Signal-to-noise Ratio Variation Research Articles

Sampling pattern discrepancy in the application of compressed sensing hyperpolarized xenon-129 lung MRI.

Although hyperpolarized (HP) 129Xe ventilation MRI can be carried out within a breath hold, it is still challenging for many sick patients. Compressed sensing (CS) is a viable alternative to accelerate this approach. However, undersampled images with identical sampling ratios differ from one another. Twenty subjects (n = 10 healthy and n = 10 patients with asthma) were scanned using a GE MR750 3 T scanner, acquiring fully sampled 2D multi-slice HP 129Xe lung ventilation images (10 s breath hold, 128 × 80 (FE × PE-frequency encoding × phase encoding) and 16 slices). Using fully sampled data, 500 variable-density Cartesian random undersampling patterns were generated, each at eight different sampling ratios from 10% to 80%. The parallel imaging and compressed sensing (PICS) command from BART was employed to reconstruct undersampled data. The signal to noise ratio (SNR), structural similarity index measurement (SSIM) and sidelobe to peak ratio of each were subsequently compared. There was a high degree of variation in both SNR and SSIM results from each of the 500 masks of each sampling rate. As the undersampling increases, there is more variation in the quantifying metrics, for both healthy and asthmatic individuals. Our study shows that random undersampling poses a significant challenge when applied at sampling ratios less than 60%, despite fulfilling CS's incoherency criteria. Such low sampling ratios will result in a large variety of undersampling patterns. Therefore, skipped segments of k-space cannot be allowed to happen randomly at low sampling rates. By optimizing the sampling pattern, CS will reach its full potential and be able to be applied to a highly undersampled 129Xe lung dataset.

Joint optimization of sampling point and sensing threshold for spectrum sensing

AbstractWith the continuous evolution and in‐depth integration between wireless communication and emerging technology such as internet of things (IoT), artificial intelligence (AI) etc., wireless terminals are growing exponentially, thus bringing great challenges to available spectrum resources. The contradiction between unlimited frequency needs and limited spectrum resources has become a bottleneck restricting the development of wireless communication technology. As an efficient way to improve spectrum efficiency, cognitive radio (CR) continues to be the focus of wireless communication within decades. To conduct CR, the main procedure is the discovery of available spectral holes by periodically monitoring the target authorized band, namely spectrum sensing (SS). Energy detector (ED) is widely accepted for SS due to its low complexity and high convenience. The essence of traditional ED based SS schemes consist in the adaptive variation of sensing threshold/sampling point with environmental signal‐to‐noise ratio (SNR) at the receiver of CR terminal, namely adaptive sensing threshold/sampling point based SS. However, the performance of both adaptive sensing threshold and adaptive sampling point based SS schemes are always at the expense of computation complexity due to the excessive sampling point. In addition, these two schemes are both about the optimization issue of a single variable under constraints. Actually, both detection probability and false alarm probability of ED are a two‐dimensional function of sensing threshold and sampling point for a given SNR. The optimal solution of sensing performance can not be obtained by optimizing sensing threshold or sampling point alone. Motivated by these, the joint optimization of sampling point and sensing threshold is considered for SS in this paper, where sampling point and sensing threshold are jointly adaptive with the variation of environmental SNR. In addition, Q‐learning is considered in this paper to obtain the sub‐optimal solution due to the non‐convexity of the considered optimization problem. Finally, the simulation experiments are made and the results validate the effectiveness of the proposed scheme.

Investigation of noise suppression in experimental multi-cell battery string voltage applying various mother wavelets and decomposition levels in discrete wavelet transform for precise state-of-charge estimation

Lithium-ion battery state-of-charge (SOC) estimation algorithms developed employing electrical circuit models (ECMs) require original experimental discharging and charging voltage (EDCV) data for very high accuracy. However, if noisy EDCV signals are detected while sensing, inaccurate SOC estimation, which causes poor BMS performance, is unavoidable. Thus, this paper introduces a novel method for implementing noise suppression employing a discrete wavelet transform (DWT) multi-resolution analysis (MRA) based method. The approach extensively performs two comparison evaluations of denoising based on the information provided by the mother wavelet and the corresponding MRA level decomposition. The former approach is to obtain the level of decomposition with high execution of the denoising under a similar mother wavelet state. The second approach, on the other hand, compares noise suppression for various mother wavelets under identical conditions of the level decomposition with similar decomposition level conditions. Signal-to-noise ratio (SNR) is used to evaluate all comparative analyses. This method compares both hard- and soft-thresholding noise reduction methods in their entirety. From these findings, the study's analytical findings indicate a precise evaluation by demonstrating the variation in SNR with the corresponding mother wavelets and their decomposition levels. Thus, it is certain that the mother wavelet and the decomposition level are optimized for noise suppression for a battery string, series/parallel (2S3P) each of 2.2 Ah, with the EDCV signal incorporated with noise.

Spatiotemporal evaluation of Rayleigh surface wave estimated from roadside dark fiber DAS array and traffic noise

Seismic imaging and monitoring of the near-surface structure are crucial for the sustainable development of urban areas. However, standard seismic surveys based on cabled or autonomous geophone arrays are expensive and hard to adapt to noisy metropolitan environments. Distributed acoustic sensing (DAS) with pre-existing telecom fiber optic cables, together with seismic ambient noise interferometry, have the potential to fulfill this gap. However, a detailed noise wavefield characterization is needed before retrievingcoherent waves from chaotic noise sources. We analyze local seismic ambient noise by tracking five-month changes in signal-to-noise ratio (SNR) of Rayleigh surface wave estimated from traffic noise recorded by DAS along the straight university campus busy road. We apply the seismic interferometry method to the 800 m long part of the Penn State Fiber-Optic For Environment Sensing (FORESEE) array. We evaluate the 160 virtual shot gathers (VSGs) by determining the SNR using the slant-stack technique. We observe strong SNR variations in time and space. We notice higher SNR for virtual source points close to road obstacles. The spatial noise distribution confirms that noise energy focuses mainly on bumps and utility holes. We also see the destructive impact of precipitation, pedestrian traffic, and traffic along main intersections on VSGs. A similar processing workflow can be applied to various straight roadside fiber optic arrays in metropolitan areas.

Performance analysis of cyclostationary spectrum sensing with dynamic thresholding using artificial neural network under varying signal to noise ratio and noise variance conditions

A key component of cognitive radio technology is spectrum sensing, which finds and accesses unused frequency bands to efficiently use the underutilized spectrum. A potential method for spectrum sensing called cyclostationary feature detection (CFD) uses the cyclostationary characteristics of signals to distinguish between the signal and noise. Artificial neural networks (ANNs) have been suggested in recent years as a method for CFD based spectrum detection, which increases detection accuracy and decreases complexity. However, the variable signal to noise ratio (SNR) and noise variance have an impact on the effectiveness of ANNs for CFD-based spectrum sensing. The effectiveness of ANNs for CFD based spectrum sensing under different SNR and noise variance conditions is evaluated in this work for the determination of threshold value in a dynamic way. We look into how SNR and noise variance affect the precision of probability of detection (Pd) and system complexity. Out analysis show how well ANNs work for CFD based spectrum detection with dynamic threshold value in the presence of changing SNR and noise variation. The findings demonstrate that ANNs may still obtain high Pd values with low SNR and large noise variance while maintaining a modest level of system complexity. According to our research, for a variety of SNR and noise variance situations, ANNs may be a viable option for CFD based spectrum detection in cognitive radio (CR) networks. The proposed approach can significantly improve the detection accuracy and reduce the complexity of the system, thereby enhancing the overall performance of cognitive radio networks. Based on the proposed work, it is determined that MPSK modulation function well with additive white Gaussian noise (AWGN), Rayleigh, and Rician channels up to a lower SNR value of – 30 dB and MQAM supports a lower SNR value of up to – 20 dB.

Variations in signal-to-noise characteristics of tissue-equivalent attenuators for mammographic automatic exposure control system performance evaluation.

This work investigates the impact of tissue-equivalent attenuator choice on measured signal-to-noise ratio (SNR) for automatic exposure control (AEC) performance evaluation in digital mammography. It also investigates how the SNR changes for each material when used to evaluate AEC performance across different mammography systems. AEC performance was evaluated for four mammography systems using seven attenuator sets at two thicknesses (4 and 8 cm). All systems were evaluated in 2D imaging mode, and one system was evaluated in digital breast tomosynthesis (DBT) mode. The methodology followed the 2018 ACR digital mammography quality control (DMQC) manual. Each system-attenuator-thickness combination was evaluated using For Processing images in ImageJ with standard ROI size and location. The closest annual physicist testing results were used to explore the impact of varying measured AEC performance on image quality. The measured SNR varied by 44%-54% within each system across all attenuators at 4 cm thickness in 2D mode. The variation appeared to be largely due to changes in measured noise, with variations of 46%-67% within each system across all attenuators at 4 cm thickness in 2D mode. Two systems had failing SNR levels for two of the materials using the minimum SNR criterion specified in the ACR DMQC manual. Similar trends were seen in DBT mode and at 8 cm thickness. Within each material, there was 115%-131% variation at 4 cm and 82%-114% variation at 8 cm in the measured SNR across the four imaging systems. Variation in SNR did not correlate with system operating level based on visual image quality and average glandular dose (AGD). Choice of tissue-equivalent attenuator for AEC performance evaluation affects measured SNR values. Depending on the material, the difference may be enough to result in failure following the longitudinal and absolute thresholds specified in the ACR DMQC manual.

On the impact of launch power optimization and transceiver noise on the performance of ultra-wideband transmission systems [Invited

Ultra-wideband transmission is a promising, cost-effective solution to meet the increasing demand for data traffic in optical fiber systems. However, system performance and quality of transmission (QoT) are limited by fiber nonlinearity, in particular, interchannel stimulated Raman scattering (ISRS), leading to a power transfer from short to long wavelengths. As the result, per-channel launch power optimization is required to maximize the system throughput. In this paper, we investigate how the transceiver noise and launch power optimization impact the total throughput and the per-channel QoT for transmission bandwidths of up to 20 THz, using the S-, C-, and L-bands. To measure the gains through the optimum launch power profile, a spectrally uniform launch power is used as a baseline. Through experimental analysis and theoretical modeling, the main limitations constraining the achieved data throughput of 178 Tb/s over a continuous bandwidth of 16.83 THz and 40 km were investigated. The impact of the launch power optimization and the transceiver constrained signal-to-noise ratio (SNR) are analyzed and compared, and the approaches to overcome data throughput limitations are considered. An extensive theoretical investigation for different system configurations is described, demonstrating the trade-off between ISRS impact and transceiver noise. The former degrades the QoT and increases the gains in performance obtained by optimizing the launch power, while the latter reduces these gains and the SNR variation across the bandwidth, with a major impact over short distances.

Onset Conditions and Features of Equatorial F Region Irregularities: New Insight From Collocated Digisonde and Radar Observations From Gadanki

AbstractIn this paper, we study the onset conditions and features of equatorial F region irregularities linked with equatorial plasma bubbles (EPBs) using observations made simultaneously by using a DPS‐4D digisonde and the Gadanki Ionospheric Radar Interferometer (GIRI), both collocated at Gadanki. Importantly, we have employed specific analysis tools on DPS‐4D observations, providing range‐time displays of radio frequency and signal‐to‐noise ratio (SNR) of the reflected/backscattered echoes and the angle‐of‐arrival of the echoes, to characterize the echoes and to study the ambient and disturbed states of the ionosphere. Observations clearly show noticeably different background conditions, in terms of the height of the F layer base, electron density gradient and vertical drift, for the freshly generated and drifting EPBs. The zonal wavelengths of the pre‐sunset large‐scale wave structures (LSWS) observed using the DPS‐4D, however, are found to show a close connection with EPB spacings for freshly generated and drifting EPBs, consistent with earlier findings. The satellite traces were observed just prior to the equatorial spread F (ESF) and were found to be associated with the bottomside upwellings. Comparison of the range‐time displays of radio frequency and SNR of the DPS‐4D observations and the height‐time variations of SNR of the GIRI observations demonstrates that the relatively weak echoes in the DPS‐4D observations, which represent the ESF echoes, correlate fairly well temporally with the plume structures observed by the GIRI. The GIRI observations also reproduce the bottomside upwellings observed by the DPS‐4D. We propose that the bottomside upwellings are due to the growth of the LSWS and these bottomside upwellings are instrumental for the growth of the Rayleigh‐Taylor instability generating EPBs. Finally, the new aspects of the digisonde observations are discussed with regard to their usefulness in understanding and forecasting EPBs/ESF.

Variation of signal-to-noise ratio of vertical array with sound source depth under wind-generated noise background

Wind-generated noise is ubiquitous in ocean environments and highly influences the passive sonar performance. Since it originates from sources near the ocean surface, one of its physical features is that it largely represents only the intermediate- and high-order modes. The array-level signal-to-noise ratio (SNR), which includes the array-sampled sound intensity, background noise power, and array gain, is an essential quantity determining the sonar array performance. What is investigated in this work is how the array-level SNR of the vertical line array (VLA) varies with the source depth in downward-refracting shallow water, contributed by the modal structure of the surface noise. On the assumption that the modes are well sampled, it is theoretically demonstrated that the SNR varying with the source depth can be approximated as a linear combination of the lower-order mode-amplitude intensities varying with the water depth. Particularly, when the surface noise especially dominates and the water channel is highly downward refractive, this variation can be represented nearly only by the 1<sup>st</sup>-order mode-amplitude intensity varying with depth. The structure is meaningful in practice. It suggests the SNR will be inherently larger when the source is submerged than it is near the ocean surface, and will be maximized at a source depth slightly below the 1<sup>st</sup>-order mode peak across different source ranges. The above assertions are demonstrated in a typical downward-refracting shallow-water channel; the effects from the dominant degree of the surface noise, sound speed gradient in water column, and array aperture are investigated numerically. The obtained results are shown below. 1) Under certain circumstances, the variation of SNR with source depth is nearly irrelevant to the source range. 2) When the surface noise is more significant, the largest SNR in a certain source range will be more significantly larger than the SNR for the source near the surface, the corresponding source depth will be closer to that presenting the 1<sup>st</sup>-order mode’s peak, and the variation of SNR with source depth is increasingly irrelevant to the source range. 3) A stronger downward-refracting sound speed also enhances this SNR superiority and irrelevance to the source range, but causes the source depth presenting the largest SNR to be more deviated from the 1<sup>st</sup>-order mode’s peak. 4) Although the structure is unraveled on the assumption that the VLA spans the full water column, it can be seen when the VLA does not but covers the low-order modes' main part; when the array aperture is insufficiently large it will become approximately periodic in the source range, with the source depth presenting the largest SNR fluctuating lightly and nearly periodically around the 1<sup>st</sup>-order mode peak.

Teleseismic body waves extracted from ambient noise cross correlation between F-net and ChinArray phase II

The vertical-vertical noise cross-correlation functions (NCFs) between two seismic arrays, the Japan F-net and ChinArray phase II, are calculated using continuous recordings during 2013–2016. After array interferometry to obtain bin stacked NCFs, clear body waves are retrieved at different period bands. Teleseismic direct P waves for distance 15–40 degrees are observed between short period 3–10 ​s while core reflected PcP/ScS waves are more obvious for longer period 30–60s. The signal-to-noise-ratio (SNR) of the short period P waves reaches its highest point with bin widths around 20 ​km while SNRs of PcP and ScS increase slowly with bin width. All those body waves demonstrate clear directivity with strong signals traveling from the east. The time-lapse SNR variations for the PcP and ScS show correlation with the occurrence of major earthquakes, while the P-wave SNR demonstrates seasonal variations with additional contribution from major earthquakes. The present results suggest teleseismic body waves can be retrieved through bin stacking, though further processing is still necessary to obtain finer waveforms such as P wave triplications.

An ACO-based cross-layer routing algorithm in space-air-ground integrated networks

Space-air-ground integrated networks (SAGINs) are heterogeneous, self-organizing and time-varying wireless networks providing massive and global connectivity. These three characteristics of SAGINs bring great challenges for routing design. In this paper, the important parameters affecting performance of SAGINs are analyzed, based on which the heterogeneous network framework is described as a vector weighted topology. Instead of a scale, the weighted parameter of the topology is a vector with elements of signal-to-noise ratio (SNR), variation of SNR, end-to-end delay and queuing length. To meet the time-varying requirements, a Wiener predictor is adopted for obtaining the estimated channel information, the expectation of queuing delay is also acquired by modeling the process of packets waiting the transmitting buffer as a M/M/1 queuing system. Considering the Ant Colony Optimization (ACO) algorithm sharing the common decentralized feature with routing algorithm in SAGINs, a novel ACO-based cross-layer routing algorithm for SAGINs is proposed. The proposed algorithm takes the link quality and end-to-end packed delay in the physical layer as deciding factors in searching for optimal routing. Simulations performed in different scenarios show that this proposed algorithm demonstrates a higher packet delivery rate.

Calibration-Less Multi-Coil Compressed Sensing Magnetic Resonance Image Reconstruction Based on OSCAR Regularization.

Over the last decade, the combination of compressed sensing (CS) with acquisition over multiple receiver coils in magnetic resonance imaging (MRI) has allowed the emergence of faster scans while maintaining a good signal-to-noise ratio (SNR). Self-calibrating techniques, such as ESPiRIT, have become the standard approach to estimating the coil sensitivity maps prior to the reconstruction stage. In this work, we proceed differently and introduce a new calibration-less multi-coil CS reconstruction method. Calibration-less techniques no longer require the prior extraction of sensitivity maps to perform multi-coil image reconstruction but usually alternate estimation sensitivity map estimation and image reconstruction. Here, to get rid of the nonconvexity of the latter approach we reconstruct as many MR images as the number of coils. To compensate for the ill-posedness of this inverse problem, we leverage structured sparsity of the multi-coil images in a wavelet transform domain while adapting to variations in SNR across coils owing to the OSCAR (octagonal shrinkage and clustering algorithm for regression) regularization. Coil-specific complex-valued MR images are thus obtained by minimizing a convex but nonsmooth objective function using the proximal primal-dual Condat-Vù algorithm. Comparison and validation on retrospective Cartesian and non-Cartesian studies based on the Brain fastMRI data set demonstrate that the proposed reconstruction method outperforms the state-of-the-art (-ESPIRiT, calibration-less AC-LORAKS and CaLM methods) significantly on magnitude images for the T1 and FLAIR contrasts. Additionally, further validation operated on 8 to 20-fold prospectively accelerated high-resolution ex vivo human brain MRI data collected at 7 Tesla confirms the retrospective results. Overall, OSCAR-based regularization preserves phase information more accurately (both visually and quantitatively) compared to other approaches, an asset that can only be assessed on real prospective experiments.

Dynamic RCS Modelling of Launch Vehicle Prior to Launch for Estimation of Real-time SNR

The Radar Tracking System plays a crucial role in tracking launch vehicles by providing continuous trajectory information in order to assess their performance in real-time for range safety purpose. The target Radar Cross Section (RCS) determines radar tracking efficiency and highly depends on both the target characteristics and the radar parameters. A good amount of RCS is warranted for space-based applications for easy detection and continuous tracking by the radar. It is essential to model and simulate the RCS fluctuations of the launch vehicle prior to launch for estimating the real-time received Signal to Noise Ratio (SNR) variations for its dynamic trajectory. This modelling will aid in selecting the best radar configuration to obtain a good amount of SNR in real-time and it also provides guidance for the radar operation in tracking the target. This paper analysed the RCS fluctuations of the launch vehicle for its dynamic trajectory prior to launch by software simulation and also compute the expected real time launch vehicle SNR variations.

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer capsule network (TL-CapsNet), and classification time is reduced by introducing a new feature clustering approach in the TL-CapsNet. Firstly, the AMC2N executes blind equalization, sampling, and quantization in trilevel preprocessing. Blind equalization is executed using a binary constant modulus algorithm to avoid intersymbol interference. To extract features from the preprocessed signal and classify signals accurately, the AMC2N employs the TL-CapsNet, in which individual lanes are incorporated to process the real and imaginary parts of the signal. In addition, it is robust to SNR variations, that is, low and high SNR rates. The TL-CapsNet extracts features from the real and imaginary parts of the given signal, which are then clustered based on feature similarity. For feature extraction and clustering, the dynamic routing procedure of the TL-CapsNet is adopted. Finally, classification is performed in the SoftMax layer of the TL-CapsNet. This study proves that the AMC2N outperforms existing methods, particularly, convolutional neural network(CNN), Robust-CNN (R-CNN), curriculum learning(CL), and Local Binary Pattern (LBP), in terms of accuracy, precision, recall, F-score, and computation time. All metrics are validated in two scenarios, and the proposed method shows promising results in both.

Robust Digital Signal Recovery for LEO Satellite Communications Subject to High SNR Variation and Transmitter Memory Effects

This paper proposes a robust digital signal recovery (DSR) technique to tackle the high signal-to-noise ratio (SNR) variation and transmitter memory effects for broadband power efficient down-link in next-generation low Earth orbit (LEO) satellite constellations. The robustness against low SNR is achieved by concurrently integrating magnitude normalization and noise feature filtering using a filtering block built with one batch normalization (BN) layer and two bidirectional long short-term memory (BiLSTM) layers. Moreover, unlike existing deep neural network-based DSR techniques (DNN-DSR), which failed to effectively take into account the memory effects of radio-frequency power amplifiers (RF-PAs) in the model design, the proposed BiLSTM-DSR technique can extracts the sequential characteristics of the adjacent in-phase (I) and quadrature (Q) samples, and hence can obtain superior memory effects compensation compared with the DNN-DSR technique. Experimental validation results of the proposed BiLSTM-DSR with a 100 MHz bandwidth OFDM signal demonstrate an excellent performance of 11.83 dB and 9.4% improvement for adjacent channel power ratio (ACPR) and error vector magnitude (EVM), respectively. BiLSTM-DSR also outperforms the existing DNN-DSR technique in terms of the ACPR and EVM by 2.4 dB and 0.9%, which provides a promising solution for developing deep learning-assisted receivers for high-throughput LEO satellite networks.

Generation of tunable linearly chirped signals with long temporal duration in the photonic time-stretched coherent radar.

Linearly chirped signals with long temporal duration are generated by adding a dispersive loop in the photonic time-stretched coherent radar. The signal-to-noise ratio (SNR) variation of the optical pulse recirculation is analyzed and simulated to evaluate the multiple extension of temporal duration. The achievable temporal duration is confined by the dispersion and the amplified spontaneous emission noise accumulation. The experimental results verify the SNR variation of the optical pulse recirculation. Moreover, tunable linearly chirped signals with temporal duration of 100 ns and a bandwidth of 4 GHz at X or Ku band are generated. With the improvement of the time-bandwidth product, a drone can be successfully observed.

A new virtue of phantom MRI data: explaining variance in human participant data

Background: Magnetic resonance imaging (MRI) is an important yet complex data acquisition technology for studying the brain. MRI signals can be affected by many factors and many sources of variance are often simply attributed to “noise”. Unexplained variance in MRI data hinders the statistical power of MRI studies and affects their reproducibility. We hypothesized that it would be possible to use phantom data as a proxy of scanner characteristics with a simplistic model of seasonal variation to explain some variance in human MRI data. Methods: We used MRI data from human participants collected in several studies, as well as phantom data collected weekly for scanner quality assurance (QA) purposes. From phantom data we identified the variables most likely to explain variance in acquired data and assessed their statistical significance by using them to model signal-to-noise ratio (SNR), a fundamental MRI QA metric. We then included phantom data SNR in the models of morphometric measures obtained from human anatomical MRI data from the same scanner. Results: Phantom SNR and seasonal variation, after multiple comparisons correction, were statistically significant predictors of the volume of gray brain matter. However, a sweep over 16 other brain matter areas and types revealed no statistically significant predictors among phantom SNR or seasonal variables after multiple comparison correction. Conclusions: Seasonal variation and phantom SNR may be important factors to account for in MRI studies. Our results show weak support that seasonal variations are primarily caused by biological human factors instead of scanner performance variation. The phantom QA metric and scanning parameters are useful for more than just QA. Using QA metrics, scanning parameters, and seasonal variation data can help account for some variance in MRI studies, thus making them more powerful and reproducible.

A new virtue of phantom MRI data: explaining variance in human participant data.

Background: Magnetic resonance imaging (MRI) is an important yet complex data acquisition technology for studying the brain. MRI signals can be affected by many factors and many sources of variance are often simply attributed to "noise". Unexplained variance in MRI data hinders the statistical power of MRI studies and affects their reproducibility. We hypothesized that it would be possible to use phantom data as a proxy of scanner characteristics with a simplistic model of seasonal variation to explain some variance in human MRI data. Methods: We used MRI data from human participants collected in several studies, as well as phantom data collected weekly for scanner quality assurance (QA) purposes. From phantom data we identified the variables most likely to explain variance in acquired data and assessed their statistical significance by using them to model signal-to-noise ratio (SNR), a fundamental MRI QA metric. We then included phantom data SNR in the models of morphometric measures obtained from human anatomical MRI data from the same scanner. Results: Phantom SNR and seasonal variation, after multiple comparisons correction, were statistically significant predictors of the volume of gray brain matter. However, a sweep over 16 other brain matter areas and types revealed no statistically significant predictors among phantom SNR or seasonal variables after multiple comparison correction. Conclusions: Seasonal variation and phantom SNR may be important factors to account for in MRI studies. Our results show weak support that seasonal variations are primarily caused by biological human factors instead of scanner performance variation. The phantom QA metric and scanning parameters are useful for more than just QA. Using QA metrics, scanning parameters, and seasonal variation data can help account for some variance in MRI studies, thus making them more powerful and reproducible.

The very low-frequency transmitter radio wave anomalies related to the 2010 Ms 7.1 Yushu earthquake observed by the DEMETER satellite and the possible mechanism

Abstract. Earthquakes may disturb the lower ionosphere through various coupling mechanisms during the seismogenic and coseismic periods. The VLF (very low-frequency) signal radiated from ground-based transmitters will be affected when it penetrates the disturbed ionosphere above the epicenter area, and this anomaly can be recorded by low-Earth orbit satellites under certain conditions. In this paper, the temporal and spatial variation of the signal-to-noise ratio (SNR) of the VLF transmitter signal in the ionosphere over the epicenter of 2010 Yushu Ms 7.1 earthquake in China is analyzed using DEMETER (Detection of Electro-Magnetic Emission Transmitted from Earthquake Regions) satellite observation. The results show that SNR over the epicenter of the Yushu earthquake especially in the southwestern region decreased (or dropped) before the main shock, and a GPS–TEC (Global Positioning System; total electron content) anomaly accompanied, which implies that the decrease in SNR might be caused by the enhancement of TEC. A full-wave method is used to study the mechanism of the change in SNR before the earthquake. The simulated results show SNR does not always decrease before an earthquake. When the electron density in the lower ionosphere increases by 3 times, the electric field will decrease about 2 dB, indicating that the disturbed-electric-field decrease of 20 % compared with the original electric field and vice versa. It can be concluded that the variation of electron density before earthquakes may be one of the important factors influencing the variation of SNR.

Quantitative analysis of SNR in bilinear time frequency domain

Signal-to-noise ratio (SNR) is an essential concept or quantity on the result of a process or on the output of a filter, which helps us in designing, analyzing or evaluating a system. In this paper, we study SNR for bilinear time–frequency transform (TFT). Firstly, according to the definition of SNR in time domain, we define a proper form for SNR in time–frequency (T–F) plane for bilinear TFT; then, we extract SNR relation in terms of TFT kernel, signals power and noise power in time domain. The extracted relation of SNR for bilinear TFT that can be represented in terms of Wigner–Ville distribution (WVD) shows its dependence on the kernel used in the TFT. Finally, to illustrate the applicability of the proposed SNR, the relations of SNR for several distributions are extracted in the T–F domain, and the variation of SNR versus the noise variance is shown by curves. The results show that the WVD has higher SNR than the Rihaczek, Page, spectrogram and Levin respectively.