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Molecular quantum-dot cellular automata (QCA) may provide high speed, low-power, classical information processing in the post-CMOS era. The readout of molecular QCA bits is challenging because the molecules may be much smaller than transistors and even single-electron transistors. This paper builds on a recent proposal for the spectroscopic readout of bits from asymmetric QCA molecules. Here, we propose circuits for fanning out a bit onto a large QCA circuit to increase the spectroscopic signal-to-noise ratio. As the number of molecules in a fanout circuit grows, the internal bias of each asymmetric cell accumulates, and the circuit may become stuck, tolerating only a very small internal bias. We also propose the use of an applied electric field to compensate for a candidate molecule's internal bias, thereby restoring switchability, even when the internal bias is significant. [ABSTRACT FROM AUTHOR]

The two-dimensional laser-based Ångstrom method measures the in-plane thermal properties for anisotropic film-like materials. It involves periodic laser heating at the center of a suspended film sample and records its transient thermal response by infrared imaging. These spatiotemporal temperature data must be analyzed to extract the unknown thermal conductivity values in the orthotropic directions, an inverse parameter fitting problem. Previous demonstration of the metrology technique used a least-squares fitting method that relies on numerical differentiation to evaluate the second-order partial derivatives in the differential equation describing transient conduction in the physical system. This fitting approach is susceptible to measurement noise, introducing high uncertainty in the extracted properties when working with noisy data. For example, when noise of a signal-to-noise ratio of 10 is added to simulated amplitude and phase data, the error in the extracted thermal conductivity can exceed 80%. In this work, we introduce a new alternative inverse parameter fitting approach using physics-informed neural networks (PINNs) to increase the robustness of the measurement technique for noisy temperature data. We demonstrate the effectiveness of this approach even for scenarios with extreme levels of noise in the data. Specifically, the PINN-approach accurately extracts the properties to within 5% of the true values even for high noise levels (a signal-to-noise ratio of 1). This offers a promising avenue for improving the robustness and accuracy of advanced thermal metrology tools that rely on inverse parameter fitting of temperature data to extract thermal properties. [ABSTRACT FROM AUTHOR]

Piezoresponse force microscopy (PFM) is one of the most widespread methods for investigating and visualizing ferroelectric domain structures down to the nanometer length scale. PFM makes use of the direct coupling of the piezoelectric response to the crystal lattice, and hence, it is most often applied to spatially map the three-dimensional (3D) near-surface domain distribution of any polar or ferroic sample. Nonetheless, since most samples investigated by PFM are at least semiconducting or fully insulating, the electric ac field emerging from the conductive scanning force microscopy (SFM) tip penetrates the sample and, hence, may also couple to polar features that are deeply buried into the bulk of the sample under investigation. Thus, in the work presented here, we experimentally and theoretically explore the contrast and depth resolution capabilities of PFM, by analyzing the dependence of several key parameters. These key parameters include the depth of the buried feature, i.e., here a domain wall (DW), as well as PFM-relevant technical parameters such as the tip radius, the PFM drive voltage and frequency, and the signal-to-noise ratio. The theoretical predictions are experimentally verified using x-cut periodically poled lithium niobate single crystals that are specially prepared into wedge-shaped samples, in order to allow the buried feature, here the DW, to be "positioned" at any depth into the bulk. This inspection essentially contributes to the fundamental understanding in PFM contrast analysis and to the reconstruction of 3D domain structures down to a 1 μ m-penetration depth into the sample. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The disco-vertebral junction (DVJ) of the lumbar spine contains thin structures with short T2 values, including the cartilaginous endplate (CEP) sandwiched between the bony vertebral endplate (VEP) and the nucleus pulposus (NP). We previously demonstrated that ultrashort-echo-time (UTE) MRI, compared to conventional MRI, is able to depict the tissues at the DVJ with improved contrast. In this study, we sought to further optimize UTE MRI by characterizing the contrast-to-noise ratio (CNR) of these tissues when either single echo or echo subtraction images are used and with varying echo times (TEs). METHODS: In four cadaveric lumbar spines, we acquired 3D Cones (a UTE sequence) images at varying TEs from 0.032 ms to 16 ms. Additionally, spin echo T1- and T2-weighted images were acquired. The CNRs of CEP-NP and CEP-VEP were measured in all source images and 3D Cones echo subtraction images. RESULTS: In the spin echo images, it was challenging to distinguish the CEP from the VEP, as both had low signal intensity. However, the 3D Cones source images at the shortest TE of 0.032 ms provided an excellent contrast between the CEP and the VEP. As the TE increased, the contrast decreased in the source images. In contrast, the 3D Cones echo subtraction images showed increasing CNR values as the second TE increased, reaching statistical significance when the second TE was above 10 ms (p < 0.05). CONCLUSIONS: Our study highlights the feasibility of incorporating UTE MRI for the evaluation of the DVJ and its advantages over conventional spin echo sequences for improving the contrast between the CEP and adjacent tissues. Additionally, modulation of the contrast for the target tissues can be achieved using either source images or subtraction images, as well as by varying the echo times.

We have observed that Variational Mode Decomposition (VMD) exhibits instability in the denoising and separation of noisy multitone signals. Specifically, minor changes in factors such as signal-to-noise ratio, frequency spacing, sampling rate, and probability distribution can significantly impact the decomposition results. To address this issue, we have developed the Dual-VMD-correlation algorithm. This algorithm effectively mitigates the impact of beat effects when harmonics of closely spaced frequencies are superimposed, enabling stable denoising of noisy multitone signals and the separation of each individual tone signal. The algorithm holds promise for applications in frequency-modulated continuous wave laser detection. It can address challenges related to denoising laser echo signals interfered with by aerosols and the difficulty in separating backscatter interference from the target reflected signal spectrum. [ABSTRACT FROM AUTHOR]

BACKGROUND: The monitoring and analysis of quasi-periodic biological signals such as electrocardiography (ECG), intracranial pressure (ICP), and cerebral blood flow velocity (CBFV) waveforms plays an important role in the early detection of adverse patient events and contributes to improved care management in the intensive care unit (ICU). This work quantitatively evaluates existing computational frameworks for automatically extracting peaks within ICP waveforms. METHODS: Peak detection techniques based on state-of-the-art machine learning models were evaluated in terms of robustness to varying noise levels. The evaluation was performed on a dataset of ICP signals assembled from 700 h of monitoring from 64 neurosurgical patients. The groundtruth of the peak locations was established manually on a subset of 13, 611 pulses. Additional evaluation was performed using a simulated dataset of ICP with controlled temporal dynamics and noise. RESULTS: The quantitative analysis of peak detection algorithms applied to individual waveforms indicates that most techniques provide acceptable accuracy with a mean absolute error (MAE) ≤ 10 ms without noise. In the presence of a higher noise level, however, only kernel spectral regression and random forest remain below that error threshold while the performance of other techniques deteriorates. Our experiments also demonstrated that tracking methods such as Bayesian inference and long short-term memory (LSTM) can be applied continuously and provide additional robustness in situations where single pulse analysis methods fail, such as missing data. CONCLUSION: While machine learning-based peak detection methods require manually labeled data for training, these models outperform conventional signal processing ones based on handcrafted rules and should be considered for peak detection in modern frameworks. In particular, peak tracking methods that incorporate temporal information between successive periods of the signals have demonstrated in our experiments to provide more robustness to noise and temporary artifacts that commonly arise as part of the monitoring setup in the clinical setting.

We demonstrate an approach to two-dimensional electronic spectroscopy (2DES) that combines the benefits of shot-to-shot detection at high-repetition rates with the simplicity of a broadband white light continuum input and conventional optical elements to generate phase-locked pump pulse pairs. We demonstrate this through mutual synchronization between the laser repetition rate, the acousto-optical deflector, the pump delay stage, and the CCD line camera, which allows for rapid scanning of pump optical delay synchronously with the laser repetition rate, while the delay stage is moved at a constant velocity. The resulting shot-to-shot detection scheme is repetition rate scalable and only limited by the CCD line rate and the maximum stage velocity. Using this approach, we demonstrate the measurement of an averaged 2DES absorptive spectrum in as much as 1.2 s of continuous sample exposure per 2D spectrum. We achieve a signal-to-noise ratio of 6.8 for optical densities down to 0.05 with 11.6 s of averaging at 100 kHz laser repetition rate. Combining rapid scanning of mechanical delay lines with shot-to-shot detection as demonstrated here provides a viable alternative to acousto-optic pulse shaping approaches that is repetition-rate scalable, has comparable throughput and sensitivity, and minimizes sample exposure per 2D spectrum with promising micro-spectroscopy applications. [ABSTRACT FROM AUTHOR]

Within aerosol-rich environments, efficient simulation of frequency-modulated continuous wave (FMCW) laser detector echo characteristics is crucial. Conventional methods often need more efficiency. To address this, we propose a photon matrix-based approach for simulating intricate photon scattering processes, enhancing simulation accuracy. This study focuses on short-range FMCW laser detection under aerosol interference, assessing performance via signal-to-noise ratio (SNR). We analyze the impact of amplitude modulation coefficient and photon count on SNR. Surprisingly, the photon count minimally affects SNR, while the amplitude modulation coefficient significantly influences it. These findings shed light on optimizing FMCW laser detection in aerosol-laden environments. Attention to the amplitude modulation coefficient can notably enhance SNR and overall detection efficiency. [ABSTRACT FROM AUTHOR]

BACKGROUND: Global shortages of iodinated contrast media (ICM) during COVID-19 pandemic forced the imaging community to use ICM more strategically in CT exams. PURPOSE: The purpose of this work is to provide a quantitative framework for preserving iodine CNR while reducing ICM dosage by either lowering kV in single-energy CT (SECT) or using lower energy virtual monochromatic images (VMI) from dual-energy CT (DECT) in a phantom study. MATERIALS AND METHODS: In SECT study, phantoms with effective diameters of 9.7, 15.9, 21.1, and 28.5 cm were scanned on SECT scanners of two different manufacturers at a range of tube voltages. Statistical based iterative reconstruction and deep learning reconstruction were used. In DECT study, phantoms with effective diameters of 20, 29.5, 34.6, and 39.7 cm were scanned on DECT scanners from three different manufacturers. VMIs were created from 40 to 140 keV. ICM reduction by lowering kV levels for SECT or switching from SECT to DECT was calculated based on the linear relationship between iodine CNR and its concentration under different scanning conditions. RESULTS: On SECT scanner A, while matching CNR at 120 kV, ICM reductions of 21%, 58%, and 72% were achieved at 100, 80, and 70 kV, respectively. On SECT scanner B, 27% and 80% ICM reduction was obtained at 80 and 100 kV. On the Fast-kV switch DECT, with CNR matched at 120 kV, ICM reductions were 35%, 30%, 23%, and 15% with VMIs at 40, 50, 60, and 68 keV, respectively. On the dual-source DECT, ICM reductions were 52%, 48%, 42%, 33%, and 22% with VMIs at 40, 50, 60, 70, and 80 keV. On the dual-layer DECT, ICM reductions were 74%, 62%, 45%, and 22% with VMIs at 40, 50, 60, and 70 keV. CONCLUSIONS: Our work provided a quantitative baseline for other institutions to further optimize their scanning protocols to reduce the use of ICM.

The emergence of on-machine measurement (OMM) technology has opened up new possibilities for error compensation in complex surface machining. Due to the harsh working environments of machine tools, the error data obtained from OMM often contains a considerable degree of random errors. To separate the systematic errors from the random errors and improve the effectiveness of the measurement results and the accuracy of surface parts machining, a method known as ICEEMDAN-VMD is proposed. This method, based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), permutation entropy, correlation coefficient, power spectral entropy, and variational mode decomposition (VMD), is entirely data-driven and does not necessitate manual intervention, making it well-suited for OMM. The accuracy of error separation using the ICEEMDAN-VMD method was assessed through simulation, utilizing root mean square error and signal-to-noise ratio as evaluation metrics. Application of this method in machining experiments resulted in a substantial reduction of 73.58% in the average error, 64.33% in the maximum error, and 67.86% in the standard deviation on surfaces. This implementation notably enhanced surface consistency and effectively avoided over-cutting. The results conclusively demonstrated the theoretical feasibility, high accuracy, and practical effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Tomographic synthetic aperture radar (TomoSAR) technology has demonstrated significant practical value in forest parameter extraction, accumulating a wealth of airborne data and research results over the years. This paper addresses the problem of TomoSAR underlying topography inversion in forested areas, where the complex vertical structure of forests contributes to diverse scattering mechanisms, making it difficult to determine the exact number of scatterers within each unit. To tackle this issue, we propose a TomoSAR underlying topography inversion method that combines density clustering with the multiple signal classification (MUSIC) technique. The proposed algorithm achieves superior estimation for the positions of unknown scatterers by utilizing eigenvalue density clustering to distinguish the signal and noise subspaces. A series of simulated experiments were conducted to showcase the robustness of the proposed algorithm in estimating scatterer phase centres under challenging conditions such as low signal-to-noise ratio and limited snapshots. Moreover, the practicality of the proposed algorithm in accurately reconstructing the underlying topography is validated by experimental results of real data obtained from the BioSAR 2008 campaign dataset. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is a critical biomedical signal and plays an imperative role in diagnosing cardiovascular disorders. During ECG data acquisition in clinical environment, noise is frequently present. Various noises such as powerline interference (PLI) and baseline wandering (BLW) distort the ECG signal which may lead to incorrect interpretation. Consequently, substantial emphasis has been dedicated to ECG denoising for reliable diagnosis and analysis. In this study, a novel hybrid ECG denoising method based on variational mode decomposition (VMD) and the empirical wavelet transform (EWT) is presented. For effective denoising using the VMD and EWT approach, the noisy ECG signal is decomposed within narrow-band variational mode functions (VMFs). The aim is to remove noise from these narrow-band VMFs. In current approach, the centre frequency of each VMF was computed and utilized to design an adaptive wavelet filter bank using EWT. This leads to effective removal of noise components from the signal. The proposed approach was applied to ECG signals obtained from the MIT-BIH Arrhythmia database. To evaluate the denoising performance, noise sources from the MIT-BIH Noise Stress Test Database (NSTDB) are used for simulation. The assessment of denoising performance in based on two key metrics: the percentage-root-mean-square difference (PRD) and the signal-to-noise ratio (SNR). The findings of the simulation experiment demonstrate that the suggested method has lower percentage root mean square difference and higher signal-to-noise ratio as compared to existing state of the art denoising methods. An average output SNR of 24.03 was achieved, along with a 5% reduction in PRD. [ABSTRACT FROM AUTHOR]

Industrial machinery relies heavily on accurate fault diagnosis to maintain its reliability and operational efficiency. However, analyzing vibration signals for early fault detection is complex, as these signals often suffer from low signal-to-noise ratios, background noise, and random interferences. While wavelet filters are frequently employed for identifying informative frequency bands, determining the optimal filter parameters is crucial for accurately extracting repetitive transients related to faults. This research proposes a novel approach that utilises a crayfish optimization algorithm (COA) to adaptively optimise the wavelet filter. The COA employs correlated kurtosis (CK) as a fitness function to guide the optimization process. This method addresses limitations related to inaccurate CK period estimation through a continuous update mechanism, ensuring more precise parameter selection. Through its application to various industrial cases, the proposed methodology demonstrates its superiority over existing techniques in accurately extracting informative frequencies. This advancement enables more precise fault diagnosis, leading to improved maintenance strategies and minimized downtime in industrial environments. [ABSTRACT FROM AUTHOR]

QRS detection within an electrocardiogram (ECG) is the basis of virtually any further processing and any error caused by this detection will propagate to further processing stages. However, standard benchmarking procedures of QRS detectors are seriously flawed because they report almost always close to 100% accuracy for any QRS detection algorithm. This is due to the use of large temporal error margins and noise-free ECG databases which grossly overestimate their performance. The use of a large fixed error margin masks temporal jitter between detection and ground truth measurements. Here, we present a new performance measure (JF) which combines temporal jitter with the F-score, and also an ECG database with decreasing levels of signal to noise ratios based on noise generated from different tasks. Our new performance measure JF fully encompasses all the types of errors that can occur, equally weights them and provides a percentage value which allows direct comparison between QRS detection algorithms. In combination with the new noisy ECG database, the JF performance measure now varies between 50% and 100% for different detectors and signal to noise conditions thereby making it possible to find the best detector for an application. [ABSTRACT FROM AUTHOR]

Methane is the second most significant greenhouse gas after carbon dioxide. As global warming intensifies, the quantification of methane point sources is becoming increasingly crucial. However, retrieving high-quality methane signals from remote sensing data remains challenging due to various factors, including surface reflectance, atmospheric interference, sensor noise, wind speed, and sensor sensitivity. In real-world scenarios, methane remote sensing quantification often encounters unfavourable conditions that lead to low signal-to-noise ratio (SNR) signals, resulting in reduced quantification accuracy. To address these challenges, we introduce a novel multi-task learning-based approach. Specifically, we incorporate a denoising auxiliary task into the quantification network by introducing an additional denoising branch that recovers clean plume column concentration maps. The network is trained with supervision using both denoising and quantification losses, which enables it to acquire robust feature representations to noise and benefits the quantification of low SNR images. During the inference phase, the denoising branch is removed, resulting in an efficient and robust single quantification network with reduced inferring time, supporting onboard computation. We construct a low SNR database based on the AVIRIS-NG sensor and evaluate the generalization ability of our method. DQNet achieves the highest RMSE, MAPE, and R of 16.478 kg/h, 15.296%, and 95.167% respectively on the synthetic dataset. Across the entire range of SNR, DQNet exhibits a greater relative advantage as SNR decreases. However, our approach is not limited to AVIRIS-NG and can be easily extended to various multispectral and hyperspectral satellites. [ABSTRACT FROM AUTHOR]

This letter presents a novel approach to sparse Bayesian underwater acoustic signal direction estimation. The proposed method incorporates interpolation of the coprime array and signal subspace fitting. It addresses the limitations of the hydrophone coprime array in utilizing all array elements' information and mitigates the interference of ocean noise in shallow waters, which impairs the accuracy and resolution of target direction estimation. Firstly, the hydroacoustic signals are received using a coprime array, then the missing information is filled by interpolating the virtual array elements in the virtual domain, and by optimizing the design of the atomic norm and reconstructing the covariance matrix, the direction-of-arrival (DOA) estimation is performed using all the information of the received signal. Then, the received signal is reconstructed in conjunction with the reconstructed covariance signal subspace, which effectively reduces the impact of background noise. Finally, we derive an off-grid sparse model for the reconstructed signal by exploiting sparsity in the null domain and use Bayesian learning to compute the maximum a posteriori probability of the source signal, thus achieving DOA estimation. The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability. [ABSTRACT FROM AUTHOR]

AbstractObjectiveDesignStudy sampleResultsConclusionsMeasurement of reference speech-recognition curves for a specific speech test in typical clinical testing environments and for different loudspeaker configurations.Speech-recognition scores were measured at four signal-to-noise ratios for five loudspeaker configurations in two anechoic rooms, and in four audiometric test rooms with low reverberation times.240 young participants (aged 18–25 years) without hearing impairment participated in the measurements.Reference speech-recognition curves for speech and noise from the front (S0N0) were similar across rooms. Compared to S0N0, lower speech-recognition thresholds (SRTs) were observed for all other loudspeaker configurations in which speech and noise were spatially separated. This spatial release from masking was significantly reduced for the audiometric test rooms compared to the anechoic rooms. A binaural speech-intelligibility model was used to verify the influence of room acoustic properties and loudspeaker configuration on SRT.Speech-recognition curves for spatially separated loudspeaker configurations depend on the room acoustic properties, even in audiometric test rooms with low reverberation times. This makes it more difficult to compare clinical measurements with reference speech-recognition curves, or even with data measured in a different test room. It is thus recommended to document the loudspeaker configuration and test room for each clinical measurement. [ABSTRACT FROM AUTHOR]

In image dehazing, non-uniform haze leads to issues such as image blurring, distortion, low contrast, and surface detail loss. Existing dehazing techniques struggle with the uneven distribution of haze, especially in scenes with varying depth, making it difficult to restore high-quality images. This research introduces the Central Difference Embedding Convoluted Generative Adversarial Network (CDECGAN), which enhances local contrast variations to address haze-induced blur and low-contrast areas. To further improve adaptability, the Updated Human Memory Algorithm (UHMA) is integrated to optimise hyperparameters and model performance. The approach also combines image denoising and dehazing, using the Generic Edge Attention (GAE) architecture to denoise while preserving edge sharpness. The Weighted Median Channel Prior (WMCP) model helps focus on high-frequency details and estimate scene depth, solving issues related to fixed-size local patches. Performance metrics confirm the effectiveness of CDECGAN, achieving an average peak signal-to-noise ratio of 25.58 dB, a structural similarity index of 0.884, a natural image quality evaluator score of 11.18, and other notable results. The CDECGAN method outperforms existing techniques by successfully handling non-uniform haze and depth-related challenges in image dehazing. [ABSTRACT FROM AUTHOR]

The rapid development of wearable and implantable electronics has enabled the real-time transmission of electrophysiological signals in situ, thus allowing the precise monitoring and regulation of biological functions. Devices based on organic materials tend to have low moduli and intrinsic stretchability, making them ideal choices for the construction of seamless bioelectronic interfaces. In this case, as an organic ionic–electronic conductor, poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) has low impedance to offer a high signal-to-noise ratio for monitoring bioelectrical signals, which has become one of the most promising conductive polymers. However, the initial conductivity and stretchability of pristine PEDOT:PSS are insufficient to meet the application requirements, and there is a trade-off between their improvement. In addition, PEDOT:PSS has poor stability in aqueous environments due to the hygroscopicity of the PSS chains, which severely limits its long-term applications in water-rich bioelectronic interfaces. Considering the growing demands of multi-function integration, the high-resolution fabrication of electronic devices is urgent. It is a great challenge to maintain both electrical and mechanical performance after miniaturization, particularly at feature sizes below 100 μm. In this review, we focus on the combined improvement in the conductivity and stretchability of PEDOT:PSS, as well as the corresponding mechanisms in detail. Also, we summarize the effective strategies to improve the stability of PEDOT:PSS in aqueous environments, which plays a vital role in long-term applications. Finally, we introduce the reliable micropatterning technologies and PEDOT:PSS-based stretchable optoelectronic devices applied at bio-interfaces. [ABSTRACT FROM AUTHOR]

Background: While there is a list of monosyllabic words available for use in speaking tests in our country, the list of sentences is limited. The aim of this study is to evaluate the effectiveness of the mobile application we developed by creating a list of 4-word sentences that are frequently encountered in daily life, well-known, and spectrally balanced, to be used in speech tests in children aged 8–12 with normal hearing. A total of 50 participants, 25 girls (mean age 9.56 ± 1.56) and 25 boys (mean age 10.60 ± 1.55) with normal hearing, aged 8–12, were included in the study. Familiarity was evaluated by listening to a list of 200 sentences consisting of 4-word sentences to 70 children in 7 geographical regions of Turkey, and the sentences that 80% of the children did not understand were excluded. A list of 60 balanced sentences was determined by performing spectral and coherence analysis of the sentences. The recording of each sentence at 80 dB, 60 dB, and 40 dB intensity levels, respectively, increased in 5 dB steps between the − 10/ + 10 signal-to-noise ratio value and was combined separately with 3 different environmental noises (classroom, shopping mall, amusement park) and uploaded to the developed mobile application. Results: For all participants, speech comprehension scores at 80 dB, 60 dB, and 40 dB sound intensity were lowest in the amusement park environment. Speech comprehension scores are lowest in noise levels of 40 dB in amusement park and shopping mall environments. Speech comprehension scores are lowest in noise levels of 60 dB in the classroom environment. There is a statistically significant difference between ambient noises (p < 0.05). When male and female participants are compared, it is seen that male participants' comprehension scores in noise in the shopping mall environment are higher than female participants. There is a statistically significant difference between genders (p < 0.05). Conclusion: When the speech in noise tests developed in the literature are examined, even though the word lists are equalized with the phonemic equalization method, they do not reflect the reality of daily life. The Turkish Mobile Speech Discrimination in Real Noise 4-Word Sentence Test that we have developed will contribute to the literature. It is recommended that future research compare the results by adding different ambient noises. [ABSTRACT FROM AUTHOR]

Previous studies demonstrated the existence of hand and tool areas in lateral and ventral occipitotemporal cortex (OTC), as well as an overlap between them. We reinvestigated this organization using 7T fMRI, benefiting from a higher signal-to-noise ratio than 3T. This enabled us to include a wider array of categories to achieve a more holistic perspective, and to omit certain spatial preprocessing steps. Despite these improvements, univariate analysis confirmed the existence of hand-tool overlap across OTC, which is striking given the omission of the spatial preprocessing steps that can influence overlap. There was significantly more overlap between hands and tools, compared to other overlap types in the left hemisphere of OTC. The overlap was also larger in the left lateral OTC as compared to the right lateral OTC. We found in all hand areas a differentiation between tools and other types of objects, although they still responded more to bodies than to tools. Regarding the tool areas, we observed a differentiation between hands and other categories such as faces and non-tool objects. Left hemisphere tool areas also differentiated between hands and bodies. When excluding the overlapping voxels from the hand and tool areas, they still showed a significant response to tools or hands (compared to objects or faces) respectively. Multi-voxel pattern analysis indicated that neural representations in the hand areas showed greater similarity between hands and tools than between hands and other objects. In the tool areas, the neural representations between tools and hands and between tools and other type of objects were all equally similar. To summarize, capitalizing on the benefits of 7T fMRI, we further substantiate the evidence in favor of hand-tool overlap in several regions of occipitotemporal cortex. [ABSTRACT FROM AUTHOR]

Purpose: Total-body dynamic positron emission tomography (PET) imaging with total-body coverage and ultrahigh sensitivity has played an important role in accurate tracer kinetic analyses in physiology, biochemistry, and pharmacology. However, dynamic PET scans typically entail prolonged durations ( 60 minutes), potentially causing patient discomfort and resulting in artifacts in the final images. Therefore, we propose a dynamic frame prediction method for total-body PET imaging via deep learning technology to reduce the required scanning time. Methods: On the basis of total-body dynamic PET data acquired from 13 subjects who received [68Ga]Ga-FAPI-04 (68Ga-FAPI) and 24 subjects who received [68Ga]Ga-PSMA-11 (68Ga-PSMA), we propose a bidirectional dynamic frame prediction network that uses the initial and final 10 min of PET imaging data (frames 1–6 and frames 25–30, respectively) as inputs. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were employed as evaluation metrics for an image quality assessment. Moreover, we calculated parametric images (68Ga-FAPI: , 68Ga-PSMA: ) based on the supplemented sequence data to observe the quantitative accuracy of our approach. Regions of interest (ROIs) and statistical analyses were utilized to evaluate the performance of the model. Results: Both the visual and quantitative results illustrate the effectiveness of our approach. The generated dynamic PET images yielded PSNRs of 36.056 ± 0.709 dB for the 68Ga-PSMA group and 33.779 ± 0.760 dB for the 68Ga-FAPI group. Additionally, the SSIM reached 0.935 ± 0.006 for the 68Ga-FAPI group and 0.922 ± 0.009 for the 68Ga-PSMA group. By conducting a quantitative analysis on the parametric images, we obtained PSNRs of 36.155 ± 4.813 dB (68Ga-PSMA, ) and 43.150 ± 4.102 dB (68Ga-FAPI, ). The obtained SSIM values were 0.932 ± 0.041 (68Ga-PSMA) and 0.980 ± 0.011 (68Ga-FAPI). The ROI analysis conducted on our generated dynamic PET sequences also revealed that our method can accurately predict temporal voxel intensity changes, maintaining overall visual consistency with the ground truth. Conclusion: In this work, we propose a bidirectional dynamic frame prediction network for total-body 68Ga-PSMA and 68Ga-FAPI PET imaging with a reduced scan duration. Visual and quantitative analyses demonstrated that our approach performed well when it was used to predict one-hour dynamic PET images. https://github.com/OPMZZZ/BDF-NET. [ABSTRACT FROM AUTHOR]

MEMS gyroscopes are well known for their outstanding advantages in Cost Size Weight and Power (CSWaP), which have inspired great research attention in recent years. A higher signal-to-noise ratio (SNR) for MEMS gyroscopes operating at larger vibrating amplitudes provides improved measuring resolution and ARW performance. However, the increment of amplitude causes strong nonlinear effects of MEMS gyroscopes due to their micron size, which has negative influences on the performance. This paper carries out detailed research on a general nonlinear mechanism on the sensors using parallel-plate capacitive transducers, which is called the gain nonlinearity in electrostatic actuation. The theoretical model established in this paper demonstrates the actuation gain nonlinearity causes the control-force coupling and brings extra angle-dependent bias with the 4th component for the whole-angle gyroscopes, which are verified by the experiments carried out on a micro-shell resonator gyroscope (MSRG). Furthermore, a real-time correction method is proposed to restore a linear response of the electrostatic actuation, which is realized by the gain modification with an online parameter estimation based on the harmonic-component relationship of capacitive detection. This real-time correction method could reduce the 4th component of the angle-dependent bias by over 95% from 0.003°/s to less than 0.0001°/s even under different temperatures. After the correction of actuation gain nonlinearity, the bias instability (BI) of whole-angle MSRG is improved by about 3.5 times from 0.101°/h to 0.029°/h and the scale factor nonlinearity (SFN) is reduced by almost one order of magnitude from 2.02 ppm to 0.21 ppm. [ABSTRACT FROM AUTHOR]

Background: Our study aims to provide an overview of existing evidence regarding the image quality of dual-energy CT (DECT) employing reduced contrast media (CM) volumes, in comparison to single-energy CT (SECT) with standard CM loads. The advantages, indications, and possible applications of DECT were investigated from the perspective of providing better patient care, minimizing CM volume and managing CM shortage. Methods: In this systematic review (PRISMA methodology), PubMed and WOS were searched from January 2010 to January 2023 by two independent reviewers. The scan and CM characteristics, radiation dose, and results of quantitative (contrast to noise ratio, CNR, and signal to noise ratio, SNR) and qualitative assessment of image quality were collected. Sixty non-duplicated records eligible for full-text screening were examined. Results: Finally, 22 articles (1818 patients) were included. The average CM reduction with DECT ranged between 43.4 ± 11%. Despite the wide variability in CT scan protocols, no differences were found in radiation doses between DECT and SECT. Conclusions: DECT scanners allow the employment of lower CM volumes with equal or better image quality evaluated by quantitative and qualitative analyses and similar dose radiation compared to SECT. Using image reconstructions at low monochromatic energy levels, DECT increases iodine conspicuity and attenuation contributing to CM containment measures. [ABSTRACT FROM AUTHOR]

Ionospheric oblique backscatter sounding is an effective means of monitoring the ionosphere which can be used as a frequency selection system to serve HF communication and ensure its quality and stability. But how to obtain effective information from the oblique backscatter ionogram is still a hot issue. Due to this situation, a frequency selecting method for HF communication based on ionospheric oblique backscatter sounding is proposed in this study. After obtaining the ionograms, pattern recognition is used to separate the vertical echoes and the oblique backscatter echoes. Next, the leading edge of the oblique backscatter echoes are extracted, and then a two-dimensional electron density profile can be reconstructed. Then, with the help of ray tracing, the usable frequency range can be estimated. Finally, according to the signal-to-noise ratio reflected by the ionograms, several optimal communication frequencies can be selected. In order to verify this method, oblique ionograms are obtained through oblique sounding experiments to evaluate its accuracy. The result indicates that the usable frequency range and the selected frequencies are in accordance with the echo of the oblique ionogram, so the practicability and accuracy of the method are validated. Eventually, the maximum usable frequencies (MUFs) obtained from oblique backscatter sounding are compared with the MUFs from the oblique sounding ionogram; its Mean Absolute Percentage Error (MAPE) is 7.8% and its root mean squared error (RMSE) is 1.34 MHz. [ABSTRACT FROM AUTHOR]

The Environmental Trace Gases Monitoring Instrument (EMI-II) onboard the Chinese GaoFen-5B (GF5B) and DaQi-1 (DQ1) satellites is the successor of the previous EMI onboard the Chinese GaoFen-5 (GF5) satellite, and has a higher spatial resolution and a better signal-to-noise ratio. The GF5B and DQ1 were launched in September 2021 and April 2022, respectively. As part of China's ultraviolet-visible hyperspectral satellite instrument series, the EMI-II aims to conduct network observations of pollution gases globally in the morning and early afternoon. In this study, NO2 data were retrieved from the EMI-II payloads on the GF5B and DQ1 satellites using the Differential Optical Absorption Spectroscopy (DOAS) algorithm. The two satellites were consistently compared, and the results showed strong consistency on various spatial and temporal scales (R2 > 0.8). In four representative regions worldwide, NO2 data from the EMI-II exhibited good spatial consistency with those from the TROPOMI. The correlation coefficient (R2) of the total vertical column density (VCD) between the EMI-II and TROPOMI exceeded 0.85, and that of the tropospheric NO2 VCD exceeded 0.57. Compared with single-satellite observations, the dual-satellite network of the GF5B and DQ1 can effectively increase the observation frequency. On a daily scale, dual-satellite observations can reduce the impact of cloud coverage by 6–8% compared to single-satellite observations, and there are two valid observations of nearly 50% of the world's regions. Additionally, the differences between the two satellites can reflect the NO2 diurnal variations, which demonstrates the potential for studying pollutant gas diurnal variations. [ABSTRACT FROM AUTHOR]

Underwater direction-of-arrival (DOA) tracking using a hydrophone array is an important research subject in passive sonar signal processing. In this study, a DOA tracking algorithm based on a novel beam-domain signal processing technique is proposed to ensure robust DOA tracking of an interested underwater target under a low signal-to-noise ratio (SNR) environment. Firstly, the beam-based observation is designed and proposed, which innovatively applies beamforming after array-based observation to achieve specific spatial directivity. Next, the proportional–integral–differential (PID)-optimized Olen–Campton beamforming method (PIDBF) is designed and proposed in the beamforming process to achieve faster and more stable sidelobe control performance to enhance the SNR of the target. The adaptive dynamic beam window is designed and proposed to focusing the observation on more likely observation area. Then, by utilizing the extended Kalman filter (EKF) tracking framework, a novel PIDBF-optimized beam-domain DOA tracking algorithm (PIDBF-EKF) is proposed. Finally, simulations with different SNR scenarios and comprehensive analyses are made to verify the superior performance of the proposed DOA tracking approach. [ABSTRACT FROM AUTHOR]

Seismic data processing plays a pivotal role in extracting valuable subsurface information for various geophysical applications. However, seismic records often suffer from inherent random noise, which obscures meaningful geological features and reduces the reliability of interpretations. In recent years, deep learning methodologies have shown promising results in performing noise attenuation tasks on seismic data. In this research, we propose modifications to the standard U-Net structure by integrating dense and residual connections, which serve as the foundation of our approach named the dense and residual (DARE U-Net) network. Dense connections enhance the receptive field and ensure that information from different scales is considered during the denoising process. Our model implements local residual connections between layers within the encoder, which allows earlier layers to directly connect with deep layers. This promotes the flow of information, allowing the network to utilize filtered and unfiltered input. The combined network mechanisms preserve the spatial information loss during the contraction process so that the decoder can locate the features more accurately by retaining the high-resolution features, enabling precise location in seismic image denoising. We evaluate this adapted architecture by applying synthetic and real data sets and calculating the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The effectiveness of this method is well noted. [ABSTRACT FROM AUTHOR]

Airborne radar forward-looking imaging holds significant promise for applications such as autonomous navigation, battlefield reconnaissance, and terrain mapping. However, traditional methods are hindered by complex system design, azimuth ambiguity, and low resolution. This paper introduces a distributed array collaborative, forward-looking imaging approach, where multiple aircraft with linear arrays fly in parallel to achieve coherent imaging. We analyze signal model characteristics and highlight the limitations of conventional algorithms. To address these issues, we propose a high-resolution imaging algorithm that combines an enhanced missing-data iterative adaptive approach with aperture interpolation technique (MIAA-AIT) for effective signal recovery in distributed arrays. Additionally, a novel reference range cell migration correction (reference RCMC) is employed for precise range–azimuth decoupling. The forward-looking algorithm effectively transforms distributed arrays into a virtual long-aperture array, enabling high-resolution, high signal-to-noise ratio imaging with a single snapshot. Simulations and real data tests demonstrate that our method not only improves resolution but also offers flexible array configurations and robust performance in practical applications. [ABSTRACT FROM AUTHOR]

The growth of the Internet of Things has focused attention on visualized sensors as a key technology. However, it remains challenging to achieve high sensing accuracy and self-power ability. Here, we propose a self-powered visualized tactile-acoustic sensor (SVTAS) based on an elaborated triboelectrification-induced electroluminescence (TIEL) unit. To date, it features a high brightness of 0.5 mW cm-2 (32 cd m-2) and a record-low detection limit of 0.5 kPa in horizontal-sliding mode. Meanwhile, the SVTAS is applicable to convert acoustic waves into TIEL signals in contact-separation mode, showing the highest response to the 44.07 Hz sound, a high signal-to-noise ratio of 8.7 dB-1, and an ultrafast response time of 0.8 ms. Furthermore, advanced artificial visualized perception systems are constructed with excellent performance in recognizing motion trajectories and human speech with different words/sentences. This work paves the way for the highly efficient and sustainable development of new-generation self-powered visualized perception systems, contributing a solution to wireless communication free from electromagnetic interference. [ABSTRACT FROM AUTHOR]

We have developed a new method for measuring mass dependent Si isotope fractionation via critical mixture double-spiking. Samples need to be spiked before column chemistry to guarantee full equilibrium between the sample and double-spike (29Si–30Si spike). An iterative addition of the double-spike to the sample, usually 2–4 times, is needed to generate a solution very close to the critically spiked mixture. We use a double-pass cyclonic quartz spray chamber, as it gives the highest signal-to-noise ratio. In conjunction with 6 μg ml−1 Si solution to yield intense Si isotope beams, this setup results in an ∼25 V (with 1011 Ω resistor) signal on 28Si+, while on-peak noise is less than 0.06 V. A typical sample analysis comprises 8 repeats (n = 8) of an individual sample measurement (for each repeat n = 1, 168 second analysis time) normalised to bracketing measurements of critically double-spiked NIST SRM 8546 (commonly known as NBS28). Each of these n = 8 analyses consumes about 13 μg of sample Si and yields a mean δ30/28Si with a precision of approximately ±0.03‰ (2 s.e., 2 × standard error of the mean). Over a 16 month period, the reproducibility of the 11 mean δ30/28Si values of such n = 8 analyses of the silicate reference material BHVO-2 is ±0.03‰ (2 s.d., 2 × standard deviation), which is 2 to 8 times better than the long-term reproducibility of traditional Si isotope measurement methods (∼±0.1‰, 2 s.d., δ30/28Si). This agreement between the long-term and short-term variability illustrates that the data sample the same population over the long and short terms, i.e., there is no scatter on the timescale of 16 months additional to what we observe over twenty hours (the typical timescale in one analytical session). Thus, for any set of n repeats, including n >8, their 2 s.e. should prove a useful metric of the reproducibility of their mean. Three international geological reference materials and a Si isotope reference material, diatomite, were characterised via the critical mixture double-spiking technique. Our results, expressed as δ30/28SiNBS28, for BHVO-2 (−0.276 ± 0.011‰, 2 s.e., n = 94), BIR-1 (−0.321 ± 0.025‰, 2 s.e., n = 27), JP-1 (−0.273 ± 0.030‰, 2 s.e., n = 19) and diatomite (1.244 ± 0.025‰, 2 s.e., n = 20), are consistent with literature data, i.e., within the error range, but much more precise. [ABSTRACT FROM AUTHOR]

Over the past few decades, the use of functional magnetic resonance imaging (fMRI) on neonates and very young children has increased dramatically in research and clinical settings. However, the specific characteristics of this population and the MRI standards largely derived from adult studies, pose serious practical challenges. The current study aims to provide general methodological guidelines for customized neonatal fMRI by assessing the performance of various fMRI hardware and software applications. Specifically, this article focuses on MR equipment (head coils) and MR sequences (singleband vs. multiband). We computed and compared the signal-to-noise ratio (SNR) and the temporal SNR (tSNR) in different fMRI protocols using a small-size spherical phantom in three different commercial receiver-only head-neck coils. Our findings highlight the importance of coil selection and fMRI sequence planning in optimizing neonatal fMRI. For SNR, the prescan normalize filter resulted in significantly higher values overall, while in general there was no difference between the different sequences. In terms of head coil performance, the 20-channel head coil showed slightly but significantly higher values compared to the others. For tSNR, there was no difference in the usage of the prescan normalize filter, but the values were significantly higher in the singleband EPI sequences compared to the multiband. In contrast to the SNR, the pediatric head coil seems to have an advantage for tSNR. We provide five practical guidelines to assist researchers and clinicians in developing fMRI studies in neonates and young infants. These recommendations are especially relevant considering ethical constraints and exogenous challenges of neonatal fMRI. [ABSTRACT FROM AUTHOR]

Objectives: With the widespread clinical application of prostate magnetic resonance imaging (MRI), there has been an increasing demand for lesion detection and accurate diagnosis in prostate MR, which relies heavily on satisfactory image quality. Focusing on the primary sequences involved in Prostate Imaging Reporting and Data System (PI-RADS), this study have evaluated common quality issues in clinical practice (such as signal-to-noise ratio (SNR), artifacts, boundaries, and enhancement). The aim of the study was to determine the impact of image quality on clinically significant prostate cancer (csPCa) detection, positive predictive value (PPV) and radiologist's diagnosis in different sequences and prostate zones. Methods: This retrospective study included 306 patients who underwent prostate MRI with definitive pathological reports from February 2021 to December 2022. All histopathological specimens were evaluated according to the recommendations of the International Society of Urological Pathology (ISUP). An ISUP Grade Group ≥ 2 was considered as csPCa. Three radiologists from different centers respectively performed a binary classification assessment of image quality in the following ten aspects: (1) T2WI in the axial plane: SNR, prostate boundary conditions, the presence of artifacts; (2) T2WI in the sagittal or coronal plane: prostate boundary conditions; (3) DWI: SNR, delineation between the peripheral and transition zone, the presence of artifacts, the matching of DWI and T2WI images; (4) DCE: the evaluation of obturator artery enhancement, the evaluation of dynamic contrast enhancement. Fleiss' Kappa was used to determine the inter-reader agreement. Wilson's 95% confidence interval (95% CI) was used to calculate PPV. Chi-square test was used to calculate statistical significance. A p-value < 0.05 was considered statistically significant. Results: High-quality images had a higher csPCa detection rate (56.5% to 64.3%) in axial T2WI, DWI, and DCE, with significant statistical differences in SNR in axial T2WI (p 0.002), the presence of artifacts in axial T2WI (p 0.044), the presence of artifacts in DWI (p < 0.001), and the matching of DWI and T2WI images (p < 0.001). High-quality images had a higher PPV (72.5% to 78.8%) and showed significant statistical significance in axial T2WI, DWI, and DCE. Additionally, we found that PI-RADS 3 (24.0% to 52.9%) contained more low-quality images compared to PI-RADS 4-5 (20.6% to 39.3%), with significant statistical differences in the prostate boundary conditions in axial T2WI (p 0.048) and the presence of artifacts in DWI (p 0.001). Regarding the relationship between csPCa detection and image quality in different prostate zones, this study found that significant statistical differences were only observed between high- (63.5% to 75.7%) and low-quality (30.0% to 50.0%) images in the peripheral zone (PZ). Conclusion: Prostate MRI quality may have an impact on the diagnostic performance. The poorer image quality is associated with lower csPCa detection rates and PPV, which can lead to an increase in radiologist's ambiguous diagnosis (PI-RADS 3), especially for the lesions located at PZ. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate deep learning reconstruction (DLR)-based accelerated rectal magnetic resonance imaging (MRI) compared with standard MRI. Materials and methods: Patients with biopsy-confirmed rectal adenocarcinoma between November/2022 and May/2023 in a single centre were prospectively enrolled for an intra-individual comparison between standard fast spin-echo (FSEstandard) and DLR-based FSE (FSEDL) sequences. Quantitative and qualitative image quality metrics of the pre-therapeutic MRIs were evaluated in all patients; diagnostic performance and evaluating time for T-staging, N-staging, extramural vascular invasion (EMVI), and mesorectal fascia (MRF) status was further analysed in patients undergoing curative surgery, with histopathologic results as the diagnostic gold standard. Results: A total of 117 patients were enrolled, with 60 patients undergoing curative surgery. FSEDL reduced the acquisition time by 65% than FSEstandard. FSEDL exhibited higher signal-to-noise ratios, contrast-to-noise ratio, and subjective scores (noise, tumour margin clarity, visualisation of bowel wall layering and MRF, overall image quality, and diagnostic confidence) than FSEstandard (p < 0.001). Reduced artefacts were observed in FSEDL for patients without spasmolytics (p < 0.05). FSEDL provided higher T-staging accuracy by junior readers than FSEstandard (reader 1, 58.33% vs 70.00%, p = 0.016; reader 3, 60.00% vs 76.67%, p = 0.021), with similar N-staging, EMVI, and MRF performance. No significant difference was observed for senior readers. FSEDL exhibited shorter diagnostic time in all readers' T-staging and overall evaluation, and junior readers' EMVI and MRF (p < 0.05). Conclusion: FSEDL provided improved image quality, reading time, and junior radiologists' T-staging accuracy than FSEstandard, while reducing the acquisition time by 65%. Clinical relevance statement: DLR is clinically applicable for rectal MRI, providing improved image quality with shorter scanning time, which may ease the examination burden. It is beneficial for diagnostic optimisation in improving junior radiologists' T-staging accuracy and reading time. Key Points: The rising incidence of rectal cancer has demanded enhanced efficiency and quality in imaging examinations. FSEDLdemonstrated superior image quality and had a 65% reduced acquisition time. FSEDLcan improve the diagnostic accuracy of T-staging and reduce the reading time for assessing rectal cancer. [ABSTRACT FROM AUTHOR]

The recently developed Frequency-Bessel (F-J) transform array technique has significantly advanced multi-mode surface wave tomography, providing precise constraints on shear wave velocity (VS) models of the crust and uppermost mantle. This technique has primarily focused on extracting inter-station surface waves from ambient noise cross-correlation. Energetic typhoons generate abundant surface wave signals, prompting growing interest in utilizing these signals to investigate subsurface structures. This study explores the feasibility and application of using microseisms excited by strong typhoons for F-J transform surface wave tomography. By analyzing microseisms recorded by a broadband seismic array in Mongolia during energetic typhoons, we observed multi-mode surface waves with high signal-to-noise ratios. Our analysis revealed that the sources of these waves closely correspond to typhoon tracks, confirming that they were primarily excited by typhoons. We successfully extracted multi-mode dispersion curves for imaging the VS structure beneath the array using the F-J transform. Additionally, we derived multi-mode dispersion curves from a two-year noise dataset recorded by the same array, which were also used to invert for the VS structure. Comparison of VS models derived from typhoon-induced microseisms and the two-year noise dataset showed negligible differences within the depth range of 0–140 km. Our findings demonstrate the potential value of microseisms generated by energetic typhoons in precisely probing the VS structure of the crust and uppermost mantle. [ABSTRACT FROM AUTHOR]

Objectives: To compare the image quality and efficacy of the adaptive imaging receiver (AIR) coil (GE Healthcare) and the traditional coil for multiplexed sensitivity encoding diffusion-weighted imaging (MUSE-DWI) in the detection of focal liver lesions (FLLs). Methods: Two groups of MUSE-DWI were obtained. Image quality was qualitatively evaluated by 3 independent blinded radiologists on a 5-point scale, and quantitative parameters were calculated by measurements of the region of interest in the liver and FLLs. McNemar's test were used to compare the characteristics and detectability. Results: Less image noise, sharper contours, milder susceptibility artefacts, and better liver lesion conspicuity were found by all radiologists in 60 livers with 140 FLLs with the AIR coil than with the traditional coil (reader average mean, 4.3-4.4 vs. 3.7-4.0, P < .001). The signal-to-noise ratio (SNR) of the liver was significantly higher with the AIR coil than with the traditional coil (right lobe: mean, 8.89 vs.7.76, P < .05; left lobe: mean, 7.14 vs.6.19, P < .001), and the SNR of FLLs (mean, 24.62 vs. 21.01, P < .001) and lesion-to-liver CNR (mean, 16.61 vs. 14.02, P < .001) exhibited significant differences between the AIR coil and the traditional coil. Besides, superior detection of FLLs was observed with the AIR coil compared to the traditional coil (95.7% [134/140] vs. 85.7% [120/140], P < .001). Conclusions: The AIR coil yields less noise, fewer distortions, better lesion detectability, higher SNR of the liver and FLLs, and improved lesion-to-liver CNR during liver MUSE-DWI. Thus, it is a feasible and effective scanning scheme in liver MRI. Advances in knowledge: The AIR coil improves SNR and the quality of liver MR imaging compared with the traditional coil. [ABSTRACT FROM AUTHOR]

In this paper, we calculate the energy, signal-to-noise ratio (SNR), detection range, and angular anisotropy of the matter, matter memory, and neutrino memory gravitational-wave (GW) signatures of 21 three-dimensional initially nonrotating core-collapse supernova (CCSN) models carried to late times. We find that inferred energy, SNR, and detection range are angle-dependent quantities, and that the spread of possible energy, signal to noise, and detection ranges across all viewing angles generally increases with progenitor mass. When examining the low-frequency matter memory and neutrino memory components of the signal, we find that the neutrino memory is the most detectable component of a CCSN GW signal, and that DECIGO is best equipped to detect both matter memory and neutrino memory. Moreover, we find that the polarization angle between the h + and h × strains serves as a unique identifier of matter and neutrino memory. Finally, we develop a Galactic density- and stellar mass-weighted formalism to calculate the rate at which we can expect to detect CCSN GW signals with the Advanced Laser Interferometer Gravitational-Wave Observatory (aLIGO). When considering only the matter component of the signal, the aLIGO detection rate is around 65% of the total Galactic supernova rate, but increases to 90% when incorporating the neutrino memory component. We find that all future detectors (Einstein Telescope, Cosmic Explorer, DECIGO) will be able to detect CCSN GW signals from the entire Galaxy, and for the higher-mass progenitors even into the Local Group of galaxies. [ABSTRACT FROM AUTHOR]

Monitoring seismic velocity changes obtained from ambient noise correlations is widely used to understand changes in rock properties in response to earthquakes, volcanic activities and environmental changes. Since continuous seismic data have been accumulated, this method can estimate long-term changes in seismic velocity, such as crustal recovery after a major earthquake and temporal variations in seismic velocity related to long-term environmental change. Changes in seismic velocity can be estimated with a high temporal resolution by measuring the phase differences of ambient noise correlations based on a seismic interferometry method. Still, these phase differences are influenced not only by seismic wave velocity changes but also by errors in clock timing in seismometers. The clock drift occurs due to out-of-synchronization with the GPS clock and the drift of the internal clock. Therefore, to accurately monitor temporal changes in crustal structure by measuring the phase differences of noise correlations, it is crucial to evaluate the contribution of errors in clock timing to the phase differences. Recently, a method using an extended Kalman filter based on a state-space model was developed for reliable detection of temporal changes in the waveforms of ambient noise correlations, with the state-space model offering the advantage of flexible modelling of time-series data. In this study, we incorporated the time-shifts caused by clock time errors of the seismometer into the state-space model of the temporal changes in ambient noise correlations. We estimated seismic velocity changes, amplitude changes of noise correlations and clock time errors from 2010 April to 2021 September at seismic stations around the Shinmoe-dake volcano in Japan, which experienced eruptions in 2011 and 2018, respectively. Several stations exhibited clear clock time offsets, and the occurrence of clock time-shifts coincided with the dates when the data logger was turned off for seismic station maintenance or replacement of the seismometer. The proposed method provides stable estimations with respect to the signal-to-noise ratio of the waveform, and this stable estimation facilitates accurate timing of seismic recordings, enabling precise analysis of seismic phase arrival times. [ABSTRACT FROM AUTHOR]

Intermediate-depth earthquakes, accommodating intraslab deformation, typically occur within subduction zone settings at depths between 60–300 km. These events are in a unique position to inform us about the geodynamics of the subducting slab, specifically the geometry of the slab and the stress state of the host material. Improvements in the density and quality of recorded seismic data enhance our ability to determine precise locations of intermediate-depth earthquakes, in order to establish connections between event nucleation and the tectonic setting. Depth phases (near-source surface reflections, e.g. pP and sP) are crucial for the accurate determination of earthquake source depth using global seismic data. However, they suffer from poor signal-to-noise ratios in the P wave coda. This reduces the ability to systematically measure differential traveltimes to the direct P arrival, particularly for the frequent lower magnitude seismicity which highlights considerable seismogenic regions of the subducted slabs. To address this limitation, we have developed an automated approach to group globally distributed stations at teleseismic distances into ad-hoc arrays with apertures of 2.5 |$^\circ$|⁠ , before optimizing and applying phase-weighted beamforming techniques to each array. Resultant vespagrams allow automated picking algorithms to determine differential arrival times between the depth phases and their corresponding direct P arrival. Using these differential times we can then determine the depths of earthquakes, which in turn can be used to create a catalogue of relocated events. This will allow new comparisons and insights into the governing controls on the distribution of earthquakes in subducted slabs. We demonstrate this method by relocating intermediate-depth events associated with northern Chile and the Peruvian flat slab regions of the subducting Nazca plate. The relocated Chilean catalogue contains comparable event depths to an established catalogue, calculated using a semi-automated global methodology, which serves to validate our fully automatic methodology. The new Peruvian catalogue we generate indicates three broad zones of seismicity approximately between latitudes 1–7 |$^\circ$| S, 7–13 |$^\circ$| S and 13–19 |$^\circ$| S. These align with flat to steep slab dip transitions and the previously identified Pucallpa Nest. We also find a regionally deeper slab top than indicated by recent slab models, with intraslab events concentrated at points where the slab bends, suggesting a link between slab flexure and intermediate-depth earthquake nucleation. [ABSTRACT FROM AUTHOR]

A cooperative network could improve energy efficiency by combining non-orthogonal multiple access (NOMA) with simultaneous wireless information and power transfer (SWIPT). This work proposes a novel model for optimal resource allocation for specified users through combined time switching and power splitting protocol. The system uses an amplified and forward-based energy harvesting protocol to broadcast the data in the Nakagami-m fading channel. An algorithm that provides the power coefficients for all NOMA users as a Levy Aquila optimiser is exploited to maximise the signal-to-noise ratio for far and near users. Further, the parameters of combined time switching and power splitting are brought by a game theoretic approach, which supports better energy harvesting in relay nodes with improved energy efficiency and high outage probability. The simulated results show that the employed scheme's energy efficiency has increased by 11.3%, improved from the GS-Dinkelbach algorithm. [ABSTRACT FROM AUTHOR]

In this research paper, we introduce an adaptive block-matching motion estimation algorithm to improve the accuracy and efficiency of motion estimation (ME). First, we present a block generation system that creates blocks of varying sizes based on the detected motion location. Second, we incorporate predictive tools such as early termination and variable window size to optimize our block-matching algorithm. Furthermore, we propose two distinct search patterns to achieve maximum quality and efficiency. We evaluated the proposed algorithms on 20 videos and compared the results with known algorithms, including the full search algorithm (FSA), which is a benchmark for ME accuracy. Our proposed quality-based algorithm shows an improvement of 0.27 dB in peak signal-to-noise ratio (PSNR) on average for reconstructed frames compared to FSA, along with a reduction of 71.66% in searched blocks. Similarly, our proposed efficiency-based method results in a 0.07 dB increase in PSNR and a 97.93% reduction in searched blocks compared to FSA. These findings suggest that our proposed method has the potential to improve the performance of ME in video coding. [ABSTRACT FROM AUTHOR]

Objective: To demonstrate the potential of low-dose ultra-high-resolution CT (UHRCT) images to generate high-quality radiographic images on extremity phantoms and to estimate the radiation dose required for this. Materials and methods: A hand and knee phantom containing real human bones was imaged on an UHRCT scanner at full-dose, half-dose, and quarter-dose levels using a high-resolution extremity protocol. The raw data was reconstructed using both filtered back projection (FBP) and an iterative reconstruction algorithm (AIDR3D). Using custom designed software, each CT volume data set was converted to attenuation coefficients, and then a synthesized radiograph (synDX) was generated by forward projecting the volume data sets from a point source onto a 2D synthetic detector. The signal-to-noise ratio (SNR) was measured in the synDXs across all dose levels and the root-mean-squared error (RMSE) was computed with the FD synDXs as the reference. Results: The proposed workflow generates high-quality synDXs at any arbitrary angle. For FBP, the SNR largely tracked with the radiation dose levels for both the knee and hand phantoms. For the knee phantom, iterative reconstruction provided a 6.1% higher SNR when compared to FBP. The RMSE was overall higher for the lowest dose levels and monotonically decreased with increasing dose. No substantial differences were observed qualitatively in the visualization of skeletal detail of the phantoms. Conclusion: The fine detail provided by UHRCT acquisitions of extremities facilitates the ability to generate quality radiographs, potentially eliminating the need for additional scanning on a conventional digital radiography system. [ABSTRACT FROM AUTHOR]

Site monitoring is indispensable for modern construction management. Contact approaches, represented by wearable devices, have problems such as privacy leaks and hindering working. Vision-based noncontact methods depend highly on light and environmental conditions, and have poor three-dimensional perception ability. To propose an all-weather noncontact activity identification approach on construction sites, four-dimensional (4D) millimeter-wave (MMW) radar is adopted in this study for the first time because of its excellent abilities of motion sensing, spatial sensing, and penetration. First, a feature processing method is proposed to convert the MMW signal to a seven-dimensional point cloud, which consists of the shape information (x , y , and z) and four attributes (Doppler′ , SNR′ , H , and V), representing the information of velocity, signal-to-noise ratio, height, and volume, respectively. Second, a novel deep learning framework is developed, which contains (1) one shape subnetwork, driven by the PointNet++ model, to capture the shape information of objects; (2) four attribute subnetworks to fully utilize the additional attribute features; and (3) a two-layer fusion module to combine all the outputs of the subnetworks. With precision of 0.963, recall of 0.961, and an F1 score of 0.962, the results show that the proposed method can accurately identify construction activities under different environmental conditions. It also can facilitate further development of MMW radar–based solutions for construction site analysis. [ABSTRACT FROM AUTHOR]

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Background: Heart rate (HR) is a critical biomarker that provides insights into overall health, stress levels, and the autonomic nervous system. Pulse wave signals contain valuable information about the cardiovascular system and heart status. However, signal acquisition in wearables poses challenges, particularly when using electrical sensors, due to factors like the distance from the heart, body movement, and suboptimal electrode placement. Methods: Electrical bioimpedance (EBI) measurements using bipolar and tetrapolar electrode systems were employed for pulse wave signal acquisition from the wrist in both perpendicular and distal configurations. Signal preprocessing techniques, including baseline removal via Hankel matrix methods, normalization, cross-correlation, and peak detection, were applied to improve signal quality. This study describes the combination of sensor-level signal acquisition and processing for accurate wearable HR estimation. Results: The bipolar system was shown to produce larger Δ Z (t) , while the tetrapolar system demonstrated higher sensitivity. Distal placement of the electrodes yielded greater Δ Z (t) (up to 0.231 Ω) when targeting both wrist arteries. Bandpass filtering resulted in a better signal-to-noise ratio (SNR), achieving 3.6 dB for the best bipolar setup and 4.8 dB for the tetrapolar setup, compared to 2.6 and 3.3 dB SNR, respectively, with the Savitzky–Golay filter. The custom HR estimation algorithm presented in this paper demonstrated improved accuracy over a reference method, achieving an average error of 1.8 beats per minute for the best bipolar setup, with a mean absolute percentage error (MAPE) of 8%. Conclusions: The analysis supports the feasibility of using bipolar electrode setups on the wrist and highlights the importance of electrode positioning relative to the arteries. The proposed signal processing method, featuring a preprocessing pipeline and HR estimation algorithm, provides a proof-of-concept demonstration for HR estimation from EBI signals acquired at the wrist. [ABSTRACT FROM AUTHOR]

Natural gas pipelines are an essential part of the economy. Natural gas pipelines may leak after aging, strong vibration signals may be generated in the pipeline when leakage occurs, and vibration signals may be noisy. Traditional variational mode decomposition (VMD) noise reduction methods need to set parameters in advance, and so may not achieve the best decomposition effect. To solve this problem, this paper proposes a method for pipeline leakage location based on the sparrow search algorithm (SSA) optimization of VMD combined with generalized quadratic cross-correlation. The method first calculates the original signal-to-noise ratio (SNR), and if the SNR is low, wavelet threshold denoising is used to process the signal. Then, SSA optimization is used to refine the two key parameters of VMD (penalty parameter α and mode decomposition number K) based on sample entropy. Subsequently, the signal undergoes decomposition into K intrinsic mode function (IMF) components through VMD according to the obtained analysis parameter combination. Then, the IMF components are screened to obtain the reconstructed signal. Finally, the noise reduction signal is obtained. The signal delay after noise reduction is obtained through a generalized quadratic cross-correlation and the accurate leakage position is obtained using the delay. Experiments showed that the minimum relative error of this method could reach 0.6%, which was more accurate than the traditional VMD method, and effectively improved the accuracy of noisy signals in pipeline leakage locations. [ABSTRACT FROM AUTHOR]

It is essential to enhance the speech signal's clarity and quality in order to maintain the message's content. By boosting the noisy voice signal, the speech signal quality can be raised. Two techniques are presented in this study to significantly minimize the additive background noise. In order to minimize non-stationary additive noise concerning the speech signal, the first approach employs modified multiband spectral subtraction. With this technique, spectral subtraction is carried out based on the signal to noise ratio (SNR) values in various noisy speech frames. When the noisy signal and noise signal are somewhat correlated, a second method is used to minimize the cross spectral components. These techniques are used to get over the drawbacks of the fundamental spectrum subtraction method. To improve the noisy speech signal, both techniques are combined. [ABSTRACT FROM AUTHOR]

This paper presents a resource allocation scheme that fulfills the maximum possible aggregate rate of the capacity region by targeting the corner points of the multiple-input multiple-output multiple access channel. This corner points of the channel's capacity region are attainable whenever each user's transmission has minimum possible interference among other users. This work aims to investigate the non-singularity of such situations by the exploitation of users' geographic location seeking the opportunity of getting users' transmission spatially multiplexed. The developed model demonstrates that similar results can be achieved with partial channel state information knowledge under certain conditions throughout the operational signal to noise ratio range. The proposed resource allocation scheme is designed for a shotgun cellular system with a random distribution of users over a circular coverage area. The proposed model uses stochastic geometry to prove that when number of users grows up within the coverage area, the probability of achieving the corner points sum rate increases rapidly. The developed model was evaluated, and the results show that for a circular coverage area with a radius of 10 km, the probability of having users whose transmissions can be spatially multiplexed with minimum interference increases as the number of users grows to 300 users. [ABSTRACT FROM AUTHOR]