PKI中几个安全问题的研究

重点分析了ＰＫＩ中可能出现的几个安全问题并给出了相应的改进措施,这些攻击包括基于证书持有者态度实施的攻击,基于ＣＲＬ的攻击及利用定制协议进行的攻击,这些问题在ＣＡ和ＰＫＫ设计中必须给予关注和解决。     

素性检测及其实现

本文在论述素性检测对于实现ＲＳＡ分开密钥密码体制重要意义的基础上，综述了素性检测的概率方法非概率方法，给出了笔者基于Ｍｉｌｌｅｒ－Ｔｒｂｏｖｉｃｈ所发现的一种确定性的方法而发研制出的素性检测子系统。该子系统的计算机运行结果表明，它至少可以快速生成２７５位十进制素数，这对于提高ＲＳＡ密码体制的安全强度有一定的现实意义。     

公开密钥加密体制引入电子邮件

本文从理论及实际可行性初步探讨了将公开密钥加密体制ＲＳＡ引入电子邮件Ｅ－ｍａｉｌ中，以解决Ｅ－ｍａｉｌ中信息保密及身份鉴别的问题。     

RAA公开密钥密码体制的密钥生成研究

密钥生成是ＲＳＡ公钥系统的一个重要研究课题，本文介绍了密钥生成的一般方法，即确定性素数产生方法和概率性产生方法，并给出了利用ＭｉｌｌｅｒＲａｂｉｎ测试和Ｐｏｃｋｌｉｎｇｔｏｎ定理生成强伪素数的算法实现。     

CAD／CAM软件在虚拟制造中的应用及展望

本文概述了虚拟制造技术的概念,分析了在虚拟制造技术中ＣＡＤ／ＣＡＭ软件的应用特点。介绍了我国中小企业目前的状况和对ＣＡＤ／ＣＡＭ软件的需求。接着介绍了我国自主版权的ＣＡＤ／ＣＡＭ软件ＣＡＸＡ在烟台北极星模具厂生产过程中的应用。最后文章展望了ＣＡＤ／ＣＡＭ软件在未来制造业的发展。     

CAD／CAM一体化技术综述

本文简要介绍了ＣＡＤ／ＣＡＭ一体化技术，着重讨论了实现ＣＡＤ／ＣＡＭ一体化系统的关键技术及其ＣＡＤ／ＣＡＭ一体化技术的发展趋势。     

CAD／CAM／CAE技术在高压电器设计制造中的应用

本文分析了我国传统高压电器设计和制造过程中现实存在的不能满足电力工业需要的问题，提出了在高压电器工业中采用ＣＡＤ／ＣＡＭ／ＣＡＥ技术所能解决的问题，指出了与高压电器ＣＡＤ／ＣＡＭ／ＣＡＥ相关的技术，分析了我国高压电器ＣＡＤ／ＣＡＭ／ＣＡＥ技术的发展及其受限制的种种问题，提出了发展我国高压电器ＣＡＤ／ＣＡＭ／ＣＡＥ技术的切实可行的措施，对于我国工业的迅速发展有重要意义。     

CAD/CAM开发平台及其发展趋势

基于ＣＡＤ／ＣＡＭ平台的系统开发是一种重要的开发方式,研究平台对ＣＡＤ／ＣＡＭ系统的开发有重要意义,介绍了三种主流ＣＤＤ／ＣＡＭ系统开发ＡＣＩＳ,ＣＡＳ．ＣＡＤＥ和ＰＡＲＡＳＯＬＩＤ,阐述了平台的主要功能、开发接口和数据结构,并分析其各自特色,最后叫结了ＣＡＤ／ＣＡＭ平台的发展趋势。     

注塑模CAD／CAE／CAM技术及其发展概况

分析了注塑模ＣＡＤ／ＣＡＥ／ＣＡＭ技术的基本内容，综述了国内外注塑模ＣＡＤ／ＣＡＥ／ＣＡＭ技术的主要特点和研究概况，并指出了存在的问题。提出采用特征造型技术、融合人工智能技术、勾通注塑模ＣＡＤ／ＣＡＥ／ＣＡＭ技术，并指出了进一步发展的方向。     

CORBA技术的新发展

为了保持生命力,ＣＯＲＢＡ技术正在继续不断地发展,本文首先介绍ＣＯＲＢＡ的发展历程,在此基础上探讨了ＣＯＲＢＡ３．０的三个主要组成部分;ＣＯＲＢＡ服务,通过值传递对象和ＣＯＲＢＡ构件。随后简述了ＣＯＲＢＡ３．０的另外几个规范：嵌埋式ＣＯＲＢＡ,实时ＣＯＲＢＡ,Ｊａｖａ／ＩＤＬ映射,防火墙和ＤＣＥ／ＣＯＲＢＡ之间的协同工作。     

The role of perceived user-interface design in continued usage intention of self-paced e-learning tools

While past studies on user-interface design focused on a particular system or application using the experimental approach, we propose a theoretical model to assess the impact of perceived user-interface design (PUID) on continued usage intention (CUI) of self-paced e-learning tools in general. We argue that the impact of PUID is mediated by two variables, namely perceived functionality (PF) and perceived system support (PSS), which influence perceived usefulness (PU) and perceived ease of use (PEOU), respectively. We empirically validated the model using data collected from a survey administered to university students in Hong Kong. We found that most hypotheses are valid and PUID is an important antecedent of CUI of a self-paced e-learning tool. We also showed that PU and user satisfaction (USat) are two essential predictors of CUI. However, the impact of PEOU on CUI is indirect via PU as a mediator. Our findings enrich the theory on the continued usage of technology, and provide e-learning developers with managerial insights on how to entice learners to continue using their e-learning tools.     

Two‐stage 3D model‐based UAV pose estimation: A comparison of methods for optimization

Particle Filters (PFs) have been successfully used in three‐dimensional (3D) model‐based pose estimation. Typically, these filters depend on the computation of importance weights that use similarity metrics as a proxy to approximate the likelihood function. In this paper, we explore the use of a two‐stage 3D model‐based approach based on a PF for single‐frame pose estimation. First, we use a classifier trained in a synthetic data set for Unmanned Aerial Vehicle (UAV) detection and a pretrained database indexed by bounding boxes properties to obtain an initial rough pose estimate. Second, we employ optimization algorithms to optimize the used similarity metrics and decrease the obtained error. We have tested four different algorithms: (a) Particle Filter Optimization (PFO), (b) Particle Swarm Optimization (PSO), (c) modified PSO, and (d) an approach based on the evolution strategies present in the genetic algorithms named Genetic Algorithm‐based Framework (GAbF). To check the quality of the estimate on each iteration, we have tested several similarity metrics (color, edge, and mask‐based) based on the UAV Computer‐Aided Design (CAD) model. The framework is applied to the outdoor pose estimation of a fixed‐wing UAV for autonomous landing in a Fast Patrol Boat (FPB). We extend our previous approach by adopting a better problem formulation, using Deep Neural Networks (DNNs) for UAV detection, making the comparison between the used similarity metrics, comparing pose optimization schemes, and showing new results. The future work will focus on the inclusion of this scheme in a tracking architecture to increase the accuracy of the result between observations.     

Gold nano-islands on FTO as plasmonic nanostructures for biosensors

Metal nanoislands (NIs) deposited on transparent surfaces can be a convenient plasmonic material for bio/organic sensors, under the condition that a stable morphology is reached.Plasmonic materials suitable for the fabrication of low cost biosensors based on localized surface plasmon resonance (LSPR) UV-Vis spectroscopy, are fabricated by a simple methodology based on thermal evaporation of Au on commercially available, transparent fluorine-doped tin oxide (FTO) surfaces. The LSPR UV-Vis spectroscopy performed in transmittance mode reveals: (i) a small energy shift, Δλ_max, of the LSPR band under immersion both in organic solvent, and significantly in aqueous media, and (ii) a sensible and reproducible Δλ_max under formation of organic SAMs on the NIs surface. These data indicate that the Au NIs when deposited on FTO substrate exhibit (i) strong adhesion and a high stability, and (ii) a good sensitivity to molecular interaction. The samples also show that the LSPR bands recover the original feature after being exposed to different type of SAMs. Significantly, the absorption maximum, λ_max of the Au-NIs LSPR spectra shows a red shift when SAMs incorporating single strands DNA are exposed to the complementary strands. The plasmonic system based on Au NI deposited on FTO surfaces because of (i) the inexpensive fabrication of stable NIs, (ii) the easy way to detect the molecular interaction occurring at their surface, and (iii) the sensitivity of their LSPR to molecular interaction represents a convenient platform for biosensors.     

Automatic selection of arterial input function on dynamic contrast-enhanced MR images

Dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) data analysis requires the knowledge of the arterial input function (AIF) to quantify the cerebral blood flow (CBF), volume (CBV) and the mean transit time (MTT). AIF can be obtained either manually or using automatic algorithms. We present a method to derive the AIF on the middle cerebral artery (MCA). The algorithm draws a region of interest (ROI) where the MCA is located. Then, it uses a recursive cluster analysis on the ROI to select the arterial voxels. The algorithm had been compared on simulated data to literature state of art automatic algorithms and on clinical data to the manual procedure. On in silico data, our method allows to reconstruct the true AIF and it is less affected by partial volume effect bias than the other methods. In clinical data, automatic AIF provides CBF and MTT maps with a greater contrast level compared to manual AIF ones. Therefore, AIF obtained with the proposed method improves the estimate reliability and provides a quantitatively reliable physiological picture.     

CUDA-enabled Sparse Matrix–Vector Multiplication on GPUs using atomic operations

Existing formats for Sparse Matrix–Vector Multiplication (SpMV) on the GPU are outperforming their corresponding implementations on multi-core CPUs. In this paper, we present a new format called Sliced COO (SCOO) and an efficient CUDA implementation to perform SpMV on the GPU using atomic operations. We compare SCOO performance to existing formats of the NVIDIA Cusp library using large sparse matrices. Our results for single-precision floating-point matrices show that SCOO outperforms the COO and CSR format for all tested matrices and the HYB format for all tested unstructured matrices on a single GPU. Furthermore, our dual-GPU implementation achieves an efficiency of 94% on average. Due to the lower performance of existing CUDA-enabled GPUs for atomic operations on double-precision floating-point numbers the SCOO implementation for double-precision does not consistently outperform the other formats for every unstructured matrix. Overall, the average speedup of SCOO for the tested benchmark dataset is 3.33 (1.56) compared to CSR, 5.25 (2.42) compared to COO, 2.39 (1.37) compared to HYB for single (double) precision on a Tesla C2075. Furthermore, comparison to a Sandy-Bridge CPU shows that SCOO on a Fermi GPU outperforms the multi-threaded CSR implementation of the Intel MKL Library on an i7-2700 K by a factor between 5.5 (2.3) and 18 (12.7) for single (double) precision.     

Design of phononic materials/structures for surface wave devices using topology optimization

We develop a topology optimization approach to design two- and three-dimensional phononic (elastic) materials, focusing primarily on surface wave filters and waveguides. These utilize propagation modes that transmit elastic waves where the energy is contained near a free surface of a material. The design of surface wave devices is particularly attractive given recent advances in nano- and micromanufacturing processes, such as thin-film deposition, etching, and lithography, which make it possible to precisely place thin film materials on a substrate with submicron feature resolution. We apply our topology optimization approach to a series of three problems where the layout of two materials (silicon and aluminum) is sought to achieve a prescribed objective: (1) a grating to filter bulk waves of a prescribed frequency in two and three dimensions, (2) a surface wave device that uses a patterned thin film to filter waves of a single or range of frequencies, and (3) a fully three-dimensional structure to guide a wave generated by a harmonic input on a free surface to a specified output port on the surface. From the first to the third example, the resulting topologies increase in sophistication. The results demonstrate the power and promise of our computational framework to design sophisticated surface wave devices.     

Visual control of two aerial micro-robots by insect-based autopilots

In the framework of our research on biologically inspired microrobotics, we have developed two novel Automatic Flight Control Systems (AFCs): OCTAVE ( Optical flow Control sysTem for Aerospace VEhicles) and OSCAR ( Optical Scanning sensor for the Control of Autonomous Robots), both based on insects' visuomotor control systems. OCTAVE confers upon a tethered aerial robot (the OCTAVE robot) the ability to perform terrain following. OSCAR gives a tethered aerial robot (the OSCAR robot) the ability to fixate and track a contrasting target with a high level of accuracy. Both OCTAVE and OSCAR robots are based on optical velocity sensors, the principle of which is based on the results of previous electrophysiological studies on the fly's Elementary Motion Detectors (EMDs) performed at our laboratory. Both processing systems described are light enough to be mounted on-board Micro-Air Vehicles (MAVs) with an avionic payload small enough to be expressed in grams rather than kilograms.     

Live semantic data from building digital twins for robot navigation: Overview of data transfer methods

Increasing reliance on automation and robotization presents great opportunities to improve the management of construction sites as well as existing buildings. Crucial in the use of robots in a built environment is their capacity to locate themselves and navigate as autonomously as possible. Robots often rely on planar and 3D laser scanners for that purpose, and building information models (BIM) are seldom used, for a number of reasons, namely their unreliability, unavailability, and mismatch with localization algorithms used in robots. However, while BIM models are becoming increasingly reliable and more commonly available in more standard data formats (JSON, XML, RDF), they become more promising and reliable resources for localization and indoor navigation, in particular in the more static types of existing infrastructure (existing buildings). In this article, we specifically investigate to what extent and how such building data can be used for such robot navigation. Data flows are built from BIM model to local repository and further to the robot, making use of graph data models (RDF) and JSON data formats. The local repository can hereby be considered to be a digital twin of the real-world building. Navigation on the basis of a BIM model is tested in a real world environment (university building) using a standard robot navigation technology stack. We conclude that it is possible to rely on BIM data and we outline different data flows from BIM model to digital twin and to robot. Future work can focus on (1) making building data models more reliable and standard (modelling guidelines and robot world model), (2) improving the ways in which building features in the digital building model can be recognized in 3D point clouds observed by the robots, and (3) investigating possibilities to update the BIM model based on robot feedback.     

Scaled Symbolic Regression

Performing a linear regression on the outputs of arbitrary symbolic expressions has empirically been found to provide great benefits. Here some basic theoretical results of linear regression are reviewed on their applicability for use in symbolic regression. It will be proven that the use of a scaled error measure, in which the error is calculated after scaling, is expected to perform better than its unscaled counterpart on all possible symbolic regression problems. As the method (i) does not introduce additional parameters to a symbolic regression run, (ii) is guaranteed to improve results on most symbolic regression problems (and is not worse on any other problem), and (iii) has a well-defined upper bound on the error, scaled squared error is an ideal candidate to become the standard error measure for practical applications of symbolic regression.