Proposed Measurement Scheme Research Articles

Digital Homodyne and Heterodyne Detection for Stationary Bosonic Modes.

Homo- and heterodyne detection are fundamental techniques for measuring propagating electromagnetic fields. However, applying these techniques to stationary fields confined in cavities poses a challenge. As a way to overcome this challenge, we propose to use repeated indirect measurements of a two-level system interacting with the cavity. We demonstrate numerically that the proposed measurement scheme faithfully reproduces measurement statistics of homo- or heterodyne detection. The scheme can be implemented in various physical architectures, including circuit quantum electrodynamics. Our results pave the way for implementation of quantum algorithms requiring linear detection of stationary modes, including quantum verification protocols.

Nanoparticle Interferometer by Throw and Catch

Matter wave interferometry with increasingly larger masses could pave the way to understanding the nature of wavefunction collapse, the quantum to classical transition, or even how an object in a spatial superposition interacts with its gravitational field. In order to improve upon the current mass record, it is necessary to move into the nanoparticle regime. In this paper, we provide a design for a nanoparticle Talbot–Lau matter wave interferometer that circumvents the practical challenges of previously proposed designs. We present numerical estimates of the expected fringe patterns that such an interferometer would produce, considering all major sources of decoherence. We discuss the practical challenges involved in building such an experiment, as well as some preliminary experimental results to illustrate the proposed measurement scheme. We show that such a design is suitable for seeing interference fringes with 106 amu SiO2 particles and that this design can be extended to even 108 amu particles by using flight times below the typical Talbot time of the system.

BPath-RO: A Performance- and Area-Efficient In Situ Delay Measurement Scheme for Digital IC

Circuit delays are increasingly sensitive to process, voltage, temperature, and aging (PVTA) variations, severely impacting circuit performance. Accurate measurement of circuit delay is essential. However, the additional hardware structures for measuring circuit delay add to the critical path delay. To address this issue, this paper proposes a bypass-based ring oscillator (BPath-RO) that reduces the impact on the critical path delay by moving the added measurement control structures to the bypass. The proposed measurement scheme requires only two transistors inserted into the critical path, which is more conducive to engineering change order (ECO). Measurement simulation experiments were performed on representative critical paths of the ISCAS’89 s298 and ITC’99 b15 benchmark circuits. The experimental results show that, in comparison with the existing architectures, the Bpath-RO delay measurement scheme improves the circuit performance by an average of 13.81% (s298) and 3.47% (b15) and reduces the hardware overhead by up to 70% for each path.

Dual beam polarization interferometry for roll angular displacement measurement

This study presents a polarization interferometry-based technique for the measurement of roll angular displacement with high resolution and excellent stability. The proposed technique uses a polarization camera to detect the phases of a light beam passed through a birefringent crystal. Variations in the phase difference are used to determine the angular displacement of the birefringent crystal. The dual beam configuration of the proposed measurement scheme helps to overcome the effects of fluctuating incident angular displacement on measurements of roll angular displacement. In experiments, the proposed system achieved resolution of 0.58 arcsec within a 10-degree measurement range. Disturbance from the incident angular displacement was much lower than that of our previous technique measurement methods. This study also investigated the occurrence of nonlinear periodic system errors. The proposed technique offers several advantages over existing methods, including higher resolution, wider range measurement capabilities, and robustness against external disturbance.

Single-port measurement scheme: An alternative approach to system calibration for 5G massive MIMO base station conformance testing

To calibrate the test system of fifth-generation (5G) massive multiple-input multiple-output (MIMO) base station (BS), this paper proposes a promising single-port measurement scheme, which is more cost-effective and convenient than conventional scheme. The conventional scheme first requires the disconnection of the measurement equipment from the BS test system, and then uses a costly vector network analyzer (VNA) for the system calibration measurement, which is cumbersome, slow, and inconvenient. Instead, the proposed scheme can conduct the system calibration with low cost directly based on the existing measurement equipment in the original test system, i.e. no need for the extra expensive VNA. The accuracy performance of the proposed scheme is evaluated by the frequency response error measured between the proposed and the conventional schemes, e.g. with an amplitude error range of [−0.45, 0.14] dB and a phase error range of [−0.2∘, 2.5∘]. Therefore, the superiority is that the proposed single-port scheme in this paper accomplished the equivalent measurement as the conventional scheme, and further reduced the cost and inconvenience for the test industry. Finally, the reduced cost and complexity by the proposed measurement scheme is beneficial to both the industry and academic scientific world in terms of faster and more efficient testing as well as increased reproducibility of research findings.

Express Analysis of Vertical Distribution of <sup>137</sup>Cs to Assess the Rates of Erosion and Accumulation Processes in the Zone of Intense Radioactive Contamination

Field gamma-sensing under conditions of intense radioactive contamination has shown high productivity in studying the migration sediment associated pollutants via erosion and accumulation processes. The purpose of the presented work is to evaluate the applicability of compact gamma detectors without a collimator that narrows the area of gamma radiation registration to determine the vertical distribution of Chernobyl-derived 137Cs. Accum-ulative strata of sediments formed within the “Plavsk radioactive hot spot” in the southern part of the Tula region were chosen as the object of research. By comparing the obtained vertical distribution of the gamma quantum counting rate and the actual distribution of 137Cs deposits, the resulting distortions in the estimation of the relative vertical distribution of radionuclides in the soil were considered, limiting the applicability of the proposed measurement scheme. The main prospects for further application of the gamma-sensing technique of soil cover at relatively high concentrations of radionuclides in accumulated sediments were identified.

Quantum-Inspired Multi-Parameter Adaptive Bayesian Estimation for Sensing and Imaging

It is well known in Bayesian estimation theory that the conditional estimator attains the minimum mean squared error (MMSE) for estimating a scalar parameter of interest. In quantum, e.g., optical and atomic, imaging and sensing tasks the user has access to the quantum state that encodes the parameter. The choice of a measurement operator, i.e. a positive-operator valued measure (POVM), leads to a measurement outcome on which the aforesaid classical MMSE estimator is employed. Personick found the optimum POVM that attains the MMSE= over all possible physically allowable measurements and the resulting MMSE [1]. This result from 1971 is less-widely known than the quantum Fisher information (QFI), which lower bounds the variance of an unbiased estimator over all measurements without considering any prior probability. For multi-parameter estimation, in quantum Fisher estimation theory the inverse of the QFI matrix provides an operator lower bound on the covariance of an unbiased estimator, and this bound is understood in the positive semidefinite sense. However, there has been little work on quantifying the quantum limits and measurement designs, for multi-parameter quantum estimation in a Bayesian setting. In this work, we build upon Personick's result to construct a Bayesian adaptive (greedy) measurement scheme for multiparameter estimation. We illustrate our proposed measurement scheme with the application of localizing a cluster of point emitters in a highly sub-Rayleigh angular field-of-view, an important problem in fluorescence microscopy and astronomy. Our algorithm translates to a multi-spatial-mode transformation prior to a photon-detection array, with electro-optic feedback to adapt the mode sorter. We show that this receiver performs superior to quantum-noise-limited focal-plane direct imaging.

Nonlinear polarization tensor measurement with a vectorial complex field in second-harmonic-generation microscopy

Polarization-sensitive second-harmonic-generation microscopy (pSHGM) allows one to determine the molecular orientation of harmonophores. Conventional point scanning based pSHGM is time consuming and subject to the assumption of the cylinder symmetry of the sample. Here, we propose a wide-field pSHGM measurement scheme that is able to measure the second-order nonlinear polarization tensor. The measurement scheme is based on first-order Born approximation, from which the relation between incident fundamental wave, second harmonic wave, and nonlinear polarization tensor has been established. It suggests that the polarization tensor can be solved by measuring the vectorial second harmonic complex fields corresponding to three independent polarization states of the incident fundamental wave. An experiment on measuring the supramolecular orientations, in terms of their symmetric axis, of myosin in rat muscle tissue has been carried out to demonstrate the proposed measurement scheme. Benefiting from the ability of recording the second harmonic signal from different positions parallelly, the proposed method possesses a higher imaging frame rate compared to point scanning based pSHGM. With the present configuration, it takes 0.01 s to acquire a 128\ifmmode\times\else\texttimes\fi{}128 pixels image, which is mainly limited by the excitation power density for wide-field illumination. For the same data throughput using pixel-by-pixel scanning, 0.16 s acquisition time is required for a pixel dwell time of 10 \textmu{}s. Having the ability of wide-field imaging and polarization measurement, the present work also lays a foundation for high-resolution SHG microscopy using computed tomography.

A Deep Neuronal Network-Based Sensor for the Detection of Oxygen inside the Carbonization Furnace

Agricultural production generates biological surpluses that can be used to produce additional products through thermal and/or chemical transformations, such as the carbonization of vegetables. The oxygen on the vegetable material during the carbonization process inside a furnace is an important parameter that determines not only the success of the process but also the quality of the final product. It is difficult to measure the oxygen inside the furnace in real time, partly because of the working environment of the sensor, and partly because of the operating characteristics of the furnace (continuous rotation on its axis). The goal of this project is to develop a reliable measurement system capable of operating in real-time. For the continuous and precise detection of the temperature inside the furnaces of a carbonization plant, we propose a method based on the characterization of the image inside the furnace using a deep neuronal network. First of all, the images of the interior of the furnace are captured through a digital camera in front of the material and the axis of rotation of the furnace. Then, the area of interest in each frame of the video is determined by image processing. Oxygen content information is then obtained using a deep model trained for the same furnace with pattern oxygen level sensors. Experiments conducted on actual operating conditions of the furnace demonstrate that the proposed method for estimating the working oxygen provides reliable data for the control of plant operation. The proposed measurement scheme demonstrated high reliability against the many changes present inside the furnace, also, its low computational consumption makes it a viable strategy for embedded implementation and operation in real-time.

Measurement of pulsed laser welding penetration based on keyhole dynamics and deep learning approach

The keyhole instability is a key concern in laser deep-penetration welding of high reflectivity materials, potentially impacting the penetration status and weld quality. Monitoring and control the keyhole behavior still remain a great challenge for obtaining a desired welded joint. For the pulsed laser welding of thin-sheet aluminum alloy, an active visual monitoring system was established to systematically probe the dynamic keyhole behavior from multi-view sensing. Combining with the image processing method and process analysis, the keyhole surface area and depth were extracted to quantify the keyhole formation dynamics under different welding conditions. Furthermore, a data-driven deep learning model with hyperparameter optimization was constructed to identify different penetration states and it has a high accuracy and good reliability. The experiment results show that our proposed measurement scheme based on multi-view monitoring and deep learning approach could guide the development of real-time control of the pulsed laser welding process.

Interaction of an array of single-electron quantum dots with a microcavity field with allowance for Coulomb correlations

We consider an ordered array of semiconductor single-electron quantum dots located in the antinode of a mode of a microcavity based on a two-dimensional photonic crystal. The influence of Coulomb effects on the energy exchange between quantum dots and the mode is studied in arrays for different numbers of dots. The stability of resonant electron – photon dynamics is analysed as a function of technological deviations of the dot parameters caused by the imperfection of the manufacturing procedure. Using the data of numerical simulation of the spectroscopic response of the system, criteria are formulated for using such a structure as a detector of localised charges. The parameters of the photonic crystal are chosen, which make it possible to implement the proposed measurement scheme.

Range-Extended Confocal Chromatic Sensor System for Double-Sided Measurement of Optical Components With High Lateral Resolution

This paper presents a strategy to break the tradeoff between the thickness measuring range of a confocal chromatic sensor (CCS) and its lateral imaging resolution. By combining the sensor with a linear stage, the thickness measuring range is no longer limited by chromatic dispersion, but by the focusing power of the sensor optics. The principle is demonstrated with a prototype setup that utilizes a CCS with a spot size of 6 μm and a spec-sheet thickness measuring range of 300 μm. By implementing the proposed mechatronic approach, a successful extension of the measuring range by a factor of 17 is achieved while preserving the lateral resolution of 2.46 μm for thickness measurements. The proposed measurement scheme is validated by measuring lateral surface features on a 1.2mm thick lenslet array from the backside.

Quantum sensing of the electron electric dipole moment using ultracold entangled Fr atoms

We propose a method to measure the electron electric dipole moment (eEDM) using ultracold entangled francium (Fr) atoms trapped in an optical lattice, yielding an uncertainty below the standard quantum limit. Among the alkali atoms, Fr offers the largest enhancement factor to the eEDM. With a Fr based experiment, quantum sensing using quantum entangled states could enable a search for the eEDM at a level below 10−30 ecm. We estimate statistical and systematic errors attached to the proposed measurement scheme based on this quantum sensing technique. A successful quantum sensing of the eEDM could enable the exploration of new physics beyond the standard model of particle physics.

Non-intrusive DC voltage measurement based on resonant electric field microsensors

Based on the resonant electric field microsensors with coplanar electrodes, a novel non-intrusive measurement scheme of DC power-line voltage suitable for actual product is presented in this paper. Compared with using microsensors directly without some protection, employing the proposed scheme can avoid the influence of humidity and electromagnetic disturbance on non-contact voltage measurement. A theoretical model is developed to analyze the influence of the structural parameters of the proposed measurement scheme on its sensitivity and to predict the voltage response. Furthermore, the theoretical analysis reveals that the proposed scheme obtains the DC power-line voltage non-intrusively by measuring the voltage of a floating electrode. A prototype of the non-intrusive measurement apparatus of DC power-line voltage has been developed, calibrated, and tested. The prototype shows a favorable linear response characteristic with a linearity of 0.86%. And the experimental results are in good agreement with the theoretical results. The maximum relative deviation noticed is −2.16% over a range of voltages from −1000 V to 1000 V.

Precise infrared thermometry with considering background radiation for gas turbine air cooling application

The turbine inlet temperature keeps increasing as high-efficient gas turbines have been developed for the last decades. The increase of turbine inlet temperature makes the turbine blades being exposed in thermally severe conditions, leading to thermal damage. The thermal management with advanced cooling techniques is thus necessary to protect turbine blades from thermal deformations. For properly evaluating the air-cooling effects on turbine blades, the more accurate surface temperature measurement must be preceded. Infrared thermometry has often been employed for high-temperature turbine blades due to its convenience. However, background radiation must be carefully considered if the surrounding environment is at a higher temperature than the surface to be measured, as in the case of a gas turbine blade. In addition, the spectral emittance of an object should also be known a priori. In this study, we propose an accurate measurement scheme of infrared thermometry by properly considering the background radiation from the high-temperature environment as well as the spectral emittance of an object. The accuracy of infrared thermometry was examined for two exemplary surfaces with low and high emittance in a high-temperature wind tunnel. The proposed measurement scheme was found to be in good agreement with the thermocouple measurements within 5% for both surfaces.

An Improved Controlled-Frequency-Band Impedance Measurement Scheme for Railway Traction Power System

Railway traction power system (TPS) impedance affects the stability and operation performance of connected power electronics devices (i.e., electric trains) with the closed-loop control system. In order to identify the detailed TPS impedance, the wideband-response-based impedance measurement technique has become attractive for its fast execution time and accuracy, especially the chirp-based technique for its bilateral controllability of injected wideband disturbance. However, for the reason that the disturbance device is directly powered by the measured system, the fundamental component separates some harmonic clusters in the frequency domain and leads to unsatisfactory uniformity of the injected wideband disturbance. In this article, a segmented-chirp-based technique is proposed to eliminate this phenomenon caused by the fundamental component, where the chirp signal is segmented and carried on the fundamental only when the amplitude is relatively smooth on top (or bottom), which facilitates a uniform wideband distribution to keep a good measurement accuracy. Meanwhile, the presented modular disturbance circuit with a flexible topology can better adapt to the various industrial engineering conditions. Finally, the detailed measurement results based on the simulation and downscaled experimental system validate the effectiveness of the proposed measurement scheme.

A Blockchain-Based Security Traffic Measurement Approach to Software Defined Networking

Software Defined Networking (SDN) architecture separates control plane and data plane, making network flexible and programmable. Since the large number of devices connected to the Internet of things (IoT) networks, the SDN-based network architecture makes the deployment and configuration of IoT much easier. In the IoT network, the fine-grained network traffic is critical to network management, then we propose a novel scheme to measure the fine-grained network traffic in the SDN-based IoT networks. In SDN-based IoT networks, the controller is very easy to be attacked, we introduce the blockchain technology into the measurement framework to ensure the security and consistency of the statistics. To measure flow traffic with low overhead and high accuracy, we collect the statistics of coarse-grained traffic of flows and fine-grained traffic of links, and model the network traffic as an ARIMA model and forecast the network traffic with the coarse-grained measurement of flows. Then, we propose an objective function to decrease the estimation errors. Due to the objective function is an NP-hard problem, we present a heuristic algorithm to obtain the optimal solution of the fine-grained measurement. Finally, we conduct some simulations to verify the validity of the proposed measurement scheme. Simulation results show that our approach is feasible and effective.

A New Network Traffic Prediction Approach in Software Defined Networks

Software Defined Networking (SDN) is a centralized management network architecture, the handling commands of flows are designed in the controller and installed into flow tables of OpenFlow switches. SDN has obtained a lot of attention due to flexible and scalable. Network traffic prediction is very important for load balancing and network planning. It is implemented to improve the quality of service of the operators. In this paper, we propose a network traffic prediction method based on Short Time Fourier Transform (STFT) and traffic modeling. We use STFT to decompose network traffic into high-frequency components and low-frequency components. The low-frequency component of network traffic describes the smoothness and long-range correlation of network traffic, we model it as Auto-regression (AR) model. Otherwise, the high-frequency component of the network traffic fluctuates strongly which shows the randomness of the network traffic, we model the network traffic as an exponential distribution. However, since the prediction error of network traffic model is large, we propose an optimization function to optimize the predictions of network traffic to reduce the errors. Finally, we conduct some simulations to verify the proposed measurement scheme. From simulations, our proposed prediction method outperforms WABR and PCA.

An intelligent optimization‐based traffic information acquirement approach to software‐defined networking

AbstractInternet of things (IoT) is a global information infrastructure that supports access to thousands of monitoring devices and user terminals. A large amount of monitoring data generated by IoT is integrated to cloud computing through the network to improve the quality of life of citizens. Fine‐grained and accurate traffic information is important for IoT network management. Software‐defined networking (SDN) is a centralized control plane as a logical control center, making network management more flexible and efficient. Then, we collect fine‐grained traffic information in SDN‐based IoT networks to improve network management. To acquire the traffic information with low overhead and high accuracy, first, we collect the statistics of coarse‐grained traffic of flows and fine‐grained traffic of links, and then we utilize the intelligent optimization methods to estimate the network traffic. To improve the granularity and accuracy of the acquired traffic information, we construct an optimization function with constraints to decrease the estimation errors. As the optimization function of traffic information is a non‐deterministic polynomial‐hard problem, we present a heuristic algorithm to obtain the optimal solution of the fine‐grained measurement. Finally, we conduct some simulations to verify the proposed measurement scheme. Simulation results show that our approach can improve the granularity and accuracy of traffic information with intelligent optimization methods.

РАЗРАБОТКА АППАРАТНОГО И ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ БЕСКОНТАКТНОГО ИЗМЕРИТЕЛЯ ЛИНЕЙНЫХ ПЕРЕМЕЩЕНИЙ И ВИБРАЦИЙ НА ОСНОВЕ МНОГОПОЛЮСНОГО РЕФЛЕКТОМЕТРА

The paper describes advantages and disadvantages of the approach to measuring parameters of vibration using contact sensors, i.e. sensors having a mechanical contact with researched object. Several contactless measuring methods were analyzed: ultrasonic, radio wave, and optical. The contactless radio wave displacement gauge based on a classical multi-port reflectometer has been described. A new contactless measuring method of object displacement based on using multi-port reflectometer with frequency down conversion unit is proposed. Scheme of this meter and its basic equations are given. The advantages of proposed measurement scheme are discussed. The optimal statistical algorithms for measuring displacement of a surface under test is developed, which consists in optimal processing of the output signals of frequency down conversion unit by maximum likelihood method. The proposed measurement scheme in combination with developed optimal algorithm for processing information allows one to create a relatively inexpensive and precision meter of displacement and vibration parameters, which is capable to measure coordinates of researched object having an arbitrary shape with submillimeter accuracy and working under conditions of poor optical visibility and high temperatures. The results of carried out computer simulation of measurement process confirmed the theoretical conclusions.