Real-time Measurement Systems Research Articles

Development and Performance Assessment of Real-time Rotational Speed Measurement System for Agricultural Application

Rotational speed is one of the most important parameters in the performance of any system. It becomes more vital when the rotational speed measurement is required on real-time basis. Measurement of rotational speed has a crucial role in automation of agricultural machineries for various purposes. The existing real-time speed measurement systems are susceptible to the varying working environment. Therefore, it was necessary to study the performance of available sensor. In this study four different sensors (inductive proximity, Infrared (IR), Hall effect and optical proximity sensors) were tested in three different working environments (indoor, outdoor and outdoor dusty condition) for two different spacings (2 and 5 mm) between sensors and target. A laboratory setup was developed to test the performance of the sensors in terms of percentage deviation in rotational speeds measurement compared to actual values. The values were recorded in Secure Digital (SD) card and analysed using full factorial design. The means of the different levels were compared using Tukey’s (b) method. There was significant difference in sensor response for different conditions. The percentage of deviation in speed for Hall effect, inductive proximity, IR and optical proximity sensors varied from 0.55 to 1.03, 1.20 to 32.29, 21.37 to 100.00 and 35.18 to 99.98%, respectively. The study concluded that the Hall effect sensor was more suitable for rotational speed measurement than the other sensors without being affected by working environment.

Development of Real-Time Cuffless Blood Pressure Measurement Systems with ECG Electrodes and a Microphone Using Pulse Transit Time (PTT).

Research has shown that pulse transit time (PTT), which is the time delay between the electrocardiogram (ECG) signal and the signal from a photoplethysmogram (PPG) sensor, can be used to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) without the need for a cuff. However, the LED of the PPG sensor requires the precise adjustment of both light intensity and light absorption rates according to the contact status of the light-receiving element. This results in the need for regular calibration. In this study, we propose a cuffless blood pressure monitor that measures real-time blood pressure using a microphone instead of a PPG sensor. The blood pulse wave is measured in the radial artery of the wrist using a microphone that can directly measure the sound generated by a body rather than sending energy inside the body and receiving a returning signal. Our blood pressure monitor uses the PTT between the R-peak of the ECG signal and two feature points of the blood pulse wave in the radial artery of the wrist. ECG electrodes and circuits were fabricated, and a commercial microelectromechanical system (MEMS) microphone was used as the microphone to measure blood pulses. The peak points of the blood pulse from the microphone were clear, so the estimated SBP and DBP could be obtained from each ECG pulse in real time, and the resulting estimations were similar to those made by a commercial cuff blood pressure monitor. Since neither the ECG electrodes nor the microphone requires calibration over time, the real-time cuffless blood pressure monitor does not require calibration. Using the developed device, blood pressure was measured three times daily for five days, and the mean absolute error (MAE) and standard deviation (SD) of the SBP and DBP were found to be 2.72 ± 3.42 mmHg and 2.29 ± 3.53 mmHg, respectively. As a preliminary study for proof-of-concept, these results were obtained from one subject. The next step will be a pilot study on a large number of subjects.

Ultrafast response of self-powered humidity sensor of flexible graphene oxide film

Next-generation humidity sensor, as one of the smart devices in artificial skin or flexible electronics, will need to be self-powered, flexible, fast and accurately detecting in the microenvironment. Although various flexible humidity sensors have already been demonstrated, most of them rely on large external power sources to operate, and still suffer from slow response, poor selectivity in environmental stimuli, and expensive and complicated procedures. Here, we realize a self-powered humidity sensor with ultrafast response and recovery time of less than 0.3 s by flexible GO film. Importantly, of all state-of-the-art humidity sensors, this is the fastest response for humidity sensor. Moreover, the sensor has a high sensitivity within a wide range of RH from 33% to 98%, and an excellent selective response to humidity under multiple environmental stimuli, due to its moist-induced generator. Theoretical computations reveal that protons generated spontaneously from oxygen-containing groups and water molecules, have a fast diffusion behavior as Grotthuss hopping. In particular, we build four real-time measurement systems capable of respiration monitoring of human, smart leaf surface humidity sensor, and energy harvesting. Our work provides a simple way to fabricate next-generation self-powered sensor using GO film with outstanding humidity sensor performance.

Optimize Ranking and Load Shedding in Microgrid Considering Improved Analytic Hierarchy Process Algorithm and Power Stability Index

This paper proposes a load shedding model for the island microgrid based on the ranking of loads and the power stability index (PSI). Loads are ranked based on the improved analytic hierarchy process (AHP) algorithm. Real‐time measurement systems have the function of collecting data for very important, important, and less‐important loads at each bus load. From this data, the improved AHP method is applied to rank the loads. The advantage of this method is that the subjectivity is eliminated and not depending on the expertise of the system operator when implementing the traditional AHP method. Besides, the minimum amount of load shedding power is calculated, taking into account both primary and secondary control methods. The objective is to minimize the impact on power consumers and ensure that the frequency returns to an acceptable range. In addition, when implementing load shedding, voltage quality, and stability are considered. The PSI serves as a crucial parameter for assessing the voltage stability of microgrid buses. This index is combined with load ranking weights to obtain combined weights for the load shedding plan. Consequently, the proposed load shedding plan prioritizes minimizing damage to customers, improving voltage quality and stability, and ensuring frequency is within permissible limits. The 16‐bus microgrid system is applied to compare with traditional methods and to prove the efficiency of the suggested technique.

Development of a low-cost vision-based real-time displacement system using Raspberry Pi

Vision-based real-time displacement measurement systems can measure the structural displacements without contact and output the results immediately, overcoming some drawbacks of traditional contact-type sensors and off-line vision sensors in field applications. However, high cost and complexity of installation restrict the widespread application of the off-the-shelf vision-based real-time displacement systems. In this work, a fast image processing software is developed for operation in single board computers (SBC), firstly, based which a low-cost and portable vision-based real-time displacement measurement system using Raspberry Pi is constructed for civil structural health monitoring. To evaluate the performance of the system in field applications, a new method for validation of on-site measurement accuracy is proposed. The experimental results show that the constructed system accurately measured multipoint structural displacements of a long-span suspension bridge in real-time, with at least 1/17 pixel accuracy, providing a cost-efficient, convenient, high-precision, and contactless approach to obtain the displacement responses of infrastructures in the field.

Flexible Screen-Printed Electrochemical Sensors Functionalized with Electrodeposited Copper for Nitrate Detection in Water.

Nitrate (NO3–) contamination is becoming a major concern due to the negative effects of an excessive NO3– presence in water which can have detrimental effects on human health. Sensitive, real-time, low-cost, and portable measurement systems able to detect extremely low concentrations of NO3– in water are thus becoming extremely important. In this work, we present a novel method to realize a low-cost and easy to fabricate amperometric sensor capable of detecting small concentrations of NO3– in real water samples. The novel fabrication technique combines printing of a silver (Ag) working electrode with subsequent modification of the electrode with electrodeposited copper (Cu) nanoclusters. The process was tuned in order to reach optimized sensor response, with a high catalytic activity toward electroreduction of NO3– (sensitivity: 19.578 μA/mM), as well as a low limit of detection (LOD: 0.207 nM or 0.012 μg/L) and a good dynamic linear concentration range (0.05 to 5 mM or 31 to 310 mg/L). The sensors were tested against possible interference analytes (NO2–, Cl–, SO42–, HCO3–, CH3COO–, Fe2+, Fe3+, Mn2+, Na+, and Cu2+) yielding only negligible effects [maximum standard deviation (SD) was 3.9 μA]. The proposed sensors were also used to detect NO3– in real samples, including tap and river water, through the standard addition method, and the results were compared with the outcomes of high-performance liquid chromatography (HPLC). Temperature stability (maximum SD 3.09 μA), stability over time (maximum SD 3.69 μA), reproducibility (maximum SD 3.20 μA), and repeatability (maximum two-time useable) of this sensor were also investigated.

Prediction of Transient Stability Using Wide Area Measurements Based on ANN

The concept of wide-area control and protection as an application on real-time wide-area measurement systems makes the transient stability prediction more accurate in early time after fault occurrences. The transient prediction is the first step in the dynamic control system to avoid any unwanted emergency or non-stable power system state. In this paper, an early predictionof the power system stability once the fault cleaning using real-time dynamic data collected by WAMS is proposed based on an artificial neural network (ANN). The dataset collected by the different contingency analyses on the IEEE 39 bus test system is used to train a multilayer perceptron network. Pre-fault, during- fault, and post-fault generators' speeds are fed to ANN as inputs, and the status of the overall system, either stable or not, is the output of ANN. The proposed model can predict an unstable state within 100 ms after the fault. NEPLAN simulator is used to simulate the dynamic analysis ofthe IEEE 39-Bus test system, and MATLAB 2019a is used to design the ANN.

Development of a Real-Time Magnetic Field Measurement System for Synchrotron Control

The precise knowledge of the magnetic field produced by dipole magnets is critical to the operation of a synchrotron. Real-time measurement systems may be required, especially in the case of iron-dominated electromagnets with strong non-linear effects, to acquire the magnetic field and feed it back to various users. This work concerns the design and implementation of a new measurement system of this kind currently being deployed throughout the European Organization for Nuclear Research (CERN) accelerator complex. We first discuss the measurement principle, the general system architecture and the technology employed, focusing in particular on the most critical and specialized components developed, that is, the field marker trigger generator and the magnetic flux integrator. We then present the results of a detailed metrological characterization of the integrator, including the aspects of drift estimation and correction, as well as the absolute gain calibration and frequency response. We finally discuss the latency of the whole acquisition chain and present an outline of future work to improve the capabilities of the system.

Intracellular detection and communication of a wireless chip in cell

The rapid growth and development of technology has had significant implications for healthcare, personalized medicine, and our understanding of biology. In this work, we leverage the miniaturization of electronics to realize the first demonstration of wireless detection and communication of an electronic device inside a cell. This is a significant forward step towards a vision of non-invasive, intracellular wireless platforms for single-cell analyses. We demonstrate that a 25 upmu m wireless radio frequency identification (RFID) device can not only be taken up by a mammalian cell but can also be detected and specifically identified externally while located intracellularly. The S-parameters and power delivery efficiency of the electronic communication system is quantified before and after immersion in a biological environment; the results show distinct electrical responses for different RFID tags, allowing for classification of cells by examining the electrical output noninvasively. This versatile platform can be adapted for realization of a broad modality of sensors and actuators. This work precedes and facilitates the development of long-term intracellular real-time measurement systems for personalized medicine and furthering our understanding of intrinsic biological behaviors. It helps provide an advanced technique to better assess the long-term evolution of cellular physiology as a result of drug and disease stimuli in a way that is not feasible using current methods.

Error characterization and calibration of real-time magnetic field measurement systems

In synchrotrons at the European Organization for Nuclear Research (CERN), magnetic measurement systems known as B-trains measure the magnetic field in the main bending magnets in real-time, and transmit this signal for the control of the synchrotron’s RF accelerating cavities, magnet power converter and beam monitoring systems. This work presents an assessment of the capabilities and performance of the new FIRESTORM (Field In REal-time STreaming from Online Reference Magnets) system as part of the first phase of commissioning. A short summary of the architecture of the measurement system is provided first, followed by the definition of an error model which can be used to characterize random and systematic errors separately. We present a procedure for the metrological calibration and qualification of the B-trains, including an experimental evaluation of the different error sources for the four new systems being commissioned in the Proton Synchrotron Booster (PSB), Low Energy Ion Ring (LEIR), Proton Synchrotron (PS) and the Extra Low ENergy Antiproton (ELENA) ring. In particular, we discuss a method to calibrate systematic gain and offset errors based on the RF cavity frequency offset needed to center the beam on its theoretical orbit.

Electron paramagnetic resonance magnetic field sensors for particle accelerators.

We report on four electron paramagnetic resonance sensors for dynamic magnetic field measurements at 36 mT, 100 mT, 360 mT, and 710 mT. The sensors are based on grounded co-planar microwave resonators operating at about 1 GHz and 3 GHz, realized using printed circuit board technology, and on single-chip integrated microwave oscillators operating at about 10 GHz and 20 GHz, realized using complementary metal-oxide-semiconductor technology. The sensors are designed to mark precisely the moment when a time-dependent magnetic field attains a specific value. The trigger from the sensor can be used to preset the output of real-time magnetic field measurement systems, called "B-trains," which are in operation at several large synchrotron installations, including five of the CERN's particle accelerators. We discuss in detail the performance achieved, in particular, the magnetic field resolution that is in the range of 0.1 nT/Hz1/2-6 nT/Hz1/2. The effects of material anisotropy and temperature are also discussed. Finally, we present a detailed characterization of the sensors with field ramps as fast as 5 T/s and field gradients as strong as 12 T/m.

Development of a Real-Time Tillage Depth Measurement System for Agricultural Tractors: Application to the Effect Analysis of Tillage Depth on Draft Force during Plow Tillage.

The objectives of this study were to develop a real-time tillage depth measurement system for agricultural tractor performance analysis and then to validate these configured systems through soil non-penetration tests and field experiment during plow tillage. The real-time tillage depth measurement system was developed by using a sensor fusion method, consisting of a linear potentiometer, inclinometer, and optical distance sensor to measure the vertical penetration depth of the attached implement. In addition, a draft force measurement system was developed using six-component load cells, and an accuracy of 98.9% was verified through a static load test. As a result of the soil non-penetration tests, it was confirmed that sensor fusion type A, consisting of a linear potentiometer and inclinometer, was 6.34–11.76% more accurate than sensor fusion type B, consisting of an optical distance sensor and inclinometer. Therefore, sensor fusion type A was used during field testing as it was found to be more suitable for use in severe working environments. To verify the accuracy of the real-time tillage depth measurement system, a linear regression analysis was performed between the measured draft and the predicted values calculated using the American Society of Agricultural and Biological Engineers (ASABE) standards-based equation. Experimental data such as traveling speed and draft force showed that it was significantly affected by tillage depth, and the coefficient of determination value at M3–Low was 0.847, which is relatively higher than M3–High. In addition, the regression analysis of the integrated data showed an R-square value of 0.715, which is an improvement compared to the accuracy of the ASABE standard prediction formula. In conclusion, the effect of tillage depth on draft force of agricultural tractors during plow tillage was analyzed by the simultaneous operation of the proposed real-time tillage depth measurement system and draft force measurement system. In addition, system accuracy is higher than the predicted accuracy of 40% based on the ASABE standard equation, which is considered to be useful for various agricultural machinery research fields. In future studies, real-time tillage depth measurement systems can be used in tractor power train design and to ensure component reliability, in accordance with agricultural working conditions, by predicting draft force and axle loads depending on the tillage depth during tillage operations.

Real-time fast Fourier transform

The paper presents a method of implementation of fast Fourier transform in real-time data processing and measurement systems. The method is based on software and hardware package «UMIKON», supporting input-output drivers. Classic Cooley and Tukey algorithm apply to real-time data vector, which formed by successive shift after every clock cycle time. This approach to data processing in real-time allows reaching high access time in information measurement systems when analyzing dynamic processes.

Ferrimagnetic resonance field sensors for particle accelerators.

We report on two ferrimagnetic resonance (FMR) sensors for absolute dynamic magnetic field measurements at 36 and 100 mT. The sensors are designed to mark precisely and reproducibly the moment when a time-transient magnetic field attains a specific value. The trigger from the sensor can then be used for real-time magnetic field measurement systems, called "B-trains," which are in operation at several large synchrotron installations including five of CERN's particle accelerators. We discuss in detail the design, the operation, and the performance of the FMR sensors based on two different types of printed circuit board (PCB) resonator structures.

Real-Time Alpine Measurement System Using Wireless Sensor Networks

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra’s wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.

Redundant and fault-tolerant algorithms for real-time measurement and control systems for weapon equipment

Because of the high availability requirements from weapon equipment, an in-depth study has been conducted on the real-time fault-tolerance of the widely applied Compact PCI (CPCI) bus measurement and control system. A redundancy design method that uses heartbeat detection to connect the primary and alternate devices has been developed. To address the low successful execution rate and relatively large waste of time slices in the primary version of the task software, an improved algorithm for real-time fault-tolerant scheduling is proposed based on the Basic Checking available time Elimination idle time (BCE) algorithm, applying a single-neuron self-adaptive proportion sum differential (PSD) controller. The experimental validation results indicate that this system has excellent redundancy and fault-tolerance, and the newly developed method can effectively improve the system availability.

Metabolic imaging in multiple time scales

We report here a novel combination of time-resolved imaging methods for probing mitochondrial metabolism in multiple time scales at the level of single cells. By exploiting a mitochondrial membrane potential reporter fluorescence we demonstrate the single cell metabolic dynamics in time scales ranging from microseconds to seconds to minutes in response to glucose metabolism and mitochondrial perturbations in real time. Our results show that in comparison with normal human mammary epithelial cells, the breast cancer cells display significant alterations in metabolic responses at all measured time scales by single cell kinetics, fluorescence recovery after photobleaching and by scaling analysis of time-series data obtained from mitochondrial fluorescence fluctuations. Furthermore scaling analysis of time-series data in living cells with distinct mitochondrial dysfunction also revealed significant metabolic differences thereby suggesting the broader applicability (e.g. in mitochondrial myopathies and other metabolic disorders) of the proposed strategies beyond the scope of cancer metabolism. We discuss the scope of these findings in the context of developing portable, real-time metabolic measurement systems that can find applications in preclinical and clinical diagnostics.

Flexible real-time natural 2D color and 3D shape measurement

The majority of existing real-time 3D shape measurement systems only generate non-nature texture (i.e., having illumination other than ambient lights) that induces shadow related issues. This paper presents a method that can simultaneously capture natural 2D color texture and 3D shape in real time. Specifically, we use an infrared fringe projection system to acquire 3D shapes, and a secondary color camera to simultaneously capture 2D color images of the object. Finally, we develop a flexible and simple calibration technique to determine the mapping between the 2D color image and the 3D geometry. Experimental results demonstrate the success of the proposed technique.

A Real-Time Terahertz Time-Domain Polarization Analyzer with 80-MHz Repetition-Rate Femtosecond Laser Pulses

We have developed a real-time terahertz time-domain polarization analyzer by using 80-MHz repetition-rate femtosecond laser pulses. Our technique is based on the spinning electro-optic sensor method, which we recently proposed and demonstrated by using a regenerative amplifier laser system; here we improve the detection scheme in order to be able to use it with a femtosecond laser oscillator with laser pulses of a much higher repetition rate. This improvement brings great advantages for realizing broadband, compact and stable real-time terahertz time-domain polarization measurement systems for scientific and industrial applications.

Intelligent-Well-Monitoring Systems: Review and Comparison

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 150195, ’Review, Analysis, and Comparison of Intelligent-Well- Monitoring Systems,’ by M.F. Silva Junior, Petrobras, and K.M. Muradov, SPE, and D.R. Davies, SPE, Heriot-Watt University, prepared for the 2012 SPE Intelligent Energy International, Utrecht, The Netherlands, 27-29 March. The paper has not been peer reviewed. Despite the maturity of intelligent-well (IW) equipment, the concept of an integrated IW as a key element in the “digital oil field” is still not developed fully. Current practice is to evaluate the IW value chain in a fit-for-purpose manner rather than by an integrated-modeling workflow. An increasing variety of real-time downhole monitoring and measurement systems is available for deployment or is in development. A well-founded understanding of data that are actually needed, the most suitable sensor types and interfaces, and availability of data-reconciliation and -validation methodology are key factors for the success of an integrated IW project. Introduction A subsea integrated production frame-work treats the reservoir, wells, flowlines, risers, and topside facilities as a single system. IWs can provide reservoir control by use of continuous monitoring and valve actuation in real-time. Integrated modeling and optimization, in this context, aims to capture the physics of the complex interactions across the processes for true operations support. Actual data are fed into the optimizer after data analysis and assimilation for model calibration and uncertainty minimization. The complete paper details how established concepts from control engineering and metrology can be used for reservoir management. Research and development efforts in permanent well monitoring are divided into two classes: deep-reservoir and near-wellbore. Additional value can be derived from these technologies because they also can identify and classify failures in downhole equipment. Deep-reservoir sensing is related to 4D seismic, dynamic 3D resistivity, and streaming potential (electrokinetic) monitoring techniques that capture the dynamics of the entire reservoir. Near-wellbore sensing includes classical downhole measurements, such as pressure and temperature. Also, monitoring systems have become a distributed sensing network instead of an autonomous system, which requires an understanding of all errors and limitations in the signal path aside from the sensor itself. IW-Monitoring Systems The physical quantities measured by equipment from different manufacturers of sensors for downhole, near- wellbore permanent monitoring of an IW are very similar: pressure, temperature, flow, acceleration (seismic and acoustic), and strain. The differences between them and their limitations are related mostly to the equipment and its installation requirements. The measurement of new physical quantities for deep-reservoir monitoring, such as seismic, electromagnetics, and streaming potential, is in the early stages of development.