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HDOJ ACM
input:The input consists of T test cases. The number of test cases ) (T is given in the first line of the input. Each test case begins with a line containing an integer N , 1<=N<=200 , that represents the number of tables to move. Each of the following N lines contains two positive integers s and t, representing that a table is to move from room number s to room number t (each room number appears at most once in the N lines). From the N+3-rd line, the remaining test cases are listed in the same manner as above.
標簽：
input
cases
test
The
上傳時間：
2015-10-18
上傳用戶：三人用菜
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a sample WDM stream class video capture driver that supports two IEEE 1394 digital cameras.
The same driver may be able to support other digital cameras that conform to 1394-based Digital Camera Specification from 1394 Trade Association.
Digital camera supported by dcam.sys is a data source that produces digital image data without any other input connection. It manifests itself in a DirectShow graph as a WDM Streaming Capture Device and as a capture filter that has output capture stream supporting image sizes of 320x240 with a UYVY color space. Its de-compressor, Msyuv.dll, is part of the OS delivery, and it can convert image data format from UYVY to RGB16, RGB8, or to a Direct Draw surface if the video card supports UYVY format.
標簽：
supports
capture
cameras
digital
上傳時間：
2014-01-13
上傳用戶：yph853211
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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.
標簽：
demonstrate
unorganized
algorithm
describe
上傳時間：
2013-12-18
上傳用戶：xc216
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Flex chip implementation
File: UP2FLEX
JTAG jumper settings: down, down, up, up
Input:
Reset - FLEX_PB1
Input n - FLEX_SW switches 1 to 8
Output:
Countdown - two 7-segment LEDs.
Done light - decimal point on Digit1.
Operation:
Setup the binary input n number.
Press the Reset switch.
See the countdown from n down to 0 on the 7-segment LEDs.
Done light lit when program terminates.
標簽：
down
implementation
settings
UP2FLEX
上傳時間：
2014-01-21
上傳用戶：sclyutian
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Batch version of the back-propagation algorithm.
％ Given a set of corresponding input-output pairs and an initial network
％ [W1,W2,critvec,iter]=batbp(NetDef,W1,W2,PHI,Y,trparms) trains the
％ network with backpropagation.
％
％ The activation functions must be either linear or tanh. The network
％ architecture is defined by the matrix NetDef consisting of two
％ rows. The first row specifies the hidden layer while the second
％ specifies the output layer.
％
標簽：
back-propagation
corresponding
input-output
algorithm
上傳時間：
2016-12-27
上傳用戶：exxxds
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％ 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.
標簽：
Levenberg-Marquardt
desired
network
neural
上傳時間：
2016-12-27
上傳用戶：jcljkh
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In some graphs, the shortest path is given by optimizing two different metrics: the sum of weights of the edges and the number of edges. For example: if two paths with equal cost exist then, the path with the least number of edges is chosen as the shortest path. Given this metric, you have find out the shortest path between a given pair of vertices in the input graph. The output should be the number of edges on the path, the cost of the shortest path, and the path itself. Input is the adjacency matrix and the two vertices.
標簽：
optimizing
different
the
shortest
上傳時間：
2014-10-25
上傳用戶：1159797854
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The LT®6552 is a specialized dual-differencing 75MHzoperational amplifier ideal for rejecting common modenoise as a video line receiver. The input pairs are designedto operate with equal but opposite large-signal differencesand provide exceptional high frequency commonmode rejection (CMRR of 65dB at 10MHz), therebyforming an extremely versatile gain block structure thatminimizes component count in most situations. The dualinput pairs are free to take on independent common modelevels, while the two voltage differentials are summedinternally to form a net input signal.
標簽：
視頻
差分放大器
低電壓
多功能
上傳時間：
2014-12-23
上傳用戶：13691535575
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Photodiodes can be broken into two categories: largearea photodiodes with their attendant high capacitance(30pF to 3000pF) and smaller area photodiodes withrelatively low capacitance (10pF or less). For optimalsignal-to-noise performance, a transimpedance amplifi erconsisting of an inverting op amp and a feedback resistoris most commonly used to convert the photodiode currentinto voltage. In low noise amplifi er design, large areaphotodiode amplifi ers require more attention to reducingop amp input voltage noise, while small area photodiodeamplifi ers require more attention to reducing op amp inputcurrent noise and parasitic capacitances.
標簽：
光電二極管
低噪聲放大器
上傳時間：
2013-10-28
上傳用戶：hanbeidang
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Differential Nonlinearity: Ideally, any two adjacent digitalcodes correspond to output analog voltages that are exactlyone LSB apart. Differential non-linearity is a measure of theworst case deviation from the ideal 1 LSB step. For example,a DAC with a 1.5 LSB output change for a 1 LSB digital codechange exhibits 1⁄2 LSB differential non-linearity. Differentialnon-linearity may be expressed in fractional bits or as a percentageof full scale. A differential non-linearity greater than1 LSB will lead to a non-monotonic transfer function in aDAC.Gain Error (Full Scale Error): The difference between theoutput voltage (or current) with full scale input code and theideal voltage (or current) that should exist with a full scale inputcode.Gain Temperature Coefficient (Full Scale TemperatureCoefficient): Change in gain error divided by change in temperature.Usually expressed in parts per million per degreeCelsius (ppm/°C).Integral Nonlinearity (Linearity Error): Worst case deviationfrom the line between the endpoints (zero and full scale).Can be expressed as a percentage of full scale or in fractionof an LSB.LSB (Lease-Significant Bit): In a binary coded system thisis the bit that carries the smallest value or weight. Its value isthe full scale voltage (or current) divided by 2n, where n is theresolution of the converter.Monotonicity: A monotonic function has a slope whose signdoes not change. A monotonic DAC has an output thatchanges in the same direction (or remains constant) for eachincrease in the input code. the converse is true for decreasing codes.
標簽：
Converters
Defini
DAC
上傳時間：
2013-10-30
上傳用戶：stvnash
