無線網卡驅動 固件程序 There are currently 3 "programming generations" of Atheros 802.11 wireless devices (some of these have multiple hardware implementations but otherwise appear identical to users): 5210 supports 11a only 5211 supports Both 11a and 11b 5212 supports 11a, 11b, and 11g
標簽： programming generations currently wireless
上傳時間： 2014-01-22
上傳用戶：aa54
The aim of this book is to provide the reader with a thorough grounding in the General Packet Radio Service – GPRS.The introduction contains a basic review of GSM to ensure that the reader is clear on the main aspects of circuit switched technology, as this will make the differences and advantages of packet oriented GPRS Both more apparent and easier to understand.
標簽： grounding the thorough General
上傳時間： 2016-03-30
上傳用戶：康郎
PCI設計指南The Xilinx LogiCORE PCI interface is a fully verified, pre-implemented PCI Bus interface. This interface is available in 32-bit and 64- bit versions, with support for multiple Xilinx FPGA device families. It is designed to support Both Verilog-HDL and VHDL. The design examples in this book are provided in Verilog.
標簽： interface PCI pre-implemented LogiCORE
上傳時間： 2016-04-03
上傳用戶：清風冷雨
The jacobi.f program solves the Helmholtz equation on a regular mesh, using an iterative Jacobi method with over-relaxation. Parallelism is exploited in Both the solver and the numerical error checking
標簽： Helmholtz iterative equation program
上傳時間： 2016-04-03
上傳用戶：杜瑩12345
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
模式識別學習綜述.該論文的英文參考文獻為303篇.很有可讀價值.Abstract— Classical and recent results in statistical pattern recognition and learning theory are reviewed in a two-class pattern classification setting. This basic model best illustrates intuition and analysis techniques while still containing the essential features and serving as a prototype for many applications. Topics discussed include nearest neighbor, kernel, and histogram methods, Vapnik–Chervonenkis theory, and neural networks. The presentation and the large (thogh nonexhaustive) list of references is geared to provide a useful overview of this field for Both specialists and nonspecialists.
標簽： statistical Classical Abstract pattern
上傳時間： 2013-11-25
上傳用戶：www240697738
In this letter, the error performance of an ultra-wideband (UWB) system with a hybrid pulse amplitude and position modulation (PAPM) scheme over indoor lognormal fading channels is analyzed. In the PAPM UWB system, input data is modulated onto Both the pulse amplitudes and pulse positions.
標簽： ultra-wideband performance amplitud letter
上傳時間： 2014-01-08
上傳用戶：yulg
1) Write a function reverse(A) which takes a matrix A of arbitrary dimensions as input and returns a matrix B consisting of the columns of A in reverse order. Thus for example, if A = 1 2 3 then B = 3 2 1 4 5 6 6 5 4 7 8 9 9 8 7 Write a main program to call reverse(A) for the matrix A = magic(5). Print to the screen Both A and reverse(A). 2) Write a program which accepts an input k from the keyboard, and which prints out the smallest fibonacci number that is at least as large as k. The program should also print out its position in the fibonacci sequence. Here is a sample of input and output: Enter k>0: 100 144 is the smallest fibonacci number greater than or equal to 100. It is the 12th fibonacci number.
標簽： dimensions arbitrary function reverse
上傳時間： 2016-04-16
上傳用戶：waitingfy
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
北京大學ACM題 Here is a geometric problem. You have an angle and some squares in the first quadrant of the plane rectangular coordinates. The vertex of the angle is fixed on the origin O of the coordinates, and Both of its radial lines are specified by the input. The sizes of the squares are also specified by the input, and the squares can shift vertically and horizontally. Now your job is to use the squares and the radial lines of the angle to enclose the maximum area, which excludes the area of the squares (see Figure 1). You should note that the edges of the squares must be parallel to the axes.
標簽： geometric quadrant problem squares
上傳時間： 2013-12-25
上傳用戶：ynzfm
