Reconstruction of atomic force microscopy image using compressed sensing

In this thesis, two inversion-based feedforward control approaches have been developed and implemented to high-speed large-range atomic force microscope (AFM) imaging and nanofabrication, respectively. High-speed large-range AFM imaging and nanofabrication are needed in many areas and have attracted great interests. Challenges, however, must be overcome because in high-speed largerange AFM operation, large positioning error of the AFM probe relative to the sample can be generated due to the adverse effects of the hardware. The nonlinear hysteresis and the vibrational dynamics effects of the piezotube actuator must be addressed during the high-speed lateral scanning of the AFM imaging over large imaging size. In addition, precision positioning of the AFM probe in the vertical direction is even more challenging (than the lateral scanning) because the desired trajectory (i.e., the sample topography profile) is unknown in general, and the probe positioning is also effected by and sensitive to the probe-sample interaction. Finally, in AFM multi-axis nanofabrication, additional positioning errors are induced by the large cross-axis dynamics coupling effect. The large positioning errors generated during high-speed, large-range fabrication will lead to large defects in the fabricated structures or devices. In the thesis, firstly, to compensate for the adverse effects generated during high-speed large-range AFM imaging, an integrated approach is proposed, which combines the enhanced inversion-based iterative control technique (EIIC) for lateral x-y axis scanning with a dual-stage piezoactuator for the vertical z-axis positioning. The main contribution of this integrated approach is the combination of an advanced control algorithm with an advanced hardware platform. This approach is demonstrated in experiments by imaging a large-size (50 μm) calibration sample at high speed (50 Hz scan rate). Then secondly, to achieve high-speed large-range multi-axis AFM nanofabrication, a recently developed model-less inversion-based iterative control technique (MIIC) is utilized to overcome the adverse effects involved in high-speed large-range multi-axis AFM nanofabrication. By using this advanced control technique,

Tetrapods have the unique property of self-aligning on a flat surface with one arm pointing in the direction perpendicular to the surface plane. The positioning of single tetrapods on the flattened tip of commercial AFM probes (see image) is reported. These tetrapod-functionalized AFM probes are used to image the topography of different samples revealing excellent spatial and vertical resolution. Detailed facts of importance to specialist readers are published as ”Supporting Information”. Such documents are peer-reviewed, but not copy-edited or typeset. They are made available as submitted by the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

The importance of the atomic force microscope (AFM), in nano-technology, demands understanding of factors affecting AFM performance and image quality. Through a simulation study, the impact of scanning parameters on AFM images acquired in contact-mode has been investigated. The performance and limitations of commercial AFM controllers was also discussed. Based on the generated images, it is seen that high speed scans may require a large force set-point to maintain probe-sample contact at the expense of higher surface deformation, contact area, and friction force. Hence, reducing resolution and increasing image distortion. The sensitivity of AFM images to scan and controller parameters, invites the development of a systematic procedure for parameter selection for a given sample and operating environment. This would greatly reduce the current guess work in parameter selection and improve image repeatability. Currently, commercial control systems do limit the capabilities of the AFM in terms of speed and data repeatability. A good controller should be able to compensate for the large uncertainty due to different cantilevers and samples that may be used, nonlinearities, and system resonances. In addition, it should provide a tight tolerance on sample surface tracking both during transient and at steady state operation. This would enable robust high-speed operation and allow small or negative force set-point to be used.

This paper demonstrates the merits of nested Compressive Sensing (CS) approach for Electrocardiogram (ECG) signals using discrete wavelet transform (DWT) and discrete cosine transform (DCT) as sensing matrix and sparsifying matrix. The implementation of CS framework is done using Gradient Projection for Sparse Reconstruction (GPSR). It is tested on 9 ECG signals of different arrhythmia categories obtained from MITBIH and BIDMC dataset. It is analysed for 3 different combinations, case (1) DCT sensing and DWT sparsification, case (2) DWT sensing and DCT sparsification and case (3) DWT sensing and DWT sparsification. A novel hybrid nested CS approach is proposed as case (4) which is a combination of case (2) in higher level and case (1) in lower level. This nested method yields the best PRD of 1.39 for CR = 0.2 and performs better than all proposed cases. This proposed approach involves a fair method of discarding the measurements in all frequency band and performs better than the state of the art work when the sampling rate is reduced by 4 times for ECG signal 100.dat from MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) database for CR = 0.2. It is shown that the reconstruction time of the proposed nested CS approach is lesser than non-nested CS approach.

We present a fast method for computing simulated Atomic Force Microscope (AFM) image scans (including tip artifacts). The basic insight is that the array of depth values in the depth buffer of a graphics system is analogous to the height field making up an AFM image, and thus the ability of graphics hardware to compute the depth of many pixels in parallel can be used to radically speed up the AFM imaging simulation. We also present a method for reconstructing better approximations to the true shape from AFM images distorted by tip artifacts. These algorithms are implemented using operations from 3D grayscale mathematical morphology operations of dilation and erosion.

Due to the broad bandwidth nature of the direct sequence spread spectrum (DSSS) signal, the application of narrowband interference (NBI) cancelation algorithms based on the Nyquist‐Shannon sampling theory are limited by the high sampling rate. To solve the sampling and processing difficulty in the traditional DSSS communication interference cancelation method, we used the compressive sensing (CS) to reduce the sampling rate of the DSSS communication system, and further proposed a compressed domain NBI elimination method in this study. We proved that NBI with a certain bandwidth and DSSS signal are both sparse in specific dictionaries and proposed the corresponding sparse dictionary construction method. Detailed analysis of the compressed domain features of NBI and the DSSS signal demonstrated that it is possible to extract the strong NBI components from the compressed measurements. Two compressed domain NBI cancelation algorithms were proposed based on this idea. The extracted NBI components were filtered out from the compressed measurements directly in the first algorithm, and then we achieved the DSSS signal demodulation in compressed domain using the separated DSSS signal components with the orthogonal matching pursuit (OMP) algorithm. A proposed extraction filter block sparse Bayesian learning (EFBSBL) framework algorithm was utilized to estimate the NBI from the compressed measurements in the second algorithm. The estimated NBI was eliminated from the received signal, and then we recompressed the DSSS signal after NBI cancelation and realized the DSSS signal demodulation in compressed domain with the OMP algorithm. Simulation results support our theoretical analysis results and reveal that the two proposed algorithms are both effective for NBI cancelation and significant interference cancelation performance are achieved.

Multiphase flow measurements, based on tomographic imaging techniques, have made substantial progress in the past several years. In particular, ultrasonic tomographic reconstruction has shown its high potential in accurately measuring fluid fractions in a flow cross-section based on direct flow measurements. These types of measurements are very attractive as the system is nonradioactive, has direct contact to fluid, yet without flow obstruction. An ultrasonic tomographic multiphase flow meter (UTM) is being developed to capture the fluid flow rates and phase compositions in producing oil wells. The UTM aims to generate cross-sectional images of the flow inside the pipe. To recover these images, complex computations and high processing time are required, based on a limited amount of available data. For this purpose, compressive sensing (CS) reconstruction methods have shown great potential in overcoming the mentioned data quality and quantity issues. CS builds upon the fact that one can recover certain data of interest from potentially very few measurements. Many CS algorithms have been proposed in the literature. In this paper, we seek to find a suitable algorithm by comparing different CS algorithms in terms of the resulting recovered image quality. Specifically, we survey three algorithms in CS, namely basis pursuit, iterative soft thresholding (IST), and orthogonal matching pursuit (OMP). We also compare the results of the CS algorithms to that of a least-square based conventional algorithm. The numerical results show the superiority of CS recovery algorithms over the conventional approach, both qualitatively and quantitatively. The results also suggest that high image quality could potentially be achieved with less complex CS algorithms, i.e., the IST and OMP algorithms. As presented in the paper, our goal is to adapt CS reconstruction algorithms for images generated by the UTM, for the purpose of accurate quantification of oil, gas, and water phase fractions produced from different wells.

Atomic force microscopy study of the antimicrobial action of Sushi peptides on Gram negative bacteria

The orthogonal matching pursuit (OMP) algorithm is widely used because of its simple algorithmic structure, the linear magnitude of its computational complexity and other advantages. Algorithms of improved and extended OMP types are continually emerging. To make the OMP algorithm more suitable for the new transmission requirements of the fifth-generation (5G) network, the orthogonal matching pursuit with multiple users (OMP_mu) algorithm is proposed for cell-free massive multi-input multi-output (MIMO) networks with multiuser spatial index modulation. The OMP_mu algorithm considers the constraint condition for the sparsity of each user on the basis of each iterative search in the OMP algorithm until all users meet the constraint. The simulation results show that compared with the original OMP algorithm, the OMP_mu algorithm has a greatly improved demodulation performance. Compared with the orthogonal matching pursuit step-by-step (OMP_step) algorithm, which takes the user sparsity constraint condition into account in two steps, the OMP_mu algorithm greatly reduces the operation time and number of iterations while achieving a slightly better performance. The OMP_mu algorithm provides an effective reference for the application of spatial index modulation in cell-free massive MIMO networks.

Traditional orthogonal matching pursuit (OMP) algorithms for direction of arrival (DOA) estimation suffer from poor angular resolution and noise suppression. In this paper, we analyze the reason why the OMP algorithm has difficulties in resolving closely separated DOAs and conclude that it lies in the rules of support detection. Moreover, we propose a solution to this problem via developing the connection between the sparse reconstruction class algorithm and the subspace algorithm from the structure of the redundant dictionary. Based on the framework of the matching pursuit (MP) algorithm, the effective information of the signal and noise subspaces is integrated, and a noise subspace reprojection orthogonal matching pursuit (NSRomp) algorithm for DOA estimation is proposed. By adopting signal subspaces to reconstruct the original signal, the proposed NSRomp can reduce both the influence of noise on the selection of the support set and the computing time. By implementing the minimum norm method to optimize the noise subspace into a vector, which corrects the selection rules of the support set during each iteration, the angular resolution of the proposed algorithm is improved. From the simulation results, when the signal to noise ratio (SNR) is lower than or near 0, the angular resolution can be improved by > 15° using OMP algorithms to by > 5° using the proposed NSRomp algorithm.

We describe in detail how atomic force microscopy (AFM) images can be routinely achieved with macroscopic silicon-based chips integrating mesoscopic tips, paving the way for the development of new near field devices combining AFM imaging with any kind of functionality integrated on a chip. The chips have been glued at the end of the free prong of 100 kHz quartz tuning forks mounted in Qplus configuration. Numerical simulations by modal analysis have been carried out to clarify the nature of the vibration modes observed in the experimental spectra. It is shown that two low frequency modes can be used to drive the system and scan the surface with a great stability in amplitude modulation as well as in frequency modulation AFM under ultrahigh vacuum. The AFM capabilities are demonstrated through a series of examples including phase and dissipation contrast imaging, force spectroscopy measurements, and investigations of soft samples in weak interaction with the substrate. The lateral resolution with the tips grown by focused ion beam deposition already matches the one achieved in standard amplitude modulation mode AFM experiments.

Nanotubes, particularly single-walled carbon nanotubes (SWCNTs), represent an important class of materials with valuable electrical, mechanical, and thermal properties. However, accurate characterization of nanotube network morphologies remains a significant challenge. We present a deep learning-based approach for extracting nanotube morphologies from atomic force microscopy (AFM) images utilizing an image-to-image (I2I) translation framework based on cycleGAN with a specialized loss function that learns to transform AFM images containing nanotubes to images of pure substrates. By subtracting these translated substrate images from the original AFM images, we effectively isolated nanotube morphologies even on substrates with roughness exceeding the nanotube diameter. We validate our approach through physics-based simulation studies and application to roll-to-roll printed carbon nanotube transistors on flexible polymeric substrates.Our method outperforms tranditional image processing and supervised learning models in sensitivity and accuracy for CNT network extraction. This improved characterization capability provides useful insights for optimizing the fabrication processes of CNT-TFTs, supporting their development for flexible electronic applications. The methodology extendsbeyond carbon nanotubes to other nanomaterial-based electronic devices, with future work aimed at expanding the model's generalization and integrating with real-time AFM imaging.

Ab initio periodic Hartree-Fock calculations with the full potential and minimum basis set are applied to interpretation of scanning tunneling microscope (STM) and atomic force microscope (AFM) images on 1TVTe2. Our results show that the simulated STM image shows asymmetry while the simulated AFM image shows the circular electron densities at the bright spots without asymmetry of electron density to agree with the experimental AFM image. The bright spots of both the STM and AFM images of VTe2 are associated with the surface Te atoms, while the patterns of bright spots of STM and AFM images are different.

Adaptive under-sampling strategy for fast imaging in compressive sensing-based atomic force microscopy

Protein-DNA interactions are pivotal for many essential biological processes. Atomic force microscopy (AFM) imaging of protein-DNA systems involved in DNA target site search, identification, and processing by proteins has contributed invaluable information to our understanding of the underlying mechanisms. The single molecule 3D resolution of AFM enables us to uncover stoichiometries and conformational properties of protein-DNA complexes. Its molecular resolution places AFM at the interface between the atomic resolution achievable by crystallography and the comparably poor (typically > hundred nanometers) spatial resolution of optical microscopy. Furthermore, the transient character of protein interactions with nonspecific DNA sites, for example during their target site search renders these complexes difficult to resolve by standard ensemble methods. Here, we review current applications and capabilities of as well as novel advances in AFM imaging in protein-DNA interaction studies.