Enhanced Parameter Estimation Research Articles

Quantum enhanced mechanical rotation sensing using wavefront photonic gears

Quantum metrology leverages quantum correlations for enhanced parameter estimation. Recently, structured light enabled increased resolution and sensitivity in quantum metrology systems. However, lossy and complex setups impacting photon flux hinder true quantum advantage while using high dimensional structured light. We introduce a straightforward mechanical rotation quantum sensing mechanism, employing high-dimensional structured light and use it with a high-flux (45 000 coincidence counts per second) N00N state source with N = 2. The system utilizes two opposite spiral phase plates with topological charge of up to ℓ = 16 that converts mechanical rotation into wavefront phase shifts and exhibit a 16-fold enhanced super-resolution and 25-fold enhanced sensitivity between different topological charges, while retaining the acquisition times, and with negligible change in coincidence count. Furthermore, the high efficiency together with the high photon flux enables detection of mechanical angular acceleration in real-time. Our approach paves the way for highly sensitive quantum measurements, applicable to various interferometric schemes.

Enhanced Quantum Metrology with Non-Phase-Covariant Noise.

The detrimental impact of noise on sensing performance in quantum metrology has been widely recognized by researchers in the field. However, there are no explicit fundamental laws of physics stating that noise invariably weakens quantum metrology. We reveal that phase-covariant noise either degrades or remains neutral to sensing precision, whereas non-phase-covariant noise can potentially enhance parameter estimation, surpassing even the ultimate precision limit achievable in the absence of noise. This implies that a non-Hermitian quantum sensor may outperform its Hermitian counterpart in terms of sensing performance. To illustrate and validate our theory, we present several paradigmatic examples of magnetic field metrology.

Contribution of landmark-tracking data to the estimation of the gravity field and rotation parameters of 311P/PANSTARRS for the incoming Tianwen-2 space mission

311P/PANSTARRS is one of the two targets of the Chinese asteroid exploration mission Tianwen-II. The estimation of the gravity field and rotation parameters is an important part of the mission. Optical landmark observations directly record the relative relationship between the asteroid and probe. For this purpose, we investigate the role of the addition of landmark observations to radio-tracking data in achieving precise orbit determination and deriving the physical parameters of 311P. More specifically, we use a linear re-weighting fusion scheme to aggregate these two heterogeneous data to determine 311P's gravitational field and rotational parameters. Our results show that landmark observations enhance parameter estimation drastically. We conduct extensive experiments to understand how to optimize the use of landmark observations. Besides, we find that the upper bound error on 311P ephemeris for estimating a 2nd-degree gravity field model is 5 km.

Multiparameter critical quantum metrology with impurity probes

Quantum systems can be used as probes in the context of metrology for enhanced parameter estimation. In particular, the delicacy of critical systems to perturbations can make them ideal sensors. Arguably the simplest realistic probe system is a spin- 12 impurity, which can be manipulated and measured in-situ when embedded in a fermionic environment. Although entanglement between a single impurity probe and its environment produces nontrivial many-body effects, criticality cannot be leveraged for sensing. Here we introduce instead the two-impurity Kondo model as a novel paradigm for critical quantum metrology, and examine the multiparameter estimation scenario at finite temperature. We explore the full metrological phase diagram numerically and obtain exact analytic results near criticality. Enhanced sensitivity to the inter-impurity coupling driving a second-order phase transition is evidenced by diverging quantum Fisher information (QFI) and quantum signal-to-noise ratio (QSNR). However, with uncertainty in both coupling strength and temperature, the multiparameter QFI matrix becomes singular—even though the parameters to be estimated are independent—resulting in vanishing QSNRs. We demonstrate that by applying a known control field, the singularity can be removed and measurement sensitivity restored. For general systems, we show that the degradation in the QSNR due to uncertainties in another parameter is controlled by the degree of correlation between the unknown parameters.

Calibration of visual measurement system for excavator manipulator pose

The automatic control of excavator operation trajectories is a pivotal technology for autonomous excavators, with the essential prerequisite being the real-time measurement of manipulator poses. Given the complexity of the operating environment, traditional sensor-based measurement methods face limitations, whereas visual measurement emerges as a promising technique. Accurately measuring excavator manipulator poses involves a crucial aspect: mapping the relationship between image information and poses. First, to address the significant errors in pose prediction encountered with machine learning techniques like artificial neural networks, this work introduces a mathematical model for mapping this relationship, referred to as the pose mapping mathematical model, which includes calibrating model parameters. Second, to address the sensitivity of initial values in the calibration process, we propose a residual-guided initialization algorithm. This algorithm aims to ensure that initial values closely approximate the ground truth values, thus preventing matrix singularity at the source and avoiding parameter estimation divergence. Third, to tackle challenges such as unstable lighting conditions and discrepancies between the dataset and the mathematical model, we introduce the random sample consensus-driven Levenberg–Marquardt parameter optimization algorithm to enhance parameter estimation accuracy. Experiments with static and dynamic online measurement demonstrate that our method reduces pose measurement errors compared to existing methods. This research lays a solid foundation for developing visual measurement techniques for excavators and automated manipulator control based on visual measurements, also serving as a valuable reference for research on mechanical arms.

Rapid and accurate navigators for motion and B0 tracking using QUEEN: Quantitatively enhanced parameter estimation from navigators.

To develop a framework that jointly estimates rigid motion and polarizing magnetic field (B0 ) perturbations ( ) for brain MRI using a single navigator of a few milliseconds in duration, and to additionally allow for navigator acquisition at arbitrary timings within any type of sequence to obtain high-temporal resolution estimates. Methods exist that match navigator data to a low-resolution single-contrast image (scout) to estimate either motion or . In this work, called QUEEN (QUantitatively Enhanced parameter Estimation from Navigators), we propose combined motion and estimation from a fast, tailored trajectory with arbitrary-contrast navigator data. To this end, the concept of a quantitative scout (Q-Scout) acquisition is proposed from which contrast-matched scout data is predicted for each navigator. Finally, navigator trajectories, contrast-matched scout, and are integrated into a motion-informed parallel-imaging framework. Simulations and in vivo experiments show the need to model to obtain accurate motion parameters estimated in the presence of strong . Simulations confirm that tailored navigator trajectories are needed to robustly estimate both motion and . Furthermore, experiments show that a contrast-matched scout is needed for parameter estimation from multicontrast navigator data. A retrospective, in vivo reconstruction experiment shows improved image quality when using the proposed Q-Scout and QUEEN estimation. We developed a framework to jointly estimate rigid motion parameters and from navigators. Combing a contrast-matched scout with the proposed trajectory allows for navigator deployment in almost any sequence and/or timing, which allows for higher temporal-resolution motion and estimates.

Zero-Inflated Count Regression Models in Solving Challenges Posed by Outlier-Prone Data; an Application to Length of Hospital Stay.

Ignoring outliers in data may lead to misleading results. Length of stay (LOS) is often considered a count variable with a high frequency of outliers. This study exemplifies the potential of robust methodologies in enhancing the accuracy and reliability of analyses conducted on skewed and outlier-prone count data of LOS. The application of Zero-Inflated Poisson (ZIP) and robust Zero-Inflated Poisson (RZIP) models in solving challenges posed by outlier LOS data were evaluated. The ZIP model incorporates two components, tackling excess zeros with a zero-inflation component and modeling positive counts with a Poisson component. The RZIP model introduces the Robust Expectation-Solution (RES) algorithm to enhance parameter estimation and address the impact of outliers on the model's performance. Data from 254 intensive care unit patients were analyzed (62.2% male). Patients aged 65 or older accounted for 58.3% of the sample. Notably, 38.6% of patients exhibited zero LOS. The overall mean LOS was 5.89 (± 9.81) days, and 9.45% of cases displayed outliers. Our analysis using the RZIP model revealed significant predictors of LOS, including age, underlying comorbidities (p<0.001), and insurance status (p=0.013). Model comparison demonstrated the RZIP model's superiority over ZIP, as evidenced by lower Akaike information criteria (AIC) and Bayesians information criteria (BIC) values. The application of the RZIP model allowed us to uncover meaningful insights into the factors influencing LOS, paving the way for more informed decision-making in hospital management.

The Rise of Intelligent Reflecting Surfaces in Integrated Sensing and Communications Paradigms

The intelligent reflecting surface (IRS) alters the behavior of wireless media and, consequently, has potential to improve the performance and reliability of wireless systems such as communications and radar remote sensing. Recently, integrated sensing and communications (ISAC) has been widely studied as a means to efficiently utilize spectrum and thereby save cost and power. This article investigates the role of IRS in the future ISAC paradigms. While there is a rich heritage of recent research into IRS-assisted communications, the IRS-assisted radars and ISAC remain relatively unexamined. We discuss the putative advantages of IRS deployment, such as coverage extension, interference suppression, and enhanced parameter estimation, for both communications and radar. We introduce possible IRSassisted ISAC scenarios with common and dedicated surfaces. The article provides an overview of related signal processing techniques and the design challenges, such as wireless channel acquisition, waveform design, and security.

Channel Estimation for Near-Field XL-RIS-Aided mmWave Hybrid Beamforming Architectures

Extremely large-scale reconfigurable intelligent surface (XL-RIS) has been a prospective capacity-enhancing technique. Faced with XL-RIS-aided millimeter wave (mmWave) communications, we propose a low-complexity channel estimation strategy with the near-field spherical wavefront assumption. It separates the whole channel estimation procedure into two phases to decrease pilot use and computational complexity: 1) estimation of angular-domain parameters of the base station (BS) and users equipped with the hybrid beamforming architecture, and 2) estimation of cascaded angular- and polar-domain parameters of the XL-RIS. Particularly, in the first and second phases, the three-dimensional multiple measurement vector compressive sensing (3D-M-CS) framework and the 3D distributed CS (3D-D-CS) framework are proposed for enhanced parameter estimation, respectively. Then the variants of Look Ahead Orthogonal Match Pursuit (LAOMP), i.e., 3D-M-LAOMP and 3D-D-LAOMP, are developed for solving our proposed CS frameworks.

Multiparameter Estimation with Two-Qubit Probes in Noisy Channels.

This work compares the performance of single- and two-qubit probes for estimating several phase rotations simultaneously under the action of different noisy channels. We compute the quantum limits for this simultaneous estimation using collective and individual measurements by evaluating the Holevo and Nagaoka-Hayashi Cramér-Rao bounds, respectively. Several quantum noise channels are considered, namely the decohering channel, the amplitude damping channel, and the phase damping channel. For each channel, we find the optimal single- and two-qubit probes. Where possible we demonstrate an explicit measurement strategy that saturates the appropriate bound and we investigate how closely the Holevo bound can be approached through collective measurements on multiple copies of the same probe. We find that under the action of the considered channels, two-qubit probes show enhanced parameter estimation capabilities over single-qubit probes for almost all non-identity channels, i.e., the achievable precision with a single-qubit probe degrades faster with increasing exposure to the noisy environment than that of the two-qubit probe. However, in sufficiently noisy channels, we show that it is possible for single-qubit probes to outperform maximally entangled two-qubit probes. This work shows that, in order to reach the ultimate precision limits allowed by quantum mechanics, entanglement is required in both the state preparation and state measurement stages. It is hoped the tutorial-esque nature of this paper will make it easily accessible.

Bayesian kinetic modeling for tracer-based metabolomic data

BackgroundStable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly 13C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{13}C$$\\end{document}-enriched glucose (13C6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{13}C_6$$\\end{document}-Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups.ResultsWe propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts’ knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux.ConclusionsOur new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data.

Dynamic Quantizer Design for Target Tracking for Wireless Sensor Network With Imperfect Channels

Wireless sensor networks (WSNs) have been demonstrated to enhance parameter estimation performance for target tracking. In this paper, a prior information based quantizer design framework is proposed for target tracking for WSNs. In the proposed framework, the imperfect wireless channels between local sensors and the fusion center are considered. To make full use of the historical states and measurements embedded into the Bayesian tracking methodology, the quantizer design is suggested to be implemented with considering the prior state information. To this end, a channel-aware posterior Cramér-Rao lower bound (PCRLB) is derived based on the state prediction and further used as the performance indicator for quantizer design. Regarding target tracking, we model the quantizer design problem as a non-convex and highly nonlinear optimization problem that is intractable in general. We split the problem in terms of different scenarios, and for one-bit quantizer design based on a binary symmetric channel (BSC), we find that the optimal solution can be analytically computed. While for the general fading channel-based quantizer design problems, we propose two polynomial-time algorithms to find the solutions. Meanwhile, an approximation-based channel-aware particle filter (A-CAPF) is proposed to improve the implementation efficiency of state filtering. Simulation results demonstrate the enhanced performance and execution efficiency of the proposed algorithms in the context of the BSC and Rayleigh fading channel.

Stochastic resonance in Bayesian estimation and CRLB for nonlinear system

In this work, noise enhanced parameter estimation problems are investigated for a general nonlinear system, where an additive noise is added to the nonlinear system input and a Bayesian estimator is developed based on the noise modified output. The optimal probability distribution of the additive noises is formulated successively for minimizing the mean square error (MSE) of the optimal Bayesian estimation and the Cramer–Rao lower bound (CRLB). Then the optimal additive noises for the two different noise enhanced optimization problems are explicitly derived as constant vectors, which implies the randomization of constant vectors is not beneficial to these optimizations. Finally, numerical results are presented to illustrate the theoretical results.

Recursive Least Squares Identification With Variable-Direction Forgetting via Oblique Projection Decomposition

In this paper, a new recursive least squares (RLS) identification algorithm with variable-direction forgetting (VDF) is proposed for multi-output systems. The objective is to enhance parameter estimation performance under non-persistent excitation. The proposed algorithm performs oblique projection decomposition of the information matrix, such that forgetting is applied only to directions where new information is received. Theoretical proofs show that even without persistent excitation, the information matrix remains lower and upper bounded, and the estimation error variance converges to be within a finite bound. Moreover, detailed analysis is made to compare with a recently reported VDF algorithm that exploits eigenvalue decomposition (VDF-ED). It is revealed that under non-persistent excitation, part of the forgotten subspace in the VDF-ED algorithm could discount old information without receiving new data, which could produce a more ill-conditioned information matrix than our proposed algorithm. Numerical simulation results demonstrate the efficacy and advantage of our proposed algorithm over this recent VDF-ED algorithm.

Bioinspired Composite Learning Control Under Discontinuous Friction for Industrial Robots

Adaptive control can be applied to robotic systems with parameter uncertainties, but improving its performance is usually difficult, especially under discontinuous friction. Inspired by the human motor learning control mechanism, an adaptive learning control approach is proposed for a broad class of robotic systems with discontinuous friction, where a composite error learning technique that exploits data memory is employed to enhance parameter estimation. Compared with the classical feedback error learning control, the proposed approach can achieve superior transient and steady-state tracking without high-gain feedback and persistent excitation at the cost of extra computational burden and memory usage. The performance improvement of the proposed approach has been verified by experiments based on a DENSO industrial robot.

A rigorous physics-based enhanced parameter estimation (EPE) methodology for calibration of building energy simulations

Buildings rarely perform as designed/simulated and there are numerous tangible benefits if this gap is reconciled. A new scientifically rigorous yet pragmatic methodology for calibrating building energy simulations - called Enhanced Parameter Estimation (EPE) - is proposed that allows physically relevant parameter estimation rather than a blind force-fit to energy use data. Starting with a rapidly created simulation model, calibration is performed in two stages: (a) building shell calibration with the HVAC system replaced by an ideal system that meets the loads (b) HVAC system calibration with the building shell and all internal loads replaced by a box with only process loads. In the first stage, EPE identifies a small number of high-level heat flows in the energy balance, calculates them with specifically tailored individual driving functions, introduces physically significant parameters to best accomplish energy balance, and, estimates the parameters and their uncertainty bounds. Calibration is thus done with corrective heat flows without any arbitrary tuning of input parameters. Calibration accuracy is enhanced by machine learning of the residual errors. The EPE methodology is demonstrated by means of: a synthetic building and an actual 75,000 sq. ft. building in Pennsylvania. Future work needed for widespread application is discussed.

Resolving Range Ambiguity via Multiple-Input Multiple-Output Radar With Element-Pulse Coding

In this paper, we propose a novel radar framework, referred to as multiple-input multiple-output (MIMO) radar with element-pulse coding (EPC). The EPC employs Fourier basis in joint transmit elements and pulses, which has the advantages of simplicity and convenience for practical realization. It is shown that the EPC-MIMO radar is advantageous in discriminating different pulses and resolving range ambiguity. The discrimination of different pulses is achieved in the transmit spatial frequency domain, and the identifiability of range ambiguity increases with the transmit element number of the radar. Moreover, coherent processing in Doppler domain can be performed after decoding. The coding and decoding procedures are closely related though they are performed in transmitter and receiver, respectively. As the EPC-MIMO radar is capable of resolving range ambiguity, an enhanced parameter estimation approach is developed to obtain the angle and range region number estimates of target. The parameter estimation performance is assessed by the Cramer-Rao bound, which reveals useful and interesting findings. Simulation examples are provided to verify the signal properties in EPC-MIMO radar and to demonstrate its superiority in resolving range ambiguity over existing systems.

Agent-based models of malaria transmission: a systematic review

BackgroundMuch of the extensive research regarding transmission of malaria is underpinned by mathematical modelling. Compartmental models, which focus on interactions and transitions between population strata, have been a mainstay of such modelling for more than a century. However, modellers are increasingly adopting agent-based approaches, which model hosts, vectors and/or their interactions on an individual level. One reason for the increasing popularity of such models is their potential to provide enhanced realism by allowing system-level behaviours to emerge as a consequence of accumulated individual-level interactions, as occurs in real populations.MethodsA systematic review of 90 articles published between 1998 and May 2018 was performed, characterizing agent-based models (ABMs) relevant to malaria transmission. The review provides an overview of approaches used to date, determines the advantages of these approaches, and proposes ideas for progressing the field.ResultsThe rationale for ABM use over other modelling approaches centres around three points: the need to accurately represent increased stochasticity in low-transmission settings; the benefits of high-resolution spatial simulations; and heterogeneities in drug and vaccine efficacies due to individual patient characteristics. The success of these approaches provides avenues for further exploration of agent-based techniques for modelling malaria transmission. Potential extensions include varying elimination strategies across spatial landscapes, extending the size of spatial models, incorporating human movement dynamics, and developing increasingly comprehensive parameter estimation and optimization techniques.ConclusionCollectively, the literature covers an extensive array of topics, including the full spectrum of transmission and intervention regimes. Bringing these elements together under a common framework may enhance knowledge of, and guide policies towards, malaria elimination. However, because of the diversity of available models, endorsing a standardized approach to ABM implementation may not be possible. Instead it is recommended that model frameworks be contextually appropriate and sufficiently described. One key recommendation is to develop enhanced parameter estimation and optimization techniques. Extensions of current techniques will provide the robust results required to enhance current elimination efforts.

(L + S)-RT-CCD for Terrain Paths Monitoring

In past research, the problem of maritime targets detection and motion parameter estimation has been tackled. This letter aims to contribute to preventing illegal immigration, solving the problem of obtaining reliable paths detection of targets in terms of temporal decorrelation observed on coherent change detection (CCD) maps occurring between two or more complex-valued synthetic aperture radar (SAR) images. Most detection problems are related to terrain clutter and platform motion instabilities which make the paths structure detection and reconnaissance difficult. This letter presents a complete procedure called low-rank plus sparse decomposition radon transform (RT) CCD for automatic estimation and tracking of target paths by evaluating the generated temporal decorrelation CCD-SAR images, observed at the desert environments. The algorithm consists of evaluating a dual-stage low-rank plus sparse decomposition (LRSD) assisted by RT for clutter reduction, sparse object detection, and precise path inclination estimation. The algorithm is based on the robust principal component analysis (RPCA) implemented by convex programming. The LRSD algorithm permits the extrapolation of sparse objects of interest consisting of the incoherent patterns generated by targets from the unchanging low-rank and more coherent background. This dual-stage RPCA and RT applied to SAR-CCD surveillance permits fast detection and enhanced parameter estimation of terrain target paths.

Estimation of protein diffusion parameters

Protein diffusion offers an essential and elegant mechanism for morphogen gradient formation. Morphogens are signalling molecules that emanate from a particular region of cells and create a gradient, which has an impact on most biological processes, including cell signalling and embryonic development. Using a method that is based on singular spectrum analysis, we estimate parameters introduced in the synthesis diffusion degradation model, which is a commonly applied model for a transcription factor known as bicoid. Our findings, consistent with simulation results, indicate that the proposed method can be practically applied as an enhanced parameter estimation technique with reduced sensitivity to various levels of noise. © 2018 John Wiley &amp; Sons, Ltd.