Flow In Real Time Research Articles

Application of near-infrared spectroscopy in clinical neurology

Near-infrared spectroscopy (NIRS) monitoring has been used mainly to detect reduced perfusion of the brain during orthostatic stress in order to assess orthostatic intolerance (OI). Many studies have investigated the use of NIRS to reveal the pathophysiology of patients with OI. Research using NIRS in other neurological diseases (e.g., stroke, epilepsy, and migraine) is continuing. NIRS may play an important role in monitoring the regional distribution of the hemodynamic flow in real time and thereby reveal the underlying pathophysiology and facilitate the management of not only patients with OI symptoms but also those with various neurological diseases.

Comprehensive approach for optimization traffic routing and using network resources in a virtual data center

Today, telecommunication networks are the basis for deploying different types of cloud applications and services in data centers. The growing use of the cloud computing concept to provide access to applications and services every year increases the amount of converged network traffic. Existing approaches for traffic routing which using in the software defined-network don’t provide enough flexible solutions, able to adapt to changes in traffic flows in real time. At peak load, this leads to an overabundance of traffic to specific physical network nodes which are not prepared to handle a large data flow. One of the approaches that make it possible to solve this problem is the organization of adaptive traffic routing. In this paper are describes the developed approaches for effective control of traffic flow in the virtual data center. Our approaches are using the methods of the data mining and machine learning to more accurately classify and identify traffic flows of cloud applications and network services. This information helps for optimization traffic routes in real time. Another element of the proposed complex solution is solving the task of identifying routes in the virtual network of a data center. Within the framework of the research, the problem of route identification has been decomposed into two consecutive subproblems. The first part is aimed at the classification of communication channels in a virtual data center for a number of features that are characteristic of all superimposed networks. At the second part, based on the received data, the routes are clustered in accordance with the QoS requirements imposed by the current traffic flows. The received data are combined with information about the current flows of the circulating network and are fed to the input of the neural network to decide on the choice of a suitable route. Thus, the solution of the task of adaptive routing allows determining the priorities when creating the bandwidth of traffic flows, as well as establishing rules for managing the network of the data center.

Checking Function-Level Kernel Control Flow Integrity for Cloud Computing

With the advancement of cloud computing, the control flow integrity (CFI) of virtual machines’ kernel becomes more and more important for the security of cloud services. Many CFI checking and protecting approaches have been proposed. Among them, dynamic analysis approaches have the best detection capability, but they are rarely used because of the high overhead introduced to the virtual machines to be monitored. In this paper, we propose a function-level kernel CFI checking approach to meet the performance requirements in the cloud. By combining the static memory analysis and the dynamic tracing, our system can achieve high detection capability with low overhead. Since the analysis and tracing targets of our system are kernel functions, our system incurs lower overhead to the monitored virtual machines than the instruction-level monitors. We propose two models to describe the kernel control flows. After building the secure control flow database by learning the normal behaviors, we can detect abnormal control flows in real time. With the help of virtualization and virtual machine introspection techniques, we implement a prototype system in the hardware virtualization environment. From the evaluation, our system has high detection capability with reasonable overhead.

Robust MPC for temperature management on electrical transmission lines

Abstract In the current context of high integration of renewable energies, maximizing infrastructures capabilities for electricity transmission is a general need for Transmission System Operators (TSO). The French TSO, RTE, is developing levers to control power flows in realtime: renewable production curtailment is already employed and large battery storage systems are planned to be installed for congestion management in early 2020. The combination of these levers with the use of Dynamic Line Rating (DLR) helps exploiting the lines at the closest of their limit by managing their temperature in real-time. Unnecessary margins can be reduced, avoiding congestion and excessive generation curtailment. In particular, there is a possible interesting correlation between the transits increase due to high wind farms generation and the cooling effect of wind on power lines in the same area. In order to optimize the electrical transmission network capacities, the present paper advocates the use of a temperature management model, mixing production curtailment and large batteries as control variables. A robust Model Predictive Control framework for local control on electrical lines temperature is presented based on the regulation within tubes of trajectories. Simulations on the French electrical network are conducted to show the effectiveness of the optimization-based control design.

Visualized localization and tracking of debris flow movement based on infrasound monitoring

During the initiation and movement of debris flow, low frequency infrasound is generated and emitted with characteristics such as strong penetration, low attenuation, and a high propagation speed that is faster than the debris flow. Monitoring infrasound has been used in debris flow detection and early warning systems; however, although the current infrasound-based warning system of debris flow can provide an alarm when debris flow occurs, it cannot locate and track the movement of debris flow in real time, and cannot determine the arrival time of debris flow to its potential victims. Thus, the potential applications of low frequency infrasound in debris flow disaster prevention and reduction have not yet been fully explored. In this study, we constructed an optimal triangular monitoring array based on factors such as the terrain of the monitoring area and characteristics of infrasound propagation. Combined with the time-delay estimation method, we determined and established an acoustic source localization model based on the time difference of arrival. Furthermore, with the help of a GIS platform, a visualized localization and tracking system for debris flow movement was developed to achieve real-time monitoring of debris flow. The performance of the system on acoustic source localization was validated using a simulation test and long-term field monitoring of debris flow at Jiangjia gully in Dongchuan, Yunnan province, China. Both the simulation and field test results showed that the system has high localization accuracy and strong real-time response. The results of the monitoring system could provide more accurate warning information of debris flow to local government and residents, allowing them to take appropriate mitigation measures well in advance to reduce the loss of life and property caused by debris flows.

A Real-Time Passenger Flow Estimation and Prediction Method for Urban Bus Transit Systems

Bus service is the most important function of public transportation. Besides the major goal of carrying passengers around, providing a comfortable travel experience for passengers is also a key business consideration. To provide a comfortable travel experience, effective bus scheduling is essential. Traditional approaches are based on fixed timetables. The wide adoptions of smart card fare collection systems and GPS tracing systems in public transportation provide new opportunities for using the data-driven approaches to fit the demand of passengers. In this paper, we associate these two independent data sets to derive the passengers’ origin and destination. As the data are real time, we build a system to forecast the passenger flow in real time. To the best of our knowledge, this is the first paper, which implements a system utilizing smart card data and GPS data to forecast the passenger flow in real time.

Application of X-Ray Sensors for In-line and Noninvasive Monitoring of Mass Flow Rate in Continuous Tablet Manufacturing

The progress in continuous downstream manufacturing of oral solid doses demands effective real-time process management, with monitoring at its core. This study evaluates the feasibility of using a commercial sensor to measure the mass flow rate of the particulates, a critical process variable in continuous manufacturing. The sensor independently measures X-ray attenuation and cross-correlation velocimetry of particulate flow in real time. Steady-state flow rates of blends comprised primarily of acetaminophen and microcrystalline-cellulose are monitored using the sensor, with simultaneous weighing scale measurements, to calibrate the sensor and investigate the measurement accuracy. The free-fall flow measurement of the powder and granule blends in a conduit is linearly proportional to the X-ray attenuation. Relative standard deviations of ∼3%-7% for 1 s monitoring are observed and a measurement error of approximately 5% suggests the usability of the sensor for real-time monitoring. The sensor measurement is robust for operational variations in composition, addition of lubricant or glidant and reuse of material for PAT tool calibration. The measurement relative standard deviations depend on particulate flow dynamics at the sensor location. This requires experimental evaluation for a given material at the sensor location, to capture the flow dynamics of the particulate stream through the sensor.

Characterizing tissue perfusion after lower extremity intervention using two-dimensional color-coded digital subtraction angiography

ObjectiveDigital subtraction angiography (DSA) of the peripheral arterial vasculature provides lumenographic information but only a qualitative assessment of blood flow. The ability to quantify adequate tissue perfusion of the lower extremities would enable real-time perfusion assessment during DSA of patients with peripheral arterial disease (PAD). In this study, we used a novel real-time imaging software to delineate tissue perfusion parameters in the foot in PAD patients. MethodsBetween March 2015 and June 2016, patients (N = 31) underwent lower extremity angiography using a two-dimensional perfusion (2DP) imaging protocol (Philips Healthcare, Andover, Mass). Of the 31 enrolled patients, 16 patients received preintervention and postintervention DSA images (18 angiograms), while contrast agent injection settings and the position of the foot, catheter, and C-arm were kept constant. The region of interest for perfusion measurements was taken at the level of the medial malleolus. Perfusion parameters included arrival time (AT) of contrast material, wash-in rate (WIR), time to peak (TTP) contrast intensity, and area under the curve (AUC). ResultsPatients (mean age, 67 years; male, 61%) undergoing 2DP had limbs classified as Rutherford class 3 (n = 9 limbs), class 4 (n = 11), and class 5 (n = 14) ischemia with a mean ankle-brachial index of 0.63. For the whole cohort, median (interquartile range) AT measured 5.20 (3.10-7.25) seconds; WIR, 61.95 (43.53-86.43) signal intensity (SI)/s; TTP, 3.80 (2.88-4.50) seconds; peak intensity, 725.00 (613.75-1138.00) SI; and AUC, 12,084.00 (6742.80-17,059.70) SI*s. A subset of patients had 2DP performed before and after intervention (n = 18 cases). A detectable improvement in SI and two-dimensional flow parameters was seen after intervention. Average AT of contrast material to the region of interest shortened after intervention with percentage decrease of 30.1% ± 49.1%, corresponding decrease in TTP of 17.6% ± 24.7%, increase in WIR of 68.8% ± 94.2% and in AUC of 10.5% ± 37.6%, decrease in mean transit time of 18.7% ± 28.1%, and increase in peak of 34.4% ± 42.2%. ConclusionsThe 2DP imaging allows measurement of blood flow in real time as an adjunct to DSA. The AT may be the most sensitive marker of perfusion change in the lower extremity. Quantitative thresholds based on 2DP hold promise for immediate treatment effectiveness assessment in patients with PAD.

Pedestrian monitoring techniques for crowd-flow prediction

The high concentration and flow rate of people in train stations during rush hours can pose a prominent risk to passenger safety and comfort. In situ counting systems are a critical element for predicting pedestrian flows in real time, and their capabilities must be rigorously tested in live environments. The focus of this paper is on evaluating the reliability of two alternative counting systems, the first using an array of infrared depth sensors and the second a visible light (RGB) camera. Both proposed systems were installed at a busy walkway in London Bridge station. The data were collected over a period of 2 months, after which, portions of the data set were labelled for quantitative evaluation against ground truth. In this paper, the implementation of the two different counting technologies is described, and the accuracy and limitations of both approaches under different conditions are discussed. The results show that the developed RGB-based system performs reliably across a wide range of conditions, while the depth-based approach proves to be a useful complement in conditions without significant ambient sunlight, such as underground passageways.

Application of Near-Infrared Spectroscopy in Neurological Disorders: Especially in Orthostatic Intolerance

Near-infrared spectroscopy (NIRS), a noninvasive optical method, utilizes the characteristic absorption spectra of hemoglobin in the near-infrared range to provide information on cerebral hemodynamic changes in various clinical situations. NIRS monitoring have been used mainly to detect reduced perfusion of the brain during orthostatic stress for three common forms of orthostatic intolerance (OI); orthostatic hypotension, neurally mediated syncope, and postural orthostatic tachycardia syndrome. Autonomic function testing is an important diagnostic test to assess their autonomic nervous systems for patients with symptom of OI. However, these techniques cannot measure dynamic changes in cerebral blood flow. There are many experimentations about study of NIRS to reveal the pathophysiology of patients with OI. Research using NIRS in other neurologic diseases (stroke, epilepsy and migraine) are ongoing. NIRS have been experimentally used in all stages of stroke and may complement the established diagnostic and monitoring tools. NIRS also provide pathophysiological approach during rehabilitation and secondary prevention of stroke. The hemodynamic response to seizure has long been a topic for discussion in association with the neuronal damage resulting from convulsion. One critical issue when unpredictable events are to be detected is how continuous NIRS data are analyzed. Besides, NIRS studies targeting pathophysiological aspects of migraine may contribute to a deeper understanding of mechanisms relating to aura of migraine. NIRS monitoring may play an important role to trend regional hemodynamic distribution of flow in real time and also highlights the pathophysiology and management of not only patients with OI symptoms but also those with various neurologic diseases. Key Words: Orthostatic intolerance, Spectroscopy, Near-Infrared, Clinical neurology

A Novel Electronic Device for Measuring Urine Flow Rate: A Clinical Investigation

Objective: Currently, most vital signs in the intensive care unit (ICU) are electronically monitored. However, clinical practice for urine output (UO) measurement, an important vital sign, usually requires manual recording of data that is subject to human errors. In this study, we assessed the ability of a novel electronic UO monitoring device to measure real-time hourly UO versus current clinical practice. Design: Patients were connected to the RenalSense Clarity RMS Sensor Kit with a sensor integrated within a standard sterile urinary catheter drainage tube to monitor urine flow in real time. The Clarity RMS Sensor Kit was modified to incorporate a standard urinometer (Unomedical) for the nursing staff to record UO as per their standard practice. The drainage bag was placed in a container on a scientific scale (Precisa BJ) to be used as the gold standard. Interventions: Nursing records for hourly UO were collected and compared with the electronically recorded UO. Sensor measurements and nursing staff manual records of UO were compared with the scale data. Setting: The study setting was the ICU of Hadassah Hospital, Jerusalem. Patients: Data from 23 patients with a urinary catheter were observed in this study. Measurements and main results: A total of 1046 hours of UO were recorded from 23 subjects. Compared with the scale data, the measurements of hourly urine flow measured with the RenalSense system were closer, had a better correlation, and narrower limits of agreement to gravimetrically determined values than the measurements obtained by the nurses. In addition, continuous monitoring of UO provided graphical display of response to repeated diuretic administration. Conclusions: An electronic device for recording UO has been shown to provide more reliable information of UO records and patient fluid status than current practice. Future applications of this device will provide valuable information to help set protocol goals such as decisions for timely fluid and diuretic administration and response.

Satellite system tracks glaciers' flow in real time

Satellite system tracks glaciers' flow in real time

Social networking data analysis tools & challenges

Online Social Network’s (OSN) considered a spark that burst the Big Data era. The unfolding of every event, breaking new or trend flows in real time inside OSN triggering a surge of opinionated networked content. An unprecedented scale of social relationships also diffuses across this vastly interconnected system affecting public behaviors and knowledge construction. Extracting intelligence from such data has becoming a quickly widening multidisciplinary area that demands the synergy of scientific tools and expertise. Key analysis practices include social network analysis, sentiment analysis, trend analysis and collaborative recommendation. Though, both their recent advent and the fact that science is still in the frontiers of processing human-generated data, provokes the need for an update and comprehensible taxonomy of the related research. In response to this chaotic emerging science of social data, this paper provides a sophisticated classification of state-of the-art frameworks considering the diversity of practices, methods and techniques. To the best of our knowledge, this is the first attempt that illustrated the entire spectrum of social data networking analysis and their associated frameworks. The survey demonstrates challenges and future directions with a focus on text mining and the promising avenue of computational intelligence.

Predicting Peak Flows in Real Time through Event Based Hydrologic Modeling for a Trans-Boundary River Catchment

Investigating the hydrological response of an area to adverse climate changes and extreme rainfall events is crucial for managing land and water resources and mitigating the natural hazards like floods. Limited availability of the in situ data, especially in case of Transboundary Rivers, further highlights the need to develop and evaluate decision support systems which may predict the flows in real time using open source rainfall data. This paper presents the study conducted in Chenab River catchment, Pakistan, to develop and evaluate a hydrologic model using HEC-HMS for predicting flows based on TRMM rainfall data. The catchment was analyzed for hydro-morphological properties using SRTM DEM in HEC-GeoHMS. To rely on open source data as much as possible, digital soil map of the world developed by FAO and global land cover map developed by European Space Agency were utilized to compute Curve Number grid data for the catchment. These preliminary data analyses were employed to set initial values of different parameters to be used for model calibration. The model was calibrated for five rainfall events occurred in the rainy seasons of 2006, 2010 and 2013. The calibrated model was then validated for four other rainfall events of similar type in the same years. Consistency in simulated and observed flows was found with percent difference in volume ranging from −6.17 % to 5.47 % and percent difference in peak flows to be in the range of 6.96 % to 7.28 %. Values of Nash-Sutcliffe Efficiency were ranging from 0.299 to 0.909 with an average value of 0.586 for all flow events. The model was found well capable of capturing the hydrologic response of the catchment due to rainfall events and can be helpful in providing alerts of peak flows in real time based on real time/forecasted rainfall data.

Near-Infrared Fluorescence Lymphatic Imaging to Reconsider Occlusion Pressure of Superficial Lymphatic Collectors in Upper Extremities of Healthy Volunteers.

Background: There are very little scientific data on occlusion pressure for superficial lymphatic collectors. Given its importance in determining the transport capacity of lymphatic vessels, it is crucial to know its value. The novel method of near-infrared fluorescence lymphatic imaging (NIRFLI) can be used to visualize lymphatic flow in real time. The goal of this study was to see if this method could be used to measure the lymphatic occlusion pressure.Methods: We observed and recorded lymph flow in the upper limb of healthy volunteers through a transparent cuff using near-infrared fluorescence lymphatic imaging. After obtaining a baseline of the lymph flow without pressure inside the cuff, the cuff was inflated by increments of 10 mm Hg starting at 30 mm Hg. A NIRFLI guided manual lymphatic drainage technique named “Fill & Flush Drainage Method” was performed during the measurement to promote lymph flow. Lymphatic occlusion pressure was determined by observing when lymph flow stopped under the cuff.Results: We measured the lymphatic occlusion pressure on 30 healthy volunteers (11 men and 19 women). Mean lymphatic occlusion pressure in the upper limb was 86 mm Hg (CI ±3.7 mm Hg, α = 0.5%). No significant differences were found between age groups (p = 0.18), gender (p = 0.12), or limb side (p = 0.85).Conclusions: NIRFLI, a transparent sphygmomanometer cuff and the “Fill and Flush” manual lymphatic drainage method were used to measure the lymphatic occlusion pressure in 30 healthy humans. That combination of these techniques allows the visualization of the lymph flow in real time, while ensuring the continuous filling of the lymph collectors during the measurement session, reducing false negative observations. The measured occlusion pressures are much higher than previously described in the medical literature.

Classification of Fontan Hemodynamics by Respiration Using Real-Time Phase-Contrast Magnetic Resonance

Objective: Outcome of Fontan patients is often complicated by impaired hemodynamics caused by increased pulmonary vascular resistance and/or ventricular stiffness. The influence of respiration and vasodilative agents on vessel flow might be of value to investigate their quality of hemodynamics. Cardiac magnetic resonance (CMR) is highly accurate and can measure flow in realtime by generation of respiratory-dependent stroke volumes.

Estimación del caudal a partir de la evolución del nivel del agua en un estuario con onda de marea estacionaria

A method to calculate the freshwater discharge in an estuary and the net exchange flow with the sea measuring the evolution of the water level in fixed monitoring stations is presented. It rises from the need to obtain reliable data of discharges under the particular morphological conditions that determine the hydrodynamic behaviour of the coastal plain Quequén Grande River estuary (Buenos Aires, Argentina), in which the tide behaves as a standing wave. The technique is calibrated with direct measurements of discharges performed with an acoustic Doppler current profiler in field studies during periods of poor and abundant rains. It is found that the exchange of water with the sea is proportional to the time derivative of the height of the free surface, and the fluvial discharge can be estimated from the height difference between monitoring stations. The results make it possible to quantify the river contribution during dry and wet seasons, and the flows generated by occasional and unusual tides. The method proposed is simple, does not require complex instruments to continuously measure velocity, and takes advantage of existing monitoring nets to provide the information of the estuarine flows in real time and of historic files to evaluate the changes produced in the past.

A distributed spatial–temporal weighted model on MapReduce for short-term traffic flow forecasting

Accurate and timely traffic flow prediction is crucial to proactive traffic management and control in data-driven intelligent transportation systems (D2ITS), which has attracted great research interest in the last few years. In this paper, we propose a Spatial–Temporal Weighted K-Nearest Neighbor model, named STW-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, STW-KNN considers the spatial–temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. Furthermore, STW-KNN is implemented on a widely adopted Hadoop distributed computing platform with the MapReduce parallel processing paradigm, for parallel prediction of traffic flow in real time. Finally, with extensive experiments on real-world big taxi trajectory data, STW-KNN is compared with the state-of-the-art prediction models including conventional K-Nearest Neighbor (KNN), Artificial Neural Networks (ANNs), Naïve Bayes (NB), Random Forest (RF), and C4.5. The results demonstrate that the proposed model is superior to existing models on accuracy by decreasing the mean absolute percentage error (MAPE) value more than 11.59% only in time domain and even achieves 89.71% accuracy improvement with the MAPEs of between 3.34% and 6.00% in both space and time domains, and also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches.

Smart Sensing Strip Using Monolithically Integrated Flexible Flow Sensor for Noninvasively Monitoring Respiratory Flow.

This paper presents a smart sensing strip for noninvasively monitoring respiratory flow in real time. The monitoring system comprises a monolithically-integrated flexible hot-film flow sensor adhered on a molded flexible silicone case, where a miniaturized conditioning circuit with a Bluetooth4.0 LE module are packaged, and a personal mobile device that wirelessly acquires respiratory data transmitted from the flow sensor, executes extraction of vital signs, and performs medical diagnosis. The system serves as a wearable device to monitor comprehensive respiratory flow while avoiding use of uncomfortable nasal cannula. The respiratory sensor is a flexible flow sensor monolithically integrating four elements of a Wheatstone bridge on single chip, including a hot-film resistor, a temperature-compensating resistor, and two balancing resistors. The monitor takes merits of small size, light weight, easy operation, and low power consumption. Experiments were conducted to verify the feasibility and effectiveness of monitoring and diagnosing respiratory diseases using the proposed system.

Urban traffic congestion estimation and prediction based on floating car trajectory data

Traffic flow prediction is an important precondition to alleviate traffic congestion in large-scale urban areas. Recently, some estimation and prediction methods have been proposed to predict the traffic congestion with respect to different metrics such as accuracy, instantaneity and stability. Nevertheless, there is a lack of unified method to address the three performance aspects systematically. In this paper, we propose a novel approach to estimate and predict the urban traffic congestion using floating car trajectory data efficiently. In this method, floating cars are regarded as mobile sensors, which can probe a large scale of urban traffic flows in real time. In order to estimate the traffic congestion, we make use of a new fuzzy comprehensive evaluation method in which the weights of multi-indexes are assigned according to the traffic flows. To predict the traffic congestion, an innovative traffic flow prediction method using particle swarm optimization algorithm is responsible for calculating the traffic flow parameters. Then, a congestion state fuzzy division module is applied to convert the predicted flow parameters to citizens’ cognitive congestion state. Experimental results show that our proposed method has advantage in terms of accuracy, instantaneity and stability.