<li id="4oicu"></li> <li id="4oicu"></li> rc5 encryption- rc5 encryption using vhdl, using state machine, more detailed description can be found in ieee papers.
標(biāo)簽： encryption using description rc5
上傳時(shí)間： 2013-12-22
上傳用戶：13517191407
RC5 decryption algorithm implementation, using vhdl, with state machine implementation, use ieee papers for more detailed description.
標(biāo)簽： implementation decryption algorithm machine
上傳時(shí)間： 2014-01-06
上傳用戶：bruce5996
rc5 key expansion algorithm implementation in vhdl, using state machine too. use ieee papers for more detailed description
標(biāo)簽： implementation expansion algorithm machine
上傳時(shí)間： 2017-07-14
上傳用戶：lyy1234
gum vending machine implementation in vhdl, state machine implementation,
標(biāo)簽： implementation machine vending state
上傳時(shí)間： 2017-07-14
上傳用戶：zycidjl
least mean square algorithm for estimation state
標(biāo)簽： estimation algorithm square least
上傳時(shí)間： 2013-12-20
上傳用戶：jackgao
The design of Kalman filter for estimation of state
標(biāo)簽： estimation Kalman design filter
上傳時(shí)間： 2013-12-23
上傳用戶：gtf1207
A 8 puzzle program solver.user have to input the current state and goal state of your 8 puzzle and it solving by the program.
標(biāo)簽： puzzle state and program
上傳時(shí)間： 2014-08-10
上傳用戶：huyiming139
Two scripts are included here. 1. convsys.m - combines the state space representation of two systems connected in series. [Ao,Bo,Co,Do]=convsys(A1,B1,C1,D1,A2,B2,C2,D2) This algorithm gives the convolution of two state space representations | A1 B1 | | A2 B2 | u ==> | | ==> | | ==> y | C1 D1 | | C2 D2 | The algorithm also accepts state space objects as inputs and gives out a state space object as output. 2. sysfeedbk.m [Ao,Bo,Co,Do]=convsys(A1,B1,C1,D1,A2,B2,C2,D2) Gives the closed loop state space representation for two systems connected with negative feedback in the following manner. | A1 B1 | u ==> | | ==> y + o | C1 D1 | | - | | | | A2 B2 | | |= | |= | | C2 D2 | The zip file also contains checkcompatibility.m , which checks the compatibility of matrix dimensions in the system and cleanss.m which can be used to clean a state space representation.
標(biāo)簽： representation included combines scripts
上傳時(shí)間： 2017-07-25
上傳用戶：semi1981
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.
標(biāo)簽： filter efficient estimates recursive
上傳時(shí)間： 2017-08-06
上傳用戶：風(fēng)之驕子
Solve the 8-puzzle problem using A * algorithme. Input: Program reads start state and goal state and heuristic (N or S) from EightPuzzle.INP file.0 representing blank. There are 2 Heuristic: 1. N: Number of misplaced tiles 2. S: Sum of Manhattan distance of current location and target location. Format: The first line write type of heuristic (N or S). Next is the status of departing and landing status. Between 2 states of 1 line blank. See examples EightPuzzle.INP
標(biāo)簽： state algorithme Program problem
上傳時(shí)間： 2017-08-12
上傳用戶：jjj0202
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