Returns weighted percentiles of a sample given the weight vector w
% The idea is to give more emphasis in some examples of data as compared to
% others by giving more weight. For example, we could give lower weights to
% the outliers.
% The motivation to write this function is to compute percentiles for Monte
% Carlos simulations Where some simulations are very bad (in terms of goodness
% of fit between simulated and actual value) than the others and to give
% the lower weights based on some goodness of fit criteria.
This the second tutorial of the Writing Device Drivers series. There seems to be a lot of interest in the topic, so this article will pick up Where the first left off. The main focus of these articles will be to build up little by little the knowledge needed to write device drivers. In this article, we will be building on the same example source code used in part one. In this article, we will expand on that code to include Read functionality, Handle Input/Ouput Controls also known as IOCTLs, and learn a bit more about IRPs.
IEEE Std 1180-1990. IEEE Standard Specifications for the Implementations of 8x8 Inverse Discrete Cosine Transform, specifies the numerical characteristics of the 8x8 inverse discrete cosine transform (IDCT) for use in visual telephony and similar applications Where the 8x8 IDCT results are used in a reconstruction loop. The specifications ensure the compatibility between different implementations of the IDCT.
A new cable fault location method based on
wavelet reconstruction is proposed. In this method the
difference between the currents of faulty phase and sound
phase under the high voltage pulse excitation is used as the
measured signal and is decomposed in multi-scale by wavelet
transform, then reconstructed in single scale. Comparing with
traditional fault location method by travelling wave, the
presented method will not be interfered by the reflected wave
from the branch joint of cables or from other positions Where
the impedances are not matched and not be influenced by fault
types, otherwise, the reflected waves can be recognized even
the faulty position is near to the measuring terminal, at the
same time, the influence of the wave speed uncertainty can be
reduced. The correctness of the proposed method is proved by
simulation results.
μC/OS-II Goals
Probably the most important goal of μC/OS-II was to make it backward compatible with μC/OS (at least from an
application’s standpoint). A μC/OS port might need to be modified to work with μC/OS-II but at least, the application
code should require only minor changes (if any). Also, because μC/OS-II is based on the same core as μC/OS, it is just
as reliable. I added conditional compilation to allow you to further reduce the amount of RAM (i.e. data space) needed
by μC/OS-II. This is especially useful when you have resource limited products. I also added the feature described in
the previous section and cleaned up the code.
Where the book is concerned, I wanted to clarify some of the concepts described in the first edition and provide
additional explanations about how μC/OS-II works. I had numerous requests about doing a chapter on how to port
μC/OS and thus, such a chapter has been included in this book for μC/OS-II.
基于OFDM的無線寬帶系統(tǒng)仿真It contains mainly two parts, i.e. link-level simulator and system-level simulator.
Link-level simulator focus on a single-cell single-user scenario, Where signal is transmitted from tx, and estimated at rx. Comparing the difference in tx/rx signal, the error rate can be found out. The output of the link-level simulator is the BLER/BER vs. SNR mapping table, that can be used for the system-level simulation.
System-level simulator focus on a multi-cell multi-user scenario. For the sake of simplicity, it takes the mapping table aquired in the link-level simulation, measure the actural SNR, and finds the corresponding error rate.
多項(xiàng)式曲線擬合 任意介數(shù) Purpose - Least-squares curve fit of arbitrary order
working in C++ Builder 2007 as a template class,
using vector<FloatType> parameters.
Added a method to handle some EMathError exceptions.
If do NOT want to use this just call PolyFit2 directly.
usage: Call PolyFit by something like this.
CPolyFit<double> PolyFitObj
double correlation_coefficiant = PolyFitObj.PolyFit(X, Y, A)
Where X and Y are vectors of doubles which must have the same size and
A is a vector of doubles which must be the same size as the number of
coefficients required.
returns: The correlation coefficient or -1 on failure.
produces: A vector (A) which holds the coefficients.
This will sample all 8 A/D-channels by the continous mode.
Each end of conversion will call an interrupt routine,
Where the AD-channel is put to Port4[2..0]
and the upper nibble of the result is put to Port4[7..4].
Port 4 is connected to the user LEDs of the FlashCan100P Evaluation-Board
This example provides a description of how to use a DMA channel to transfer a
word data buffer from memory (Flash) to memory (RAM).
The dedicated DMA channel is configured to transfer once a time a 32 word data buffer
stored as constant in the Flash memory to another buffer in the RAM memory.
The received data are stored in the DST_Buffer.
The DMA channel transfer complete interrupt is enabled to generate an interrupt at
the end of the buffer transfer. As soon as the transfer is completed an interrupt is
generated and in the DMA channel interrupt routine the transfer complete interrupt
pending bit is cleared.
The data counter is stored before and after the transfer to show that all data has been
transfered.
TransferStatus gives the data transfer status Where it is PASSED if transmitted and
received data are the same otherwise it is FAILED
Hybrid Monte Carlo sampling.SAMPLES = HMC(F, X, OPTIONS, GRADF) uses a hybrid Monte Carlo
algorithm to sample from the distribution P ~ EXP(-F), Where F is the
first argument to HMC. The Markov chain starts at the point X, and
the function GRADF is the gradient of the `energy function F.