Analysis of Imperfect Rephasing in Photon Echo-Based Quantum Memories

Over the last two decades, quantum memories have been intensively studied for potential applications of quantum repeaters in quantum networks. Various protocols have also been developed. To satisfy no noise echoes caused by spontaneous emission processes, a conventional two-pulse photon-echo scheme has been modified. The resulting methods include double-rephasing, ac Stark, dc Stark, controlled echo, and atomic frequency comb methods. In these methods, the main purpose of modification is to remove any chance of a population residual on the excited state during the rephasing process. Here, we investigate a typical Gaussian rephasing pulse-based double-rephasing photon-echo scheme. For a complete understanding of the coherence leakage by the Gaussian pulse itself, ensemble atoms are thoroughly investigated for all temporal components of the Gaussian pulse, whose maximum echo efficiency is 26% in amplitude, which is unacceptable for quantum memory applications.     

An Efficient Supervised Deep Hashing Method for Image Retrieval

In recent years, searching and retrieving relevant images from large databases has become an emerging challenge for the researcher. Hashing methods that mapped raw data into a short binary code have attracted increasing attention from the researcher. Most existing hashing approaches map samples to a binary vector via a single linear projection, which restricts the flexibility of those methods and leads to optimization problems. We introduce a CNN-based hashing method that uses multiple nonlinear projections to produce additional short-bit binary code to tackle this issue. Further, an end-to-end hashing system is accomplished using a convolutional neural network. Also, we design a loss function that aims to maintain the similarity between images and minimize the quantization error by providing a uniform distribution of the hash bits to illustrate the proposed technique’s effectiveness and significance. Extensive experiments conducted on various datasets demonstrate the superiority of the proposed method in comparison with state-of-the-art deep hashing methods.     

Fuzzy measures for color image retrieval

In this paper we present a scheme for fuzzy similarity based strategy to retrieve an image from a library of color images. Color features are among the most important features used in image database retrieval. Due to its compact representation and low complexity, direct histogram comparison is the most commonly used technique in measuring color similarity of images. A gamma membership function, derived from the Gamma distribution, has been proposed to find the membership values of the gray levels of the histogram. We present here an image retrieval scheme with some popular vector fuzzy distance measures using a gamma membership function for finding the membership values of the gray levels and evaluate the matching function to select the appropriate retrieval mechanism.     

A continuous model of the dynamical systems capable to memorise multiple shapes

This paper proposes the novel approach to the mathematical synthesis of continuous self-organising systems capable to memorise and restore own multiple shapes defined by means of functions of single spatial variable or parametric models in two-dimensional space. The model is based on the certain universal form of the integral operator with the kernel representing the system memory. The technique for memorising shapes uses the composition of singular kernels of integral operators. The whole system is described by the potential function, whose minimisation leads to the non-linear dynamics of shape reconstruction by integro-differential non-linear equations with partial derivatives. The corresponding models are proposed and analysed for both parametric and non-parametric shape definitions. Main features of the proposed model are considered, and the results of numerical simulation are shown in case of three shapes memorising and retrieval. The proposed model can be used in theory of smart materials, artificial intelligence and some other branches of non-linear sciences where the effect of multiple shapes memorising and retrieval appears as the core feature.     

A New Fourier Iterative Algorithm for the Design of Phase－only Diffractive Optical Element Used in Laser Beam Shaping

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基于多层次特征的跨场景服装检索

针对跨场景服装检索如何提取更有表述力的服装特征问题,提出了一种新的基于高层公共特征约束的相似性度量算法。首先,通过类别空间学习提取不同场景域的类别信息;然后,在场景域空间网络用类别信息约束传统对比损失函数,增大对类间负样本对的惩罚以减轻过拟合;最后,融合类别公共特征和域特定特征并通过类别判断进行辅助检索。分析和实验结果表明,新算法对跨场景服装检索的准确度要优于当前前沿的方法。     

Constrained regularization methods for ozone profile retrieval from UV/VIS nadir spectrometers

In this paper we present several constrained regularization methods for ozone profile retrieval from UV/VIS nadir sounding instruments such as GOME, SCIAMACHY, OMI and GOME-2. These methods extend the Tikhonov regularization and the iteratively regularized Gauss-Newton method with equality and inequality constraints imposed on the vertical column. It will be shown that this type of information, which is delivered by an independent algorithm like DOAS or GODFIT, significantly improves the accuracy and stability of the profile retrieval.     

Travel time models for automated warehouses with aisle transferring storage and retrieval machine

This paper presents analytical travel time models for the computation of travel time for automated warehouses with the aisle transferring S/R machine (in continuation multi-aisle AS/RS). These models consider the operating characteristics of the storage and retrieval machine such as acceleration and deceleration and the maximum velocity. Assuming uniform distributed storage rack locations and pick aisles and using the probability theory, the expressions of the cumulative distribution functions with which the mean travel time is calculated, have been determined. The computational models enable the calculation of the mean travel time for the single and dual command cycles, from which the performance of multi-aisle AS/RS can be evaluated. A simulation model of multi-aisle AS/RS has been developed to compare the performances of the proposed analytical travel time models. The analyses show that regarding all examined types of multi-aisle AS/RS, the results of proposed analytical travel time models correlate with the results of simulation models of multi-aisle AS/RS.     

Coherent X‐ray diffractive imaging of protein crystals

The technique of coherent X‐ray diffraction imaging (CXDI) has recently shown great promise for the study of inorganic nanocrystals. In this work the CXDI method has been applied to the study of micrometer‐size protein crystals. Finely sampled diffraction patterns of single crystals were measured and iterative phase‐retrieval algorithms were used to reconstruct the two‐dimensional shape of the crystal. The density maps have limited reproducibility because of radiation damage, but show clear evidence for crystal facets. Qualitative analysis of a number of single‐crystal diffraction peaks indicates the presence of inward surface contraction on 2 µm size crystals. A survey of several hundred diffraction patterns yielded a number of examples with dramatic single‐sided streaks, for which a plausible model is constructed.