SNR Improvement Research Articles

Influence of anatomical structure on antiscatter grid performance in 2D: application to x-ray angiography and a prototype 29:1 ratio grid

Objective. The use of uniform phantoms to assess the influence of x-ray scatter and antiscatter grids on x-ray angiography and fluoroscopy image quality disregards the influence of spatially variable x-ray attenuation of patients. The purpose of this work was to measure scatter to primary ratio (SPR) and antiscatter grid SNR improvement factor (K SNR) using experimental conditions which better mimic patient imaging conditions. Approach. Three adult-sized anthropomorphic phantoms were used. AP and lateral projection images of the thorax and abdomen were acquired with and without an antiscatter grid. Grids with ratio 15:1 and 29:1 (r15, r29) and x-ray fields of view 20, 25 (thorax) and 32, 42 cm (abdomen) were tested. Combined with a-priori measurements of grid scatter and primary transmission fractions, these images were used to calculate 2D SPR and K SNR maps. Main results. Results demonstrated that measurements by uniform phantom do not describe the complex 2D SPR and K SNR distributions associated with anthropomorphic phantoms. The regions of the images with the lowest primary x-ray intensity (greatest attenuation) had the highest SPR and the highest K SNR attributable to the grids. Considering all conditions, the 95th percentile of the SPR maps was in the range 42%–185% greater than the median values and that of the K SNR maps was 4%–20% higher than the median values. The combined influences of SID 120 vs. 107 cm and r29 vs. r15 grid resulted in K SNR in the range 1.05–1.49. Significance. Performance of anti-scatter grids using anatomically complex phantoms highlights the substantial variation of SPR and K SNR within 2D images. Also, this work demonstrates the benefit of the prototype r29 grid for thoracic and abdominal angiography imaging conditions is substantial, especially for large patients and radiodense image regions.

Enhancing corrosion detection in pulsed eddy current testing systems through autoencoder-based unsupervised learning

Pulsed Eddy Current Testing (PECT) stands out as an advanced method in Non-Destructive Testing due to its extensive spectrum characteristics in comparison to traditional ECT techniques, making it exceptionally suitable for identifying corrosion. Nonetheless, the analysis of PECT signals for corrosion detection poses a challenge due to the transient nature of these signals and the impact of sensor lift-off effects. As a result, conventional methods are facing hurdles in dealing with corrosion signals of poor quality. In this study, the challenge is addressed by employing unsupervised learning methods utilizing an autoencoder neural network. This autoencoder integrates Long Short-Term Memory and 1D convolutional layers, acquiring the underlying features of normal PECT signals from non-corrosive regions. Significantly, the model is trained exclusively on this normal data, thereby obviating the necessity for pre-existing corrosion information. Through learning the inherent structure of normal signals, the model can detect anomalies in unseen data, potentially indicating corrosion. The unsupervised framework presents several advantages, such as reducing reliance on prior corrosion knowledge, mitigating inherent noise, and addressing sensor lift-off effects. Experimental results were conducted to compare with traditional methods like the lift-off of intersection and lift-off compensation methods. This approach resulted in a significant improvement in SNR, ranging from 100 % to 200 %, thus facilitating more robust NDT applications employing smart PECT sensors empowered by unsupervised learning techniques.

Spatiotemporal energy regulation strategy for enhancing SNR and spatial resolution in the damage detection with SH0 waves

The fundamental shear horizontal (SH0) wave, which can be generated by electromagnetic acoustic transducers (EMATs), is widely used in the structural health monitoring fields owing to its non-dispersive nature and the ability to detect over long distances. However, current SH0 wave methods hardly achieve the excellent signal-to-noise ratio (SNR) of the echo signals and satisfactory spatial resolution to defects due to the co-restriction between them. To address these issues, a spatiotemporal energy regulation strategy (STERS) is proposed in this paper to optimize energy distribution in the temporal and spatial dimensions. First, spatiotemporal pulse compression technology (STPCT) is proposed to improve both SNR and spatial range resolution for defect detection. Second, a staggered dual-exciter employing two sets of excitation signals is designed to improve the spatial azimuth resolution. Besides, a double sidelobe suppression algorithm (DSSA) is proposed to suppress sidelobe issues arising from the application of STPCT, thereby further improving the performance of STPCT. Finally, multiple simulations and experiments are executed to demonstrate the advantages of the proposed damage detection system. Results show that our methods can achieve comprehensive improvement in SNR, spatial range resolution, spatial azimuth resolution, and sidelobe suppression ability.

Pathlength-selective, interferometric diffuse correlation spectroscopy (PaLS-iDCS).

Diffuse correlation spectroscopy (DCS) is an optical method that offers non-invasive assessment of blood flow in tissue through the analysis of intensity fluctuations in diffusely backscattered coherent light. The non-invasive nature of the technique has enabled several clinical applications for deep tissue blood flow measurements, including cerebral blood flow monitoring as well as tumor blood flow mapping. While a promising technique, in measurement configurations targeting deep tissue hemodynamics, the standard DCS implementations suffer from insufficient signal-to-noise ratio (SNR), depth sensitivity, and sampling rate, limiting their utility. In this work, we present an enhanced DCS method called pathlength-selective, interferometric DCS (PaLS-iDCS), which improves upon both the sensitivity of the measurement to deep tissue hemodynamics and the SNR of the measurement using pathlength-specific coherent gain. Through interferometric detection, PaLS-iDCS can provide time-of-flight (ToF) specific blood flow information without the use of expensive time-tagging electronics and low-jitter detectors. The new technique is compared to time-domain DCS (TD-DCS), another enhanced DCS method able to resolve photon ToF in tissue, through Monte Carlo simulation, phantom experiments, and human subject measurements. PaLS-iDCS consistently demonstrates improvements in SNR (>2x) for similar measurement conditions (same photon ToF), and the SNR improvements allow for measurements at extended photon ToFs, which have increased sensitivity to deep tissue hemodynamics (~50% increase). Further, like TD-DCS, PaLS-iDCS allows direct estimation of tissue optical properties from the sampled ToF distribution without the need for a separate spectroscopic measurement. This method offers a relatively straightforward way to allow DCS systems to make robust measurements of blood flow with greatly enhanced sensitivity to deep tissue hemodynamics, enabling further applications of this non-invasive technology.

Linearly-coupled sigmoid bistable stochastic resonance for weak signal detection

Abstract The paper focuses on developing a stochastic resonance (SR) system designed for the detection of weak signals under alpha-stable-distributed noises. Initially, in view of the strong impulsive characteristics of noises, a linearly-coupled sigmoid bistable stochastic resonance (LSBSR) system is proposed, which is constructed by potential function and sigmoid function. Through formula derivation, it is theoretically proved that the output signal-to-noise ratio (SNR) of the LSBSR system is superior to that of the classical bistable SR system. Then, a new signal processing strategy based on the LSBSR system is introduced. Simulation experiments have demonstrated that under the input SNR = −20 dB, the detection probability of the LSBSR system exceeds 95% for the alpha-stable-distributed noise with α= 1.5. When α is reduced to 0.1, the detection probability approaches 80%, significantly outperforming other detection methods. Finally, the LSBSR system is applied to detect sea-trial signals with an SNR improvement of 22.5 dB, which further validates the practicability of the proposed system.

FPGA implementation of IIR elliptic filters for de-noising ECG signal

De-noising of ECG signal is very much necessary to monitor the heart health and to diagnose disease. This paper presents a real time de-noising system that efficiently wiped out the noises from corrupted ECG signal and improves its SNR which enhances the interpretation of features present in ECG signal. This proposed system is designed with three Infinite Impulse Response (IIR) elliptic filters in cascaded direct form II structure and is implemented on the FPGA platform. Total 376 ECG samples are taken from the open access public database physiobank. All the architectural level FPGA designs have been developed in Xilinx System Generator (XSG) and implemented on Xilinx Zynq 7000 board. Our design utilized only 3.87 % of the total available resource and consumed 0.321 watt on-chip power. Worst Negative Slack (WNS) for the critical path is 2.101 ns. The positive value of WNS signifies our hardware design implementation run successfully by meeting the required time constraint. To judge the performance of the proposed design, the measurement matrices SNR Improvement, RMSE, PRD, SSNR and SINAD are calculated and achieved 81.11% , 88.89% and 85.41% accuracy for ECG recordings of ECG-ID, MIT-BIH Normal Sinus Rhythm and MIT-BIH Arrhythmia database respectively. The novelty of this proposed denoising system is its robustness, which work satisfactorily for Baseline Wander (BW), Electrode Motion (EM), Power Line Interference (PLI), Muscle or Electromyography (EMG) and as well as Additive White Gaussian Noise (AWGN).

Delay Segmented Tristable Stochastic Resonance System Driven by Non-Gaussian Colored Noise and Its Application in Bearing Fault Detection

Abstract This study investigates the Delayed Segmented Tristable Stochastic Resonance (DSTSR) system under the influence of additive non-Gaussian colored noise. The research employs an improved segmented tristable potential function, wherein the equilibrium points and barrier heights can be independently controlled by parameters. Simultaneously, the segmented function on both sides reduces the restrictions of higher-order terms on the walls of the potential wells. The equivalent Langevin equation for the DSTSR system is obtained using the path integral method, the unified colored noise approximation method, and the small-delay approximation. Subsequently, the theoretical expressions for the steady-state probability density, mean first passage time (MFPT), and Signal-to-Noise Ratio (SNR) are derived from the resulting equations, and the impact of variations in system parameters on these performance metrics is discussed. Additionally, Monte Carlo simulations for MFPT are conducted to verify the accuracy of the theoretical derivations. Combining the results from the theoretical section and the impact of parameters on system performance, the article employs an adaptive genetic algorithm to optimize system parameters. This algorithm is then applied to simulation experiments and bearing fault detection. In the simulation experiments, the DSTSR system is compared with other systems. The results indicate that the DSTSR system exhibits the highest SNR improvement. Furthermore, in bearing fault detection under non-Gaussian colored noise, the DSTSR system shows higher spectral amplitude and SNR at the fault frequency compared to the tristable stochastic resonance system and the segmented tristable stochastic resonance system without time delay feedback. This suggests that stochastic resonance can effectively detect weak signals in non-Gaussian non-white noise scenarios, and the introduction of time delay contributes to the occurrence of stochastic resonance to a certain extent.

Enhancement of single-lead dry-electrode ECG through wavelet denoising

Neonatal electrocardiogram (ECG) monitoring is an important diagnostic tool for identifying cardiac issues in infants at birth. Long-term remote neonatal dry-electrode ECG monitoring solutions can be an additional step for preventive healthcare measures. In these solutions, power and computationally efficient embedded signal processing techniques for denoising newborn ECGs can assist in increasing neonatal medical wearable time. Wavelet denoising is an appropriate denoising mechanism with low computational complexity that can be implemented on embedded microcontrollers for long-term remote ECG monitoring. Discrete wavelet transform (DWT) denoising for neonatal dry-electrode ECG using different wavelet families is investigated. The wavelet families and mother wavelets used include Daubechies (db1, db2, db3, db4, and db6), symlets (sym5), and coiflets (coif5). Different levels of added white Gaussian noise (AWGN) were added to 19 newborn ECG signals, and denoising was performed to select the appropriate wavelets for neonatal dry-electrode ECG. The selected wavelets then undergo real noise additions of baseline wander and electrode motion to determine their robustness and accuracy. Signal-to-noise ratio (SNR), mean squared error (MSE), and power spectral density (PSD) are used to examine denoising performance. db1, db2, and db3 wavelets are eliminated from analysis where the 30 dB AWGN led to negative SNR improvement for at least one newborn ECG, removing important ECG information. db4 and sym5 are eliminated from selection due to their different waveform morphology compared to the dry-electrode newborn ECG’s QRS complex. db6 and coif5 are selected due to their highest SNR improvement and lowest MSE of 6.26 × 10−6 and 1.65 × 10−7 compared to other wavelets, respectively. Their wavelet shapes are more like a newborn ECG’s QRS morphology, validating their selection. db6 and coif5 showed similar denoising performance, decreasing electrode motion and baseline wander noisy ECG signals by 10 dB and 14 dB, respectively. Further denoising of inherent dry-electrode noise is observed. DWT with coif5 or db6 wavelets is appropriate for denoising newborn dry-electrode ECGs for long-term neonatal dry-electrode ECG monitoring solutions under different noise types. Their similarity to newborn dry-electrode ECGs yields accurate and robust reconstructed denoised newborn dry-electrode ECG signals.

The Output Signal-to-Noise Ratio and Speech Perception in Noise: The Effect of Multichannel and Free-Channel Hearing Aids

Background and Aim: The output Signal-to-Noise Ratio (SNR) is one of the essential factors in hearing aid benefits. There is limited evidence regarding SNR improvement by the Channel-Free (CFHA) and Multi-Channel Hearing Aid (MCHA) and the speech understanding in noise through them. This study aimed to investigate the extent to which output SNR was modified by CFHA and MCHA processing and the variation in aided speech recognition abilities with a change in output SNR. Methods: Thirty-six participants aged 50–65 years were included. A chosen CFHA and MCHA were used to obtain the output SNR and sentence recognition in noise in four different processing algorithms (linear, linear+noise reduction, WDRC, WDRC+noise reduction). Hagerman’s phase inversion technique was used to measure the attenuation of noise and, in turn, to obtain the output SNR of the hearing aid. Results: In all hearing aid processing algorithms among those with normal hearing and people with hearing loss, the output of CFHA revealed higher attenuation values than that of MCHA. There was a significant effect of the hearing aids and processing algorithms in both normal and individuals with hearing impairment on the mean SNR. Further, multiple linear regression analysis results showed that whether the hearing is channel-free or multi- channel significantly predicted speech recognition scores, while output SNR and processing algorithms did not. Conclusion: The signal processing algorithms in CFHA had greater noise attenuation values, better output SNR, and speech recognition scores, showing an advantage over the modern MCHA among individuals with hearing impairment. Keywords: Channel-free hearing aids; multi-channel hearing aids; sensorineural hearing loss; speech recognition

Value of vendor-agnostic deep learning image denoising in brain computed tomography: A multi-scanner study.

To evaluate the effect of a vendor-agnostic deep learning denoising (DLD) algorithm on diagnostic image quality of non-contrast cranial computed tomography (ncCT) across five CT scanners.This retrospective single-center study included ncCT data of 150 consecutive patients (30 for each of the five scanners) who had undergone routine imaging after minor head trauma. The images were reconstructed using filtered back projection (FBP) and a vendor-agnostic DLD method. Using a 4-point Likert scale, three readers performed a subjective evaluation assessing the following quality criteria: overall diagnostic image quality, image noise, gray matter-white matter differentiation (GM-WM), artifacts, sharpness, and diagnostic confidence. Objective analysis included evaluation of noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and an artifact index for the posterior fossa.In subjective image quality assessment, DLD showed constantly superior results compared to FBP in all categories and for all scanners (p<0.05) across all readers. The objective image quality analysis showed significant improvement in noise, SNR, and CNR as well as for the artifact index using DLD for all scanners (p<0.001).The vendor-agnostic deep learning denoising algorithm provided significantly superior results in the subjective as well as in the objective analysis of ncCT images of patients with minor head trauma concerning all parameters compared to the FBP reconstruction. This effect has been observed in all five included scanners. · Significant improvement of image quality for 5 scanners due to the vendor-agnostic DLD. · Subjects were patients with routine imaging after minor head trauma. · Reduction of artifacts in the posterior fossa due to the DLD. · Access to improved image quality even for older scanners from different vendors. · Kapper C, Müller L, Kronfeld A et al. Value of vendor-agnostic deep learning image denoising in brain computed tomography: A multi-scanner study. Fortschr Röntgenstr 2024; DOI 10.1055/a-2290-4781.

K-means non-uniform-quantization digital-analog radio-over-fiber scheme for THz-band photonics-aided wireless fronthaul.

We propose a non-uniform-quantization digital-analog radio-over-fiber (NUQ-DA-RoF) scheme based on an advanced K-means NUQ algorithm and demonstrate it experimentally in a 2-m 300-GHz photonics-aided wireless fronthaul system. Results show that the NUQ-DA-RoF scheme achieves a SNR gain of ∼1.9 dB compared to the uniform-quantization DA-RoF (UQ-DA-RoF) at an equivalent Common Public Radio Interface equivalent data rate (CPRI-EDR). Remarkably, the NUQ-DA-RoF scheme exhibits an ∼1.6-dB power sensitivity enhancement over the UQ-DA-RoF at the 256-QAM SNR threshold. These findings highlight the advantages of the NUQ-DA-RoF scheme over UQ-DA-RoF in terms of power budget and SNR improvement, suggesting promising prospects for future radio access networks and wireless fronthaul.

Automated detection and removal of artifacts from sEMG signals based on fuzzy inference system and signal decomposition methods

Surface electromyography (sEMG) signal quality decreases when it is contaminated by different types of artifacts. Detection and removal of the contaminants from sEMG signals were a major and interesting task for several research works in recent years. In this paper, a novel automatic approach was designed for the detection and removal of artifacts from sEMG signals. The proposed method consists of a combined model that based on a fuzzy inference system (FIS), a signal decomposition method, and a denoising technique. Based on three spectral statistical features, which are calculated in frequency domain using power spectral density: composite multiscale entropy (CMSE), kurtosis (K), and skewness (S), FIS detects the artifactual epochs of the sEMG signal. Thereafter, those epochs are decomposed into their components by a signal decomposition. In this work, we have tested the performance of two decomposition methods namely stationary wavelet transform (SWT) and variational mode decomposition (VMD). A second FIS is used to select the contaminated components. Depending on the type of artifact, two procedures are considered, the first aims to denoise motion artifact (MOA), electrocardiography interference (ECG) and power line interference (PLI), whereas the second procedure aims to eliminate the additive white gaussian noise (AWGN). Finally, the denoised signal is reconstructed. The evaluation performance of the proposed method is compared with the performance of some existing methods using the following metrics: SNR improvement (ΔSNR), the reduction in artifact (λ(%)), improvement in signal to noise and distortion ratio (ΔSNDR) and improvement in the root mean square error (ΔRMSE). The results emphazed the superiority of our method over others in terms of all the used metrics and computational time.

Low‐density algebra‐check codes for ultra reliable low latency communications

AbstractA novel channel code, the low‐density algebra‐check (LDAC) code, is introduced in this letter. LDAC code is constructed by combining sparse matrice with algebraic constraints from two types of subcodes. Detailed explanations for the encoding and decoding process of the LDAC codes are provided in the paper. Simulation results demonstrate that LDAC codes offer advantages over LDPC codes in constructing low rate codes. The proposed ultra‐low rate LDAC code can achieve approximately 0.4 and 0.2 dB improvement in SNR compared to CA‐polar and 5G LDPC, respectively, at the block error rate (BER) over the additive white Gaussian noise (AWGN) channel. LDAC codes demonstrate superior performance over LDPC codes at low rates, due to the enhanced error correction capabilities of the two types of subcodes and reduced rates without introducing short cycles.

Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation.

Ultrasound is extremely efficient for wireless signal transmission through metal barriers due to no limit of the Faraday shielding effect. Echoing in the ultrasonic channel is one of the most challenging obstacles to performing high-quality communication, which is generally coped with by using a channel equalizer or pre-distorting filter. In this study, a deep learning algorithm called a dual-path recurrent neural network (DPRNN) was investigated for echo cancellation in an ultrasonic through-metal communication system. The actual system was constructed based on the combination of software and hardware, consisting of a pair of ultrasonic transducers, an FPGA module, some lab-made circuits, etc. The approach of DPRNN echo cancellation was applied to signals with a different signal-to-noise ratio (SNR) at a 2 Mbps transmission rate, achieving higher than 20 dB SNR improvement for all situations. Furthermore, this approach was successfully used for image transmission through a 50 mm thick aluminum plate, exhibiting a 24.8 dB peak-signal-to-noise ratio (PSNR) and a about 95% structural similarity index measure (SSIM). Additionally, compared with three other echo cancellation methods-LMS, RLS and PNLMS-DPRNN has demonstrated higher efficiency. All those results firmly validate that the DPRNN algorithm is a powerful tool to conduct echo cancellation and enhance the performance of ultrasonic through-metal transmission.

Comparison of model-based versus deep learning-based image reconstruction for thin-slice T2-weighted spin-echo prostate MRI.

To compare a previous model-based image reconstruction (MBIR) with a newly developed deep learning (DL)-based image reconstruction for providing improved signal-to-noise ratio (SNR) in high through-plane resolution (1mm) T2-weighted spin-echo (T2SE) prostate MRI. Large-area contrast and high-contrast spatial resolution of the reconstruction methods were assessed quantitatively in experimental phantom studies. The methods were next evaluated radiologically in 17 subjects at 3.0 Tesla for whom prostate MRI was clinically indicated. For each subject, the axial T2SE raw data were directed to MBIR and to the DL reconstruction at three vendor-provided levels: (L)ow, (M)edium, and (H)igh. Thin-slice images from the four reconstructions were compared using evaluation criteria related to SNR, sharpness, contrast fidelity, and reviewer preference. Results were compared using the Wilcoxon signed-rank test using Bonferroni correction, and inter-reader comparisons were done using the Cohen and Krippendorf tests. Baseline contrast and resolution in phantom studies were equivalent for all four reconstruction pathways as desired. In vivo, all three DL levels (L, M, H) provided improved SNR versus MBIR. For virtually, all other evaluation criteria DL L and M were superior to MBIR. DL L and M were evaluated as superior to DL H in fidelity of contrast. For 44 of the 51 evaluations, the DL M reconstruction was preferred. The deep learning reconstruction method provides significant SNR improvement in thin-slice (1mm) T2SE images of the prostate while retaining image contrast. However, if taken to too high a level (DL High), both radiological sharpness and fidelity of contrast diminish.

Adaptive augmented cubature Kalman filter/smoother for ECG denoising.

Model-based Bayesian approaches have been widely applied in Electrocardiogram (ECG) signal processing, where their performances heavily rely on the accurate selection of model parameters, particularly the state and measurement noise covariance matrices. In this study, we introduce an adaptive augmented cubature Kalman filter/smoother (CKF/CKS) for ECG processing, which updates the noise covariance matrices at each time step to accommodate diverse noise types and input signal-to-noise ratios (SNRs). Additionally, we incorporate the dynamic time warping technique to enhance the filter's efficiency in the presence of heart rate variability. Furthermore, we propose a method to significantly reduce the computational complexity required for CKF/CKS implementation in ECG processing. The denoising performance of the proposed filter was compared to those of various nonlinear Kalman-based frameworks involving the Extended Kalman filter/smoother (EKF/EKS), the unscented Kalman filter/smoother (UKF/UKS), and the ensemble Kalman filter (EnKF) that was recently proposed for ECG enhancement. In this study, we conducted a comprehensive evaluation and comparison of the performance of various nonlinear Kalman-based frameworks for ECG signal processing, which have been proposed in recent years. Our assessment was carried out on multiple normal ECG segments extracted from different entries in the MIT-BIH Normal Sinus Rhythm Database (NSRDB). This database provides a diverse set of ECG recordings, allowing us to examine the filters' denoising capabilities across various scenarios. By comparing the performance of these filters on the same dataset, we aimed to provide a thorough analysis and identification of the most effective approach for ECG denoising. Two kinds of noises were introduced to such segments: 1-stationary white Gaussian noise and 2-non-stationary real muscle artifact noise. For evaluation, four comparable measures namely the SNR improvement, PRD, correlation coefficient and MSEWPRD were employed. The findings demonstrated that the suggested algorithm outperforms the EKF/EKS, EnKF/EnKS, UKF/UKS methods in both stationary and nonstationary environments regarding SNR improvement, PRD, correlation coefficient and MSEWPRD metrics.

Complete Complementary Coded Excitation Scheme for SNR Improvement of 2D Sparse Array Ultrasound Imaging.

Driving the numerous elements of 2D matrix arrays for 3D ultrasound imaging is very challenging in terms of cable size, wiring and data rate. The sparse array approach tackles this problem by optimally distributing a reduced number of elements over a 2D aperture while preserving a decent image quality and beam steering capabilities. Unfortunately, reducing the number of elements significantly reduces the active probe footprint reducing as a consequence the sensitivity and at the end the signal-to-noise ratio. Here we propose a new coded excitation scheme based on complete complementary codes to increase the signal-to-noise ratio in 3D ultrasound imaging with sparse arrays. These codes are known for their ideal auto-correlation and cross-correlation properties and have been widely used in Code-Division Multiple Access systems (CDMA). An algorithm for generating such codes is presented as well as the adopted imaging sequence. The proposed method has been compared in simulations to other coded excitation schemes and showed significant increase in the signal-to-noise ratio of sparse arrays with no correlation artifacts and no frame rate reduction. The gain in signal-to-noise ratio compared to the case where no coded excitation is used was around [Formula: see text] and the contrast was also improved by [Formula: see text] while the resolution was unchanged.

A multi-aperture encoding scheme for increased SNR in photoacoustic Imaging

Photoacoustic imaging creates light-induced ultrasonic signals to provide valuable information on internal body structures and tissue morphology non-invasively. A multi-aperture photoacoustic imaging (MP-PAI) system is an improvement over conventional photoacoustic imaging (PAI) systems in terms of resolution, contrast, and field of view. Previously, a prototype MP-PAI system was introduced based on multiple capacitive micromachined ultrasound transducers (CMUTs) with shared channels, such that each element in a CMUT shares its channel with its counterpart in other CMUTs. The system uses the biasing voltages of the CMUTs to switch between them and multiplex the received signals in time. Notwithstanding all the enhancements, the signal-to-noise ratio (SNR) remains limited in PAI. To address this issue, we are proposing a multi-aperture encoding scheme (MAES) to further increase the SNR in a multi-aperture PAI system. The proposed method involves receiving signals with multiple CMUTs simultaneously based on an encoding matrix, instead of switching between individual CMUTs. As a result, shared channels contain a superposition of signals, which are later recovered by applying a decoding matrix. Here, an analytical model for computing SNR with an arbitrary encoding sequence is presented, and the method is validated through numerical simulations and in an experimental study. Bipolar and unipolar encoding sequences were considered for the experiments. The numerical results show, in comparison to conventional MP-PAI, that MAES will obtain an SNR gain of 5.8 and 8.8 dB for S-sequence and truncated Hadamard encodings, respectively, when using 15 transducers. In experiments, three transducers are encoded by S-sequences and show 1.5 dB improvement in SNR over conventional MP-PAI method, which aligns well with the analytical model.

SNR of a combined mode-locked fiber laser generating conventional and dissipative solitons.

In this paper, we utilized a combined mode-locked fiber laser including a saturable absorber mirror (SESAM) and nonlinear polarization evolution (NPE) to generate conventional solitons (CSs) and dissipative solitons (DSs), respectively, in order to investigate the difference in signal-to-noise ratio (SNR) between the outputs of these two types of solitons in artificial and natural saturators. Both simulation and experimental results demonstrated that, under the shared pump power, the DSs from the NPE-based mode-locked fiber output exhibited a higher SNR of approximately 60dB, compared to the CSs from the SESAM-based mode-locked fiber output of 45dB. Furthermore, we conducted theoretical analysis of these results. We believe that this work can provide new approaches for SNR improvement research in the fields of passively mode-locked fiber lasers.

Factors Predicting Subjective Satisfaction for Successful Hearing Aid Adaptation.

(1) Background: For successful hearing aid (HA) use during daily life, an objective parameter reflecting the subjective satisfaction is required. We explored the aided hearing status, hearing in noise test (HINT) scores, and subjective outcomes to predict performance improvements in everyday living. (2) Methods: A total of 406 patients with hearing loss (HL) who were prescribed HAs were included and were divided into two groups according to the symmetricity of HL. The relationship between audiometric data and subjective questionnaires under unaided and aided (3 months) conditions were investigated. (3) Results: Patients with symmetric HL showed a significant HINT signal-to-noise ratio (SNR) change and significant increase in their subjective satisfaction questionnaire score under the bilateral HA condition. On the other hand, the HINT SNR change and subjective questionnaire score showed various significances according to the side of HA (better or worse hearing) in asymmetric HL HINT SNR and was significantly correlated with the subjective questionnaire score in symmetric HL patients and AHL patients with unilateral HA in their better ear. (4) Conclusions: The HINT SNR improvement after long-term HA use could be an effective tool for predicting the subjective satisfaction of HA use and HA validation.