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Cognitive neuroimaging studies typically require fast whole brain image acquisition with maximal sensitivity to small BOLD signal changes. To increase the sensitivity, higher field strengths are often employed, since they provide an increased image signal-to-noise ratio (SNR). However, as image SNR increases, the relative contribution of physiological noise to the total time series noise will be greater compared to that from thermal noise. At 7T, we studied how the physiological noise contribution can be best reduced for EPI time series acquired at three different spatial resolutions (1.1mm×1.1mm×1.8mm, 2mm×2mm×2mm and 3mm×3mm×3mm). Applying optimal physiological noise correction methods improved temporal SNR (tSNR) and increased the numbers of significantly activated voxels in fMRI visual activation studies for all sets of acquisition parameters. The most dramatic results were achieved for the lowest spatial resolution, an acquisition parameter combination commonly used in cognitive neuroimaging which requires high functional sensitivity and temporal resolution (i.e. 3mm isotropic resolution and whole brain image repetition time of 2s). For this data, physiological noise models based on cardio-respiratory information improved tSNR by approximately 25% in the visual cortex and 35% sub-cortically. When the time series were additionally corrected for the residual effects of head motion after retrospective realignment, the tSNR was increased by around 58% in the visual cortex and 71% sub-cortically, exceeding tSNR ~140. In conclusion, optimal physiological noise correction at 7T increases tSNR significantly, resulting in the highest tSNR per unit time published so far. This tSNR improvement translates into a significant increase in BOLD sensitivity, facilitating the study of even subtle BOLD responses. ► Impact of physiological noise correction on tSNR versus SNR was characterized at 7T. ► tSNR was improved by 50 to 70% using physiological noise correction in task-free EPI. ► The reported results exceed values for tSNR per unit time published so far. ► tSNR improvements translated into more than 10% increase in BOLD activity in fMRI.

Temporal signal-to-noise ratio (tSNR) is a key metric for assessing the ability to detect brain activation in fMRI data. A recent study has shown substantial variation of tSNR between multiple runs of accelerated EPI acquisitions reconstructed with the GRAPPA method using protocols commonly used for fMRI experiments. Across-run changes in the location of high-tSNR regions could lead to misinterpretation of the observed brain activation patterns, reduced sensitivity of the fMRI studies, and biased results. We compared conventional EPI autocalibration (ACS) methods with the recently-introduced FLEET ACS method, measuring their tSNR variability, as well as spatial overlap and displacement of high-tSNR clusters across runs in datasets acquired from human subjects at 7T and 3T. FLEET ACS reconstructed data had higher tSNR levels, as previously reported, as well as better temporal consistency and larger overlap of the high-tSNR clusters across runs compared with reconstructions using conventional multi-shot (ms) EPI ACS data. tSNR variability across two different runs of the same protocol using ms-EPI ACS data was about two times larger than for the protocol using FLEET ACS for acceleration factors (R) 2 and 3, and one and half times larger for R=4. The level of across-run tSNR consistency for data reconstructed with FLEET ACS was similar to within-run tSNR consistency. The displacement of high-tSNR clusters across two runs (inter-cluster distance) decreased from ∼8mm in the time-series reconstructed using conventional ms-EPI ACS data to ∼4mm for images reconstructed using FLEET ACS. However, the performance gap between conventional ms-EPI ACS and FLEET ACS narrowed with increasing parallel imaging acceleration factor. Overall, the FLEET ACS method provides a simple solution to the problem of varying tSNR across runs, and therefore helps ensure that an assumption of fMRI analysis—that tSNR is largely consistent across runs—is met for accelerated acquisitions. [Display omitted] •Improved across-run tSNR consistency using FLEET ACS reconstruction vs. ms-EPI ACS.•High-tSNR cluster displacement decreased by factor of two by using FLEET ACS.•FLEET ACS reconstructed data increases sensitivity of BOLD fMRI measurements.

Echo-planar imaging (EPI) is a standard procedure in functional magnetic resonance imaging (fMRI) for measuring changes in the blood oxygen level-dependent (BOLD) signal associated with neuronal activity. The images obtained from fMRI with EPI, however, exhibit signal dropouts and geometric distortions. Parallel imaging (PI), due to its short readout, accelerates image acquisition and might reduce dephasing in phase-encoding direction. The concomitant loss of signal-to-noise ratio (SNR) might be compensated through single-shot multi-echo EPI (mEPI). We systematically compared the temporal SNR and BOLD sensitivity of single echoes (TE=15, 45, and 75ms) and contrast-optimized mEPI with and without PI and mEPI-based denoising. Audio-visual stimulation under natural viewing conditions activated distributed neural networks. Heterogeneous SNR, noise gain, and sensitivity maps emerged. In single echoes, SNR and BOLD sensitivity followed the predicted dependency on echo time (TE) and were reduced under PI. However, the combination of echoes with mEPI recovered the quality parameters and increased BOLD signal changes at circumscribed fronto-polar and deep brain structures. We suggest applying PI only in combination with mEPI to reduce imaging artifacts and conserve BOLD sensitivity. •Parallel imaging reduces SNR in fMRI globally with local improvements.•Multi-echo EPI recovers quality parameters and yields locally improved BOLD signal.•Combined multi-echo and parallel imaging EPI optimizes BOLD imaging.•Natural viewing activates distributed networks for BOLD sensitivity mapping.•Multi-echo EPI can be used for denoising.

Everyday environments often contain multiple concurrent sound sources that fluctuate over time. Normally hearing listeners can benefit from high signal-to-noise ratios (SNRs) in energetic dips of temporally fluctuating background sound, a phenomenon called dip-listening. Specialized mechanisms of dip-listening exist across the entire auditory pathway. Both the instantaneous fluctuating and the long-term overall SNR shape dip-listening. An unresolved issue regarding cortical mechanisms of dip-listening is how target perception remains invariant to overall SNR, specifically, across different tone levels with an ongoing fluctuating masker. Equivalent target detection over both positive and negative overall SNRs (SNR invariance) is reliably achieved in highly-trained listeners. Dip-listening is correlated with the ability to resolve temporal fine structure, which involves temporally-varying spike patterns. Thus the current work tests the hypothesis that at negative SNRs, neuronal readout mechanisms need to increasingly rely on decoding strategies based on temporal spike patterns, as opposed to spike count. Recordings from chronically implanted electrode arrays in core auditory cortex of trained and awake Mongolian gerbils that are engaged in a tone detection task in 10 Hz amplitude-modulated background sound reveal that rate-based decoding is not SNR-invariant, whereas temporal coding is informative at both negative and positive SNRs.

Speech perception often takes place in noisy environments, where multiple auditory signals compete with one another. The addition of visual cues such as talkers’ faces or lip movements to an auditory signal can help improve the intelligibility of speech in those suboptimal listening environments. This is referred to as audiovisual benefits. The current study aimed to delineate the signal-to-noise ratio (SNR) conditions under which visual presentations of the acoustic amplitude envelopes have their most significant impact on speech perception. Seventeen adults with normal hearing were recruited. Participants were presented with spoken sentences in babble noise either in auditory-only or auditory-visual conditions with various SNRs at −7, −5, −3, −1, and 1 dB. The visual stimulus applied in this study was a sphere that varied in size syncing with the amplitude envelope of the target speech signals. Participants were asked to transcribe the sentences they heard. Results showed that a significant improvement in accuracy in the auditory-visual condition versus the audio-only condition was obtained at the SNRs of −3 and −1 dB, but no improvement was observed in other SNRs. These results showed that dynamic temporal visual information can benefit speech perception in noise, and the optimal facilitative effects of visual amplitude envelope can be observed under an intermediate SNR range.

Under the condition of low signal-to-noise ratio, for the problem of insufficient speech feature extraction and speech enhancement effect of the traditional neural network, this paper is based on empirical mode decomposition (EMD), temporal convolutional network (TCN), and gated convolution recurrent neural network (GCRN), while combining with feature fusion module (FFM), the adaptive mean median-empirical mode decomposition-multilayer gated feature fusion module convolutional recurrent neural networks (ME-MGFCRNs) for speech enhancement modeling. The network model uses a split-frequency learning strategy to learn low-frequency features and high-frequency features, i.e., the TCN and MGFCRN networks are used to obtain low-frequency and high-frequency features, and FFM processes the two sets of features to achieve speech enhancement in the form of feature mapping. The model proposed in this paper performs ablation and comparison experiments on the dataset to evaluate the enhancement effect of speech using PESQ, FwSegSNR, and STOI metrics. The research shows that under different noise environments and SNR conditions, the model proposed in this paper improves compared with other baseline models, especially under the low SNR condition of − 5 dB, FwSegSNR and PESQ improve by more than 0.86 dB and 0.02 compared with other baseline models.

In addition to traditional tide gauges, the ground-based global navigation satellite system reflectometry (GNSS-R) that utilizes signal-to-noise ratio data from a single GNSS receiver has become another promising alternative for sea level monitoring. However, its application is limited by retrieval precision, especially in large tidal variation environments. On the other hand, previous studies have focused on performance improvement by using post-processing strategies, which cannot support practical (near-) real-time applications. In this work, we present a method using a robust Kalman filter to provide near real-time sea level measurements based on cloud service, achieving both high precision and high temporal resolution. A coastal GNSS station BRST with large tidal variations was selected for experimental validation. First, 30 days of archived GNSS observations were used for performance assessment. It is observed that high-precision sea level retrievals with a 5-min sampling interval can be obtained, which reaches a root-mean-square error of 5.87 cm and a correlation of 99.93% compared to the tide gauge records. Then, based on the Alibaba cloud service, we implemented a near real-time sea level monitoring system by using the real-time GNSS observations streamed by the International GNSS Service real-time service. It is shown that no detectable bias is found compared with the retrievals obtained in post-processing mode, which indicates that we can remotely sense sea level variations in near real-time and further promotes ground-based GNSS-R in practical sea level monitoring applications.

Accuracy and resolution are the two primary challenges that impose limitations on the practical implementation of classical tide-level remote sensing. To improve the accuracy and applicability and increase the temporal resolution of the inversion point near the shore area, the influence of coastal reflection signals in the signal-to-noise ratio (SNR) residual sequence should be weakened significantly. This contribution proposes an anti-interference GNSS Multipath Reflectometry (GNSS-MR) algorithm called VMD_SNR, which is enhanced using variational mode decomposition (VMD). Compared with wavelet decomposition and empirical mode decomposition (EMD) methods, VMD_SNR exhibits superior capabilities in reducing the interference caused by noisy signals. The measurements of ground-based GNSS stations are used to verify the performance improvement in the VMD_SNR algorithm. The results show that the proposed algorithm is better than the wavelet decomposition method and EMD method in terms of accuracy and stability in the shore area, where the effective number is higher than 99% of the total number, and the accuracy is better than 13.80 cm. Moreover, the accuracy improvement is more significant in the high-elevation range, which is 30.16% higher than the wavelet decomposition method and 38.34% higher than the EMD method.

Global Navigation Satellite System interferometric reflectometry (GNSS-IR) can be used to monitor a series of geophysical parameters in a cost-effective manner with high temporal resolution. The technique makes use of the simultaneous reception of direct and reflected GNSS signals with a single antenna. Based on the low-cost u-blox M8N chips, a GNSS-IR instrument is developed, which could be used to collect and process GNSS signals automatically and save and transmit generated GNSS data. Details about the instrument are described here for the first time. Then, the recorded SNR observation characteristics are analyzed by comparing three in-situ SNR sequences, which are simultaneously collected by the instrument with a low-cost patch antenna and a geodetic antenna and by a geodetic GNSS receiver with a geodetic antenna. Based on the developed function relating the peak power spectral density to peak frequency estimation error of the low-cost instrument, a weighting method is proposed to fuse multiple estimations of the reflector height to improve the estimation accuracy of the GNSS-IR-based reflector height. The performances of the developed low-cost instrument and the proposed reflector height estimation method are evaluated using a data set collected in Xinxiang City, Henan, China, over 6 days. The results show that there exists good agreement between the instrument-based reflector height estimates and the ground-truth estimates, with root-mean-square errors of 1.1 cm and 0.4 cm for the normal average and the proposed weighted average results, respectively, when the antenna height is in the range of 0.65 m to 2.15 m and the reflecting surface is flat, silty loam soil ground.

Moving target detection in optical remote sensing is important for satellite surveillance and space target monitoring. Here, a new moving point target detection framework under a low signal-to-noise ratio (SNR) that uses an end-to-end network (1D-ResNet) to learn the distribution features of transient disturbances in the temporal profile (TP) formed by a target passing through a pixel is proposed. First, we converted the detection of the point target in the image into the detection of transient disturbance in the TP and established mathematical models of different TP types. Then, according to the established mathematical models of TP, we generated the simulation TP dataset to train the 1D-ResNet. In 1D-ResNet, the structure of CBR-1D (Conv1D, BatchNormalization, ReLU) was designed to extract the features of transient disturbance. As the transient disturbance is very weak, we used several skip connections to prevent the loss of features in the deep layers. After the backbone, two LBR (Linear, BatchNormalization, ReLU) modules were used for further feature extraction to classify TP and identify the locations of transient disturbances. A multitask weighted loss function to ensure training convergence was proposed. Sufficient experiments showed that this method effectively detects moving point targets with a low SNR and has the highest detection rate and the lowest false alarm rate compared to other benchmark methods. Our method also has the best detection efficiency.