Relevant bibliographies by topics / Particle sensor

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Particle sensor'

Author: Grafiati
Published: 1 March 2025

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Journal articles on the topic "Particle sensor"

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Zhang, Siqi, Yucai Xie, Lianfeng Zhang, Yuwei Zhang, Shuyao Zhang, Chenzhao Bai, and Wei Li. "Investigation of the Effect of Debris Position on the Detection Stability of a Magnetic Plug Sensor Based on Alternating Current Bridge." Sensors 24, no. 1 (December 21, 2023): 55. http://dx.doi.org/10.3390/s24010055.

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Magnetic plug-type abrasive particle sensors have a wide range of applications in oil detection, but there is little literature on the effect of abrasive particle position on detection accuracy. In this paper, an alternating current (AC) bridge-type abrasive particle detection sensor is designed, in which the sensing module utilizes permanent magnets to attract iron particles, and the induction coil is specially designed to detect the magnetic field fluctuation caused by iron particles. A corresponding model was also designed to evaluate the sensor’s sensitivity at different locations. In this paper, the magnetic field distribution of the sensor was first analyzed using finite element analysis software to obtain the magnetic field strength at different positions. Then, the response sensitivity of the sensor to particles and the effect of different positions on the detection results are explored through experiments. The simulation and the experimental results show substantial signal difference signal at different sensor positions. The method outlined in this article can determine the optimal sensing range for subsequent magnetic plug-type abrasive particle detection sensors and subsequently improve their reliability.
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Fardi, B., B. MacGibbon, S. Tripathi, and F. Moghadam. "Feasibility of an In-Situ Particle Monitor on a Tungsten LPCVD Reactor." Journal of the IEST 39, no. 3 (May 31, 1996): 25–30. http://dx.doi.org/10.17764/jiet.2.39.3.f109749056q17677.

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Reduction or elimination of particles generated within the semiconductor process equipment is vital to achieving high yields in the semiconductor industry. In-situ laser particle sensors that detect contaminants within the process equipment are gaining increasing acceptance because of their ability to detect problems in real-time. The prompt detection of particle problems followed by corrective actions will minimize impact to parts (wafers) sensitive to particle contamination. It is hypothesized that a relationship exists between the quantities of particles depositing on a wafer and particles detected by an in-situ particle sensor in the exhaust line. An extensive study was conducted to evaluate this hypothesis. An in-situ laser particle sensor was installed on the exhaust line of a Tungsten Low Pressure Chemical Vapor Deposition (W-LPCVD) chamber. The exhaust line particle counts were collected during the processing of device wafers and during particle tests using monitor wafers. Correlations between the ISPM counts, particles deposited on the wafer, and yields are examined. The response of the particle sensor to the periodic chamber cleans is also presented and discussed.
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Huang, Ching-Hsuan, Jiayang He, Elena Austin, Edmund Seto, and Igor Novosselov. "Assessing the value of complex refractive index and particle density for calibration of low-cost particle matter sensor for size-resolved particle count and PM2.5 measurements." PLOS ONE 16, no. 11 (November 11, 2021): e0259745. http://dx.doi.org/10.1371/journal.pone.0259745.

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Low-cost optical scattering particulate matter (PM) sensors report total or size-specific particle counts and mass concentrations. The PM concentration and size are estimated by the original equipment manufacturer (OEM) proprietary algorithms, which have inherent limitations since particle scattering depends on particles’ properties such as size, shape, and complex index of refraction (CRI) as well as environmental parameters such as temperature and relative humidity (RH). As low-cost PM sensors are not able to resolve individual particles, there is a need to characterize and calibrate sensors’ performance under a controlled environment. Here, we present improved calibration algorithms for Plantower PMS A003 sensor for mass indices and size-resolved number concentration. An aerosol chamber experimental protocol was used to evaluate sensor-to-sensor data reproducibility. The calibration was performed using four polydisperse test aerosols. The particle size distribution OEM calibration for PMS A003 sensor did not agree with the reference single particle sizer measurements. For the number concentration calibration, the linear model without adjusting for the aerosol properties and environmental conditions yields an absolute error (NMAE) of ~ 4.0% compared to the reference instrument. The calibration models adjusted for particle CRI and density account for non-linearity in the OEM’s mass concentrations estimates with NMAE within 5.0%. The calibration algorithms developed in this study can be used in indoor air quality monitoring, occupational/industrial exposure assessments, or near-source monitoring scenarios where field calibration might be challenging.
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Hong, Sung-Ho. "Numerical Approach and Verification Method for Improving the Sensitivity of Ferrous Particle Sensors with a Permanent Magnet." Sensors 23, no. 12 (June 6, 2023): 5381. http://dx.doi.org/10.3390/s23125381.

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This study aimed to improve the sensitivity of ferrous particle sensors used in various mechanical systems such as engines to detect abnormalities by measuring the number of ferrous wear particles generated by metal-to-metal contact. Existing sensors collect ferrous particles using a permanent magnet. However, their ability to detect abnormalities is limited because they only measure the number of ferrous particles collected on the top of the sensor. This study provides a design strategy to boost the sensitivity of an existing sensor using a multi-physics analysis method, and a practical numerical method was recommended to assess the sensitivity of the enhanced sensor. The sensor’s maximum magnetic flux density was increased by around 210% compared to the original sensor by changing the core’s form. In addition, in the numerical evaluation of the sensitivity of the sensor, the suggested sensor model has improved sensitivity. This study is important because it offers a numerical model and verification technique that may be used to enhance the functionality of a ferrous particle sensor that uses a permanent magnet.
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Hagan, David H., and Jesse H. Kroll. "Assessing the accuracy of low-cost optical particle sensors using a physics-based approach." Atmospheric Measurement Techniques 13, no. 11 (November 26, 2020): 6343–55. http://dx.doi.org/10.5194/amt-13-6343-2020.

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Abstract. Low-cost sensors for measuring particulate matter (PM) offer the ability to understand human exposure to air pollution at spatiotemporal scales that have previously been impractical. However, such low-cost PM sensors tend to be poorly characterized, and their measurements of mass concentration can be subject to considerable error. Recent studies have investigated how individual factors can contribute to this error, but these studies are largely based on empirical comparisons and generally do not examine the role of multiple factors simultaneously. Here, we present a new physics-based framework and open-source software package (opcsim) for evaluating the ability of low-cost optical particle sensors (optical particle counters and nephelometers) to accurately characterize the size distribution and/or mass loading of aerosol particles. This framework, which uses Mie theory to calculate the response of a given sensor to a given particle population, is used to estimate the fractional error in mass loading for different sensor types given variations in relative humidity, aerosol optical properties, and the underlying particle size distribution. Results indicate that such error, which can be substantial, is dependent on the sensor technology (nephelometer vs. optical particle counter), the specific parameters of the individual sensor, and differences between the aerosol used to calibrate the sensor and the aerosol being measured. We conclude with a summary of likely sources of error for different sensor types, environmental conditions, and particle classes and offer general recommendations for the choice of calibrant under different measurement scenarios.
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Hong, Sung-Ho. "Numerical Analysis for Appropriate Positioning of Ferrous Wear Debris Sensors with Permanent Magnet in Gearbox Systems." Sensors 24, no. 3 (January 26, 2024): 810. http://dx.doi.org/10.3390/s24030810.

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In order to improve the measurement sensitivity of ferrous wear debris sensors with a permanent magnet, a new numerical approach to the appropriate position of the sensor is presented. Moreover, a flow guide wall is proposed as a way to concentrate flow around the ferrous particle sensors. The flow guide wall is intended to further improve measurement sensitivity by allowing the flow containing ferrous particles to flow around the sensor. Numerical analysis was performed using the multi-physics analysis method for the most representative gearbox of the sump-tank type. In condition diagnosis using ferrous wear debris sensors, the position of the sensor has a great influence. In other words, there are cases where no measurements occur, despite the presence of abnormal wear and damage due to the wrong sensor position. To determine the optimal sensor position, this study used flow analysis for the flow caused by the movement of the gear, electric and magnetic field analysis to implement the sensor, and a particle tracing technique to track particle trajectory. The new analysis method and results of this study will provide important information for selecting the optimal sensor location and for the effective application of ferrous wear debris sensors, and will contribute to the oil sensor-based condition diagnosis technology.
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Kittimanapun, Kritsada, Natthawut Laojamnongwong, Jetnipit Kaewjai, Chinorat Kobdaj, and Wanchaloem Poonsawat. "Commissioning of Pixel Sensor Telescope for Monolithic Active Pixel Sensor Characterization." Journal of Physics: Conference Series 2653, no. 1 (December 1, 2023): 012029. http://dx.doi.org/10.1088/1742-6596/2653/1/012029.

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Abstract The Monolithic Active Pixel Sensors (MAPS) have been promisingly demonstrated as a core part of tracking detectors used in many experiments. In order to obtain decent particle tracks generated after high-energy particle collision, it is especially important that the detector must consist of high-performance MAPS. A pixel sensor telescope is a tool employed to investigate the properties of MAPS using high-energy particle test beams. At the Synchrotron Light Research Institute (SLRI), the pixel sensor telescope assembled with five layers of 50-µm thick pixel sensors has been commissioned and tested both in the laboratory with a radioactive source and by a high-energy electron test beam produced at the SLRI Beam Test Facility. With sensor signal threshold ranging from 9.73 to 10.15 DAC and noises between 0.62 and 0.87 DAC, each plane of the pixel sensor telescope can detect particles decaying from the radioactive source. Moreover, the test beam profiles have been measured to be 4.00 - 4.56 mm and 1.15 - 2.29 mm in horizontal and vertical axes, respectively. Correlations between each plane have been observed and confirm the proper operation of the pixel sensor telescope. Consequently, the pixel sensor telescope can be used to characterize the pixel sensor prototype.
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Yuan, Changrong, Zhongsheng Sun, and Xiaoning Li. "Mechanism and Modeling of Contaminant Accumulation on Hot-Film Air Flow Sensor." Mathematical Problems in Engineering 2019 (February 19, 2019): 1–15. http://dx.doi.org/10.1155/2019/6246259.

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Hot-film air flow sensors are now widely applied in many industrial fields. Their accuracy can be seriously affected when there are contaminants accumulated on the sensor chip. To predict the decrease of accuracy caused by contamination, the accumulation mechanism of contaminant particles on the surface of the sensor chip is studied in this paper. The adsorption process of particle is analyzed and a theoretical model of the cumulative thickness of the particles over time is established. A test platform for the particle accumulation is built and a long-term cumulative test is conducted. According to the tests, cumulative thickness of the particles increases while the growth rate slows over time. The results of the test fit those of the theoretical model. Various factors affecting the accumulation of contaminants are analyzed. The results indicate that the cumulative thickness of the particles increases along with the increase of the particle concentration, the particle charge, and the electric field strength on the chip surface but decreases along with the increase of the particle radius. The test results also show that the electrical force is the dominant reason of particle adsorption accumulation. By decreasing the electric field strength on the sensor surface, the cumulative thickness of the particles can be effectively reduced.
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Santos da Silva, Safire Torres, Nikola Jerance, and Harijaona Lalao Rakotoarison. "Simulating metallic contamination in permanent magnets used in magnetic sensors." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 5 (September 2, 2019): 1683–95. http://dx.doi.org/10.1108/compel-12-2018-0515.

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Purpose The purpose of this paper is to provide a model for simulating contamination by ferromagnetic particles in sensors that use permanent magnets. This topic is especially important for automotive applications, where magnetic sensors are extensively used and where metallic particles are present, particularly because of friction between mechanical parts. The aim of the model is to predict the particle accumulation and its effect on the sensor performance. Design/methodology/approach Magnetostatic moment method is used to calculate particles' magnetization and magnetic field. Magnetic saturation is included and Newton–Raphson method is used to solve the non-linear system. Magnetic force on particles is calculated as a gradient of energy. Dynamic simulation provides the positions of agglomerated particles. Findings A simulation of magnetic park lock sensor shows a significant impact of ferromagnetic particles on sensor's accuracy. Moreover, gains on computational time because of model optimizations are reported. Research limitations/implications Only magnetic force and gravity are taken into account for particle dynamics. Mechanical forces such as friction and particle interactions might be considered in future works. Practical implications This paper provides the possibility to evaluate and improve magnetic sensor design with respect to particles contamination. Originality/value The paper presents a novel simulation tool developed to answer the growing need for reliable and fast prediction of magnetic position sensors’ degradation in the presence of metallic particles.
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Fan, Bin, Lianfu Wang, Yong Liu, Peng Zhang, and Song Feng. "Simulation and Optimization Design of Inductive Wear Particle Sensor." Sensors 23, no. 10 (May 19, 2023): 4890. http://dx.doi.org/10.3390/s23104890.

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In order to monitor the diagnosis of mechanical equipment by monitoring the metal wear particles carried in large aperture lubricating oil tubes, the simulation optimization structure design was carried out based on the traditional three-coil inductance wear particle sensor. The numerical model of electromotive force induced by the wear particle sensor was established, and the coil distance and coil turns were simulated by finite element analysis software. When permalloy is covered on the surface of the excitation coil and induction coil, the background magnetic field at the air gap increases, and the induced electromotive force amplitude generated by wear particles is increased. The effect of alloy thickness on the induced voltage and magnetic field was analyzed to determine the optimum thickness, and increase the induction voltage of the alloy chamfer detection at the air gap. The optimal parameter structure was determined to improve the detection ability of the sensor. Ultimately, by comparing the extreme values of the induced voltage of various types of sensors, the simulation determined that the minimum allowable detection of the optimal sensor was 27.5 µm ferromagnetic particles.
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Dissertations / Theses on the topic "Particle sensor"

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Ing, Garrick. "Distributed particle filters for object tracking in sensor networks." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98971.

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A particle filter (PF) is a simulation-based algorithm used to solve estimation problems, such as object tracking. The PF works by maintaining a set of "particles" as candidate state descriptions of an object's position. The filter determines how well the set of particles describe the observations and fit the dynamic model, in order to form an object state estimate. The drawback of the basic PF is that the algorithm functions by collecting all data at a fusion centre. This leads to high communication and energy costs in a resource-limited network such as the sensor network. In this thesis, we analyze the PF to determine how it can be modified for efficient use in a sensor network. Our main priority is to keep communication and energy costs low since this increases the network lifetime. We propose two innovative particle filtering algorithms which minimizes the associated costs.
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Latiff, Nurul Mu'azzah Abdul. "Particle swarm optimisation for clustering in wireless sensor networks." Thesis, University of Newcastle Upon Tyne, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.489298.

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One of the mam characteristics which set wireless sensor networks apart from traditional networks is the inclusion of energy consumption as the highest priority optimisation goal. This is because these types of networks work under the general concept that the system lifetime needs to be extended as much as possible, whilst at the same time achieving efficient data forwarding and preventing route disconnections due to sensor node failure. Hence, the use of energy efficient infrastructure such as clustering n:ay lengthen the lifetime of the network and prevent network connectivity degradation through the utilisation of cluster heads. Since the optimal selection of cluster heads in a network belongs to nondeterministic polynomial (NP) hard problem, the use of approximation algorithms such as Particle Swarm Optimisation (PSO) are generally more suitable due to its simplicity and outstanding search strength. This PhD thesis investigates the application of the PSO algorithm in clustering ofwireless sensor networks. In view of the need to prolong sensor network lifetime, a centralised, energy efficient, cluster-based protocol is developed using the PSO algorithm. A new cost function has been defined, which takes into consideration three important factors, namely the expected network energy consumption, the intracluster distance and the remaining energy of the cluster heads. The clustering problem is then transformed into an optimisation problem, and the PSO algorithm is employed to search for the optimal set of cluster heads. Simulation results demonstrate that the proposed protocol using PSO obtains better data delivery and network lifetime, as well as improves network connectivity over its comparatives. In addition, the results confirm the efficiency of PSG in clustering problems, compared to other evolutionary algorithms. This thesis also considers the use of the PSO algorithm in clustering the wireless sensor networks with mobile nodes. For this purpose, the 'mobility factor is taken into account when defining the cluster membership and selecting the cluster heads in order to maintain network connectivity. Simulation results prove that this approach outperforms other well known protocols in terms of data delivery and network lifetime. Finally, a dynamic multi-objective clustering algorithm which automatically determines the optimum number of clusters is introduced. This algorithm, based on binary PSG, eliminates the need to set the number of clusters a priori. Furthermore, the use of multi-objective PSG can tackle the difficulty of tuning the cost function weights that properly scales the sub-objectives. Performance evaluation through simulation exhibits the superior strength of this algorithm in enhancing the network survivability.
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Ihler, Alexander T. (Alexander Thomas) 1976. "Inference in sensor networks : graphical models and particle methods." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33206.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 175-183).
Sensor networks have quickly risen in importance over the last several years to become an active field of research, full of difficult problems and applications. At the same time, graphical models have shown themselves to be an extremely useful formalism for describing the underlying statistical structure of problems for sensor networks. In part, this is due to a number of efficient methods for solving inference problems defined on graphical models, but even more important is the fact that many of these methods (such as belief propagation) can be interpreted as a set of message passing operations, for which it is not difficult to describe a simple, distributed architecture in which each sensor performs local processing and fusion of information, and passes messages locally among neighboring sensors. At the same time, many of the tasks which are most important in sensor networks are characterized by such features as complex uncertainty and nonlinear observation processes. Particle filtering is one common technique for dealing with inference under these conditions in certain types of sequential problems, such as tracking of mobile objects.
(cont.) However, many sensor network applications do not have the necessary structure to apply particle filtering, and even when they do there are subtleties which arise due to the nature of a distributed inference process performed on a system with limited resources (such as power, bandwidth, and so forth). This thesis explores how the ideas of graphical models and sample-based representations of uncertainty such as are used in particle filtering can be applied to problems defined for sensor networks, in which we must consider the impact of resource limitations on our algorithms. In particular, we explore three related themes. We begin by describing how sample-based representations can be applied to solve inference problems defined on general graphical models. Limited communications, the primary restriction in most practical sensor networks, means that the messages which are passed in the inference process must be approximated in some way. Our second theme explores the consequences of such message approximations, and leads to results with implications both for distributed systems and the use of belief propagation more generally.
(cont.) This naturally raises a third theme, investigating the optimal cost of representing sample-based estimates of uncertainty so as to minimize the communications required. Our analysis shows several interesting differences between this problem and traditional source coding methods. We also use the metrics for message errors to define lossy or approximate4 encoders, and provide an example encoder capable of balancing communication costs with a measure on inferential error. Finally, we put all of these three themes to work to solve a difficult and important task in sensor networks. The self-localization problem for sensors networks involves the estimation of all sensor positions given a set of relative inter-sensor measurements in the network. We describe this problem as a graphical model, illustrate the complex uncertainties involved in the estimation process, and present a method of finding for both estimates of the sensor positions and their remaining uncertainty using a sample-based message passing algorithm. This method is capable of incorporating arbitrary noise distributions, including outlier processes, and by applying our lossy encoding algorithm can be used even when communications is relatively limited.
(cont.) We conclude the thesis with a summary of the work and its contributions, and a description of some of the many problems which remain open within the field.
y Alexander T. Ihler.
Ph.D.
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Zhang, Zheng. "RESISTIVE PULSE SENSORS FOR POLLEN PARTICLE MEASUREMENTS." University of Akron / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=akron1145070142.

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Campbell, Steven Conner. "DETERMINATION OF ACOUSTIC RADIATION EFFICIENCY VIA PARTICLE VELOCITY SENSOR WITH APPLICATIONS." UKnowledge, 2019. https://uknowledge.uky.edu/me_etds/133.

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Acoustic radiation efficiency is defined as the ratio of sound power radiated to the surface vibration power of a piston with equivalent surface area. It has been shown that the radiation efficiency is maximized and may exceed unity when the structural and acoustic wavelengths are approximately equal. The frequency at which this occurs is called the critical frequency and can be shifted with structural modifications. This has proven to be an effective way to reduce noise. The standard radiation efficiency measurement is comprised of an intensity scan for sound power measurement and accelerometer array for spatially averaged vibration determination. This method is difficult to apply to lightweight structures, complicated geometries, and when acoustic sources are in close proximity to one another. Recently, robust particle velocity sensors have been developed. Combined with a small microphone in the same instrument, particle velocity and sound pressure can be measured simultaneously and at the same location. This permits radiation efficiency to be measured using a non-contact approach with a single sensor. A suggested practice for measuring radiation efficiency has been developed and validated with several examples including two flat plates of different thickness, an oil pan, and components on a running small engine.
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Jagtiani, Ashish V. "DEVELOPMENT OF NOVEL MULTICHANNEL RESISTIVE PULSE SENSORS FOR MICRO-PARTICLE DETECTION AND DIFFERENTIATION." University of Akron / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=akron1196284929.

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Kornilin, Dmitriy V. "Investigation of size, concentration and particle shapes in hydraulic systems using an in-line CMOS image matrix sensor." Thesis, University of Chester, 2018. http://hdl.handle.net/10034/621947.

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The theoretical and experimental investigation of the novel in-line CMOS image sensor was performed. This sensor is aimed to investigate particle size distribution, particle concentration and shape in hydraulic liquid in order to implement the proactive maintenance of hydraulic equipment. The existing instruments such as automatic particle counters and techniques are not sufficiently enough to address this task because of their restricted sensitivity, limit of concentration to be measured and they cannot determine particle shape. Other instruments cannot be used as inline sensors because they are not resistant to the arduous conditions such as high pressure and vibration. The novel mathematical model was proposed as it is not possible to use previously developed techniques based on using optical system and complicated algorithms. This model gives the output signal of the image sensor depending on the particle size, its distance from the light source (LED) and image sensor. Additionally, the model takes into account the limited exposure time and particle track simulation. The results of simulation based on the model are also performed in thesis. On the basis of the mathematical model the image processing algorithms were suggested in order to determine particle size even when this size is lower than pixel size. There are different approaches depending on the relation between the size of the particle and the pixel size. The approach to the volume of liquid sample estimation was suggested in order to address the problem of low accuracy of concentration measurement by the conventional automatic particle counters based on the single photodiode. Proposed technique makes corrections on the basis of particle velocity estimation. Approach to the accuracy estimation of the sensor was proposed and simulation results are shown. Generally, the accuracy of particle size and concentration measurement was considered. Ultimately, the experimental setup was used in order to test suggested techniques. The mathematical model was tested and the results showed sufficient correlation with the experiment. The zinc dust was used as a reference object as there are the particles within the range from 1 to 25 microns which is appropriate to check the sensitivity. The results of experiments using reference instrument showed the improved sensitivity and accuracy of volume measured compared to the reference one.
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Fan, Zihao, and Wei Zhao. "Network Coverage Optimization Strategy in Wireless Sensor Networks Based on Particle Swarm Optimization." Thesis, Högskolan i Gävle, Akademin för teknik och miljö, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-9764.

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Wireless sensor network is an intelligent network system which has the self-monitoring functionality. It consists of many low-cost, low-power and small-sized sensor nodes that can communicate with each other to perform sensing and data processing. Acting as an important role in the system, network coverage usually has a huge effect on the system’s lifetime.In this thesis, particle swarm algorithm was used as a method to optimize the coverage in the coverage of wireless sensor network. A network coverage optimization strategy based on particle swarm optimization was proposed and MATLAB was used as a tool to apply the algorithm. The model used in this thesis is the probability sensing model and the coverage type is area coverage. Effectiveness of the algorithm is proved by simulation. The simulation of the algorithm suggests the optimal deployment can be determined if a certain parameter which in this thesis is the sensing range is given.
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Barboza, Kris Leo. "A Diagnostic Technique for Particle Characterization Using Laser Light Extinction." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/52000.

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Increased operations of aircraft, both commercial and military, in hostile desert environments have increased risks of micro-sized particle ingestion into engines. The probability of increased sand and dust ingestion results in increased life cycle costs, in addition to increased potential for performance loss. Thus, abilities to accurately characterize inlet sand would be useful for engine diagnostics and prognostic evaluation. Previous characterization studies were based on particle measurements performed a posteriori. Thus, there exists a need for in situ quantification of ingested particles. The work presented in this thesis describes initial developments of a line-of-sight optical technique to characterize ingested particles at concentrations similar to those experienced by aircraft in brownout conditions using light extinction with the end goal of producing an onboard aircraft diagnostic sensor. By measuring the extinct light intensity in presence of particles over range of concentrations, a relationship between diameters, concentration and light extinction was used for characterization. The particle size distribution was assumed log-normal and size range of interest 1-10 μm. To validate the technique, particle characterization in both static and flow based tests were performed on polystyrene latex spheres of sizes 1.32 μm, 3.9 μm, 5.1 μm, and 7 μm in mono-disperse and poly-disperse mixtures. Results from the static experiments were obtained with a maximum relative error of 11%. Concentrations from the static experiments were obtained with a maximum relative error of 18%. Mono-dispersed and poly-dispersed particle samples were sized in a flow setup, with a maximum relative error of 12% and 10% respectively across all diameter samples tested. Uncertainty in measurements were quantified, with results indicating a maximum error of 17% in diameters due to sources of variability and showed that shorter wavelength lasers provide lower errors in concentration measurements, compared to longer wavelengths. For real time, on-board measurements, where path lengths traveled by light are much larger than distances traveled in initial proof of concept experimental setups, requirements would be to install sensitive detectors and powerful lasers to prevent operation near noise floors of detectors. Vibration effects from the engine can be mitigated by using larger area collection optics to ensure that the transmitted light falls on active detector areas. Results shown in this thesis point towards validity of the light extinction technique to provide real time characterization of ingested particles, and will serve as an impetus to carry out further research using this technique to characterize particles entering aircraft engine inlets.
Master of Science
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Kiring, Aroland. "Shrinkage based particle filters for tracking in wireless sensor networks with correlated sparse measurements." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/20105/.

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This thesis focuses on the development of mobile tracking approaches in wireless sensor networks (WSNs) with correlated and sparse measurements. In wireless networks, devices have the ability to transfer information over the network nodes via wireless signals. The strength of a wireless signal at a receiver is referred as the received signal strength (RSS) and many wireless technologies such as Wi-Fi, ZigBee, the Global Positioning Systems (GPS), and other Satellite systems provide the RSS measurements for signal transmission. Due to the availability of RSS measurements, various tracking approaches in WSNs were developed based on the RSS measurements. Unfortunately, the feasibility of tracking using the RSS measurements is highly dependent on the connectivity of the wireless signals. The existing connectivity may be intermittently disrupted due to the low-battery status on the sensor node or temporarily sensor malfunction. In ad-hoc networks, the number of observation of the RSS measurements rapidly changing due to the movements of network nodes and mobile user. As a result, the tracking algorithms have limited data to perform state inference and this prevents accurate tracking. Furthermore, consecutive RSS measurements obtained from nearby sensor nodes exhibit spatio-temporal correlation, which provides extra information to be exploited. Exploiting the statistical information on the measurements noise covariance matrix increases the tracking accuracy. When the number of observations is relatively large, estimating the measurement noise covariance matrix is feasible. However, when they are relatively small, the covariance matrix estimation becomes ill-conditioned and non-invertible. In situations where the RSS measurements are corrupted by outliers, state inference can be misleading. Outliers can come from the sudden environmental disturbances, temporary sensor failures or even from the intrinsic noise of the sensor device. The outliers existence should be considered accordingly to avoid false and poor estimates. This thesis proposes first a shrinkage-based particle filter for mobile tracking in WSNs. It estimates the correlation in the RSS measurement using the shrinkage estimator. The shrinkage estimator overcomes the problems of ill-conditioned and non-invertibility of the measurement noise covariance matrix. The estimated covariance matrix is then applied to the particle filter. Secondly, it develops a robust shrinkage based particle filter for the problem of outliers in the RSS measurements. The proposed algorithm provides a non-parametric shrinkage estimate and represents a multiple model particle filter. The performances of both proposed filters are demonstrated over challenging scenarios for mobile tracking.
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Books on the topic "Particle sensor"

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Hartmann, Frank. Evolution of Silicon Sensor Technology in Particle Physics. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64436-3.

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Hartmann, Frank. Evolution of Silicon Sensor Technology in Particle Physics. Cham: Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-59720-6.

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Goddard Space Flight Center. Engineering Procurement Office., ed. [Measuring electrically charged particle fluxes in space using a fiber optic loop sensor]: Final report. Greenbelt, MD: NASA Goddard Space Flight Center, Engineering Procurement Office, 1993.

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A, Lindemulder Elizabeth, Jovaag Kari, and United States. National Aeronautics and Space Administration., eds. Temperature-dependent daily variability of precipitable water in special sensor microwave/imager observations. [Washington, DC: National Aeronautics and Space Administration, 1995.

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A, Lindemulder Elizabeth, Jovaag Kari, and United States. National Aeronautics and Space Administration., eds. Temperature-dependent daily variability of precipitable water in special sensor microwave/imager observations. [Washington, DC: National Aeronautics and Space Administration, 1995.

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1922-, Soo S. L., ed. Instrumentation for fluid-particle flow. Norwich, N.Y: Noyes Publications, 1999.

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(Firm), Knovel, ed. Instrumentation for fluid-particle flow. Park Ridge, N.J: Noyes Publications, 1999.

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Inc, ebrary, ed. Nanomedicine design of particles, sensors, motors, implants, robots, and devices. Boston, Mass: Artech House, 2009.

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Evolution of Silicon Sensor Technology in Particle Physics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/b106762.

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Hartmann, Frank. Evolution of Silicon Sensor Technology in Particle Physics. Springer, 2010.

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Book chapters on the topic "Particle sensor"

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Eveland, Christopher K. "Particle Filtering with Evidential Reasoning." In Sensor Based Intelligent Robots, 305–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45993-6_17.

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Jacobsen, Finn, and Hans-Elias de Bree. "The Microflown Particle Velocity Sensor." In Handbook of Signal Processing in Acoustics, 1283–91. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-30441-0_68.

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Kreucher, Christopher M., Mark Morelande, Keith Kastella, and Alfred O. Hero. "Joint Multi-Target Particle Filtering." In Foundations and Applications of Sensor Management, 59–93. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-49819-5_4.

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Becker, Aaron, Erik D. Demaine, Sándor P. Fekete, Golnaz Habibi, and James McLurkin. "Reconfiguring Massive Particle Swarms with Limited, Global Control." In Algorithms for Sensor Systems, 51–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45346-5_5.

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Majumdar, Ivy, B. N. Chatterji, and Avijit Kar. "Particle Swarm Optimisation Method for Texture Image Retrieval." In Computational Intelligence in Sensor Networks, 405–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-57277-1_17.

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Ristic, Branko. "Sensor Control for Random Set BasedParticle Filters." In Particle Filters for Random Set Models, 85–119. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6316-0_5.

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Narkhede, Parag, Shripad Deshpande, and Rahee Walambe. "Sensor Data Cleaning Using Particle Swarm Optimization." In Advances in Intelligent Systems and Computing, 182–91. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16681-6_18.

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Zhao, Mengying, Yuqi Ni, Tao Chao, and Ke Fang. "An Inertia Weight Variable Particle Swarm Optimization Algorithm with Mutation." In Sensor Networks and Signal Processing, 269–80. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4917-5_21.

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Gning, Amadou, Lyudmila Mihaylova, Fahed Abdallah, and Branko Ristic. "Particle Filtering Combined with Interval Methods for Tracking Applications." In Integrated Tracking, Classification, and Sensor Management, 43–74. Hoboken, New Jersey: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118450550.ch02.

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Hamster, A. W., M. J. van Duuren, G. C. S. Brons, J. Flokstra, and H. Rogalla. "Squid Readout of Cryogenic Particle Detectors." In Sensor Technology in the Netherlands: State of the Art, 281–85. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5010-1_45.

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Conference papers on the topic "Particle sensor"

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Coates, M., and G. Ing. "Sensor network particle filters: motes as particles." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628769.

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Zhou, Gui, Hang Wang, and Minjun Peng. "Research on Optimization and Verification Method of Sensor Arrangement in the Chemical and Volume Control System." In 2021 28th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/icone28-65466.

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Abstract In order to avoid the nuclear accidents during the operation of nuclear power plants, it is necessary to always monitor the status of relevant facilities and equipment. The premise of condition monitoring is that the sensor can provide sufficient and accurate operating parameters. Therefore, the sensor arrangement must be rationalized. As one of the nuclear auxiliary systems, the chemical and volume control system plays an important role in ensuring the safe operation of nuclear power plants. There are plenty of sensor measuring points arranged in the chemical and volume control system. These sensors are not only for detecting faults, but also for running and controlling services. Particle swarm algorithm has many applications in solving the problem of sensor layout optimization but the disadvantage of the basic particle swarm optimization algorithm is that the parameters are fixed, the particles are single, and it is easy to fall into the local optimization. In this paper, the basic particle swarm optimization algorithm is improved by Non-linearly adjusting inertia weight factor, asynchronously changing learning factor, and variating particle. The improved particle swarm optimization algorithm is used to optimize the sensor placement. The numerical analysis verified that a smaller number of sensors can meet the fault detection requirements of the chemical and volume control system in this paper, and Experiments have proved that the improved particle swarm algorithm can improve the basic particle swarm algorithm, which is easy to fall into the shortcomings of local optimization and single particles. This method has good applicability, and could be also used to optimize other systems with sufficient parameters and consistent objective function.
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Johansen, Per, Michael M. Bech, Sune Dupont, Uffe N. Christiansen, Jens L. Sørensen, David N. Østedgaard-Munck, and Anders Bentien. "An Experimental Study on High-Flowrate Ultrasonic Particle Monitoring in Oil Hydraulics." In BATH/ASME 2022 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/fpmc2022-89721.

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Abstract In this article an experimental analysis on detection of particles in oil hydraulics by an in-line, high flowrate ultrasonic sensor is presented. This experimental study is conducted by the implementation of a particle concentration sensor prototype in a simple hydraulic circuit. In this system the contamination particles are introduced in an open tank. The sensor prototype is based on the particle scattering of ultrasound measured in a 90° angle in relation to the incoming wave. An experimental analysis of the sensors ability to detect specific concentrations of silicon dioxide particles in oils of varying viscosity grade is presented. Furthermore, an experiment involving a used hydraulic oil from an industrial plant is shown, where the sensor is able to detect increased particle contamination in the system, when the open tank is actively stirred. It is from the experimental analysis found that the prototype can detect particles of above 40 microns in common hydraulic oils, however the background field intensity and frequency could be increased to lower the size of the detectable particles. The paper is concluded with a theoretical analysis, which is examining the background field intensities and frequencies needed to meet standards within measurements of oil cleanliness.
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Pan, Feng, and Adam Huang. "Investigation and Measurement of Electrical Transport of Metal Particle Polymer Composites for the Development of MEMS-Based Corrosion Sensor." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-12041.

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Recently, our research group has proposed a MEMS-based solid state corrosion sensor based on the carbon black and metal particle polymer composite materials. The chemical and dimensional properties of the particles and polymer matrix will provide the tailorability in sensor sensitivity, selectivity, time response, and operating life-span. This paper will analyze the electrical resistivity of the sensor relative to the percentage of different type of particles in PDMS matrix in order to understand the effect of corrosion mechanism on metal particle polymer composites. This paper will also present the electrical quantification techniques for the micro corrosion sensors, the understanding of which is need for the transduction from corrosion to electronic output. Electronic transduction is the natural form of output due to the redox reactions of the electrochemical corrosion of particles and the electric current generated is directly proportional to the rate of the reaction.
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Kaikkonen, Ville A., Eero O. Molkoselkä, Harri J. Juttula, and Anssi J. Mäkynen. "UAV Cloud Particle Sensor." In 2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2024. http://dx.doi.org/10.1109/i2mtc60896.2024.10560651.

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Yunpeng Li, Lingling Zhao, and Mark Coates. "Particle flow auxiliary particle filter." In 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2015. http://dx.doi.org/10.1109/camsap.2015.7383760.

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Pokusevski, Z., I. G. Evans, T. A. York, and T. Dyakowski. "A Novel Micro Capacitance Sensor for Studying Hydrodynamics of Particle Laden Flow." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2043.

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Abstract Particle laden flows occur in a wide range of industrial applications. Detection of dispersed particles and drops is of enormous importance since they may cause “erosion-corrosion effects” in industrial equipment. This paper presents a novel technique for particle detection. The results show 20 and 50 micron particles detected in the static tests. CFD simulations were carried out in order to design the sensor mounting and its optimal inclination to the flow. It was shown that sensor inclination of 5 or 10 degrees to the flow is highly beneficial. Dynamic tests were carried out and the full results show good agreement with the predicted particle tracks. 50 and 20 micron polyamide particles were detected at air velocities up to 12 metres per second.
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Namin, Parham H., and Mohammad A. Tinati. "Node localization using Particle Swarm Optimization." In 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2011. http://dx.doi.org/10.1109/issnip.2011.6146558.

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Chen, Sen, Yitao Shen, Guiyan Qiang, Zheng Zheng, Zheyu Wang, Yin Hao, and Ting Hu. "Simulation Study on the Influence of Multi-Magnetic Particles on Oil Sensor Signals." In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2826.

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<div class="section abstract"><div class="htmlview paragraph">Engine operation produces particles that contaminate the lubricating oil and can damage the engine's internal components. This paper presents a model for a three-coil inductive metal particle sensor and verifies the rationality and accuracy of the model by simulating the motion of a single spherical iron particle passing through the sensor. On this basis, the simulation of coupling double particles with different sizes, distances, and shapes is carried out. The study explores the influence of particle motion on the sensor-induced signal under various conditions. The research shows that when two particles pass through the sensor, the induced voltage signal will produce superposition when the distance between the two particles is small. The peak value of the induced voltage is 1-2 times the peak value of the induced voltage of a single particle. As the distance increases, the peak value of the induced voltage initially decreases, then slowly increases, and finally stabilizes. When the double particle signal is superimposed, the enhancement effect of the peak value of the induced voltage gradually increases as the particle diameter decreases. For particles with the same volume but different shapes, the peak value of the induced voltage increases with the increase of the cross-sectional area of the particles.</div></div>
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Boiarski, Anthony A. "Fiber Optic Particle Concentration Sensor." In 29th Annual Technical Symposium. SPIE, 1986. http://dx.doi.org/10.1117/12.949775.

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Reports on the topic "Particle sensor"

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SEA TECH INC CORVALLIS OR. Development of an Expendable Particle Sensor. Fort Belvoir, VA: Defense Technical Information Center, May 1992. http://dx.doi.org/10.21236/ada251708.

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Bartz, Robert. Development of an Expendable Particle Sensor. Fort Belvoir, VA: Defense Technical Information Center, May 1992. http://dx.doi.org/10.21236/ada251942.

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Bartz, Robert. Development of an Expendable Particle Sensor. Fort Belvoir, VA: Defense Technical Information Center, April 1992. http://dx.doi.org/10.21236/ada252185.

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Bartz, Robert. Development of an Expendable Particle Sensor. Fort Belvoir, VA: Defense Technical Information Center, May 1992. http://dx.doi.org/10.21236/ada252186.

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Bartz, Robert. Development of an Expendable Particle Sensor. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada255702.

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Bartz, Robert. Development of an Expendable Particle Sensor. Fort Belvoir, VA: Defense Technical Information Center, February 1994. http://dx.doi.org/10.21236/ada303901.

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Siegel, David A., Ivona Cetinic, Andrew F. Thompson, Norman B. Nelson, Michaela Sten, Melissa Omand, Shawnee Traylor, et al. EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) North Atlantic sensor calibration and intercalibration documents. NASA STI Program and Woods Hole Oceanographic Institution, October 2023. http://dx.doi.org/10.1575/1912/66998.

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The following documents collect information regarding the calibration and intercalibration of various sensors that were deployed during the North Atlantic field component of the NASA EXPORTS project (EXPORTS NA), which took place between May 4 and June 1, 2021 (Johnson et al., 2023). The EXPORTS NA campaign was designed to to provide a contrasting end member to the earlier North Pacific field campaign, and focused on carbon export associated with the North Atlantic spring bloom in which gravitational sinking of organic particles, the physical advection and mixing, and active transport by vertically migrating zooplankton are all expected to provide significant flux pathways. During EXPORTS NA data sets were collected from a variety of shipbased, autonomously-piloted, and Lagrangian platforms. Intercalibration activities were tasked to different groups within the EXPORTS project team. Team leads and contact information are listed below. The overarching goal of these activities was to identify a trusted sensor, carry out a careful calibration of this sensor, then base any intercalibraiton needs off of this sensor, occasionally propagating information across platforms. Full details of the intercalibration approach, assumptions, and summary are provided in the attached documents. All calibration and intercalibration activities were completed before data set were uplaoded to the NASA SeaBASS data repository. Data related to this cruise can be publicly accessed at: https://seabass.gsfc.nasa.gov/cruise/EXPORTSNA Updates to calibration and intercalibration documents required to reflect revised data sets will also be provided through SeaBASS. Questions concerning referencing these documents or accessing data sets should be directed to Inia Soto Ramos. NASA EXPORTS Science Lead: David Siegel, davesiegel@ucsb.edu NASA EXPORTS Project Scientist: Ivona Cetini´c, ivona.cetinic@nasa.gov NASA EXPORTS Data Manager: Inia Soto Ramos, inia.m.sotoramos@nasa.gov Calibration and intercalibration leads Temperature and salinity sensors: Andy Thompson, andrewt@caltech.edu Chlorophyll fluorescence sensors: Melissa Omand & Kaley Sten, momand@uri.edu Oxygen sensors: Shawnee Traylor & Roo Nicholson, shawnee@mit.edu Optical backscatter sensors: Xiaodong Zhang, Xiaodong.Zhang@usm.edu Lagrangian float sensors: Eric D’Asaro, dasaro@apl.washington.edu Underway sensors: Leah Johnson, leahjohn@uw.edu Underwater Vision Profiler (UVP)-Particle Size Distribution (PSD): David Siegel, davesiegel@ucsb.edu
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Chang, Enson, and R. Patton. Moored optical particle flux sensor (MOPAR). SBIR Phase II interim report. Office of Scientific and Technical Information (OSTI), June 1993. http://dx.doi.org/10.2172/10200461.

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Dichter, Bronislaw K., Edward G. Mullen, and Gary E. Galica. Space Particle Modeling, Measurements, and Effects: Compact Environmental Anomaly Sensor (CEASE) Proton Calibration. Fort Belvoir, VA: Defense Technical Information Center, February 2011. http://dx.doi.org/10.21236/ada536723.

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Bontha, Jagannadha R., Nancy G. Colton, Eric A. Daymo, T. D. Hylton, C. K. Bayne, and T. H. May. Qualification of the Lasentec M600P Particle Size Analyzer and the Red Valve Model 1151 Pressure Sensor. Office of Scientific and Technical Information (OSTI), January 2000. http://dx.doi.org/10.2172/15002697.

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