Unbiased State Research Articles

Generation and trapping of a mesoderm biased state of human pluripotency

We postulate that exit from pluripotency involves intermediates that retain pluripotency while simultaneously exhibiting lineage-bias. Using a MIXL1 reporter, we explore mesoderm lineage-bias within the human pluripotent stem cell compartment. We identify a substate, which at the single cell level coexpresses pluripotent and mesodermal gene expression programmes. Functionally these cells initiate stem cell cultures and exhibit mesodermal bias in differentiation assays. By promoting mesodermal identity through manipulation of WNT signalling while preventing exit from pluripotency using lysophosphatidic acid, we ‘trap’ and maintain cells in a lineage-biased stem cell state through multiple passages. These cells correspond to a normal state on the differentiation trajectory, the plasticity of which is evidenced by their reacquisition of an unbiased state upon removal of differentiation cues. The use of ‘cross-antagonistic’ signalling to trap pluripotent stem cell intermediates with different lineage-bias may have general applicability in the efficient production of cells for regenerative medicine.

Air Data Sensor Fault Detection and Diagnosis in the Presence of Atmospheric Turbulence: Theory and Experimental Validation With Real Flight Data

Managing air data sensor fault detection and diagnosis (FDD) in the presence of atmospheric turbulence is challenging since the effects of faults and turbulence are coupled. Existing FDD approaches cannot decouple the faults from the turbulence. To address this challenge, this brief first proposes a novel kinematic model that incorporates the effects of the turbulence. This model is valid inside the entire flight envelope, and there is no need to design a linear parameter varying system. Then, the double-model adaptive estimation algorithm is extended to achieve unbiased state estimation even in the presence of unknown disturbances. The proposed approach is validated using generated turbulence data with various scale lengths and intensities. More importantly, the proposed approach is successfully validated using the real flight test data of a business jet when it is experiencing atmospheric turbulence.

Tunable Higher Order Mode-Based Dual-Beam CRLH Microstrip Leaky-Wave Antenna for V-Band Backward–Broadside–Forward Radiation Coverage

This article proposes a composite right-/left-handed (CRLH) dual-beam leaky-wave microstrip antenna with a tunable operating region for $V$ -band radiation coverage. Due to wideband excitation for second higher order modes, the symmetrically formed side-by-side dual beam initially scans in one quadrant from 52.5 to 65 GHz. Further, by appropriate placement of metallic via holes, a pair of back-to-back half-modes realizes generating two symmetrical side beams in E-plane with an enhanced gain over 40–52 GHz. Mode switching is performed electronically by incorporating p-i-n diodes and proper biasing network. Finally, incorporating half-sinusoidal interdigital slot variations and periodically placed switching diodes enables CRLH media having frequency beam steering from backward–broadside–forward quadrant. Leaky bands in microstrip higher order modes were analyzed by dispersion characteristics, and the proposed antenna achieved −10 dB impedance bandwidth of 29.2%, scanning range in H-plane from −65° to +69.7°, and peak gain of 18.7 dBi for the unbiased state and 28%, −78° to +69.6°, and 18 dBi, respectively, for the biased state. The average sidelobe level and minimum cross polar level are obtained as 13.5 and 23 dB, respectively. The fabricated prototype responses closely matched theoretical predictions, verifying the proposed concept.

Structural response reconstruction in physical coordinate from deficient measurements

For structural health monitoring, the monitored data is limited to several locations in space. To overcome the measurement incompleteness, many approaches have developed, such as the shape sensing and the dynamic reduction expansion. However, the measurement noise will affect the calculated displacement. Then, the unbiased filtering approaches have developed to estimate the input and to update the state simultaneously, combining with Kalman filter. This study proposes an unbiased input estimation and state updating method that can process the complex process using deficient measurements (the complex process in this paper indicates the process contains both dynamic and quasi-static components). The proposed approach involves two steps: the first step adopts the principal component analysis to solve an under-determined equation, and obtains the noised complete displacement vector; the second step uses the Gillijns De Moor filter to eliminate the measurement noise and gets the unbiased displacement at all positions. Different from previous papers, this study expresses the motion of a structure in the physical coordinate, not in the modal coordinate, because the modal coordinate is not sufficient to describe the static and quasi-static responses. Besides, this study makes no assumption and has no prior knowledge over the input, and even the input position is unknown. Numerical simulations, using a twenty-floor frame and a three-span continuous bridge, have validated that the approach is efficient and accurate under the existence of white noise. Finally, an experiment has been conducted to validate the algorithm adopting a two-span continuous beam bridge model, which has demonstrated that the approach is robust to modeling errors and applies to complex input.

Continuous-time quantum-walk spatial search on the Bollobás scale-free network

The scale-free property emerges in various real-world networks and is an essential property that characterizes the dynamics or features of such networks. In this work, we investigate the effect of this scale-free property on a quantum information processing task of finding a marked node in the network, known as the quantum spatial search. We analyze the quantum spatial search algorithm using a continuous-time quantum walk on the Bollob\'as network, and we evaluate the time $T$ to localize the quantum walker on the marked node starting from an unbiased initial state. Our main finding is that $T$ is determined by the global structure around the marked node, while some local information of the marked node, such as the degree, does not identify $T$. We discuss this by examining the correlation between $T$ and some centrality measures of the network, and we show that the closeness centrality of the marked node is highly correlated with $T$. We also characterize the distribution of $T$ by marking different nodes in the network, which displays a multimode log-normal distribution. Especially on the Bollob\'as network, $T$ is a few orders of magnitude shorter depending on whether the marked node is adjacent to the largest degree hub node. However, as $T$ depends on the property of the marked node, one requires some amount of prior knowledge about such a property of the marked node in order to identify the optimal time to measure the quantum walker and achieve a fast search. These results indicate that the existence of the hub node in the scale-free network plays a crucial role in the quantum spatial search.

Polarization-Independent Reconfigurable Graphene Gas Sensor Using Crescent Plasmonic Antenna

A polarization-independent gas sensor based on crescent plasmonic dipole antenna loaded with graphene monolayer is introduced in this paper for environment monitoring applications. Single, dual, and four arms crescent dipoles are designed, analyzed and investigated. The proposed gas sensor has reconfigurable absorption characteristics in the wavelength range from 900 to 1600 nm which covers the different gases responses. A parametric study on the effect of the sensor dimensions and polarization of the illuminated waves on the total absorption of different gases is investigated. A graphene monolayer is used to enhance the gas molecule absorption in its unbiased state. An enhancement of the total absorption cross section (ACS) values for n = 1.4 is 1.028 × 105 nm2 for the unloaded sensor case and is 1.77 × 105 for the graphene loaded sensor case. The unloaded graphene sensor sensitivity is 630 nm/RIU, and for the graphene loaded case is 677.5 nm/RIU. Dual-polarized crescent dipole consists of two orthogonal arms is designed to maintain the same sensitivity for x- and y-polarized E-field illumination. A polarization independent gas sensor consists of four crescents dipole arms rotated with 45° is explained. An enhancement of gas absorption is achieved by using a sensor array of four-arm dipole sensor. Different array sizes loaded with graphene monolayer are investigated. The total ACS peak value is 7.84 × 106 nm2 for 6 × 6 dipole array is obtained.

Data processing of field measurements of expedition research for mathematical modeling of hydrodynamic processes in the Azov Sea

This paper describes a three-dimensional mathematical hydrodynamic model capable of taking into account the processes of salt and heat transfer in the Azov Sea. The model allows obtaining three-dimensional fields of the vector of water flow rates, pressure, sea water density, salinity and temperature. The model is based on the equations of motion (Navier-Stokes), the continuity equation in the case of variable density, and the equations of heat and salt transport. The boundary and initial conditions are indicated. To approximate the equation of diffusion-convection-reaction in time, we analyzed the schemes with weights. The approximation of the problem of calculating the velocity field of the aquatic environment in terms of spatial variables was carried out on the basis of the balance method taking into account the occupancy ratios of the control areas. The stationary modes of the heat and salt transfer problem were investigated. The initial distribution of the salinity and temperature functions, which have a sufficient degree of smoothness at the points of setting the field values, was calculated using the Laplace equation. Using the interpolation algorithm and by superimposing the boundaries of the region, maps of salinity and temperature of the Sea of Azov were obtained. Based on the monitoring of the water area, three-dimensional mathematical models of the movement of the aquatic environment designed to predict possible scenarios for the development of the Azov Sea ecosystem were constructed in order to avoid the occurrence of anaerobic infection areas and take timely measures for their localization. The full-scale data obtained using different types of measuring instruments was used to develop observation models for prediction of the changes in hydrodynamic processes. A modified Kalman filter algorithm was applied to obtain unbiased minimum-variance state estimation for the dynamic system. A description is given of a software package that allows modeling hydrodynamic processes in shallow water bodies with complex spatial structures of currents, taking into account the transport of salts and heat.

Spectral response of Josephson junctions with low-energy quasiparticles

We study nanowire-based Josephson junctions shunted by a capacitor and take into account the presence of low-energy quasiparticle excitations. These are treated by extending conventional models used to describe superconducting qubits to include the coherent coupling between fermionic quasiparticles, in particular the Majorana zero modes that emerge in topological superconductors, and the plasma mode of the junction. Using accurate, unbiased matrix-product state techniques, we compute the energy spectrum and response function of the system across the topological phase transition. Furthermore, we develop a perturbative approach, valid in the harmonic limit with small charging energy, illustrating how the presence of low-energy quasiparticles affects the spectrum and response of the junction. Our results are of direct interest to on-going experimental investigations of nanowire-based superconducting qubits.

On the equivalence between the unbiased minimum-variance estimation and the infinity augmented Kalman filter

ABSTRACT In this paper, the augment Kalman filter (AKF) approach is discussed under the infinity condition. It is proved that the AKF in infinity condition is equivalent to the minimum-variance unbiased (MVU) estimation proposed in Gillijns & De Moor [(2007). Unbiased minimum-variance input and state estimation for linear discrete-time systems. Automatica, 43(1), 111–116]. This result can be regarded as a new contribution in the field of joint input and state estimation. Meanwhile, this result gives another way to estimate the state and unknown input simultaneously. Finally, the theoretical result is validated by simulations.

Extended Kalman Filter with Input Detection and Estimation for Tracking Manoeuvring Satellites

The technique of tracking a non-cooperative manoeuvring satellite is important for Space Situation Awareness (SSA). However, the classical extended Kalman filter cannot work successfully in this situation. Motivated by this problem, a novel Extended Kalman Filter with Input Detection and Estimation (EKF/IDE) method is proposed in this paper for tracking a non-cooperative satellite with impulsive manoeuvres. The impulsive manoeuvre is modelled as an unknown acceleration without any prior information. An unbiased minimum-variance input and state estimation method is introduced to estimate the manoeuvre acceleration. An approach based on the Mahalanobis distance of the manoeuvre estimate error is proposed for manoeuvre detection. With the impulsive manoeuvre being detected and estimated accurately, an adaptive extended Kalman filter is proposed to estimate the state of the target. An approach of covariance inflation is proposed to deal with the manoeuvre during the unobserved period. Simulations and Monte Carlo experiments are implemented to demonstrate the feasibility and validity of the proposed method. The results of simulations show that the proposed method can accurately detect and estimate unknown impulsive manoeuvres of a non-cooperative satellite. Through the compensation of the estimated manoeuvre, the estimation of position and velocity after the manoeuvre maintains the accuracy of the pre-manoeuvre period, demonstrating the robustness of the proposed method against unknown manoeuvres.

Augmented unbiased minimum-variance input and state estimation for tracking a maneuvering satellite

The technique of tracking a maneuvering satellite is significantly important for Space Situation Awareness (SSA). Traditional Kalman filters (KF) cannot robustly track unknown maneuvers. Motivated by the problem of tracking a non-cooperative maneuvering satellite, an augmented unbiased minimum-variance input and state estimation (AUMVISE) method is developed for estimating the state and the maneuver acceleration in this study. The maneuver acceleration estimate of the proposed method is proven to be more accurate than the original unbiased minimum-variance input and state estimation (UMVISE) method. Approaches based on the measurement residuals and the acceleration estimates are developed for maneuver start and end detection. The filter is switched between the AUMVISE method and the classical extended Kalman filter (EKF) to obtain an accurate tracking result during both the maneuver period and the non-maneuver period. Simulation results show that the proposed method has a suitable maneuver detection delay and outperforms the UMVISE method in estimating the state and maneuver acceleration.

Optimal Position and Velocity Estimation for Multi-USV Positioning Systems with Range Measurements

This paper investigates the problem on simultaneously estimating the velocity and position of the target for range-based multi-USV positioning systems. According to the range measurement and kinematics model of the target, we formulate this problem in a mixed linear/nonlinear discrete-time system. In this system, the input and state represent the velocity and position of the target, respectively. We divide the system into two components and propose a three-step minimum variance unbiased simultaneous input and state estimation (SISE) algorithm. First, we estimate the velocity in the local level plane and predict the corresponding position. Then, we estimate the velocity in the heave direction. Finally, we estimate the 3-dimensional (3D) velocity and position. We establish the unbiased conditions of the input and state estimation for the MLBL system. Simulation results illustrate the effectiveness of the problem formulation and demonstrate the performance of the proposed algorithm.

Robust extended Kalman filter with input estimation for maneuver tracking

This study investigates the problem of tracking a satellite performing unknown continuous maneuvers. A new method is proposed for estimating both the state and maneuver acceleration of the satellite. The estimation of the maneuver acceleration is obtained by the combination of an unbiased minimum-variance input and state estimation method and a low-pass filter. Then a threshold-based maneuver detection approach is developed to determinate the start and end time of the unknown maneuvers. During the maneuvering period, the estimation error of the maneuver acceleration is modeled as the sum of a fluctuation error and a sudden change error. A robust extended Kalman filter is developed for dealing with the acceleration estimate error and providing state estimation. Simulation results show that, compared with the Unbiased Minimum-variance Input and State Estimation (UMISE) method, the proposed method has the same position estimation accuracy, and the velocity estimation error is reduced by about 5 times during the maneuver period. Besides, the acceleration detection and estimation accuracy of the proposed method is much higher than that of the UMISE method.

Agents' beliefs and economic regimes polarization in interacting markets.

In the present paper, a model of a market consisting of real and financial interacting sectors is studied. Agents populating the stock market are assumed to be not able to observe the true underlying fundamental, and their beliefs are biased by either optimism or pessimism. Depending on the relevance they give to beliefs, they select the best performing strategy in an evolutionary perspective. The real side of the economy is described within a multiplier-accelerator framework with a nonlinear, bounded investment function. We study the effect of market integration, in particular, of the financialization of the real market. We show that strongly polarized beliefs in an evolutionary framework can introduce multiplicity of steady states, which, consisting in enhanced or depressed levels of income, reflect and reproduce the optimistic or pessimistic nature of the agents' beliefs. The polarization of these steady states, which coexist with an unbiased steady state, positively depends on that of the beliefs and on their relevance. Moreover, with a mixture of analytical and numerical tools, we show that such static characterization is inherited also at the dynamical level, with possibly complex attractors that are characterized by endogenously fluctuating pessimistic and optimistic prices and levels of national income, with the effect of having several coexisting business cycles. This framework, when stochastic perturbations are included, is able to account for stylized facts commonly observed in real financial markets, such as fat tails and excess volatility in the returns distributions, as well as bubbles and crashes for stock prices.

Switching and Data Injection Attacks on Stochastic Cyber-Physical Systems

In this article, we consider the problem of attack-resilient state estimation, that is, to reliably estimate the true system states despite two classes of attacks: (i) attacks on the switching mechanisms and (ii) false data injection attacks on actuator and sensor signals, in the presence of stochastic process and measurement noise signals. We model the systems under attack as hidden mode stochastic switched linear systems with unknown inputs and propose the use of a multiple-model inference algorithm to tackle these security issues. Moreover, we characterize fundamental limitations to resilient estimation (e.g., upper bound on the number of tolerable signal attacks) and discuss the topics of attack detection, identification, and mitigation under this framework. Simulation examples of switching and false data injection attacks on a benchmark system and an IEEE 68-bus test system show the efficacy of our approach to recover resilient (i.e., asymptotically unbiased) state estimates as well as to identify and mitigate the attacks.

Hydrogenation-induced strengthening of exchange bias coupling in antiferromagnetic Pd-rich alloy films

Hydrogenation has recently been proposed for use in modulating the magnetic properties of Pd-rich ferromagnetic (FM) alloy films. This study successfully fabricated Pd-rich Mn/MnPd/Fe antiferromagnetic/FM films with an established exchange bias field at RT, and determined that hydrogenation can enhance the exchange bias coupling in such films. Specifically, magnetic hysteresis loops revealed that the magnetic state of the Pd-rich Mn/MnPd/Fe films was gradually changed from an unbiased state to an exchange-biased state upon exposure to a hydrogen environment; moreover, hydrogenation engendered a larger magnitude of the exchange bias field than conventional field cooling. This phenomenon can be attributed to the enhanced long-range antiferromagnetic ordering of the Pd-rich MnPd film upon uptake of hydrogen atoms.

Density matrix approach to photon-assisted tunneling in the transfer Hamiltonian formalism

The transfer Hamiltonian tunneling current is derived in a time-dependent density matrix formulation and is used to examine photon-assisted tunneling. Bardeen's tunneling expression arises as the result of first order perturbation theory in a mean-field expansion of the density matrix. Photon-assisted tunneling from confined electromagnetic fields in the forbidden barrier region occurs due to time-varying polarization and wavefunction overlap in the gap, which leads to a non-zero tunneling current in asymmetric device structures, even in an unbiased state. The photon energy is seen to act as an effective temperature dependent bias in a uniform barrier asymmetric tunneling example problem. Higher order terms in the density matrix expansion give rise to multi-photon enhanced tunneling currents that can be considered an extension of non-linear optics where the non-linear conductance plays a similar role as the non-linear susceptibilities in the continuity equations.

Minimum-variance unbiased unknown input and state estimation for multi-agent systems with direct feedthrough by using distributed cooperative filters

This paper addresses the problem of simultaneous estimation of unknown inputs and states in multi-agent systems with direct feedthrough. A group of cooperative distributed recursive filters, in the sense of minimum-variance unbiased (MVU), is developed, where the estimations of the unknown input and state are interconnected. Theoretical and numerical analyses show that the existing condition of the proposed filters is significantly relaxed compared with that of the conventional decentralized filters.

Minimum-Variance Unbiased Unknown Input and State Estimation for Multi-Agent Systems by Distributed Cooperative Filters

This paper addressed the problem of the simultaneous estimation of unknown inputs and states in a multi-agent system with time-invariant and time-varying topology. A group of distributed cooperative recursive filters, in the sense of minimum-variance unbiased, was developed, where the estimations of unknown input and state were combined. A necessary and sufficient existing condition is presented and proven for the proposed distributed cooperative filters. Theoretical and numerical analyses demonstrate that the existing condition of the proposed filters is significantly relaxed, in comparison to that of conventional decentralized filters.

Unbiased Minimum Variance Fault and State Estimation for Linear Discrete Time-Varying Two-Dimensional Systems

The fault and state estimation problem is addressed for a class of linear discrete time-varying two-dimensional systems subject to state and measurement noises. Two estimators are proposed to compute the estimation of the system state and/or fault recursively, both of which are unbiased with minimum variance. Through formulating the estimation problem as the solvability problem of the corresponding matrix equations of estimator gains and system constraint, the necessary and sufficient condition of the existence and the solution for the proposed estimators are given. An example is used to demonstrate the effectiveness of the proposed estimators.