The Kalman filter is an efficient recursive filter that estimates the state of a linear dynamic system from a series of noisy measurements. It is used in a wide range of engineering applications from radar to computer vision, and is an important topic in control theory and control systems engineering. Together with the linear-quadratic regulator (LQR), the Kalman filter solves the linear-quadratic-Gaussian control problem (LQG). The Kalman filter, the linear-quadratic regulator and the linear-quadratic-Gaussian controller are solutions to what probably are the most fundamental problems in control theory.
The use of FPGAs for cryptographic applications is highly attractive for a variety of reasons but at the same time there are many open issues related to the general security of FPGAs. This contribution attempts to provide a state-of-the-art description of this topic. First, the advantages of reconfigurable hardware for cryptographic applications are discussed from a systems perspective. Second, potential security problems of FPGAs are described in detail, followed by a proposal of a some countermeasure. Third, a list of open research problems is provided. Even though there have been many contributions dealing with the algorithmic
aspects of cryptographic schemes implemented on FPGAs, this contribution appears to be the first comprehensive treatment of system and security aspects.
mp3設計程序資料，采用c語言編寫。
README file for
yampp-3 source code 2001-05-27
This is the current state of the yampp-3 source code, 2001-05-27.
This code is intended to run on Rev. B of the yampp-3 PCB, but can
ofcourse be used on compatible systems as well.
It still uses the "old" song selection system as the yampp-2.
However, the disk handling routines has improved a lot and the
obviosly, the new VS1001 handling has been put in.
The codesize is almost at it s maximum at 1F40 bytes.
A .ROM file is included if you don t have the compiler set up.
For now, the documentation is in the code
The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application.
Knowledge of the process noise covariance matrix
is essential for the application of Kalman filtering. However,
it is usually a difficult task to obtain an explicit expression of
for large time varying systems. This paper looks at an adaptive
Kalman filter method for dynamic harmonic state estimation and
harmonic injection tracking.
this a pack include source code for quartus 2.
It is an implementation of the LC2. The LC-2 computer is described in Introduction to Computing Systems from Bits & Gates to C & Beyond by Yale Patt and Sanjay Patel, McGraw Hill, 2001. The LC2 model can be run as a simulation or downloaded to the UP3 in a larger model, TOP_LC2 that adds video output. Push buttons reset and single step the processor and a video output display of registers is generated. This state machine VHDL-based model of the LC-2 includes all source files. Currently compiled for a Cyclone EP1C6Q240 FPGA.
The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application. For details, please refer to Rao-Blackwellised Particle Filtering for Fault Diagnosis and On Sequential Simulation-Based Methods for Bayesian Filtering After downloading the file, type "tar -xf demo_rbpf_gauss.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab and run the demo.
EKF-SLAM Simulator
This version of the simulator uses global variables for
all large objects, such as the state covariance matrix.
While bad programming practice, it is a necessary evil
for MatLab efficiency, as MatLab has no facility to avoid
gratuitous memory allocation and copying when passing
(and modifying) variables between functions. With this
concession, effort has been made to keep the code as
clean and modular as possible.
The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order
A Design and Implementation of The Online Shopping System
Abstract: Along with the development of the computer ,the database technique is widespread in the application of Internet, more and more , providing more thoughtful and humanized service for the large network customer. Characteristic services have already become gradually the current as the application of Wed nowadays. This text studied a method of project which composed of characteristic page and dynamic state of technical customer according to the data connection rule, compared with some projects that adopt already on present nets, this project have the advantage of providing customers more simple ,reflective obvious meticulous use and so on..
