Here an embedded System-on-Chip is build, in an Xilinx Spartan-3 FPGA with Microblaze as the processor.A PLB core System is made with the VGA IP core attached to it. The software written for the MicroBlaze processor specifies the object, the color and the movement of the display. The functionality of the module is verified by implementation on Spartan 3.
Implementation of GPU (Graphics Processing Unit) that rendered triangle based models. Our goal was to generate complex models with a movable camera. We wanted to be able to render complex images that consisted of hundreds to thousands of triangles. We wanted to apply interpolated shading on the objects, so that they appeared more
smooth and realisitc, and to have a camera that orbitted around the object, which allowed us to
look arond the object with a stationary light source. We chose to do this in hardware, because our initial implementation using running software on the NIOS II processor was too slow. Implementing parallelism in hardware is also easier to do than in software, which allows for more efficiency. We used Professor Land s floating point hardware, which allowed us to do calculations efficiency, which is essential to graphics.
Cocoa Framework 簡稱Cocoa，它是Mac OS X 上的快速應(yīng)用程序開發(fā)（RAD, Rapid Application Development）框
架，一個高度面向?qū)ο蟮模∣bject Oriented）開發(fā)框架。無論您是資深的Mac 開發(fā)人員，還是即將踏入Mac 開發(fā)世
界的新新人類，Cocoa 都是您開發(fā)應(yīng)用程序的瑞士軍刀、樂高積木，它是您構(gòu)建Mac OS X 應(yīng)用程序最強大、最高
效的工具。值得一提的是，蘋果公司之所以能夠開發(fā)出眾多頂級軟件，其實也正是因為有著Cocoa 這個秘密武器。
Cocoa 是Mac OS X 上原生支持的應(yīng)用程序開發(fā)框架，蘋果公司強烈推薦所有Mac 開發(fā)人員使用。
If you are a C++ programmer who desires a fuller understanding of what is going on "under the hood," then Inside the C++ Object Model is for you!
Inside the C++ Object Model focuses on the underlying mechanisms that support object-oriented programming within C++: constructor semantics, temporary generation, support for encapsulation, inheritance, and "the virtuals"-virtual functions and virtual inheritance. This book shows how your understanding the underlying implementation models can help you code more efficiently and with greater confidence. Lippman dispells the misinformation and myths about the overhead and complexity associated with C++, while pointing out areas in which costs and trade offs, sometimes hidden, do exist. He then explains how the various implementation models arose, points out areas in which they are likely to evolve, and why they are what they are. He covers the semantic implications of the C++ object model and how that model affects your programs.
The object detector described below has been initially proposed by
P.F. Felzenszwalb in [Felzenszwalb2010]. It is based on a
Dalal-Triggs detector that uses a single filter on histogram of
oriented gradients (HOG) features to represent an object category.
This detector uses a sliding window approach, where a filter is
applied at all positions and scales of an image. The first
innovation is enriching the Dalal-Triggs model using a
star-structured part-based model defined by a “root” filter
(analogous to the Dalal-Triggs filter) plus a set of parts filters
and associated deformation models. The score of one of star models
at a particular position and scale within an image is the score of
the root filter at the given location plus the sum over parts of the
maximum, over placements of that part, of the part filter score on
its location minus a deformation cost easuring the deviation of the
part from its ideal location relative to the root. Both root and
part filter scores are defined by the dot product between a filter
(a set of weights) and a subwindow of a feature pyramid computed
from the input image. Another improvement is a representation of the
class of models by a mixture of star models. The score of a mixture
model at a particular position and scale is the maximum over
components, of the score of that component model at the given
location.
