This designs demonstrates how to use the Ethernet port using a Nios II system on the DE2 board. It sends packets, and using a loopback Ethernet cable, it ll receive the same packets which are then displayed. It also works if the board is CONNECTED to another packet source.
WAP browser is based on the Wireless Application Protocol web browser, WAP1.3 will enable mobile phones and other devices CONNECTED to the Internet, browse the Internet website.
The Inter IC bus or I2C bus is a simple bidirectional two wire bus designed primarily for general control
and data transfer communication between ICs.
Some of the features of the I2C bus are:
• Two signal lines, a serial data line (SDA) and a serial clock line (SCL), and ground are required. A
12V supply line (500mA max.) for powering the peripherals often may be present.
• Each device CONNECTED to the bus is software addressable by a unique address and simple
master/ slave relationships exist at all times masters can operate as master-transmitters or as
master-receivers.
• The I2C bus is a true multi-master bus including collision detection and arbitration to prevent data
corruption if two or more masters simultaneously initiate data transfer systems.
• Serial, 8-bit oriented, bidirectional data transfers can be made at up to 100 KBit/s in the standard
mode or up to 400 KBit/s in the fast mode.
ST32
基于（英蓓特）STM32V100的EXTI程序
This example shows how to configure an external interrupt line.
In this example, the EXTI line 9 is configured to generate an interrupt on each
falling edge. In the interrupt routine a led CONNECTED to PC.06 is toggled.
This led will be toggled due to the softawre interrupt generated on EXTI Line9
then at each falling edge.
The ADC0803 family is a series of three CMOS 8-bit successive
approximation A/D converters using a resistive ladder and
capacitive array together with an auto-zero comparator. These
converters are designed to operate with microprocessor-controlled
buses using a minimum of external circuitry. The 3-State output data
lines can be CONNECTED directly to the data bus.
15篇光流配準(zhǔn)經(jīng)典文獻(xiàn)，目錄如下：
1、A Local Approach for Robust Optical Flow Estimation under Varying
2、A New Method for Computing Optical Flow
3、Accuracy vs. Efficiency Trade-offs in Optical Flow Algorithms
4、all about direct methods
5、An Introduction to OpenCV and Optical Flow
6、Bayesian Real-time Optical Flow
7、Color Optical Flow
8、Computation of Smooth Optical Flow in a Feedback CONNECTED Analog Network
9、Computing optical flow with physical models of brightness Variation
10、Dense estimation and object-based segmentation of the optical flow with robust techniques
11、Example Goal Standard methods Our solution Optical flow under
12、Exploiting Discontinuities in Optical Flow
13、Optical flow for Validating Medical Image Registration
14、Tutorial Computing 2D and 3D Optical Flow.pdf
15、The computation of optical flow
neural network utility is a Neural Networks library for the
C++ Programmer. It is entirely object oriented and focuses
on reducing tedious and confusing problems of programming neural networks.
By this I mean that network layers are easily defined. An
entire multi-layer network can be created in a few lines, and
trained with two functions. Layers can be CONNECTED to one another
easily and painlessly.
％ Train a two layer neural network with the Levenberg-Marquardt
％ method.
％
％ If desired, it is possible to use regularization by
％ weight decay. Also pruned (ie. not fully CONNECTED) networks can
％ be trained.
％
％ Given a set of corresponding input-output pairs and an initial
％ network,
％ [W1,W2,critvec,iteration,lambda]=marq(NetDef,W1,W2,PHI,Y,trparms)
％ trains the network with the Levenberg-Marquardt method.
％
％ The activation functions can be either linear or tanh. The
％ network architecture is defined by the matrix NetDef which
％ has two rows. The first row specifies the hidden layer and the
％ second row specifies the output layer.
Train a two layer neural network with a recursive prediction error
％ algorithm ("recursive Gauss-Newton"). Also pruned (i.e., not fully
％ CONNECTED) networks can be trained.
％
％ The activation functions can either be linear or tanh. The network
％ architecture is defined by the matrix NetDef , which has of two
％ rows. The first row specifies the hidden layer while the second
％ specifies the output layer.
