In this paper we describe a control methodology for
catching a fast moving object with a robot manipulator,
Where visual information is employed to track the
trajectory of the target. Sensing, planning and control
are performed in real-time to cope with possible
unpredictable trajectory changes of the moving target,
and prediction techniques are adopted to compensate the
time delays introduced by visual processing and by the
robot controller. A simple but reliable model of the
robot controller has been taken into account in the
control architecture for improving the performance of the
system. Experimental results have shown that the robot
system is capable of tracking and catching an object
moving on a plane at velocities of up to 700 mm/s and
accelerations of up to 1500 mm/s2.
The tca package is a Matlab program that implements the tree-dependent
component analysis (TCA) algorithms that extends the independent
component analysis (ICA), Where instead of looking for a linear transform
that makes the data components independent, we are looking for components
that can be best fitted in a tree structured graphical model. The TCA model
can be applied in any situation Where the data can be assumed to have been
transformed by an unknown linear transformation.
At can be given its arguments in a file. You can comment
out lines by preceding them with either # or -
characters. This is an easy way to temporarily disable
some commands.
The CONTINUE-command is most useful at the end of the
file. When this command is read, the file is started
again from the beginning. You can use it situations Where
the machine is not shut down for the night and you want
to run some commands every day.
simulating a convolutional encoder
allows the user to input a source code to be encoded and also input the values of the generator polynomials. It outputs the encoded data bits, Where 1/n is the code rate
This paper presents a visual based localization
mechanism for a legged robot. Our proposal, fundamented
on a probabilistic approach, uses a precompiled topological
map Where natural landmarks like doors or ceiling lights
are recognized by the robot using its on-board camera.
Experiments have been conducted using the AIBO Sony
robotic dog showing that it is able to deal with noisy sensors
like vision and to approximate world models representing
indoor ofce environments. The two major contributions of
this work are the use of this technique in legged robots, and
the use of an active camera as the main sensor
The "GEE! It s Simple" package illustrates Gaussian elimination with partial pivoting, which produces a factorization of P*A into the product L*U Where P is a permutation matrix, and L and U are lower and upper triangular, respectively.
The functions in this package are accurate, but they are far slower than their MATLAB equivalents (x=A\b, [L,U,p]=lu(A), and so on). They are presented here merely to illustrate and educate. "Real" production code should use backslash and lu, not this package.
The "GEE! It s Simple" package illustrates Gaussian elimination with partial pivoting, which produces a factorization of P*A into the product L*U Where P is a permutation matrix, and L and U are lower and upper triangular, respectively.
The functions in this package are accurate, but they are far slower than their MATLAB equivalents (x=A\b, [L,U,p]=lu(A), and so on). They are presented here merely to illustrate and educate. "Real" production code should use backslash and lu, not this package.
This demo shows the BER performance of linear, decision feedback (DFE), and maximum likelihood sequence estimation (MLSE) equalizers when operating in a static channel with a deep null. The MLSE equalizer is invoked first with perfect channel knowledge, then with an imperfect, although straightforward, channel estimation algorithm. The BER results are determined through Monte Carlo simulation. The demo shows how to use these equalizers seamlessly across multiple blocks of data, Where equalizer state must be maintained between data blocks.
Problem B:Longest Ordered Subsequence
A numeric sequence of ai is ordered if a1 < a2 < ... < aN. Let the subsequence of the given numeric sequence (a1, a2, ..., aN) be any sequence (ai1, ai2, ..., aiK), Where 1 <= i1 < i2 < ... < iK <= N. For example, sequence (1, 7, 3, 5, 9, 4, 8) has ordered subsequences, e. g., (1, 7), (3, 4, 8) and many others. All longest ordered subsequences are of length 4, e. g., (1, 3, 5, 8).
