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The fault diagnosis of rotating machinery involves revealing the possible faults in advance to reduce dispensable breakdowns; the difficulty of this lies in identifying the periodic features of rotating machines contaminated by background noise. Time-domain synchronous averaging (TSA) has been studied to eliminate random noise in signals. However, TSA sometimes cannot extract useful signals more accurately because of the accumulative phase error caused by the discrete sampling process and the difficulties in obtaining accurate prior information. Hence, the moving interpolation and Kurtosis searching criterion are used for more accurate extraction of harmonics and transient impacts. Also, an improved compensation algorithm based on moving interpolation is proposed to overcome the amplitude attenuation caused by cumulative phase error for low signal to noise ratio (SNR) signal. To determine some parameters in the algorithm such as the number of periods and the time delay of windows which depend on a priori information relevant to the fault period, a searching method for the prior information in vibration signals including transient impacts and harmonics with Kurtosis and minimum mean square error criterion is proposed to optimize the algorithm in the process of feature extraction. Finally, the improved TSA (ITSA) is applied to extracting the fault features in a real factory, and the performance of fault feature extraction in low SNR signal conditions with the ITSA has been enhanced effectively. [ABSTRACT FROM AUTHOR]

Objective . In-beam positron emission tomography (PET) is a promising technology for real-time monitoring of proton therapy. Random coincidences between prompt radiation events and positron annihilation photon pairs can deteriorate imaging quality during beam-on operation. This study aimed to improve the PET image quality by filtering out the prompt radiation events. Approach . We investigated a prompt radiation event filtering method based on the accelerator radio frequency phase and assessed its performance using various prompt gamma energy thresholds. An in-beam PET prototype was used to acquire the data when the 70 MeV proton beam irradiated a water phantom and a mouse. The signal-to-background ratio (SBR) indicator was utilized to evaluate the quality of the PET reconstruction image. Main results . The selection of the prompt gamma energy threshold will affect the quality of the reconstructed image. Using the optimal energy threshold of 580 keV can obtain a SBR of 1.6 times for the water phantom radiation experiment and 2.0 times for the mouse radiation experiment compared to those without background removal, respectively. Significance . Our results show that using this optimal threshold can reduce the prompt radiation events, enhancing the SBR of the reconstructed image. This advancement contributes to more accurate real-time range verification in subsequent steps., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective. The objective of this work is to: (1) demonstrate fluorine-19 ( 19 F) MRI on a 3T clinical system with a large field of view (FOV) multi-channel torso coil (2) demonstrate an example parameter selection optimization for a 19 F agent to maximize the signal-to-noise ratio (SNR)-efficiency for spoiled gradient echo (SPGR), balanced steady-state free precession (bSSFP), and phase-cycled bSSFP (bSSFP-C), and (3) validate detection feasibility in ex vivo tissues. Approach. Measurements were conducted on a 3.0T Discovery MR750w MRI (GE Healthcare, USA) with an 8-channel 1 H/ 19 F torso coil (MRI Tools, Germany). Numerical simulations were conducted for perfluoropolyether to determine the theoretical parameters to maximize SNR-efficiency for the sequences. Theoretical parameters were experimentally verified, and the sensitivity of the sequences was compared with a 10 min acquisition time with a 3.125 × 3.125 × 3 mm 3 in-plane resolution. Feasibility of a bSSFP-C was also demonstrated in phantom and ex vivo tissues. Main Results . Flip angles (FAs) of 12 and 64° maximized the signal for SPGR and bSSFP, and validation of optimal FA and receiver bandwidth showed close agreement with numerical simulations. Sensitivities of 2.47, 5.81, and 4.44ms-0.5mM-1 and empirical detection limits of 20.3, 1.5, and 6.2 mM were achieved for SPGR, bSSFP, and bSSFP-C, respectively. bSSFP and bSSFP-C achieved 1.8-fold greater sensitivity over SPGR ( p < 0.01). Significance. bSSFP-C was able to improve sensitivity relative to simple SPGR and reduce both bSSFP banding effects and imaging time. The sequence was used to demonstrate the feasibility of 19 F MRI at clinical FOVs and field strengths within ex-vivo tissues., (Creative Commons Attribution license.)

Objective. The mean squared error (MSE), also known as L 2 loss, has been widely used as a loss function to optimize image denoising models due to its strong performance as a mean estimator of the Gaussian noise model. Recently, various low-dose computed tomography (LDCT) image denoising methods using deep learning combined with the MSE loss have been developed; however, this approach has been observed to suffer from the regression-to-the-mean problem, leading to over-smoothed edges and degradation of texture in the image. Approach. To overcome this issue, we propose a stochastic function in the loss function to improve the texture of the denoised CT images, rather than relying on complicated networks or feature space losses. The proposed loss function includes the MSE loss to learn the mean distribution and the Pearson divergence loss to learn feature textures. Specifically, the Pearson divergence loss is computed in an image space to measure the distance between two intensity measures of denoised low-dose and normal-dose CT images. The evaluation of the proposed model employs a novel approach of multi-metric quantitative analysis utilizing relative texture feature distance. Results. Our experimental results show that the proposed Pearson divergence loss leads to a significant improvement in texture compared to the conventional MSE loss and generative adversarial network (GAN), both qualitatively and quantitatively. Significance. Achieving consistent texture preservation in LDCT is a challenge in conventional GAN-type methods due to adversarial aspects aimed at minimizing noise while preserving texture. By incorporating the Pearson regularizer in the loss function, we can easily achieve a balance between two conflicting properties. Consistent high-quality CT images can significantly help clinicians in diagnoses and supporting researchers in the development of AI-diagnostic models., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective . The primary objective of this study is to address the reconstruction time challenge in magnetic particle imaging (MPI) by introducing a novel approach named SNR-peak-based frequency selection (SPFS). The focus is on improving spatial resolution without compromising reconstruction speed, thereby enhancing the clinical potential of MPI for real-time imaging. Approach . To overcome the trade-off between reconstruction time and spatial resolution in MPI, the researchers propose SPFS as an innovative frequency selection method. Unlike conventional SNR-based selection, SPFS prioritizes frequencies with signal-to-noise ratio (SNR) peaks that capture crucial system matrix information. This adaptability to varying quantities of selected frequencies enhances versatility in the reconstruction process. The study compares the spatial resolution of MPI reconstruction using both SNR-based and SPFS frequency selection methods, utilizing simulated and real device data. Main results. The research findings demonstrate that the SPFS approach substantially improves image resolution in MPI, especially when dealing with a limited number of frequency components. By focusing on SNR peaks associated with critical system matrix information, SPFS mitigates the spatial resolution degradation observed in conventional SNR-based selection methods. The study validates the effectiveness of SPFS through the assessment of MPI reconstruction spatial resolution using both simulated and real device data, highlighting its potential to address a critical limitation in the field. Significance. The introduction of SPFS represents a significant breakthrough in MPI technology. The method not only accelerates reconstruction time but also enhances spatial resolution, thus expanding the clinical potential of MPI for various applications. The improved real-time imaging capabilities of MPI, facilitated by SPFS, hold promise for advancements in drug delivery, plaque assessment, tumor treatment, cerebral perfusion evaluation, immunotherapy guidance, and in vivo cell tracking., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective . Photon-counting detector (PCD) CT enables routine virtual-monoenergetic image (VMI) reconstruction. We evaluated the performance of an automatic VMI energy level (keV) selection tool on a clinical PCD-CT system in comparison to an automatic tube potential (kV) selection tool from an energy-integrating-detector (EID) CT system from the same manufacturer. Approach. Four torso-shaped phantoms (20-50 cm width) containing iodine (2, 5, and 10 mg cc -1 ) and calcium (100 mg cc -1 ) were scanned on PCD-CT and EID-CT. Dose optimization techniques, task-based VMI energy level and tube-potential selection on PCD-CT (CARE keV) and task-based tube potential selection on EID-CT (CARE kV), were enabled. CT numbers, image noise, and dose-normalized contrast-to-noise ratio (CNR d ) were compared. Main results . PCD-CT produced task-specific VMIs at 70, 65, 60, and 55 keV for non-contrast, bone, soft tissue with contrast, and vascular settings, respectively. A 120 kV tube potential was automatically selected on PCD-CT for all scans. In comparison, EID-CT used x-ray tube potentials from 80 to 150 kV based on imaging task and phantom size. PCD-CT achieved consistent dose reduction at 9%, 21% and 39% for bone, soft tissue with contrast, and vascular tasks relative to the non-contrast task, independent of phantom size. On EID-CT, dose reduction factor for contrast tasks relative to the non-contrast task ranged from a 65% decrease (vascular task, 70 kV, 20 cm phantom) to a 21% increase (soft tissue with contrast task, 150 kV, 50 cm phantom) due to size-specific tube potential adaptation. PCD-CT CNR d was equivalent to or higher than those of EID-CT for all tasks and phantom sizes, except for the vascular task with 20 cm phantom, where 70 kV EID-CT CNR d outperformed 55 keV PCD-CT images. Significance . PCD-CT produced more consistent CT numbers compared to EID-CT due to standardized VMI output, which greatly benefits standardization efforts and facilitates radiation dose reduction., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective . Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels. Method . The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error, structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image. Results. DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25%-83% in the small phantom and by 50%-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR. Conclusion . DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments., (Creative Commons Attribution license.)

Deep neural networks (DNNs) have been widely applied in medical image classification and achieve remarkable classification performance. These achievements heavily depend on large-scale accurately annotated training data. However, label noise is inevitably introduced in the medical image annotation, as the labeling process heavily relies on the expertise and experience of annotators. Meanwhile, DNNs suffer from overfitting noisy labels, degrading the performance of models. Therefore, in this work, we innovatively devise a noise-robust training approach to mitigate the adverse effects of noisy labels in medical image classification. Specifically, we incorporate contrastive learning and intra-group mixup attention strategies into vanilla supervised learning. The contrastive learning for feature extractor helps to enhance visual representation of DNNs. The intra-group mixup attention module constructs groups and assigns self-attention weights for group-wise samples, and subsequently interpolates massive noisy-suppressed samples through weighted mixup operation. We conduct comparative experiments on both synthetic and real-world noisy medical datasets under various noise levels. Rigorous experiments validate that our noise-robust method with contrastive learning and mixup attention can effectively handle with label noise, and is superior to state-of-the-art methods. An ablation study also shows that both components contribute to boost model performance. The proposed method demonstrates its capability of curb label noise and has certain potential toward real-world clinic applications., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective . To improve intravoxel incoherent motion imaging (IVIM) magnetic resonance Imaging quality using a new image denoising technique and model-independent parameterization of the signal versus b -value curve. Approach . IVIM images were acquired for 13 head-and-neck patients prior to radiotherapy. Post-radiotherapy scans were also acquired for five of these patients. Images were denoised prior to parameter fitting using neural blind deconvolution, a method of solving the ill-posed mathematical problem of blind deconvolution using neural networks. The signal decay curve was then quantified in terms of several area under the curve (AUC) parameters. Improvements in image quality were assessed using blind image quality metrics, total variation (TV), and the correlations between parameter changes in parotid glands with radiotherapy dose levels. The validity of blur kernel predictions was assessed by the testing the method's ability to recover artificial 'pseudokernels'. AUC parameters were compared with monoexponential, biexponential, and triexponential model parameters in terms of their correlations with dose, contrast-to-noise (CNR) around parotid glands, and relative importance via principal component analysis. Main results . Image denoising improved blind image quality metrics, smoothed the signal versus b -value curve, and strengthened correlations between IVIM parameters and dose levels. Image TV was reduced and parameter CNRs generally increased following denoising. AUC parameters were more correlated with dose and had higher relative importance than exponential model parameters. Significance . IVIM parameters have high variability in the literature and perfusion-related parameters are difficult to interpret. Describing the signal versus b -value curve with model-independent parameters like the AUC and preprocessing images with denoising techniques could potentially benefit IVIM image parameterization in terms of reproducibility and functional utility., (Creative Commons Attribution license.)

Objective . The textures and detailed structures in computed tomography (CT) images are highly desirable for clinical diagnosis. This study aims to expand the current body of work on textures and details preserving convolutional neural networks for low-dose CT (LDCT) image denoising task. Approach . This study proposed a novel multi-scale feature aggregation and fusion network (MFAF-net) for LDCT image denoising. Specifically, we proposed a multi-scale residual feature aggregation module to characterize multi-scale structural information in CT images, which captures regional-specific inter-scale variations using learned weights. We further proposed a cross-level feature fusion module to integrate cross-level features, which adaptively weights the contributions of features from encoder to decoder by using a spatial pyramid attention mechanism. Moreover, we proposed a self-supervised multi-level perceptual loss module to generate multi-level auxiliary perceptual supervision for recovery of salient textures and structures of tissues and lesions in CT images, which takes advantage of abundant semantic information at various levels. We introduced parameters for the perceptual loss to adaptively weight the contributions of auxiliary features of different levels and we also introduced an automatic parameter tuning strategy for these parameters. Main results . Extensive experimental studies were performed to validate the effectiveness of the proposed method. Experimental results demonstrate that the proposed method can achieve better performance on both fine textures preservation and noise suppression for CT image denoising task compared with other competitive convolutional neural network (CNN) based methods. Significance . The proposed MFAF-net takes advantage of multi-scale receptive fields, cross-level features integration and self-supervised multi-level perceptual loss, enabling more effective recovering of fine textures and detailed structures of tissues and lesions in CT images., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective. Up to this point, 1.5 T linac-compatible coil array layouts have been restricted to one or two rows of coils because of the desire to place radiation-opaque circuitry adjacent to the coils and outside the window through which the linac beam travels. Such layouts can limit parallel imaging performance. The purpose of this work was to design and build a three-row array in which remotely located circuits permitted a central row of coils while preserving the radiolucent window. Approach. The remote circuits consisted of a phase shifter to cancel the phase introduced by the coaxial link between the circuit and coil, followed by standard components for tuning, matching, detuning, and preamplifier decoupling. Tests were performed to compare prototype single-channel coils with remote or local circuits, which were followed by tests comparing two and three-row arrays . Main results. The single-channel coil with the remote circuit maintained 85% SNR at depths of 30 mm or more as compared to a coil with local circuit. The three-row array provided similar SNR as the two-row array, along with geometry factor advantages for parallel imaging acceleration in the head-foot direction. Significance. The remote circuit strategy could potentially support future MR-linac arrays by allowing greater flexibility in array layout compared to those confined by local circuits, which can be leveraged for parallel imaging acceleration., (© 2024 Institute of Physics and Engineering in Medicine.)

Cardiac magnetic resonance imaging (MRI) usually requires a long acquisition time. The movement of the patients during MRI acquisition will produce image artifacts. Previous studies have shown that clear MR image texture edges are of great significance for pathological diagnosis. In this paper, a motion artifact reduction method for cardiac MRI based on edge enhancement network is proposed. Firstly, the four-plane normal vector adaptive fractional differential mask is applied to extract the edge features of blurred images. The four-plane normal vector method can reduce the noise information in the edge feature maps. The adaptive fractional order is selected according to the normal mean gradient and the local Gaussian curvature entropy of the images. Secondly, the extracted edge feature maps and blurred images are input into the de-artifact network. In this network, the edge fusion feature extraction network and the edge fusion transformer network are specially designed. The former combines the edge feature maps with the fuzzy feature maps to extract the edge feature information. The latter combines the edge attention network and the fuzzy attention network, which can focus on the blurred image edges. Finally, extensive experiments show that the proposed method can obtain higher peak signal-to-noise ratio and structural similarity index measure compared to state-of-art methods. The de-artifact images have clear texture edges., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective. Performing positron emission tomography (PET) denoising within the image space proves effective in reducing the variance in PET images. In recent years, deep learning has demonstrated superior denoising performance, but models trained on a specific noise level typically fail to generalize well on different noise levels, due to inherent distribution shifts between inputs. The distribution shift usually results in bias in the denoised images. Our goal is to tackle such a problem using a domain generalization technique. Approach. We propose to utilize the domain generalization technique with a novel feature space continuous discriminator (CD) for adversarial training, using the fraction of events as a continuous domain label. The core idea is to enforce the extraction of noise-level invariant features. Thus minimizing the distribution divergence of latent feature representation for different continuous noise levels, and making the model general for arbitrary noise levels. We created three sets of 10%, 13%-22% (uniformly randomly selected), or 25% fractions of events from 97 18 F-MK6240 tau PET studies of 60 subjects. For each set, we generated 20 noise realizations. Training, validation, and testing were implemented using 1400, 120, and 420 pairs of 3D image volumes from the same or different sets. We used 3D UNet as the baseline and implemented CD to the continuous noise level training data of 13%-22% set. Main results. The proposed CD improves the denoising performance of our model trained in a 13%-22% fraction set for testing in both 10% and 25% fraction sets, measured by bias and standard deviation using full-count images as references. In addition, our CD method can improve the SSIM and PSNR consistently for Alzheimer-related regions and the whole brain. Significance. To our knowledge, this is the first attempt to alleviate the performance degradation in cross-noise level denoising from the perspective of domain generalization. Our study is also a pioneer work of continuous domain generalization to utilize continuously changing source domains., (© 2024 Institute of Physics and Engineering in Medicine.)

In this work, a time-varying pulse waveform spin readout sequence method is proposed to improve the spin readout efficiency of nitrogen-vacancy (NV) centers. The method consists of performing the readout signal-to-noise ratio (SNR) analysis via the construction of a rate equation model of the NV center. We demonstrate a superior performance from the time-varying waveform readout approach and focus on the role of the readout optimization in quantum measurements. A detailed comparison of the readout SNR of the time-varying waveform and conventional waveform is provided. We experimentally demonstrate that the readout SNR is increased by 8% by utilizing an optimized waveform. It is anticipated that the improved method will effectively benefit the sensing capability of NV center ensembles and provide support for high-precision measurements of solid systems. [ABSTRACT FROM AUTHOR]

For the first time, we present the simultaneous detection and characterization of three distinct phases at >105 K in z = 0 absorption using deep Chandra observations toward Mrk 421. The extraordinarily high signal-to-noise ratio (≥60) of the spectra has allowed us to detect a hot phase of the Milky Way circumgalactic medium (CGM) at 107 K coexisting with a warm-hot phase at 1.5 ± 0.1 × 106 K and a warm phase at 3.0 ± 0.4 × 105 K. The warm-hot phase is at the virial temperature of the Galaxy, and the warm phase may have cooled from the warm-hot phase, but the supervirial hot phase remains a mystery. We find that [C/O] in the warm and warm-hot phases, [Mg/O] in the warm-hot phase, and [Ne/O] in the hot phase are supersolar, and the hot and the warm-hot phases are α-enhanced. Nonthermal line broadening is evident in the warm-hot and the hot phases, and it dominates the total line broadening. Our results indicate that the >105 K CGM is a complex ecosystem. It provides insights on the thermal and chemical history of the Milky Way CGM and theories of galaxy evolution. [ABSTRACT FROM AUTHOR]

Quasi-static ultrasound elastography (USE) is an imaging modality that measures deformation (i.e. strain) of tissue in response to an applied mechanical force. In USE, the strain modulus is traditionally obtained by deriving the displacement field estimated between a pair of radio-frequency data. In this work we propose a recurrent network architecture with convolutional long-short-term memory decoder blocks to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames. The network is trained in an unsupervised way, by optimising a similarity metric between the reference and compressed image. Our training loss is also composed of a regularisation term that preserves displacement continuity by directly optimising the strain smoothness, and a temporal continuity term that enforces consistency between successive strain predictions. In addition, we propose an open-access in vivo database for quasi-static USE, which consists of radio-frequency data sequences captured on the arm of a human volunteer. Our results from numerical simulation and in vivo data suggest that our recurrent neural network can account for larger deformations, as compared with two other feed-forward neural networks. In all experiments, our recurrent network outperformed the state-of-the-art for both learning-based and optimisation-based methods, in terms of elastographic signal-to-noise ratio, strain consistency, and image similarity. Finally, our open-source code provides a 3D-slicer visualisation module that can be used to process ultrasound RF frames in real-time, at a rate of up to 20 frames per second, using a standard GPU. [ABSTRACT FROM AUTHOR]

Raman spectroscopy has been widely used to characterize the physical properties of two-dimensional materials (2DMs). The signal-to-noise ratio (SNR or S/N ratio) of Raman signal usually serves as an important indicator to evaluate the instrumental performance rather than Raman intensity itself. Multichannel detectors with outstanding sensitivity, rapid acquisition speed and low noise level have been widely equipped in Raman instruments for the measurement of Raman signal. In this mini-review, we first introduce the recent advances of Raman spectroscopy of 2DMs. Then we take the most commonly used CCD detector and IGA array detector as examples to overview the various noise sources in Raman measurements and analyze their potential influences on SNR of Raman signal in experiments. This overview can contribute to a better understanding on the SNR of Raman signal and the performance of multichannel detector for numerous researchers and instrumental design for industry, as well as offer practical strategies for improving spectral quality in routine measurement. [ABSTRACT FROM AUTHOR]

Bone fatigue accumulation is a factor leading to bone fracture, which is a progressive process of microdamage deteriorating under long-term and repeated stress. Since the microdamage of the early stage in bone is difficult to be investigated by linear ultrasound, the second harmonic generation method in nonlinear ultrasound technique is employed in this paper, which is proved to be more sensitive to microdamage. To solve the deficiency that the second harmonic component is easily submerged by noise in traditional nonlinear measurement, a weighted chirp coded sinusoidal signal was applied as the ultrasonic excitation, while pulse inversion is implemented at the receiving side. The effectiveness of this combination to improve the signal-to-noise ratio has been demonstrated by in vitro experiment. Progressive fatigue loading experiments were conducted on the cortical bone plate in vitro for microdamage generation. There was a significant increase in the slope of the acoustic nonlinearity parameter with the propagation distance (increased by 8% and 24% respectively) when the bone specimen was at a progressive level of microdamage. These results indicate that the coded nonlinear ultrasonic method might have the potential in diagnosing bone fatigue. [ABSTRACT FROM AUTHOR]

Driven chains, also called current injection blocks, are one of the main sub-blocks of electrical impedance tomography (EIT) systems. In most applications, the noise performance of a driven chain is a critical figure of merit, which limits the signal-to-noise ratio of an EIT system. Accurate noise analysis of driven chains with improved Howland circuits is still missing in the EIT literature. In this paper, an accurate noise model of a driven chain with an improved Howland current source for an EIT system is presented. Both simulated and lab-built driven-chain circuits are employed to validate our model. It is shown that the simulation and test results closely match. This work provides both an accurate noise model and a noise budget method for EIT driven chains with an improved Howland current source. [ABSTRACT FROM AUTHOR]

To reduce the influence of MEMS gyroscope random errors on navigation systems, the improved variational mode decomposition-wavelet threshold de-noising (WTD) method is proposed in this paper. First, to suppress the endpoint effect caused by the signal truncation and Hilbert transform during decomposition, the triangular waveform matching method is used to search for the waveform which most matches the endpoints in the whole signal. Secondly, the grid search algorithm is used to select the optimal parameters for the extended signal to realize the optimal decomposition. Finally, all the components are analyzed to determine autocorrelation characteristics. According to the variance of the autocorrelation function, all the components can be divided into noise components, mixed components and signal components. The noise components are directly removed, and the mixed components are de-noised using the WTD method. Then, the signal components and the mixed components are reconstructed after de-noising. Analysis of simulation and measured data de-noising experiments proves the effectiveness of the proposed method. For the static signal, the mean square error (MSE) of the proposed method is reduced by 10.1% and the signal to noise ratio (SNR) is increased by 14.2%. For the dynamic signal, the MSE of the proposed method decreases by 16.9% and the SNR increases by 18.8%. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the low signal-to-noise ratio in the complex indoor environment will lead to the complex data association and low accuracy of the simultaneous localization and mapping (SLAM) method, a PHD-SLAM method based on the memory attenuation (MA) filter and mixed newborn maps information (MBMA-PHD-SLAM) is proposed in this paper. First of all, this method based on a probability hypothesis density (PHD) filter. Therefore, this method avoids data association and solves the problem of high computational complexity. Besides, the general PHD-SLAM method tends to cause the filter to diverge when the indoor signal-to-noise ratio is low. Therefore, MA filter is combined into PHD-SLAM. This method can reduce the influence of old data on SLAM, which improves the filtering divergence problem and improves the accuracy of SLAM. What's more, since conventional SLAM algorithms usually have a problem of lack of prior information and in order to further improve the accuracy of SLAM on the basis of the above method, a method of mixed newborn maps information is proposed to solve this problem. Finally, the experiment mainly compares the method in this paper with the classic Rao-Blackwellised (RB) implementation of the PHD-SLAM (RB-PHD-SLAM). The results show that this method outperforms the PHD-SLAM method in terms of estimating map features and localization accuracy. The method in this paper effectively improves the ability of the mobile robot to explore unknown indoor environments. [ABSTRACT FROM AUTHOR]

Direct inversion (DI) derives tissue shear modulus by inverting the Helmholtz equation. However, conventional DI is sensitive to data quality due to the ill-posed nature of Helmholtz inversion and thus providing reliable stiffness estimation can be challenging. This becomes more problematic in the case of estimating shear stiffness of the lung in which the low tissue density and short T2* result in considerably low signal-to-noise ratio during lung MRE. In the present study, we propose to perform MRE inversion by compressive recovery (MICRo). Such a technique aims to improve the numerical stability and the robustness to data noise of Helmholtz inversion by using prior knowledge on data noise and transform sparsity of the stiffness map. The developed inversion strategy was first validated in simulated phantoms with known stiffness. Next, MICRo was compared to the standard clinical multi-modal DI (MMDI) method for in vivo liver MRE in healthy subjects and patients with different stages of liver fibrosis. After establishing the accuracy of MICRo, we demonstrated the robustness of the proposed technique against data noise in lung MRE with healthy subjects. In simulated phantoms with single-directional or multi-directional waves, MICRo outperformed DI with Romano filter or Savitsky and Golay filter, especially when the stiffness and/or noise level was high. In hepatic MRE application, agreement was observed between MICRo and MMDI. Measuring in vivo lung stiffness, MICRo demonstrated its advantages over filtered DI by yielding stable stiffness estimation at both residual volume and total lung capacity. These preliminary results demonstrate the potential value of the proposed technique and also warrant further investigation in a larger clinical population. [ABSTRACT FROM AUTHOR]

Our study aims to improve the signal-to-noise ratio of positron emission tomography (PET) imaging using conditional unsupervised learning. The proposed method does not require low- and high-quality pairs for network training which can be easily applied to existing PET/computed tomography (CT) and PET/magnetic resonance (MR) datasets. This method consists of two steps: populational training and individual fine-tuning. As for populational training, a network was first pre-trained by a group of patients' noisy PET images and the corresponding anatomical prior images from CT or MR. As for individual fine-tuning, a new network with initial parameters inherited from the pre-trained network was fine-tuned by the test patient's noisy PET image and the corresponding anatomical prior image. Only the last few layers were fine-tuned to take advantage of the populational information and the pre-training efforts. Both networks shared the same structure and took the CT or MR images as the network input so that the network output was conditioned on the patient's anatomic prior information. The noisy PET images were used as the training and fine-tuning labels. The proposed method was evaluated on a 68Ga-PPRGD2 PET/CT dataset and a 18F-FDG PET/MR dataset. For the PET/CT dataset, with the original noisy PET image as the baseline, the proposed method has a significantly higher contrast-to noise ratio (CNR) improvement (71.85% ± 27.05%) than Gaussian (12.66% ± 6.19%, P = 0.002), nonlocal mean method (22.60% ± 13.11%, P = 0.002) and conditional deep image prior method (52.94% ± 21.79%, P = 0.0039). For the PET/MR dataset, compared to Gaussian (18.73% ± 9.98%, P < 0.0001), NLM (26.01% ± 19.40%, P < 0.0001) and CDIP (47.48% ± 25.36%, P < 0.0001), the CNR improvement ratio of the proposed method (58.07% ± 28.45%) is the highest. In addition, the denoised images using both datasets also showed that the proposed method can accurately restore tumor structures while also smoothing out the noise. [ABSTRACT FROM AUTHOR]

The transient radial shearing interferometry technique based on fast Fourier transform (FFT) provides a means for the measurement of the wavefront phase of transient light field. However, which factors affect the spatial bandwidth of the wavefront phase measurement of this technology and how to achieve high-precision measurement of the broad-band transient wavefront phase are problems that need to be studied further. To this end, a theoretical model of phase-retrieved bandwidth of radial shearing interferometry is established in this paper. The influence of the spatial carrier frequency and the calculation window on phase-retrieved bandwidth is analyzed, and the optimal carrier frequency and calculation window are obtained. On this basis, a broad-band transient radial shearing interference phase-retrieval method based on chirp Z transform (CZT) is proposed, and the corresponding algorithm is given. Through theoretical simulation, a known phase is used to generate the interferogram and it is retrieved by the traditional method and the proposed method respectively. The residual wavefront RMS of the traditional method is 0.146λ, and it is 0.037λ for the proposed method, which manifests an improvement of accuracy by an order of magnitude. At the same time, different levels of signal-to-noise ratios (SNRs) from 50 dB to 10 dB of the interferogram are simulated, and the RMS of the residual wavefront is from 0.040λ to 0.066λ. In terms of experiments, an experimental verification device based on a phase-only spatial light modulator is built, and the known phase on the modulator is retrieved from the actual interferogram. The RMS of the residual wavefront retrieved through FFT is 0.112λ, and it decreases to 0.035λ through CZT. The experimental results verify the effectiveness of the method proposed in this paper. Furthermore, the method can be used in other types of spatial carrier frequency interference, such as lateral shearing interference, rotational shearing interference, flipping shearing interference, and four-wave shearing interference. [ABSTRACT FROM AUTHOR]

To address the problem that it is difficult to detect an intermediate frequency (IF) signal at the receiving end of a communication system under extremely low signal-to-noise ratio (SNR) conditions, we propose a stochastic resonance (SR)-enhanced sine-signal detection method based on the sign function. By analyzing the SR mechanism of the sine signal and combining it with the characteristics of a dual-sequence frequency-hopping (DSFH) receiver, a periodic stationary solution of the Fokker–Planck equation (FPE) with a time parameter is obtained. The extreme point of the sine signal is selected as the decision time, and the force law of the electromagnetic particles is analyzed. A receiving structure based on the sign function is proposed to maximize the output difference of the system, and the value condition of the sign function is determined. In order to further improve the detection performance, in combination with the central-limit theorem, the sampling points are averaged N times, and the signal-detection problem is transformed into a hypothesis-testing problem under a Gaussian distribution. The theoretical analysis and simulation experiment results confirm that when N is 100 and the SNR is greater than 20 dB, the bit-error ratio (BER) is less than 1.5 × 10−2 under conditions in which the signal conforms to the optimal SR parameters. [ABSTRACT FROM AUTHOR]

Cardiac chemical exchange saturation transfer-magnetic resonance imaging (CEST-MRI) has been used to probe levels of various metabolites that provide insight into myocardial structure and function. However, imaging of the heart using CEST-MRI is prolonged by the need to repeatedly acquire multiple images for a full Z-spectrum and to perform saturation and acquisition around cardiac and respiratory cycles. Compressed sensing (CS) reconstruction of sparse data enables accelerated acquisition, but reconstruction artifacts may bias subsequently derived measures of CEST contrast. In this study, we examine the impact of CS reconstruction of increasingly under-sampled cardiac CEST-MRI data on subsequent CEST contrasts of amine-containing metabolites and amide-containing proteins. Cardiac CEST-MRI data sets were acquired in six mice using low and high RF saturation for single and dual contrast generation, respectively. CEST-weighted images were reconstructed using CS methods at 2–5× levels of under-sampling. CEST contrasts were derived from corresponding Z-spectra and the impact of accelerated imaging on accuracy was assessed via analysis of variance. CS reconstruction preserved myocardial signal to noise ratio as compared to conventional reconstruction. However, greater absolute error and distribution of derived contrasts was observed with increasing acceleration factors. The results from this study indicate that acquisition of radial cardiac CEST-MRI data can be modestly, but meaningfully, accelerated via CS reconstructions with little error in CEST contrast quantification. [ABSTRACT FROM AUTHOR]

The purpose of this work was to assess technical performance of a prototype high-ratio (r29), 80 line cm−1 grid for imaging conditions which mimic those for adult cardiovascular angiography. The standard equipment r15, 80 line cm−1 grid was used as a reference. Plastic Water® LR phantoms with thickness in the range 20–44 cm were used to simulate adult patient attenuation and scatter. Grids were tested using x-ray field of view 20 and 25 cm and x-ray source to detector distance (SID) 107 and 120 cm. The primary transmission fraction (TP) was measured using both narrow beam geometry and a lead beam stop (BS) technique. Scatter transmission (TS) was measured with the lead BS technique. The quantum signal to noise ratio improvement factor (KSNR) was used to describe relative grid performance. The experimental conditions required revised theory to assess grid performance. Theory to account for the detector glare and underestimation of scatter intensity by the lead BS method was developed. Also, novel KSNR theory was developed to allow direct comparison of two grids operated at different SID. Mean TP was modestly lower for the r29 versus r15 grid (0.69 versus 0.75). When tested under equivalent scatter condition, TS of the r29 grid was approximately ˝ that of the r15 grid (0.18 versus 0.34). KSNR of the r29 grid at SID 120 cm compared to the r15 grid at SID 107 cm increased linearly with phantom thickness (range 1.0 to ∼1.16). Findings of this work indicate that the r29 grid used at SID 120 cm is expected to provide improved image quality (or reduced patient radiation dose) when compared to the r15 grid used at SID 107 cm for adult cardiovascular patients and that the potential benefit of the r29 grid increases with patient thickness >20 cm. [ABSTRACT FROM AUTHOR]

We present a detailed analysis for a subset of the high-resolution (∼35 mas, or 5 au) ALMA observations from the Disk Substructures at High Angular Resolution Project (DSHARP) to search for faint 1.3 mm continuum emission associated with dusty circumplanetary material located within the narrow annuli of depleted emission (gaps) in circumstellar disks. This search used the Jennings et al. frank modeling methodology to mitigate contamination from the local disk emission and then deployed a suite of injection–recovery experiments to statistically characterize point-like circumplanetary disks in residual images. While there are a few putative candidates in this sample, they have only marginal local signal-to-noise ratios and would require deeper measurements to confirm. Associating a 50% recovery fraction with an upper limit, we find that these data are sensitive to circumplanetary disks with flux densities ≳50–70 μJy in most cases. There are a few examples where those limits are inflated (≳110 μJy) owing to lingering nonaxisymmetric structures in their host circumstellar disks, most notably for a newly identified faint spiral in the HD 143006 disk. For standard assumptions, this analysis suggests that these data should be sensitive to circumplanetary disks with dust masses. While those bounds are comparable to some theoretical expectations for young giant planets, we discuss how plausible system properties (e.g., relatively low host planet masses or the efficient radial drift of solids) could require much deeper observations to achieve robust detections. [ABSTRACT FROM AUTHOR]

This paper reports an IC-compatible method for fabricating a PDMS-based resistive pulse sensing (RPS) device with embedded nanochannel (nanochannel-RPS) for label-free analysis of biomolecules and bionanoparticles, such as plasmid DNAs and exosomes. Here, a multilayer lithography process was proposed to fabricate the PDMS mold for the microfluidic device, comprising a bridging nanochannel, as the sensing gate. RPS was performed by placing the sensing and excitation electrodes symmetrically upstream and downstream of the sensing gate. In order to reduce the noise level, a reference electrode was designed and placed beside the excitation electrode. To demonstrate the feasibility of the proposed nanochannel-RPS device and sensing system, polystyrene micro- and nanoparticles with diameters of 1 μm and 300 nm were tested by the proposed device with signal-to-noise ratios (SNR) ranging from 9.1–30.5 and 2.2–5.9, respectively. Furthermore, a nanochannel with height of 300 nm was applied for 4 kb plasmid DNA detection, implying the potential of the proposed method for label-free quantification of nanoscale biomolecules. Moreover, HeLa cell exosomes, known as a well-studied subtype of extracellular vesicles, were measured and analyzed by their size distribution. The result of the resistive pulse amplitude corresponded well to that of nanoparticle tracking analysis (NTA). The proposed nanochannel-RPS device and the sensing strategy are not only capable of label-free analysis for nanoscale biomolecules and bionanoparticles, but are also cost-effective for large-scale manufacturing. [ABSTRACT FROM AUTHOR]

The [C ii ] fine-structure transition at 158 μm is frequently the brightest far-infrared line in galaxies. Due to its low ionization potential, C+ can trace the ionized, atomic, and molecular phases of the ISM. We present velocity-resolved [C ii ] and [N ii ] pointed observations from SOFIA/GREAT on ∼500 pc scales in the nearby galaxies M101 and NGC 6946 and investigate the multiphase origin of [C ii ] emission over a range of environments. We show that ionized gas makes a negligible contribution to the [C ii ] emission in these positions using [N ii ] observations. We spectrally decompose the [C ii ] emission into components associated with the molecular and atomic phases using existing CO (2–1) and H i data and show that a peak signal-to-noise ratio of 10–15 is necessary for a reliable decomposition. In general, we find that in our pointings ≳50% of the [C ii ] emission arises from the atomic phase, with no strong dependence on star formation rate, metallicity, or galactocentric radius. We do find a difference between pointings in these two galaxies, where locations in NGC 6946 tend to have larger fractions of [C ii ] emission associated with the molecular phase than in M101. We also find a weak but consistent trend for fainter [C ii ] emission to exhibit a larger contribution from the atomic medium. We compute the thermal pressure of the cold neutral medium through the [C ii ] cooling function and find , a value slightly higher than similar determinations, likely because our observations are biased toward star-forming regions. [ABSTRACT FROM AUTHOR]

Objective. Muscle artifacts (MA) and electrode motion artifacts (EMA) in electrocardiogram (ECG) signals lead to a large number of false alarms from cardiac diagnostic systems. To reduce false alarms, it is necessary to improve the performance of the diagnostic algorithm in noisy environments by removing excessively noisy signals. However, existing methods focus on signal quality assessment and contain too many artificial features. Here, we present a novel method to flexibly eliminate noisy signals without any artificial features. Approach. Our method contains an improved lightweight deep neural network (DNN) to capture the signal portions damaged by EMA and MA, uses the sample entropy to quantize noisy portions, and discards those portions that exceed a defined threshold. Our method was tested in conjunction with Pan-Tompkins (PT), Filter Bank (FB), and 'UNSW' R-peak detection algorithms along with two heartbeat classification algorithms on datasets synthesized from the MIT-BIH Noise Stress Test Database, the China Physiological Signal Challenge 2018 Database and the MIT-BIH Arrhythmia Database. Main results. For PT R-peak detection algorithms, the sensitivity (Se) increased noticeably from 89.01% to 99.42% in the synthesized datasets with a signal-to-noise ratio of 6 dB. With the same datasets, the Se of the FB algorithm increased about 9.29%, and a 3.64% increase occurred in the Se of the 'UNSW' algorithm. For heartbeat classification algorithms, the overall F1-score increased about 6% in the synthesized one-heartbeat datasets. It is the first study to utilize a DNN to capture noisy segments of the ECG signal. Significance. Too many false alarms can cause alarm fatigue. Our method can be utilized as the preprocessing before signal analysis, thereby reducing false alarms from the ECG diagnostic systems. [ABSTRACT FROM AUTHOR]

Convergent cross mapping (CCM) is an analytical technique with various applications in ecology, biology, and any other fields where causal links must be distinguished in dynamical systems. An extended version (herein referred to as eCCM) is used to determine the causal links between probe and grid signals in Hall effect thruster simulation results obtained from HPHall, though this technique can also be applied to experimental data. To curtail some of the limitations of eCCM due to signal noise and data capture rate, a new methodology is developed by introducing data splitting (into training and testing sets) and normalization by the self-reconstruction skill. This version of eCCM is validated with probe to probe cross mapping tests to ensure its accuracy and calibrate input parameters that balance resolution and computational cost. Probe to probe cross mapping results agreed to within 10% for most cases (once filtering was applied to remove noise), and probe to anode cross mapping results for several quiescent and an oscillatory operating modes agree very well with the expected flow of information carried by the ions. For instance, eCCM is shown to be able to distinguish the first two harmonic frequencies to less than to within 0.5% when the signal to noise ratio is sufficient. These results strongly affirm the usefulness of eCCM in Hall-effect thruster analysis. [ABSTRACT FROM AUTHOR]

According to the properties of Fourier transform, Fourier single-pixel imaging uses the illumination lights with cosine distributions to obtain the Fourier spectrum of the object, and then apply the inverse Fourier transform to reconstruct the spatial information of the object. This technique does not require detector arrays, such as charge coupled device and has proven to be insensitive to distortion, which is a great improvement over traditional photography techniques. In this manuscript, we present a detailed analysis and discussion on the signal-to-noise ratio (SNR) of Fourier single-pixel imaging. Compared with conventional imaging whose SNR is independent of pixel number N, Fourier single-pixel imaging achieves an improved SNR which is up to N times as high as the dynamic range of detection. Furthermore, this SNR benefit is further confirmed experimentally, in cases of one dimension and two dimensions. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of post-deposition annealing (PDA) temperature on H2S gas sensing and low-frequency noise characteristics of In2O3 gas sensors. In2O3 thin-films are deposited using the radio frequency (RF) sputtering method at an RF power of 150 W and post-annealed at various temperatures (200, 300, and 400 °C). The response of the In2O3 gas sensor to H2S decreases with increasing PDA temperature due to the increase of grain size. However, the In2O3 post-annealed at 200 °C shows the largest 1/f noise since the damaged sensing material-substrate interface is not fully recovered by the PDA. The sensors post-annealed at a higher temperature (300 °C and 400 °C) recover the damaged interface. Thanks to its moderate response and noise level, the sensor post-annealed at 300 °C shows the largest signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Herein, Ti3C2Tx MXene/N-doped reduced graphene oxide (MXene/N-rGO) composite was employed as the electrocatalyst to construct a new electrochemical sensing platform for the determination of adrenaline (AD). The MXene/N-rGO was synthesized via a facile one-step hydrothermal method, where ethylenediamine acted as a reducing agent and N source. The doped N in rGO served as a bridge between MXene and rGO through tight hydrogen bonds. Scanning electron microscopy showed that large numbers of MXenes with accordion-like morphology were distributed on the surface of the N-rGO. The MXene/N-rGO composite displayed a synergetic catalytic effect for oxidizing AD, originating from the unique catalytic activity of N-rGO and the large surface area and satisfactory conductivity of MXene. These characteristics of composite material led to a remarkable effect on signal amplification for the detection of AD, with a wide linear range from 10.0 nM to 90.0 μM and a low detection limit of 3.0 nM based on a signal to noise ratio of 3. Moreover, the MXene/N-rGO electrode displayed good stability, repeatability, and reproducibility. Additionally, the proposed sensor was successfully applied for voltammetric sensing of AD in urine with recoveries from 97.75% to 103.0%. [ABSTRACT FROM AUTHOR]

We show how the observable number of binaries in LISA is affected by eccentricity through its influence on the peak gravitational-wave frequency, enhanced binary number density required to produce the LIGO observed rate, and the reduced signal-to-noise ratio (S/N) for an eccentric event. We also demonstrate how these effects should make it possible to learn about the eccentricity distribution and formation channels by counting the number of binaries as a function of frequency, even with no explicit detection of eccentricity. We also provide a simplified calculation for S/N of eccentric binaries. [ABSTRACT FROM AUTHOR]

We propose and apply a new test of Einstein's equivalence principle (EEP) based on the gravitational redshift induced by the central supermassive black hole of quasars in the surrounding accretion disk. Specifically, we compare the observed gravitational redshift of the Fe III λλ 2039-2113 emission line blend in quasars with the predicted values in a wide, uncharted, cosmic territory (0 ≲ zcosm ≲ 3). For the first time we measure, with statistical uncertainties comparable or better than those of other classical methods outside the solar system, the ratio between the observed gravitational redshifts and the theoretical predictions in 10 independent cosmological redshift bins in the 1 ≲ zcosm ≲ 3 range. The average of the measured over predicted gravitational redshifts ratio in this cosmological redshift interval is with scatter 0.13 ± 0.05 showing no cosmological evolution of EEP within these limits. This method can benefit from larger samples of measurements with better signal-to-noise ratios, paving the way for high-precision tests (below 1%) of EEP on cosmological scales. [ABSTRACT FROM AUTHOR]

High-performance ultraviolet (UV) detectors with both high responses and fast speeds are hard to make on homogeneous crystal semiconductor materials. Here, the UV response characteristics of mixed-phase MgZnO thin films with different internal structure distributions are studied. The mixed-phase MgZnO-based detector with the given crystal composition has a high response at both deep UV light (96 A W−1 at 240 nm) and near UV light (80 A W−1 at 335 nm). Meanwhile, because of the quasi-tunneling breakdown mechanism within the device, the high-response UV detector also shows a fast response speed (tr = 0.11 μs) and recovery speed (td1 = 26 μs) at deep UV light, which is much faster than both low-response mixed-phase MgZnO-based UV detectors with other structure constitutions and reported high-response UV devices on homogenous crystal materials. The Idark of the device is just 4.27 pA under a 5 V bias voltage, so the signal-to-noise ratio of the device reached 23852 at 5.5 uW cm−2 235 nm UV light. The new quasi-tunneling breakdown mechanism is observed in some mixed-phase MgZnO thin films that contain both c-MgZnO and h-MgZnO parts, which introduce a high response, signal-to-noise ratio, and fast speed into mixed-phase MgZnO-based UV detectors at weak deep UV light. [ABSTRACT FROM AUTHOR]

Weak value amplification has shown its superiority in measurement of small physical effects. Here we introduce a signal-recycled weak-value-based velocity measurement strategy to decrease the attenuation of detected photons during the post-selection. Like the power-recycled scheme, we can improve the number of detected photons and signal-to-noise ratio of velocity by forming a cavity. However, optimal improvements of number of detected photons and signal-to-noise ratio cannot be obtained simultaneously in our signal-recycled scheme owing to the walk-off effect. Furthermore, we find that the reflected light is relatively strong compared with the power-recycled scheme, which may increase the collection-detection efficiency in prospective relevant experiment. [ABSTRACT FROM AUTHOR]

We use combined South Pole Telescope (SPT)+Planck temperature maps to analyze the circumgalactic medium (CGM) encompassing 138,235 massive, quiescent 0.5 ≤ z ≤ 1.5 galaxies selected from data from the Dark Energy Survey (DES) and Wide-Field Infrared Survey Explorer (WISE). Images centered on these galaxies were cut from the 1.85 arcmin resolution maps with frequency bands at 95, 150, and 220 GHz. The images were stacked, filtered, and fit with a graybody dust model to isolate the thermal Sunyaev–Zel'dovich (tSZ) signal, which is proportional to the total energy contained in the CGM of the galaxies. We separated these M⋆ = 1010.9M⊙–1012M⊙ galaxies into 0.1 dex stellar mass bins, detecting tSZ per bin up to 5.6σ and a total signal-to-noise ratio of 10.1σ. We also detect dust with an overall signal-to-noise ratio of 9.8σ, which overwhelms the tSZ at 150 GHz more than in other lower-redshift studies. We corrected for the 0.16 dex uncertainty in the stellar mass measurements by parameter fitting for an unconvolved power-law energy-mass relation, , with the peak stellar mass distribution of our selected galaxies defined as M⋆,peak = 2.3 × 1011M⊙. This yields an erg and. These are consistent with z ≈ 0 observations and within the limits of moderate models of active galactic nucleus feedback. We also computed the radial profile of our full sample, which is similar to that recently measured at lower-redshift by Schaan et al. [ABSTRACT FROM AUTHOR]

Contemporary scanning laser Kerr magnetometers are very efficient measurement devices that achieve very good signal to noise ratios and spatial resolution for magnetization measurements. When going beyond simple hysteresis loop measurements, large amounts of data can be acquired quickly and, thus, control software and automated processing becomes necessary to go beyond these simple measurements. Here, we present an open source software that realizes first-order reversal curves and magnetic property mapping based on scanning laser Kerr magnetometry. The modular software is easily extensible and provides a convenient user interface to make these more complex measurements accessible to a broader user base. [ABSTRACT FROM AUTHOR]

Red giants show a large number of absorption lines in both optical and near-infrared wavelengths. Still, the characteristics of the lines in different wave passbands are not necessarily the same. We searched for lines of Mg i , Si i , Ca i , Ti i , Cr i , and Ni i in the , Y, and J bands (0.91–1.33 μm), that are useful for precise abundance analyses, from two different compilations of lines, namely, the third release of Vienna Atomic Line Database (VALD3) and the catalog published by Meléndez & Barbuy in 1999 (MB99). We selected sufficiently strong lines that are not severely blended and ended up with 191 lines (165 and 141 lines from VALD3 and MB99, respectively), in total, for the six elements. Combining our line lists with high-resolution (λ/Δλ = 28,000) and high signal-to-noise ratio (>500) spectra taken with the WINERED spectrograph, we measured the abundances of the six elements in addition to Fe i of two prototype red giants, i.e., Arcturus and μ Leo. The resultant abundances show reasonable agreement with the values in the literature within ∼0.2 dex, indicating that the available oscillator strengths are acceptable, although the abundances based on the two line lists show systematic differences by 0.1–0.2 dex. Furthermore, to improve the precision, solid estimation of the microturbulence (or the microturbulences if they are different for different elements) is necessary as far as the classical hydrostatic atmosphere models are used for the analysis. [ABSTRACT FROM AUTHOR]

Modern astronomical surveys are observing spectral data for millions of stars. These spectra contain chemical information that can be used to trace the Galaxy's formation and chemical enrichment history. However, extracting the information from spectra and making precise and accurate chemical abundance measurements is challenging. Here we present a data-driven method for isolating the chemical factors of variation in stellar spectra from those of other parameters (i.e., Teff, log g, [Fe/H]). This enables us to build a spectral projection for each star with these parameters removed. We do this with no ab initio knowledge of elemental abundances themselves and hence bypass the uncertainties and systematics associated with modeling that rely on synthetic stellar spectra. To remove known nonchemical factors of variation, we develop and implement a neural network architecture that learns a disentangled spectral representation. We simulate our recovery of chemically identical stars using the disentangled spectra in a synthetic APOGEE-like data set. We show that this recovery declines as a function of the signal-to-noise ratio but that our neural network architecture outperforms simpler modeling choices. Our work demonstrates the feasibility of data-driven abundance-free chemical tagging. [ABSTRACT FROM AUTHOR]

In this study, we investigated the capacity of various ion beams available for radiotherapy to produce high quality relative stopping power map acquired from energy-loss measurements. The image quality metrics chosen to compare the different ions were signal-to-noise ratio (SNR) as a function of dose and spatial resolution. Geant4 Monte Carlo simulations were performed for: hydrogen, helium, lithium, boron and carbon ion beams crossing a 20 cm diameter water phantom to determine SNR and spatial resolution. It has been found that protons possess a significantly larger SNR when compared with other ions at a fixed range (up to 36% higher than helium) due to the proton nuclear stability and low dose per primary. However, it also yields the lowest spatial resolution against all other ions, with a resolution lowered by a factor 4 compared to that of carbon imaging, for a beam with the same initial range. When comparing for a fixed spatial resolution of 10 lp cm−1, carbon ions produce the highest image quality metrics with proton ions producing the lowest. In conclusion, it has been found that no ion can maximize all image quality metrics simultaneously and that a choice must be made between spatial resolution, SNR, and dose. [ABSTRACT FROM AUTHOR]

This work presents a H2S selective resistive gas sensor design based on a chemical field effect transistor (ChemFET) with open gate formed by hundreds of high temperature chemical vapour deposition (CVD) grown zinc oxide nanowires (ZnO NW). The sensing ability of pristine ZnO NWs and surface functionalized ZnO NWs for H2S is analysed systematically. ZnO NWs are functionalized by deposition of discontinuous gold (Au) nanoparticle films of different thicknesses of catalyst layer ranging from 1 to 10 nm and are compared in their gas sensing properties. All experiments were performed in a temperature stabilized small volume compartment with adjustable gas mixture at room temperature. The results allow for a well-founded understanding of signal-to-noise ratio, enhanced response, and improved limit of detection due to the Au functionalisation. Comprehension and controlled application of the beneficial effects of Au catalyst on ZnO NWs allow for the detection of very low H2S concentrations down to 10 ppb, and a theoretically estimated 500 ppt in synthetic air at room temperature. [ABSTRACT FROM AUTHOR]

Ultrasound localization microscopy (ULM) has recently enabled the mapping of the cerebral vasculature in vivo with a resolution ten times smaller than the wavelength used, down to ten microns. However, with frame rates up to 20000 frames per second, this method requires large amount of data to be acquired, transmitted, stored, and processed. The transfer rate is, as of today, one of the main limiting factors of this technology. Herein, we introduce a novel reconstruction framework to decrease this quantity of data to be acquired and the complexity of the required hardware by randomly subsampling the channels of a linear probe. Method performance evaluation as well as parameters optimization were conducted in silico using the SIMUS simulation software in an anatomically realistic phantom and then compared to in vivo acquisitions in a rat brain after craniotomy. Results show that reducing the number of active elements deteriorates the signal-to-noise ratio and could lead to false microbubbles detections but has limited effect on localization accuracy. In simulation, the false positive rate on microbubble detection deteriorates from 3.7% for 128 channels in receive and 7 steered angles to 11% for 16 channels and 7 angles. The average localization accuracy ranges from 10.6 μm and 9.93 μm for 16 channels/3 angles and 128 channels/13 angles respectively. These results suggest that a compromise can be found between the number of channels and the quality of the reconstructed vascular network and demonstrate feasibility of performing ULM with a reduced number of channels in receive, paving the way for low-cost devices enabling high-resolution vascular mapping. [ABSTRACT FROM AUTHOR]

Conventional parameter estimation methods for pseudo-random binary code-linear frequency modulation (PRBC-LFM) signals require prior knowledge, are computationally complex, and exhibit poor performance at low signal-to-noise ratios (SNRs). To overcome these problems, a blind parameter estimation method based on a Duffing oscillator array is proposed. A new relationship formula among the state of the Duffing oscillator, the pseudo-random sequence of the PRBC-LFM signal, and the frequency difference between the PRBC-LFM signal and the periodic driving force signal of the Duffing oscillator is derived, providing the theoretical basis for blind parameter estimation. Methods based on amplitude method, short-time Fourier transform method, and power spectrum entropy method are used to binarize the output of the Duffing oscillator array, and their performance is compared. The pseudo-random sequence is estimated using Duffing oscillator array synchronization, and the carrier frequency parameters are obtained by the relational expressions and characteristics of the difference frequency. Simulation results show that this blind estimation method overcomes limitations in prior knowledge and maintains good parameter estimation performance up to an SNR of –35 dB. [ABSTRACT FROM AUTHOR]

To improve the signal to noise ratio (SNR) and the short-term stability of cesium atomic fountain clocks, the work of two-laser optical pumping is presented theoretically and experimentally. The short-term stability of the NIM6 fountain clock has been improved by preparing more cold atoms in the | F = 4, mF = 0〉 clock state with a shortened cycle time. Two π-polarized laser beams overlapped in the horizontal plane have been applied after launching, one is resonant with | F = 4〉 → | F′ = 4〉 transition and the other is resonant with | F = 3〉 → | F′ = 4〉 transition. With optical pumping, the population accumulated in the | mF = 0〉 clock state is improved from 11% to 63%, and the detection signal is increased by a factor of 4.2, the SNR of the clock transition probability and the short-term stability are also improved accordingly. [ABSTRACT FROM AUTHOR]

We present initial results from the Cosmic Origins Spectrograph (COS) and Gemini Mapping the Circumgalactic Medium (CGMCGM ≡ CGM2) survey. The CGM2 survey consists of 1689 galaxies, all with high-quality Gemini-GMOS spectra, within 1 Mpc of 22 z ≲ 1 quasars, all with a signal-to-noise ratio of ∼10 Hubble Space Telescope/COS G130M+G160M spectra. For 572 of these galaxies with stellar masses 107 M⊙ < M⋆ < 1011 M⊙ and z ≲ 0.5, we show that the H i covering fraction above a threshold of NHI > 1014cm−2 is ≳0.5 within 1.5 virial radii (Rvir ∼ R200m). We examine the H i kinematics and find that the majority of absorption lies within ±250 km s−1 of the galaxy systemic velocity. We examine H i covering fractions over a range of impact parameters to infer a characteristic size of the CGM, , as a function of galaxy mass. is the impact parameter at which the probability of observing an absorber with NHI >1014 cm−2 is >50%. In this framework, the radial extent of the CGM of M⋆ > 109.9 M⊙ galaxies is kpc or. Intermediate-mass galaxies with 109.2 < M⋆/M⊙ < 109.9 have an extent of kpc or. Low-mass galaxies, M⋆ < 109.2 M⊙, show a smaller physical scale of kpc and extend to. Our analysis suggests that using Rvir as a proxy for the characteristic radius of the CGM likely underestimates its extent. [ABSTRACT FROM AUTHOR]

We present new proper-motion measurements of optically emitting oxygen-rich knots of supernova remnant 1E 0102.2-7219 (E0102), which are used to estimate the remnant's center of expansion and age. Four epochs of high-resolution Hubble Space Telescope images spanning 19 yr were retrieved and analyzed. We found a robust center of expansion of α = 1h04m02.ˢ48 and δ = −72°01′53.″92 (J2000) with 1σ uncertainty of 1.″77 using 45 knots from images obtained with the Advanced Camera for Surveys using the F475W filter in 2003 and 2013 having the highest signal-to-noise ratio. We also estimate an upper limit explosion age of 1738 ± 175 yr by selecting the knots with the highest proper motions and these knots are assumed to be the least decelerated. We find evidence of an asymmetry in the proper motions of the knots as a function of position angle. We conclude that these asymmetries were most likely caused by interaction between E0102's original supernova blast wave and an inhomogeneous surrounding environment, as opposed to intrinsic explosion asymmetry. The observed nonhomologous expansion suggests that the use of a free expansion model inaccurately offsets the center of expansion and leads to an overestimated explosion age. We discuss our findings as they compare to previous age and center of expansion estimates of E0102, as well as their relevance to a recently identified candidate central compact object. [ABSTRACT FROM AUTHOR]