Quantum state estimation

New algorithm for quantum state estimation based on the maximum likelihood estimation is proposed. Existing techniques for state reconstruction based on the inversion of measured data are shown to be overestimated since they do not guarantee the positive definiteness of the reconstructed density matrix.Comment: 4 pages, twocolumn Revte

Real-time state of charge estimation of electrochemical model for lithium-ion battery

This paper proposes the real-time Kalman filter based observer for Lithium-ion concentration estimation for the electrochemical battery model. Since the computation limitation of real-time battery management system (BMS) micro-processor, the battery model which is utilized in observer has been further simplified. In this paper, the Kalman filter based observer is applied on a reduced order model of single particle model to reduce computational burden for real-time applications. Both solid phase surface lithium concentration and battery state of charge (SoC) can be estimated with real-time capability. Software simulation results and the availability comparison of observers in different Hardware-in- the-loop simulation setups demonstrate the performance of the proposed method in state estimation and real-time application

State estimation: direct state measurement vs. tomography

We compare direct state measurement (DST or weak state tomography) to conventional state reconstruction (tomography) through accurate Monte-Carlo simulations. We show that DST is surprisingly robust to its inherent bias. We propose a method to estimate such bias (which introduces an unavoidable error in the reconstruction) from the experimental data. As expected we find that DST is much less precise than tomography. We consider both finite and infinite-dimensional states of the DST pointer, showing that they provide comparable reconstructions.Comment: 4 pages, 4 figure

State Estimation in the Cerebellum

An exciting hypothesis about the cerebellum is that its role is one of state estimation—a process that combines afferent copies of motor commands with afferent sensory signals to produce a representation of the current status of the peripheral motor system. Sensory inputs alone cannot provide a perfect state signal because of inevitable delays in their afferent pathways. We have recently reported the effects of transcranial magnetic stimulation (TMS) over the ipsilateral cerebellum as healthy subjects made rapid reaching movements towards visually defined targets (Miall et al. in PLoS Biology 5:2733–2744, 2007). Errors in the initial direction and in the final finger position of this reachto-target movement were consistent with the reaching movements being planned and initiated from an estimated hand position that was about 138 ms out of date. This interval is consistent with estimates of the delays in sensory motor pathways that would inform the central nervous system of the peripheral status. We now report new data using the same paradigm, testing the effects of varying the TMS stimulus train from one, two, or three pulses. We show that the errors in movement are relatively insensitive to the TMS pulse-train duration. The estimated time interval by which the hand position is mislocalized varied by only 12 ms as the TMS train duration increased by 100 ms. Thus, this interval is likely to reflect physiological processes within the cerebellum rather than the TMSstimulus duration. This new evidence supports our earlier claim that the cerebellum is responsible for predictively updating a central state estimate over an interval of about 120–140 ms. Dysfunction of the cerebellum, whether through disease or experimental procedures, leads to motor errors consistent with a loss of knowledge of the true state of the motor system

Two-state filtering for joint state-parameter estimation

This paper presents an approach for simultaneous estimation of the state and unknown parameters in a sequential data assimilation framework. The state augmentation technique, in which the state vector is augmented by the model parameters, has been investigated in many previous studies and some success with this technique has been reported in the case where model parameters are additive. However, many geophysical or climate models contains non-additive parameters such as those arising from physical parametrization of sub-grid scale processes, in which case the state augmentation technique may become ineffective since its inference about parameters from partially observed states based on the cross covariance between states and parameters is inadequate if states and parameters are not linearly correlated. In this paper, we propose a two-stages filtering technique that runs particle filtering (PF) to estimate parameters while updating the state estimate using Ensemble Kalman filter (ENKF; these two "sub-filters" interact. The applicability of the proposed method is demonstrated using the Lorenz-96 system, where the forcing is parameterized and the amplitude and phase of the forcing are to be estimated jointly with the states. The proposed method is shown to be capable of estimating these model parameters with a high accuracy as well as reducing uncertainty while the state augmentation technique fails