基于PIC單片機的低功耗讀卡器硬件設計:本文提出了一個完整的基于串口的智能讀卡器子系統設計方案并將其實現。讀卡器的設計突出了小型化的要求，全部器件使用貼片封裝。為了減小讀卡器的體積，設計中還使用了串口竊電的技術，使用串口信號線直接給讀卡器供電。為此，讀卡器使用了省電的設計，采用了省電的集成電路，并大膽簡化了許多傳統的設計電路。關鍵字： 讀卡器， 單片機， 串口竊電
Abstract： This paper aims to put forward a complete design of Smart IC card reader based onSerial Port and propose the way of realizing it for the purpose of Network Security. SMD isadopted to make Smart IC reader smaller in this design. To reduce the volume of Smart ICreader， Serial Port powered technology is employed to get power from the signal line of Serial Port. For this reason， low-power consumption components are adopted in the design and some traditional designs are simplified to reduce the power consumption.Keywords： Card Reader； Single-chip Computer； Serial Port Powered
IC 卡系統保存了加密算法所需要的工作密鑰，供加密算法對網絡上傳輸的數據加密使用，是整個系統網絡安全的核心。在IC 卡子系統中，讀卡器是一個重要的部分。它起著管理IC卡、在IC 卡和PC或網絡計算機間傳遞數據的重要作用。本文以一片PIC單片機為核心完成了基于RS232 串口的讀卡器的硬件設計。
This paper presents an interactive technique that
produces static hairstyles by generating individual hair strands
of the desired shape and color, subject to the presence of gravity
and collisions. A variety of hairstyles can be generated by
adjusting the wisp parameters, while the deformation is solved
efficiently, accounting for the effects of gravity and collisions.
Wisps are generated employing statistical approaches. As for
hair deformation, we propose a method which is based on
physical simulation concepts but is simplified to efficiently
solve the static shape of hair. On top of the statistical wisp
model and the deformation solver, a constraint-based styler
is proposed to model artificial features that oppose the natural
flow of hair under gravity and hair elasticity, such as a hairpin.
Our technique spans a wider range of human hairstyles than
previously proposed methods, and the styles generated by this
technique are fairly realistic.
Rao-Blackwellised Particle Filters (RBPFs) are a class of Particle
Filters (PFs) that exploit conditional dependencies between
parts of the state to estimate. By doing so, RBPFs can
improve the estimation quality while also reducing the overall
computational load in comparison to original PFs. However,
the computational complexity is still too high for many
real-time applications. In this paper, we propose a modified
RBPF that requires a single Kalman Filter (KF) iteration per
input sample. Comparative experiments show that while good
convergence can still be obtained, computational efficiency is
always drastically increased, making this algorithm an option
to consider for real-time implementations.
The need for accurate monitoring and analysis of sequential data arises in many scientic, industrial
and nancial problems. Although the Kalman lter is effective in the linear-Gaussian
case, new methods of dealing with sequential data are required with non-standard models.
Recently, there has been renewed interest in simulation-based techniques. The basic idea behind
these techniques is that the current state of knowledge is encapsulated in a representative
sample from the appropriate posterior distribution. As time goes on, the sample evolves and
adapts recursively in accordance with newly acquired data. We give a critical review of recent
developments, by reference to oil well monitoring, ion channel monitoring and tracking
problems, and propose some alternative algorithms that avoid the weaknesses of the current
methods.
Abstract
The Lucene Server project is an attempt to extend the Jakarta Lucene tool with server capabilities.
Lucene is a robust Java API that enables you creating indexes from text sources and perform powerful searches on these indexes. With Lucene, creating an index must be done programmatically and there are almost no possibilities of integrating index management in a distributed environment. In other words, out of the box, Lucene is suitable for integrating indexing and searching possibilities in a single application but not for providing index/search services for multiple applications.
The Lucene Server project comes with a Java API that propose the following
make it easy to create indexes in a declarative way by simply providing an XML configuration document.
make it easy to personalize the way Lucene must handle different kind of data sources.
provide services for index management and searching that can be accessed from several applications.
enable batch tasks scheduling.
Sequential Monte Carlo without Likelihoods
粒子濾波不用似然函數的情況下
本文摘要:Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributions
in the presence of analytically or computationally intractable likelihood functions.
Despite representing a substantial methodological advance, existing methods based on rejection
sampling or Markov chain Monte Carlo can be highly inefficient, and accordingly
require far more iterations than may be practical to implement. Here we propose a sequential
Monte Carlo sampler that convincingly overcomes these inefficiencies. We demonstrate
its implementation through an epidemiological study of the transmission rate of tuberculosis.
Introduction
Computer security is undeniably important, and as new vulnerabilities are discovered and
exploited, the perceived need for new security solutions grows. "Trusted computing"
initiatives propose to solve some of today s security problems through hardware changes
to the personal computer. Changing hardware design isn t inherently suspicious, but the
leading trusted computing proposals have a high cost: they provide security to users
while giving third parties the power to enforce policies on users computers against the
users wishes -- they let others pressure you to hand some control over your PC to
someone else. This is a "feature" ready-made for abuse by software authors who want to
anticompetitively choke off rival software.
It needn t be this way: a straightforward change to the plans of trusted computing vendors
could leave the security benefits intact while ensuring that a PC owner s
Behavioral models are used in games and computer graphics for
realistic simulation of massive crowds. In this paper, we present a
GPU based implementation of Reynolds [1987] algorithm for simulating
flocks of birds and propose an extension to consider environment
self occlusion. We performed several experiments and
the results showed that the proposed approach runs up to three
times faster than the original algorithm when simulating high density
crowds, without compromising significantly the original crowd
behavior.
We consider the problem of target localization by a
network of passive sensors. When an unknown target emits an
acoustic or a radio signal, its position can be localized with multiple
sensors using the time difference of arrival (TDOA) information.
In this paper, we consider the maximum likelihood formulation
of this target localization problem and provide efficient convex
relaxations for this nonconvex optimization problem.We also propose
a formulation for robust target localization in the presence of
sensor location errors. Two Cramer-Rao bounds are derived corresponding
to situations with and without sensor node location errors.
Simulation results confirm the efficiency and superior performance
of the convex relaxation approach as compared to the
existing least squares based approach when large sensor node location
errors are present.
We review the current applications of photonic technologies to Smart Cities. Inspired
by the future needs of Smart Cities, we then propose potential applications of advanced
photonic technologies. We find that photonics already has a major impact on Smart
Cities, in terms of smart lighting, sensing, and communication technologies. We further
find that advanced photonic technologies could lead to vastly improved infrastructure,
such as smart water‐supply systems. We conclude by proposing directions for future
research that will have the greatest impact on realizing Smart City initiatives.
