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Limited data on the correlation between the perineal body (PB) and stress urinary incontinence (SUI) are available. The objectives of this study were to quantify the PB using shear wave elastography (SWE) technology with a high-frequency linear array probe to evaluate the relationship between the properties of PB and stress urinary incontinence (SUI). This study included 64 women with SUI and 70 female control participants. The length, height, perimeter, and area of PB in all participants were calculated using transperineal ultrasound, and the elasticity of PB was assessed by SWE at rest and during the maximal Valsalva maneuver, respectively. In addition, the comparison of PB parameters between the patients with SUI and the healthy participants was conducted. The transperineal ultrasound and SWE examination was performed in 134 participants, and the elastic modulus values were significantly increased from participants at rest to those during the maximal Valsalva maneuver in all participants (Emax: 35.59 versus 53.13 kPa, P < 0.001; and Emean: 26.97 versus 40.25 kPa, P < 0.001). Emax and Emean of PB exhibited significant differences during the maximal Valsalva maneuver between the SUI group and the control group (47.73 versus 58.06 kPa, P < 0.001; and 35.78 versus 44.33 kPa, P < 0.001) and had a negative correlation with SUI. The BMI and PB height during the maximal Valsalva maneuver in the SUI group were found to be significantly higher than that in healthy volunteers. Emax and Emean of PB negatively correlated with BMI during the maximal Valsalva maneuver (r = -0.277, P = 0.001 and r = -0.211, P = 0.014). ROC curve analysis demonstrated that PB perimeter of less than 12.68mm was strongly associated with SUI during the maximal Valsalva maneuver, and an Emax of less than 55.76 kPa had a 100% specificity in predicting SUI. SWE can quantify the elasticity of PB, identifying a significant difference between participants at rest and during Valsalva maneuver. In addition, the stiffness of the PB was significantly lower in women with SUI than in healthy women, which may provide a noninvasive clinical practice in SUI prediction.

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Railway transportation has been integrated into people's lives. According to the "Notice on the release of the General Technical Specification of High-speed Railway Power Supply Safety Testing (6C System) System" issued by the National Railway Administration of China in 2012, it is required to install pantograph and slide monitoring devices in high-speed railway stations, station throats and the inlet and exit lines of high-speed railway sections, and it is required to detect the damage of the slider with high precision. It can be seen that the good condition of the pantograph slider is very important for the normal operation of the railway system. As a part of providing power for high-speed rail and subway, the pantograph must be paid attention to in railway transportation to ensure its integrity. The wear of the pantograph is mainly due to the contact power supply between the slide block and the long wire during high-speed operation, which inevitably produces scratches, resulting in depressions on the upper surface of the pantograph slide block. During long-term use, because the depression is too deep, there is a risk of fracture. Therefore, it is necessary to monitor the slider regularly and replace the slider with serious wear. At present, most of the traditional methods use automation technology or simple computer vision technology for detection, which is inefficient. Therefore, this paper introduces computer vision and deep learning technology into pantograph slide wear detection. Specifically, this paper mainly studies the wear detection of the pantograph slider based on deep learning and the main purpose is to improve the detection accuracy and improve the effect of segmentation. From a methodological perspective, this paper employs a linear array camera to enhance the quality of the data sets. Additionally, it integrates an attention mechanism to improve segmentation performance. Furthermore, this study introduces a novel image stitching method to address issues related to incomplete images, thereby providing a comprehensive solution.

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Skin architecture and its underlying vascular structure could be used to assess the health status of skin. A non-invasive, high resolution and deep imaging modality able to visualize skin subcutaneous layers and vasculature structures could be useful for determining and characterizing skin disease and trauma. In this study, a multispectral high-frequency, linear array-based photoacoustic/ultrasound (PAUS) probe is developed and implemented for the imaging of rat skin in vivo. The study seeks to demonstrate the probe capabilities for visualizing the skin and its underlying structures, and for monitoring changes in skin structure and composition during a 5-day course of a chemical burn. We analayze composition of lipids, water, oxy-hemoglobin, and deoxy-hemoglobin (for determination of oxygen saturation) in the skin tissue. The study successfully demonstrated the high-frequency PAUS imaging probe was able to provide 3D images of the rat skin architecture, underlying vasculature structures, and oxygen saturation, water, lipids and total hemoglobin.

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We recently read with great interest a study by Zhang et al in the World Journal of Gastroenterology. In our practice, we focus specifically on examining appendiceal mucinous neoplasms (AMNs) with endoscopic ultrasound (EUS) using different scopes. AMNs are rare neoplastic lesions characterized by an accumulation of mucin inside a cystic dilatation of the appendix. Clinically, they can present as nonspecific acute appendicitis. AMNs can turn into a life-threatening condition, termed pseudomyxoma peritonei, in which the ruptured appendix causes accumulation of mucin in the abdomen. Therefore, accurate and rapid diagnosis of AMN is essential. EUS is able to confirm and stage AMNs; although, EUS examination was once limited to the rectal and anal regions due to the conventional oblique-view scopes. With the emergence of new forward-view linear echoendoscopes and instruments like EUS miniprobes and overtubes, the scope of examination is changing. Herein, we discuss the feasibility of using the curved linear array echoendoscopes to examine cecal and appendiceal orifice lesions.

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Real-time source localization is crucial for high-end automation and artificial intelligence (AI) products. However, a low signal-to-noise ratio (SNR) and limited processing time can reduce localization accuracy. This work proposes a new architecture for a time-domain feedback-based beamformer that meets real-time processing demands. The main objective of this design is to locate reflective sources by estimating their direction of arrival (DOA) and signal range. Incorporating a feedback mechanism in this architecture refines localization precision, a unique aspect of this approach. We conducted an in-depth analysis to compare the effectiveness of time-domain feedback beamforming against conventional time-domain methods, highlighting their benefits and limitations. Our evaluation of the proposed architecture, based on critical performance indicators such as peak-to-sidelobe ratio, mainlobe width, and directivity factor, demonstrates its ability to improve beamformer effectiveness significantly.

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0D Bi-based 329-type halide perovskite is demonstrated as a promising semiconductor for X-ray detection due to its strong X-ray absorption, superior stability, availability of large single crystals (SCs) and solution processibility at low temperature. However, its low mobility-lifetime product (µτ) limits its further improvement in detection sensitivity. Based on the first-principles calculations, this work designs a new 2D Bi-based 329-type halide perovskite using a mixed-halide-induced structural dimension regulation strategy. By using a continuous supply of a precursor solution, this work successfully grows inch-sized high-quality SCs. These SCs exhibit large µτ product, high resistivity, and low ion migration. The detectors fabricated using the SCs show X-ray detection sensitivity as high as 24,509 µC Gyair -1 cm-2, short response time of 315 µs, low detection limit of 4.3 nGy s-1, and superior stability. These properties are the best among all lead-free perovskite detectors and are comparable to those of the best lead-based perovskite detectors. The linear array detector assembled on the SCs for the first time also shows a high spatial resolution of 10.6 lp mm-1 during X-ray imaging. The high performance combined with superior stability of these new 329-type lead-free halide perovskite SCs is expected to promote a new generation of X-ray detection technologies.

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The exceptional performance of graphene has driven the advancement of its preparation techniques and applications. Laser-induced graphene (LIG), as a novel graphene preparation technique, has been applied in various fields. Graphene periodic structures created by the LIG technique exhibit superhydrophobic characteristics and can be used for deicing and anti-icing applications, which are significantly influenced by the laser parameters. The laser surface treatment process was simulated by a finite element software analysis (COMSOL Multiphysics) to optimize the scanning parameter range, and the linear array surface structure was subsequently fabricated by the LIG technique. The generation of graphene was confirmed by Raman spectroscopy and energy-dispersive X-ray spectroscopy. The periodic linear array structure was observed by scanning electron microscopy (SEM) and confocal laser imaging (CLSM). In addition, CLSM testings, contact angle measurements, and delayed icing experiments were systematically performed to investigate the effect of scanning speed on surface hydrophobicity. The results show that high-quality and uniform graphene can be achieved using the laser scanning speed of 125 mm/s. The periodic linear array structures can obviously increase the contact angle and suppress delayed icing. Furthermore, these structures have the enhanced ability of the electric heating deicing, which can reach 100 °C and 240 °C within 15 s and within 60 s under the DC voltage power supply ranging from 3 to 7 V, respectively. These results indicate that the LIG technique can be developed to provide an efficient, economical, and convenient approach for preparing graphene and that the hydrophobic surface array structure based on LIG has considerable potential for deicing and anti-icing applications.

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Various reconstruction algorithms have been implemented for linear array photoacoustic imaging systems with the goal of accurately reconstructing the strength absorbers within the tissue being imaged. Since the existing algorithms have been introduced by different research groups and the context of performance evaluation was not consistent, it is difficult to make a fair comparison between them. In this study, we systematically compared the performance of 10 published image reconstruction algorithms (DAS, UBP, pDAS, DMAS, MV, EIGMV, SLSC, GSC, TR, and FD) using in-vitro phantom data. Evaluations were conducted based on lateral resolution of the reconstructed images, computational time, target detectability, and noise sensitivity. We anticipate the outcome of this study will assist researchers in selecting appropriate algorithms for their linear array PA imaging applications.

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Infrared linear array detectors frequently experience vertical, low-frequency, and periodic stripe noise during imaging, stemming from electro-mechanical interference. Unlike conventional periodic disturbances, this interference showcases long periodicities and is uniquely columnar in orientation. Its presence, especially within the low-frequency domain, renders conventional filtering techniques ineffective and, at times, detrimental to image quality. Addressing this challenge, we introduce Fourier-Assisted Correlative Denoising (FACD), a correlation-centric denoising approach tailored for such unique interference patterns. This mechanism begins with the capture of a pure background image, inclusive of periodic noise, during the non-uniform correction phase of the infrared detector. Leveraging the noise's frequency domain attributes, we extract a one-dimensional single-cycle noise signal. The infrared image is subsequently segmented into parts, and using the detected noise periodicity, the one-dimensional signals for each segment are computed. By leveraging the correlation between these signals and the benchmark one-dimensional noise pattern, we ascertain the noise profile within each segment. This profile is then employed for spatial domain denoising across the entire image frame. Empirical assessments confirm that the FACD outperforms contemporary denoising techniques by augmenting the peak signal-to-noise ratio by approximately 2.5 dB, underscoring its superior robustness. Furthermore, in light of its specificity to this noise model, FACD rapidly denoises high-resolution real infrared linear array scans, thus meeting the stringent real-time and resolution imperatives of advanced infrared linear array scanning apparatuses.

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Nowadays, sparse arrays have been a hotspot for research in the direction of arrival (DOA). In order to achieve a big value for degrees of freedom (DOFs) using spatial smoothing methods, researchers try to use multiple uniform linear arrays (ULAs) to construct sparse arrays. But, with the number of subarrays increasing, the complexity also increases. Hence, in this paper, a design method, named as the cross-coarray consecutive-connected (4C) criterion, and the sparse array using Q ULAs (SA-UQ) are proposed. We first analyze the virtual sensor distribution of SA-U2 and extend the conclusions to SA-UQ, which is the 4C criterion. Then, we give an algorithm to solve the displacement between subarrays under the given Q ULAs. At last, we consider a special case, SA-U3. Through the analysis of DOFs, SA-UQ can find underdetermined signals. Moreover, SA-U3 can obtain DOFs close to other sparse arrays using three ULAs. The simulation experiments prove the performance of SA-UQ.

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In this article, we propose a light weight, low profile Multiple Input Multiple Output (MIMO) antenna system for compact 5th Generation (5G) mmwave devices. Using a RO5880 substrate that is incredibly thin, the suggested antenna is made up of circular rings stacked vertically and horizontally on top of one another. The single element antenna board has dimensions of 12 × 12 × 0.254 mm3 while the size of the radiating element is 6 × 2 × 0.254 mm3 (0.56λ0 × 0.19λ0 × 0.02λ0). The proposed antenna showed dual band characteristics. The first resonance showed a bandwidth of 10 GHz with a starting frequency of 23 GHz to an ending frequency point of 33 GHz followed by a second resonance bandwidth of 3.25 GHz ranging from 37.75 to 41 GHz, respectively. The proposed antenna is transformed into a four element Linear array system with size of 48 × 12 × 0.254 mm3 (4.48λ0 × 1.12λ0 × 0.02λ0). The isolation levels at both resonance bands were noted to be >20 dB which shows high levels of isolation among radiating elements. The MIMO parameters such as Envelope Correlation Co-efficient (ECC), Mean Effective Gain (MEG) and Diversity Gain (DG) were derived and were found to be in satisfactory limits. The proposed MIMO system model is fabricated and through validation and testing of the prototype, the results were found to be in good agreement with simulations.

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Passive radar is an interesting approach in the context of non-cooperative target detection. Because the signal source takes advantage of the so-called illuminator of opportunity (IoO), the deployed system is silent, allowing the operator cheap, portable, and practically undetectable deployments. These systems match perfectly with the use of antenna arrays to take advantage of the additional gains provided by the coherent combination of the signals received at each element. To obtain these benefits, linear processing methods are required to enhance the system's performance. In this work, we summarize the main beamforming methods in the literature to provide a clear picture of the current state of the art. Next, we perform an analysis of the benefits and drawbacks and explore the chance of increasing the number of antenna elements. Finally, we identify the major challenges to be addressed by researchers in the future.

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This paper proposes a joint estimation method for source number and DOA based on an improved convolutional neural network for unknown source number and undetermined DOA estimation. By analyzing the signal model, the paper designs a convolutional neural network model based on the existence of a mapping relationship between the covariance matrix and both the source number and DOA estimation. The model, which discards the pooling layer to avoid data loss and introduces the dropout method to improve generalization, takes the signal covariance matrix as input and the two branches of source number estimation and DOA estimation as outputs, and achieves the unfixed number of DOA estimation by filling in invalid values. Simulation experiments and analysis of the results show that the algorithm can effectively achieve the joint estimation of source number and DOA. Under the conditions of high SNR and a large snapshot number, both the proposed algorithm and the traditional algorithm have high estimation accuracy, while under the conditions of low SNR and a small snapshot, the algorithm is better than the traditional algorithm, and under the underdetermined conditions, where the traditional algorithm often fails, the algorithm can still achieve the joint estimation.

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Video 1Video demonstrating the use of a DEIP to facilitate both radial and linear EUS in the proximal colon.

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Australia's cervical screening program transitioned from cytology to HPV-testing with genotyping for HPV16/18 in Dec'2017. We investigated whether program data could be used to monitor HPV vaccination program impact (commenced in 2007) on HPV16/18 prevalence and compared estimates with pre-vaccination benchmark prevalence. Pre-vaccination samples (2005-2008) (n = 1933; WHINURS), from 25 to 64-year-old women had been previously analysed with Linear Array (LA). Post-vaccination samples (2013-2014) (n = 2989; Compass pilot), from 25 to 64-year-old women, were analysed by cobas 4800 (cobas), and by LA for historical comparability. Age standardised pre-vaccination HPV16/18 prevalence was 4.85% (95%CI:3.81-5.89) by LA; post-vaccination estimates were 1.67% (95%CI:1.21-2.13%) by LA, 1.49% (95%CI:1.05-1.93%) by cobas, and 1.63% (95%CI:1.17-2.08%) for cobas and LA testing of non-16/18 cobas positives (cobas/LA). Age-standardised pre-vaccination oncogenic HPV prevalence was 15.70% (95%CI:13.79-17.60%) by LA; post-vaccination estimates were 9.06% (95%CI:8.02-10.09%) by LA, 8.47% (95%CI:7.47-9.47%) by cobas and cobas/LA. Standardised rate ratios between post-vs. pre-vaccination rates were significantly different for HPV16/18, non-16/18 HPV and oncogenic HPV: 0.34 (95%CI:0.23-0.50), 0.68 (95%CI:0.55-0.84) and 0.58 (95%CI:0.48-0.69), respectively. Additional strategies (LA for all cobas positives; combined cobas and LA results on all samples) had similar results. If a single method is applied consistently, it will provide important data on relative changes in HPV prevalence following vaccination.

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This article proposes a direction-of-arrival (DOA) estimation algorithm for a random sparse linear array based on a novel graph neural network (GNN). Unlike convolutional layers and fully connected layers, which do not interact well with information between different antennas, the GNN model can adapt to the goniometry problem of non-uniform random sparse linear arrays without any prior information by applying neighbor nodes' aggregation and update operations. This helps the model in learning signal features under complex environmental conditions. We train the model in an end-to-end way to reduce the complexity of the network. Experiments are conducted on the uniform and sparse linear arrays for various signal-to-noise ratio (SNR) and numbers of snapshots for comparison. We prove that the GNN model has superior angle estimation performance on arrays with large sparsity that cannot be used by traditional algorithms and surpasses existing deep learning models based on convolutional or fully connected structures. The proposed algorithm shows excellent DOA estimation performance under the complex conditions of limited snapshots, low signal-to-noise ratio, and large array sparsity as well. In addition, the algorithm has a low time calculation cost and is suitable for scenarios that require low latency.

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Nonuniform array geometries provide freedom for increased aperture and reduced mutual coupling. A necessary and sufficient condition is given for an array of isotropic sensor elements to be unambiguous for any specified set of directions of arrival. The set of unambiguous spatial frequencies is shown to be a parallelepiped, admitting simple geometrical interpretation. Results are used in design of linear, planar, and 3D arrays.

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This paper presents a new scheme of applying CORPS (coherently radiating periodic structures) for reducing the number of phase shifters in linear antenna arrays. This scheme can be seen as a combination of the properties of two techniques: CORPS and butler. The proposed system applies an interleaving of several blocks of 2 × 3 CORPS networks. This interleaving of two stages of 2 × 3 CORPS networks is made in a convenient way to provide the required progressive phase for beam-scanning and the level of amplitude excitations necessary for achieving the radiation characteristics of low SLL. Interesting results are provided based on experimental measurements and full-wave simulations to analyze and evaluate the performance of the feeding network based on CORPS and the reduction capability of the number of phase shifters in the antenna system. The proposed design methodology achieves a reduction capability of 66% in the number of phase shifters used in linear antenna arrays. This reduction in the complexity of the antenna system is reached maintaining a peak SLL of -22 dB with scanning ranges of until ±25°. A good design option is provided to simplify the complexity of the feeding network in antenna array applications.

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In this paper, we present a design method for a wideband non-uniformly spaced linear array (NUSLA), with both symmetric and asymmetric geometries, using the modified reinforcement learning algorithm (MORELA). We designed a cost function that provided freedom to the beam pattern by setting limits only on the beam width (BW) and side-lobe level (SLL) in order to satisfy the desired BW and SLL in the wide band. We added the scan angle condition to the cost function to design the scanned beam pattern, as the ability to scan a beam in the desired direction is important in various applications. In order to prevent possible pointing angle errors for asymmetric NUSLA, we employed a penalty function to ensure the peak at the desired direction. Modified reinforcement learning algorithm (MORELA), which is a reinforcement learning-based algorithm used to determine a global optimum of the cost function, is applied to optimize the spacing and weights of the NUSLA by minimizing the proposed cost function. The performance of the proposed scheme was verified by comparing it with that of existing heuristic optimization algorithms via computer simulations.

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Ultrasonic linear arrays have great potential to generate high-quality three-dimensional (3D) images by scanning the array. However, the generated images suffer from low resolution in the elevation plane, limiting the image quality for a reliable 3D Non-Destructive Testing (NDT) inspection. Although several ultrasonic imaging methods have been implemented to inspect different types of defects, there has been limited research to characterise surface-breaking cracks (SBCs) in 3D quantitatively. To improve the characterisation of surface-breaking cracks (SBCs), a 3D hybrid imaging method is proposed by combining the Half Skip Total Focusing Method (HSTFM) and the Synthetic Aperture Focusing Technique (SAFT) using a linear array. This paper proposed the implementation of an array with a reduced element length for full matrix capture (FMC) data acquisition. In conjunction with the hybrid imaging method, a reduced element array enables the utilisation of the information from a broad ultrasonic beam in the elevation direction to achieve improved image resolution. The imaging capability is assessed via a point spread function (PSF) as well as numerical simulations. From the PSF measurements, the image resolution is shown to improve with the smaller element length of the array, which is attributable to the combination of wide beamwidth and hybrid imaging method. Thereafter, experimental validation was performed with arrays of different elevation lengths, where an excellent match with the numerical results was observed. Furthermore, the crack sizing was performed using a 6-dB-drop rule, which assisted in accurately predicting the shape and size of the SBCs and is shown to measure the depth of SBCs with greater confidence. It is shown that a reduced array elevation with the hybrid imaging method and sizing method yields improved image resolution contrary to conventional linear arrays. This approach can offer a significant improvement in manifesting complete comprehension of the spatial defect relationship, enabling NDT engineers to analyse the inspection results quantitatively in 3D for progressive reliability.