Optimal Data Acquisition Research Articles

Parametrization of groundwater quality of the Quaternary aquifer in N'Djamena (Chad), Lake Chad Basin: application of numerical and multivariate analyses.

The urban groundwater of the Quaternary aquifer of the Lake Chad basin in N'Djamena has been subject to many hydrochemical studies. However, the results are often not presented in a way that enables water quality managers to make an appropriate decisions, which restrict development and poverty reduction efforts. The objective of the present study was to contribute the improved management of the local groundwater resources. A total of 85 groundwater samples were interpreted using hydrochemical techniques associated with integrated numerical indices and multivariate statistical analysis. The hydrochemical results coupled with the relative residence time of water have shown that the chemical composition of these waters is linked to geogenic and anthropogenic factors and to their proximity to the Chari-Logone rivers. These investigations showed that the groundwater quality in N'Djamena is characterized by a high spatial variability. This study also assessed the suitability of groundwater for user needs and identified areas which are more/less favorable for a specific use. The evaluation of water quality and its suitability for human consumption is also a problem of optimizing data acquisition strategy, and this study used the correlation between water quality index (WQI) and electrical conductivity (EC) to orientate future data acquisition strategies. This parametrization can assist the decision makers and water management professionals in evaluating groundwater availability and setting up a robust water quality management plan in areas with similar hydrogeological and climatic conditions.

Ensemble-Guided Tropical Cyclone Track Forecasting for Optimal Satellite Remote Sensing

Within the realm of satellite remote sensing, optimal data acquisition to study natural phenomena under time, resource, and cost constraints is a well-known problem. Furthermore, since the sensors themselves are at remote locations with sparse ground connectivity, the optimal method must use a computationally light forecasting algorithm, which assimilates information from the observations at possibly irregular intervals, in near real time. In this article, we propose and demonstrate the ensemble -guided cyclone track forecasting (EGCTF) method for application in remote tropical cyclone tracking. The algorithm uses ensemble data produced by numerical weather prediction models to guide the forecasting process while assimilating measured cyclone center positions. The algorithm was tested and analyzed with the Global Ensemble Forecasting System (GEFS) data and the National Hurricane Center data for the 2018 year hurricanes within the Atlantic basin. Compared with a baseline method that uses the GEFS-issued mean ensemble track (AEMN) for forecasting and no data assimilation, the proposed algorithm exhibited positive forecast skill for more than 290 test cases over forecast periods spanning 6-48 h. The skill is seen to improve with lengthening forecast periods, with five test cases showing greater than 75% skill for a forecast period of 6 h to 247 test cases for the forecast period of 48 h.

MicroCT analysis of connectivity in porous structures: optimizing data acquisition and analytical methods in the context of tissue engineering.

Micro-computed X-ray tomography (MicroCT) is one of the most powerful techniques available for the three-dimensional characterization of complex multi-phase or porous microarchitectures. The imaging and analysis of porous networks are of particular interest in tissue engineering due to the ability to predict various large-scale cellular phenomena through the micro-scale characterization of the structure. However, optimizing the parameters for MicroCT data capture and analyses requires a careful balance of feature resolution and computational constraints while ensuring that a structurally representative section is imaged and analysed. In this work, artificial datasets were used to evaluate the validity of current analytical methods by considering the effect of noise and pixel size arising from the data capture, and intrinsic structural anisotropy and heterogeneity. A novel ‘segmented percolation method’ was developed to exclude the effect of anomalous, non-representative features within the datasets, allowing for scale-invariant structural parameters to be obtained consistently and without manual intervention for the first time. Finally, an in-depth assessment of the imaging and analytical procedures are presented by considering percolation events such as micro-particle filtration and cell sieving within the context of tissue engineering. Along with the novel guidelines established for general pixel size selection for MicroCT, we also report our determination of 3 μm as the definitive pixel size for use in analysing connectivity for tissue engineering applications.

Digital transformation of obtaining data for an express assessment of regional sustainable development

Sustainable development of the Russian Federation is impossible without the sustainable development of its regions. To make managerial decisions aimed at sustainable development of the regions, it is necessary to carry out appropriate monitoring. It is proposed to use the methodology and tools of an express assessment, which are based on the dynamics of wages at the enterprises of the region, as operational control over the processes taking place in the economy of the regions. This is due to the fact that in a market economy there is a clear direct relationship between the timeliness and size of wages paid to employees and the state of corporate businesses. The aim of this study is to select the optimal data acquisition scheme for an express assessment of the sustainable development of the regions. For this purpose, a comparative analysis of three schemes for obtaining paper-based data was applied, using personal accounts on the Internet and the information system of the Federal State Statistics Service using the system of interdepartmental electronic exchange in terms of the following indicators: completeness, delay and discreteness of the wages reflection and the number of enterprises in the region in the digital spatio-temporal model and complexity of organizational measures. It has been established that in the near future, it will the most effective to use the scheme for obtaining data from the Internet, which provides high completeness of reflection of the source data and minimizes the delay time of an express assessment relative to the real state of the regional economy. This creates objective conditions for the rapid identification of trends in the regional economy and contributes to the timely formation of legislative initiatives and the adoption of managerial decisions by the regional legislative and executive authorities to improve the legal framework conducive to business development in the region. The data collected by state statistics bodies as per the form No. P-4 “Information on the wages and number of employees” can also serve as a source for an express assessment. However, the federal law currently prohibits state statistics from providing access to this information to regional executive bodies.

Mixed method review for integrating building information modeling and life-cycle assessments

Previous studies have suggested that building information modeling (BIM) and life-cycle assessments (LCAs) could be integrated to quantify and alleviate the environmental effects of buildings while simplifying and optimizing data acquisition. However, there has been no expert-based research into the true potential of BIM-based LCAs in a wider context with consideration of the advantages and disadvantages. This study aims to identify, classify, and prioritize these advantages and disadvantages using mixed methodological research and subject matter experts (SMEs). This includes a comprehensive literary review, semi-structured interviews with SMEs, and use of the Delphi method. A total of 21 advantages and 7 disadvantages associated with BIM-based LCAs were identified and classified. The advantages were grouped into four categories: decision making, material selection, sustainability performance, and waste reduction. The disadvantages were grouped into two categories: standardization and data processing. The Delphi method was applied via two rounds of questionnaires with ten SMEs to prioritize the advantages and disadvantages. The results of this study will provide practitioners and researchers with constructive information for the integrated use of BIM and LCAs and will create awareness in the architecture, engineering, and construction industry. The identified disadvantages show the requirements for future work and the priority rankings for both the advantages and disadvantages could be used by researchers and key stakeholders.

Low Power Optimisations for IoT Wearable Sensors Based on Evaluation of Nine QRS Detection Algorithms

This paper aims to reduce the power consumption of electrocardiography based wearable healthcare devices, by introducing power reduction approaches and considerations at system level design, where we have the highest potential to influence power. It focuses, in particular, on algorithm design and implementation, data acquisition, and transmission under constrained resources. A thorough investigation of the suitability of nine existing algorithms for on-sensor QRS feature detection is conducted, with respect to metrics such as sensitivity, positive predictivity, power consumption, parameter choice and time delay. Optimisation of data acquisition on CPU-based IoT systems is performed, and the current consumption is reduced by a factor of 3 using a combination of direct memory access (DMA) list approach and low-level register manipulations for task delegation. The acquisition data rate, sampling rate, buffer and batch size are also optimised. To reduce the power consumption by data transmission, the effect of on-sensor versus off-sensor processing is investigated. While focusing on CPU-based systems with experiments performed on a generic low-power wearable platform, the design optimisation and considerations proposed in this work could be extended to custom designs and allow further investigation into QRS detection algorithm optimisation for wearable devices.

Scanning Conditions in Functional Connectivity Magnetic Resonance Imaging: How to Standardise Resting-State for Optimal Data Acquisition and Visualisation?

Functional connectivity magnetic resonance imaging (fcMRI), performed during resting wakefulness without tasks or stimulation, is a non-invasive technique to assess and visualise functional brain networks in vivo. Acquisition of resting-state imaging data has become increasingly common in longitudinal studies to investigate brain health and disease. However, the scanning protocols vary considerably across different institutions creating challenges for comparability especially for the interpretation of findings in patient cohorts and establishment of diagnostic or prognostic imaging biomarkers. The aim of this chapter is to discuss the effect of two experimental conditions (i.e. a low cognitive demand paradigm and a pure resting-state fcMRI) on the reproducibility of brain networks between a baseline and a follow-up session, 30 (±5) days later, acquired from 12 right-handed volunteers (29±5yrs). A novel method was developed and used for a direct statistical comparison of the test-retest reliability using 28 well-established functional brain networks. Overall, both scanning conditions produced good levels of test-retest reliability. While the pure resting-state condition showed higher test-retest reliability for 18 of the 28 analysed networks, the low cognitive demand paradigm produced higher test-retest reliability for 8 of the 28 brain networks (i.e. visual, sensorimotor and frontal areas); in 2 of the 28 brain networks no significant changes could be detected. These results are relevant to planning of longitudinal studies, as higher test-retest reliability generally increases statistical power. This work also makes an important contribution to neuroimaging where optimising fcMRI experimental scanning conditions, and hence data visualisation of brain function, remains an on-going topic of interest. In this chapter, we provide a full methodological explanation of the two paradigms and our analysis so that readers can apply them to their own scanning protocols.

Data-Acquisition Optimization Maximizes Value of Mature Oil-Rim Reservoirs

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 196208, “Value Creation From the Reservoir, Well, and Facilities Management (RWFM) Planning in Multistacked Mature Oil-Rim Reservoir, Offshore Sarawak, Malaysia,” by Yeek Huey Ho, SPE, Nor Baizurah Ahmad Tajuddin, SPE, Muhammed Mansor Elharith, SPE, Hui Xuan Dan, Kwang Chian Chiew, Kok Liang Tan, SPE, Raj Deo Tewari, SPE, and Rahim Masoudi, SPE, Petronas, prepared for the 2019 SPE Annual Technical Conference and Exhibition, Calgary, 29 September–2 October. The paper has not been peer previewed. The XamXung field offshore Sarawak, Malaysia, is a 47-year brownfield with thin remaining oil rims that have made field management challenging. The dynamic oil-rim movement has been a key subsurface uncertainty, particularly with the commencing of a redevelopment project. A reservoir, well, and facilities-management (RWFM) plan was implemented to optimize development decisions. This paper is a continuation of paper SPE 174638 and outlines the outcome of the RWFM plan and the results’ effect on development decisions such as infill well placement and gas/water injection-scheme optimization (Fig. 1). Key decisions affected by the RWFM findings are highlighted in the complete paper. Introduction The XamXung field was discovered in 1967, with commercial production established in 1972. The field is a simple faulted anticline bounded by two major faults in the north and south. The field consists primarily of clastic deposits characterized by thick sands interbedded with thin shale layers of late Miocene to Pliocene age. The field consists of multiple stacked gas and oil reservoirs, with key producing intervals being the oil-rim reservoirs XE/XF and XH/XI, and the deeper oil reservoir XL, as well as major nonassociated gas reservoirs XA, XC, XD, XM, and XN. The main discussion of the complete paper focuses on XE/XF and XH/XI reservoir redevelopment. XE/XF is a saturated oil-rim reservoir with initial oil-rim thicknesses of 95 and 85 ft, respectively. The XE/XF oil rim is overlain by a gas cap with m size (ratio of initial free-gas reservoir volume to initial reservoir oil volume) of 1.1 and 0.4, respectively. XE and XF reservoirs are separated by a fieldwide sealed shale layer of 10–20-ft thickness. Initial reservoir pressure and well log data acquired during the early production his-tory suggested that XE/XF reservoirs originally shared a common gas/oil con-tact (GOC) but different free-water levels. XE/XF reservoirs have an average porosity of 27–32% and average permeability of 500–1500 md. They started production in 1974 and reached peak production in 1977. The reservoir pressure has declined by only 200 psi after 45 years of production, indicating that the reservoir is under a strong aquifer drive. Gas injection into XE reservoir commenced in 1995 following an increasing water-cut trend in most of the producers, with the objective to counteract the aquifer influx and to allow uniform gas-cap gas expansion throughout the reservoir. However, the reservoir historically has produced more free gas compared with the amount of gas reinjected, resulting in the reservoir gas-cap shrinkage.

Optimal data acquisition in multi‐pass geosynchronous SAR tomography

Geosynchronous SAR tomography (GEO TomoSAR) techniques exploit multi-pass SAR acquisitions of the same scene, taken with slightly different view angles, and allow generating fully 3D images. Compared to low earth orbit (LEO) SAR case, GEO SAR has the advantages of shorter revisit time period and wider coverage, which can greatly shorten the data acquisition time, improve the coherence of collected data and effectively improve the 3D imaging accuracy. However, GEO SAR has curved trajectories and un-parallel repeated trajectories, which introduce the along-track component of the spatial baseline. The obtained GEO TomoSAR data based on the data acquisition method using in LEO TomoSAR have the significant rotation-induced decorrelation. Thus, the imaging performance in the elevation is severely degraded. In this paper, we first analyse the feasibility of GEO TomoSAR to achieve 3D imaging. In view of the special issues existing in GEO TomoSAR, we adopt an optimal minimal rotation-induced decorrelation data acquisition method to obtain repeated trajectories data, which can effectively improve the coherence between the collected data. Then, performance of imaging in elevation was analysed based on the spatial baseline distribution. Finally, the feasibility and validity of GEO TomoSAR techniques are validated through computer simulation.

Measurements of soil profiles in the vadose zone using the high-frequency surface waves method

For soil exploration in the vadose zone, a high-frequency multi-channel analysis of surface waves (HF-MASW) method with three enhanced techniques has been developed. In this paper, we apply this enhanced HF-MASW method to re-process field testing data that were collected over the past several years on five different sites. These sites are selected for their different soil properties and data acquisition configurations. The derived overtone images, extracted dispersion curves, and inverted soil profiles of these soil sites are shown and discussed. Penetration tests were conducted and comparisons are made between the soil profiles taken from the HF-MASW tests and those from the penetration tests. One of the objectives of the paper is to determine the optimal data acquisition parameters, including the source parameters and sensor array geometric configuration through the discussions of these case studies. Practical concerns of the HF-MASW method and lessons learnt from these tests are addressed. Recommendations for data acquisition, signal processing, and inversion are provided.

Optimization of data acquisition and analysis for fiber ball imaging

The inverse Funk transform of high angular resolution diffusion imaging (HARDI) data provides an estimate for the fiber orientation density function (fODF) in white matter (WM). Since the inverse Funk transform is a straightforward linear transformation, this technique, referred to as fiber ball imaging (FBI), offers a practical means of calculating the fODF that avoids the need for a response function or nonlinear numerical fitting. Nevertheless, the accuracy of FBI depends on both the choice of b-value and the number of diffusion-encoding directions used to acquire the HARDI data. To inform the design of optimal scan protocols for its implementation, FBI predictions are investigated here with in vivo data from healthy adult volunteers acquired at 3 T for b-values spanning 1000 to 10,000 s/mm2, for diffusion-encoding directions varying in number from 30 to 256 and for TE ranging from 90 to 120 ms. Our results suggest b-values above 4000 s/mm2 with at least 64 diffusion-encoding directions are adequate to achieve reasonable accuracy with FBI for calculating axon-specific diffusion measures and for performing WM fiber tractography (WMFT).

Optimization of Scanning Parameters in Coordinate Metrology Using Grey Relational Analysis and Fuzzy Logic

The phenomenon of coordinate measuring machines has led to a significant improvement in accuracy, adaptability, and reliability for measurement jobs. The coordinate measuring machines with scanning capabilities provide the alternative to output precise acquisition at a faster rate. However, they are less accurate as compared to discrete probing systems and slower than the noncontact techniques. Therefore, the data acquisition using a scanning touch probe needs improvement, so that it can provide commendable performance both in terms of accuracy and scanning time. The determination of appropriate scanning parameters is crucial to minimize the inaccuracy and time associated with the scanning process. However, it can be demanding as well as unreliable owing to the presence of uncertainty from a multitude of factors that may influence the measurement process. The optimization of data acquisition using a scanning touch probe is a multiresponse process which involves definite uncertainties from various sources. Therefore, multioptimization tools based on grey relational analysis coupled with principal component analysis and fuzzy logic were employed to enhance the utilization of the scanning touch probe. The work described here has the objective to identify the appropriate combination of scanning factors which can simultaneously boost the accuracy and lessen the scanning time. This study demonstrates the capability and effectiveness of the uncertainty theory based optimization methods in coordinate metrology. It also suggests that the uncertainty associated with the parameter optimization can be significantly reduced using these techniques. It has also been noticed that the results from the two techniques are in accord, which corroborates their application in coordinate metrology. The result from this study can be applied to other probing systems and can be broadened to include more experiments and parameters in various scenarios as needed by the specific application.

Optimization of data acquisition parameters for single gamma line sensing: KSr2I5:Eu2+ and NaI(Tl) compared

Abstract KSr2I5:Eu 2 + scintillators exhibit better than 3% energy resolution at 662 keV and may be significantly less expensive to produce than other, similar energy resolution scintillators. However, KSr2I5:Eu 2 + exhibits a 6.5 Bq/cm3 intrinsic background from the presence of 40K. This background is not only the well-known 1.46 MeV gamma, but also the 1.31 MeV end point of the beta spectrum. This paper computationally evaluates the optimum data acquisition parameters of KSr2I5:Eu 2 + and compares them to NaI(Tl) for the detection of single line gamma sources between 0.01–2.2 MeV with incident source strengths between 5 s−1 and 100 s−1 striking the detector. We found a strong dependence of the time-to-detection on the data acquisition parameters, where the time-to-detection of KSr2I5:Eu 2 + was 1–3 times larger than NaI(Tl) across the parameter space. The largest discrepancy observed was between 0.6 MeV and 1.2 MeV, where the intrinsic 1.31 MeV beta dominates the background.

Internet of things in healthcare monitoring to enhance acquisition performance of respiratory disorder sensors

The implementation of the Internet of things in healthcare is a promising challenge to achieve coverage for a bigger number of users in different places at lower costs. Internet of things might mean better technology regarding response time and proper control of medical parameters. This study introduces an Internet-of-things system for healthcare with possibilities to control medical variables according to recent breakthroughs in sensors and data processing. The goal of the system is to optimize the development of applications to obtain variables in real time and with less energy consumption. The proposed model is validated on the measurement and monitoring of oxygen saturation, heart rate, and body temperature in patients with respiratory disorders. This was achieved by the optimization of data acquisition, integrated into a secure architecture using Message Queuing Telemetry Transport protocol. A cloud architecture with interconnection to low-cost and open-source devices was implemented, which interconnect to the sensors and actuators’ network. The experimental results were statistically treated against the device pattern data, through hypothesis tests for mean differences to probe the accuracy of the model. Finally, the proposed model demonstrates an efficient performance in several clinical parameters, such as oxygen saturation and heart rate per minute.

Implementation and configuration of GB-SAR for landslide monitoring: case study in Minami-Aso, Kumamoto

ABSTRACTIn this paper, the applicability of ground-based synthetic aperture radar (GB-SAR) as an early warning system for landslide monitoring is discussed. The effectiveness of the differential interferometric SAR (DInSAR) technique used in GB-SAR depends strongly on the geography of the monitored location. Therefore, an assessment of the system compatibility to select the most appropriate remote monitoring method is essential prior to any hardware implementation. In the preliminary part of this study, a 3D model was created using a LiDAR survey, and proposed locations for GB-SAR installation were examined. A 3D simulation was carried out to estimate the illumination from each of the proposed GB-SAR locations. The proposed model increased the efficiency of the GB-SAR positioning by minimising installation cost and time. Hardware configuration parameters, such as platform height, maximum range, and the direction and view angle of the radar line of sight were estimated by considering the optimum reflected power and ground illumination. Unlike on flat terrain, deployment of GB-SAR in a mountainous area is challenging because of surface anomalies and continuous changes in meteorological parameters, such as atmospheric temperature, pressure and relative humidity. In this study, the experimental site was located 3 km from the Aso volcano, and the weather conditions in the Aso caldera became a critical factor in accurately estimating the interferometric phase. The presence of atmospheric artefacts also compromises the applicability of the classical DInSAR technique. Here, we minimised the atmospheric phase screen by estimating the optimum data acquisition interval from GB-SAR monitoring under extreme weather conditions. The developed methodologies were then used to design a new landslide early warning system that measures real-time displacement over an area of 1 km2 within 10 s of scanning. This fully automatic monitoring system updates every 15 min and presents displacement information in a 3D interface. The system we have developed has been deployed for continuous monitoring of the mountainous environment of a road reconstruction site in Minami-Aso, Kumamoto, Japan where a large-scale landslide was triggered following the Kumamoto earthquake in 2016.

Strategies for Optimizing the Determination of Second-Order Nonlinear Susceptibility Tensor Coefficients for Collagen in Histological Samples

Quantitative second harmonic generation microscopy methods that provide an estimate for the molecular structure of collagen in tissues by fitting theoretical models with experimental data at pixel-level are gaining increasing momentum. Such methods offer reliable diagnostic-relevant information that complement traditional histology procedures. We provide guidelines on selecting the optimal experimental data acquisition parameters (frame or pixel averaging, pixel dwell time, pixel size, zoom factor) and the most suited pre-processing steps (thresholding, averaging of pixels) for ensuring experimental data fitting with very high performance. Under controlled image acquisition conditions, we also propose pixel fitting efficiency calculation which varies across distinct tissue types and has potential to be used to differentiate tissues exhibiting distinct collagen architectures.

Hybrid PET-MRI Applications in Movement Disorders.

Even before the success of combined positron emission tomography and computed tomography (PET/CT), the neuroimaging community was conceiving the idea to integrate the positron emission tomography (PET), with very high molecular quantitative data but low spatial resolution, and magnetic resonance imaging (MRI), with high spatial resolution. Several technical limitations have delayed the use of a hybrid scanner in neuroimaging studies, including the full integration of the PET detector ring within the MRI system, the optimization of data acquisition, and the implementation of reliable methods for PET attenuation, motion correction, and joint image reconstruction. To be valid and useful in clinical and research settings, this instrument should be able to simultaneously acquire PET and MRI, and generate quantitative parametric PET images comparable to PET-CT. While post hoc co-registration of combined PET and MRI data acquired separately became the most reliable technique for the generation of "fused" PET-MRI images, only hybrid PET-MRI approach allows merging these measurements naturally and correlating them in a temporal manner. Furthermore, hybrid PET-MRI represents the most accurate tool to investigate in vivo the interplay between molecular and functional aspects of brain pathophysiology. Hybrid PET-MRI technology is still in the early stages in the movement disorders field, due to the limited availability of scanners with integrated optimized methodological models. This technology is ideally suited to investigate interactions between resting-state functional/arterial spin labeling MRI and [18F]FDG PET glucose metabolism in the evaluation of the brain "hubs" particularly vulnerable to neurodegeneration, areas with a high degree of connectivity and associated with an efficient synaptic neurotransmission. In Parkinson's disease, hybrid PET-MRI is also the ideal instrument to deeper explore the relationship between resting-state functional MRI and dopamine release at [11C]raclopride PET challenge, in the identification of early drug-naïve Parkinson's disease patients at higher risk of motor complications and in the evaluation of the efficacy of novel neuroprotective treatment able to restore at the same time the altered resting state and the release of dopamine. In this chapter, we discuss the key methodological aspects of hybrid PET-MRI; the evidence in movement disorders of the key resting-state functional and perfusion MRI; [18F]FDG PET and [11C]raclopride PET challenge studies; the potential advantages of using hybrid PET-MRI to investigate the pathophysiology of movement disorders and neurodegenerative diseases. Future directions of hybrid PET-MRI will be discussed alongside with up-to-date technological innovations on hybrid systems.

Strategies for data acquisition using ultrasonic phased arrays.

Ultrasonic phased arrays have produced major benefits in a range of fields, from medical imaging to non-destructive evaluation. The maximum information, which can be measured by an array, corresponds to the Full Matrix Capture (FMC) data acquisition technique and contains all possible combinations of transmitter–receiver signals. However, this method is not fast enough for some applications and can result in a very large volume of data. In this paper, the problem of optimal array data acquisition strategy is considered, that is, how to make the minimum number of array measurements without loss of information. The main result is that under the single scattering assumption the FMC dataset has a specific sparse structure, and this property can be used to design an optimal data acquisition method. An analytical relationship between the minimum number of array firings, maximum steering angle and signal-to-noise ratio is derived, and validated experimentally. An important conclusion is that the optimal number of emissions decreases when the angular aperture of the array increases. It is also shown that plane wave imaging data are equivalent to the FMC dataset, but requires up to an order of magnitude fewer array firings.

PySight: plug and play photon counting for fast continuous volumetric intravital microscopy

Imaging increasingly large neuronal populations at high rates pushed multi-photon microscopy into the photon-deprived regime. We present PySight, an add-on hardware and software solution tailored for photon-deprived imaging conditions. PySight more than triples the measured median amplitude of neuronal calcium transients in awake mice and facilitates single-trial intravital voltage imaging in fruit flies. Its unique data streaming architecture allowed us to image a fruit fly’s brain olfactory response over 234 μm×600 μm×330 μm at 73 volumes per second, while retaining over 200 times lower data rates than those of a conventional data acquisition system with comparable voxel sizes (1.2 μm×1.2 μm×2.2 μm). PySight requires no electronics expertise or custom synchronization boards, and its open-source software is extensible to any imaging method based on single-pixel (bucket) detectors. PySight offers an optimal data acquisition scheme for ever increasing imaging volumes of turbid living tissue.

Optimal Data Acquisition Methods for Single-Pixel Imaging

Single-pixel imaging is an image reconstruction technique, which uses spatially modulated illumination of an object and a single-pixel detector collecting the light scattered by the object. This technique is complementary to the conventional imaging based on multipixel matrices and becomes especially interesting when one needs to go beyond the capabilities of the standard silicon-based sensors, e.g., to perform imaging in the infrared range. One of the factors that limit the resolution of the reconstructed images is the signal-to-noise ratio during the data acquisition. We propose several methods for processing the photodetector signal and develop a universal method to determine the optimal pixel number in light patterns with respect to illuminance of the object. We use these methods to capture the standard test images and discuss their advantages.