To lessen the positioning error of the piezoelectric actuator (PEA) caused by hysteresis nonlinearity and unknown external disturbance, a neural network based adaptive controller is designed to realize the accurate trajectory tracking of the PEA. Specifically, a more universal model, consisting of a hysteresis submodel and a dynamics submodel, is first built for the PEA without the requirement of parameter identification. On this basis, a sliding mode adaptive controller capable of handling unknown parameters of the dynamics submodel is designed to weaken the damage of external disturbance to the system stability. Furthermore, to deal with the hysteresis submodel with unknown structure and parameters, a neural network based self‐tuning control scheme is developed to enable the PEA to accurately track the desired trajectory. Moreover, Lyapunov stability analysis is performed to strictly prove that the tracking error of the system can asymptotically converge to zero. Finally, the performance of the designed controller is verified via sufficient comparative simulations and experiments.

>Dear editor,The inherent hysteresis of a piezoelectric actuator(PEA) results in intricate nonlinearity between the output displacement and input voltage, which restricts positioning accuracy of the actuator [1, 2]. Hysteresis behavior appears as a coupling of nonlinearity, frequency-dependence and memory characteristic, which makes it difficult to comprehensively characterize hysteresis [3]. To eliminate the effect of hysteresis on the positioning accuracy of PEAs,

To shorten the scanning time by focusing on the local scanning for such specimens as cells, this article proposes an advanced scanning strategy based on autonomous exploration, so as to detect the specimen in real time and achieve fast imaging for an atomic force microscopy. More specifically, fast raster scanning is first performed to locate the initial boundary point of the specimen. On this basis, a boundary tracking algorithm is proposed to construct the internal boundary of the specimen online through the autonomous exploration of the probe. Afterward, the boundary is expanded according to the internal boundary tracking direction, based on which a convex hull of the specimen is further constructed for the local scanning. Furthermore, the local slow scanning is performed for the specimen according to the generated scanning trajectory. Subsequently, unscanned areas in the sample can also be scanned by this autonomous method. Experimental results verify that the proposed method can achieve fast scanning while ensuring high-quality imaging.

To improve the scanning speed of an atomic force microscopy (AFM), a smooth scanning pattern is elaborately devised via trajectory shaping in this paper, so as to achieve fast imaging without hardware modification. Specifically, in the proposed scanning method, the piezoelectric actuator tracks a well-designed smooth periodic signal in x-direction, and simultaneously tracks a step signal in y-direction. The advantage of the proposed method is that it does not require additional data reprocessing to construct the morphology of the sample surface, while significantly increasing the scanning bandwidth restricted by the raster scanning method. Particularly, to directly utilize the height data collected by scanning to produce the sample morphology, the forward process in the common raster scanning mode is retained in the proposed method, the tracking signal in the forward process is thus set to a ramp function in x-direction. In addition, to ensure the continuity and smoothness of the entire tracking signal in x-direction, a segment of a sine curve is uniquely determined as the backward tracking signal by position and acceleration constraints, so as to ensure that the forward and backward curves are continuous and acceleration-continuous at the intersection point. Moreover, the frequency spectrum analysis of the designed smooth signal is carried out to exhibit the depressed amplitudes of high-frequency components, which demonstrates that the proposed method is able to reduce the resonance in AFM high-speed scanning, so as to improve the capacity of rapidly generating high-quality images. Finally, convincing comparison experiments are implemented to verify the imaging performance of the designed scanning algorithm.

In this study, a novel digital compound compensation method is proposed to compensate for the hysteresis nonlinearity and the drift disturbance of a piezoelectric nanopositioning system with a large range. The overall hysteresis behaviors can be divided into the static amplitude-dependent behavior and the dynamic rate-dependent behavior, where the static hysteresis is compensated for by a novel discrete feedforward controller, while the dynamic hysteresis and the drift disturbance are compensated for by a novel discrete composite feedback controller composed of a drift observer-based state feedback controller and a repetitive learning controller. Compared with traditional control strategies, the proposed compound control strategy, including feedforward and feedback components, can eliminate system errors more effectively when tracking large range signals with obvious hysteresis. Moreover, the proposed online drift observer is superior over a traditional offline drift compensator both in response speed and compensation accuracy. Sufficient simulation tests and convincing tracking experiments, with large range periodic signals up to 90 μm, are carried out. And comparisons with the two classical control algorithms are performed. The tracking results show that the mean absolute error of the proposed control method is minor compared with the other two algorithms, which validates that the proposed strategy can efficiently compensate for the hysteresis nonlinearity and the drift disturbance.

In this study, a novel method combining pulse coupled neural network (PCNN) and social network search (SNS) is proposed to achieve accurate image segmentation for an atomic force microscopy (AFM). The proposed method utilizes the biological visual characteristics of PCNN and the solution space search ability of SNS to determine the optimal key parameters, which can address the issue of incorrect image segmentation caused by different topographic heights of specimens in an AFM image. In the tests, the performance of the proposed method is compared with the traditional PCNN method and the Otsu method, which demonstrates that the proposed method can automatically segment the AFM image with higher accuracy and robustness.

Hybrid systems are common in real life and have been studied in many different fields. However, due to the interaction of different sub-systems, a hybrid system is much more complex than a mono-dynamic one, and great challenges are confronted when seeking stable controllers either for linear or nonlinear hybrid systems. The traditional design scheme for such a system, namely, to design a controller for each sub-system separately, cannot yield satisfactory performance, since the switches between sub-systems are not specifically considered. In fact, the controllers constructed in this way are usually of poor performance, or even unstable, as a disastrous effect caused by the alternation of sub-systems. In this paper, considering the aforementioned problem, a control scheme is proposed to design suitable controllers for hybrid systems to achieve overall stability by taking account of the switching behaviors of the system, as well as its sub-systems carefully. The design scheme consists of two steps, wherein the first step aims to design sub-controllers for different sub-systems, usually with different sub-Lyapunov functions, while a common Lyapunov function candidate is composed in the second step to modify the previously designed sub-controllers correspondingly. Following this design scheme, not only the stability, but also the closed-loop performance is successfully guaranteed. Some simulation results are provided to show the satisfactory performance of the proposed design scheme.

在对细胞、生物大分子等柔软样品进行纳米操作时,原子力显微镜（atomic force microscope,AFM）面临缺乏实时视觉反馈的问题.为此,搭建了一套面向柔软样品的AFM纳米操作可视化系统.具体而言,首先建立了AFM形貌图像坐标系到虚拟场景坐标系之间的映射关系,从而得到虚拟场景中样品的顶点信息,进而通过3维图形引擎渲染样品的虚拟形貌.在此基础上,提出了一种基于接触力学理论的样品形变估计和仿真方法,对探针按压导致的样品形变进行了虚拟视觉反馈,从而使得刻画的虚拟形貌能够和样品的真实形貌保持一致,并准确地还原按压过程中样品表面的形貌变化.仿真和实验结果表明,所设计的纳米操作可视化系统能够在虚拟场景中实时呈现AFM纳米操作过程.

Thanks to the ability to perform imaging and manipulation at the nanoscale, atomic force microscopy (AFM) has been widely used in biology, materials, chemistry, and other fields. However, as common error sources, vertical drift and illusory slope severely impair AFM imaging quality. To address this issue, this paper proposes a robust algorithm to synchronously correct the image distortion caused by vertical drift and slope, thus achieving accurate morphology characterization. Specifically, to eliminate the damage of abnormal points and feature areas on the correction accuracy, the laser spot voltage error acquired in the AFM scanning process is first utilized to preprocess the morphology height data of the sample, so as to obtain the refined alternative data suitable for line fitting. Subsequently, this paper proposes a novel line fitting algorithm based on sparse sample consensus, which accurately simulates vertical drift and slope in the cross-sectional profile of the topographic image, thereby achieving effective correction of the image distortion. In the experiments and applications, a nanoscale optical grating sample and a biological cell sample are adopted to perform topography imaging and distortion correction, so as to verify the ability of the proposed algorithm to promote AFM imaging quality.

Hysteresis and thermal drift cause image distortion of atomic force microscopy (AFM). To address this issue, a hysteresis compensation algorithm based on B-spline curve fitting is first designed to correct the image distortion due to hysteresis, so as to provide a basis for further thermal drift correction. Afterward, a novel off-line drift correction algorithm based on cross diagonals’ scanning is proposed to reconstruct high-quality AFM images. More precisely, according to the distorted image and the leading diagonal profile obtained by initial scanning, the drift is first calculated by template matching with an increasing sliding window. Furthermore, since the calculated drift contains a part of incorrect data, an adaptive filter is designed to generate more accurate horizontal thermal drift, based on which an undistorted image is constructed by bilinear interpolation. Finally, the performance of the proposed method is verified by convincing experimental and application results. Compared with online correction methods, the proposed approach is easier to implement since it does not require hardware improvement. Besides, compared with other off-line methods, the proposed method is simpler with less time consumption since it only needs two additional scanning lines to correct image distortion.

An atomic force microscopy generally adopts a raster scanning method to obtain the image of the sample morphology. However, the raster method takes too much time on the base part without focusing enough on the object, thereby restricting the scanning speed of an AFM. To solve this problem, this paper proposes a novel path planning based scanning method to achieve high-speed scanning with super resolution for AFMs. Specifically speaking, a fast scanning process is first carried out to generate a low-resolution image with less time, then a convolutional neural network is designed to construct a super-resolution image based on the fast scanning image. Afterwards, an advanced detection algorithm is proposed to achieve the accurate object detection and localization. Furthermore, an improved ant colony optimization algorithm is proposed to realize the path planning for scanning the objects with high quality, whose imaging result is then matched with the previous super-resolution image to construct the entire sample image, thus achieving fast scanning with super resolution. Experimental and application results demonstrate the good performance of the proposed scanning method.

An atomic force microscope (AFM) is able to overcome the limitation of its own imaging range while ensuring high imaging accuracy with the assistance of an optical microscope (OM). However, it is difficult and necessary to realize accurate positioning of AFM images in optical field of view by linkage control between an AFM and an OM, and the key to solving this issue is to achieve AFM and OM image registration. Therefore, in this paper, a geometric feature similarity evaluation based cross-scale image registration algorithm is proposed for AFM and OM imaging, which provides the basis for further precise positioning and imaging in the AFM and OM confocal system. To be specific, to facilitate accurate image registration, the ratio between AFM and OM imaging scales is first calibrated by utilizing a known size pattern imprinted by an AFM probe, which is then applied as a priori knowledge for image registration. Furthermore, an advanced image processing algorithm is designed to extract the geometric feature for AFM and OM images, which are stored as inner angle vectors and side length vectors. Moreover, based on the calibrated scale ratio and the geometric feature, a novel similarity evaluation function is proposed to achieve cross-scale image registration with high accuracy. Experiments and analysis of different imprinted geometries are implemented to demonstrate the good performance of the proposed method.

Atomic force microscopy (AFM) generally relies on a raster scanning method to obtain the sample morphology, which limits its scanning speed and application prospect. To solve this issue, this article proposes an optimized scanning-based fast imaging method to improve the scanning speed of the AFM system. More precisely, a class of novel tracking signals is constructed to achieve smooth scanning, for which the effect of the parameters is analyzed to provide a basis for trajectory optimization. The scanning performance of the proposed method is evaluated from such aspects as scanning uniformity, scanning coverage, scanning/imaging time, and imaging quality, wherein the scanning uniformity is analyzed with Hopkins statistic, while the scanning coverage is studied with the logistic regression. Based on these evaluation indexes, the scanning performance is investigated to determine the scanning parameters and generate the optimized trajectory. Moreover, a three-nearest-neighbor interpolation method is proposed to deal with the difficulty involved with nonlinear sampling imaging, which facilitates to reconstruct images with satisfactory quality. Finally, multiple convincing experiments and applications are implemented to further verify the good performance of the proposed method.

Nuclear transplantation is an effective method for processing, modifying, and reconstructing cells, which is widely used in biological experiments. Microinjections and extractions are key steps in nuclear transplantation. It is beneficial to the promotion and the application of nuclear transplantation by increasing the efficiency and improving the quality of microinjections. At present, little research has been implemented for the speed/volume control problem in the microinjection process. Motivated by this observation, this article designs an update-law-improved adaptive control scheme to implement precise injections and extractions. Specifically, a precise mathematical model of the overall system is established first, which takes the imaging, pneumatic pump, cell, and micropipette elements into full consideration. Since parameters in the model are difficult to calibrate or measure, an update-law-improved adaptive control algorithm, together with a trajectory planner, is constructed for the microinjection and extraction system, which shows great tolerance to parameter uncertainties, avoids overshoot of the system to ensure great protection for the manipulated cells, and demonstrates fast convergence rate. The stability of the designed control system is proved theoretically by LaSalle’s principle. Finally, the controller is tested by both numerical simulation and practical experiments, with the obtained data demonstrating satisfactory performances of the overall system.

As the kernel part in such precise instruments as an atomic force microscopy, a piezoelectric actuator achieves nano-scale displacement resolution with fast response. However, the inherent hysteresis of a piezoelectric actuator badly limits its position accuracy and further results in image distortion of an atomic force microscopy. Hysteresis occurs with three coupled characteristics, respectively, nonlinearity, memory, and frequency-dependence, thereby increasing the difficulty of hysteresis modeling. Aiming at this problem, this paper sets up a gated recurrent unit based frequency-dependent hysteresis model and then proposes an end-to-end compensation method to correct image distortion. To be specific, a gated recurrent unit layer is designed to accurately describe the nonlinearity and memory of hysteresis, based on which, a modified back propagation neural network is constructed by introducing the frequency of input voltage to simulate the frequency-dependence characteristic, finally yielding a very accurate hysteresis model with strong generalization ability. Based on the constructed model, a novel piecewise Hermitian interpolation method is then proposed to deal with the uncompensated AFM images, obtained in both forward and backward scanning directions, so as to implement end-to-end compensation for hysteresis to generate a high-quality image. Experimental and application results are presented to demonstrate the satisfactory performance of the proposed modeling and compensation methods. (C) 2019 Elsevier Ltd. All rights reserved.

The atomic force microscope (AFM) has become a powerful tool in many fields. However, environmental noise and other disturbances are very likely to cause the AFM probe to vibrate, which lead to vertical drift in AFM imaging and limit its further application. Therefore, to correct image distortion caused by vertical drift, a morphology prediction based image correction algorithm is proposed in this paper. Specifically, a Gaussian-Hann filter is first designed for distorted AFM images, based on which, an adaptive image binarization algorithm is developed to achieve accurate object detection and background extraction. Furthermore, an advanced morphology prediction algorithm, consisting of morphological approximation prediction and morphological detail prediction, is proposed to correct image distortion by using the extracted substrate of a sample image. Approximate morphology is generated by an improved weighted fusion autoregressive model, and morphological detail is obtained by energy analysis based on discrete wavelet transform. Experimental and application results are presented to illustrate that the proposed algorithm is able to effectively eliminate vertical drift of AFM images.

Advanced atomic force microscope (AFM) scanning strategy with high speed and accuracy plays a very important role in nano-control field. A novel contact scanning strategy is designed to enhance both the scanning speed and the imaging accuracy. The strategy in this paper fully explores the property of the dynamics in piezo-ceramic and cantilever, through which the torsion angle of cantilever is able to be controlled by adjusting the input voltage of piezo-ceramic. Moreover, a novel image data fusion algorithm is proposed, wherein both forward and backward scanning images are utilized to generate a new image, which can eliminate the distortion caused by transient property in control loops and improve the accuracy of scanning image. The performance of proposed strategy is tested on AFM platform, and the experiment result shows that this novel strategy is effective in high scanning frequency. Then the superior performance of the proposed strategy is validated through the experimental comparison with the traditional AFM scanning algorithm.

Image registration is usually used to transform different images from different sensors, times, depths or viewpoints into one coordinate. This paper proposes a novel template matching algorithm based on geometrical patterns, which is proved to be effective in matching AFM and optical images. As for the procedures of the proposed algorithm, firstly the resolution of the AFM image is calibrated as a priori knowledge for image processing in the next few steps. Then, traditional image processing methods, including filtering, binarization and contour-searching, are applied to the raw images sequentially. Centroids of every patternmade up of edges are connected to extract the geometrical feature information of the image. In the end, a designed assessment function is applied to the whole image to calculate each point's matching possibility. Experiments show that, compared with traditional methods, the proposed algorithm provides much more reliable matching results in AFM-optical template matching, offering an effective way for cross-scale images registration.

This paper designs a vision assistant system for an atomic force microscopy based on object detection. The system includes a visual interface, a focus assessment subsystem, a function that evaluates the uniformity of the object distribution and an object detection subsystem. A visual interface can provide researchers a convenient way to obtain the area of interest. Afterwards, the sharpest image in the region of interest can be obtained by the automatic focus of the focus assessment subsystem. Furthermore, the object distribution uniformity function is proposed to find an optimal region with uniform object distribution. Based on the above modules, the object detection algorithm is proposed to detect and locate the objects, which provides reliable visual assistant for the imaging and manipulation of an atomic force microscopy. Experimental results are exhibited to illustrate the good performance and the satisfactory robustness of the system.

As an excellent model organism, zebrafish have been widely applied in many fields. The accurate identification and tracking of individuals are crucial for zebrafish shoaling behaviour analysis. However, multi-zebrafish tracking still faces many challenges. It is difficult to keep identified for a long time due to fish overlapping caused by the crossings. Here we proposed an improved Histogram of Oriented Gradient (HOG) algorithm to calculate the stable back texture feature map of zebrafish, then tracked multi-zebrafish in a fully automated fashion with low sample size, high tracking accuracy and wide applicability. The performance of the tracking algorithm was evaluated in 11 videos with different numbers and different sizes of zebrafish. In the Right-tailed hypothesis test of Wilcoxon, our method performed better than idTracker, with significant higher tracking accuracy. Throughout the video of 16 zebrafish, the training sample of each fish had only 200-500 image samples, one-fifth of the idTracker's sample size. Furthermore, we applied the tracking algorithm to analyse the depression and hypoactivity behaviour of zebrafish shoaling. We achieved correct identification of depressed zebrafish among the fish shoal based on the accurate tracking results that could not be identified by a human.