Instantaneous Measurements Research Articles

Ultra-wideband instantaneous frequency measurement based on differential photonic time-stretch

A novel technique to perform ultra-wideband instantaneous frequency measurement in real-time based on differential photonic time-stretch is proposed and experimentally demonstrated. Wideband frequency measurement is enabled by time-stretching the RF input signal via the dispersion compensating fiber. Distortion of the measured frequency signal caused by the non-uniformity of the spectrum of the pulsed laser source, which can be found in most of traditional time-stretch systems, is eliminated here by implementing a dual-output Mach–Zehnder modulator (DOMZM) with complementary outputs and a balanced photodetector. Multi-tone tests with a 20 GHz frequency measurement range are conducted to verify the feasibility of the system of measuring multiple frequency signals. Followed by the proposed automated digital signal processing algorithms, our instantaneous frequency measurement system offers an ultra-fast sweep time of 10 ns, a frequency resolution of 660 MHz, and a measurement error within ±380 MHz. A larger frequency measurement range can be realized by implementing a DOMZM with a larger bandwidth.

Protected area planning to conserve biodiversity in an uncertain future.

Protected areas are a key instrument for conservation. Despite this, they are vulnerable to risks associated with weak governance, land-use intensification, and climate change. We used a novel hierarchical optimization approach to identify priority areas for expanding the global protected area system that explicitly accounted for such risks while maximizing protection of all known terrestrial vertebrate species. To incorporate risk categories, we built on the minimum set problem, where the objective is to reach species distribution protection targets while accounting for 1 constraint, such as land cost or area. We expanded this approach to include multiple objectives accounting for risk in the problem formulation by treating each risk layer as a separate objective in the problem formulation. Reducing exposure to these risks required expanding the area of the global protected area system by 1.6% while still meeting conservation targets. Incorporating risks from weak governance drove the greatest changes in spatial priorities for protection, and incorporating risks from climate change required the largest increase (2.52%) in global protected area. Conserving wide-ranging species required countries with relatively strong governance to protect more land when they bordered nations with comparatively weak governance. Our results underscore the need for cross-jurisdictional coordination and demonstrate how risk can be efficiently incorporated into conservation planning. Planeación de las áreas protegidas para conservar la biodiversidad en un futuro incierto.

Groundwater Modeling to Assess Climate Change Impacts and Sustainability in the Tana Basin, Upper Blue Nile, Ethiopia

Climate change effects on long-term groundwater (GW) resource developments in the Tana Basin, Ethiopia, are a growing concern. Efforts to provide estimates under various climatic uncertainties are lacking in the region. To address this need, we deployed a fine-resolution (500 m) GW model using MODFLOW-NWT for the Tana Basin, Upper the Blue Nile region. The GW model was calibrated based on 98 historical instantaneous well-level measurements (RMSE = 16.36 m, 1.6% of range), and 38 years of monthly lake level data (RMSE = 0.2 m, 6.7% of range). We used the model to simulate long-term climate change impacts by considering two representative concentration pathways, (RCPs) 4.5 and 8.5, from the two extreme global circulation models (MIROC5 for wetter conditions and CSIRO-Mk3 for drier conditions) available in the region. While the MIROC5 simulated GW table (GWT) was found to be stable, the CSIRO-Mk3 simulated GWT exhibited large fluctuations within +2 m to −4 m by 2100 due to climate change. More critical impacts were predicted for the lake, where total lake releases from the baseline scenario were foreseen to be changed by +50% (MIROC5) or −22% (CSIRO-Mk3) by the end of 2100.

Optical investigation of ammonia rich combustion based on methanol jet ignition by means of an ignition chamber

Ammonia, as a carbon-free alternative fuel, has attracted a lot of attention. However, ammonia has slow laminar burning velocity and high ignition energy and NOx emissions. The ignition chamber has been proven to greatly enhance ignition energy. Methanol, as a low-carbon liquid fuel with a low evaporation temperature (64.5 °C), is easy to store and transport and has the potential to become a good jet ignition fuel in the ignition chamber (pre-chamber). In addition, chemical reaction kinetics analysis of ammonia demonstrated that there are few NOx emissions when the equivalent ratio is higher than 1.2. In this study, the ignition chamber fueled with methanol was used to investigate the jet flow, jet ignition, and combustion process of rich ammonia using visualization experiments and instantaneous pressure measurement. The research results showed that the ignition chamber fueled with methanol improved the jet ignition and combustion of ammonia compared to the ignition chamber fueled with ammonia. Experimental studies of energy substitution ratios of methanol from 1% to 4% showed that a 2% energy substitution ratio achieved better jet ignition performance. Comparing with the stoichiometric combustion of ammonia, the combustion performance deteriorated with the increase of ammonia rich equivalent ratio. A slightly richer equivalent ratio of 1.1 tended to be a good choice for jet ignition of ammonia. The difference in channel diameter between the ignition chamber and the main chamber had large effects on the performance of jet ignition ammonia. The 3 mm channel diameter achieved the highest ammonia-rich combustion speed under the rich equivalent ratios of 1.1 and 1.2 in the main chamber, compared to the diameters of 4.5 mm and 6 mm.

The Quantum Regime Operation of Beam Splitters and Interference Filters

The presence of quantum Rayleigh scattering, or spontaneous emission, inside a dielectric medium such as a beam splitter or an interferometric filter prevents a single photon from propagating in a straight line. Modelling a beam splitter by means of a unitary transformation is physically meaningless because of the loss of photons. Additional missing elements from the conventional theory are the quantum Rayleigh-stimulated emission, which can form groups of photons of the same frequency, and the unavoidable parametric amplification of single photons in the original parame-tric crystal. An interference filter disturbs, through multiple internal reflections, the original stream of single photons, thereby confirming the existence of groups of photons being spread out to lengthen the coherence time. The approach of modelling individual, single measurements with probability amplitudes of a statistical ensemble leads to counterintuitive explanations of the experimental outcomes and should be replaced with pure states describing instantaneous measurements whose values are afterwards averaged.

Application of kalman filter for radar target tracking.

In this paper, the principles of recursive tracking and prediction filters are used to track a single maneuvering aerial target and implemented using instantaneous noisy measurements. Kalman filter is used to estimate (predict) the system state (bearing, range, target course, and speed) as they cannot be measured directly from measurements. Furthermore, after mathematical formulation of the equations for the Kalman filter fitting the application, it was then applied and evaluated. Monte Carlo Simulation technique was applied to quantify the results and the simulation results are obtained using a python program.

Wetting-induced soil CO2 emission pulses are driven by interactions among soil temperature, carbon, and nitrogen limitation in the Colorado Desert.

Warming-induced changes in precipitation regimes, coupled with anthropogenically enhanced nitrogen (N) deposition, are likely to increase the prevalence, duration, and magnitude of soil respiration pulses following wetting via interactions among temperature and carbon (C) and N availability. Quantifying the importance of these interactive controls on soil respiration is a key challenge as pulses can be large terrestrial sources of atmospheric carbon dioxide (CO2 ) over comparatively short timescales. Using an automated sensor system, we measured soil CO2 flux dynamics in the Colorado Desert-a system characterized by pronounced transitions from dry-to-wet soil conditions-through a multi-year series of experimental wetting campaigns. Experimental manipulations included combinations of C and N additions across a range of ambient temperatures and across five sites varying in atmospheric N deposition. We found soil CO2 pulses following wetting were highly predictable from peak instantaneous CO2 flux measurements. CO2 pulses consistently increased with temperature, and temperature at time of wetting positively correlated to CO2 pulse magnitude. Experimentally adding N along the N deposition gradient generated contrasting pulse responses: adding N increased CO2 pulses in low N deposition sites, whereas adding N decreased CO2 pulses in high N deposition sites. At a low N deposition site, simultaneous additions of C and N during wetting led to the highest observed soil CO2 fluxes reported globally at 299.5μmol CO2 m-2 s-1 . Our results suggest that soils have the capacity to emit high amounts of CO2 within small timeframes following infrequent wetting, and pulse sizes reflect a non-linear combination of soil resource and temperature interactions. Importantly, the largest soil CO2 emissions occurred when multiple resources were amended simultaneously in historically resource-limited desert soils, pointing to regions experiencing simultaneous effects of desertification and urbanization as key locations in future global C balance.

Construction, calibration and performance evaluation of a lysimeter, with a double weighing system: Hydraulic and mechanical

According to the literature, hydraulic and mechanical weighing lysimeters, which use electronic load cells, are considered the most accurate equipment to directly determine reference evapotranspiration (ETo) and crop evapotranspiration (ETc). However, the lack of comparative performance studies between these systems makes it impossible to make accurate statements about which is the most stable system and has the highest accuracy measurement. To evaluate the performance, it is important to consider parameters such as calibration, error analysis, accuracy test, hysteresis, and also the susceptibility to interference from external factors. Therefore, this study aimed to compare hydraulic and mechanical weighing systems through the construction, calibration and performance evaluation of a lysimeter with a dual weighing system operating simultaneously under the same environmental conditions. This study aimed to fill in the gaps found in the literature and help to solve questions related to the performance of these systems. Both systems have a high degree of linearity, with R² above 0.999, responding proportionally to mass variations in the lysimeter. However, the mechanical weighing system performed better with lower hysteresis, higher accuracy measurement, and more stable behavior, being less susceptible to environmental interference than the hydraulic weighing system. Both systems are suitable for instantaneous measurements of mass and determination of ETo and ETc, but for purposes that require greater accuracy measurement, it is recommended to opt for the mechanical weighing system.

Microwave Photonic Reconfigurable High Precision Instantaneous Frequency Measurement System Assisted by Stacking Ensemble Learning Method

This paper reports a microwave photonic reconfigurable high precision simple instantaneous frequency measurement system. With the first assistance of stacking ensemble learning method, it can achieve high measurement accuracy over a wide bandwidth. For introducing electrical and optical delays, the range and accuracy of the instantaneous frequency measurement method can be flexibly adjusted to suit the needs of various scenarios. Sufficient and systematic experimental results show that from 11 GHz to 17 GHz, the average measurement error is 4.39 MHz. This simple system acquits reconfigurability and on-chip integration possibilities without dispersion elements, which reveals great potential in the future integrated array radar, detection, and other fields.

Broadband tunable instantaneous frequency measurement system based on stimulated Brillouin scattering

Broadband tunable instantaneous frequency measurement system based on stimulated Brillouin scattering

An Efficient Digital Channelized Receiver for Low SNR and Wideband Chirp Signals Detection

Synthetic aperture radar (SAR) is essential for obtaining intelligence in modern information warfare. Wideband chirp signals with a low signal-to-noise ratio (SNR) are widely used in SAR. Intercepting low-SNR wideband chirp signals is of great significance for anti-SAR reconnaissance. Digital channelization technology is an effective means to intercept wideband signals. The existing digital channelization methods have the following problems: the contradiction of reception blind zone and signal spectrum aliasing, high computational complexity, and low estimating accuracy for chirp signals with a low SNR. This paper proposes a non-critical sampling digital channelized receiver architecture to intercept chirp signals. The receiver architecture has no blind zone in channel division and no aliasing of signal spectrum in the channel, which can provide reliable instantaneous frequency measurements. An adaptive threshold generation algorithm is proposed to detect signals without prior information. In addition, an improved instantaneous frequency measurement (IFM) algorithm is proposed, improving low SNR chirp signals’ frequency estimation accuracy. Moreover, a simple channel arbitration logic is proposed to complete the cross-channel combination of wideband signals. Simulations show that the proposed receiver architecture is reliable and robust for low SNR and wideband chirp signal detection. When the input SNR is 0 dB, the absolute frequency root-mean-square error (RMSE) of bandwidth and the center frequency is 0.57 MHz and 1.05 MHz, respectively. This frequency accuracy is great for radio frequency (RF) wideband systems.

Forecasting of volumetric flow rate of Ergene river using machine learning

Today, the significance of the estimation of physical parameters has considerably increased; for example, the prediction of water flow rate (WFR) is one of the types that will gain a substantial importance among the others. The predictions of WFR of rivers play a prominent role in the plans and constructions of new water dams, or to operate the ones that were formerly constructed. In this study, a variety of machine learning algorithms have been suggested in the estimations of one-ahead instantaneous measurement of river WFR. In this regard, four different prediction algorithms including long short-term memory (LSTM) neural network, adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM), ANFIS with subtractive clustering (SC), and the ANFIS with grid partition (GP) were initially trained and secondly tested. The study region was selected as Inanli measurement station, which is located on the Ergene River. A cumulative of 134 different models were formed using these algorithms. Instantaneous WFR predictions in terms of WFR data have shown that ANFIS-FCM and ANFIS-SC had given the best statistical error outcomes. Accordingly, the values of 0.34 m3/s, 0.72 m3/s, and 0.9728 have been found as a result of machine learning establishment corresponding to statistical accuracy results of MAE, RMSE, and R values, respectively. Namely, it has been concluded that error values of the ANFIS-FCM and ANFIS-SC computations have outcome to be sufficiently good. Also, it was concluded and shown in the current study that both tools of the ANFIS can be two efficacious methods to WFR forecasting.

Study of the Instantaneous Water Level Measurement Method on Unsteady Flow Based on Single Camera and Fixed Scale Compensation in the Tunnel Model

The shape of instantaneous water surface profiles for the unsteady flow in the tunnel model can be determined by measuring the instantaneous water levels at multiple points synchronously. However, the measurement accuracy of the ultrasonic method is affected greatly because of the complex state of the water surface when water level changes rapidly. Using an optical method, multicamera synchronous measurement is difficult to control, and the cost is high. For single camera measurement, the reading of the steel ruler in the image cannot be clearly seen when the distance between the measuring point and single camera is far. When the distance is short, all steel rulers cannot be captured at the same time. Hence, in this paper, a single camera and fixed scale compensation method for instantaneous water level observation in unsteady flow is proposed. A series of fixed scales is placed with the same height and width near the water surface to be measured. In order to capture dynamic images of all fixed scales with a clear water surface, a single camera is set at a far position to continuously record the water level change process within the model range. According to the height of the fixed scale, the image accuracy is compensated and the effect of the wide-angle image distortion is automatically avoided. The water level elevation is obtained according to the relationship between the fixed scale and the water surface. In this paper, the unsteady flow model measuring test is applied to the Southern Main Tunnel and the Water Distribution Pool of the Huangchigou Water Diversion Project. The accuracy of this method in measuring the dynamic water level is 1–2 mm, which meets the accuracy requirement and greatly reduces the observation cost of the model test. The proposed method may also be suitable for measuring the instantaneous water level of the unsteady flow in other similar hydraulic model tests.

Study on Hydraulic Characteristic of Unsteady Flow at Huangchigou Water Distribution Hub by Observing Instantaneous Water Level Based on Single-Camera and Fixed-Scale Compensation Method

The Huangchigou Water Distribution Hub is the core water distribution structure in the Hanjiang-to-Weihe Valley Water Diversion Project. It plays a decisive role in ensuring the safety of downstream water transport, allocating water reasonably and reducing the waste of abandoned water. The study on its unsteady-flow hydraulic characteristics is of key significance in the formulation for the opening and closing rules of the hub gates. The shape of the instantaneous water surface profile is an important factor for the hydraulic characteristics of the unsteady flow in the hub. The effect of the traditional instantaneous water level measurement method is not ideal. A single-camera and fixed-scale compensation method is proposed for an instantaneous water level observation. The proposed method meets the requirement of measurement accuracy and reduces the observation cost of the model test. The unsteady-flow test of the hydraulic model in the Southern Main Tunnel of the Huangchigou Water Distribution Hub was carried out under two operating conditions when the model flow is 0.0246 m3/s. Precise data such as wave front propagation velocity and hydraulic oscillation recovery time of unsteady flows in the Southern Main Tunnel was obtained. It has an important reference value for the research and design of water transfer dispatching and gate-opening and gate-closing rules for the Huangchigou Water Distribution Hub.

Spatially lagged predictors from a wider area improve PM2.5 estimation at a finer temporal interval—A case study of Dallas-Fort Worth, United States

Fine particulate matter, also known as PM2.5, has many adverse impacts on human health. However, there are few ground monitoring stations measuring PM2.5. Satellite data help fill the gaps in ground measurements, but most studies focus on estimating daily PM2.5 levels. Studies examining the effects of environmental exposome need accurate PM2.5 estimates at fine temporal intervals. This work developed a Convolutional Neural Network (CNN) to estimate the PM2.5 concentration at an hourly average using high-resolution Aerosol Optical Depth (AOD) from the MODIS MAIAC algorithm and meteorological data. Satellite-acquired AOD data are instantaneous measurements, whereas stations on the ground provide an hourly average of PM2.5 concentration. The current work aimed to refine PM2.5 estimates at temporal intervals from 24-h to 1-h averages. Our premise posited the enabling effects of spatial convolution on temporal refinements in PM2.5 estimates. We trained a CNN to estimate PM2.5 corresponding to the hour of AOD acquisition in the Dallas-Fort Worth and surrounding area using 10 years of data from 2006–2015. The CNN accepts images as input. For each PM2.5 station, we strategically subset temporal MODIS images centering at the PM2.5 station. Hence, the resulting image-patch size represented the size of the area around the PM2.5 station. It thus was analogous to spatial lag in spatial statistics. We systematically increased the image-patch size from 3 × 3, 5 × 5, … , to 19 × 19 km2 and observed how increasing the spatial lag impacted PM2.5 estimation. Model performance improved with a larger spatial lag; the model with a 19 × 19 km2 image-patch as input performed best, with a correlation coefficient of 0.87 and a RMSE of 2.57 g/m3 to estimate PM2.5 at in situ stations corresponding to the hour of satellite acquisition time. To overcome the problem of a reduced number of image-patches available for training due to missing AOD, the study employed a data augmentation technique to increase the number of samples available to train the model. In addition to avoiding overfitting, data augmentation also improved model performance.

Flow measurement in open channels using imaging techniques in conjunction with a convolutional neural network

Data related to river velocity and discharge are important for water resource management. Non-intrusive image measurement techniques based on direct cross-correlation (DCC) algorithms, such as particle image velocimetry (PIV), are widely used to measure velocity and discharge in the field. However, environmental noise is highly complex and uncontrollable in the field, significantly reducing the accuracy of DCC-based methods. Convolutional neural networks (CNNs) are commonly used in image recognition because of their outstanding accuracy, which far exceeds that of conventional imaging methods. However, these accuracy levels cannot be directly extrapolated for flow movement estimation. Therefore, in this study, we developed an innovative sub-pixel correction technique that allows CNN-based methods to obtain stable measurements in the PIV framework. This is the first study that successfully applied the concept of CNN to measure velocity data using images. The Hamel-Oseen vortex-flow, uniform steady-flow, and plane laminar jet-flow models are established as benchmark vector fields. Non-uniform illumination and Gaussian noise with varying degrees of interference are added to the synthetic data to evaluate the performance of the CNN-based method in PIV. For noiseless images, the DCC-based and CNN-based methods achieve lower measurement errors than benchmark errors. For noisy images, the DCC suffers a fold error increase ranging from 2.77 to 31.13, whereas the CNN suffers only a fold error increase ranging from 1.25 to 1.68. A 30-m-long flume is then used in an uncontrolled environment to mimic real-world flow measurement. The dispersion of the instantaneous velocity measurements for the CNN is more concentrated than that for the DCC. The acoustic Doppler velocimetry yields an error of only 7.87% in discharge estimation using CNN. These results indicate that the CNN-based method is more robust than conventional methods and has the potential to be effectively applied to measurements in the field.

Instantaneous measurement of surface roughness spectra using white-light scattering projected on a spectrometer.

Following on from previous studies on motionless scatterometers based on the use of white light, we propose a new, to the best of our knowledge, experiment of white-light scattering that should overtake the previous ones in most situations. The setup is very simple as it requires only a broadband illumination source and a spectrometer to analyze light scattering at a unique direction. After introducing the principle of the instrument, roughness spectra are extracted for different samples, and the consistency of results is validated at the intersection of bandwidths. The technique will be of great use for samples that cannot be moved.

Dynamic correlations in lipid bilayer membranes over finite time intervals.

Recent single-molecule measurements [Schoch et al., Proc. Natl. Acad. Sci. U. S. A. 118, e2113202118 (2021)] have observed dynamic lipid-lipid correlations in membranes with submicrometer spatial resolution and submillisecond temporal resolution. While short from an instrumentation standpoint, these length and time scales remain long compared to microscopic molecular motions. Theoretical expressions are derived to infer experimentally measurable correlations from the two-body diffusion matrix appropriate for membrane-bound bodies coupled by hydrodynamic interactions. The temporal (and associated spatial) averaging resulting from finite acquisition times has the effect of washing out correlations as compared to naive predictions (i.e., the bare elements of the diffusion matrix), which would be expected to hold for instantaneous measurements. The theoretical predictions are shown to be in excellent agreement with Brownian dynamics simulations of experimental measurements. Numerical results suggest that the experimental measurement of membrane protein diffusion, in complement to lipid diffusion measurements, might help to resolve the experimental ambiguities encountered for certain black lipid membranes.

Upscaling of longwave downward radiation from instantaneous to any temporal scale: Algorithms, validation, and comparison

Surface longwave downward radiation (LWDR) is a key factor affecting the surface energy balance. The daily LWDR and the diurnal variations of LWDR are of great significance for studies of climate change and surface processes. How to obtain LWDR at an averaged temporal scale from instantaneous LWDR is one of the long-standing problems in the field of radiation budget from remote sensing. In this paper, two temporal upscaling methods are introduced, namely, a method based on the diurnal variations of LWDR (diurnal variation based, DVB) and a method based on random forest regression (RFR). The results reveal that: (1) The DVB method has a global hourly and daily LWDR root-mean-square error (RMSE) of less than 21 W/m2 and 15 W/m2, respectively, and the RMSE of the daily LWDR based on RFR is less than 7 W/m2; (2) When compared with four existing statistical interpolation methods, the DVB method can not only ensure the accuracy, but also can overcome the problem of missing samples and/or an abnormal samples during upscaling; (3) Except for directly predict daily LWDR, the DVB methods can also obtain more accurate LWDR diurnal variations such as hourly, half-hourly etc. The RFR method enables high-efficiency and accurate estimation of daily averaged LWDR from instantaneous measurements. Compared with existing methods and products, the proposed methods are not only efficient, but also have a superior applicability and reliable accuracy. The proposed strategies provide new ideas for the community in estimating LWDR at continuous temporal scales from remotely sensed measurements.

A distance Protection/Fault location algorithm for Double-Circuit transmission lines with asymmetrical circuits during Cross-Country faults

The impedance-measuring elements and fault locators in distance relays suffer from measuring the correct fault impedance and fault location when facing cross-country faults (CCFs). During such atypical faults, this paper proposes a distance protection along with a fault location algorithm for double-circuit lines with asymmetrical circuits. This is fulfilled through instantaneous fault-branch currents innovatively derived from the line six-phase circuit model analysis on a time-domain basis. The currents derived lead to the relations that are mathematically linear combinations of designated vectors. This makes the possibility of establishing vector-input adaptive linear combiners (VI-ALC) in accordance with the relations derived. Being fed from the vectors of instantaneous measurements at the relaying side, the VI-ALCs with a distinctive capability of online training would swiftly give an accurate estimation of the fault distances during CCFs. The estimations are exploited in a logical procedure for both of the distance protection and fault location functions, taking account of different position status of the CCFs’ comprising LG faults. The proposed algorithm is feasibly implementable and is independent of the system configuration. It operates robustly in high-ground resistance and noisy conditions. Through extensive simulation studies in PSCAD/EMTDC, performance of the proposed algorithm would be investigated and verified in a variety of fault scenarios.