AM-AFM System Analysis and Output Feedback Control Design With Sensor Saturation

Fang, Yongchun; Zhang, Yudong; Qi, Ningning; Dong, Xiaokun

doi:10.1109/tnano.2013.2241450

Cited by 11 publications

(3 citation statements)

References 31 publications

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“…As a result, fast converging amplitude estimation technique has attracted a lot of attention in the control of AFM research area. One of the basic techniques in this regard is the well known lock-in amplifier (LIA) [4][5][6][7][8][9]. By multiplying the cantilever deflection signal with sine and cosine signals, LIA estimates the amplitude and phase.…”

Section: Introductionmentioning

confidence: 99%

Gradient Estimator-Based Amplitude Estimation for Dynamic Mode Atomic Force Microscopy: Small-Signal Modeling and Tuning

Ahmed

Benbouzid

2020

Sensors

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Atomic force microscopy (AFM) plays an important role in nanoscale imaging application. AFM works by oscillating a microcantilever on the surface of the sample being scanned. In this process, estimating the amplitude of the cantilever deflection signal plays an important role in characterizing the topography of the surface. Existing approaches on this topic either have slow dynamic response e.g., lock-in-amplifier or high computational complexity e.g., Kalman filter. In this context, gradient estimator can be considered as a trade-off between fast dynamic response and high computational complexity. However, no constructive tuning rule is available in the literature for gradient estimator. In this paper, we consider small-signal modeling and tuning of gradient estimator. The proposed approach greatly simplifies the tuning procedure. Numerical simulation and experimental results are provided to demonstrate the suitability of the proposed tuning procedure.

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Section: Introductionmentioning

confidence: 99%

Gradient Estimator-Based Amplitude Estimation for Dynamic Mode Atomic Force Microscopy: Small-Signal Modeling and Tuning

Ahmed

Benbouzid

2020

Sensors

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“…In practical engineering, sensors cannot generate measured outputs with unbounded amplitudes due to physical and technological limitations such as non‐linear transition shift sensors [10], position sensors [11], displacement sensors [12], pressure sensors [13] and so on. This phenomenon, often referred to as sensor saturation, would bring in extra challenges to the filtering problem.…”

Section: Introductionmentioning

confidence: 99%

filtering for non‐linear systems with stochastic sensor saturations and Markov time delays: the asymptotic stability in probability

Liu

Wang

et al. 2016

IET Control Theory & Applications

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This paper is concerned with the filtering problem for a class of nonlinear systems with stochastic sensor saturations and Markovian measurement transmission delays, where the asymptotic stability in probability is considered. The sensors are subject to random saturations characterized by a Bernoulli distributed sequence. The transmission time-delays are governed by a discrete-time Markov chain with finite states. In the presence of the nonlinearities, stochastic sensor saturations and Markovian time-delays, sufficient conditions are established to guarantee that the filtering process is asymptotically stable in probability without disturbances and also satisfies the H ∞ criterion with respect to nonzero exogenous disturbances under the zero-initial condition. Moreover, it is illustrated that the results can be specialized to linear filters. Two simulation examples are presented to show the effectiveness of the proposed algorithms.

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“…When an observation p i equals s + or s − , we say that this measurement is saturated. The saturation phenomenon may happen when the detector has a narrow range [1], [2], [3]. In this paper, we discuss how to recover x from a sensing system with saturation and then apply the proposed method to correct overexposure for C-arm computed tomography (C-arm CT).…”

mentioning

confidence: 99%

Mixed one-bit compressive sensing with applications to overexposure correction for CT reconstruction

Huang,

Xia,

Shi

et al. 2017

Preprint

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When a measurement falls outside the quantization or measurable range, it becomes saturated and cannot be used in classical reconstruction methods. For example, in Carm angiography systems, which provide projection radiography, fluoroscopy, digital subtraction angiography, and are widely used for medical diagnoses and interventions, the limited dynamic range of C-arm flat detectors leads to overexposure in some projections during an acquisition, such as imaging relatively thin body parts (e.g., the knee). Aiming at overexposure correction for computed tomography (CT) reconstruction, we in this paper propose a mixed one-bit compressive sensing (M1bit-CS) to acquire information from both regular and saturated measurements. This method is inspired by the recent progress on one-bit compressive sensing, which deals with only sign observations. Its successful applications imply that information carried by saturated measurements is useful to improve recovery quality. For the proposed M1bit-CS model, alternating direction methods of multipliers is developed and an iterative saturation detection scheme is established. Then we evaluate M1bit-CS on one-dimensional signal recovery tasks. In some experiments, the performance of the proposed algorithms on mixed measurements is almost the same as recovery on unsaturated ones with the same amount of measurements. Finally, we apply the proposed method to overexposure correction for CT reconstruction on a phantom and a simulated clinical image. The results are promising, as the typical streaking artifacts and capping artifacts introduced by saturated projection data are effectively reduced, yielding significant error reduction compared with existing algorithms based on extrapolation.

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AM-AFM System Analysis and Output Feedback Control Design With Sensor Saturation

Cited by 11 publications

References 31 publications

Gradient Estimator-Based Amplitude Estimation for Dynamic Mode Atomic Force Microscopy: Small-Signal Modeling and Tuning

Gradient Estimator-Based Amplitude Estimation for Dynamic Mode Atomic Force Microscopy: Small-Signal Modeling and Tuning

filtering for non‐linear systems with stochastic sensor saturations and Markov time delays: the asymptotic stability in probability

Mixed one-bit compressive sensing with applications to overexposure correction for CT reconstruction

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