Phase Estimation Research Articles

Extraction and evaluation of cable forces of a cable-stayed bridge based on amplitude and phase estimation method

In order to identify the time-varying frequency and amplitude of structural vibration based on the bridge structural health monitoring data and obtain the cable force of cable-stayed bridges in real time, a spectrum analysis method based on amplitude and phase estimation (APES) was proposed in this study. The amplitude spectrum of the acceleration data is first calculated by the APES method, the real-time spectrogram of the cable is obtained by the sliding window method. Then the modal frequency and amplitude are automatically extracted from the real-time spectrum by using a frequency extrusion post-processing technique, which can be regarded as the average value of the instantaneous frequency and amplitude respectively. Next, the fundamental frequency of the cable is extracted by using an automatic identification method, and the performance of the proposed method is verified. Finally, real-time scoring of cable forces and structural condition assessment is achieved with consideration of the moderation index model as well as the material strength. The results show that the APES method can use shorter calculation samples than the traditional Fast Fourier Transform (FFT) to obtain higher resolution and more accurate amplitude, which provides a theoretical basis for the real-time identification of fundamental frequency based on short-term monitoring data. The frequency extrusion post-processing-algorithm can reduce the spectrum recognition delay and improve timeliness of the cable force evaluation. The time-varying cable force with an interval of 10 s can be used to reflect the health status of the cable in time. The research results can provide technical support for the real-time extraction of cable force of long-span cable-stayed bridges, and can also provide an effective basis for component condition evaluation and bridge maintenance decision-making.

Study of the Bias of the Initial Phase Estimation of a Sinewave of Known Frequency in the Presence of Phase Noise.

The estimation of the parameters of a sinusoidal signal is of paramount importance in various applications in the fields of sensors, signal processing, parameter estimation, and device characterization, among others. The presence, in the measurement system, of non-ideal phenomena such as additive noise in the signals, phase noise in the stimulus generation, jitter in the sampling system, frequency error in the experimental setup, among others, leads to increased uncertainty and bias in the estimated quantities obtained by least squares methods and those derived from them. Therefore, from a metrological point of view, it is important to be able to theoretically predict and quantify those uncertainties in order to properly design the measurement system and its parameters, such as the number of samples to acquire or the stimulus signal amplitude to use to minimize the uncertainty in the estimated values. Previous works have shown that the presence of these non-ideal phenomena leads to increased uncertainty and bias in the estimation of the sinewave amplitude. The present work complements this knowledge by focusing specifically on the effect of phase noise and sampling jitter in the bias of the initial phase estimation of a sinusoidal signal of known frequency (three‑parameter sine fitting procedure). A theoretical derivation of the bias of initial phase estimation that takes into consideration the presence of phase noise in the sinewave is presented. Since a Taylor series approximation was used where only the first term was retained, it was necessary to validate the analytical derivations with numerical simulations using a Monte Carlo type of procedure. This process was applied to different conditions regarding the phase noise standard deviation, initial phase value, and number of samples. It is concluded that, in most scenarios, initial phase estimation using sine fitting is unbiased in the presence of phase noise or jitter. It is shown, however, that in cases of extremely high phase noise standard deviation and a very low number of samples, a bias occurs.

Inter- and sub-harmonics estimation: Treatise on raptor-inspired Harris Hawks collective wisdom

In contemporary power systems, harmonics as undesired electrical phenomena, have drawn considerable attention of the research community investigating in design and development of optimal electrical networks. Harmonics affect the power electronics devices with a number of negative impacts, including higher power losses, worse power quality, and interference with other delicate grid connected equipment. An objective function of inter- and sub-harmonics is constructed for joint estimation of amplitude and phase with known frequency. Accurate estimation of the harmonics model is carried out with stupendous knacks of raptor-inspired Harris Hawks optimization algorithm (HHOA), a revolutionary population-based, nature-motivated optimization approach. The primary sources of inspiration for HHOA are Harris Hawks cooperative demeanor and surprise pounce chasing style in nature. The HHOA’s performance is assessed for harmonics parameter estimation with several noise conditions, differing particle sizes and iterations. The proposed HHOA-based estimation technique provides accurate, convergent and robust performance for harmonics estimation. The reliable level of fitness is achieved consistently in the range of 10−3 to 10−10, an evidence of valuable precision, stability and robustness of the proposed algorithm.

Turbulence image correction using focused light field camera

Under turbulent conditions, the images of objects may be severely blurred and become unrecognizable because of the complex aberrations introduced by turbulence. Using adaptive optics (AO) to compensate for these wavefront aberrations can theoretically solve the problem of image blurring under turbulent conditions. However, due to its small field of view and high cost, the AO system is difficult to widely use. In recent years, using the technology of light field photography to correct turbulence images has been proposed. However, the current studies are either based on the structure of the non-focused light field camera, which makes it hard to achieve good image quality, or on the need to redesign the core sensor of the non-focused light field camera, which greatly increases the cost and complexity. We believe that the structure of a focused light field camera has a good potential for turbulence image correction and propose a new method of achieving a corrected focused image using a focused light field camera in this paper. We propose to use phase estimation instead of depth estimation for a focused light field camera and phase maps instead of a depth map as the basis for achieving a focused image. By the proposed method, a corrected focused image with a large field of view under turbulent conditions is achieved without using independent wavefront correction devices or redesigning the light field camera. The experiments are given to verify the effectiveness of the proposed method under strong turbulent conditions in the laboratory and real turbulent conditions in the outdoors. The proposed method in this paper provides a new and low-cost solution for image correction for optical imaging systems under turbulent conditions, so it has a high application value.

Efficiency and mass optimization for space nuclear power systems with closed Brayton cycle loops

Space nuclear power systems should be strictly limited in mass and size. Lack of detailed designs, mass estimation in the general design phase usually relies on simple and empirical models that leave too much design margin from the limits. Accurate mass estimation of space nuclear power systems is key to minimize the design margin and maximize the system performance in the general design phase. In this paper, new mass estimation models are proposed for the main components of space nuclear power systems, including nuclear reactor, shield, turbine, compressor, heat exchanger, and thermal radiator. The mass estimation models are more accurate by incorporating principles of nuclear physics, thermal hydraulics, and structural mechanics to the component designs. Combined with thermodynamic models and genetic algorithm, the MEGREZ code is developed to optimize the system performance by setting efficiency and mass as dual objectives. The code verification results indicate that the deviations between and the Jupiter Icy Moons Orbiter project parameters and the code calculated values are only 0.4% for system efficiency and 2.1% for system mass. The optimization for the Jupiter Icy Moons Orbiter project shows that the optimal molecular weight of helium-xenon mixture is the minimum value for single-stage turbomachinery limits. Additionally, the impacts of potential technological degradation, such as increased heat exchanger thermal resistances, higher frictional factors, and decreased turbomachinery efficiency, on system performances are discussed. The results show that turbomachinery efficiency presents the strongest influence on the system-specific mass; a 0.04 reduction in the polytropic efficiency of the compressor or turbine results in more than a 10% increase in system-specific mass.

Developing a cost estimation model for efficient road improvement planning: A case study in Aceh Jaya regency

Abstract Estimation is a method used to predict financial requirements by analyzing, calculating, and engineering based on field conditions and bidding documents. To enhance efficiency, there is a pressing need for a simple, swift, accurate cost estimation model. Therefore, this study aimed to establish cost estimation model at the initial planning phase of construction work and to assess its accuracy in estimating cost for road improvement using cost significant model (CSM) compared to actual expenses. The fundamental objective was to facilitate the execution of construction work, ensuring the successful completion. Data collection occurred at the (PUPR) in Aceh Jaya Regency, consisting of 25 contracts for road improvement work spanning from 2018 to 2022. The data included the execution year, road area, total variable cost (y), and the categorization of work item as independent variables (xn). Subsequently, the work data was transformed into data per square meter (m2) for each work item by dividing each independent variable by the area of road improvement work. This showed the cost/m2 for each work item (xn) and the total cost (y)/m2. Annual inflation data was obtained from (BPS) of Aceh province used for calculation projection year. The identification of cost-significant item variables occurred after organizing the data according to work item, adjusting the work cost for the execution year and the work-year cost while considering inflation factor. Additionally, it was observed that 3 most expensive work items represented 80% work item had a substantial impact on the total cost. Classic assumption tests were conducted to confirm the suitability of data for further analysis. The analysis was conducted using multiple linear regression, resulting in a model with y = 69850.515 + 0.879(x4) + 1.085(x5) + 1.221(x7). When cost significant model estimates were compared to the actual total cost of road improvement in Aceh Jaya, the accuracy ranged from +17.46% to -19.30%, which was consistent with Class 3 classification of (AACE) International standards.This classification was suitable for semi-detailed cost estimation in the conceptual phase.

Phase and Channel Estimation for High-Capacity Phase-Asynchronous Mode-Division Multiplexing Multiple-Input Multiple-Output Free-Space Optical Communications in Strong Turbulent Channels

Phase and Channel Estimation for High-Capacity Phase-Asynchronous Mode-Division Multiplexing Multiple-Input Multiple-Output Free-Space Optical Communications in Strong Turbulent Channels

Adaptive Grid Search Based Pulse Phase and Doppler Frequency Estimation for XNAV

Adaptive Grid Search Based Pulse Phase and Doppler Frequency Estimation for XNAV

Enhanced Gait Phases Recognition by EMG and Kinematics Information Fusion and a Minimal Recording Setuping Setup

The limited mobility of lower limb amputees highlights the need for advancements in prosthetic control strategies to restore natural locomotion. This paper proposes an information fusion approach for gait phase recognition using surface electromyography (sEMG) and kinematics data. Time-domain (TD) features were extracted from the myoelectric data and three data-driven models, specifically Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Artificial Neural Network (ANN), were compared in three different input conditions i.e. sEMG features, hip angle, and their fusion. Gait phase estimation results averaged from 40 healthy participants during normal walking with 10 strides per each demonstrated that the proposed fusion approach has consistently outperformed (p<0.0001) the other two conditions achieving a maximum accuracy of 85.48% with SVM. The findings suggest promising applications in prosthetic motion control and rehabilitative exoskeletons, highlighting the potential for improved user-driven strategies in lower limb prostheses.

Thin-film lithium niobate-based electro-optic comb cloning for self-homodyne coherent communication.

As the optical communication industry advances, metropolitan area networks (MANs) and radio access networks (RANs) are extensively deployed on a large scale, demanding energy-efficient integrated light sources and simplified digital signal processing (DSP) technologies. The emergence of thin-film lithium niobate (TFLN) has given rise to high-performance, energy-efficient on-chip modulators, making on-chip optical frequency comb (OFC) more appealing. Owing to the phase uniformity and stability of this chip-scale device, it has been possible to eliminate the carrier frequency phase estimation (CPE) in DSP stacks using comb-clone-enabled self-homodyne detection. Here we report the first use, to our knowledge, of a TFLN on-chip electro-optic (EO) frequency comb to realize comb cloning and self-homodyne coherent detection. We transmit three optical pilot tones and eight data channels encoded with 20 Gbaud polarization-multiplexed 16-ary quadrature amplitude modulation (PM-16-QAM) over 10 km and 80 km standard single-mode fibers. The bit error ratios (BERs) of the eight channels reach below 10-3, a result made possible by our on-chip comb. The scalability and mass producibility of on-chip EO combs, combined with the simplified DSP, show potential in our proposed fifth-generation (5G) RAN and MAN transmission scheme.

Accelerated phase retrieval using adaptive support and statistical fringe processing of phase estimates.

A technique for accelerated multiple-plane phase retrieval is demonstrated by creating adaptive support through the statistical analysis of phase estimates. Its technical advantage arises from, what we believe to be, the first time use of both phase estimates and a statistical metric, enabling the fast generation of noise-robust support masks. This results in a fourfold improvement in convergence speed when compared to the conventional multiple-plane method. Evaluating data fitting performance with fewer intensity recordings showed that using four or more recordings resulted in accurate fitting, three recordings caused overfitting, and two recordings led to underfitting for the test object waves used. In principle, the adaptive support strategy based on the statistical analysis of phase estimates may be applied to other iterative phase retrieval methods.

Beating the Standard Quantum Limit Electronic Field Sensing by Simultaneously Using Quantum Entanglement and Squeezing.

Quantum entanglement and quantum squeezing are two typical approaches to beat the standard quantum limit (SQL) for the sensitive phase estimations in quantum metrology. Each of them has already been utilized individually and sequentially to improve the sensitivity of electric field sensing with the trapped ion platform. However, the upper bound of the demonstrated sensitivity gain is still limited, i.e., the theoretical 6dB and experimental 3dB over the corresponding SQL, for electric field sensing. By simultaneously using the internal (spin)-external (oscillator) state entanglement and the oscillator squeezing to effectively amplify the accumulation phase, we show here that such a theoretical sensitivity gain upper bound can be significantly surpassed. The proposal provides a novel approach to implement the stronger beat of the SQL and even approach the Heisenberg limit, for the sensitive sensings of the desired electric field and also the other metrologies.

Quantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction.

Toxicity is a roadblock that prevents an inordinate number of drugs from being used in potentially life-saving applications. Deep learning provides a promising solution to finding ideal drug candidates; however, the vastness of chemical space coupled with the underlying matrix multiplication means these efforts quickly become computationally demanding. To remedy this, we present a hybrid quantum-classical neural network for predicting drug toxicity utilizing a quantum circuit design that mimics classical neural behavior by explicitly calculating matrix products with complexity . Leveraging the Hadamard test for efficient inner product estimation rather than the conventionally used swap test, we reduce the number of qubits by half and remove the need for quantum phase estimation. Directly computing matrix products quantum mechanically allows for learnable weights to be transferred from a quantum to a classical device for further training. We apply our framework to the Tox21 data set and show that it achieves commensurate predictive accuracy to the model's fully classical analogue. Additionally, we demonstrate that the model continues to learn, without disruption, once transferred to a fully classical architecture. We believe that combining the quantum advantage of reduced complexity and the classical advantage of noise-free calculation will pave the way for more scalable machine learning models.

Estimation and Compensation of the Ionospheric Path Delay Phase in PALSAR-3 and NISAR-L Interferograms

Spatial and temporal variation in the free electron concentration in the ionosphere affects SAR interferograms, in particular at low radar frequencies. In this work, the identification, estimation, and compensation of ionospheric path delay phases in PALSAR-3 and NISAR-L interferograms are discussed. Both of these L-band sensors simultaneously acquire SAR data in a main spectral band and in an additional, spectrally separated, narrower second band to support the mitigation of ionospheric path delays. The methods presented permit separating the dispersive and the non-dispersive phase terms based on the double-difference interferogram between the two available spectral bands and the differential interferogram of the main band. The applicability of the proposed methods is demonstrated using PALSAR-3-like data that were simulated based on PALSAR-2 SM1 mode data.

Reducing the Runtime of Fault-Tolerant Quantum Simulations in Chemistry through Symmetry-Compressed Double Factorization.

Quantum phase estimation based on qubitization is the state-of-the-art fault-tolerant quantum algorithm for computing ground-state energies in chemical applications. In this context, the 1-norm of the Hamiltonian plays a fundamental role in determining the total number of required iterations and also the overall computational cost. In this work, we introduce the symmetry-compressed double factorization (SCDF) approach, which combines a CDF of the Hamiltonian with the symmetry shift technique, significantly reducing the 1-norm value. The effectiveness of this approach is demonstrated numerically by considering various benchmark systems, including the FeMoco molecule, cytochrome P450, and hydrogen chains of different sizes. To compare the efficiency of SCDF to other methods in absolute terms, we estimate Toffoli gate requirements, which dominate the execution time on fault-tolerant quantum computers. For the systems considered here, SCDF leads to a sizable reduction of the Toffoli gate count in comparison to other variants of DF or even tensor hypercontraction, which is usually regarded as the most efficient approach for qubitization.

Optimizing quantum-enhanced Bayesian multiparameter estimation of phase and noise in practical sensors

Achieving quantum-enhanced performances when measuring unknown quantities requires developing suitable methodologies for practical scenarios, which include noise and the availability of a limited amount of resources. Here, we report on the optimization of substandard quantum limit Bayesian multiparameter estimation in a scenario where a subset of the parameters describes unavoidable noise processes in an experimental photonic sensor. We explore how the optimization of the estimation changes depending on which parameters are either of interest or are treated as nuisance ones. Our results show that optimizing the multiparameter approach in noisy apparata represents a significant tool to fully exploit the potential of practical sensors operating beyond the standard quantum limit for broad resources range. Published by the American Physical Society 2024

Estimation of passive gastrointestinal absorption of new dual DNA gyrase and topoisomerase IV inhibitors using PAMPA and biopartitioning micellar chromatography and quantitative structure-retention relationship analysis

DNA gyrase and topoisomerase IV play significant role in maintaining the correct structure of DNA during replication and they have been identified as validated targets in antibacterial drug discovery. Inadequate pharmacokinetic properties are responsible for many failures during drug discovery and their estimation in the early phase of this process maximizes the chance of getting useful drug candidates. Passive gastrointestinal absorption of a selected group of thirteen dual DNA gyrase and topoisomerase IV inhibitors was estimated using two in vitro tests – parallel artificial membrane permeability assay (PAMPA) and biopartitioning micellar chromatography (BMC). Due to good correlation between obtained results, passive gastrointestinal absorption of remaining ten compounds was estimated using only BMC. With this experimental setup, it was possible to identify compounds with high values of retention factors (k) and highest expected passive gastrointestinal absorption, and compounds with low values of k for which low passive gastrointestinal absorption is predicted. Quantitative structure-retention relationship (QSRR) modelling was performed by creating multiple linear regression (MLR), partial least squares (PLS) and support vector machines (SVM) models. Descriptors with the highest influence on retention factor were identified and their interpretation can be used for the design of new compounds with improved passive gastrointestinal absorption.

An algorithm based on quantum phase estimation for the identification of patterns

The quantum phase estimation algorithm has been utilized by a variety of quantum algorithms, most notably Shor’s algorithm, to obtain information regarding the period of a function that is appropriately encoded into a unitary operator. In many cases, it is desired to estimate whether a specific state exhibits a certain pattern quickly. In this paper, we exhibit a methodology based on the QPE algorithm to identify certain patterns. In particular, starting from a properly encoded state, we demonstrate how to construct unitary operators whose eigenvectors correspond to states with proper diagonals. QPE will then output an eigenphase equal to zero with certainty for these states, thereby identifying this class of matrices. For partial matches, our algorithm, based on the tolerance threshold used, will show areas of high similarity, and it will outperform classical ones when the number of mismatches defined by the tolerance is significantly low when compared to the size of the diagonal.

Optimizing one-axis twists for variational Bayesian quantum metrology

Quantum metrology and sensing seek advantages in estimating an unknown parameter of some quantum state or channel, using entanglement such as spin squeezing produced by one-axis twists or other quantum resources. In particular, qubit phase estimation, or rotation sensing, appears as a ubiquitous problem with applications to electric field sensing, magnetometry, atomic clocks, and gyroscopes. By adopting the Bayesian formalism to the phase estimation problem to account for limited initial knowledge about the value of the phase, we formulate variational metrology and treat the state preparation (or encoding) and measurement (or decoding) procedures as parameterized quantum circuits. It is important to understand how effective various parametrized protocols are as well as how robust they are to the effects of complex noise such as spatially correlated noise. First, we propose a family of parametrized encoding and decoding protocols called arbitrary-axis twist ansatzes, and show that it can lead to a substantial reduction in the number of one-axis twists needed to achieve a target estimation error. Furthermore, we demonstrate that the estimation error associated with these strategies decreases with system size in a faster manner than classical (or no-twist) protocols, even in the less-explored regimes where the prior information is limited. Last, using a polynomial-sized tensor network algorithm, we numerically analyze practical variational metrology beyond the symmetric subspace of a collective spin and find that quantum advantage persists for the arbitrary-axis twist ansatzes with a few one-axis twists and smaller total twisting angles for practically relevant noise levels. Published by the American Physical Society 2024

Improving 2–5 Qubit Quantum Phase Estimation Circuits Using Machine Learning

Quantum computing has the potential to solve problems that are currently intractable to classical computers with algorithms like Quantum Phase Estimation (QPE); however, noise significantly hinders the performance of today’s quantum computers. Machine learning has the potential to improve the performance of QPE algorithms, especially in the presence of noise. In this work, QPE circuits were simulated with varying levels of depolarizing noise to generate datasets of QPE output. In each case, the phase being estimated was generated with a phase gate, and each circuit modeled was defined by a randomly selected phase. The model accuracy, prediction speed, overfitting level and variation in accuracy with noise level was determined for 5 machine learning algorithms. These attributes were compared to the traditional method of post-processing and a 6x–36 improvement in model performance was noted, depending on the dataset. No algorithm was a clear winner when considering these 4 criteria, as the lowest-error model (neural network) was also the slowest predictor; the algorithm with the lowest overfitting and fastest prediction time (linear regression) had the highest error level and a high degree of variation of error with noise. The XGBoost ensemble algorithm was judged to be the best tradeoff between these criteria due to its error level, prediction time and low variation of error with noise. For the first time, a machine learning model was validated using a 2-qubit datapoint obtained from an IBMQ quantum computer. The best 2-qubit model predicted within 2% of the actual phase, while the traditional method possessed a 25% error.