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In the field of studies on the "Neural Synapses" in the nervous system, its experts manually (or pseudo-automatically) detect the bio-molecule clusters (e.g., of proteins) in many TIRF (Total Internal Reflection Fluorescence) images of a fluorescent cell and analyze their static/dynamic behaviors. This paper proposes a novel method for the automatic detection of the bio-molecule clusters in a TIRF image of a fluorescent cell and conducts several experiments on its performance, e.g., mAP @ IoU (mean Average Precision @ Intersection over Union) and F1-score @ IoU, as an objective/quantitative means of evaluation. As a result, the best of the proposed methods achieved 0.695 as its mAP @ IoU = 0.5 and 0.250 as its F1-score @ IoU = 0.5 and would have to be improved, especially with respect to its recall @ IoU. But, the proposed method could automatically detect bio-molecule clusters that are not only circular and not always uniform in size, and it can output various histograms and heatmaps for novel deeper analyses of the automatically detected bio-molecule clusters, while the particles detected by the Mosaic Particle Tracker 2D/3D, which is one of the most conventional methods for experts, can be only circular and uniform in size. In addition, this paper defines and validates a novel similarity of automatically detected bio-molecule clusters between fluorescent cells, i.e., SimMolCC, and also shows some examples of SimMolCC-based applications. [ABSTRACT FROM AUTHOR]

Synthetic amorphous silica (SAS) is used as additive in a variety of industrial applications for many decades and has been approved to be used in food, food contact materials, pharmaceuticals, and cosmetics. Due its internal structure, SAS is considered as a nanomaterial, thus it is affected by a general safety discussion. Based on the production process, SAS for cosmetic application is a nanomaterial by the EU Recommendation, although it was not considered as such, because the solely size-dependent definitions of the term "nanomaterial" emerged in recent times first in Recommendation 2011/696/EU. Therefore, former physicochemical and toxicological evaluations of SAS were already performed on nanomaterials, however, without being addressed as such. Safety concerns can only emerge if two criteria, (toxicological) hazard and exposure towards the substance is fulfilled at the same time. In case of SAS, the Scientific Committee on Consumer Safety (SCCS) challenged provided data to be insufficient to draw a conclusion regarding the safety of SAS and thus, requested further investigations, in particular by exploring skin penetration of particulate SAS.Investigation of specific particulate substances in skin penetration tests is an analytical challenge. The number of available analytical techniques that are capable to detect nanomaterials in complex matrices, like receptor fluids from skin penetration testing, are limited and still emerging. In the new studies, a comprehensive set of analytical techniques were used to investigate the skin penetration potential of SAS. Particle-sensitive, element and particle-specific combinations of techniques and different sample preparation procedures, that respected the particulate nature of SAS, were used to detect SAS in receptor fluids directly. In addition, electron microscopic techniques were used to examine different layers of skin to detect adsorbed SAS.The combination of Asymmetric Flow Field-Flow Fractionation (AF4) in combination with Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for examination of receptor fluids and Scanning Electron Microscopy coupled with Energy Dispersive X-ray spectroscopy (SEM/EDX) for examination of skin itself, were identified as suitable techniques for the detection of SAS in skin penetration tests. Data from literature was used to compare the results of the studies with the outcome of other test systems (other particles, other techniques). Both, the test results, and literature evaluation led to the conclusion, that SAS does not penetrate skin. Based on this outcome and local and systemic dermal toxicity review of SAS, it can be concluded that dermal application of SAS in cosmetic formulations is negligible. • New analytical systems to detect particles in report fluids are presented. • Synthetic amorphous particles do not penetrate through pig and human skin. • A critical literature review is provided to understand different results reported. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence (AI), especially in machine learning and deep learning technologies, the particle and impurity detection and removal processes employed in many industries have been improved. Particles and impurities of any size, shape and in any condition can be detected using advanced technology in both areas. This paper presents a comprehensive overview of research papers that discuss the application of AI techniques for the detection and removal of particles and impurities. The publications featured in this review were mainly retrieved from the Web of Science (WoS) database, covering the timeframe from 2000 to 2023. This paper also covers the review on the impurity detection and removal specifically in edible bird’s nest (EBN). The aim of this paper is to provide a valuable resource for the future development of AI applications in particle and impurity detection and removal technologies that have not been addressed in this study. Through the review and analysis of AI for particle and impurity detection and removal techniques in recent years, this paper includes the following parts: research trend in particle and impurity detection in general and AI methods in particle and impurity detection, applications of AI in particle and impurity detection in related industries including in EBN and AI applications in particle and impurity removal. This review study will offer advantages to researchers engaged in the field of AI with regards to the detection and removal of particles and impurities.

In the field of studies on the “Neural Synapses” in the nervous system, its experts manually (or pseudo-automatically) detect the bio-molecule clusters (e.g., of proteins) in many TIRF (Total Internal Reflection Fluorescence) images of a fluorescent cell and analyze their static/dynamic behaviors. This paper proposes a novel method for the automatic detection of the bio-molecule clusters in a TIRF image of a fluorescent cell and conducts several experiments on its performance, e.g., mAP @ IoU (mean Average Precision @ Intersection over Union) and F1-score @ IoU, as an objective/quantitative means of evaluation. As a result, the best of the proposed methods achieved 0.695 as its mAP @ IoU = 0.5 and 0.250 as its F1-score @ IoU = 0.5 and would have to be improved, especially with respect to its recall @ IoU. But, the proposed method could automatically detect bio-molecule clusters that are not only circular and not always uniform in size, and it can output various histograms and heatmaps for novel deeper analyses of the automatically detected bio-molecule clusters, while the particles detected by the Mosaic Particle Tracker 2D/3D, which is one of the most conventional methods for experts, can be only circular and uniform in size. In addition, this paper defines and validates a novel similarity of automatically detected bio-molecule clusters between fluorescent cells, i.e., SimMolCC, and also shows some examples of SimMolCC-based applications.

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Particle detection aims to accurately locate and count valid particles in pad images to ensure the performance of electrical connections in the chip-on-glass (COG) process. However, existing methods fail to achieve both high detection accuracy and inference efficiency in real applications. To solve this problem, we design a computation-efficient particle detection network (PAD-Net) with an encoder–decoder architecture, making a good trade-off between inference efficiency and accuracy. In the encoder part, MobileNetV3 is tailored to greatly reduce parameters at a little cost of accuracy drop. And the decoder part is designed by using the light-weight RefineNet, which can further boost particle detection performance. Besides, the proposed network adopts the adaptive attention loss (termed AAL), which improves the detection accuracy with a negligible increase in computation cost. Finally, we employ a knowledge distillation strategy to further enhance the final detection performance without increasing the parameters and floating-point operations (FLOPs) of PAD-Net. Experimental results on the real datasets demonstrate that our methods can achieve high-accuracy and real-time detection performance on valid particles compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The automated search software integrated with a scanning electron microscope (SEM/EDS) has been the standard tool for detecting inorganic gunshot residues (iGSR) for several decades. The detection of these particles depends on various factors such as collection, preservation, contamination with organic matter, and the method for sample analysis. This article focuses on the influence of equipment resolution setup on the backscattered electron images of the sample. The pixel size of these images plays a crucial role in determining the detectability of iGSR particles, especially those with sizes close to the pixel size. In this study, we calculated the probability of missing all characteristic iGSR particles in a sample using an SEM/EDS automated search and how it depends on the image pixel resolution setup. We developed and validated an iGSR particle detection model that links particle size with equipment registers and applied it to 320 samples analyzed by a forensic science laboratory. Our results show that the probability of missing all characteristic iGSR particles due to their size is below 5% for pixel sizes below 0.32 μm2. These findings indicate that pixel sizes as large as twice the one commonly used in laboratory casework, that is, 0.16 μm2, are effective for initial sample scanning, yielding good detection rates of characteristic particles that could exponentially reduce laboratory workload. [ABSTRACT FROM AUTHOR]

Identifying large particles are a prerequisite for slurry health monitoring. This study presents a real-time monitoring system using an infrared coaxial light source to detect large particles in slurry. The imaging system acquires real-time images under infrared light, which are processed and analyzed to quantify the particles within the fluid flowing through the monitored area. The proposed approach consists of different illumination modes—fixed, random, and continuous. For particles with regular and irregular shapes, various scintillation modes are applied. The results show that the method is highly feasible and effective, and enables real-time monitoring of the slurry. Moreover, the real-time detection approach for different particle samples, particularly, undiluted opaque fluids, is proposed. The feasibility of the proposed method is verified in the irregular particle detection experiment, where the difference between the detection value and the real value is very small. The accuracy and intuitive properties of the method indicate a massive potential across diverse applications in the forthcoming years. [ABSTRACT FROM AUTHOR]

The contactless interrogation of an inductive sensor (IS) for conductive target detection is presented. The IS comprises a solenoidal coil of copper wire wrapped around a plastic pipe which is connected to a series capacitor to form an LC circuit resonating at the frequency fr. A conductive target placed at different positions inside the pipe modifies the inductance of the coil, and in turn, fr. An external interrogation coil (IC) electromagnetically coupled to the IS allows the fr to be read through a contactless interrogation technique. The approach has been tested by varying both the position of a lead sphere adopted as the target and the interrogation distance d between the IS and IC. Without the sphere, the LC circuit has fr0 = 2.51 MHz. The target sphere has been detected at up to |x| = 7.5 mm from the center of the IS coil with a frequency variation ∆ fr = 180 kHz at x = 0.

We describe a user-friendly, open source software for single-particle detection/counting in a continuous-flow. The tool automatically processes video images of particles, including pre-conditioning, followed by size-based discrimination for independent detection of fluorescent and non-fluorescent particles of different sizes. This is done by interactive tuning of a reduced set of parameters that can be checked with a robust, real-time quality control of the original video files. The software provides a concentration distribution of the particles in the transverse direction of the fluid flow. The software is a versatile tool for many microfluidic applications and does not require expertise in image analysis. [ABSTRACT FROM AUTHOR]

A lightweight Mask R-CNN instance segmentation model was developed here to analyze particle size and shape accurately and quickly. Firstly, a hybrid Depthwise Dilated Convolutional Network (DDNet) is proposed, and the feature pyramid layers and the shared convolutional layers of the region proposal network are simplified, reducing the model complexity while ensuring robust feature extraction capabilities. Then, segmentation accuracy is significantly improved without sacrificing computational speed and performance by introducing the Dice loss function and clustering algorithm. Experimental results show that the model parameters are significantly reduced by 49.46%, and the segmentation speed increases from 2.15 FPS (frames per second) to 5.88 FPS. Meanwhile, the segmentation accuracy (AP50) increased from 90.56% to 91.21%. In addition, it was proven that the particle size distribution and shape could be analyzed accurately and rapidly with the proposed model, providing essential information for multiphase flow process optimization and equipment design in industrial applications. [Display omitted] • A lightweight Mask R-CNN instance segmentation model was proposed. • Adopting lightweight hybrid network increases segmentation speed markedly. • Loss function and candidate box significantly improved segmentation accuracy. • Particle size distribution and shape were measured accurately and rapidly. [ABSTRACT FROM AUTHOR]

In the context of the CERN EP R&D on monolithic sensors and the ALICE ITS3 upgrade, the Tower Partners Semiconductor Co (TPSCo) 65 nm process has been qualified for use in high energy physics, and adopted for the ALICE ITS3 upgrade. An Analog Pixel Test Structure (APTS) featuring fast per pixel operational-amplifier-based buffering for a small matrix of four by four pixels, with a sensor with a small collection electrode and a very non-uniform electric field, was designed to allow detailed characterization of the pixel performance in this technology. Several variants of this chip with different pixel designs have been characterized with a 120 GeV / c positive hadron beam. This result indicates that the APTS-OA prototype variants with the best performance achieve a time resolution of 63 ps with a detection efficiency exceeding 99% and a spatial resolution of 2 μm, highlighting the potential of TPSCo 65 nm CMOS imaging technology for high-energy physics and other fields requiring precise time measurement, high detection efficiency, and excellent spatial resolution. [ABSTRACT FROM AUTHOR]

Current methods used for testing the rehydration quality of infant formula (IF) are mainly subjective. For a better understanding of rehydration, objective measurements are required. A computer vision (CV) system was synchronized with a collaborative robot (cobot) to automatically estimate foam height, sediment height, and the number of white particles after IF powder rehydration. Two different robotic agitations were used to prepare the mixtures in a commercially available baby bottle. To evaluate the platform, twenty-four stage-1 IF powders were rehydrated. Cobot-captured images were processed by CV algorithms and independently rated by eight participants. The participants' and platform's estimates of foam height, sediment height, and white particles score, respectively, showed agreements of 2.1 mm, 3.4 mm, and 1.7 scores, and correlation coefficients of 0.82, 0.77, and 0.68. The results show that the platform has the potential to enable objective rehydration tests and to monitor changes in visible foam and sediment over time. [Display omitted] • An automated platform for testing infant formula rehydration quality was developed. • Using computer vision, the platform estimated three rehydration attributes. • Platform estimates were compared to estimates from eight human participants. • Reference "white particles" images were digitally generated for participants. • The platform can monitor foam and sediment levels over time. [ABSTRACT FROM AUTHOR]

Analogue test structures were fabricated using the Tower Partners Semiconductor Co. CMOS 65 nm ISC process. The purpose was to characterize and qualify this process and to optimize the sensor for the next generation of Monolithic Active Pixels Sensors for high-energy physics. The technology was explored in several variants which differed by: doping levels, pixel geometries and pixel pitches (10–25 μ m). These variants have been tested following exposure to varying levels of irradiation up to 3 MGy and 1 0 16 1 MeV n eq cm−2. Here the results from prototypes that feature direct analogue output of a 4 × 4 pixel matrix are reported, allowing the systematic and detailed study of charge collection properties. Measurements were taken both using 55Fe X-ray sources and in beam tests using minimum ionizing particles. The results not only demonstrate the feasibility of using this technology for particle detection but also serve as a reference for future applications and optimizations. [ABSTRACT FROM AUTHOR]

Wear is an unavoidable phenomenon that generates debris particles. i.e., ferromagnetic and non-ferromagnetic particles. If the system's condition is not monitored effectively, wear can lead to the breakdown of the system. Therefore, an inductive-resistance sensor was designed to monitor and detect debris particles. Moreover, Finemet (Fe 73.5 Si 13.5 B 9 Nb 3 Cu 1) was applied to the sensor to improve its sensitivity. A comparison simulation and experiment examined the sensor's performance and Finemet's impact. The study shows that the induction mode has greater sensitivity in detecting ferromagnetic particles, while the resistance mode is more sensitive in detecting non-ferromagnetic particles. This study also demonstrates that the application of Finemet boosts the sensitivity of the sensors in both findings, indicating the significance of Finemet for the sensor's applications. [ABSTRACT FROM AUTHOR]

This paper describes a benchmark dataset for the detection of fuel particles in 2D and 3D image data in a rotary kiln combustion chamber. The specific challenges of detecting the small particles under demanding environmental conditions allows for the performance of existing and new particle detection techniques to be evaluated. The data set includes a classification of burning and non-burning particles, which can be in the air but also on the rotary kiln wall. The light-field camera used for data generation offers the potential to develop and objectively evaluate new advanced particle detection methods due to the additional 3D information. Besides explanations of the data set and the contained ground truth, an evaluation procedure of the particle detection based on the ground truth and results for an own particle detection procedure for the data set are presented. Dataset: 10.5281/zenodo.6358536. Dataset License: Creative Commons Attribution 4.0 International [ABSTRACT FROM AUTHOR]

The presented work addresses the problem of particle detection with neural networks (NNs) in defocusing particle tracking velocimetry. A novel approach based on synthetic training data refinement is introduced, with the scope of revising the well documented performance gap of synthetically trained NNs, applied to experimental recordings. In particular, synthetic particle image (PI) data is enriched with image features from the experimental recordings by means of deep learning through an unsupervised image-to-image translation. It is demonstrated that this refined synthetic training data enables the neural-network-based particle detection for a simultaneous increase in detection rate and reduction in the rate of false positives, beyond the capability of conventional detection algorithms. The potential for an increased accuracy in particle detection is revealed with NNs that utilise small scale image features, which further underlines the importance of representative training data. In addition, it is demonstrated that NNs are able to resolve overlapping PIs with a higher reliability and accuracy in comparison to conventional algorithms, suggesting the possibility of an increased seeding density in real experiments. A further finding is the robustness of NNs to inhomogeneous background illumination and aberration of the images, which opens up defocusing PTV for a wider range of possible applications. The successful application of synthetic training-data refinement advances the neural-network-based particle detection towards real world applicability and suggests the potential of a further performance gain from more suitable training data. [ABSTRACT FROM AUTHOR]

Particle field measurement is an important topic in many industrial branches. However, there are always complex imaging scenes in the engineering experiments, resulting in severe imaging artifact, noise, and blur, such as the optical holography. In this article, we propose a primary-auxiliary coupled neural network (PANet) for 3-D holographic particle field characterization, which can obtain a comprehensive particle measurement, including the identification, focus determination, segmentation, and size estimation. PANet is constituted by two subnets that are arranged in a coupled architecture, i.e., a Primary-Net (PNet) and an AuxiliaryNet (ANet). As the main frame, PNet is designed to accomplish the detection of most particles, while ANet aims to detect the tiny particles that PNet cannot identify. We exploit an alternative training method to realize their functional differentiation and complementation. PANet is evaluated on two kinds of holographic particle field data, i.e., high-energy laser shock aluminum target and droplet breakup in high Mach shock wave. By means of semisupervised learning and a specific loss function, the effect of deficient particle labeling can be alleviated. Experimental results demonstrate that PANet can achieve excellent performance in particle field characterization, especially for those with a wide size span and complex image background. [ABSTRACT FROM AUTHOR]

• Impact of Solid Particles in Transformer Oil: The text highlights the significant influence of solid particles in transformer oil on insulation strength, indicating that these particles can form weak points in insulation, leading to local discharge exceeding limits or insulation breakdown. • Emphasis on Clean New Transformer Oil: Manufacturing companies are encouraged to prioritize the cleanliness of new transformer oil, using methods such as vacuum filtration to reduce the introduction of impurity particles, thereby enhancing the reliability of transformer oil. • Innovative No-Lens Holographic Microscope: The text introduces the rapid development of lensless imaging systems based on CMOS image sensors, which address the high cost and lack of portability associated with traditional high-resolution optical microscopes, providing an effective solution for downsized microscopic imaging and detection. • Simplicity and Speed of Operation: It emphasizes that the lensless holographic microscope is characterized by its simplicity of operation, rapid measurement, and high precision, making it a convenient tool for assessing the condition of impurity particles in power transformers and preemptively identifying issues. • Enhancing Power Transformer Reliability: By employing this novel detection method, power transformer manufacturing companies can more effectively control and manage impurity particles in transformer oil, ultimately improving the reliability of power transformers, reducing maintenance costs, and extending equipment lifespan. The insulation effectiveness of transformer oil has a substantial impact on the safety of transformers. In the presence of particles within the transformer oil, they are able to adversely affect its performance, potentially originating from electrical arcs or severe carbonization processes. These particles are commonly capable of reducing the insulating capability of the transformer oil, thereby enhancing the risk of transformer failure. To overcome this dilemma, we propose a miniaturized quantitative particle detection approach in transformer oil based on a lensless digital holographic microscope. In general, methods based on traditional high-resolution optical microscopy are essentially limited by their fairly huge cost and importability. However, the proposed approach utilizes a homemade miniaturized model combined with a CMOS image sensor to achieve microscopic imaging. The deployed device here weighs ∼ 50 g, making it portable and affordable for on-the-go detection. By simplifying the microscope system, we can quickly and accurately detect tiny particles in transformer oils. This enables us to efficiently assess the status of impurities in power transformers and to identify potential issues in advance, thereby enhancing the reliability of transformer oil. This detection method not only features simplicity of operation, convenient on-the-go detection, low cost, and high precision but also provides a convenient approach for accurately assessing the condition of impurity particles in power transformers and preemptively identifying potential issues. [ABSTRACT FROM AUTHOR]

Monolithic sensors, and CMOS sensors in particular, revolutionized visible imaging, and will be widely applied in high energy physics, but with different requirements and implementations than sensors for visible light (Snoeys, 2023). [ABSTRACT FROM AUTHOR]

Within the ATTRACT FASTPIX project, a monolithic pixel sensor demonstrator chip has been developed in a modified 180 n m CMOS imaging process, targeting sub-nanosecond timing measurements for single ionizing particles. It features a small collection electrode design on a 25 micron thick epitaxial layer and contains 32 mini matrices of 68 hexagonal pixels each, with pixel pitches ranging from 8.66 to 20 micron. Four pixels are transmitting an analog output signal and 64 are transmitting binary hit information. Various design variations are explored, aiming at accelerating the charge collection and making the timing of the charge collection more uniform over the pixel area. Signal treatment of the analog waveforms, as well as reconstruction of time and charge information, is carried out off-chip. This contribution introduces the design of the sensor and readout system and presents the first performance results for 10 μ m and 20 μ m pixel pitch achieved in measurements with particle beams. [ABSTRACT FROM AUTHOR]

Online wear particle detectors have been widely studied due to their ability to monitor the wear state of machines, and to help to prevent serious mechanical failures caused by excessive wear of components. This paper presents a review of the state-of-the-art wear debris detectors based on different principles. That mainly includes optical/imaging particle detectors, electrical particle detectors, ultrasonic particle detectors and magnetic debris detection sensors. Meanwhile, the characteristics and the performance (sensitivity, maximum flow rate, and the detectable information) of each type of sensor are detailed discussed. In conclusion, the optical/imaging and magnetic debris detectors have become promising technologies in the field of wear monitoring. Meanwhile, that the rapid extraction algorithm and three-dimensional reconstruction method of wear debris for optical/imaging debris detectors, and improving the sensitivity and detectability of magnetic debris detectors are the two important research contents in the field. Besides that, it can be predicted that the integrated wear monitoring system that can collect multiple wear-information in-situ, and the intelligent wear particle detector that have the ability of wear monitoring, wear evaluation and fault warning are the main research directions in the future. [ABSTRACT FROM AUTHOR]

An automated hologram acquisition system for big-data analysis and for improving the statistical precision of phase analysis has been upgraded with automated particle detection technology. The coordinates of objects in low-magnification images are automatically detected using zero-mean normalized cross-correlation with preselected reference images. In contrast with the conventional scanning acquisitions from the whole area of a microgrid and/or a thin specimen, the new method allows efficient data collections only from the desired fields of view including the particles. The acquisition time of the cubic/triangular nanoparticles that were observed was shortened by about one-fifty eighth that of the conventional scanning acquisition method because of efficient data collections. The developed technology can improve statistical precision in electron holography with shorter acquisition time and is applicable to the analysis of electromagnetic fields for various kinds of nanoparticles. [ABSTRACT FROM AUTHOR]

Particle identification and shape evaluation of granular materials from their realistic packing images are challenging and of great interest to many engineers and researchers. In this study, a systematic tool is developed based on computing techniques, including deep learning and computational geometry. First, image datasets of the target granular particles with well-labeled masks are established. The Mask Region Convolutional Neural Network (Mask R-CNN) is employed to implement the end-to-end instance segmentation and contour extraction of particles on different realistic images. Since Mask R-CNN models have several different feature extraction backbones, the optimal model is selected and then trained on the established datasets using transfer learning technique. After the particles are successfully identified from images of cobble and ballast, the elongation, angularity, and roughness are evaluated and the statistical shape analysis is conducted. The proposed method has strong generalization ability, especially for densely-packed particles. [Display omitted] • Automated particle identification using the Mask R-CNN model. • Successful instance segmentation of densely-packed granular particles. • Application to automated shape evaluation of ballast and cobble samples. [ABSTRACT FROM AUTHOR]

This research proposed the design, fabrication, and experiments of a surface acoustic wave resonator (SAWR)-based multi-sized particles monitor. A wide range selection and monitoring of large coarse particles (LCP), inhalable particles (PM10), and fine inhalable particles (PM2.5) were achieved by combining high-performance 311 MHz SAWRs and a specially designed cascade impactor. This paper calculated the normalized sensitivity distribution of the chip to the mass loading effect, extracted the optimal response area for particle attachment, analyzed the influence of the distance between nozzle and chip surface on the particle distribution, and evaluated the collection efficiency of the specially designed 2 LPM (L/min) impactor through computational fluid dynamics simulation software. An experimental platform was built to conduct the response experiment of the sensor to particle-containing gas generated by the combustion of leaf fragments and repeatability test. We verified the results of the particle diameter captured at each stage. This research suggests that the sensor's response had good linearity and repeatability, while the particles collected on the surface of the SAWR in each impactor stage met the desired diameter, observed through a microscope. [ABSTRACT FROM AUTHOR]

This paper describes a benchmark dataset for the detection of fuel particles in 2D and 3D image data in a rotary kiln combustion chamber. The specific challenges of detecting the small particles under demanding environmental conditions allows for the performance of existing and new particle detection techniques to be evaluated. The data set includes a classification of burning and non-burning particles, which can be in the air but also on the rotary kiln wall. The light-field camera used for data generation offers the potential to develop and objectively evaluate new advanced particle detection methods due to the additional 3D information. Besides explanations of the data set and the contained ground truth, an evaluation procedure of the particle detection based on the ground truth and results for an own particle detection procedure for the data set are presented.

The correlation of molecular neuroimaging and behavior studies in preclinical PET imaging is of major interest to unlock progress in the understanding of brain processes and assess the validity of preclinical studies in drug development. However, fully achieving this ambition requires performing molecular images of awake and freely moving animals, whereas most of the preclinical imaging procedures are currently performed under anesthesia. To overcome this issue, the MAPSSIC project aims to develop a pixelated intracerebral probe to be implanted into awake and freely moving rats. The aforementioned probe relies on IMIC (Imageur Moléculaire Intra Cérébral), a Monolithic Active Pixel Sensor (MAPS) prototype set to directly detect positrons. The IMIC sensors were produced in 5 different configurations. Measurements using a 204Tl source showed that the sensor parameters can be optimized to boost its performance allowing to increase the sensitivity and reduce the average cluster size. In addition, comparisons between sensor configurations show a clear gain provided by the introduction of CMOS process modifications. Finally, the choice of the optimal sensor configuration will depend on the expected in vivo conditions. [ABSTRACT FROM AUTHOR]

Monolithic active pixel sensors (MAPS) produced in a 65 nm CMOS imaging technology are being investigated for applications in particle physics. The MAPS design has a small collection electrode characterized by an input capacitance of ∼ fF, granting a high signal-to-noise ratio and low power consumption. Additionally, the 65 nm CMOS imaging technology brings a reduction in material budget and improved logic density of the readout circuitry, compared to previously studied technologies. Given these features, this technology was chosen by the TANGERINE project to develop the next generation of silicon pixel sensors. The sensor design targets temporal and spatial resolutions compatible with the requirements for a vertex detector at future lepton colliders. Simulations and test-beam characterization of technology demonstrators have been carried out in close collaboration with the CERN EP R&D program and the ALICE ITS3 upgrade. TCAD device simulations using generic doping profiles and Monte Carlo simulations have been used to build an understanding of the technology and predict the performance parameters of the sensor. Technology demonstrators of a 65 nm CMOS MAPS with a small collection electrode have been characterized in laboratory and test-beam facilities by studying performance parameters such as cluster size, charge collection, and efficiency. This work compares simulation results to test-beam data. The experimental results establish this technology as a promising candidate for a vertex detector at future lepton colliders and give valuable information for improving the simulation approach. [ABSTRACT FROM AUTHOR]

Silicon Photomultipliers (SiPMs) are state-of-the-art photon detectors used in particle physics, medical imaging, and beyond. They are sensitive to individual photons in the optical wavelength regime and achieve time resolutions of a few tens of picoseconds, which makes them interesting candidates for timing detectors in tracking systems for particle physics experiments. The Geiger discharges triggered in the sensitive elements of a SiPM, Single-Photon Avalanche Diodes (SPADs), yield signal amplitudes independent of the energy deposited by a photon or ionizing particle. This intrinsically digital nature of the signal motivates its digitization already on SPAD level. A digital SiPM (dSiPM) was designed at Deutsches Elektronen Synchrotron (DESY), combining a SPAD array with embedded CMOS circuitry for on-chip signal processing. A key feature of the DESY dSiPM is its capability to provide hit-position information on pixel level, and one hit time stamp per quadrant at a 3 MHz readout-frame rate. The pixels comprise four SPADs and have a pitch of about 70 μm. The four time stamps are provided by 12 bit Time-to-Digital Converters (TDCs) with a resolution better than 100 ps. The chip was characterized in the laboratory to determine dark count rate, breakdown voltage, and TDC characteristics. Test-beam measurements are analyzed to assess the DESY dSiPMs performance in the context of a 4D-tracking applications. The results demonstrate a spatial hit resolution on a pixel level, a minimum-ionizing particle detection efficiency of about 30 % and a time resolution in the order of 50 ps. [ABSTRACT FROM AUTHOR]

Abstract The separation and detection of particles in suspension are essential for a wide spectrum of applications including medical diagnostics. In this field, microfluidic deterministic lateral displacement (DLD) holds a promise due to the ability of continuous separation of particles by size, shape, deformability, and electrical properties with high resolution. DLD is a passive microfluidic separation technique that has been widely implemented for various bioparticle separations from blood cells to exosomes. DLD techniques have been previously reviewed in 2014. Since then, the field has matured as several physics of DLD have been updated, new phenomena have been discovered, and various designs have been presented to achieve a higher separation performance and throughput. Furthermore, some recent progress has shown new clinical applications and ability to use the DLD arrays as a platform for biomolecules detection. This review provides a thorough discussion on the recent progress in DLD with the topics based on the fundamental studies on DLD models and applications for particle separation and detection. Furthermore, current challenges and potential solutions of DLD are also discussed. We believe that a comprehensive understanding on DLD techniques could significantly contribute toward the advancements in the field for various applications. In particular, the rapid, low-cost, and high-throughput particle separation and detection with DLD have a tremendous impact for point-of-care diagnostics.

Copyright of Technisches Messen is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract When optical navigation images acquired by the OSIRIS‐REx (Origins, Spectral Interpretation, Resource Identification, and Security‐Regolith Explorer) mission revealed the periodic ejection of particles from asteroid (101955) Bennu, it became a mission priority to quickly identify and track these objects for both spacecraft safety and scientific purposes. The large number of particles and the mission criticality rendered time‐intensive manual inspection impractical. We present autonomous techniques for particle detection and tracking that were developed in response to the Bennu phenomenon but that have the capacity for general application to particles in motion about a celestial body. In an example OSIRIS‐REx data set, our autonomous techniques identified 93.6% of real particle tracks and nearly doubled the number of tracks detected versus manual inspection alone.

Within the ATTRACT FASTPIX project, a monolithic pixel sensor demonstrator chip has been developed in a modified 180 nm CMOS imaging process, targeting sub-nanosecond timing measurements for single ionizing particles. It features a small collection electrode design on a 25 micron thick epitaxial layer and contains 32 mini matrices of 68 hexagonal pixels each, with pixel pitches ranging from 8.66 to 20 micron. Four pixels are transmitting an analog output signal and 64 are transmitting binary hit information. Various design variations are explored, aiming at accelerating the charge collection and making the timing of the charge collection more uniform over the pixel area. Signal treatment of the analog waveforms, as well as reconstruction of time and charge information, is carried out off-chip. This contribution introduces the design of the sensor and readout system and presents the first performance results for 10 μm and 20 μm pixel pitch achieved in measurements with particle beams.

In the interaction of cosmic rays (CRs) with Earth’s atmosphere, neutrons are formed in a wide range of energies: from thermal (E≈0.025 eV) to ultrarelativistic (E>1 GeV). To detect and study CRs, Polar Geophysical Institute (PGI) uses a complex monitoring system containing detectors of various configurations. The standard neutron monitor (NM) 18-NM-64 is sensitive to neutrons with energies E>50 MeV. The lead-free section of the neutron monitor (BSRM) detects neutrons with energies E≈(0.1÷1) MeV. Also, for sharing with standard detectors, the Apatity NM station has developed and installed a neutron spectrometer with three energy channels and a particle reception angle of 15 degrees. The configuration of the device makes it possible to study the degree of anisotropy of the particle flux from different directions. We have obtained characteristics of the detector (response function and particle reception angle), as well as geometric dimensions through numerical simulation using the GEANT4 toolkit [Agostinelli et al., 2003]. During operation of the device, we collected database of observations and received preliminary results.

Flow imaging microscopy (FIM) is widely used to analyze subvisible particles starting from 2 μm in biopharmaceuticals. Recently, an oil-immersion FIM system emerged, the FlowCam Nano, designed to enable the characterization of particle sizes even below 2 μm. The aim of our study was to evaluate oil-immersion FIM (by using FlowCam Nano) in comparison to microfluidic resistive pulse sensing and resonant mass measurement for sizing and counting of particles in the submicron range. Polystyrene beads, a heat-stressed monoclonal antibody formulation and a silicone oil emulsion, were measured to assess the performance on biopharmaceutical relevant samples, as well as the ability to distinguish particle types based on instrument-derived morphological parameters. The determination of particle sizes and morphologies suffers from inaccuracies due to a low image contrast of small particles and light-scattering effects. The ill-defined measured volume impairs an accurate concentration determination. Nevertheless, FlowCam Nano in its current design complements the limited toolbox of submicron particle analysis of biopharmaceuticals by providing particle images in a size range that was previously not accessible with commercial FIM instruments. [ABSTRACT FROM AUTHOR]

When optical navigation images acquired by the OSIRIS‐REx (Origins, Spectral Interpretation, Resource Identification, and Security‐Regolith Explorer) mission revealed the periodic ejection of particles from asteroid (101955) Bennu, it became a mission priority to quickly identify and track these objects for both spacecraft safety and scientific purposes. The large number of particles and the mission criticality rendered time‐intensive manual inspection impractical. We present autonomous techniques for particle detection and tracking that were developed in response to the Bennu phenomenon but that have the capacity for general application to particles in motion about a celestial body. In an example OSIRIS‐REx data set, our autonomous techniques identified 93.6% of real particle tracks and nearly doubled the number of tracks detected versus manual inspection alone. Key Points: We describe autonomous techniques for the identification and tracking of particles in motion about a celestial bodyWe demonstrate these techniques using images from the OSIRIS‐REx mission to the active asteroid (101955) BennuIn the OSIRIS‐REx dataset, our autonomous algorithms detected 93.6% of real particle tracks, including 244 tracks not identified by manual inspection [ABSTRACT FROM AUTHOR]

• A wavelet-EMD denoising method is developed for strong gas turbulence noise. • A multiscale statistical method is established to enhance weak sand signals. • Weak sand information in strong noise is characterized by deep learning algorithms. • A quantitative model to detect solid particles in sand-gas flow is constructed. A method to quantitate sand particles in turbulent gas flow that combines the multiscale triaxial vibration response and deep learning algorithms is proposed. First, an optimized adaptive wavelet-empirical mode decomposition (EMD) denoising method is proposed based on multifrequency coherent and statistical analysis. Second, complex gas–solid turbulent flow information under multiple gas–particle coupling is characterized based on Hilbert-Huang transform (HHT), Hurst analysis, EMD entropy, etc. In addition, a deep learning algorithm that integrates multiscale flow information to determine sand content that includes two independent branches, a pure deep convolutional neural network (CNN) model driven by microscale triaxial response and a shallow long short-term memory (LSTM) network with regularization driven by mesoscale triaxial response, is proposed. Finally, a quantitative model to characterize sand-carrying turbulent gas flow based on the entropy weight effect of the triaxial vibration response is constructed as follows: C sand = A · p. ∑ i = x z S i Q i . Experimental validation indicates that the proposed deep learning algorithm has recognition and prediction accuracy of 97.8% and 96.97% for sand particle size and the power spectrum, respectively, which are higher than those of the existing intelligent models to characterize sand information. Moreover, the quantitative sand content model based on the multiscale response and deep learning algorithm has a maximum error of only 1.56% under strong turbulence. [ABSTRACT FROM AUTHOR]

Counting grape berries and measuring their size can provide accurate data for robot picking behavior decision-making, yield estimation, and quality evaluation. When grapes are picked, there is a strong uncertainty in the external environment and the shape of the grapes. Counting grape berries and measuring berry size are challenging tasks. Computer vision has made a huge breakthrough in this field. Although the detection method of grape berries based on 3D point cloud information relies on scanning equipment to estimate the number and yield of grape berries, the detection method is difficult to generalize. Grape berry detection based on 2D images is an effective method to solve this problem. However, it is difficult for traditional algorithms to accurately measure the berry size and other parameters, and there is still the problem of the low robustness of berry counting. In response to the above problems, we propose a grape berry detection method based on edge image processing and geometric morphology. The edge contour search and the corner detection algorithm are introduced to detect the concave point position of the berry edge contour extracted by the Canny algorithm to obtain the best contour segment. To correctly obtain the edge contour information of each berry and reduce the error grouping of contour segments, this paper proposes an algorithm for combining contour segments based on clustering search strategy and rotation direction determination, which realizes the correct reorganization of the segmented contour segments, to achieve an accurate calculation of the number of berries and an accurate measurement of their size. The experimental results prove that our proposed method has an average accuracy of 87.76% for the detection of the concave points of the edge contours of different types of grapes, which can achieve a good edge contour segmentation. The average accuracy of the detection of the number of grapes berries in this paper is 91.42%, which is 4.75% higher than that of the Hough transform. The average error between the measured berry size and the actual berry size is 2.30 mm, and the maximum error is 5.62 mm, which is within a reasonable range. The results prove that the method proposed in this paper is robust enough to detect different types of grape berries.

This research proposed the design, fabrication, and experiments of a surface acoustic wave resonator (SAWR)-based multi-sized particles monitor. A wide range selection and monitoring of large coarse particles (LCP), inhalable particles (PM10), and fine inhalable particles (PM2.5) were achieved by combining high-performance 311 MHz SAWRs and a specially designed cascade impactor. This paper calculated the normalized sensitivity distribution of the chip to the mass loading effect, extracted the optimal response area for particle attachment, analyzed the influence of the distance between nozzle and chip surface on the particle distribution, and evaluated the collection efficiency of the specially designed 2 LPM (L/min) impactor through computational fluid dynamics simulation software. An experimental platform was built to conduct the response experiment of the sensor to particle-containing gas generated by the combustion of leaf fragments and repeatability test. We verified the results of the particle diameter captured at each stage. This research suggests that the sensor’s response had good linearity and repeatability, while the particles collected on the surface of the SAWR in each impactor stage met the desired diameter, observed through a microscope.