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.
obot control, a subject aimed at making robots behave as desired, has been
extensively developed for more than two decades. Among many books being
published on this subject, a common feature is to treat a robot as a single
system that is to be controlled by a variety of control algorithms depending on
different scenarios and control objectives. However, when a robot becomes more
complex and its degrees of freedom of motion increase substantially, the needed
control computation can easily go beyond the scope a modern computer can
handle within a pre-specified sampling period. A solution is to base the control
on subsystem dynamics.
In this book we focus on the basic signal processing that underlies current and
future ultra wideband systems. By looking at signal processing in this way we
hope this text will be useful even as UWB applications mature and change or
regulations regarding ultra wideband systems are modified. The current UWB
field is extremely dynamic, with new techniques and ideas being presented at every
communications and signal-processing conference. The basic signal-processing
techniques presented in this text though will not change for some time to come.
Thus, we have taken a somewhat theoretical approach, which we believe is longer
lasting and more useful to the reader in the long term than an up-to-the-minute
summary that is out of date as soon as it is published.
A modern power grid needs to become smarter in order to provide an affordable,
reliable, and sustainable supply of electricity. For these reasons, a smart grid is
necessary to manage and control the increasingly complex future grid. Certain
smart grid elements including renewable energy, storage, microgrid, consumer
choice, and smart appliances like electric vehicles increase uncertainty in both
supply and demand of electric power.
Modern power systems involve large amount of investment. An electric power
system comprises of generation, transmission, and distribution of electric energy.
Growth of power systems has led to very complex networks extended across large
areas. In such situations, the proper functioning of a modern power system is
heavily dependent upon the healthy operation of the transmission lines within it.
Transmission lines are used to transmit a huge amount of power over a long
distance. But as these lines are located in the open atmosphere, they are highly
affected by different types of abnormal conditions or faults.
Radio frequency identifi cation (RFID) is a modern wireless data transmission and
reception technique for applications including automatic identifi cation, asset track-
ing and security surveillance. As barcodes and other means of identifi cation and
asset tracking are inadequate for recent demands, RFID technology has attracted
interest for applications such as logistics, supply chain management, asset tracking
and security access control.
AR0231AT7C00XUEA0-DRBR（RGB濾光）安森美半導體推出采用突破性減少LED閃爍 (LFM)技術的新的230萬像素CMOS圖像傳感器樣品AR0231AT，為汽車先進駕駛輔助系統(ADAS)應用確立了一個新基準。新器件能捕獲1080p高動態范圍(HDR)視頻，還具備支持汽車安全完整性等級B(ASIL B)的特性。LFM技術(專利申請中)消除交通信號燈和汽車LED照明的高頻LED閃爍，令交通信號閱讀算法能于所有光照條件下工作。AR0231AT具有1/2.7英寸(6.82 mm)光學格式和1928(水平) x 1208(垂直)有源像素陣列。它采用最新的3.0微米背照式(BSI)像素及安森美半導體的DR-Pix?技術，提供雙轉換增益以在所有光照條件下提升性能。它以線性、HDR或LFM模式捕獲圖像，并提供模式間的幀到幀情境切換。 AR0231AT提供達4重曝光的HDR，以出色的噪聲性能捕獲超過120dB的動態范圍。AR0231AT能同步支持多個攝相機，以易于在汽車應用中實現多個傳感器節點，和通過一個簡單的雙線串行接口實現用戶可編程性。它還有多個數據接口，包括MIPI(移動產業處理器接口)、并行和HiSPi(高速串行像素接口)。其它關鍵特性還包括可選自動化或用戶控制的黑電平控制，支持擴頻時鐘輸入和提供多色濾波陣列選擇。封裝和現狀：AR0231AT采用11 mm x 10 mm iBGA-121封裝，現提供工程樣品。工作溫度范圍為-40℃至105℃(環境溫度)，將完全通過AEC-Q100認證。
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.
