In this project we analyze and design the minimum mean-square error (MMSE) multiuser receiver for uniformly quantized synchronous code division multiple access (CDMA) signals in additive white Gaussian noise (AWGN) channels.This project is mainly based on the representation of uniform quantizer by gain plus additive noise model. Based on this model, we derive the weight vector and the output signal-to-interference ratio (SIR) of the MMSE receiver. The effects of quantization on the MMSE receiver performance is characterized in a single parameter named 鈥漞quivalent noise variance鈥? The optimal quantizer stepsize which maximizes the MMSE receiver output SNR is also determined.
統(tǒng)計(jì)模式識(shí)別工具箱（Statistical Pattern Recognition Toolbox）包含：
1，Analysis of linear discriminant function
2，F(xiàn)eature extraction: Linear Discriminant Analysis
3，Probability distribution estimation and clustering
4，Support Vector and other Kernel Machines
OTSU Gray-level image segmentation using Otsu s method.
Iseg = OTSU(I,n) computes a segmented image (Iseg) containing n classes
by means of Otsu s n-thresholding method (Otsu N, A Threshold Selection
Method from Gray-Level Histograms, IEEE Trans. Syst. Man Cybern.
9:62-66 1979). Thresholds are computed to maximize a separability
criterion of the resultant classes in gray levels.
OTSU(I) is equivalent to OTSU(I,2). By default, n=2 and the
corresponding Iseg is therefore a binary image. The pixel values for
Iseg are [0 1] if n=2, [0 0.5 1] if n=3, [0 0.333 0.666 1] if n=4, ...
[Iseg,sep] = OTSU(I,n) returns the value (sep) of the separability
criterion within the range [0 1]. Zero is obtained only with images
having less than n gray level, whereas one (optimal value) is obtained
only with n-valued images.
how to add arrays
* Use of const (constant) values.
* Creation of vectors.
* Passing vectors as function arguments.
* Reading from files of unknown size (monitoring istream status).
* Repetitive structures (while and for loops).
* The increment operators (++).
* Selection structures (if-else statements).
* Use of the .size, .empty, .begin, .insert, .erase, .resize, .clear and .swap vector class member functions.
The frequency domain plays an important role in image
processing to smooth, enhance, and detect edges of images. Although
image data typically does not include imaginary values, the fast Fourier
transform (FFT) has been used for obtaining spectra. In this paper,
the fast Hartley transform (FHT) is used to transform two-dimensional
image data. Because the Hartley transform is real valued, it does
not require complex operations. Both spectra and autocorrelations of
two-dimensional ultrasound images of normal and abnormal livers were
computed.
Learn how to:
*
Tokenize a null-terminated string
*
Create a search and replace function for strings
*
Implement subtraction for string objects
* Use the vector, deque, and list sequence containers
*
Use the container adaptors stack, queue, and priority_queue
* Use the map, multimap, set, and multiset associative containers
*
Reverse, rotate, and shuffle a sequence
*
Create a function object
*
Use binders, negators, and iterator adapters
*
Read and write files
*
Use stream iterators to handle file I/O
*
Use exceptions to handle I/O errors
*
Create custom inserters and extractors
*
Format date, time, and numeric data
* Use facets and the localization library
*
Overload the [ ], ( ), and -> operators
*
Create an explicit constructor
*
And much, much more
Specification File
adjacencyListGragh
class GeneralGraph:
use adjacency list to implement the graph which data structure is vector
Construct methods:
* public GeneralGraph():
contain an empty vector store the vertex and a boolean determines whether graph is directed or not, defaulted is undirected
