Signal-to-noise Research Articles

Characterization of Optokinetic Nystagmus in Healthy Participants With a Novel Oculography Device.

To develop a proof-of-concept smart-phone-based eye-tracking algorithm to assess non-pathologic optokinetic (OKN) nystagmus in healthy participants. Current videonystagmography (VNG) is typically restricted to in-office use, and advances in portable vestibular diagnostics would yield immense public health benefits. Prospective cohort study. Tertiary academic medical center. Healthy participants (n = 39) without dizziness or vertigo were recruited. A smart-phone attached to a custom head stabilization device illuminated by a white LED circuit was used to record nystagmus induced with a 30 frames per second OKN stimulus over a 60-second period. A centroid tracking algorithm was created to detect slow-phase velocity (SPV) of horizontal nystagmus in a diverse subject cohort in a variety of lighting conditions. Nystagmus recordings were compared to those obtained with a standard VNG system. Non-pathologic nystagmus from an OKN stimulus was measured across multiple lighting conditions, with high signal-to-noise ratios (SNR) and mean SPV 22.13 ± 5.26°/s. Nystagmus SPV was not significantly different between the device and standard VNG system (t = -0.5, P = .6). Lighting conditions produced SNRs of 57.30 (ideal), 50.59 (backlit), 51.33 (side-lit), 49.28 (dark), 54.52 (outdoor lighting). We demonstrate the feasibility of a novel portable oculography system in the detection of non-pathologic nystagmus in healthy subjects. Future applications of this system include: (1) to obtain real-time measurements of nystagmus during an acute vertigo attack; (2) to test patients unable or unwilling to present to a specialized vestibular laboratory; (3) to efficiently repeat testing overtime; (4) to improve accessibility of vestibular testing.

In-pixel foreground and contrast enhancement circuits with customizable mapping

This paper presents an in-pixel contrast enhancement circuit that performs image processing directly within the pixel circuit. The circuit leverages HyperFET, a hybrid device combining a MOSFET and a phase transition material (PTM), to enhance performance. It can be tuned for different modes of operation. In foreground enhancement mode, it suppresses low-intensity background pixels to nearly zero, isolating the foreground for better object visibility. In contrast enhancement mode, it improves overall image contrast. The contrast enhancement function is customizable both during the design phase and in real-time, allowing the circuit to adapt to specific applications and varying lighting conditions. A model of the designed pixel circuit is developed and applied to a full pixel array, demonstrating significant improvements in image quality. Simulations performed in HSPICE show a nearly 6x increase in Michelson Contrast Ratio (CR) in the foreground enhancement mode. Furthermore, process variation and Signal-to-Noise Ratio (SNR) analysis has been conducted to evaluate the robustness of the design under manufacturing variations. The simulation results indicate its potential for real-time, adaptive contrast enhancement across various imaging environments.

Pilot Data for a New Headphone-Based Assessment of Absolute Localization in the Assessment of Auditory Processing Disorder (APD)

Background/Objectives: Localization deficit is often said to be a symptom of Auditory Processing Disorder (APD). However, no clinically viable assessment of localization ability has been developed to date. The current study presents pilot data for a new assessment of absolute auditory localization using headphones. Methods: Speech phrases encoded with non-individualized head-related transfer functions (HRTF) using real-time digital processing were presented to two cohorts of participants with normal hearing. Variations in the simulated environment (anechoic and reverberant) and signal to noise ratio (SNR) were made to assess each of these factors’ influences on localization performance. Experiment 1 assessed 30 young adults aged 21–33 years old and Experiment 2 assessed 28 young adults aged 21–29 years old. All participants had hearing thresholds better than 20 dB HL. Results: Participants performed the localization task with a moderate degree of accuracy (Experiment 1: Mean RMS error = 25.9°; Experiment 2: Mean RMS error 27.2°). Front–back errors (FBEs) were evident, contributing to an average RMS error that was notably elevated when compared to similar free-field tasks. There was no statistically significant influence from the simulated environment or SNR on performance. Conclusions: An exploration of test viability in the pediatric and APD-positive populations is warranted alongside further correction for FBEs; however, the potential for future clinical implementation of this measure of absolute auditory localization is encouraging.

Reliable quantification of neural entrainment to rhythmic auditory stimulation: simulation and experimental validation.

Entrainment has been considered as a potential mechanism underlying the facilitatory effect of rhythmic neural stimulation on neurorehabilitation. The inconsistent effects of brain stimulation on neurorehabilitation found in the literature may be caused by the variability in neural entrainment. To dissect the underlying mechanisms and optimize brain stimulation for improved effectiveness, it is critical to reliably assess the occurrence and the strength of neural entrainment. However, the factors influencing entrainment assessment are not yet fully understood. This study aims to investigate whether and how the relevant factors (i.e., data length, frequency bandwidth, signal-to-noise ratio (SNR), center frequency, and the constant component of stimulus-response phase-difference) influence the assessment reliability of neural entrainment. We simulated data for 28 scenarios to answer above questions. We also recorded experimental data to verify the findings from our simulation study. A minimal data length is required to achieve reliable neural entrainment assessment, and this requirement critically depends on the bandwidth and SNR, but is independent of the center frequency and the constant component of stimulus-response phase-difference. Furthermore, changing of bandwidth is accompanied by the change of SNR. The present study has revealed how data length, bandwidth, and SNR critically affect the assessment reliability of neural entrainment. The findings provide a foundation for the parameter setting in experiment design and data analysis in neural entrainment studies. While this study is within the context of rhythmic auditory stimulation, the conclusions may be applicable for neural entrainment to other rhythmic stimulations.

Combining Low-energy Images in Dual-energy Spectral CT With Deep Learning Image Reconstruction Algorithm to Improve Inferior Vena Cava Image Quality.

To explore the application of low-energy image in dual-energy spectral CT (DEsCT) combined with deep learning image reconstruction (DLIR) to improve inferior vena cava imaging. Thirty patients with inferior vena cava syndrome underwent contrast-enhanced upper abdominal CT with routine dose, and the 40, 50, 60, 70, and 80keV images in the delayed phase were first reconstructed with the ASiR-V40% algorithm. Image quality was evaluated both quantitatively [CT value, SD, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for inferior vena cava] and qualitatively to select an optimal energy level with the best image quality. Then, the optimal-energy images were reconstructed again using deep learning image reconstruction medium strength (DLIR-M) and DLIR-H (high strength) algorithms and compared with that of ASiR-V40%. The objective CT value, SD, SNR, and CNR increased with the decrease in energy level, with statistically significant differences (all P<0.05). The 40keV images had the highest CT values, SNR, and CNR and good diagnostic acceptability, and 40keV was selected as the best energy level. Compared with ASiR-V40% and DLIR-M, DLIR-H had the lowest SD, highest SNR and CNR, and subjective score (all P<0.001) with good consistencies between the 2 physicians (all k ≥0.75). The 40keV images with DLIR-H had the highest overall image quality, showing sharper edges of inferior vena cava vessels and clearer lumen in patients with Budd-Chiari syndrome. Compared with the ASiR-V algorithm, DLIR-H significantly reduces image noise and provides the highest CNR and best diagnostic image quality for the 40keV DEsCT images in imaging inferior vena cava.

A User Pairing Algorithm for the Uplink NOMA With Imperfect CSI

ABSTRACTIn this paper, the problem of user pairing across subcarriers is investigated for the uplink non‐orthogonal multiple‐access (NOMA) systems, in which every subcarrier is assigned to exactly two users. Considering fixed transmit power for all users, we pair users such that sum‐rate is maximized over all subcarriers. While different (near) optimum allocation schemes exist in the literature, they pertain to perfect channel state information (CSI), which is impractical. To overcome this limitation, we assume statistical CSI is available in the form of the first two moments of channel gains. Then, we maximize a lower bound on the sum rate over user pairings utilizing a block coordinate ascent (BCA) approach with guaranteed convergence to a suboptimal solution. Numerical results corroborate a satisfactory performance for our proposed algorithm versus the perfect CSI benchmark and a simple random assignment approach. For high signal to noise ratios (SNRs) and the more challenging frequency selective fading setup, the solution of our proposed algorithm comes close to those of the optimal pairing with perfect CSI. Furthermore, simulations revealed that if the proposed BCA is utilized assuming perfect CSI, it performs similar to the well‐known global optimum in the frequency flat fading setup and outperforms the best existing near‐optimal solution in the frequency selective fading scenario. These observations offer a testament to the strength of the proposed BCA algorithm in both perfect and imperfect CSI conditions.

NeOPT: neural optical projection tomography with low-cost imaging setup

Optical projection tomography (OPT) is an imaging technology that employs light to reconstruct 3D models of objects. Recent advancements in neural radiance field (NeRF) have improved 3D biomedical imaging, yet they perform suboptimally with low signal-to-noise ratio (SNR) data in low-cost setups and are not directly applicable to OPT systems. Here, we introduce neural optical projection tomography (NeOPT), leveraging a low-cost OPT imaging setup combined with a pose-robust neural-based 3D reconstruction method to achieve high-quality OPT results. NeOPT incorporates discrete wavelet transform (DWT) to reduce imaging noise under low-SNR conditions. Experiments demonstrate that NeOPT significantly outperforms traditional OPT techniques.

The Clinical Value of the MAR+ Metal Artifact Reduction Algorithm for Postoperative Assessment of Lumbar Internal Fixation.

With the widespread use of lumbar pedicle screws for internal fixation, the morphology of the screws and the surrounding tissues should be evaluated. The metal artifact reduction (MAR) technique can reduce the artifacts caused by pedicle screws, improve the quality of computed tomography (CT) images after pedicle fixation, and provide more imaging information to the clinic. To explore whether the MAR+ method, a projection-based algorithm for correcting metal artifacts through multiple iterative operations, can reduce metal artifacts and have an impact on the structure of the surrounding metal. A total of 57 patients who underwent lumbar spine CT examination after lumbar internal fixation from January to December 2023 in our hospital were retrospectively enrolled. The CT images were reconstructed using MAR+ and non-MAR+ techniques and were subdivided into MAR+ and non-MAR+ groups. The CT number (in Hounsfield units) and the SD noise values of the spinal canal, vertebral body, psoas major muscle, and adjacent fat were measured in the 2 groups of CT images, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The subjective score was evaluated by two diagnostic radiologists using a double-blind method for image quality evaluation of the MAR+ group and the non-MAR+ group, and the image quality was classified on a 5-point scale. The rank-sum test was utilized to compare the subjective and objective scores of the 2 groups. The SD values of the spinal canal (Z=-4.12, P<0.01), vertebral body (Z=-3.81, P<0.01), and psoas major muscle (Z=-3.87, P<0.01) in the MAR+ group were significantly lower than those in the non-MAR+ group (P<0.05). However, the SD values of the adjacent fat (Z=-2.03, P=0.42) in the MAR+ group, although smaller than those in the non-MAR+ group, were not statistically significant. The CNR values of vertebral canal (Z=-2.67, P=0.008) and fat (Z=-2.60, P=0.009) were higher in the MAR+ group than in the non-MAR+ group, whereas the CNR values of the vertebral body (Z=-6.74, P<0.01) in the MAR+ group were smaller than those in the non-MAR+ group, and the difference of all of them was statistically significant (P<0.05). Furthermore, for both CT and SNR values, the MAR group's values were all less than those of the non-MAR group and were statistically significant (P<0.05). The subjective scores of the measurement points were all higher in the MAR+ group than in the non-MAR+ group. The MAR+ technique has a noise reduction effect on different tissues and artifacts are significantly reduced. Although the artifacts caused by metal screws were not completely eliminated, the MAR+ technique was able to reduce the interference of artifacts in the diagnosis of CT images, thus improving their diagnostic quality.

Raman measurements using an acousto-optic tunable filter spatial heterodyne Raman spectrometer with an echelle-mirror structure

This study presents an acousto-optic tunable filter technology-based echelle-mirror spatial heterodyne Raman spectrometer (AOTF-EMSHRS) that integrates echelle gratings with mirror spatial heterodyne detection. The instrument uses the grating’s high resolution and the AOTF’s rapid wavelength selection capability to optimize the optical path through a mirror design. This results in a high signal-to-noise ratio (SNR), high spectral resolution, and broad wavelength range measurements. After calibration, the theoretical resolution reached 1.53 cm−1, with a single-order spectral detection range of 1574.3 cm−1. The spectral detection range spans from 100 cm−1 to 4397 cm−1. Experimentally, we detected and analyzed various organic and inorganic substances and solutions. We compared EMSHRS performances with and without the AOTF and found that the SNR of the AOTF-EMSHRS system has improved by at least twofold. Additionally, we measured solutions with different molar concentrations, various sulfate mixtures, and mixtures in transparent containers made from different materials, obtained comprehensive spectral information, and conducted detailed analyses. The AOTF-EMSHRS demonstrated excellent measurement performance during complex sample detection and analysis.

Correlated Photon Lidar Based on Time-Division Multiplexing

Single-photon lidar (SPL) exhibits high sensitivity, making it particularly suitable for detecting weak echoes over long distances. However, its susceptibility to background noise necessitates the implementation of advanced filtering techniques and complex algorithms, which can significantly increase system cost and complexity. To address these challenges, we propose a time-division-multiplexing-based correlated photon lidar system that employs a narrowband pulsed laser with stable time delays and variable pulse intensities, thereby establishing temporal and intensity correlations. This all-fiber solution not only simplifies the system architecture but also enhances operational efficiency. An adaptive cross-correlation method incorporating time slicing has been developed to extract histogram signals, enabling successful 1.5 km distance measurements under intense daytime noise conditions, using a 1 s accumulation time and a 20 mm receiving aperture. The experimental results demonstrate a 38% (from 1.11 to 1.52) improvement in the signal-to-noise ratio (SNR), thereby enhancing the system’s anti-noise capability, facilitating rapid detection, and reducing overall system costs.

A Survey on WiFi-based Human Identification: Scenarios, Challenges, and Current Solutions

With the evolution of wireless sensing technology, WiFi-based human identification has demonstrated tremendous potential in human-computer interaction and home security. However, most existing research operates in controlled environments, overlooking the complexities of real-world scenarios, such as signal fading, signal interference and uncertainty, and the diversity of application requirements. This article presents a comprehensive analysis of a series of representative research articles on WiFi-based human identification and summarizes five major challenges in achieving high-precision identification in complex application scenarios. Non-line-of-sight (NLOS) user sensing, coexistence user sensing, dynamic group user sensing, cross-domain user sensing, and multi-task sensing. Additionally, this article proposes a series of current solutions, including improving the signal-to-noise ratio (SNR) of NLOS sensing from the perspective of communication signals and sensing models, addressing coexistence sensing issues, designing adaptable tasks for dynamic user groups, leveraging techniques like adversarial learning and transfer learning for cross-domain problems, and employing modular deep learning architectures. By providing a comprehensive overview of WiFi-based human identification, this survey not only offers insights into current research but also charts a roadmap for future investigations. It is anticipated that this survey will stimulate innovative research endeavors and foster the expansion of wireless sensing technology across diverse application domains.

Rapid Micro-Motion Feature Extraction of Multiple Space Targets Based on Improved IRT

Micro-motion feature extraction is of great significance for target recognition. However, traditional methods mostly focus on single target and struggle to correctly separate the severely overlapping micro-motion curves of multiple targets. In this paper, a rapid micro-motion feature extraction algorithm of multiple space targets based on inverse radon transform (IRT) with a modified model is proposed. First, the high-resolution range profile (HRRP) generated from echo is subject to binarization to improve the unstable estimation caused by noise. Then, the micro-motion period in a complicated multi-target scenario is obtained by a period estimation method based on the autocorrelation coefficients of binarized HRRP. To further improve the extraction accuracy, the IRT model of the micro-range curve is modified from the sine function to second-order sine function. By searching for the remaining unknown parameters in the model in conjunction with the period, the precise micro-range curves are quickly separated. Each time the curves of a target are extracted, they are removed, and the next extraction is carried out until all the targets have been searched. Finally, simulation and experimental results indicate that the proposed algorithm can not only correctly separate the micro-motion feature curves of multiple space targets under low signal-to-noise ratio (SNR) conditions but also significantly outperforms the original IRT in terms of extraction speed.

Selective use of ferumoxytol-enhanced magnetic resonance angiography in patients with renal insufficiency: insights from a pilot study.

The use of conventional contrast agents in computed tomography (CT) and magnetic resonance (MR) imaging is often limited in patients with chronic kidney disease (CKD) due to potential nephrotoxicity. Ferumoxytol, originally developed for iron supplementation, has emerged as a promising alternative MR contrast agent that is safer for patients with CKD. This study aims to present our center's experience with ferumoxytol as a contrast agent in CKD patients. We retrospectively reviewed 24 MR imaging studies of the chest, abdomen, and pelvis performed in CKD patients at our center. All patients were deemed suitable for ferumoxytol administration, receiving a dose of 4mg/kg with post-injection monitoring. The imaging quality of the ascending, descending, suprarenal and infrarenal aortic segments was assessed by three independent observers using a qualitative scoring system (nondiagnostic, poor vascular definition, good vascular definition, and excellent vascular definition). Quantitative analyses, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and heterogeneity index, were also performed. No adverse reactions to ferumoxytol were observed. Of the 72 vascular segments evaluated, 90.8% of the images were rated as excellent vascular definition, and 9.2% were rated as good vascular definition. Inter-observer agreement was substantial (k = 0.647), with no statistically significant differences in ratings between observers. Ferumoxytol is a safe and effective alternative to conventional contrast agents for MR vascular imaging, particularly in patients with renal insufficiency. These findings support its selective use in appropriate clinical scenarios, offering a reliable imaging option for CKD patients.

Reconfigurable ScAlN Piezoelectric Micromachined Ultrasonic Transducer Arrays for Range Finding

Due to their compact sizes, low power consumption levels, and convenient integration capabilities, piezoelectric micromachined ultrasonic transducers (PMUTs) have gained significant attention for enabling environmental sensing functionalities. However, the frequency inconsistency of the PMUT arrays often leads to directional errors with the ultrasonic beams. Herein, we propose a reconfigurable PMUT array based on a Sc0.2Al0.8N piezoelectric thin film for in-air ranging. Each element of the reconfigurable PMUT array possesses the ability to be independently replaced, enabling matching of the required frequency characteristics, which enhances the reusability of the device. The experimental results show that the frequency uniformity of the 2 × 2 PMUT array reaches 0.38% and the half-power beam width (θ−3dB) of the array measured at 20 cm is 60°. At a resonance of 69.7 kHz, the sound pressure output reaches 7.4 Pa (sound pressure level of 108.2 dB) at 19 mm, with a reception sensitivity of approximately 11.6 mV/Pa. Ultimately, the maximum sensing distance of the array is 7.9 m, and it extends to 14.1 m with a horn, with a signal-to-noise ratio (SNR) of 19.5 dB. This research significantly expands the ranging capability of PMUTs and showcases their great potential in environmental perception applications.

Transformer Architecture for Micromotion Target Detection Based on Multi-Scale Subaperture Coherent Integration

In recent years, long-time coherent integration techniques have gained significant attention in maneuvering target detection due to their ability to effectively enhance the signal-to-noise ratio (SNR) and improve detection performance. However, for space targets, challenges such as micromotion phenomena and complex scattering characteristics make envelope alignment and phase compensation difficult, thereby limiting integration gain. To address these issues, in this study, we conducted an in-depth analysis of the echo model of cylindrical space targets (CSTs) based on different types of scattering centers. Building on this foundation, the multi-scale subaperture coherent integration Transformer (MsSCIFormer) was proposed, which integrates MsSCI with a Transformer architecture to achieve precise detection and motion parameter estimation of space targets in low-SNR environments. The core of the method lies in the introduction of a convolutional neural network (CNN) feature extractor and a dual-attention mechanism, covering both intra-subaperture attention (Intra-SA) and inter-subaperture attention (Inter-SA). This design efficiently captures the spatial distribution and motion patterns of the scattering centers of space targets. By aggregating multi-scale features, MsSCIFormer significantly enhances the detection performance and improves the accuracy of motion parameter estimation. Simulation experiments demonstrated that MsSCIFormer outperforms traditional moving target detection (MTD) methods and other deep learning-based algorithms in both detection and estimation tasks. Furthermore, each module proposed in this study was proven to contribute positively to the overall performance of the network.

Aluminium foil micro-defect detection method based on motion-induced eddy current using differential TMR sensor spatial position compensation

ABSTRACT Lithium-ion batteries are a key technology in the energy storage field, and aluminum foil, a common cathode current collector, can develop micro-defects that significantly affect battery performance, especially during high-speed manufacturing. Motion-induced eddy current (MIEC) sensing is effective for detecting defects in conductive materials. However, lift-off fluctuations degrade the signal-to-noise ratio (SNR), making it challenging to detect small or shallow defects. Traditional methods, such as differential sensor outputs, help reduce these fluctuations, but baseline signal deviations caused by varying relative positions between the sensor and the permanent magnet limit their effectiveness. To address this, this study proposes a spatial compensation method for differential tunnel magnetoresistance (TMR) sensors, which reduces baseline deviations caused by position differences, mitigating lift-off fluctuations and improving the SNR for micro-defect detection.

Clinical application of third-generation dual-source CT-based dynamic imaging reconstruction for pulmonary embolism imaging

BackgroundTo evaluate the clinical diagnostic value of third-generation dual-source CT for pulmonary embolism, focusing on the optimization of dual-source CT scanning with dynamic reconstruction in acute pulmonary embolism (PE) and various imaging manifestations.MethodsEighty-two patients with pulmonary embolism were enrolled and randomly divided into standard CT angiography (SCTA) and dynamic CT angiography (DCTA). DCTA patients were divided into dynamic CT angiography arterial phase (DCTAa), time phase Angiography reconstruction (TMIP-CTA), and 4D noise reduction TMIP-CTA according to the image reconstruction. The region of interest was selected in the region of the pulmonary trunk and its branches, respectively. The vessel CT value and image background noise (IN) of each subgroup were also determined, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Simultaneously two radiologists performed a subjective evaluation of the quality of the picture images.ResultsThe DCTA group had a lower contrast dose than the SCAT group, but the vessel CT value, IN, CNR, and SNR were significantly higher in the DCTA group compared with the SCTA group. CT of the vascular lumen was generally higher in all subgroups of DCTA than in SCTA, with the highest in the TMIP-CIA group. IN was significantly higher in both the DCTAa and TMIP-CTA groups than in the SCTA group. SNR and CNR were elevated in TMIP-CTA and 4D noise reduction TMIP-CTA compared to the SCTA group. In addition, the subjective image quality scores of the DCTA group were significantly higher than those of SCTA, and the 4D noise reduction TMIP-CTA had the most. However, the ED of the SCTA group was lower than that of the DCTA group.Conclusion4D noise reduction TMIP-CTA based on DCTA reconstruction significantly improves the quality of pulmonary artery CTPA images and increases the clinical diagnostic rate, with potential for clinical dissemination.

Non-parametric Bayesian deep learning approach for whole-body low-dose PET reconstruction and uncertainty assessment.

Positron emission tomography (PET) imaging plays a pivotal role in oncology for the early detection of metastatic tumors and response to therapy assessment due to its high sensitivity compared to anatomical imaging modalities. The balance between image quality and radiation exposure is critical, as reducing the administered dose results in a lower signal-to-noise ratio (SNR) and information loss, which may significantly affect clinical diagnosis. Deep learning (DL) algorithms have recently made significant progress in low-dose (LD) PET reconstruction. Nevertheless, a successful clinical application requires a thorough evaluation of uncertainty to ensure informed clinical judgment. We propose NPB-LDPET, a DL-based non-parametric Bayesian framework for LD PET reconstruction and uncertainty assessment. Our framework utilizes an Adam optimizer with stochastic gradient Langevin dynamics (SGLD) to sample from the underlying posterior distribution. We employed the Ultra-low-dose PET Challenge dataset to assess our framework's performance relative to the Monte Carlo dropout benchmark. We evaluated global reconstruction accuracy utilizing SSIM, PSNR, and NRMSE, local lesion conspicuity using mean absolute error (MAE) and local contrast, and the clinical relevance of uncertainty maps employing correlation between the uncertainty measures and the dose reduction factor (DRF). Our NPB-LDPET reconstruction method exhibits a significantly superior global reconstruction accuracy for various DRFs (paired t-test, , N=10,631). Moreover, we demonstrate a 21% reduction in MAE (573.54 vs. 723.70, paired t-test, , N=28) and an 8.3% improvement in local lesion contrast (2.077 vs. 1.916, paired t-test, , N=28). Furthermore, our framework exhibits a stronger correlation between the predicted uncertainty 95th percentile score and the DRF ( vs. , N=10,631). The proposed framework has the potential to improve clinical decision-making for LD PET imaging by providing a more accurate and informative reconstruction while reducing radiation exposure.

Full-rank imaging detection of multi-frames of ultrasonic-guided waves in welded structural plate based on arc sparse array

Abstract The utilization of ultrasonic-guided wave array imaging in the detection of weld structural plates holds significant importance. This study presents a novel approach for full-rank imaging of shear horizontal (SH)-guided wave arc sparse arrays in welding structure imaging detection, The paper discusses the mechanism behind the ultrasonic-guided wave arc sparse array, specifically focusing on its full-rank imaging of multi-frames (FRIMR) capabilities in the presence of weld scattering. The receiving aperture of the arc sparse array is analyzed and discussed using the amplitude matrix and time-transition matrix. Both synthetic aperture and FRIMR of the arc sparse array are examined. The research findings demonstrate that FRIMR detection using ultrasonic-guided wave arc sparse array effectively captures scattering information equivalent to the wavelength size in the imaging detection area. This results in an average signal-to-noise ratio (SNR) improvement of 39.2% in full-frame imaging compared to multi-frames imaging. The imaging experiment validates the feasibility of the proposed imaging principle, highlighting its potential for imaging detection of welded structures. The research presented in this paper serves as a crucial foundation for future studies and applications of array imaging detection using ultrasonic-guided waves in welded structural plates.&amp;#xD;

Traffic and Scenario Adaptive OFDM-IM for Vehicular Networks: A Fuzzy Logic Based Optimization Approach

Orthogonal Frequency Division Multiplexing with Index Modulation (OFDM-IM) holds significant importance in vehicle-to-everything (V2X) communications, with its main advantages being outstanding spectral efficiency and strong interference resistance. However, the existing OFDM-IM systems in vehicular networks overlook actual vehicular network channels and the impact of scatterers, thus failing to accurately reflect the system performance. Moreover, these systems focus solely on the bit error rate (BER) and ignore user requirements for low energy consumption and high spectral efficiency. To address these issues, we propose a user demand- and scenario-adaptive OFDM-IM method that optimizes the OFDM-IM index parameter by considering the spectral efficiency, BER, and energy consumption. Firstly, considering non-line-of-sight components and roadside reflectors, we establish a vehicle-to-vehicle (V2V) communication channel model for straight road scenarios. Then, we construct a transmission framework for vehicular network communication using OFDM-IM. Specifically, we develop an energy efficiency maximization formula, in which fuzzy logic is used to adjust the weights of the three performance indicators to meet various environmental and user requirements. In detail, we discuss the minimum signal-to-noise ratio (SNR) required for OFDM-IM to achieve a lower BER than traditional OFDM in various vehicular communication scenarios. Thus, we can make appropriate choices based on the robustness of the simulation results. The simulation results presented in this paper indicate our method’s effectiveness in enhancing the system’s reliability, efficiency, and flexibility.