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The miniaturization of semiconductor chips creates discrepancies between the designed node size and physical values. It has resulted in a tightened on-product overlay (OPO) budget and increased the demand for improved measurement noise reduction and accuracy in optical systems. A solution utilizing moiré targets can address such challenges by enabling the amplification of small misalignments that cannot be achieved with conventional overlay targets using an image-based overlay (IBO) estimator. However, moiré patterns formed within a layer introduce noise sources and problems owing to interference from the reflected light, adversely affecting the precision of overlay measurements and limiting the effective utilization of moiré patterns. We investigate the problems associated with moiré patterns in the IBO measurement method and propose a novel overlay measurement algorithm to mitigate the problems. The proposed algorithm increases the accuracy of the filtering method in the spatial frequency domain and improves the overlay precision by approximately 2% compared with conventional measurement algorithms. The proposed low-frequency selection algorithm and signal indexing algorithm effectively address the challenges posed by high-frequency problems and signal strength degradation in moiré patterns. The proposed practical solution achieves more accurate overlay measurements in semiconductor manufacturing, enabling better control and optimization of chip fabrication processes. [ABSTRACT FROM AUTHOR]

In recirculating aquaculture system, precise estimation of perch size from images is essential for developing intelligent management system. However, variations in fish postures and visual field lead to different fish sizes in RGB images, posing challenges for accurate fish detection and localization. To address the above issues, this paper proposes a nondestructive target detection and size measurement method for perch, based on depth information and RGB-D images. The details are as follows: firstly, the capture ability of perch key point features is augmented by a bi-level routing attention (BRA) mechanism. Secondly, the enhanced CSPDarknet53 to 2-Stage FPN(C2f) module and new detection layer are introduced into the model’s backbone and neck, further improving the learning ability of perch features and the recognition accuracy of small-size targets. Finally, based on the detected key point coordinates, the perch size is calculated by combining the three-dimensional transformation from depth camera and measurement model. The experimental results show that the mAP@.5:.95 for key point detection reaches 86.4%, which is 3.6% higher than the baseline model, and the average relative error of perch size measurement is ± 5%. The proposed model provides an important basis for developing scientific feeding strategies and harvest plans. [ABSTRACT FROM AUTHOR]

Objectives: Ultrasound (US) imaging has been observed to underestimate tumor size in clinical practice. This study aims to compare the size measurements of breast cancer and benign tumors using two‐dimensional ultrasound (2DUS) and contrast‐enhanced ultrasound (CEUS). Methods: The study included 42 clinically confirmed breast cancer and 47 benign breast tumors. Two experienced physicians independently measured the maximal longitudinal and transverse diameters of the masses in 2DUS and CEUS. All analyses were performed in R (4.2.2) and GraphPad Prism 6. Results: The maximal longitudinal and transverse diameters of breast cancer measured by CEUS were 26.61 ± 0.21% and 26.24 ± 0.19% larger compared with 2DUS, and benign breast tumors had an 11.74 ± 0.21% and 11.06 ± 0.14% increase in size compared with 2DUS. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) for the difference between 2DUS and CEUS was 0.870 for longitudinal diameters (95% CI: 0.795–0.945, sensitivity 0.842, specificity 0.783, threshold value 0.215), and 0.863 for transverse diameters (95% CI: 0.785–0.942, sensitivity 0.667, specificity 0.936, threshold value 0.203). Conclusions: The size measurements of both breast cancer and benign tumors were larger in CEUS compared with 2DUS, with CEUS measurements of breast cancer being more pronounced than those of benign breast tumors. These findings suggest that CEUS may provide a more precise assessment of tumor size, which is crucial for determining optimal treatment strategies and improving patient outcomes in breast cancer management. [ABSTRACT FROM AUTHOR]

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Single-particle inductively coupled plasma mass spectrometry (spICP-MS) has been used for particle size measurement of diverse types of individual nanoparticles and micrometer-sized carbon-based particles such as microplastics. However, its applicability to the measurement of micrometer-sized non-carbon-based particles such as silica (SiO2) particles is unclear. In this study, the applicability of spICP-MS to particle size measurement of non-porous/mesoporous SiO2 microspheres with a nominal diameter of 5.0 µm or smaller was investigated. Particle sizes of these microspheres were measured using both spICP-MS based on a conventional calibration approach using an ion standard solution and scanning electron microscopy as a reference technique, and the results were compared. The particle size distributions obtained using both techniques were in agreement within analytical uncertainty. The applicability of this technique to the detection of metal-containing protein-binding mesoporous SiO2 microspheres was also investigated. Bound iron (Fe)-containing proteins (i.e., lactoferrin and transferrin) of mesoporous SiO2 microspheres were detected using Fe as a presence marker for the proteins. Thus, spICP-MS is applicable to the particle size measurement of large-sized and non-porous/mesoporous SiO2 microspheres. It has considerable potential for element-based detection and qualification of bound proteins of mesoporous SiO2 microspheres in a variety of applications. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Polyp size determination plays an important role in endoscopic decision making and follow-up determination.However, there is a lack of knowledge of endoscopist accuracy for polyp sizing and efficacy of available tools for sizemeasurement.Our aimwas to compare the accuracy of visual assessment, snare, forceps, and virtual scale endoscope (VSE) in estimating polyp size among a diverse group of endoscopists. METHODS: We conducted a prospective video-based study. One hundred twenty polyps measured and recorded along with all available measurement tools were randomized to visual assessment, snare, forceps, or VSE group. Eleven endoscopists conducted video-based measurement using the randomized measurement tool. Primary outcome was relative accuracy in polyp size measurement compared with caliper measurement immediately postresection. RESULTS: One thousand three hundred twenty measurements were performed. VSE had statistically significantly higher relative accuracy when compared to forceps (79.3 vs 71.3%; P < 0.0001). Forceps had statistically significantly higher relative accuracy when compared to visual assessment (71.3 vs 63.6%; P 5 0.0036). There was no statistically significant difference when comparing visual assessment and snare-based measurements (63.6 vs 62.8%; P 5 0.797). Overall, 21.5% of polyps >5 mm were misclassified as £5 mm and 17.3% of polyps ‡10 mm were misclassified as <10 mm. VSE had the lowest percentage of polyps >5 mm misclassified as £5 mm (2.6%), polyps £5 mm misclassified as >5 mm (5.1%), and polyps <10 mm misclassified as ‡10 mm (1.7%). DISCUSSION: Visual size estimation of polyps is inaccurate independently of training level, sex, and specialty. Size measurement accuracy can be improved using forceps and yields the highest relative accuracy when an adaptive scale technology is used. [ABSTRACT FROM AUTHOR]

Simple Summary: Intraductal papillary mucinous neoplasms are non-invasive precursor lesions for pancreatic cancer that are often surgically resected when certain diagnostic criteria are met. While multiple cyst features play a role in determining their resectability, cyst size is an important factor. The aim of this study is to compare the cyst size on seven commonly-obtained MRI sequences with surgically-resected specimen pathology size in a retrospective cohort of 52 patients. The maximum MRI size across the sequences correlated well but tended to overestimate length compared to pathology measurement. BACKGROUND: While multiple cyst features are evaluated for stratifying pancreatic intraductal papillary mucinous neoplasms (IPMN), cyst size is an important factor that can influence treatment strategies. When magnetic resonance imaging (MRI) is used to evaluate IPMNs, no universally accepted sequence provides optimal size measurements. T2-weighted coronal/axial have been suggested as primary measurement sequences; however, it remains unknown how well these and maximum all-sequence diameter measurements correlate with pathology size. This study aims to compare agreement and bias between IPMN long-axis measurements on seven commonly obtained MRI sequences with pathologic size measurements. METHODS: This retrospective cohort included surgically resected IPMN cases with preoperative MRI exams. Long-axis diameter tumor measurements and the presence of worrisome features and/orhigh-risk stigmata were noted on all seven MRI sequences. MRI size and pathology agreement and MRI inter-observer agreement involved concordance correlation coefficient (CCC) and intraclass correlation coefficient (ICC), respectively. The presence of worrisome features and high-risk stigmata were compared to the tumor grade using kappa analysis. The Bland-Altman analysis assessed the systematic bias between MRI-size and pathology. RESULTS: In 52 patients (age 68 ± 13 years, 22 males), MRI sequences produced mean long-axis tumor measurements from 2.45–2.65 cm. The maximum MRI lesion size had a strong agreement with pathology (CCC = 0.82 (95% CI: 0.71–0.89)). The maximum IPMN size was typically observed on the axial T1 arterial post-contrast and MRCP coronal series and overestimated size versus pathology with bias +0.34 cm. The radiologist interobserver agreement reached ICCs 0.74 to 0.91 on the MRI sequences. CONCLUSION: The maximum MRI IPMN size strongly correlated with but tended to overestimate the length compared to the pathology, potentially related to formalin tissue shrinkage during tissue processing. [ABSTRACT FROM AUTHOR]

Brain network provides an essential perspective for studying normal and pathological brain activities. Reconstructing the brain network in the source space becomes more needed, for example, as a target in non-invasive neuromodulation. Precise estimating source activities from the scalp EEG is still challenging because it is an ill-posed question and because of the volume conduction effect. There is no consensus on how to reconstruct the EEG source network. This study uses simultaneous scalp EEG and stereo-EEG to investigate the effect of inverse solutions, connectivity measures, and node sizes on the reconstruction of the source network. We evaluated the performance of different methods on both source activity and network. Numerical simulation was also carried out for comparison. The weighted phase-lag index (wPLI) method achieved significantly better performance on the reconstructed networks in source space than five other connectivity measures (directed transfer function (DTF), partial directed coherence (PDC), efficient effective connectivity (EEC), Pearson correlation coefficient (PCC), and amplitude envelope correlation (AEC)). There is no significant difference between the inverse solutions (standardized low-resolution brain electromagnetic tomography (sLORETA), weighted minimum norm estimate (wMNE), and linearly constrained minimum variance (LCMV) beamforming) on the reconstructed source networks. The source network based on signal phases can fit intracranial activities better than signal waveform properties or causality. Our study provides a basis for reconstructing source space networks from scalp EEG, especially for future neuromodulation research. [ABSTRACT FROM AUTHOR]

Background: There is consensus that postoperative adjuvant therapy is not recommended in patients with stage 1a non-small cell lung cancer (NSCLC). Meanwhile, it is still controversial whether postoperative adjuvant chemotherapy is recommended for NSCLC patients with T2aN0M0 (stage 1b). In some patients with stage 1b NSCLC without pleural invasion, tumor diameter was measured between 3 and 4 cm by preoperative imaging and less than 3 cm by postoperative pathology specimens. TNM staging in such patients is both radiologic stage 1b and pathologic stage 1a. Thoracic surgeons are often confused about whether such patients with NSCLC will require subsequent treatment and how the survival prognosis for this group of patients will be. Methods: All data of radiographic TNM stage 1b patients who underwent radical R0 resection at the department of thoracic surgery, the First Affiliated Hospital of Soochow University between January 2013 and July 2017 were retrieved, and 208 patients were finally included in the study. Clinical data, including imaging data, pathology data, were obtained by reviewing the patients' electronic medical records. Disease-free survival (DFS) and overall survival (OS) were obtained by telephone interview. Statistical analysis was performed using SPSS (SPSS 26.0 for windows, SPSS). Results: A total of 208 patients were included in this study, 61 patients with T-stage migration (observation group) and 147 patients without T-stage migration (control group). There were significant statistical differences between the two groups in terms of preoperative FEV1/FVC and tumor diameter (specimens, CT and 3-dimensional measurements). Logistic regression results showed that lower FEV1/FVC and smaller CT measurements would make the patient's T stage more likely to migrate. Bland–Altman plots showed that tumor length measured by imaging was significantly higher than that measured by pathological specimens. Taking DFS as the outcome, the survival curve of the observation group was significantly better than that of the control group. Similarly, there was a significant difference in OS between the two groups. Conclusions: For NSCLC patients whose preoperative imaging evaluation was stage 1b (tumor diameter more than 3 cm, no main bronchus, pleura, no atelectasis), the presence of lung tissue with smaller tumor diameter and/or higher air content may indicate that the postoperative pathological staging may be changed to stage 1a (tumor diameter less than 3 cm). These patients had better survival prognosis than those who did not undergo TNM stage change and were diagnosed with stage 1b non-small cell lung cancer before and after surgery. [ABSTRACT FROM AUTHOR]

Harvesting is an important procedure for hydroponic lettuces in plant factories. At present, hydroponic lettuces are mainly harvested manually, and the key difficulty in mechanical harvesting is reducing the occurrence of leaf injury. Measuring the size of hydroponic lettuces using the image processing method and intelligently adjusting the operating parameters of the harvesting device are the foundation of high-quality harvesting for lettuces. The overlapped leaves of adjacent hydroponic lettuces cause difficulties in measuring lettuce size, especially the leaves expansion size. Therefore, we proposed an image processing method for measuring lettuce height and leaves expansion size according to the upper contour feature of lettuces and an image included three lettuces. Firstly, the upper contours of the lettuces were extracted and segmented via image preprocessing. Secondly, lettuce height was measured according to the maximum ordinate of the contour. Lastly, the lettuce's upper contour was fitted to a function to measure the leaves expansion size. The measurement results showed that the maximal relative error of the lettuce height measurements was 5.58%, and the average was 2.14%. The effect of the quadratic function in fitting the upper contour was the best compared with the cubic function and sine function. The maximal relative error of the leaves expansion size measurements was 8.59%, and the average was 4.03%. According to the results of the lettuce height and leaves expansion size measurements, the grabbing parameters of each lettuce were intelligently adjusted to verify the harvesting effect. The harvesting success rates of lettuces was above 90%, and the injured leaves areas of the left, middle, and right lettuces in each image were 192.6 mm2, 228.1 mm2, and 205.6 mm2, respectively. This paper provides a reference for the design and improvement of intelligent harvesters for hydroponic lettuces. [ABSTRACT FROM AUTHOR]

Sonar imaging is one of the important means of underwater target detection. It can measure underwater targets and draw underwater topographic map. However, the existing methods can not rely on sonar images alone to measure underwater targets. In this paper, an underwater measurement technology based on sonar image is proposed. We combine the third-party reference object, artificial landmark to make up for the defects of sonar image measurement. The proposed method includes angle measurement method and three-dimensional mapping algorithm for size measurement. In the experiment, we combine image processing technology to restore the image contour information to obtain the key points in the sonar image. The experimental results show that the accuracy of this method can reach millimeter level, and the error range is within 5%. This technology realizes the shape correction and precise length measurement of underwater targets. [ABSTRACT FROM AUTHOR]

By combining a multitask deep learning method and a nitrogen PROSPECT and scattering by arbitrarily inclined leaves (N-PROSAILs) model, we proposed a multitask learning-based hybrid model (ML-HM) for leaf nitrogen content (LNC) prediction in a previous study. To provide an optimal ML-HM design for LNC prediction, this study focused on analyzing how factors, such as the simulated data distribution and sample size and the simulated and measured data batch sizes, affect the ML-HM accuracy. For this purpose, different scenarios for the above three factors were generated. ML-HMs were designed under these scenarios, and the performance was evaluated. The results showed that the simulated data distribution affects the ML-HM inversion accuracy, and it is better to use a priori knowledge to set the range and sampling strategy for the N-PROSAIL input variables to obtain a generated simulated data distribution that is similar to that of the measured data. The ML-HM accuracy increases with increasing measured sample size, but it does not change in an obvious manner once a certain threshold is reached. Thus, it is better to apply the sample size determination method based on simple random sampling to calculate the required sample size. The simulated and measured data batch sizes significantly affect the ML-HM accuracy, and we recommended creating a model for ML-HM accuracy prediction based on a certain number of batch size scenarios and using it to estimate suitable batch sizes of simulated and measured data to design an ML-HM. [ABSTRACT FROM AUTHOR]

A multiband fusion (MF) method can estimate a full-band echo (FBE) with a bandwidth larger than the sum of bandwidths of several subbands echoes. To improve estimation speed and estimation accuracy of the FBE, a novel MF method based on a small multiband-measurement matrix and a nonconvex log-sum regularization (LSR) is proposed and experimentally verified. In the method, the multiband-measurement matrix, which is in a subbands-echoes expression, provides a beneficial tool for speeding up the estimation of the FBE due to its small size. The small size is obtained by constructing the matrix only using the parameters of existing subbands and ignoring parameters of gaps between subbands. The LSR is developed in a multiband cost function to improve the estimation accuracy of the FBE, for its corresponding geometric property is similar to a geometric property of an $l_{0}$ -norm regularization that can guarantee an unbiased estimate of FBE. In addition, since the imaging scene is sparse and the lack of several pulses does not affect the sparsity, the proposed method is robust to missing pulses. Simulation-data and real-date experimental results proved that the bandwidth of a fused FBE is 1.67 times the sum bandwidth of two subbands, the estimation accuracy and the estimation speed corresponding to the proposed method are 5.56 times and 1.11 times those of the traditional MF methods, respectively, and the proposed MF method is still feasible even when 10% of pulses are randomly missing. [ABSTRACT FROM AUTHOR]

Due to the presence of numerous surface defects, the inadequate contrast between defective and non-defective regions, and the resemblance between noise and subtle defects, edge detection poses a significant challenge in dimensional error detection, leading to increased dimensional measurement inaccuracies. These issues serve as major bottlenecks in the domain of automatic detection of high-precision metal parts. To address these challenges, this research proposes a combined approach involving the utilization of the YOLOv6 deep learning network in conjunction with metal lock body parts for the rapid and accurate detection of surface flaws in metal workpieces. Additionally, an enhanced Canny–Devernay sub-pixel edge detection algorithm is employed to determine the size of the lock core bead hole. The methodology is as follows: The data set for surface defect detection is acquired using the labeling software lableImg and subsequently utilized for training the YOLOv6 model to obtain the model weights. For size measurement, the region of interest (ROI) corresponding to the lock cylinder bead hole is first extracted. Subsequently, Gaussian filtering is applied to the ROI, followed by a sub-pixel edge detection using the improved Canny–Devernay algorithm. Finally, the edges are fitted using the least squares method to determine the radius of the fitted circle. The measured value is obtained through size conversion. Experimental detection involves employing the YOLOv6 method to identify surface defects in the lock body workpiece, resulting in an achieved mean Average Precision ( m A P ) value of 0.911. Furthermore, the size of the lock core bead hole is measured using an upgraded technique based on the Canny–Devernay sub-pixel edge detection, yielding an average inaccuracy of less than 0.03 mm. The findings of this research showcase the successful development of a practical method for applying machine vision in the realm of the automatic detection of metal parts. This achievement is accomplished through the exploration of identification methods and size-measuring techniques for common defects found in metal parts. Consequently, the study establishes a valuable framework for effectively utilizing machine vision in the field of metal parts inspection and defect detection. [ABSTRACT FROM AUTHOR]

The problems of measuring the dimensions of relief elements on a scanning electron microscope (SEM) in the technological process of manufacturing microcircuits are considered. The first problem is related to the fact that the increase in the SEM during operation can vary over a wide range depending on the measured dimensions. The second problem is that the probe diameter determined in the SEM calibration process differs from the diameter used in operational measurements. The third problem is related to the fact that it is not known which relief parameter is measured in the SEM probe defocusing method. It is shown that to solve the first problem, it is necessary to calibrate the mark on the image using structures with a trapezoidal profile and large angles of inclination of the side walls. The solution of the second problem is based on the method of defocusing the SEM probe: determining the dependence of the sizes between certain points on the SEM signals on the probe diameter and extrapolating this dependence to the zero value of the diameter. The third problem is solved with the help of a virtual scanning electron microscope. [ABSTRACT FROM AUTHOR]

Flow cytometry is a widespread and powerful technique whose resolution is determined by its capacity to accurately distinguish fluorescently positive populations from negative ones. However, most informative results are discarded while performing the measurements of conventional flow cytometry, e.g., the cell size, shape, morphology, and distribution or location of labeled exosomes within the unpurified biological samples. Herein, we propose a novel approach using an anti-diffraction light sheet with anisotroic feature to excite fluorescent tags. Constituted by an anti-diffraction Bessel–Gaussian beam array, the light sheet is 12 μ m wide, 12 μ m high, and has a thickness of ~0.8 μ m . The intensity profile of the excited fluorescent signal can, therefore, reflect the size and allow samples in the range from O (100 nm ) to 10 μ m (e.g., blood cells) to be transported via hydrodynamic focusing in a microfluidic chip. The sampling rate is 500 kHz , which provides a capability of high throughput without sacrificing the spatial resolution. Consequently, the proposed anti-diffraction light sheet flow cytometry (ADLSFC) can obtain more informative results than the conventional methodologies, and is able to provide multiple characteristics (e.g., the size and distribution of fluorescent signal) helping to distinguish the target samples from the complex backgrounds. [ABSTRACT FROM AUTHOR]

The principle of compensating size effect by special structure design or structure design value has its limitation. In this article, the internal size effect and the rotation center size effect are considered, and the coupling between them is eliminated by the constraint of virtual point position. Moreover, in order to avoid the influence of other error sources on the size effect error calibration, such as the gyro-accelerometer asynchronous time and quadratic error of the accelerometer are considered in the state equation to develop a 46D Kalman filter self-calibration model. The calibration accuracy of the size effect is better than 1.2 mm by comparing the design values of the high precision inertial measurement unit physical structure dimensions. The navigation experiment shows that the accuracy of velocity can be increased by more than 4.4 times after the size effect error is compensated. [ABSTRACT FROM AUTHOR]

The environment of operation and maintenance of locomotives and rolling stocks is complex. Factors such as dust and reflection make point cloud missing, noise and false points, which seriously affect the accuracy of locomotive robot brake shoe image detection. To this end, this paper presents an size measurement algorithm of locomotive brake shoe for locomotive robot based on 3D image. Firstly, it realizes automatic transformation between the template region of interest (ROI) and measurement ROI according to the camera perspective transformation and point cloud registration algorithm, which makes up for the repeated positioning error of the locomotive robot to a certain extent. For the problem of point cloud missing and noise caused by reflection, RANSAC algorithm is used to fit the reference ROI in plane to make up for the loss of precision. The algorithm of automatic filtering of ordered point cloud is used to filter the point cloud between brake disc and brake shoe effectively. For the difficulty of edge discrimination by the false point cloud caused by lock brake and specular reflection of brake disc surface, this paper designs a filtering operator to preliminarily separate the edge of brake shoe and extract the ROI on the side of the brake shoe. Then, according to the imaging characteristics of the brake shoe, the gradient-based edge extraction and tracking algorithm is used to realize the accurate extraction of point cloud boundary of brake shoe and the accurate measurement of brake release gap. Finally, the accurate dimension measurement is realized by using the statistical method of sorting strategy. By the algorithm presented in this paper, accurate measurement can be realized under the condition that slack gap is less than 2mm. The method is used to measure the brake shoe in different shape, position and lighting complex environment, and the Vernier Caliper is used to measure it. The results show that the measuring accuracy of brake shoe wear size is within ±1mm range, and the algorithm has certain robustness.

Technology market has been proposed to benefit technology development and increase productivity, and there is a growing interest on technology market in recent years. However, there is little specific guidance documented the link between patent-based measures and patent collateral. Therefore, the purpose of this article attempts to investigate how patent-based measures and patents used as collateral are related. This article employed eight frequently used patent-based measures categorized into three dimensions, i.e., knowledge flow, technological diversity, and legal protection, to analyze which factors increase patent collateral likelihood and patent collateral speed. The results of this article shows that reference count, times cited count, originality index, generality index, claim count, and family size all increase patent collateral likelihood. On the other hand, reference count, nonpatent reference count, originality index, claim count, and family size accelerate patent collateral speed. [ABSTRACT FROM AUTHOR]

How can we assess a network's ability to maintain its functionality under attacks? Network robustness has been studied extensively in the case of deterministic networks. However, applications such as online information diffusion and the behavior of networked public raise a question of robustness in probabilistic networks. We propose three novel robustness measures for networks hosting a diffusion under the Independent Cascade or Linear Threshold model, susceptible to attacks by an adversarial attacker who disables nodes. The outcome of such a process depends on the selection of its initiators, or seeds, by the seeder, as well as on two factors outside the seeder's discretion: the attacker's strategy and the probabilistic diffusion outcome. We consider three levels of seeder awareness regarding these two uncontrolled factors, and evaluate the network's viability aggregated over all possible extents of an attack. We introduce novel algorithms from building blocks found in previous works to evaluate the proposed measures. A thorough experimental study with synthetic and real, scale-free and homogeneous networks establishes that these algorithms are effective and efficient, while the proposed measures highlight differences among networks in terms of robustness and the surprise they furnish when attacked. Last, we devise a new measure of diffusion entropy, and devise ways to enhance the robustness of probabilistic networks. [ABSTRACT FROM AUTHOR]

In this paper, an image measurement method using a high-speed imaging system is proposed for the evolution of crystal population sizes during cooling crystallization processes. Firstly, to resist the negative effect from solution stirring and particle motion during crystallization, a U-net network-based image processing method is established to efficiently detect sufficiently clear crystals from the online captured microscopic images. Accordingly, the crystal size distribution model is analyzed in terms of the counted probability densities of these crystal images. Subsequently, a measurement method of size growth rate based on crystal population distribution is proposed to estimate the growth condition. An experimental case on a crystallization process of β-form LGA is used to show the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

In the semiconductor manufacturing industry, gold wires are commonly used to connect integrated circuits for transmitting signal and power. Hence, gold wire bonding quality inspection is a vital task for the functional stability of the integrated circuit chip. The shape of each gold wire bonding is one of the most important factors influencing the stability of chip performance. However, as the size of gold wire is quite small (around 0.02 mm), it is difficult to quantitatively measure the shape, i.e., the size-related indices. In this article, we propose a novel framework for fully automated gold wire bonding size-related measurement, which is the key step in gold wire bounding quality inspection. First, we employ a 3-D scanning device to obtain the 3-D point cloud of the chip area where gold wire bonding structures are. We then propose a 3-D deep learning-based algorithm to extract multiple gold wire bounding structures from the whole scanned point cloud. With the extracted points, a postprocessing method is introduced to group the gold wire-solder joint pairs. Moreover, we design a point cloud-based calculation model to perform the final size-related measurement. The proposed automated framework achieves high-accuracy quantitative measurement of gold wire bonding structures on the scale of 0.02 mm for the first time. The effectiveness of the proposed measurement framework is demonstrated by the experiments. [ABSTRACT FROM AUTHOR]

Railway Automatic Switch (RAS) plays a crucial role in Turnout Switching System (TSS). The size of RAS’s Stationary Contacts (SCs) directly affects connectivity of the pivotal control and feedback circuit, which further influences the remaining useful life of TSS. However, it is impossible to avoid normal wear and tear or fractures of SC during daily operation, resulting in size change of SCs. Therefore, it is vital to monitor the size of SCs. However, due to lack of wear samples, it is hard to design automatic algorithms for this task, especially for developing currently popular deep learning. To this end, this paper proposes a computer vision method for railway automatic switch stationary contacts wear detection under few-shot occasions. Our method includes two key modules: a Few Shot SC DETection (FSDet) module and a Contour-based Size MEAsurement (CSMea) module, which together form a system that achieves accurate SC detection and size monitoring. The FSDet module formulates a multi-template deep feature matching pipeline, which plays the role of detecting all SCs in an image under the few shot manner. Then, the CSMea module takes the above detected SC patches as input and detects wear regions utilizing contour features and key point features. Finally, size of SCs can be calculated in image level by computing average pixels distance in wear regions and rescaled into real world level using image calibration tools. Experimental results demonstrate that the proposed method can accurately and robustly detect and measure the size of different SC structures in few-shot occasions. [ABSTRACT FROM AUTHOR]

The undesirable impacts of solder void defects on the reliability of die packages have prompted stringent requirements on solder void size control in the semiconductor packaging industry. Vacuum reflow process using Pb95Sn5 solder was studied, where critical process parameters within temperature and pressure profiles were varied for enhanced solder void size reduction, in comparison to conventional reflow process. Higher reflow temperature profile, faster pressure pump-down rate, and longer vacuum dwell time above threshold levels are critical conditions required for consistently achieving minimum solder void sizes well below the industry criteria. Application of threshold conditions using large volume reflow oven with industrial settings has achieved the single and the total solder void size over die size well below the standard 2.5% and 5% requirements, respectively, with majority of the samples attained the single and the total solder void size over die size below 1% and 2%, respectively. Solder bond line thickness exhibited a significant influence on the solder void size reduction and thus needs to be administered appropriately during die packaging assembly to achieve the maximum solder void size reduction. [ABSTRACT FROM AUTHOR]

The road network serves as one of the most fundamental infrastructure systems for the society but is at risk from different types of disruptions. Due to the variety and unpredictability of disruptions, the road network has stochastic behavior, which refers to the state change of the network. In this paper, we propose an approach based on percolation theory to study the stochastic behavior of road networks impacted by disruptions. Two performance metrics, network connectivity and network efficiency, are defined to quantify the behavior of road networks. Instead of relying on detailed network structure, the proposed percolation theory-based approach uses only basic topology information, such as the distribution of node degrees and link capacities, which makes it suitable for large-scale networks. It is found that the efficiency of a disruption-impacted road network is the product of global connectivity, local connectivity, and connectivity strength. Validation by a real-world case shows that this new approach provides detailed and accurate evaluation of the behavior of disruption-impacted road networks. [ABSTRACT FROM AUTHOR]

In this study, we fabricated adenosine imprinted poly(hydroxyethyl methacrylate-N-methacryloyl-L-cysteine methyl ester-silver nanoparticles (AgNPs)-N-methacryloyl-L-phenylalanine methyl ester) (MIP-Ag) sensor. It is a sensitive, simple, low-cost, and label-free adenosine determination in an aqueous solution without the need for any complicated coupling processes. The sensor without the addition of AgNPs (MIP) was prepared for control experiments to examine the effect of incorporated AgNPs to increase the signal response of surface plasmon resonance. Also, non-imprinted (NIP-Ag) sensor were designed using the same polymerization recipe without adding adenosine to evaluate the imprinting efficiency. The characterization studies of MIP-Ag, NIP-Ag and MIP sensors were carried out by atomic force microscopy and contact angle measurements. It was determined that the MIP-Ag sensor detected the target adenosine molecule 5.10 times more selectively than the competitor guanosine and 3.92 times more than thymidine. Imprinting efficiency was determined by comparing the signal response of MIP-Ag and NIP-Ag sensors. Repeatability studies of the MIP-Ag sensor were statistically analyzed for the 1.0 ppm adenosine, and the percent relative standard deviation of the intraday assays of less than 1.0% showed an insignificant decrease in the detection of adenosine after four adsorption-desorption cycles. [ABSTRACT FROM AUTHOR]

Due to the presence of numerous surface defects, the inadequate contrast between defective and non-defective regions, and the resemblance between noise and subtle defects, edge detection poses a significant challenge in dimensional error detection, leading to increased dimensional measurement inaccuracies. These issues serve as major bottlenecks in the domain of automatic detection of high-precision metal parts. To address these challenges, this research proposes a combined approach involving the utilization of the YOLOv6 deep learning network in conjunction with metal lock body parts for the rapid and accurate detection of surface flaws in metal workpieces. Additionally, an enhanced Canny–Devernay sub-pixel edge detection algorithm is employed to determine the size of the lock core bead hole. The methodology is as follows: The data set for surface defect detection is acquired using the labeling software lableImg and subsequently utilized for training the YOLOv6 model to obtain the model weights. For size measurement, the region of interest (ROI) corresponding to the lock cylinder bead hole is first extracted. Subsequently, Gaussian filtering is applied to the ROI, followed by a sub-pixel edge detection using the improved Canny–Devernay algorithm. Finally, the edges are fitted using the least squares method to determine the radius of the fitted circle. The measured value is obtained through size conversion. Experimental detection involves employing the YOLOv6 method to identify surface defects in the lock body workpiece, resulting in an achieved mean Average Precision (mAP) value of 0.911. Furthermore, the size of the lock core bead hole is measured using an upgraded technique based on the Canny–Devernay sub-pixel edge detection, yielding an average inaccuracy of less than 0.03 mm. The findings of this research showcase the successful development of a practical method for applying machine vision in the realm of the automatic detection of metal parts. This achievement is accomplished through the exploration of identification methods and size-measuring techniques for common defects found in metal parts. Consequently, the study establishes a valuable framework for effectively utilizing machine vision in the field of metal parts inspection and defect detection.

The Thin Element Approximation (TEA) is widely used in diffraction analyses since it can be applied in efficient plane-to-plane propagation algorithms. While refinements have been developed for dielectric objects to obtain more accurate results, TEA is assumed precise for opaque objects. However, through numerical simulations presented in this work, based on Wave Propagation Method, we analyze the tendency of TEA to overestimate the dimensions of opaque objects in diffractometry. This effect was acknowledged in the case of cylinders, and we obtain similar results for the case of rectangular thick strips. On the other hand, TEA demonstrates a high level of accuracy when applied to objects with thickness concentrated at the center and thin edges, such as the isosceles triangular obstacle. Finally, we analyze objects whose shape is defined using the superellipse function, which is chosen for its versatility in generating objects of equivalent width and maximum thickness but with different shapes just changing a single parameter. Our results highlight the importance of considering the object geometry when employing this approximation in diffraction studies of opaque three-dimensional objects. [Display omitted] • TEA is not always valid for opaque thick objects. • Precise method for determining the size of small objects. • Use of Wave Propagation Method algorithm. • Analysis of versatile superellipse object. [ABSTRACT FROM AUTHOR]

In this work, we investigated a platform for real-time emulsion droplet detection and size measurement in optofluidic platforms. An 8.2 µm core diameter input optical fiber and a multi-mode Gradient Refractive Index (GRIN) output fiber were integrated into an acrylic microfluidic channel platform consisting of three layers. Water-in-oil emulsions were investigated, since relevant applications have emerged in the recent past for these types of emulsions, such as drug encapsulation as well as droplet-based Polymerase Chain Reaction (PCR) amplification of DNA, among others. The main contribution of this work is in understanding the main physical phenomena (i.e., total internal reflection, refraction, and interference) behind the complex transmittance pattern obtained for these droplets. For this purpose, a frequency domain electromagnetic wave propagation modelling of the structure using the Finite Element Method (FEM) was used along with experimental measurements. [ABSTRACT FROM AUTHOR]

The deposited particulate material within a fibrous filter affects the pressure drop which develops through three different stages. The implementation of a time-adjustable matrix is intended to cause detachment of particle structures from fibers within the upstream layers at low flow velocities. The deposited particle structures are transported further within the filter and clear up void space for an extension of filter service life. As in previous studies observed fiber stretching initiate cracks and following detachment of particle structures with a simultaneously applied airflow. For complete detachment of the particle structure, five consecutive stretching cycles are performed in this study. The elongation velocity, the flow velocity and the particle loading are varied. Using an image analysis technique and a laser-light measurement technique simultaneously, the cumulative fraction of detached particle structures and the size of detached particle structures are determined. A high initial particle loading on the fiber induces early detachment of larger structures from the fiber. The size of detached structures is increased by the increase of the elongation velocity. The mean value remains almost constant whether the elongation velocity or superficial velocity are increased. For small initial structures on the fiber, a decrease in superficial velocity causes detachment of larger particle fragments. [ABSTRACT FROM AUTHOR]

A hybrid Energy Storage Systems (ESS) consists of two or more energy storage technologies, with different power and energy characteristics. Using a hybrid ESS, both high-frequency and low-frequency power variations can be addressed at the same time. For an accurate sizing of a hybrid ESS, the use of high-resolution data is required. However, high-resolution data over long periods leads to large data sets, which are difficult to handle. In this paper, an improved motif discovery algorithm is introduced to find the most recurring daily consumption patterns within the time series of interest. The most recurring pattern is selected as the representative of the time series for sizing the hybrid ESS. Next, a simple optimization framework is proposed for selecting the cut-off frequency of a low-pass filter, used for allocating the power to different storage technologies. Finally, the proposed sizing approach is applied for sizing a hybrid battery-flywheel ESS at four different low voltage distribution grids in southern Germany using real measurement data. It is demonstrated that a hybrid ESS, with the characteristics derived from the most recurring patterns only, can effectively provide their intended grid services for most of the days during the whole period of the time series. [ABSTRACT FROM AUTHOR]

The railway turnout system is a critical infrastructure that is responsible for steering trains. The closeness state of the turnout is directly related to the safety of the passing trains. To avoid derailment accidents caused by insufficient turnout closeness degrees, it is necessary to monitor the closeness degrees of the turnout. This article introduces a new condition-monitoring strategy that evaluates the turnout closeness degree by measuring the size of the switch gap. To implement this strategy, a detector based on image sensors is designed, and an automatic algorithm is proposed to measure the size of the switch gap based on the images acquired by the detector. The proposed algorithm directly processes the original images of the switch gap and can adaptively extract the region of interest (ROI) and the hysteresis threshold of the Canny operator for each image, which enables workers to avoid using a fixed ROI and hysteresis threshold with different images. Finally, the turnout closeness degree is calculated through a simple conversion of the size of the switch gap, measured by the proposed algorithm. Experiments based on data collected from the actual turnouts of a station show that the proposed turnout closeness monitoring method has high accuracy and robustness, which simplifies the turnout closeness maintenance process and improves its efficiency. [ABSTRACT FROM AUTHOR]

In general, a distributed system can be considered as an aggregation of several units which, even though physically located in different positions, are able to jointly carry out some specified tasks, communicating together by means of a suitable network infrastructure. This type of system was born with the introduction of the first communication networks, and it started growing quickly with the establishment of the first world-wide network: the Internet. As a matter of fact, the spreading of the Internet provided developers with a new and almost-ubiquitous network that is able to connect virtually any kinds of devices and make them operate together. [ABSTRACT FROM AUTHOR]

We consider the two-dimensional multi-target detection (MTD) problem of estimating a target image from a noisy measurement that contains multiple copies of the image, each randomly rotated and translated. The MTD model serves as a mathematical abstraction of the structure reconstruction problem in single-particle cryo-electron microscopy, the chief motivation of this study. We focus on high noise regimes, where accurate detection of image occurrences within a measurement is impossible. To estimate the image, we develop an expectation-maximization framework that aims to maximize an approximation of the likelihood function. We demonstrate image recovery in highly noisy environments, and show that our framework outperforms the previously studied autocorrelation analysis in a wide range of parameters. [ABSTRACT FROM AUTHOR]

In this paper, a crystal image analysis method is presented to measure and predict crystal sizes, based on cooling crystallization of β-form L-glutamic acid (LGA) by using an in-situ non-invasive imaging system. The proposed method consists of image restoration, image segmentation, crystal size measurement, and size prediction. To cope with the effects of noise pollution, uneven illumination and movement blurring, the image processing method is conducted for segmenting crystal images captured from the stirring reactor. Thus, the crystal size distribution for crystal population is obtained by using a probability density function. In addition, a short-term prediction method is given for crystal sizes. An experimental study on the cooling crystallization process of β-form LGA is shown to demonstrate the effectiveness of the proposed method.

The maximum clique problem (MCP) has various applications to reveal the structure and function of graphs. Graphs are constantly updated in the real life. However, no algorithm is specifically designed for dynamic graph. Although MCP in dynamic graphs can be solved by simply invoking a state-of-the-art static approach, such as PMC, when the graph is updated, such an approach of simply re-calculating from scratch is inefficient. The key issue with MCP algorithm is to find a large clique, namely a seed, as fast as possible. Thus, search space can be pruned based on the seed. Size of the seed greedily found by PMC cannot be guaranteed, as it fluctuates considerably. Moreover, the time required to find a seed under PMC is up to $O(| E| \cdot \Delta (G))$ O (| E | · Δ (G)) , where $\Delta (G)$ Δ (G) is the highest degree in G. In this article, we intend to find a sizable seed by updating the previous maximum clique with the incident vertices of the inserted/deleted edge. Size of the seed now is guaranteed to be no less than $\omega (G^{\prime})\; - \;1$ ω (G ') - 1 , where $\omega (G^{\prime})$ ω (G ') is the size of the maximum clique on the updated graph. Moreover, the seed can be found in a time complexity of $O(\Delta (G)^{2})$ O (Δ (G) 2) . Two other crucial issues related to the MCP in dynamic graphs are refreshing rate and refreshing overhead. After a tight upper bound is imposed on $\omega (G^{\prime})$ ω (G ') , the necessity of refreshing is evaluated by comparing the seed with its largest challenger, then unnecessary refreshing is wiped out effectively. The size of the largest challenger is judiciously estimated using a lazy growth strategy. Subsequently, the search space in refreshing is confined on a much smaller subgraph using a local refreshing strategy. Extensive experiments indicate that the proposed approach outperforms the baseline algorithm by approximately one order of magnitude. [ABSTRACT FROM AUTHOR]

The actual image resolution achieved by synthetic aperture systems often differs from the theoretical performance, and it is advantageous to automatically measure the realized resolution of these image products to monitor their quality. This article describes a novel image processing algorithm for measuring resolution based on the detection of prominent point scatterers. The algorithm proceeds in the following three main steps. 1) Identify candidate point scatterers via connected-component labeling and dual thresholding. 2) Prune the candidate scatterers and measure the size of the accepted detections by fitting an ellipse to the half-power contour traced in an oversampled image snippet. 3) Calculate the mode of the ellipse semiaxes lengths from a collection of scatterers to represent the local resolution estimate. The fitting of an ellipse to the half-power contour is a new contribution to resolution estimation that facilitates both detection and measurement, and it provides estimates of resolution in any direction, instead of only the range and along-track directions, which is important for images with excessive sensor motion. The accuracy and robustness of the algorithm are tested by applying it to simulated imagery with a series of parameter sweeps. Finally, the algorithm is applied to collected synthetic aperture sonar data and shown to be insensitive to seafloor textures. [ABSTRACT FROM AUTHOR]

Sketches, as a kind of probabilistic data structures, have been considered as the most promising solution for network measurement in recent years. Most sketches do not work well for skewed network traffic. To address this problem, we propose a family of sketch frameworks, namely the Pyramid family. The first member of our Pyramid family is the S-Pyramid framework, which includes two techniques: counter-pair sharing for high accuracy, and word acceleration for fast speed. The second member of our Pyramid family is the Mini-Pyramid framework, which projects the S-Pyramid framework into one counter, bringing more flexibility in application while keeping the accuracy. To demonstrate the generality of our Pyramid family, we apply both frameworks to sketches of CM, CU, Count, and Augmented. To demonstrate the flexibility of the Mini-Pyramid framework, we further apply Mini-Pyramid to SBF and the On-Off sketch. The experimental results show that, the S-Pyramid framework can reduce the ARE by up to 7.12 times compared with the original sketches, while improving the throughput by up to 2.37 times; the Mini-Pyramid framework can reduce the ARE by up to 29.2 times, at the cost of 21.3% lower throughput on average. [ABSTRACT FROM AUTHOR]

Superparamagnetic nanoparticles are attracting increasing attention. For most applications, an accurate determination of the superparamagnetic particle size distribution is required. In this work, we report about determination of the core size distribution of superparamagnetic $\gamma $ -Fe2O3/SiO2 core–shell nanoparticle samples. The approach is based on a fit of the experimentally determined $m$ – $H$ curves by a superposition of Langevin functions. The scheme used for solving the minimization problem does not use pre-defined model distributions (lognormal or normal). This allows for a larger variability of the obtained distribution, e.g., bi- or multi-modal. We applied our method to determine the core size distributions of five different $\gamma $ -Fe2O3/SiO2 samples. We find that using our approach, we can obtain excellent agreement with the experimental data, and the core size distribution and mean core size values obtained compare well with the results from transmission electron microscopy (TEM) image analysis and X-ray diffraction (XRD) measurements. [ABSTRACT FROM AUTHOR]

Super spreader identification has a lot of applications in network management and security monitoring. It is a more difficult problem than heavy hitter identification because flow spread is harder to measure than flow size due to the requirement of duplicate removal. The prior work either incurs heavy memory overhead or requires heavy computations. This paper designs a new super-spreader monitor capable of identifying all flows whose spreads are greater than a user-specified threshold with a probability that can be arbitrarily set. It introduces a generalized geometric hash function, a generalized geometric counter, and a novel geometric-min filter that blocks out the vast majority of small/medium flows from being tracked, allowing us to focus on a small number of flows in which super spreaders are identified. We provide an analytical way of properly setting the system threshold to meet probabilistically guaranteed identification of super spreaders, and implement it on both hardware (FPGA) and software platforms. We perform extensive experiments based on real Internet traffic traces from CAIDA. The results show that with proper parameter settings, the new monitor can identify more than 99% super spreaders with a low memory requirement, better than the prior art. [ABSTRACT FROM AUTHOR]

Ferroelectric properties of individual TiN/Hf0.5Zr0.5O2/TiN capacitors have been measured for technologically relevant areas as small as 60 nm $\times70$ nm, using direct detection of ultralow charge. A crossbar architecture has been utilized to fabricate the ferroelectric capacitors. Switching polarization of $40.1 \mu \text{C}$ /cm $^{2} \ge \text{P}_{\text{sw}} \ge 16.7 \mu \text{C}$ /cm2 and coercive field 1.4 MV/cm $\ge \text{E}_{c} \ge1.09$ MV/cm have been observed for sizes between $1.1 \mu \text{m}$ 2 and 4200 nm2 without discernable trend as function of capacitor size in the present data set. Measured bipolar stress endurance exceeds the maximum measured cycles of 1010 although some capacitors start to show signs of degradation after 109 cycles. A clear correlation between Psw degradation and increased leakage current is observed. The direct measurement of such small area capacitors has been enabled by equipment and circuit design innovations. The present setup allows direct measurement of ferroelectric properties for capacitors as small as 1900 nm2 while further improvements could extend the size limit to 950 nm2 where the design window appears to close. [ABSTRACT FROM AUTHOR]

Abstract Background Ultrasound (US) imaging is known to underestimate tumor size in clinical. This study is aimed to compare the size measurements of hepatocellular carcinoma (HCC) in three US imaging modalities, i.e. two-dimensional (2D) imaging, the arterial phase (AP) and the late phase (LP) imaging of contrast-enhanced US (CEUS). Methods Fifty-eight clinically proved HCC patients were included. The 2D and CEUS imaging were performed with Siemens S2000, Philips iu22 and BioSound Twice. 2.5 mL of SonoVue® was injected for every CEUS performance. Two physicians measured the maximal longitudinal and the transverse diameters of the tumors in 2D, the AP and the LP of CEUS from one image section. The three measurements were compared by paired t test. Results The mean longitudinal diameter of HCC appeared to be maximal in the AP (4.73 ± 2.04 cm) of CEUS and minimal in the LP (3.98 ± 1.99 cm) of CEUS. The 2D diameter (4.26 ± 2.07 cm) was in the middle between two CEUS measurements. There were significant differences between any two measurements. Conclusion There is size difference between the three kinds of HCC measurement. It appeared to be maximal in the AP of CEUS and minimal in the LP. The 2D diameter was in the middle.

Flow cytometry is a widespread and powerful technique whose resolution is determined by its capacity to accurately distinguish fluorescently positive populations from negative ones. However, most informative results are discarded while performing the measurements of conventional flow cytometry, e.g., the cell size, shape, morphology, and distribution or location of labeled exosomes within the unpurified biological samples. Herein, we propose a novel approach using an anti-diffraction light sheet with anisotroic feature to excite fluorescent tags. Constituted by an anti-diffraction Bessel–Gaussian beam array, the light sheet is 12 μm wide, 12 μm high, and has a thickness of ~0.8 μm. The intensity profile of the excited fluorescent signal can, therefore, reflect the size and allow samples in the range from O (100 nm) to 10 μm (e.g., blood cells) to be transported via hydrodynamic focusing in a microfluidic chip. The sampling rate is 500 kHz, which provides a capability of high throughput without sacrificing the spatial resolution. Consequently, the proposed anti-diffraction light sheet flow cytometry (ADLSFC) can obtain more informative results than the conventional methodologies, and is able to provide multiple characteristics (e.g., the size and distribution of fluorescent signal) helping to distinguish the target samples from the complex backgrounds.

[Display omitted] • ADIPI is proposed to measure the 3D position and size for opaque droplets. • A general theoretical model is established to characterize ADIPI signal. • Theoretical simulation and experimental study are both carried out. • Digital inline holography is used to demonstrate the accuracy of ADIPI. This work investigates the astigmatic dual-beam interferometric particle imaging (ADIPI) to enable the measurement of the three-dimensional (3D) spatial position and particle size of spherical opaque particles. A general theoretical model based on the generalized Huygens-Fresnel integral and the ray transfer matrix model is established to characterize ADIPI signal, revealing the orientation and spacing of typical ADIPI fringes relating to the depth position and size. The model is applied to study the effects of several parameters, such as the focal length of cylindrical lens, the focal length of spherical lens and the distances between the elements in the optical system, on the relationship between the inclination angle of fringes and depth position by theoretical simulation. Experiments of ADIPI, incorporating dual-view digital inline holography (DIH), is performed to demonstrate the utility of ADIPI in the measurement of opaque spherical particles. Through the analysis of the general ADIPI signal based on two-dimensional Fourier transforms, 3D position and the particle size are obtained. The results of ADIPI, in both of the depth position and size measuring, exhibit a high degree of consistency with DIH's, given the average relative deviations of 5% in size measuring and 1% in depth position, respectively. As a new measurement approach, the ADIPI signal exists at arbitrary angle except the two laser beam axes, determining the flexibility of ADIPI configurations which facilitates its great prospect in opaque particles measurement, e.g., metal combustion, chip manufacturing, 3D metal printing. [ABSTRACT FROM AUTHOR]

This letter reveals the prevailing scenario of raindrop size distribution (DSD) in terms of mass-weighted mean drop diameter ($D_{m}$) over a tropical metropolis, Kolkata (22.57°N, 88.37°E), India, in a contrasting aerosol environment that occurred during the COVID-19 pandemic in the absence of usual human activities. In the premonsoon months (March–May), the probability of $D_{m}$ values exceeding 2 mm has increased in 2020, indicating the dominance of large raindrops, compared to the years 2017–2019. Increased number densities of larger drops have influenced the drop fall velocity spectrum as measured by a laser precipitation monitor in terms of the percentage occurrence of high-velocity small drops (superterminal) and low-velocity large drops (subterminal) for both convective and stratiform precipitations. As measured from a Ka-band microrain Doppler radar, the mean melting layer altitude during stratiform rain has decreased by ~800 m during the premonsoon of 2020 compared to 2017–2019. According to the ERA-5 reanalysis data, changing rain microphysical characteristics are related to decreasing zero-degree isotherm height and reduced wind shear. [ABSTRACT FROM AUTHOR]

Particle size distribution (PSD) characteristics of mature squall lines are investigated through global precipitation measurement (GPM) dual-frequency precipitation radar (DPR) measurements. These squall lines consist of a leading convective (LC) line, a weak-echo transition (WT) region and a trailing stratiform (TS) region. Their PSD characteristics are quite different from the existing conceptual models of mature squall line, given that many small raindrops/ice particles are found in the WT region while in the TS region raindrops/ice particles are sparse. Analysis shows that it is likely due to the short distance from LC to WT, where more particles may be dispersed from LC region and fall into WT region but barely have time to grow in size. In the TS region further behind LC, the particles have more time to get larger. Analysis also reveals that the mesoscale updraft generally occurs at mid-to-high levels in the TS region so that aggregations and collisions-coalescences could be promoted to increase the particle size but decrease the particle number. Through the GPM PSD data analysis, a refined conceptual model of a mesoscale convective system (MCS) with squall line is presented in this study. [ABSTRACT FROM AUTHOR]

We consider the high-dimensional linear regression problem, where the algorithmic goal is to efficiently infer an unknown feature vector $\beta ^{*}\in \mathbb {R}^{p}$ from its linear measurements, using a small number $n$ of samples. Unlike most of the literature, we make no sparsity assumption on $\beta ^{*}$ , but instead adopt a different regularization: In the noiseless setting, we assume $\beta ^{*}$ consists of entries, which are either rational numbers with a common denominator $Q\in \mathbb {Z}^{+}$ (referred to as $Q-$ rationality); or irrational numbers taking values in a rationally independent set of bounded cardinality, known to learner; collectively called as the mixed-range assumption. Using a novel combination of the Partial Sum of Least Squares (PSLQ) integer relation detection, and the Lenstra-Lenstra-Lovász (LLL) lattice basis reduction algorithms, we propose a polynomial-time algorithm which provably recovers a $\beta ^{*}\in \mathbb {R}^{p}$ enjoying the mixed-range assumption, from its linear measurements $Y=X\beta ^{*}\in \mathbb {R}^{n}$ for a large class of distributions for the random entries of $X$ , even with one measurement ($n=1$). In the noisy setting, we propose a polynomial-time, lattice-based algorithm, which recovers a $\beta ^{*}\in \mathbb {R}^{p}$ enjoying the $Q-$ rationality property, from its noisy measurements $Y=X\beta ^{*}+W\in \mathbb {R}^{n}$ , even from a single sample ($n=1$). We further establish that for large $Q$ , and normal noise, this algorithm tolerates information-theoretically optimal level of noise. We then apply these ideas to develop a polynomial-time, single-sample algorithm for the phase retrieval problem. Our methods address the single-sample ($n=1$) regime, where the sparsity-based methods such as the Least Absolute Shrinkage and Selection Operator (LASSO) and the Basis Pursuit are known to fail. Furthermore, our results also reveal algorithmic connections between the high-dimensional linear regression problem, and the integer relation detection, randomized subset-sum, and shortest vector problems. [ABSTRACT FROM AUTHOR]