本代碼對BPSK的誤比特率性能進行了仿真，給出了Mente carlo仿真結果與理論分析結果的對比。
上傳時間： 2015-10-07
上傳用戶：亞亞娟娟123
Uniform random number generators by Agner Fog, 2001 - 2007 randomc.zip contains a C++ class library of uniform random number generators of good quality. The random number generators found in standard libraries are often of a poor quality, insufficient for large Monte carlo calculations. This C++ implementation provides random number generators of a much better quality: Better randomness, higher resolution, and longer cycle lengths. The same random number generators are available as libraries coded in assembly language for higher speed. These libraries can be linked into projects coded in other programming languages under Windows, Linux, BSD, etc. The library files are available in the archive asmlib.zip. Non-uniform random number generators are provided in stocc.zip.
標簽： generators contains Uniform randomc
上傳時間： 2014-12-01
上傳用戶：royzhangsz
Computes BER v EbNo curve for convolutional encoding / soft decision Viterbi decoding scheme assuming BPSK. Brute force Monte carlo approach is unsatisfactory (takes too long) to find the BER curve. The computation uses a quasi-analytic (QA) technique that relies on the estimation (approximate one) of the information-bits Weight Enumerating Function (WEF) using A simulation of the convolutional encoder. Once the WEF is estimated, the analytic formula for the BER is used.
標簽： convolutional Computes encoding decision
上傳時間： 2013-12-24
上傳用戶：咔樂塢
On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標簽： demonstrates sequential Selection Bayesian
上傳時間： 2016-04-07
上傳用戶：lindor
pMatlab is a toolsbox from MIT for running matlab in parallel style on a multi-core PC or a cluster environment. These two documents summary the usage of pMatlab and running time measurements on three simple Monte carlo simulation codes.
標簽： multi-core toolsbox parallel pMatlab
上傳時間： 2014-12-05
上傳用戶：zhliu007
The package includes 3 Matlab-interfaces to the c-code: 1. inference.m An interface to the full inference package, includes several methods for approximate inference: Loopy Belief Propagation, Generalized Belief Propagation, Mean-Field approximation, and 4 monte-carlo sampling methods (Metropolis, Gibbs, Wolff, Swendsen-Wang). Use "help inference" from Matlab to see all options for usage. 2. gbp_preprocess.m and gbp.m These 2 interfaces split Generalized Belief Propagation into the pre-process stage (gbp_preprocess.m) and the inference stage (gbp.m), so the user may use only one of them, or changing some parameters in between. Use "help gbp_preprocess" and "help gbp" from Matlab. 3. simulatedAnnealing.m An interface to the simulated-annealing c-code. This code uses Metropolis sampling method, the same one used for inference. Use "help simulatedAnnealing" from Matlab.
標簽： Matlab-interfaces inference interface the
上傳時間： 2016-08-27
上傳用戶：gxrui1991
This demo shows the BER performance of linear, decision feedback (DFE), and maximum likelihood sequence estimation (MLSE) equalizers when operating in a static channel with a deep null. The MLSE equalizer is invoked first with perfect channel knowledge, then with an imperfect, although straightforward, channel estimation algorithm. The BER results are determined through Monte carlo simulation. The demo shows how to use these equalizers seamlessly across multiple blocks of data, where equalizer state must be maintained between data blocks.
標簽： performance likelihood decision feedback
上傳時間： 2013-11-25
上傳用戶：1079836864
課程設計中首先采用Ising model的思想建立一個二維的模型，然后利用重要性抽樣和Monte carlo方法及其思想模擬鐵磁-順磁相變過程。計算了順磁物質的能量平均值Ev、熱容Cv、磁化強度M及磁化率X的值，進而研究Ev、Cv、M、X與溫度T的變化關系并繪制成Ev-T圖、Cv-T圖、M-T圖、X-T圖，得出順磁物質的內能隨著溫度的升高先增大而后趨于穩定值；熱容Cv、磁化率X隨著溫度的升高先增大后減小；磁化強度M在轉變溫度Tc處迅速減小為零，找出鐵磁相變的轉變溫度Tc大約為2.35
上傳時間： 2017-05-29
上傳用戶：水中浮云
根據BS公式，通過Mente carlo模擬對歐式期權進行定價的源碼。即使不是做期權定價的，該源碼也是一個非常好的理解如何做Mente carlo模擬的實例。
標簽：
上傳時間： 2014-01-06
上傳用戶：cc1915
VHDL implementation of the twofish cipher for 128,192 and 256 bit keys. The implementation is in library-like form All needed components up to, including the round/key schedule circuits are implemented, giving the flexibility to be combined in different architectures (iterative, rolled out/pipelined etc). Manual in English is included with more details about how to use the components and/or how to optimize some of them. All testbenches are provided (tables, variable key/text, ECB/CBC monte carlo) for 128, 192 and 256 bit key sizes, along with their respective vector files.
標簽： implementation twofish cipher VHDL
上傳時間： 2017-06-25
上傳用戶：王小奇
