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.
Rao-Blackwellised Particle Filters (RBPFs) are a class of Particle
Filters (PFs) that exploit conditional dependencies between
parts of the state to estimate. By doing so, RBPFs can
improve the estimation quality while also reducing the overall
computational load in comparison to original PFs. However,
the computational complexity is still too high for many
real-time applications. In this paper, we propose a modified
RBPF that requires a single Kalman Filter (KF) iteration per
input sample. Comparative experiments show that while good
convergence can still be obtained, computational efficiency is
always drastically increased, making this algorithm an option
to consider for real-time implementations.
Generate 100 samples of a zero-mean white noise sequence with variance , by using a uniform random number generator.
a Compute the autocorrelation of for .
b Compute the periodogram estimate and plot it.
c Generate 10 different realizations of , and compute the corresponding sample autocorrelation sequences , and . Compute the average autocorrelation sequence as and the corresponding periodogram for .
d Compute and plot the average periodogram using the Bartlett method.
e Comment on the results in parts (a) through (d).
用Fourier變換求取信號的功率譜---周期圖法
用Fourier變換求取信號的功率譜---分段周期圖法
用Fourier變換求取信號的功率譜---welch方法
功率譜估計----多窗口法（multitaper method ,MTM法）
功率譜估計----最大熵法（maxmum entmpy method,MEM法）
功率譜估計----多信號分類法（multiple signal classification,music法）Fourier transform to strike a signal to the power spectrum - the cycle of plans
Fourier transform to strike a signal to the power spectrum - Sub-cycle Method
Fourier transform to strike a signal to the power spectrum --- welch method
Power spectrum estimated more than window ---- Law (multitaper method, MTM)
---- Power spectrum estimate of maximum entropy (maxmum entmpy method, MEM)
---- More than the estimated power spectrum signal classification (multiple signal classification, music)
SuperLU is a general purpose library for the direct solution of large, sparse, nonsymmetric systems of linear equations on high performance machines. The library is written in C and is callable from either C or Fortran. The library routines will perform an LU decomposition with partial pivoting and triangular system solves through forward and back substitution. The LU factorization routines can handle non-square matrices but the triangular solves are performed only for square matrices. The matrix columns may be preordered (before factorization) either through library or user supplied routines. This preordering for sparsity is completely separate from the factorization. Working precision iterative refinement subroutines are provided for improved backward stability. Routines are also provided to equilibrate the system, estimate the condition number, calculate the relative backward error, and estimate error bounds for the refined solutions.
The Kalman filter is a set of mathematical equations that provides an efficient computational
[recursive] means to estimate the state of a process, in a way that minimizes
the mean of the squared error. The filter is very powerful in several aspects:
it supports estimations of past, present, and even future states, and it can do so even
when the precise nature of the modeled system is unknown.
Abstract—In this paper, we propose transform-domain algorithms to
effectively classify the characteristics of blocks and estimate the strength
of the blocky effect. The transform-domain algorithms require much
lower computational complexity and much less memory than the spatial
ones. Along with the estimated blocky strength,
This includes the project using a stereo vision to catch the ball shot from a high pressure air cannon. The trajectory of the ball is located first by finding the ball color in the left and right camera. Then the trajectory is calculated to estimate the depth from the ball to the camera. The camera is calibrated to map the world coordinate to the camera coordinate and it can reach an accuracy over 90 . Enjoy
Reconstruction- and example-based super-resolution
(SR) methods are promising for restoring a high-resolution
(HR) image from low-resolution (LR) image(s). Under large
magnification, reconstruction-based methods usually fail
to hallucinate visual details while example-based methods
sometimes introduce unexpected details. Given a generic
LR image, to reconstruct a photo-realistic SR image and
to suppress artifacts in the reconstructed SR image, we
introduce a multi-scale dictionary to a novel SR method
that simultaneously integrates local and non-local priors.
The local prior suppresses artifacts by using steering kernel regression to predict the target pixel from a small local
area. The non-local prior enriches visual details by taking
a weighted average of a large neighborhood as an estimate
of the target pixel. Essentially, these two priors are complementary to each other. Experimental results demonstrate
that the proposed method can produce high quality SR recovery both quantitatively and perceptually.
The Internet of Things is considered to be the next big opportunity, and challenge, for the
Internet engineering community, users of technology, companies and society as a whole. It
involves connecting embedded devices such as sensors, home appliances, weather stations
and even toys to Internet Protocol (IP) based networks. The number of IP-enabled embedded
devices is increasing rapidly, and although hard to estimate, will surely outnumber the
number of personal computers (PCs) and servers in the future. With the advances made over
the past decade in microcontroller,low-power radio, battery and microelectronic technology,
the trend in the industry is for smart embedded devices (called smart objects) to become
IP-enabled, and an integral part of the latest services on the Internet. These services are no
longer cyber, just including data created by humans, but are to become very connected to the
physical world around us by including sensor data, the monitoring and control of machines,
and other kinds of physical context. We call this latest frontier of the Internet, consisting of
wireless low-power embedded devices, the Wireless Embedded Internet. Applications that
this new frontier of the Internet enable are critical to the sustainability, efficiency and safety
of society and include home and building automation, healthcare, energy efficiency, smart
grids and environmental monitoring to name just a few.
