The algorm of viterbi. You talk to your friend three days in a row and discover that on the first day he went for a walk, on the second day he went shopping, and on the third day he cleaned his apartment. You have two questions: What is the overall probability of this sequence of observations? And what is the most likely sequence of rainy/sunny days that would explain these observations? The first question is answered by the forward algorithm the second question is answered by the Viterbi algorithm. These two algorithms are structurally so similar (in fact, they are both instances of the same abstract algorithm) that they can be implemented in a single function:
Determining embedding dimension for phase-space reconstruction using a geometrical construction.
It is very important reference for time forecast in chaos sequence.
contains synchronous routines to open queries with possibility to cancel execution,is intended for Direct Oracle Access library by Allround Automations
The first task at hand is to set up the endpoints appropriately for this example. The following code switches the CPU clock speed
to 48 MHz (since at power-on default it is 12 MHz), and sets up EP2 as a Bulk OUT endpoint, 4x buffered of size 512, and EP6
as a Bulk IN endpoint, also 4x buffered of size 512. This set-up utilizes the maximum allotted 4-KB FIFO space. It also sets up
the FIFOs for manual mode, word-wide operation, and goes through a FIFO reset and arming sequence to ensure that they are
ready for data operations
This project aim was to build wireless software modem for data communication
between two computers using an acoustic interface in the voice frequency range (20Hz–
20,000Hz). The transmitting antenna is a speaker (frequency response of: 90Hz –
20,000Hz) and the receiving antenna is a microphone (frequency response of: 100Hz –
16,000Hz). The test files used as information files were text files.
This goal was attained both in an incoherent scheme and in a coherent scheme.
Build under Matlab code, our modem uses OFDM (orthogonal frequency division
multiplexing) modulation, synchronization by LMS sequence, channel estimation (no
equalizer) via pilot tones. The symbols are either PSK or ASK for a constellation size of
2 or 4. To optimize the probability of error, these symbols were mapped using Gray
mapping.
Report
ET++ is a portable and homogenous object-oriented class library integrating user
interface building blocks, basic data structures, and high level application framework
components. ET++ eases the building of highly interactive applications with consistent
user interfaces following the direct manipulation principle. The ET++ class library is
implemented in C++ and can be used on several operating systems and window system
platforms. Since its initial conception the class library has been continuously
redesigned and improved. It started with an architecture which was close to MacApp.
During several iterations a new and unique architecture evolved. A byproduct of the
ET++ project is a set of tools, which were designed to support the exploration of ET++
applications at run-time.
設計模式一書引用的主要參考例程，一個跨平臺的應用框架，基于C++實現，是學習面向對象的經典源碼.
1) Write a function reverse(A) which takes a matrix A of arbitrary dimensions as input and returns a matrix B consisting of the columns of A in reverse order. Thus for example, if
A = 1 2 3 then B = 3 2 1
4 5 6 6 5 4
7 8 9 9 8 7
Write a main program to call reverse(A) for the matrix A = magic(5). Print to the screen both A and reverse(A).
2) Write a program which accepts an input k from the keyboard, and which prints out the smallest fibonacci number that is at least as large as k. The program should also print out its position in the fibonacci sequence. Here is a sample of input and output:
Enter k>0: 100
144 is the smallest fibonacci number greater than or equal to 100.
It is the 12th fibonacci number.
runs Kalman-Bucy filter over observations matrix Z
for 1-step prediction onto matrix X (X can = Z)
with model order p
V = initial covariance of observation sequence noise
returns model parameter estimation sequence A,
sequence of predicted outcomes y_pred
and error matrix Ey (reshaped) for y and Ea for a
along with inovation prob P = P(y_t | D_t-1) = evidence
Interface for Microsoft Audio Compression Manager. - Delphi Source
The ACM uses existing driver interface hooks to override the default mapping algorithm for waveform audio devices. This allows the ACM to intercept device-open calls. After a call has been intercepted, the ACM can perform a variety of tasks to process the audio data, such as inserting an external compressor or decompressor into the sequence.
ofdm信道特性
Channel transmission simulator
Channel transmission simulator
％
％ inputs:
％ sig2 - noise variance
％ Mt - number of Tx antennas
％ Mr - number of Rx antennas
％ x - vector of complex input symbols (for MIMO, this is a matrix, where each column
％ is the value of the antenna outputs at a single time instance)
％ H - frequency selective channel - represented in block-Toeplitz form for MIMO transmission
％ N - number of symbols transmitted in OFDM frame
％
％ outputs:
％ y - vector of channel outputs (matrix for MIMO again, just like x matrix)
％ create noise vector sequence (each row is a different antenna, each column is a
％ different time index) note: noise is spatially and temporally white
