Versatile visual servoing without knowledge of true jacobian.pdf cobian matrix estimator.
The Jacobian matrix estimator does not need a priori
knowledge of the kinematic structure and parameters
of the robot system, such as camera and link parameters.
The proposed visual servoing control scheme ensures
the convergence of the image-features to desired
trajectories, by using the estimated Jacobian matrix,
which is proved by the Lyapunov stability theory. To
show the effectiveness of the proposed scheme, simulation
and Experimental results are presented.
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.
一篇關于TCP-Vegas的文獻:Vegas is an implementation of TCP that achieves between 37 and 71％ better throughput on the Internet, with onefifth to one-half the losses, as compared to the implementation of TCP in the Reno distribution of BSD Unix. This paper motivates and describes the three key techniques employed by Vegas, and presents the results of a comprehensive Experimental performance study—using both simulations and measurements on the Internet—of the Vegas and Reno implementations of TCP.
This paper analyzes the vector control theory of asynchronous motors based on the magnetic orientation of motor rotors, and its mathematical model is made. Then the variable frequency vector speed-adjusting Experimental system is built with the DSP TMS320F2812 which works as the core control chip and intelligent power module.
his procedure is the power spectral density of the simulation, 3 signal source on the specific circumstances, see the "modern digital signal processing" Introduction to the first volume, P202, Exercise 5. Experimental Methods
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.
In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data classification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local scaling heuristic strategy, then optimize a pair of nonparallel hyperplanes through two smaller sized support vector machine (SVM)-typed problems. In the prediction stage, BDTWSVMs assign to the class label depending
on the kernel density degree-based distances from each test point to the two hyperplanes. BDTWSVMs not only inherit good properties from twin support vector machines (TWSVMs) but also give good description for data points. The Experimental results on toy as well as publicly available datasets
indicate that BDTWSVMs compare favorably with classical SVMs and TWSVMs in terms of generalization
The idea of writing this book entitled “Cognitive Networked Sensing and Big Data”
started with the plan to write a briefing book on wireless distributed computing
and cognitive sensing. During our research on large-scale cognitive radio network
(and its Experimental testbed), we realized that big data played a central role. As a
result, the book project reflects this paradigm shift. In the context, sensing roughly
is equivalent to “measurement.”
