We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation,
phase-shift keying, and pulse amplitude modulation
communications systems.We study the performance of a standard
CFO estimate, which consists of first raising the received signal to
the Mth power, where M is an integer depending on the type and
size of the symbol constellation, and then applying the nonlinear
least squares (NLLS) estimation approach. At low signal-to noise
ratio (SNR), the NLLS method fails to provide an accurate CFO
estimate because of the presence of outliers. In this letter, we derive
an approximate closed-form expression for the outlier probability.
This enables us to predict the mean-square error (MSE) on CFO
estimation for all SNR values. For a given SNR, the new results
also give insight into the minimum number of samples required in
the CFO estimation procedure, in order to ensure that the MSE
on estimation is not significantly affected by the outliers.
This paper examines the asymptotic (large sample) performance
of a family of non-data aided feedforward (NDA FF) nonlinear
least-squares (NLS) type carrier frequency estimators for burst-mode
phase shift keying (PSK) modulations transmitted through AWGN and
flat Ricean-fading channels. The asymptotic performance of these estimators
is established in closed-form expression and compared with the
modified Cram`er-Rao bound (MCRB). A best linear unbiased estimator
(BLUE), which exhibits the lowest asymptotic variance within the family
of NDA FF NLS-type estimators, is also proposed.
We present a particle filter construction for a system that exhibits
time-scale separation. The separation of time-scales allows two simplifications
that we exploit: i) The use of the averaging principle for the
dimensional reduction of the system needed to solve for each particle
and ii) the factorization of the transition probability which allows the
Rao-Blackwellization of the filtering step. Both simplifications can be
implemented using the coarse projective integration framework. The
resulting particle filter is faster and has smaller variance than the particle
filter based on the original system. The convergence of the new
particle filter to the analytical filter for the original system is proved
and some numerical results are provided.
NAME Control_AT24Cxx
This collection of routines allows an AT89C2051 microcontroller to read
and write the AT24Cxx family of serial CMOS EEPROMS. This version of the
code is compatible only with the AT89C2051 due to the location of the
data buffer and stack in RAM. The code may be modified to work with the
AT89C1051 by relocating or resizing the buffer and stack to fit into the
smaller amount of RAM available in the AT89C1051. Note that the minimum
size of the buffer is determined by the page size of the AT24Cxx.
The Funambol J2ME Mail Client aims to be a light, easy to use, free email
client for J2ME devices.
The first release comes with a simple but effective UI, and a storage limited
to the internal RMS only. This makes the application compatible with most of
the phones around (minimum requirements are: MIDP2.0, CLDC 1.0, 512k of
Storage).
The mail synchronization using SyncML 1.2 over HTTP, thus removing
any problem related to the access to IMAP or POP ports.
The mail client synchonizes its address book with the one on the server,
Its design is modular and can be extended in future with other mail
protocols, a more sophisticated UI and access to the phone s AddressBook or
filesystem for the devices allowing this.
See the javadoc for more information on the structure of the library.
When working with mathematical simulations or engineering problems, it is not unusual to handle curves that contains thousands of points. Usually, displaying all the points is not useful, a number of them will be rendered on the same pixel since the screen precision is finite. Hence, you use a lot of resource for nothing!
This article presents a fast 2D-line approximation algorithm based on the Douglas-Peucker algorithm (see [1]), well-known in the cartography community. It computes a hull, scaled by a tolerance factor, around the curve by choosing a minimum of key points. This algorithm has several advantages:
這是一個基于Douglas-Peucker算法的二維估值算法。
//
// Histogram Sample
// This sample shows how to use the Sample Grabber filter for video image processing.
// Conceptual background:
// A histogram is just a frequency count of every pixel value in the image.
// There are various well-known mathematical operations that you can perform on an image
// using histograms, to enhance the image, etc.
// Histogram stretch (aka automatic gain control):
// Stretches the image histogram to fill the entire range of values. This is a "point operation,"
// meaning each pixel is scaled to a new value, without examining the neighboring pixels. The
// histogram stretch does not actually require you to calculate the full histogram. The scaling factor
// is calculated from the minimum and maximum values in the image.
北京大學ACM比賽題目
Consider an infinite full binary search tree (see the figure below), the numbers in the nodes are 1, 2, 3, .... In a subtree whose root node is X, we can get the minimum number in this subtree by repeating going down the left node until the last level, and we can also find the maximum number by going down the right node. Now you are given some queries as "What are the minimum and maximum numbers in the subtree whose root node is X?" Please try to find answers for there queries.
KMEANS Trains a k means cluster model.CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means
algorithm to set the centres of a cluster model. The matrix DATA
represents the data which is being clustered, with each row
corresponding to a vector. The sum of squares error function is used.
The point at which a local minimum is achieved is returned as
CENTRES.
