固体浸没透镜飞行高度的气浮控制

采用固体浸没透镜的光存储方法是提高光存储密度的比较实用的近场光存储方法,而严格控制SIL下底面与光存储介质之间的亚波长级距离是此光存储系统正常工作的前提.本文采用电容法测量SIL的飞行高度,采用弹性悬臂将SIL加载在转盘表面上,转盘以不同速度转动时SIL将悬浮在不同的高度.计算机首先采集到SIL的飞行高度信息,再与设定的飞行高度作比较,根据比较结果调整转盘转速,从而达到调整SIL飞行高度的目的.采用此方法,可以动态地将SIL的下底面控制在距高速转动的转盘表面上150～600 nm范围内的一定高度上.     

合成孔径雷达光学处理器的检测方法

本文以一个通用合成孔径雷达光学处理器(简称OSARP)的检测为例,来说明OSARP的检测方法。给出OSARP的每个可调部件与合成孔径雷达(简称SAR)数据片的各特征参数间的关系,通过测量调整量求得OSARP的适用范围;用普通检测目标加位移透镜,来检测OSARP的成像质量。同时用模拟的SAR数据片,以另一种方法对OSARP做检测,结果相同。最后处理了真实SAR数据片,用来展示SAR和OSARP的综合成像质量。文中给出一些实验照片和数据,结果表明,本检测方法是可行的。     

反射镜系统的光学图像处理

在文章中，对凹面反射镜在光学图像处理领域中的应用作了概述，并证明了反射镜对入射光场的影响可用一个只考虑其几何开头引起的位相延迟函数来表示，该函数类似于薄透镜     

高频金融数据的标度分析

高频金融数据通常具有尖峰厚尾的非正态特征，常用稳定分布来拟合。本文采用稳定分布的性质对高频上证指数收益率的分布作了标度分析，求得特征指数为1．48，说明我们在分析证券指数分布时可以用稳定分布，而非正态分布。     

Multiple Observations for Secret-Key Binding with SRAM PUFs

We present a new Multiple-Observations (MO) helper data scheme for secret-key binding to an SRAM-PUF. This MO scheme binds a single key to multiple enrollment observations of the SRAM-PUF. Performance is improved in comparison to classic schemes which generate helper data based on a single enrollment observation. The performance increase can be explained by the fact that the reliabilities of the different SRAM cells are modeled (implicitly) in the helper data. We prove that the scheme achieves secret-key capacity for any number of enrollment observations, and, therefore, it is optimal. We evaluate performance of the scheme using Monte Carlo simulations, where an off-the-shelf LDPC code is used to implement the linear error-correcting code. Another scheme that models the reliabilities of the SRAM cells is the so-called Soft-Decision (SD) helper data scheme. The SD scheme considers the one-probabilities of the SRAM cells as an input, which in practice are not observable. We present a new strategy for the SD scheme that considers the binary SRAM-PUF observations as an input instead and show that the new strategy is optimal and achieves the same reconstruction performance as the MO scheme. Finally, we present a variation on the MO helper data scheme that updates the helper data sequentially after each successful reconstruction of the key. As a result, the error-correcting performance of the scheme is improved over time.     

An Entropy-Based Approach to Measurement of Stock Market Depth

The aim of this study is to investigate market depth as a stock market liquidity dimension. A new methodology for market depth measurement exactly based on Shannon information entropy for high-frequency data is introduced and utilized. The proposed entropy-based market depth indicator is supported by an algorithm inferring the initiator of a trade. This new indicator seems to be a promising liquidity measure. Both market entropy and market liquidity can be directly measured by the new indicator. The findings of empirical experiments for real-data with a time stamp rounded to the nearest second from the Warsaw Stock Exchange (WSE) confirm that the new proxy enables us to effectively compare market depth and liquidity for different equities. Robustness tests and statistical analyses are conducted. Furthermore, an intra-day seasonality assessment is provided. Results indicate that the entropy-based approach can be considered as an auspicious market depth and liquidity proxy with an intuitive base for both theoretical and empirical analyses in financial markets.     

Estimation of Feeding Composition of Industrial Process Based on Data Reconciliation

For an industrial process, the estimation of feeding composition is important for analyzing production status and making control decisions. However, random errors or even gross ones inevitably contaminate the actual measurements. Feeding composition is conventionally obtained via discrete and low-rate artificial testing. To address these problems, a feeding composition estimation approach based on data reconciliation procedure is developed. To improve the variable accuracy, a novel robust M-estimator is first proposed. Then, an iterative robust hierarchical data reconciliation and estimation strategy is applied to estimate the feeding composition. The feasibility and effectiveness of the estimation approach are verified on a fluidized bed roaster. The proposed M-estimator showed better overall performance.     

A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies

Network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities. Therefore, this paper offers an exhaustive overview of different aspects of anomaly-based network intrusion detection systems (NIDSs). Additionally, contemporary malicious activities in network systems and the important properties of intrusion detection systems are discussed as well. The present survey explains important phases of NADSs, such as pre-processing, feature extraction and malicious behavior detection and recognition. In addition, with regard to the detection and recognition phase, recent machine learning approaches including supervised, unsupervised, new deep and ensemble learning techniques have been comprehensively discussed; moreover, some details about currently available benchmark datasets for training and evaluating machine learning techniques are provided by the researchers. In the end, potential challenges together with some future directions for machine learning-based NADSs are specified.     

Knowledge Discovery from Medical Data and Development of an Expert System in Immunology

Artificial intelligence is one of the fastest-developing areas of science that covers a remarkably wide range of problems to be solved. It has found practical application in many areas of human activity, also in medicine. One of the directions of cooperation between computer science and medicine is to assist in diagnosing and proposing treatment methods with the use of IT tools. This study is the result of collaboration with the Children’s Memorial Health Institute in Warsaw, from where a database containing information about patients suffering from Bruton’s disease was made available. This is a rare disorder, difficult to detect in the first months of life. It is estimated that one in 70,000 to 90,000 children will develop Bruton’s disease. But even these few cases need detailed attention from doctors. Based on the data contained in the database, data mining was performed. During this process, knowledge was discovered that was presented in a way that is understandable to the user, in the form of decision trees. The best models obtained were used for the implementation of expert systems. Based on the data introduced by the user, the system conducts expertise and determines the severity of the course of the disease or the severity of the mutation. The CLIPS language was used for developing the expert system. Then, using this language, software was developed producing six expert systems. In the next step, experimental verification was performed, which confirmed the correctness of the developed systems.