這是我工作做過的一個無線數值傳輸系統，提供原理圖和PCB，希望能對學習AVR mega 系列單片機的朋友提供學習測試的代碼。
上傳時間： 2016-01-11
上傳用戶：yzy6007
OFDM vs CDMA，一份report，而且還有之后的勘誤
上傳時間： 2016-01-14
上傳用戶：busterman
常見AVR單片機的選型，包括mega、tiny系列
上傳時間： 2014-11-30
上傳用戶：561596
aDABOOST This package contains the following files: learner.jar - is a platform independent java package. In order to run it on windows/linux open the command prompt/shell and type the command "java -jar learner.jar". Make sure the java installation path is set in the system enviroment. learner.exe - A windows executable version of the application. Doubleclick to run. learner.pdf - The digital version of the report. SRC\ - The source code of the program is in this directory
標簽： independent following aDABOOST contains
上傳時間： 2014-12-05
上傳用戶：xsnjzljj
this procedure is a game of basketball or subsystems time, the competition will record the entire time, and can amend the Competition time suspended contest time, the two can be set at any time during the competition process of the match, the two teams exchanged scores midfielder position, Competition can end report issued directives
標簽： competition basketball subsystems procedure
上傳時間： 2016-02-14
上傳用戶：cuibaigao
Application Note Abstract This Application Note introduces a complete and detailed PSoC® project. Telephone Call Logger keeps the detailed record of approximately 945 phone calls (7-digit number is assumed to be one phone call) including date, start time and the duration of the phone call in the PSoC device. Users can get this detailed report into the PC environment by using free software, which is included in the project file. When records reach near full capacity of the Flash memory, an LED will turn on to show that it is necessary to backup the data. Software gets the data from PSoC, organizes it and prepares a printable version. Additionally, it sends the date and time information to the PSoC. The external parts in this project can be obtained easily in the market.
標簽： Application Note introduces Abstract
上傳時間： 2014-01-01
上傳用戶：集美慧
This project aim was to build wireless software modem for data communication between two computers using an acoustic interface in the voice frequency range (20Hz– 20,000Hz). The transmitting antenna is a speaker (frequency response of: 90Hz – 20,000Hz) and the receiving antenna is a microphone (frequency response of: 100Hz – 16,000Hz). The test files used as information files were text files. This goal was attained both in an incoherent scheme and in a coherent scheme. Build under Matlab code, our modem uses OFDM (orthogonal frequency division multiplexing) modulation, synchronization by LMS sequence, channel estimation (no equalizer) via pilot tones. The symbols are either PSK or ASK for a constellation size of 2 or 4. To optimize the probability of error, these symbols were mapped using Gray mapping. Report
標簽： communication computers software wireless
上傳時間： 2014-05-29
上傳用戶：wangdean1101
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
This demo nstrates 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： sequential reversible algorithm nstrates
上傳時間： 2014-01-18
上傳用戶：康郎
This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". 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： reversible algorithm the nstrates
上傳時間： 2014-01-08
上傳用戶：cuibaigao
