Data Acquisition Rate Research Articles

Data analysis for the characterization of nanoparticles with single particle inductively coupled plasma mass spectrometry: From microsecond to millisecond dwell times.

Single particle-inductively coupled plasma-mass spectrometry (SP-ICP-MS) has become a powerful technique for the characterization of nanoparticles (NPs). However, the accuracy of the characterization of NPs by SP-ICP-MS is greatly affected by the data acquisition rate and the way of data processing. For SP-ICP-MS analysis, ICP-MS instruments typically apply microsecond to millisecond dwell times (10 μs-10ms). Considering the duration of one nanoparticle event in the detector is 0.4-0.9ms, NPs will show different data forms when working with microsecond and millisecond dwell times. In this work, the effects of dwell times from microsecond to millisecond (50 μs, 100 μs, 1ms and 5ms) on the data forms in SP-ICP-MS analysis are discussed. The data analysis and data processing for different dwell times is discussed in detail, including the measurement of transport efficiency (TE), the distinction of signal and background, the evaluation of diameter limit of detection (LODd) and the quantification of mass, size and particle number concentration (PNC) of NPs. This work provides data support for the data processing process and aspects to be considered in the characterization of NPs by SP-ICP-MS, which is expected to provide guidance and reference for researchers in SP-ICP-MS analysis.

탄두 파편데이터 검출시스템의 중복검출 제거를 위한 convLSTM 기반 필터 연구

Warhead arena fragmentation test is a test used for analyzing a warhead’s lethal effect on the target by using fragmentation data such as speed, mass, and distribution. Detecting objects from a high-speed video that captures fragments just to penetrate a target plate in arena test, vision-based fragmentation detection system acquires fragment velocities and fragmentation distribution. Because only the first penetration data must be obtained to determine the velocity, in this study, the bounding box filter-based detector was used to remove the consecutive duplicate detection result. However, the bounding box filter has an additional dummy area surrounding the fragment area; if new detection occurs in this dummy area, it gets removed by the bounding box filter. This decreases the data acquisition rate and hinders a tester from obtaining sufficient fragmentation data for analyzing the warhead effect. In this paper, a pixel-level filter was proposed based on convolutional long short-term memory (convLSTM); the proposed filter utilizes a spatial association loss function for filtering out duplicate results. Owing to the spatial association loss function, which yields a strong correlation between successive frames, the convLSTM-based filter can generate pixel-level filter masks and remove duplicate results effectively. The experimental results demonstrated the superior performance of the filter.

Ultra-High-Throughput and Low-Volume Analysis of Intact Proteins with LAP-MALDI MS.

High-throughput (HTP) mass spectrometry (MS) is a rapidly growing field, with many techniques evolving to accommodate ever increasing sample analysis rates. Many of these techniques, such as AEMS and IR-MALDESI MS, require volumes of at least 20-50 μL for analysis. Here, liquid atmospheric pressure-matrix-assisted laser desorption/ionization (LAP-MALDI) MS is presented as an alternative for ultra-high-throughput analysis of proteins requiring only femtomole quantities of protein in 0.5 μL droplets. By moving a 384-well microtiter sample plate with a high-speed XY-stage actuator, sample acquisition rates of up to 10 samples per second have been achieved at a data acquisition rate of 200 spectra per scan. It is shown that protein mixture solutions with concentrations of ≤2 μM can be analyzed at this speed, while individual protein solutions can be analyzed at concentrations of ≤0.2 μM. Thus, LAP-MALDI MS provides a promising platform for multiplexed HTP protein analysis.

The GlycoPaSER Prototype as a Real-Time N-Glycopeptide Identification Tool Based on the PaSER Parallel Computing Platform.

Real-time database searching allows for simpler and automated proteomics workflows as it eliminates technical bottlenecks in high-throughput experiments. Most importantly, it enables results-dependent acquisition (RDA), where search results can be used to guide data acquisition during acquisition. This is especially beneficial for glycoproteomics since the wide range of physicochemical properties of glycopeptides lead to a wide range of optimal acquisition parameters. We established here the GlycoPaSER prototype by extending the Parallel Search Engine in Real-time (PaSER) functionality for real-time glycopeptide identification from fragmentation spectra. Glycopeptide fragmentation spectra were decomposed into peptide and glycan moiety spectra using common N-glycan fragments. Each moiety was subsequently identified by a specialized algorithm running in real-time. GlycoPaSER can keep up with the rate of data acquisition for real-time analysis with similar performance to other glycoproteomics software and produces results that are in line with the literature reference data. The GlycoPaSER prototype presented here provides the first proof-of-concept for real-time glycopeptide identification that unlocks the future development of RDA technology to transcend data acquisition.

MHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics.

Hyperspectral imaging acquires data in both the spatial and frequency domains to offer abundant physical or biological information. However, conventional hyperspectral imaging has intrinsic limitations of bulky instruments, slow data acquisition rate, and spatiospectral trade-off. Here we introduce hyperspectral learning for snapshot hyperspectral imaging in which sampled hyperspectral data in a small subarea are incorporated into a learning algorithm to recover the hypercube. Hyperspectral learning exploits the idea that a photograph is more than merely a picture and contains detailed spectral information. A small sampling of hyperspectral data enables spectrally informed learning to recover a hypercube from a red-green-blue (RGB) image without complete hyperspectral measurements. Hyperspectral learning is capable of recovering full spectroscopic resolution in the hypercube, comparable to high spectral resolutions of scientific spectrometers. Hyperspectral learning also enables ultrafast dynamic imaging, leveraging ultraslow video recording in an off-the-shelf smartphone, given that a video comprises a time series of multiple RGB images. To demonstrate its versatility, an experimental model of vascular development is used to extract hemodynamic parameters via statistical and deep learning approaches. Subsequently, the hemodynamics of peripheral microcirculation is assessed at an ultrafast temporal resolution up to a millisecond, using a conventional smartphone camera. This spectrally informed learning method is analogous to compressed sensing; however, it further allows for reliable hypercube recovery and key feature extractions with a transparent learning algorithm. This learning-powered snapshot hyperspectral imaging method yields high spectral and temporal resolutions and eliminates the spatiospectral trade-off, offering simple hardware requirements and potential applications of various machine learning techniques.

Feasibility and efficacy of an activity-monitoring approach using pedometer in patients undergoing subacute rehabilitation: A pilot study.

Wearable devices for the quantification of walking have recently been adopted for gait rehabilitation. To apply this method in subacute rehabilitation settings, this approach must be effective in these populations and implemented as a feasible method in terms of adherence and safety, especially the risk of falling. This study aimed to investigate the feasibility and efficacy of an activity monitoring approach in subacute rehabilitation using a commercially available pedometer validated with slow walking. This randomized controlled study with blinded assessors recruited 29 patients admitted to a rehabilitation ward. The participants were randomly assigned to either the feedback (intervention) or the no-feedback (control) group. Participants in both groups received at least 120 min of therapy sessions every day for 6 or 7 days per week while wearing pedometers on their unaffected ankles from the day they were permitted to walk independently till discharge. Only participants in the feedback group received weekly encouragement and the next goals. The primary outcome was the change in the 6-minute walking distance (Δ6MD). Feasibility (percentage of pedometer data acquisition days in the total observational period and the number of falls) and other efficacy outcomes (step counts, gait speed, 30-seconds chair stand test, Berg Balance Scale, and Timed Up and Go Test) were also evaluated. Regarding feasibility outcomes, the data acquisition rate was 94.1% and the number of falls during the observation period was one in the feedback group. Regarding efficacy outcomes, Δ6MD was not significantly greater in the feedback group [mean (standard deviation): 79.1 (51.7) m] than in the no-feedback group [86.1 (65.4) m] (p = 0.774) and the other five secondary outcomes showed no between-group difference. Considering the large number of steps per day in both groups [6,912 (4,751) and 5,600 (5,108) steps in the feedback and no-feedback group, respectively], the effect of the intended intervention might have been masked by the effect of simply wearing pedometers in the control group. This study revealed that the activity monitoring approach using an ankle-worn pedometer was practical in terms of adherence and safety. Further clinical trials are required to elucidate ways to effectively use wearable devices in subacute rehabilitation.

Evaluation of clinical assessments of social abilities for use in autism clinical trials by the autism biomarkers consortium for clinical trials.

Clinical trials in autism spectrum disorder (ASD) often rely on clinician rating scales and parent surveys to measure autism-related features and social behaviors. To aid in the selection of these assessments for future clinical trials, the Autism Biomarkers Consortium for Clinical Trials (ABC-CT) directly compared eight common instruments with respect to acquisition rates, sensitivity to group differences, equivalence across demographic sub-groups, convergent validity, and stability over a 6-week period. The sample included 280 children diagnosed with ASD (65 girls) and 119 neurotypical children (36 girls) aged from 6 to 11 years. Full scale IQ for ASD ranged from 60 to 150 and for neurotypical ranged from 86 to 150. Instruments measured clinician global assessment and autism-related behaviors, social communication abilities, adaptive function, and social withdrawal behavior. For each instrument, we examined only the scales that measured social or communication functioning. Data acquisition rates were at least 97.5% at T1 and 95.7% at T2. All scales distinguished diagnostic groups. Some scales significantly differed by participant and/or family demographic characteristics. Within the ASD group, most clinical instruments exhibited weak (≥ |0.1|) to moderate (≥ |0.4|) intercorrelations. Short-term stability was moderate (ICC: 0.5-0.75) to excellent (ICC: >0.9) within the ASD group. Variations in the degree of stability may inform viability for different contexts of use, such as identifying clinical subgroups for trials versus serving as a modifiable clinical outcome. All instruments were evaluated in terms of their advantages and potential concerns for use in clinical trials.

Rapid generation and number-resolved detection of spinor rubidium Bose-Einstein condensates

High data acquisition rates and low-noise detection of ultracold neutral atoms present important challenges for the state tomography and interferometric application of entangled quantum states in Bose-Einstein condensates. In this article, we present a high-flux source of $^{87}$Rb Bose-Einstein condensates combined with a number-resolving detection. We create Bose-Einstein condensates of $2\times10^5$ atoms with no discernible thermal fraction within $3.3$ s using a hybrid evaporation approach in a magnetic/optical trap. For the high-fidelity tomography of many-body quantum states in the spin degree of freedom [arXiv:2207.01270], it is desirable to select a single mode for a number-resolving detection. We demonstrate the low-noise selection of subsamples of up to $16$ atoms and their subsequent detection with a counting noise below $0.2$ atoms. The presented techniques offer an exciting path towards the creation and analysis of mesoscopic quantum states with unprecedented fidelities, and their exploitation for fundamental and metrological applications.

Toward 4-D Imaging of On-the-Move Object at 2500 Volumetric Frames per Second by Software-Defined Millimeter-Wave MIMO With Compressive Reflector Antenna

Conventional millimeter-wave (mm-wave) security screening systems with synthetic or phased arrays require either mechanical or electronic scans which are slow, thus not capable to image on-the-move objects. This article presents the 4-D imaging of on-the-move objects (1-D velocity and 3-D profile) by leveraging cost-effective compressive reflector antennas (CRAs) and software-defined mm-wave (SDMMW) multiple-in multiple-out (MIMO) arrays. The CRA creates spatial aperture coding to achieve informative measurements and high sensing capacity. The SDMMW MIMO array generates fast space-time-coded frequency-modulated continuous wave (FMCW) for quick data collection and simultaneous MIMO operation. The signal model using the CRAs fed by the SDMMW MIMO arrays in a multistatic configuration is derived, followed by the sensing-matrix-based imaging theory. Then, a proof-of-concept imaging system is designed with a made-in-lab CRA and a modularized 8-by-8 SDMMW array where the fast FMCW modulation achieves a linearized frequency sweep of 5.02 GHz per 50 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mu }\text{s}$ </tex-math></inline-formula> , corresponding to a high data acquisition rate of 2500 volumetric frames/s and an unambiguous velocity of ±2.23 m/s. Both simulations and experiments have shown good imaging performance of on-the-move objects, giving great potential for developing future cost-effective high-throughput mm-wave security screening systems.

High-resolution and high-speed 3D tracking of microrobots using a fluorescent light field microscope.

Imaging and tracking are crucial for microrobots which navigate through complex 3D environments. Fluorescent imaging (FI) by microscope offers a high-resolution and high-sensitive imaging method to study the property of microrobots. However, conventional microscope suffers from shallow depth of field (DOF) and lacks 3D imaging capability. We proposed a high-resolution and high-speed 3D tracking method for microrobots based on a fluorescent light field microscope (FLFM). We designed the FLFM system according to the size of a representative helical microrobot (150 μm body length, 50 μm screw diameter), and studied the system's performance. We also proposed a 3D tracking algorithm for microrobots using digital refocusing. We validated the method by simulations and built an FLFM system to perform the tracking experiments of microrobots with representative size. Our 3D tracking method achieves a 30 fps data acquisition rate, 10 μm lateral resolution and approximately 40 μm axial resolution over a volume of 1,200×1,200×326 μm3. Results indicate that the accuracy of the method can reach about 9 μm. Compared with the FI by a conventional microscope, the FLFM-based method gains wider DOF and 3D imaging capability with a single-shot image. The tracking method succeeds in providing the trajectory of the microrobot with a good lateral resolution.

Low-Cost Data Acquisition System for Automotive Electronic Control Units.

The vehicle testing-validation phase is a crucial and demanding task in the automotive development process for vehicle manufacturers. It ensures the correct operation, safety, and efficiency of the vehicle. To meet this demand, some commercial solutions are available on the market, but they are usually expensive, have few connectivity options, and are PC-dependent. This paper presents an IoT-based intelligent low-cost system for vehicle data acquisition during on-road tests as an alternative solution. The system integrates low-cost acquisition hardware with an IoT server, collecting and transmitting data in near real-time, while artificial intelligence (AI) algorithms process the information and report errors and/or failures to the manufacturing engineers. The proposed solution was compared with other commercial systems in terms of features, performance, and cost. The results indicate that the proposed system delivers similar performance in terms of the data acquisition rate, but at a lower cost (up to 13 times cheaper) and with more advanced features, such as near real-time intelligent data processing and reduced time to find and correct errors or failures in the vehicle.

Optimization of information acquisition security of broadband carrier communication based on linear equation

Abstract In order to study the optimization of information acquisition security of broadband carrier communication and solve the problem of low baud rate of data acquisition in traditional communication operation data acquisition systems, this article reconstructs the required arrangement of communication operation data signals in the acquisition path by taking broadband carrier communication in the station area as an example through the linear equation method of genetic algorithm. The baud rate of the designed acquisition system is significantly higher than that of the control group, and the acquisition accuracy is 100% by using phase shift key modulation and high carrier frequency. It can solve the problem of low baud rate of data acquisition in traditional communication operation data acquisition systems and improve the security of information acquisition. The security (loss, anti-interference) of broadband carrier communication information collection based on the improved legacy algorithm is better than that of the traditional genetic algorithm, indicating that the security of broadband carrier communication information collection based on the improved legacy algorithm does not increase with the number of iterations and decrease.

CODAS for long lasting experiments. The SPIDER experience

The SPIDER experiment is the first of two experiments being held at the ITER Neutral Beam Test Facility in Padova (Italy). SPIDER has been operating since 2018, initially with pulse duration of a few seconds and currently with pulses lasting up to 3000 s. The paper reports the CODAS experience gained after three years of operation. In particular, Data Storage and Data Access adopted strategies will be discussed, that proved to be of high impact in overall system performance and maintainability.Regarding Data Storage strategy, a tradeoff must be defined between the continuous and event driven data acquisition. Continuous data acquisition, i.e. sampling data at a constant frequency, represents the normal operation in short experiments, but can easily lead to an unmanageable amount of data for long lasting experiments. On the other side, Data acquisition at a varying rate, that is increased upon the occurrence of given events leading to an improved signal dynamics, is required for a subset of signals that describe physical phenomena with fast dynamics. Several strategies have been adopted in SPIDER to handle varying rate data acquisition and are discussed here.Considering data access strategy, an important Use Case, especially when the pulse duration is long, is the concurrent access to the pulse file for online analysis and visualization. Concurrent data read and write is supported by MDSplus, the adopted data system, but performance can be affected by the required locks in file access. For this reason it is important to limit as far as possible useless data access. This has been achieved in different ways, such as setting a Region of Interest (ROI) in data access and by the extended usage of on-the-fly resampling in conjunction with the availability in the pulse file of different versions of the same data item, acquired at different sampling speeds.

Mapping spectroscopic micro-ellipsometry with sub-5 microns lateral resolution and simultaneous broadband acquisition at multiple angles.

Spectroscopic ellipsometry is a widely used optical technique in both industry and research for determining the optical properties and thickness of thin films. The effective use of spectroscopic ellipsometry on micro-structures is inhibited by technical limitations on the lateral resolution and data acquisition rate. Here, we introduce a spectroscopic micro-ellipsometer (SME), capable of recording spectrally resolved ellipsometric data simultaneously at multiple angles of incidence in a single measurement of a few seconds, with a lateral resolution down to 2μm in the visible spectral range. The SME can be easily integrated into generic optical microscopes by the addition of a few standard optical components. We demonstrate complex refractive index and thickness measurements by using the SME, which are in excellent agreement with a commercial spectroscopic ellipsometer. The high lateral resolution is displayed by complex refractive index and thickness maps over micron-scale areas. As an application for its accuracy and high lateral resolution, the SME can characterize the optical properties and number of layers of exfoliated transition-metal dichalcogenides and graphene, for structures that are a few microns in size.

Assessment of hearing loss risk due to impact noise in industrial environments

Impact noises are often found in industrial environments and they are predominant in mining, construction, factories, workshops, and shipyards. It is well-known that impact noises are more likely to cause noise-induced hearing loss than continuous noise of equal energy. Impulse noises are characterized by their high intensity over a short period of time and many countries have defined impulse noise exposure limits and criteria in occupational settings. They are based on the sound level measurements made using standard sound pressure level meters and dosemeters. However, because of their metrological limitations, it is not appropriate to use these instruments when dealing with such high peak levels and short duration times. Extensive research on hearing damage produced by impulse noise generated by firearms has been presented, mainly on police and military personnel. These studies have led to damage risk criteria contained in various versions of the standard MIL-STD-1474. Although industrial noises can reach similar peak sound pressure levels, not many results have been published on the subject. In this work, several common sources of industrial impact noise were measured in-situ, at the worker locations, using a specialized system equipped with high-dynamic-range microphones and a very high data acquisition rate. The signals were post-processed to obtain the main metrics defined for impulse noise exposure assessment. Then, occupational hearing loss risk was estimated using different criteria. It is shown that many common industrial processes reported a very high risk of impulsive noise to human hearing.

Blue Laser for Polymerization of Bulk-Fill Composites: Influence on Polymerization Kinetics.

The objective of this study was to compare the polymerization kinetics of bulk-fill resin composites cured with a LED-curing device and a diode laser (449 nm). Three bulk-fill composites were light-cured with constant radiation exposure at 10 J/cm2 by varying radiant exitance and curing time. The following three light-curing protocols were used: (I) 3300 mW/cm2 for 3 s; (II) 2000 mW/cm2 for 5 s; and (III) 1000 mW/cm2 for 10 s. The degree of conversion (DC) was monitored in real time at a data acquisition rate of 2 spectra/s over a 5-min period and again after seven days using Fourier transform infrared spectroscopy. DC amounted to 30.9-61.7% at 4-mm depth after 5 min. DC values of two sculptable composites were significantly higher with the laser, regardless of the curing protocol used, but not for the flowable composite. The maximum polymerization rate (2.0-22.1%/s) was less affected by the type of curing device for one of the composites, while the other two composites achieved significantly higher values when cured with the laser. Laser curing generally increased the DC and the maximum polymerization rate while it shortened the onset of the maximum reaction rate. New handheld laser devices with adjustable power have the potential to be used as a photopolymerization light source for new generations of bulk-fill composites.

Feasibility of Using Wearables for Home Monitoring during Radiotherapy for Head and Neck Cancer-Results from the OncoWatch 1.0 Study.

Background: Consumer wearables allow objective health data monitoring, e.g., of physical activity and heart rate, which might change over a cancer treatment course. Patients with head and neck cancer (HNC) receiving radiotherapy (RT) with curative intent typically experience side effects such as pain, decreased appetite, and dehydration, which may lead to hospitalizations. Therefore, health data monitoring could be important to understand a patient’s condition outside the hospital. The OncoWatch 1.0 study investigated the feasibility of using smartwatches for patients with HNC receiving RT. Methods: This study was a prospective, single-cohort feasibility study. The inclusion criteria were patients ≥ 18 years of age who planned to receive curatively intended radiotherapy for HNC. Consenting patients were asked to wear a smartwatch during RT and until two weeks after the end of RT. The primary endpoint was adherence. The secondary endpoints were data acquisition and variations in heart rate and physical activity. Results: Ten patients were included, with a median age of 62 years and eight males. The adherence rate for wearing the watch >12 h/d over the study period was 31%. The data acquisition rate was 61%. Conclusions: Although the primary endpoint was not reached, new knowledge has been established, including the secure data setup and key points that need to be addressed in future studies.

MEC-Assisted Real-Time Data Acquisition and Processing for UAV With General Missions

As a promising technology, unmanned aerial vehicles (UAVs)-enabled information gathering has received intensive interest in both academia and industry in recent years. However, the familiar paradigm, namely, data collection can hardly fulfill the active and real-time information gathering demands of many thriving smart applications, such as smart grid, where the UAV needs to serve as an intelligent aerial entity, gathering and analysing the information of surroundings actively and timely. To this aim, this article studies the paradigm <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data acquisition</i> , where the UAV dynamically gathers information by on-board sensors. We consider a general mission scenario where a UAV acquires data in real time, which needs to be timely processed with the assistance of a mobile edge computing server. To address the real-time characteristics, we construct a novel theoretical <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data acquisition rate</i> model, based on which the real-time processing quality of requirement (QoR) is defined. We respectively minimize the UAV energy consumption and mission completion time, under the constraints including real-time processing QoR and UAV mobility, by jointly optimizing the UAV trajectory, resources allocation, and time duration. To tackle these highly complex problems, we propose efficient algorithms by means of successive convex approximation and block coordinates descent techniques, where a closed-form solution for the UAV transmission and computation resources allocation is rigorously derived. Moreover, we consider a practical multi-location trajectory optimization where the UAV needs to traverse multiple target locations or areas in addition to the initial and final locations. The simulation results demonstrate the significant system performance gains brought by the proposed design and the effectiveness of the algorithms.

A Simple Lossless Algorithm for On-Board Satellite Hyperspectral Data Compression

As resolution of on-board imaging spectrometer keeps improving, data acquisition rate increases and resource limited satellite environment necessitates for computationally simple data compression methods to meet timing, bandwidth and resource requirements with error resilience. This letter proposes a new lossless, prediction based algorithm for on-board satellite hyperspectral data compression that utilizes spectral as well as spatial correlation and at the same time, is computationally less complex. Concept of non-binary tree traversal is used with nearest neighbor method and implemented using neighbor driven decision making in pre-processing stage. Previously processed pixels are used to minimize the prediction residual, which makes more than 80% calculations causal in nature and thereby reducing the computational complexity of the algorithm. The prediction residual is then encoded using sample adaptive Golomb coding in band-sequential order. CCSDS corpus of data for hyperspectral images is used for evaluating the performance of the algorithm. The proposed method shows reduced computational complexity and lesser data dependencies compared to the CCSDS 123.0-B-1 standard when similar spectral vicinity is considered, and comparable compression performance compared to other state-of-the-art on-board lossless compression methods.

Automated control and optimization of laser-driven ion acceleration

Abstract The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimization of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimization. Here, an automated, HRR-compatible system produced high-fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimization of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimization of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.