Real-time Flow Measurement Research Articles

Comprehensive Assessment of Context-Adaptive Street Lighting: Technical Aspects, Economic Insights, and Measurements from Large-Scale, Long-Term Implementations.

This paper addresses the growing importance of efficient street lighting management, driven by rising electricity costs and the need for municipalities to implement cost-effective solutions. Central to this study is the UNI 11248 Italian regulation, which extends the European EN 13201-1 standard introduced in 2016. These standards provide guidelines for designing, installing, operating, and maintaining lighting systems in pedestrian and vehicular traffic areas. Specifically, the UNI 11248 standard introduces the possibility to dynamically adjust light intensity through two alternative operating modes: (a) Traffic Adaptive Installation (TAI), which dims the light based solely on real-time traffic flow measurements; and (b) Full Adaptive Installation (FAI), which, in addition to traffic measurements, also requires evaluating road surface luminance and meteorological conditions. In this paper, we first present the general architecture and operation of an FAI-enabled lighting infrastructure, which relies on environmental sensors and a heterogeneous wireless communication network to connect intelligent, remotely controlled streetlights. Subsequently, we examine large-scale, in-field FAI infrastructures deployed in Vietnam and Italy as case studies, providing substantial measurement data. The paper offers insights into the measured energy consumption of these infrastructures, comparing them to that of conventional light-control strategies used in traditional installations. The measurements demonstrate the superiority of FAI as the most efficient solution.

Artificial intelligence (AI) inspired an innovative approach for real-time flow measurements – water distributions at outlets, in Punjab province Pakistan

ABSTRACT Accurate water metering is essential for effective water management, especially for measured and equitable distribution of canal flows, most imperative in Pakistan. The inflows distributed through outlets must adhere to pre-designated levels, defined by Punjab Irrigation Department from head to tail. With recent channels lining, the outlets sanctioned flow are found changed requiring adjustments. Furthermore, there is lack of reliable mechanisms for continuous flow regulations, resulting in unmanaged distributions. The focus of the reported work was on precise and real-time discharge monitoring at outlets using a newly developed device utilizing Artificial Intelligence. During validation training, strong agreement was observed, and all evaluation parameters supported the device accuracy for measurements. In detailed studies of canal operations, the sanctioned discharges of individual outlets along selected distributaries were found significantly inequitable. Out of 162 outlets, 42 were suspended for being unmanaged, with tail users receiving ≤ 50% of their fair intakes. Additionally, there were instances of massive irrational supplies exceeding 200% of allocated discharges. The developed device has potential to accurately measure real-time discharges for managed distributions. Conclusively, the challenging task of fair and equity-based discharge allocations, in conjunction with Warabandi system, necessitates the reevaluation of the allocated flow quantity at each outlet.

Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method.

Diffuse correlation spectroscopy (DCS) is a powerful, noninvasive optical technique for measuring blood flow. Traditionally the blood flow index (BFi) is derived through nonlinear least-square fitting the measured intensity autocorrelation function (ACF). However, the fitting process is computationally intensive, susceptible to measurement noise, and easily influenced by optical properties (absorption coefficient and reduced scattering coefficient ) and scalp and skull thicknesses. We aim to develop a data-driven method that enables rapid and robust analysis of multiple-scattered light's temporal ACFs. Moreover, the proposed method can be applied to a range of source-detector distances instead of being limited to a specific source-detector distance. We present a deep learning architecture with one-dimensional convolution neural networks, called DCS neural network (DCS-NET), for BFi and coherent factor () estimation. This DCS-NET was performed using simulated DCS data based on a three-layer brain model. We quantified the impact from physiologically relevant optical property variations, layer thicknesses, realistic noise levels, and multiple source-detector distances (5, 10, 15, 20, 25, and 30mm) on BFi and estimations among DCS-NET, semi-infinite, and three-layer fitting models. DCS-NET shows a much faster analysis speed, around 17,000-fold and 32-fold faster than the traditional three-layer and semi-infinite models, respectively. It offers higher intrinsic sensitivity to deep tissues compared with fitting methods. DCS-NET shows excellent anti-noise features and is less sensitive to variations of and at a source-detector separation of 30mm. Also, we have demonstrated that relative BFi (rBFi) can be extracted by DCS-NET with a much lower error of 8.35%. By contrast, the semi-infinite and three-layer fitting models result in significant errors in rBFi of 43.76% and 19.66%, respectively. DCS-NET can robustly quantify blood flow measurements at considerable source-detector distances, corresponding to much deeper biological tissues. It has excellent potential for hardware implementation, promising continuous real-time blood flow measurements.

A Learning-based Data Placement Framework for Low Latency in Data Center Networks

Abstract: The challenge of achieving low-latency data services has become increasingly crucial for data center applications. In modern distributed storage systems, efficient data placement plays a vital role in minimizing data movement delays, thereby significantly reducing service latency. While previous solutions for data placement have often relied on pre-assumed data request distributions or traced analysis, the dynamic nature of network conditions and evolving user request patterns poses a complex online decision-making challenge. Traditional static model-based approaches are limited in their ability to cope with such dynamic system dynamics. To address these challenges and consider both data movement and analytical latency, we introduce a novel framework named DataBot+. This framework leverages reinforcement learning techniques to autonomously learn optimal data placement policies. DataBot+ employs neural networks that are trained using a modified version of Q-learning. The input to these networks comprises real-time data flow measurements, while the output is a value function that estimates the imminent future latency. To ensure timely decision-making, DataBot+ is designed with a two-fold asynchronous architecture, consisting of production and training components. This separation guarantees that the training process does not introduce additional overhead to the handling of data flows. Our approach is validated through extensive evaluations using real-world traces, which convincingly demonstrate the efficacy of our proposed design.

Bluetooth-Based Vehicle Counting: Bridging the Gap to Ground-Truth With Machine Learning

Traffic flow, number of vehicles passing a particular point over a given period of time, is an essential indicator for evaluating the performance and condition of road networks, detecting congestion, and predicting traffic trends. Accurate and reliable measurement of traffic flow in urban roads is challenging due to the dynamic nature of intersection signals and comes with high equipment and maintenance cost. WaveTraf™ [1] is a Bluetooth-based Intelligent Traffic System solution widely deployed in the State of Qatar which detects and monitors the movement of Bluetooth-enabled devices anonymously using their unique MAC addresses. Systems such as WaveTraf allow for real-time, low-cost, scalable and non-intrusive traffic flow measurement; however, they could suffer from low detection and sampling rates leading to uncertain and unreliable estimates. In this research, we investigate various machine learning techniques such as Random Forrest, Support Vector Regression Machines and XGBoost for building a generalized model for estimating the actual traffic flow utilizing inexpensive, scalable, and real-time Bluetooth sensors. To increase the accuracy of the estimations, we train and evaluate our algorithms using vehicle count data from expensive cameras. We aim to create a more reliable and cost-effective solution for measuring real-time traffic flow by integrating machine learning techniques.

Real-time flow measurement system: physics-informed reconstruction and sampling strategy

In this work, we focus on a multi-hole pressure-probe-based flow measurement system for wind tunnel measurements that provides real-time feedback to a robot probe-manipulator, rendering the system autonomous. The system relies on a novel, computationally efficient flow analysis technique that translates the probe's point measurements of velocity and pressure into an updatable mean flow map that is accompanied by an uncertainty metric. The latter provides guidance to the manipulator when planning the optimal probe path. The probe is then guided by the robot in the flow domain until an available time budget has been exhausted, or until the uncertainty metric falls below a prescribed target threshold in the entire flow domain. We assess the capabilities of our new measurement system using computational fluid dynamics data, for which the ground truth is available in the form of a mean flow field. An application in a real wind tunnel setting is provided as well.

Surgical Coiled Tubing Stimulations: Step Changes in Effectiveness, Efficiency, and Characterization of Waterflooding Interventions

Summary An innovative coiled tubing (CT) real-time flow measurement tool was introduced in Ecuador to reformulate the stimulation workflow in water injectors, which comprised evaluation and treatment. This new technology enabled an integrated, single-run workflow instead—initial injectivity measurements, diagnostics, treatment, post-stimulation injectivity measurements, and final diagnostics. This novel, rigless approach reduced equipment footprint, operational time, and cost, and it improved production as compared with the conventional approach, despite accrued capital discipline constraints. Conventionally, operators rely on workover rigs and multiple product lines to diagnose, stimulate, and evaluate injector wells. Several challenges and inefficiencies were addressed by deploying the CT real-time flow measurement tool. Each intervention was designed to be completed with a single CT run and without the need for a workover rig, thus saving cost and time. Tailored diversion methods substituted the need for drillpipe to set mechanical packers. Prestimulation injection logging test (ILT) results obtained with that innovative tool, coupled with real-time control of depth and high-pressure jetting during execution, enabled effective placement of the stimulation treatment. Ultimately, post-treatment ILTs confirmed treatment effectiveness and final wellbore downhole conditions. Introduction of the CT real-time downhole flow measurement tool allowed operational objectives to be met in a single run, without additional interventions, with or without a workover rig on site. When workover rigs were present, this improved workflow saved an average of 15% operational time. In cases without a workover rig, 105 hours of rig time were saved (without considering rig mobilization time). Four case studies are presented. The first two cases demonstrate how acquisition of ILTs throughout the intervention enabled optimization of fluid placement and introduction of diverter methods. The third case covers a scenario where there was an initially low injectivity and highlights the challenges and lessons associated with recovering injectivity. The fourth case presents challenges unique to flowmeter measurements in heavy-oil environments. In each case, effectiveness of the optimized treatment was measured by two metrics: improvements in net injectivity and uniformity of injection profile, both of which drive the effectiveness of secondary recovery in connected producer wells. On average, wells intervened with this approach featured an improvement in injectivity of 301% (compared with 226% conventionally) and in their injection profile homogeneity by 13%. As a result, the productivity in connected wells improved by as much as 74% and an average of 39% (compared with 14% conventionally). This innovative workflow is a step change over conventional approaches to rejuvenate waterflooding. It combines the capabilities of delivering treatments via CT and the power of real-time downhole flow measurements to break the paradigm of multiline, multirun operations to remediate and stimulate injector wells. This yields logistically leaner operations, which are less costly, and it enables breakthroughs in secondary recovery through data-enriched interventions in times of budget pressure, not only in Ecuador, but also across the globe.

Comparison of the mid-term clinical efficacy and short-term complications of Y-type coronary artery bypass grafting and sequential bypass grafting of the great saphenous vein: a retrospective cohort study.

BackgroundThis study sought to compare and evaluate the clinical efficacy and safety of Y-type coronary artery bypass grafting (CABG) and sequential CABG. However, the prognosis and complication rate of the two treatments are different. Therefore, we need to systematically compare the efficacy and safety of the two surgical schemes.MethodsA total of 112 patients who underwent Y-type CABG and 113 patients who underwent sequential CABG were selected from January 2020 to December 2020. The patients undergoing Y-type CABG of the great saphenous vein (SV) were classified as the experimental group, and those undergoing sequential anastomosis were classified as the control group. The intraoperative blood flow at each anastomotic site of the venous sequential CABG, left ventricular ejection fraction (LVEF), and left ventricular diastolic diameter (LVEDD) at the end of 3 months, 6 months, and 1 year after surgery, the incidence rate of major adverse cardiovascular events, and coronary angiography (CAG) after readmissions due to similar symptoms were compared between the 2 groups. The bridging vascular blockage rate was also determined.ResultsThere was no significant difference in cardiac function between the 2 groups in the short term, and the incidence of major adverse cardiovascular events in the 2 groups mainly occurred in the middle-postoperative period (1 year after surgery) or later. There was no statistical difference in the intraoperative real-time blood flow measurements at each anastomosis of the venous bridge between the 2 groups. Compared to the control group, the LVEF of the experimental group was significantly increased at the 1-year follow-up point (51.6±5.1 vs. 67.6±5.6, P=0.001). During the operation of Y-type coronary artery bypass grafting, the incidence of major adverse cardiovascular events, vascular bridge, and anastomotic blockage were significantly decreased (16 vs. 39, P=0.023).ConclusionsLarge SV Y-type CABG can improve postoperative left heart function and reduce the incidence of postoperative adverse events, which may be of great significance for improving the postoperative mid-term survival rate of patients.

A Learning-Based Data Placement Framework for Low Latency in Data Center Networks

Low-latency data service is an increasingly critical challenge for data center applications. In modern distributed storage systems, proper data placement helps reduce the data movement delay, which can contribute to the service latency reduction tremendously. Existing data placement solutions have often assumed the prior distribution of data requests or discovered it via trace analysis. However, data placement is a difficult online decision-making problem faced with dynamic network conditions and time-varying user request patterns. The conventional static model-based solutions are less effective to handle the dynamic system. With an overall consideration of data movement and analytical latency, we develop a reinforcement learning-based framework DataBot+, automatically learning the optimal placement policies. DataBot+ adopts neural networks, trained with a variant of <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning, whose input is the real-time data flow measurements and whose output is a value function estimating the near-future latency. For instantaneous decision making, DataBot+ is decoupled into two asynchronous production and training components, ensuring that the training delay will not introduce extra overheads to handle the data flows. Evaluation results driven by real-world traces demonstrate the effectiveness of our design.

A Pulsed Wave Doppler Ultrasound Blood Flowmeter by PMUTs

This letter describes a blood flowmeter by the pulsed wave Doppler (PWD) ultrasound detection procedure utilizing piezoelectric micromachined ultrasonic transducers (pMUTs) with the aluminum nitride (AlN) film as the piezoelectric material. The transceiver electronics for the PWD scheme are constructed for system control and signal processing, and a blood-mimicking fluid (BMF) is synthesized for flow velocity tests. Both the average Doppler frequency-shift and Doppler power spectrum density (PSD) shift with respect to the flow velocity have been characterized and validated, and the flow direction can also be recognized. Finally, a real-time pulsatile flow measurement is demonstrated to emulate the feasibility of monitoring blood flows in practice. [2021-0077]

A Real-Time Blood Flow Measurement Device for Patients with Peripheral Artery Disease

PurposeTo evaluate the feasibility of a new optical device that measures peripheral blood flow as a diagnostic and monitoring tool for patients with peripheral artery disease (PAD). Materials and MethodsIn this prospective study, 167 limbs of 90 patients (mean age, 76 y; 53% men) with suspected PAD were evaluated with the FlowMet device, which uses a new type of dynamic light-scattering technology to assess blood flow in real time. Measurements of magnitude and phasicity of blood flow were combined into a single-value flow–waveform score and compared vs ankle–brachial index (ABI), toe–brachial index (TBI), and clinical presentation of patients per Rutherford category (RC). Receiver operating characteristic curves were constructed to predict RC. Area under the curve (AUC), sensitivity, and specificity were compared among flow–waveform score, ABI, and TBI. ResultsQualitatively, the FlowMet waveforms were analogous to Doppler velocity measurements, and degradation of waveform phasicity and amplitude were observed with increasing PAD severity. Quantitatively, the flow, waveform, and composite flow–waveform scores decreased significantly with decreasing TBI. In predicting RC ≥ 4, the flow–waveform score (AUC = 0.83) showed a linear decrease with worsening patient symptoms and power comparable to that of TBI (AUC = 0.82) and better than that of ABI (AUC = 0.71). Optimal sensitivity and specificity pairs were found to be 56%/83%, 72%/81%, and 89%/74% for ABI, TBI, and flow–waveform score, respectively. ConclusionsThe technology tested in this pilot study showed a high predictive value for diagnosis of critical limb ischemia. The device showed promise as a diagnostic tool capable of providing clinical feedback in real time.

Study on Development of Real-time Blood Flow Measurement System Using Multi-point Simultaneous Laser Doppler Velocimeter

Study on Development of Real-time Blood Flow Measurement System Using Multi-point Simultaneous Laser Doppler Velocimeter

Airborne Feature Matching Velocimetry for surface flow measurements in rivers

A new airborne river surface flow measurement technique is presented, called Airborne Feature Matching Velocimetry (AFMV). It uses matchings of characteristic image features for orthorectification and velocimetry. Riparian matching points with an arbitrarily chosen base image serve to find individual projective transformation matrices to stabilize airborne video recordings. Transformed matching points’ distances of feature shifts between subsequent video frames lead to surface velocity vectors. To test this approach, a riverine moving water surface was recorded by an airborne video camera. Results are compared to (i) image frames rectified by 3D photogrammetry and (ii) related velocimetry results obtained by Particle Image Velocimetry (PIV). Generally, both time averaged and instantaneous surface velocities obtained by AFMV are shown to be of almost equal quality to the PIV approach. AFMV gives slightly less spatially-dense results in poorly textured areas, but clearly outperforms the 3D photogrammetry reference method in relation to computational power. Thus, the new method bears the potential to provide almost real-time instantaneous airborne river surface flow measurements.

Flow monitoring for continuous steel casting using Contactless Inductive Flow Tomography (CIFT)

The control of the liquid steel flow in the mould of a continuous caster based on real time flow measurements is a challenging task due to the lack of appropriate measurement techniques. The opaqueness, the high temperature of 1500°C and the chemical aggressiveness of the melt require non-optical contactless methods. In order to reconstruct the complex flow structure in the mould, the Contactless Inductive Flow Tomography (CIFT) is a promising candidate, since it allows the visualization of the flow structure in the melt by applying a magnetic field to the melt, measuring the flow induced perturbation of that field and solving subsequently a linear inverse problem. The combination of this new measurement technique with typical electromagnetic actuators like electromagnetic brakes used in continuous casting pose a challenge to the CIFT measurement system, because the flow induced magnetic field is in the range of 100 nT and has to be measured robustly on the background of the static magnetic field with the amplitude of 300 mT generated by the brake. In this work we will show recent developments regarding this topic for a small model of a continuous caster in the lab. Furthermore, we will present a new method on how the complex linear inverse problem can be solved in real time providing a time resolution of about 1 Hz.

Technology Focus: Stimulation (June 2019)

Technology Focus The fourth industrial revolution is taking the oil and gas business by storm. Many companies have increased resources for developing and using big-data analytics and machine learning to catch the wind that could bring the fragrance of the new economy. In a conversation with management of a major service company, I learned that the downturn made them want to shift from a position as a capital-intensive stimulation service provider to that of a higher-profit-margin data-service provider. Though no one sees the actual physical oilfield services—such as pumping, measurement, and chemicals—as in decline, technology development in these areas may take a back seat to artificial intelligence. That said, technological innovation is not a privilege exclusive to scientists and high spenders in research and development but is often the work of good engineers. Integrating and applying existing (or even old) technologies to do the things we know how to do better is our operational model in many ways in this low- price environment. If we do not feed the right data to the machine-learning process, we are not doing justice to the smart machines we build. An example of the right data is downhole pressure, particularly during acid fracturing. We do not know whether we routinely conduct high-rate matrix acidizing or acid fracturing unless we have reliable downhole pressure data. Otherwise, we may dissolve a large rock mass in an undesirable manner and location, failing to optimize stimulation design. The other technology area to improve in stimulation is the measure of diversion. We have been using particulate diverters for ages. Now, we need such products to function under more-challenging conditions. Instead of diverting from matrix, we want them to divert from fractures. Advanced experimental equipment and testing procedures are necessary to gain better quantitative information to determine what size diverter pill is appropriate to build sufficient pressure for inducing new fractures. Matrix-stimulation techniques predominantly rely on chemicals to attack the rock and mechanical tools to place the chemicals. The aspect of physics-enhanced chemical reactions has not caught many eyes, though it has been around for a while. Some oil-production increases have been reported after earthquakes. Geophysicists provided plausible reasons, but petroleum engineers only explored such applications superficially. Not every well can be hydraulically fractured nor every rock dissolved by acids. We need all the help we can get in this kind of situation. Combining acoustic waves with chemicals for stimulation definitely should be studied more. It is encouraging to see people persevere in pursuing continuous improvements in stimulation technologies by going back to the shelf. Recommended additional reading at OnePetro: www.onepetro.org. SPE 191981 Introduction of Real-Time Flow Measurements Opens New Paths To Overcome Challenges Encountered During the Acid Stimulation of Extended-Reach Wells by Laurie Duthie, Saudi Aramco, et al. SPE 192069 Development and Application of Key Equipment of CO2 Waterless Fracturing by Lichen Zheng, PetroChina, et al. SPE 193723 Effective Matrix Acidizing Based in Chelating Agents: A Case Study in Romanian Heavy-Oil Reservoirs by Emil Panait, OMV Petrom, et al.

In-situ water level measurement using NIR-imaging video camera

Water level is a critical component for observation and management of water resources. Since video surveillance is becoming a standard configuration of gauging stations, more attentions have been paid on the image-based water level measurement techniques in recent years. Instead of human eyes, images of staff gauge can be captured by a camera and automatically processed to detect the readings of water line. However, it is quite unreliable to identify the readings on staff gauges due to low imaging resolution, tilt viewing angle and complex illumination in field conditions. For that reason, most of the existing methods failed to make effective automatic measurements in practice. To resolve difficulties of poor visibility, image distortions and ambient noises in in-situ water level measurement with standard bicolor staff gauges, an all-weather, real-time and automatic flow measurement system using single near infrared (NIR)-imaging video camera is developed. Lab and in-situ experiments demonstrate that NIR-imaging is efficient to enhance image contrast and suppress reflection noises on the water surface, which successfully overcomes the limitation of water line detection with current visible light (VIS)-imaging systems in clear water and low velocity conditions. The proposed water level conversion method is based on image ortho-rectification. It requires no on-site calibration by utilizing natural corresponding points to build the perspective transformation between the staff gauge Region of Interest (ROI) and an orthographic template image. The measurement resolution of water level (1 mm) is controlled by the physical resolution (1 mm/pixel) of the template. The proposed water line detection approach is integrated by a series of algorithms: the Order-Statistic Filtering for adaptive thresholding, the Morphological Opening Operation for local noise suppression, the Multi-points Continuity Criterion for water line locating and the Median Filtering for random noise elimination. It overcomes the deficiency of traditional Otsu method for images with uneven illumination. The reliability of system under various weather and illumination conditions is proved with water level accuracy up to 1 cm and Effective Data Ratios up to 95%.

Simultaneous Blood Flow Measurement and Dermoscopy of Skin Lesions Using Dual-Mode Dermascope

Dermascopes are commonly utilized for the qualitative visual inspection of skin lesions. While automated image processing techniques and varied illumination strategies have been used to aid in structural analysis of lesions, robust quantification of functional information is largely unknown. To address this knowledge gap, we have developed a compact, handheld dermascope that enables real-time blood flow measurements of skin during conventional visual inspection. In-vitro characterization demonstrated that the dermascope is capable of quantifying changes in flow across a physiologically relevant range even when used in a handheld manner with clinic lighting and dermascope LEDs on. In a small pilot clinical study, we demonstrated the dermascope’s ability to detect flow differences between two distinct lesion types.

Non-Contact Capacitive Technique for Biomass Flow Sensing

To facilitate real-time flow measurement, this paper aims to realize biomass flow sensing through electronic non-contact capacitive means. Hardware implementation has been carried out using a modified OP-AMP-based bridge circuit, with one arm made of a standard capacitance while the other arm is made from two specifically designed capacitive electrodes fitted on a piping system sensing biomass flow. The experimental results are targeted to obtain data for given biomass types through a custom-developed biomass flow piping system. Several flow affecting parameters namely: electrodes’ shapes, the location of electrodes on the piping system, biomass material type, and particle size have been considered in obtaining experimental data. Also, the circuit has been simulated to analyze flow sensing behavior for the proposed technique by evaluating the measurement data and assessing conformity between experimentally obtained and simulated data.

Control strategies for dynamic motorway traffic subject to flow uncertainties

ABSTRACTThis paper analyses the performance of motorway control strategies subject to real-time flow measurement and modeling uncertainties. The control strategies are derived and tested on the cell transmission model with which global optimal solutions can be derived through solving linear programs. In particular, we present an adaptive control strategy which incorporates prevailing variations in traffic flow through a rolling horizon optimization framework. This adaptive strategy is compared with a min–max robust control formulation on a Monte Carlo stochastic simulation test bed. The robust control delivers the best performance in terms of minimizing delay variability due to its underlying conservativeness, while it comes at the expense of overall delay reduction. In contrast, the adaptive controller outperforms the robust controller in terms of delay reduction. Nevertheless, the benefit gained from the adaptive control diminishes as the motorway system gets saturated with traffic. It is also found that the adaptive controller is not effective in improving travel time reliability, at least under recurrent conditions. The findings reveal the limitation of adaptive control and provide insight to control design and infrastructure planning concerning installation of an advanced traffic control system.

Passive imaging of wind surface flow using an infrared camera

We present a method for passive imaging of wind motion against surfaces in a scene using an infrared video camera. Because the method does not require the introduction of contrast agents for visualization, it is possible to obtain real-time surface flow measurements across large areas and in natural outdoor conditions, without prior preparation of surfaces. We show that this method can be used not just for obtaining single snapshot images but also for real-time flow video, and demonstrate that it is possible to measure under a wide range of conditions.