Biblio

Scanning Transmission Electron Microscopy (STEM) offers high-resolution images that are used to quantify the nanoscale atomic structure and composition of materials and biological specimens. In many cases, however, the resolution is limited by the electron beam damage, since in traditional STEM, a focused electron beam scans every location of the sample in a raster fashion. In this paper, we propose a scanning method based on the theory of Compressive Sensing (CS) and subsampling the electron probe locations using a line hop sampling scheme that significantly reduces the electron beam damage. We experimentally validate the feasibility of the proposed method by acquiring real CS-STEM data, and recovering images using a Bayesian dictionary learning approach. We support the proposed method by applying a series of masks to fully-sampled STEM data to simulate the expectation of real CS-STEM. Finally, we perform the real data experimental series using a constrained-dose budget to limit the impact of electron dose upon the results, by ensuring that the total electron count remains constant for each image.

At present, in the face of the huge and complex data in cloud computing, the parallel computing ability of quantum computing is particularly important. Quantum principal component analysis algorithm is used as a method of quantum state tomography. We perform feature extraction on the eigenvalue matrix of the density matrix after feature decomposition to achieve dimensionality reduction, proposed quantum principal component extraction algorithm (QPCE). Compared with the classic algorithm, this algorithm achieves an exponential speedup under certain conditions. The specific realization of the quantum circuit is given. And considering the limited computing power of the client, we propose a quantum homomorphic ciphertext dimension reduction scheme (QHEDR), the client can encrypt the quantum data and upload it to the cloud for computing. And through the quantum homomorphic encryption scheme to ensure security. After the calculation is completed, the client updates the key locally and decrypts the ciphertext result. We have implemented a quantum ciphertext dimensionality reduction scheme implemented in the quantum cloud, which does not require interaction and ensures safety. In addition, we have carried out experimental verification on the QPCE algorithm on IBM's real computing platform. Experimental results show that the algorithm can perform ciphertext dimension reduction safely and effectively.

Acoustic tomography experiments for ocean observation require low-frequency, broadband, high power, small size underwater acoustic transducer, but there are contradictions between the performance of the transducer, therefore a longitudinal-bending fluid-cavity coupled broadband underwater acoustic transducer is presented. The difference between the transducer and the traditional JH transducer is that the opening position of the Helmholtz resonant cavity is arranged between the radiation cover plate and the cylindrical cavity. Based on the optimization results of the finite element software ANSYS produced a transducer test prototype. The test results show that the simulation results and experimental results are basically consistent, and the transmitting voltage response can reach 136dB, the transmitting voltage response fluctuation shall no more than 6dB through the range of 700-1200Hz in the horizontal direction, verified the longitudinal-bending mode and the fluid-cavity mode of the transducer are well coupled, and the transducer is an ideal low-frequency, broadband, high power, small size underwater acoustic transducer.

Density information has been used as a property of sound to restore objects in a quantitative manner in ultrasound tomography based on backscatter theory. In the traditional method, the authors only study the distorted Born iterative method (DBIM) to create density images using Tikhonov regularization. The downside is that the image quality is still low, the resolution is low, the convergence rate is not high. In this paper, we study the DBIM method to create density images using compressive sampling technique. With compressive sampling technique, the probes will be randomly distributed on the measurement system (unlike the traditional method, the probes are evenly distributed on the measurement system). This approach uses the l1 regularization to restore images. The proposed method will give superior results in image recovery quality, spatial resolution. The limitation of this method is that the imaging time is longer than the one in the traditional method, but the less number of iterations is used in this method.

Optical Coherence Tomography (OCT) has shown a great potential as a complementary imaging tool in the diagnosis of skin diseases. Speckle noise is the most prominent artifact present in OCT images and could limit the interpretation and detection capabilities. In this work we evaluate various denoising filters with high edge-preserving potential for the reduction of speckle noise in 256 dermatological OCT B-scans. Our results show that the Enhanced Sigma Filter and the Block Matching 3-D (BM3D) as 2D denoising filters and the Wavelet Multiframe algorithm considering adjacent B-scans achieved the best results in terms of the enhancement quality metrics used. Our results suggest that a combination of 2D filtering followed by a wavelet based compounding algorithm may significantly reduce speckle, increasing signal-to-noise and contrast-to-noise ratios, without the need of extra acquisitions of the same frame.