (PSOCT). The latter provides direct measurements of axonal orientations at microscopic resolutions, allowing us to evaluate the mesoscopic orientation estimates obtained from diffusion MRI, in terms of their angular error and the presence of spurious peaks. We test two fast, dictionary-based, L2-regularized algorithms for CS-DSI reconstruction. We find that, for a CS acceleration factor of R=3, i.e., an acquisition with 171 gradient directions, one of these methods is able to achieve both low angular error and low number of spurious peaks. With a scan length similar to that of high angular resolution multi-shell acquisition schemes, this CS-DSI approach is able to approximate both fully sampled DSI and multi-shell data with high accuracy. Thus it is suitable for orientation reconstruction and microstructural modeling techniques that require either grid- or shell-based acquisitions. We find that the signal-to-noise ratio (SNR) of the training data used to construct the dictionary can have an impact on the accuracy of CS-DSI, but that there is substantial robustness to loss of SNR in the test data. Finally, we show that, as the CS acceleration factor increases beyond R=3, the accuracy of these reconstruction methods degrade, either in terms of the angular error, or in terms of the number of spurious peaks. Our results provide useful benchmarks for the future development of even more efficient q-space acceleration techniques.

Self-calibrating the look-elsewhere effect: fast evaluation of the statistical significance using peak heights

ABSTRACT In experiments where one searches a large parameter space for an anomaly, one often finds many spurious noise-induced peaks in the likelihood. This is known as the look-elsewhere effect, and must be corrected for when performing statistical analysis. This paper introduces a method to calibrate the false alarm probability (FAP), or p-value, for a given dataset by considering the heights of the highest peaks in the likelihood. Specifically, we derive an equation relating the global p-value to the rank and height of local maxima. In the simplest form of self-calibration, the look-elsewhere-corrected $\chi^2$ of a physical peak is approximated by the $\chi^2$ of the peak minus the $\chi^2$ of the highest noise-induced peak, with accuracy improved by considering lower peaks. In contrast to alternative methods, this approach has negligible computational cost as peaks in the likelihood are a byproduct of every peak-search analysis. We apply to examples from astronomy, including planet detection, periodograms, and cosmology.

Frequency domain analysis of optoelectronic oscillators utilizing optical and RF resonators with arbitrary transfer functions

Optoelectronic oscillators (OEOs) have attracted much attention for producing ultra-low phase-noise microwave/millimeter-wave oscillations. Traditional delay-based OEOs usually suffer from strong spurious peaks in their phase noise power spectral densities and possible mode-hopping phenomena. Some methods have been proposed in the literature such as using multi-loop architectures or injection locking to other OEOs or radio frequency (RF) oscillators to reduce these spurious peaks. In other approaches, optical filters/resonators other than optical fibers have been proposed to reduce or suppress these peaks and prevent the mode-hopping phenomenon, such as whispering gallery mode resonators (WGMRs), fiber Bragg gratings, and other forms of microwave photonic filters. Usually, approximate single-purpose approaches have been presented to analyze OEOs utilizing such resonators. Here a general framework for analyzing the performance of OEOs implementing RF and optical filters/resonators with arbitrary linear transfer functions is presented. Consequently, it can consider, for example, the most general dispersion models of the fibers as well as any OEO architecture using a combination of different optical resonators. It can also consider the noise transfer between any sidebands of the RF or optical signals and any kind of amplitude noise to phase noise transfers and vice versa. The non-idealities of the electro-optic modulators such as the chirping and finite extinction ratios can also be taken into account. The validity of the new approach is verified by comparing its results with those previously published in the literature. In particular, the case of a WGMR plus delay line OEO is considered for comparisons.

DOA Estimation Based on Weighted l1-norm Sparse Representation for Low SNR Scenarios.

In this paper, a weighted l1-norm is proposed in a l1-norm-based singular value decomposition (L1-SVD) algorithm, which can suppress spurious peaks and improve accuracy of direction of arrival (DOA) estimation for the low signal-to-noise (SNR) scenarios. The weighted matrix is determined by optimizing the orthogonality of subspace, and the weighted l1-norm is used as the minimum objective function to increase the signal sparsity. Thereby, the weighted matrix makes the l1-norm approximate the original l0-norm. Simulated results of orthogonal frequency division multiplexing (OFDM) signal demonstrate that the proposed algorithm has s narrower main lobe and lower side lobe with the characteristics of fewer snapshots and low sensitivity of misestimated signals, which can improve the resolution and accuracy of DOA estimation. Specifically, the proposed method exhibits a better performance than other works for the low SNR scenarios. Outdoor experimental results of OFDM signals show that the proposed algorithm is superior to other methods with a narrower main lobe and lower side lobe, which can be used for DOA estimation of UAV and pseudo base station.

Fiber orientation distribution from diffusion MRI: Effects of inaccurate response function calibration.

ABSTRACTBackground and PurposeDiffusion MRI of the brain enables to quantify white matter fiber orientations noninvasively. Several approaches have been proposed to estimate such characteristics from diffusion MRI data with spherical deconvolution being one of the most widely used methods. Spherical deconvolution requires to define––or derive from the data––a response function, which is used to compute the fiber orientation distribution (FOD). Different characteristics of the response function are expected to affect the FOD computation and the subsequent fiber tracking.MethodsIn this work, we explored the effects of inaccuracies in the shape factors of the response function on the FOD characteristics.ResultsWith simulations, we show that the apparent fiber density could be doubled in the presence of underestimated shape factors in the response functions, whereas the overestimation of the shape factor will cause more spurious peaks in the FOD, especially when the signal‐to‐noise ratio is below 15. Moreover, crossing fiber populations with a separation angle smaller than 60° were more sensitive to inaccuracies in the response function than fiber populations with more orthogonal separation angles. Results with in vivo data demonstrate angular deviations in the FODs and spurious peaks as a result of modified shape factors of the response function, while the reconstruction of the main parts of fiber bundles is well preserved.ConclusionsThis work sheds light on how specific aspects of the response function shape can affect the estimated FODs, and highlights the importance of a proper calibration/definition of the response function.

Laplacian-Level Quantum Hydrodynamic Theory for Plasmonics

An accurate description of the optical response of subwavelength metallic particles and nanogap structures is a key problem of plasmonics. Quantum hydrodynamic theory (QHT) has emerged as a powerful method to calculate the optical response of metallic nanoparticles (NPs) since it takes into account nonlocality and spill-out effects. Nevertheless, the absorption spectra of metallic NPs obtained with conventional QHT, i.e., incorporating Thomas-Fermi (TF) and von Weizsäcker (vW) kinetic energy (KE) contributions, can be affected by several spurious resonances at energies higher than the main localized surface plasmon (LSP). These peaks are not present in reference time-dependent density-functional-theory spectra, where, instead, only a broad shoulder exists. Moreover, we show here that these peaks incorrectly reduce the LSP peak intensity and have a strong dependence on the simulation domain size so that a proper calculation of QHT absorption spectra can be problematic. In this article, we introduce a more general QHT method accounting for KE contributions depending on the Laplacian of the electronic density (q), thus, beyond the gradient-only dependence of the TFvW functional. We show that employing a KE functional with a term proportional to q2 results in an absorption spectrum free of spurious peaks, with LSP resonance of correct intensity and numerically stable Bennett state. Finally, we present a novel Laplacian-level KE functional that is very accurate for the description of the optical properties of NPs of different sizes as well as for dimers. Thus, the Laplacian-level QHT represents a novel, efficient, and accurate platform to study plasmonic systems.4 MoreReceived 5 June 2020Revised 21 October 2020Accepted 24 December 2020DOI:https://doi.org/10.1103/PhysRevX.11.011049Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasDensity functional theoryPlasmonicsPlasmonsSurface plasmonsPhysical SystemsNanoparticlesTechniquesClassical electromagnetismDensity functional approximationsDensity functional calculationsFree-electron modelGGACondensed Matter, Materials & Applied PhysicsAtomic, Molecular & Optical

Determination of anisotropic intensive piezoelectric loss in polycrystalline ceramics

This paper presents an advanced characterization method to obtain anisotropic intensive piezoelectric loss for k31, k33 and k15 mode. By redefining half-power bandwidth, new procedure is suggested with effective k31-k15 geometry using crystallographic approach. Without using low capacitance k33 bar structure, the relative error is not worth consideration. Difficulty of shear mode analysis due to spurious peaks are also resolved with the angled polarization. 1% Nb-doped Pb(Zr0.56Ti0.44)O3 are prepared and analyzed. The shear vibration could be analyzed for A- and B-type resonance where the polarization is angled by 60 and 75° from the applied electric field, respectively. Using suggested method, piezoelectric losses for k31, k33 and k15 vibration mode are obtained as 2.0, 1.3 and 2.2%, respectively. The piezoelectric loss anisotropy could successfully be obtained.

High Angular Resolution for 77GHz FMCW Radar via a Sparse Weighted Quadratic Minimization

Combining the covariance matching criterion with sparse representation, much effort was devoted to improve the angular resolution. However, the requirement of hyperparameters or the high sidelobe makes them unsatisfactory in practical radar applications. In this paper, we propose a Sparse Capon-like Weighted Quadratic Estimator (SCWQE) with hyperparameter-free and apply it to a 77GHz Frequency Modulated Continuous Wave (FMCW) radar for the high-resolution Direction Of Arrival (DOA) estimation. Under the assumption of the uncorrelated sources, SCWQE is formulated by converting the covariance matching criterion to a quadratic function with respect to the unknown power spectrum, in which a symmetrical and non-diagonal matrix called as the Capon-like spectrum is embedded into the quadratic form. The consequent multiple weighting operation would further promote the sparsity because multiple and different weighting values are exerted on each element of the spatial power spectrum. This is fundamentally different from the traditional weighted approach that employs the diagonal weighting matrix and assigns a single weighting value to each element. Therefore, SCWQE could further eliminate the spurious peaks and sharpen the desired peaks. Numerical examples including simulations and actual data collected from a 77GHz FMCW radar sensor demonstrate that, the proposed SCWQE algorithm produces better accuracy of DOA estimation and higher angular resolution compared to some typical sparse recovery methods.

Refocusing of Ground Moving Targets with Doppler Ambiguity Using Keystone Transform and Modified Second-Order Keystone Transform for Synthetic Aperture Radar

Ground moving targets will typically be defocused because of the range migration (RM) and Doppler frequency migration (DFM) caused by the unknown relative motions between the platform of synthetic aperture radar (SAR) and the ground moving targets. The received signal of the ground moving target easily exhibits the Doppler ambiguity, and the Doppler ambiguity leads to the refocusing difficulty of ground moving targets. To address these problems, a SAR refocusing method of ground moving targets with Doppler ambiguity based on modified second-order keystone transform (MSOKT) and keystone transform (KT) is presented in this paper. Firstly, the second-order phase is separated by the time reversing process. Secondly, MSOKT is performed to compensate the range curvature migration and DFM, and then the coefficient of the second-order phase is estimated. Finally, a well-refocused result of the moving target is achieved after KT and the estimated Doppler ambiguity number are used to eliminate residual range walk migration. The proposed method can accurately remove RM and DFM and effectively focus the moving targets without residual correction errors. Moreover, the effects of Doppler ambiguity (including Doppler center blur and spectrum split) and blind speed sidelobe are further avoided. On the basis of the analysis of cross-term for the multiple target case, the identification strategy of spurious peak of cross-term is proposed. Additionally, the developed method can be sped up by nonuniform fast Fourier transform without the interpolation operation. The effectiveness of the proposed method is verified by both airborne and spaceborne real data processing results.

Assessment of Rotational Accelerometer Mountings for Rotational Frequency Response Function Measurement

Kistler Kshear 8840 piezoelectric rotational accelerometer is designed with an M5 bolt type of mounting and a 5mm diameter of through or pre-tapped hole is required in a structure under test. However, the mounting is not applicable for some cases, and other types of mounting need to be used. This work investigates the applicability and accuracy of five different configurations of mounting for Kistler Ksher 8840 piezoelectric rotational accelerometer for the measurement of the rotational FRFs of a steel plate. The mounting configurations consist of 3D printed PLA and ABS mounting, bee’s wax, steel, and bolt mounting. The rotational FRF of the steel plate was measured by using impact testing method. The applicability and accuracy of the mountings were evaluated by comparing the measured FRFs with the finite element (FE) FRFs counterparts. The comparisons of the results revealed that using the bee’s wax mounting had produced the most accurate and least noisy FRF. It was also found that using the 3D printed PLA mounting had contributed to the highest contamination of noise and several spurious peaks in the measured FRFs. These findings provide useful information for selecting the appropriate types and designs of mounting for the Kistler 8840 rotational accelerometer.

Improvement in Signal-to-Noise Ratio of Liquid-State NMR Spectroscopy via a Deep Neural Network DN-Unet.

Nuclear magnetic resonance (NMR) is one of the most powerful analytical tools and is extensively applied in many fields. However, compared to other spectroscopic techniques, NMR has lower sensitivity, impeding its wider applications. Using data postprocessing techniques to increase the NMR spectral signal-to-noise ratio (SNR) is a relatively simple and cost-effective method. In this work, a deep neural network, termed as DN-Unet, is devised to suppress noise in liquid-state NMR spectra to enhance SNR. It combines structures of encoder-decoder and convolutional neural network. Different from traditional deep learning training strategy, M-to-S strategy is developed to enhance DN-Unet capability that multiple noisy spectra (inputs) correspond to a same single noiseless spectrum (label) in the training stage. The trained 1D model can be used for denoising not only 1D but also high dimension spectra, further improving DN-Unet's performance. 1D, 2D, and 3D NMR spectra were utilized to evaluate DN-Unet performance. The results suggest that DN-Unet provides larger than 200-fold increase in SNR with weak peaks hidden in noise perfectly recovered and spurious peaks suppressed well. Since DN-Unet developed here to increase SNR is based on data postprocessing, it is universal for a variety of samples and NMR platforms. The great SNR enhancement and extreme excellence in differentiating signal and noise would greatly promote various liquid-state NMR applications.

MSIWarp: A General Approach to Mass Alignment in Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) is a technique that provides comprehensive molecular information with high spatial resolution from tissue. Today, there is a strong push toward sharing data sets through public repositories in many research fields where MSI is commonly applied; yet, there is no standardized protocol for analyzing these data sets in a reproducible manner. Shifts in the mass-to-charge ratio (m/z) of molecular peaks present a major obstacle that can make it impossible to distinguish one compound from another. Here, we present a label-free m/z alignment approach that is compatible with multiple instrument types and makes no assumptions on the sample’s molecular composition. Our approach, MSIWarp (https://github.com/horvatovichlab/MSIWarp), finds an m/z recalibration function by maximizing a similarity score that considers both the intensity and m/z position of peaks matched between two spectra. MSIWarp requires only centroid spectra to find the recalibration function and is thereby readily applicable to almost any MSI data set. To deal with particularly misaligned or peak-sparse spectra, we provide an option to detect and exclude spurious peak matches with a tailored random sample consensus (RANSAC) procedure. We evaluate our approach with four publicly available data sets from both time-of-flight (TOF) and Orbitrap instruments and demonstrate up to 88% improvement in m/z alignment.

Is So Called "Split Alpha" in EEG Spectral Analysis a Result of Methodological and Interpretation Errors?

This paper attempts to explain some methodological issues regarding EEG signal analysis which might lead to misinterpretation and therefore to unsubstantiated conclusions. The so called “split-alpha,” a “new phenomenon” in EEG spectral analysis described lately in few papers is such a case. We have shown that spectrum feature presented as a “split alpha” can be the result of applying improper means of analysis of the spectrum of the EEG signal that did not take into account the significant properties of the applied Fast Fourier Transform (FFT) method. Analysis of the shortcomings of the FFT method applied to EEG signal such as limited duration of analyzed signal, dependence of frequency resolution on time window duration, influence of window duration and shape, overlapping and spectral leakage was performed. Our analyses of EEG data as well as simulations indicate that double alpha spectra called as “split alpha” can appear, as spurious peaks, for short signal window when the EEG signal being studied shows multiple frequencies and frequency bands. These peaks have no relation to any frequencies of the signal and are an effect of spectrum leakage. Our paper is intended to explain the reasons underlying a spectrum pattern called as a “split alpha” and give some practical indications for using spectral analysis of EEG signal that might be useful for readers and allow to avoid EEG spectrum misinterpretation in further studies and publications as well as in clinical practice.