搜索结果：measurement

Accurate and efficient modeling of indoor wireless signal propagation is crucial for the deployment of next-generation Wi-Fi. This paper presents a digital twin-based measurement system that integrates real-world 3D environment reconstruction with deterministic ray tracing for physically grounded electromagnetic modeling. Building geometry is obtained through LiDAR scanning, followed by object segmentation and assignment of ITU-R standard material parameters. The propagation process is simulated with a GPU-accelerated ray-tracing engine that generates path-level channel attributes, including delay, power, angular dispersion, and Ricean K-factor. Under identical runtime constraints, the proposed system is evaluated against a commercial measurement simulator, demonstrating up to 21 dB higher path gain and consistently improved signal-to-interference-plus-noise ratio in line-of-sight conditions. Additionally, experiments against onsite RSSI measurements confirm a high spatial correlation of 0.98 after calibration, proving the system's fidelity in real-world settings. Furthermore, coverage analysis across 2.4 GHz, 5 GHz, and 6 GHz bands demonstrates the capability of system to model fr

In camera measurement systems, specialized equipment such as telecentric lenses is often employed to measure parts with narrow tolerances. However, despite the use of such equipment, measurement errors can occur due to mechanical and software-related factors within the system. These errors are particularly evident in applications where parts of different diameters are measured using the same setup. This study proposes two innovative approaches to enhance measurement accuracy using multiple known reference parts: a conversion factor-based method and a pixel-based method. In the first approach, the conversion factor is estimated from known references to calculate the diameter (mm) of the unknown part. In the second approach, the diameter (mm) is directly estimated using pixel-based diameter information from the references. The experimental setup includes an industrial-grade camera and telecentric lenses. Tests conducted on glass samples (1-12 mm) and metal workpieces (3-24 mm) show that measurement errors, which originally ranged from 13-114 micrometers, were reduced to 1-2 micrometers using the proposed methods. By utilizing only a few known reference parts, the proposed approach en

We show how the effects of large numbers of measurements on many-body quantum ground and thermal states can be studied using Quantum Monte Carlo (QMC). Density matrices generated by measurement in this setting feature products of many local non-unitary operators, and by expanding these density matrices as sums over operator strings we arrive at a generalized stochastic series expansion (SSE). Our `post-measurement SSE' is based on importance sampling of operator strings contributing to a measured thermal density matrix. We demonstrate our algorithm by probing the effects of measurements on the spin-$1/2$ Heisenberg antiferromagnet on the square lattice. Thermal states of this system have \SU{2} symmetry, and at first we preserve this symmetry by measuring \SU{2} symmetric observables. We identify classes of post-measurement states for which correlations can be calculated efficiently, as well as states for which \SU{2} symmetric measurements generate a QMC sign problem when working in any site-local basis. For the first class, we show how deterministic loop updates can be leveraged. Using our algorithm we demonstrate the creation of long-range Bell pairs and symmetry-protected topol

The fusion of non-Abelian anyons or topological defects is a fundamental operation in measurement-only topological quantum computation. In topological superconductors, this operation amounts to a determination of the shared fermion parity of Majorana zero modes. As a step towards this, we implement a single-shot interferometric measurement of fermion parity in indium arsenide-aluminum heterostructures with a gate-defined nanowire. The interferometer is formed by tunnel-coupling the proximitized nanowire to quantum dots. The nanowire causes a state-dependent shift of these quantum dots' quantum capacitance of up to 1 fF. Our quantum capacitance measurements show flux h/2e-periodic bimodality with a signal-to-noise ratio of 1 in 3.7 $μ$s at optimal flux values. From the time traces of the quantum capacitance measurements, we extract a dwell time in the two associated states that is longer than 1 ms at in-plane magnetic fields of approximately 2 T. These results are consistent with a measurement of the fermion parity encoded in a pair of Majorana zero modes that are separated by approximately 3 $μ$m and subjected to a low rate of poisoning by non-equilibrium quasiparticles. The large

Particle physics and radio-frequency (RF) superconductivity have driven each other on since the 1970s. The unique properties of superconductors (SC) have been the enabling keys for the realization of accelerators with always increased performances thanks to the realization of all-superconducting cavities. The use of increasingly pure superconducting coatings for accelerating cavities, with lower and lower RF losses, determined such high-quality factors that the need to operate at low temperatures (below the superconducting transition temperature Tc) was well paid for. Recently, with respect to the path followed by the high frequency superconductivity, a new field opened since SCs are being considered for GHz operation in high dc magnetic fields, and measurements (and optimization) of totally different quantities are needed. The possibility of successfully using SCs in high magnetic fields for these purposes is far from obvious and it depends on the outcome of accurate measurements of usually overlooked quantities.

In multisensor systems, time synchronization is particularly challenging for underwater integrated navigation systems (INSs) incorporating acoustic positioning, where time delays can significantly degrade accuracy when measurement and fusion epochs are misaligned. This article introduces a tightly coupled navigation framework that integrates a passive inverted ultrashort baseline (piUSBL) acoustic positioning system, a strapdown inertial navigation system (SINS), and a depth gauge under precise time synchronization. The framework fuses piUSBL azimuth and slant range with depth measurements, avoiding poor vertical-angle observability in planar arrays. By combining synchronized timing with acoustic signal processing, the proposed method transforms delay from an unobservable error into a measurable parameter, enabling explicit quantification of both acoustic propagation and system processing delays. Field experiments demonstrate that the proposed approach reduces position RMSE by 44.02% and maximum error (MAXERR) by 40.79% compared to the uncompensated baseline while achieving further RMSE reductions of 37.66% and 35.82% in horizontal directions relative to filter-based delay compensa

Flow-level network measurement is critical to many network applications. Among various measurement tasks, packet loss detection and heavy-hitter detection are two most important measurement tasks, which we call the two key tasks. In practice, the two key tasks are often required at the same time, but existing works seldom handle both tasks. In this paper, we design ChameleMon to support the two key tasks simultaneously. One key design/novelty of ChameleMon is to shift measurement attention as network state changes, through two dimensions of dynamics: 1) dynamically allocating memory between the two key tasks; 2) dynamically monitoring the flows of importance. To realize the key design, we propose a key technique, leveraging Fermat's little theorem to devise a flexible data structure, namely FermatSketch. FermatSketch is dividable, additive, and subtractive, supporting the two key tasks. We have fully implemented a ChameleMon prototype on a testbed with a Fat-tree topology. We conduct extensive experiments and the results show ChameleMon supports the two key tasks with low memory/bandwidth overhead, and more importantly, it can automatically shift measurement attention as network st

The integration of artificial intelligence (AI) in educational measurement has revolutionized assessment methods, enabling automated scoring, rapid content analysis, and personalized feedback through machine learning and natural language processing. These advancements provide timely, consistent feedback and valuable insights into student performance, thereby enhancing the assessment experience. However, the deployment of AI in education also raises significant ethical concerns regarding validity, reliability, transparency, fairness, and equity. Issues such as algorithmic bias and the opacity of AI decision-making processes pose risks of perpetuating inequalities and affecting assessment outcomes. Responding to these concerns, various stakeholders, including educators, policymakers, and organizations, have developed guidelines to ensure ethical AI use in education. The National Council of Measurement in Education's Special Interest Group on AI in Measurement and Education (AIME) also focuses on establishing ethical standards and advancing research in this area. In this paper, a diverse group of AIME members examines the ethical implications of AI-powered tools in educational measure

This paper presents the integration of OPC UA as a communication protocol in a wireless sensor network and the associated companion specifications as a semantic template for an information model. The Cyber Physical Finite Element Sensor Network (CPFEN ) for Shape Measurements, a distributed wireless system, uses IO-Link Wireless for data transmission at the sensor level, OPC UA provides a unified interface for data access, configuration, monitoring, and calibration tailored to the needs of the CPFEN for all level above. This opens up additional possibilities, such as integrated quality assurance or creating a digital twin, while improving scalability.

To effectively process impulse noise for narrowband powerline communications (NB-PLCs) transceivers, capturing comprehensive statistics of nonperiodic asynchronous impulsive noise (APIN) is a critical task. However, existing mathematical noise generative models only capture part of the characteristics of noise. In this study, we propose a novel generative adversarial network (GAN) called noise generation GAN (NGGAN) that learns the complicated characteristics of practically measured noise samples for data synthesis. To closely match the statistics of complicated noise over the NB-PLC systems, we measured the NB-PLC noise via the analog coupling and bandpass filtering circuits of a commercial NB-PLC modem to build a realistic dataset. To train NGGAN, we adhere to the following principles: 1) we design the length of input signals that the NGGAN model can fit to facilitate cyclostationary noise generation; 2) the Wasserstein distance is used as a loss function to enhance the similarity between the generated noise and training data; and 3) to measure the similarity performances of GAN-based models based on the mathematical and practically measured datasets, we conduct both quantitative

For certain joint measurements on a pair of spatially separated particles, we ask how much entanglement is needed to carry out the measurement exactly. For a class of orthogonal measurements on two qubits with partially entangled eigenstates, we present upper and lower bounds on the entanglement cost. The upper bound is based on a recent result by D. Berry [Phys. Rev. A 75, 032349 (2007)]. The lower bound, based on the entanglement production capacity of the measurement, implies that for almost all measurements in the class we consider, the entanglement required to perform the measurement is strictly greater than the average entanglement of its eigenstates. On the other hand, we show that for any complete measurement in d x d dimensions that is invariant under all local Pauli operations, the cost of the measurement is exactly equal to the average entanglement of the states associated with the outcomes.

A misunderstanding of entangled states has spawned decades of concern about quantum measurements and a plethora of quantum interpretations. The "measurement state" or "Schrodinger's cat state" of a superposed quantum system and its detector is nonlocally entangled, suggesting that we turn to nonlocality experiments for insight into measurements. By studying the full range of superposition phases, these experiments show precisely what the measurement state does and does not superpose. These experiments reveal that the measurement state is not, as had been supposed, a paradoxical superposition of detector states. It is instead a nonparadoxical superposition of two correlations between detector states and system states. In this way, the experimental results resolve the problem of definite outcomes ("Schrodinger's cat"), leading to a resolution of the measurement problem. However, this argument does not yet resolve the measurement problem because it is based on the results of experiments, while measurement is a theoretical problem: How can standard quantum theory explain the definite outcomes seen experimentally? Thus, we summarize the nonlocality experiments' supporting theory, which

This paper presents a detailed analysis of uncertainty matrices (i.e. measurement covariance matrices) associated with multiple complex-valued microwave scattering S-parameter measurements. The analysis is based on forming combinations of (2 X 2) sub-matrices from selected elements in the uncertainty matrix and using these sub-matrices to define uncertainty ellipses in two-dimensional measurement planes. These uncertainty ellipses can be used to assess the quality, accuracy and validity of the measurements. A simple example is given using measurements made on a waveguide mismatched line section at frequencies from 75 GHz to 110 GHz. The analysis is also useful when multiple S-parameter measurements are used as input quantities for measurement models used to determine other measurement quantities (i.e. as output quantities).

The ED-XRF (Energy-Dispersive X-ray Fluorescence) measurements and the FLUKA simulations have been made to discuss the possibility of recognition of coating layer as well as to its thickness measurement. In this work the $I_{Kα}$(Cu)/$I_{Kα}$(Ag) intensity ratios as well as $I_{Kβ}$(Ag)/$I_{Kα}$(Ag) and $I_{Kβ}$(Cu)/$I_{Kα}$(Cu) for copper samples coated with various thickness of sputtered silver have been analyzed. The results show strong dependence of these factors with coating silver layer thickness. The measurements show the performance of this method in archaeometry. Since the use of non-destructive methods during tests on ancient silver artifacts may not supply to obtain reliable bulk results and should be considered applicable for only surface analyses, the measured intensity ratios can be applied as a tool to estimation of surface silver enrichment thickness.

A statistical analysis of optimal universal cloning shows that it is possible to identify an ideal (but non-positive) copying process that faithfully maps all properties of the original Hilbert space onto two separate quantum systems. The joint probabilities for non-commuting measurements on separate clones then correspond to the real parts of the complex joint probabilities observed in weak measurements on a single system, where the measurements on the two clones replace the corresponding sequence of weak measurement and post-selection. The imaginary parts of weak measurement statics can be obtained by replacing the cloning process with a partial swap operation. A controlled-swap operation combines both processes, making the complete weak measurement statistics accessible as a well-defined contribution to the joint probabilities of fully resolved projective measurements on the two output systems.

High frequency applications of superconductors in a dc magnetic field rely on the estimate of the characteristic crossover frequency $ν_c$ between low and high losses. Customarily, high sensitivity resonant techniques, intrinsically operating at discrete frequencies, are used to estimate $ν_c$. We exploit here a method based on a dual frequency resonator. We show that single-frequency evaluations of $ν_c$ lead to heavy underestimations of the superconductor surface resistance. We describe a combined analytical and experimental approach that gives more accurate estimates for $ν_c$, and additionally allows to test the underlying physical model.

Active DNS measurement is fundamental to understanding and improving the DNS ecosystem. However, the absence of an extensible, high-performance, and easy-to-use DNS toolkit has limited both the reproducibility and coverage of DNS research. In this paper, we introduce ZDNS, a modular and open-source active DNS measurement framework optimized for large-scale research studies of DNS on the public Internet. We describe ZDNS' architecture, evaluate its performance, and present two case studies that highlight how the tool can be used to shed light on the operational complexities of DNS. We hope that ZDNS will enable researchers to better -- and in a more reproducible manner -- understand Internet behavior.

Binary feature descriptors have been widely used in various visual measurement tasks, particularly those with limited computing resources and storage capacities. Existing binary descriptors may not perform well for long-term visual measurement tasks due to their sensitivity to illumination variations. It can be observed that when image illumination changes dramatically, the relative relationship among local patches mostly remains intact. Based on the observation, consequently, this study presents an illumination-insensitive binary (IIB) descriptor by leveraging the local inter-patch invariance exhibited in multiple spatial granularities to deal with unfavorable illumination variations. By taking advantage of integral images for local patch feature computation, a highly efficient IIB descriptor is achieved. It can encode scalable features in multiple spatial granularities, thus facilitating a computationally efficient hierarchical matching from coarse to fine. Moreover, the IIB descriptor can also apply to other types of image data, such as depth maps and semantic segmentation results, when available in some applications. Numerical experiments on both natural and synthetic datasets

Deep learning has emerged as an effective solution for addressing the challenges of short-term voltage stability assessment (STVSA) in power systems. However, existing deep learning-based STVSA approaches face limitations in adapting to topological changes, sample labeling, and handling small datasets. To overcome these challenges, this paper proposes a novel phasor measurement unit (PMU) measurements-based STVSA method by using deep transfer learning. The method leverages the real-time dynamic information captured by PMUs to create an initial dataset. It employs temporal ensembling for sample labeling and utilizes least squares generative adversarial networks (LSGAN) for data augmentation, enabling effective deep learning on small-scale datasets. Additionally, the method enhances adaptability to topological changes by exploring connections between different faults. Experimental results on the IEEE 39-bus test system demonstrate that the proposed method improves model evaluation accuracy by approximately 20% through transfer learning, exhibiting strong adaptability to topological changes. Leveraging the self-attention mechanism of the Transformer model, this approach offers signifi