Over the past years, we have witnessed destructions of various kinds caused by human actions. As a university student, write a letter to our society to
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.
Where we ve Been Where we re Going
Back in February (that s 1998, but it almost seems longer), Dr. GUI set off to start a set of columns on the Active Template Library (ATL).
we both want to thank everyone at Microsoft Press and all the people who reviewed the manuscript for their kind support throughout this project. Eric Stroo, thanks for your patience as we completed the chapters (not always as fast as you wanted them). Sally Stickney and Rebecca McKay, thanks for your excellent job editing the manuscript and making it readable. Sally Stickney and John Pierce, thanks for managing the project. Jim Fuchs at Microsoft Press, Jason Whittington at DevelopMentor, and Rick Watson at RogueWave Software, thanks for doing technical reviews of our manuscript.
In the previous article, we presented an approach for capturing similarity between words that was concerned with the syntactic similarity of two strings. Today we are back to discuss another approach that is more concerned with the meaning of words. Semantic similarity is a confidence score that reflects the semantic relation between the meanings of two sentences. It is difficult to gain a high accuracy score because the exact semantic meanings are completely understood only in a particular context.
ARP test mode. According to the idea we design the arithmetic for the key part, first the system sends a message to the target machine, and then system wait for the response. Once system receives a message, it starts to analyze the message, according to the message s parameter system judges whether the message satisfies the conditions. Once the message satisfies all the conditions, the system thinks the machine is sniffing, and adds this machine into the list of sniffing machines. On this basis the detection has done well, and at the same time we insert the result into the log database for inquire and analyze later.
we propose a technique that allows a person to design a new photograph
with substantially less effort. This paper presents a method that generates a composite image when a user types
in nouns, such as “boat” and “sand.” The artist can optionally design an intended image by specifying other
constraints. Our algorithm formulates the constraints as queries to search an automatically annotated image
database. The desired photograph, not a collage, is then synthesized using graph-cut optimization, optionally
allowing for further user interaction to edit or choose among alternative generated photos. An implementation of
our approach, shown in the associated video, demonstrates our contributions of (1) a method for creating specific
images with minimal human effort, and (2) a combined algorithm for automatically building an image library with
semantic annotations from any photo collection.
This book is designed to teach you the best practices in developing Windows DNA applications.
we have avoided making this book a primer on every technology associated with
Windows DNA. If we had followed this course, this would be an encyclopedia set.
Everyone has their favorite authors and books on the various technical subject areas. The
market is full of books to teach you the basics, the how, this book tries to be different in
that we pull out the important points to teach you about the why. If you need training in
a particular technology covered in this book, Sams has a number of 24-hour and 21-day
books that cover a wide range of topics.
we describe and demonstrate an algorithm that takes as input an
unorganized set of points fx1 xng IR3 on or near an unknown
manifold M, and produces as output a simplicial surface that
approximates M. Neither the topology, the presence of boundaries,
nor the geometry of M are assumed to be known in advance — all
are inferred automatically from the data. This problem naturally
arises in a variety of practical situations such as range scanning
an object from multiple view points, recovery of biological shapes
from two-dimensional slices, and interactive surface sketching.
