Loop Probe Research Articles

Impact of Air Gaps Between Microstrip Line and Magnetic Sheet on Near-Field Magnetic Shielding

This study experimentally analyzed the impact of air gaps between a magnetic sheet and a test board with a microstrip line, which is used to measure the near-field magnetic shielding effectiveness (NSE) of magnetic sheets made of metallic powder. To conduct the measurements, a material fixture equipped with a microstrip line to generate the near magnetic field, a rectangular loop probe, and an automatic probe positioning system capable of moving the loop probe along three axes were designed and fabricated. In addition, to systematically vary the thickness of the gaps, three paper spacers with a thickness of 0.11 mm per paper were used, and a 1.0 mm thick acrylic sheet, along with a specially designed material fixture, was used to press down the magnetic sheets during measurement. The magnetic shielding properties were measured and compared under various air gap conditions using a near-field magnetic loop probe. The effect of the gaps on the shielding performance of the magnetic sheets was quantitatively evaluated for three different magnetic sheets. The experimental results showed that as the gap thickness increased, NSE tended to improve up to a frequency around 1 GHz, while in the higher frequency range of a few GHz, NSE tended to decrease. The physical background of this phenomenon was explained using an equivalent magnetic circuit represented by reluctances for the structure, where the magnetic sheet is placed above the microstrip line with an air gap. This model helps to elucidate how the presence of the air gap affects the near-field magnetic shielding performance.

Travel Time Estimation for Urban Arterials Based on the Multi-Source Data

Accurate traffic information, such as travel time, becomes more important since it could help provide more efficient traffic management strategies. This paper presents a method for estimating the travel time of segments on urban arterials by leveraging multi-source data from loop detectors and probe vehicles. Travel time is defined into three distinct sections based on floating car trajectories, i.e., accelerating, constant speed, and decelerating. Considering the traffic flow characteristics, different methods are developed using various data for each section. The proposed methodology is validated using field data collected in Shanghai, China. The results validated the proposed method with absolute percentage errors (APEs) of approximately 5% in constrained traffic flow conditions and 10–20% in less constrained traffic flow. The results also show that the proposed method has better performance than the method with loop detector data and another data fusion model. It is expected that the proposed method could help improve traffic management efficiency, such as traffic signal control, by providing more accurate travel time information.

Speed Data Reconstruction: Analyzing and Improving the Accuracy of MFD Estimation Using Loop Detector and Floating Car Data

Accurate MFD estimation is essential for the efficient management of urban road networks. Loop detectors and probe vehicles collect traffic data required for empirical MFD estimation. Different road network coverages of loop detectors and probe vehicles and penetration rates of probe vehicles affect the accuracy of the MFD estimation. The literature calculated the error in the MFD at different road coverage of loop detectors and penetration rates of probe vehicles. However, the error in MFD at different road coverage of probe vehicle was missing and explained the quality of FCD data as the product of probe vehicle road coverage and the square root of their penetration rate. The linear behavior leads to conflicting results and affects the accuracy of the MFD estimation. In this study, the impact of probe vehicle road coverage was initially determined, and a methodology for improving the quality of speed data was developed. Later, the study analyzed the combined effect of road coverage and penetration rate on the MFD estimation and proposed a performance factor for probe vehicle data.The results showed that the error in the MFD is a negative power function of the probe vehicle road coverage, and the intensity of the error is negatively correlated with the penetration rate. The methodology of enhancing the speed data quality improved the quality significantly, notably at lower penetration rates and road coverages of probe vehicles. Further, the proposed performance factor is consistent with the error of the MFD compared to the former approach.

Physics-informed deep learning for traffic state estimation based on the traffic flow model and computational graph method

Traffic state estimation (TSE) is a critical task for intelligent transportation systems. However, it is extremely challenging because the traffic data quality is often affected by the installation position of devices, data collection frequency, interference during the transmission process, etc., thus causing the problem of data sparsity or data missing. To address the issue of traffic state estimation under the scenario of data sparsity, we propose a TSE model that combines the computational graph with physics-informed deep learning (PIDL) methods. Firstly, we apply the computational graph method to determine the parameters of the traffic fundamental diagram. These parameters are embedded into the computational graph framework, and their values are determined through the forward propagation of variables and the backward propagation of errors. Next, we employ the PIDL method to realize TSE (taking the LWR model based on the Greenshields fundamental diagram as an example). The PIDL leverages the advantages of data-driven and model-driven approaches to achieve accurate traffic state estimation. Case studies are conducted using the NGSIM dataset under two sparse data scenarios: loop detectors and probe vehicles. Experimental results demonstrate that PIDL can accurately reconstruct the traffic state of the entire road segment based on partially observed data. Furthermore, compared to pure deep learning methods and other baseline models, PIDL performs better in situations with sparse data, thereby proving the feasibility of integrating domain knowledge with deep learning frameworks. This paper fully acknowledges the issue of data sparsity in TSE and effectively addresses it by applying the PIDL method to achieve precise TSE, which holds significant implications for the control and management of real traffic flow.

A Two-Turn Shielded-Loop Magnetic Near-Field PCB Probe for Frequencies up to 3 GHz.

This paper proposes a novel design of shielded two-turn near-field probe with focus on high sensitivity and high electric field suppression. A comparison of different two-turn loop topologies and their influence on the probe sensitivity in the frequency range up to 3 GHz is presented. Furthermore, a comparison between a single loop probe and a two-turn probe is given and different topologies of the two-turn probe are analyzed and evaluated. The proposed probes were simulated using Ansys HFSS and manufactured on a standard FR4 substrate four-layer printed circuit board (PCB). A measurement setup for determining probe sensitivity and electric field suppression ratio using an in-house made PCB probe stand, vector network analyzer, microstrip line (MSL) and the manufactured probe is presented. It is shown that using a two-turn probe design it is possible to increase the probe sensitivity while minimizing the influence on the probe spatial resolution. The average sensitivity of the proposed two-turn probe compared to the conventional design is increased by 10.1 dB in the frequency range from 10 MHz up to 1 GHz.

Preliminary Analysis of Burn Degree Using Non-invasive Microwave Spiral Resonator Sensor for Clinical Applications.

The European “Senseburn” project aims to develop a smart, portable, non-invasive microwave early effective diagnostic tool to assess the depth(d) and degree of burn. The objective of the work is to design and develop a convenient non-invasive microwave sensor for the analysis of the burn degree on burnt human skin. The flexible and biocompatible microwave sensor is developed using a magnetically coupled loop probe with a spiral resonator (SR). The sensor is realized with precise knowledge of the lumped element characteristics (resistor (R), an inductor (L), and a capacitor (C) RLC parameters). The estimated electrical equivalent circuit technique relies on a rigorous method enabling a comprehensive characterization of the sensor (loop probe and SR). The microwave resonator sensor with high quality factor (Q) is simulated using a CST studio suite, AWR microwave office, and fabricated on the RO 3003 substrate with a standard thickness of 0.13 mm. The sensor is prepared based on the change in dielectric property variation in the burnt skin. The sensor can detect a range of permittivity variations (εr 3–38). The sensor is showing a good response in changing resonance frequency between 1.5 and 1.71 GHz for (εr 3 to 38). The sensor is encapsulated with PDMS for the biocompatible property. The dimension of the sensor element is length (L) = 39 mm, width (W) = 34 mm, and thickness (T) = 1.4 mm. The software algorithm is prepared to automate the process of burn analysis. The proposed electromagnetic (EM) resonator based sensor provides a non-invasive technique to assess burn degree by monitoring the changes in resonance frequency. Most of the results are based on numerical simulation. We propose the unique circuit set up and the sensor device based on the information generated from the simulation in this article. The clinical validation of the sensor will be in our future work, where we will understand closely the practical functioning of the sensor based on burn degrees. The senseburn system is designed to support doctors to gather vital info of the injuries wirelessly and hence provide efficient treatment for burn victims, thus saving lives.

Development of a Magnetic Field Probe for Near-Field Measurement

Current electronic circuits are characterised by their compact design of miniaturisation; however, such a feature has also contributed to the rise of electromagnetic compatibility issues. This study introduces a technique to develop a magnetic field probe for near-field measurement. The probe aims at performing the debugging of electromagnetic interference on an integrated circuit. Certain processes, such as designing a microstrip line in simulation and a prototype for the validation as well as developing a functional magnetic field probe for near-field scanning and calibration measurement to determine the probe factor, have been performed under a frequency domain. The microstrip line model is built both as a simulation and a real prototype, and the handcrafted loop probe is developed with the same diameter as a commercial probe. Calibration measurement is set-up with a vector network analyser (VNA) to capture the magnetic field on radiated plane. The performance of the handcrafted probe in terms of measuring the configuration of the H-field source on the scattering region is conducted. Two types of measurements which are S11 and S21 will be conducted using a VNA. The comparison of the S11 and S21 results against simulation results are plotted using MATLAB. S11 represents the return loss of the microstrip line while S21 represents the power received by the magnetic field probe relative to power input to the microstrip line. The results of the S11 parameter indicate that the customised microstrip line board has a similar waveform pattern that matches the one on the simulation model. The S21 results for the handcrafted probe revealed that it can only function well up to the frequency of 2.644 GHz, the abnormal result obtained after the frequency mentioned. Some factors may have affected the results, such as material loss, fabrication tolerance and interferences from other devices. Therefore, calibration measurement is conducted under a less reflected radiation environment, and the designated substrate should be a perfect material correlated with the measurement.

Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models

Traffic state estimation (TSE) reconstructs the traffic variables (e.g., density or average velocity) on road segments using partially observed data, which is important for traffic managements. Traditional TSE approaches mainly bifurcate into two categories: model-driven and data-driven, and each of them has shortcomings. To mitigate these limitations, hybrid TSE methods, which combine both model-driven and data-driven, are becoming a promising solution. This paper introduces a hybrid framework, physics-informed deep learning (PIDL), to combine second-order traffic flow models and neural networks to solve the TSE problem. PIDL can encode traffic flow models into deep neural networks to regularize the learning process to achieve improved data efficiency and estimation accuracy. We focus on highway TSE with observed data from loop detectors and probe vehicles, using both density and average velocity as the traffic variables. With numerical examples, we show the use of PIDL to solve a popular second-order traffic flow model, i.e., a Greenshields-based Aw-Rascle-Zhang (ARZ) model, and discover the model parameters. We then evaluate the PIDL-based TSE method using the Next Generation SIMulation (NGSIM) dataset. Experimental results demonstrate the proposed PIDL-based approach to outperform advanced baseline methods in terms of data efficiency and estimation accuracy.

Improved grey system models for predicting traffic parameters

In transportation applications such as real-time route guidance, ramp metering, congestion pricing and special events traffic management, accurate short-term traffic flow prediction is needed. For this purpose, this paper proposes several novel online Grey system models (GM): GM(1,1|cos(ωt)), GM(1,1|sin(ωt),cos(ωt)), and GM(1,1|e-at,sin(ωt),cos(ωt)). To evaluate the performance of the proposed models, they are compared against a set of benchmark models: GM(1,1) model, Grey Verhulst models with and without Fourier error corrections, linear time series model, and nonlinear time series model. The evaluation is performed using loop detector and probe vehicle data from California, Virginia, and Oregon. Among the benchmark models, the error corrected Grey Verhulst model with Fourier outperformed the GM(1,1) model, linear time series, and non-linear time series models. In turn, the three proposed models, GM(1,1|cos(ωt)), GM(1,1|sin(ωt),cos(ωt)), and GM(1,1|e-at,sin(ωt),cos(ωt)), outperformed the Grey Verhulst model in prediction by at least 65%, 16% and 11%, in terms of Root Mean Squared Error, and by 82%, 58% and 42%, in terms of Mean Absolute Percentage Error, respectively. It is observed that the proposed Grey system models are more adaptive to location (e.g., perform well for all roadway types) and traffic parameters (e.g., speed, travel time, occupancy, and volume), and they do not require as many data points for training (4 observations are found to be sufficient).

A dynamic approach to predict travel time in real time using data driven techniques and comprehensive data sources

Having access to accurate travel time is of great importance for both highway network users and traffic engineers. The travel time which is currently reported on highways is usually estimated by employing naïve methods and using limited sources of data. This could result in unreliable and inaccurate travel time prediction which causes inconvenience for travelers. Despite abundant effort on predicting travel time, the dynamic nature of travel time was neglected in many studies. Therefore, the main objective of this study is to predict the short-term travel time of highways dynamically, using multiple data sources including loop detectors, probe vehicles, weather condition, geometry, accidents, road works, special events, and sun glare. To this end, three well known machine learning methods, Artificial Neural Network, K-Nearest Neighbors and Random Forest, are employed to predict and compare short-term travel time for prediction horizons of 5 min in one hour ahead. The results confirm satisfying performance of models, especially Random Forest, in short-term travel time prediction. Our analyses also show that traffic variables, especially occupancy, are the most effective variables in predicting the travel time. Finally, we proposed an approach for a dynamic prediction of travel time for a corridor. Interestingly, the results of our dynamic approach improved the accuracy of travel time prediction over the snapshot travel time prediction.

Calibration of the loop probe for the near-field measurement

AbstractAccurate near-field measurements for either deterministic or stochastic electromagnetic fields characterization require a relevant process that removes the influence of the probes, transmission lines, and measurement circuits. The main part of the experimental work presented here is related to a calibration procedure of a test setup consisting of a microstrip test structure and a scanning loop probe. The calibration characteristic, obtained by comparing measured and simulated results, is then used to convert the measured voltage into the magnetic field across and along the microstrip line at the specific height above it. By performing the measurements and simulations of the same test structure with the loop probe in the presence of an additional scanning probe, the influence of the additional probe to the measured output is thoroughly investigated and relevant corrections are given. These corrections can be important when two-point correlation measurement is required, especially in scanning points when two probes are mutually close.

Evaluation of Rationally Designed Label-free Stem-loop DNA Probe Opening in the Presence of miR-21 by Circular Dichroism and Fluorescence Techniques

The characteristic features of stem-loop structured probes make them robust tools to detect targets with high sensitivity and selectivity. The basis of the hairpin based sensors operation is a conformational change that occurs upon hybridization of target with stem-loop probe. The design of the stem-loop probe has an important role in target recognition. Therefore, we designed a label-free stem loop probe for targeting miR-21 as a cancer biomarker investigated by web-based tools; its thermodynamic parameters obtained by thermal UV spectroscopy. The efficiency of stem-loop structure opening in the presence of target and non-target sequences was evaluated by fluorescence spectroscopy and circular dichroism spectro-polarimetry. The results showed that the target sequence opens the structure of hairpin efficiently in comparison to non-target sequences. To optimize the stem-loop hybridization to its target, the buffer ionic strength was changed by adding different concentrations of NaCl, KCl and MgCl2. It was shown that buffering conditions have a significant role in loop structure opening and its optimization, led to an increase in sensitivity detection and have improved LOD from 60 pM to 45 pM.

Chalcogenide fiber loop probe for the mid-IR spectroscopy of oil products.

A theoretical approach based on the electromagnetic theory of optical fibers has been applied in the analysis of the evanescent modes of a chalcogenide fiber bend used as a probe in a fiber-based spectroscopic sensor, by the example of the detection of small amounts of an antigel additive in a diesel fuel. The absorbance of the loop probe calculated for each mode was compared with the results of spectrometer-based measurements. The role of the higher-order evanescent modes of a fiber bend has been revealed. The efficiency of using a loop probe has been shown to depend on conditions of light launching into the probe.

Measurement of Transient Magnetic Field caused by ESD between Spherical Electrodes using a Shielded Loop Probe and Current Estimation of EM Radiation Model

Measurement of Transient Magnetic Field caused by ESD between Spherical Electrodes using a Shielded Loop Probe and Current Estimation of EM Radiation Model

Traffic State Estimation in Heterogeneous Networks with Stochastic Demand and Supply: Mixed Lagrangian–Eulerian Approach

A network fundamental diagram (NFD) represents the relationship between network-wide average flow and average density. Network traffic state estimation to observe NFD when congestion is heterogeneously distributed, as a result of a time-varying and asymmetric demand matrix, is a challenging problem. Recent studies have formulated the NFD estimation problem using both fixed measurements and probe trajectories. They are often based on a given ground-truth NFD for a single day demand. Stochastic variations in network demand and supply may significantly affect the approximation of an NFD. This study proposes a modified framework to estimate network traffic states to observe NFD while capturing the stochasticity in transportation networks. A mixed integer problem with non-linear constraints is formulated to address stochasticity in the NFD estimation problem. To solve this Nondeterministic Polynomial-hard (NP-hard) problem, a solution algorithm based on the simulated annealing method is applied. The problem is formulated and the solution algorithm is implemented to find an optimal configuration of loop detectors and probe vehicles to estimate the NFD of the Chicago downtown network and capture its day-to-day variations, considering a given available budget. Ground-truth NFDs and estimated NFDs based on a subset of loop detectors and probe vehicles are calculated using a simulation-based dynamic traffic assignment model, which is the best surrogate available to replicate real-world conditions. The main contribution of this study is to capture stochasticity in the demand and supply sides to find a more robust subset of links and trajectories to be acquired for the NFD estimation.

Constructing a Network Fundamental Diagram: Synthetic Origin–Destination Approach

The network fundamental diagram (NFD) is increasingly used in traffic monitoring and control. One obstacle to a wider application of NFDs in network control is the difficulty of obtaining data from all vehicles traveling in the network to construct an accurate NFD. One solution is to estimate the NFD using data from only a fraction of vehicles (i.e., probe vehicles), where the probe vehicle market penetration rate (MPR) needs to be estimated. A previous study conducted by the authors demonstrated that a distance or time-weighted harmonic mean was needed to estimate the flow- and density-based MPRs, respectively, using a pairing k-mean clustering approach. This paper proposes another approach that utilizes probe vehicle and observed link volume data to estimate the MPR. A heuristic model is proposed to identify the optimum locations from which to collect link traffic volume data for use in the MPR estimation. The estimated MPR can then be used to construct the NFD. Results show that these models can accurately estimate the NFD with limited probe vehicle and link traffic volume data. Accordingly, the models can be used in the field to estimate the NFD using readily available loop detector and probe vehicle data. The ideal locations for traffic volume data collection can also be proactively chosen to generate optimum estimation results. As the models proposed here show no significant gains with an increased magnitude of collected data after a certain threshold, they will be helpful, particularly when large-scale data collection is not affordable or realistic.

Rapid detection of different DNA analytes using a single electrochemical sensor

A unique platform for the detection of two distinct DNA sequences by using one single electrochemical sensor was developed. The sensor consists of a universal stem loop probe attached to a solid surface, and two analyte-specific adaptor strands. These adaptor strands hybridize to a nucleic acid analyte and provide both highly specific recognition and high binding affinity. The universal probe can be regenerated by a simple and quick rinse with urea and water. As a proof of concept, we differentiated long target sequences of Zika (141 nt.) and Dengue (114 nt.) viruses that contain secondary structures. The proposed sensor provides a platform for rapid and cost-efficient detection of potentially any DNA or RNA sequence using a single electrochemical sensor.

Fast Far-Field Estimation Method by Compact Single Cut Near-Field Measurements for Electrically Long Antenna Array

This paper proposes a fast far-field estimation method of electrically long antenna in compact measurement space. The presented method estimates a far-field pattern on an orthogonal cut plane from the linear equivalent electric current and circular current distribution. The equivalent electric current data is measured in a reactive near-field region using a small loop probe. In the proposed method, the linear equivalent current distribution is extended by a simple numerical extrapolation formula for improving the accuracy of the estimated far field in the broadside direction. In the horizontal plane, the far-field pattern is calculated from both the circular equivalent electric and the magnetic distribution approximating equivalent magnetic current with equivalent electric current. We confirmed the validity of the proposed method from the numerical simulation and measurements results. The method allows accurate directivity measurement in a small space in about a few minutes.

Real-time Detection and Monitoring of Loop Mediated Amplification (LAMP) Reaction Using Self-quenching and De-quenching Fluorogenic Probes

Loop-mediated isothermal amplification (LAMP) is an isothermal nucleic acid amplification (iNAAT) technique known for its simplicity, sensitivity and speed. Its low-cost feature has resulted in its wide scale application, especially in low resource settings. The major disadvantage of LAMP is its heavy reliance on indirect detection methods like turbidity and non-specific dyes, which often leads to the detection of false positive results. In the present work, we have developed a direct detection approach, whereby a labelled loop probe quenched in its unbound state, fluoresces only when bound to its target (amplicon). Henceforth, referred to as Fluorescence of Loop Primer Upon Self Dequenching-LAMP (FLOS-LAMP), it allows for the sequence-specific detection of LAMP amplicons. The FLOS-LAMP concept was validated for rapid detection of the human pathogen, Varicella-zoster virus, from clinical samples. The FLOS-LAMP had a limit of detection of 500 copies of the target with a clinical sensitivity and specificity of 96.8% and 100%, respectively. The high level of specificity is a major advance and solves one of the main shortcomings of the LAMP technology, i.e. false positives. Self-quenching/de-quenching probes were further used with other LAMP primer sets and different fluorophores, thereby demonstrating its versatility and adaptability.

A homogenous electrochemical sensing DNA sensor by using bare Au electrode based on potential-assisted chemisorption technique

A novel electrochemical DNA sensing strategy based on potential-assisted Au-S deposition was presented in this article. Specifically, the 3′ terminal of double hairpin DNA probe was used to modify ferrocene (Fc), while the 5′ terminal was employed to modify thiol. The loop probe could prevent thiol from arriving on the surface of bare gold electrode under the action of an applied potential, without hybridization with the target DNA. In comparison, the stem-loop structure of probe would be opened after hybridization with the target DNA, which would lead to the formation of a hairpin DNA structure. This had facilitated the contact of thiol with electrode, resulting in potential-assisted Au-S self-assembly. Additionally, electrochemical signals of Fc were measured by differential pulse voltammetry (DPV), which were subsequently used for target quantitative detection. Such strategy has established a detection limit of down to 3.4 pM and an inherently high specificity in detecting even for a single-base mismatch.