In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata
linear filtering problem [Kalman60]. Since that time, due in large part to advances in digital
computing, the
Kalman filter
has been the subject of extensive research and application,
particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the
general idea of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete
introductory discussion can be found in [Sorenson70], which also contains some interesting
historical narrative.
his paper provides a tutorial and survey of methods for parameterizing
surfaces with a view to applications in geometric modelling and computer graphics.
We gather various concepts from di® erential geometry which are relevant to surface
mapping and use them to understand the strengths and weaknesses of the many
methods for parameterizing piecewise linear surfaces and their relationship to one
another.
Verilog HDL: Magnitude
For a vector (a,b), the magnitude representation is the following:
A common approach to implementing these arithmetic functions is to use the Coordinate Rotation Digital Computer (CORDIC) algorithm. The CORDIC algorithm calculates the trigonometric functions of sine, cosine, magnitude, and phase using an iterative process. It is made up of a series of micro-rotations of the vector by a set of predetermined constants, which are powers of two. Using binary arithmetic, this algorithm essentially replaces multipliers with shift and add operations. In a Stratix™ device, it is possible to calculate some of these arithmetic functions directly, without having to implement the CORDIC algorithm.
The Engineering Vibration Toolbox is a set of educational programs
written in Octave by Joseph C. Slater. Also included are a number of help files,
demonstration examples, and data files containing raw experimental data. The
codes include single degree of freedom response, response spectrum, finite
elements, numerical integration, and phase plane analysis.
A one-dimensional calibration object consists of three or more collinear points with known relative positions.
It is generally believed that a camera can be calibrated only when a 1D calibration object is in planar motion or rotates
around a ¯ xed point. In this paper, it is proved that when a multi-camera is observing a 1D object undergoing general
rigid motions synchronously, the camera set can be linearly calibrated. A linear algorithm for the camera set calibration
is proposed，and then the linear estimation is further re¯ ned using the maximum likelihood criteria. The simulated and
real image experiments show that the proposed algorithm is valid and robust.
A system simulation environment in Matlab/Simulink of RFID is constructed in this paper.
Special attention is emphasized on the analog/RF circuit.Negative effects are concerned in the system
model,such as phase noise of the local oscillator,TX-RX coupling,reflection of the environment,
AWGN noise,DC offset,I/Q mismatch,etc.Performance of the whole system can be evaluated by
changing the coding method,parameters of building blocks,and operation distance.Finally,some
simulation results are presented in this paper.
ITU-T G.729語音壓縮算法。
description:
Fixed-point description of commendation G.729 with ANNEX B Coding of Speech at 8 kbit/s using Conjugate-Structure Algebraic-Code-Excited Linear-Prediction (CS-ACELP) with Voice Activity Decision(VAD), Discontinuous Transmission(DTX), and Comfort Noise Generation(CNG).
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.
