function y_cum = cum2x (x,y, maxlag, nsamp, overlap, flag)
％CUM2X Cross-covariance
％ y_cum = cum2x (x,y,maxlag, samp_seg, overlap, flag)
％ x,y - data vectors/matrices with identical dimensions
％ if x,y are matrices, rather than vectors, columns are
％ assumed to correspond to independent realizations,
％ overlap is set to 0, and samp_seg to the row dimension.
％ maxlag - maximum lag to be computed [default = 0]
％ samp_seg - samples per segment [default = data_length]
％ overlap - percentage overlap of segments [default = 0]
％ overlap is clipped to the allowed range of [0,99].
This book contains a detailed analysis of the International Standard for the C language,-3.1 excluding the
library from a number of perspectives. The organization of the material is unusual in that it is based on
the actual text of the published C Standard. The unit of discussion is the individual sentences from the C
Standard (2022 of them).
Readers are assumed to have more than a passing familiarity with C.
The information in this publication is believed to be accurate as of its publication date. Such information is subject
to change without notice and The ATM Forum is not responsible for any errors. The ATM Forum does not assume
any responsibility to update or correct any information in this publication. Notwithstanding anything to the
contrary, neither The ATM Forum nor the publisher make any representation or warranty, expressed or implied,
concerning the completeness, accuracy, or applicability of any information contained in this publication. No
liability of any kind shall be assumed by The ATM Forum or the publisher as a result of reliance upon any
information contained in this publication.
//Basic packet sending test at the MAC level, used for internal testing only.
//This packet test has one node sending out a variety of
//differently formatted packets to two assumed destination nodes.
The Joint Video Team (JVT) of ISO/IEC MPEG and ITU-T VCEG are finalising a new standard for
the coding (compression) of natural video images. The new standard [1] will be known as H.264 and
also MPEG-4 Part 10, “Advanced Video Coding”. The standard specifies two types of entropy coding:
Context-based Adaptive Binary Arithmetic Coding (CABAC) and Variable-Length Coding (VLC).
This document provides a short introduction to CABAC. Familiarity with the concept of Arithmetic
Coding is assumed.
PCA and PLS aims:to get some
insight into the bilinear factor models Principal Component Analysis
(PCA) and Partial Least Squares (PLS) regression, focusing on the
mathematics and numerical aspects rather than how s and why s of
data analysis practice. For the latter part it is assumed (but not
absolutely necessary) that the reader is already familiar with these
methods. It also assumes you have had some preliminary experience
with linear/matrix algebra.
Application Note Abstract
This Application Note introduces a complete and detailed PSoC® project. Telephone Call Logger keeps the detailed record of
approximately 945 phone calls (7-digit number is assumed to be one phone call) including date, start time and the duration of
the phone call in the PSoC device. Users can get this detailed report into the PC environment by using free software, which is
included in the project file. When records reach near full capacity of the Flash memory, an LED will turn on to show that it is
necessary to backup the data. Software gets the data from PSoC, organizes it and prepares a printable version. Additionally, it
sends the date and time information to the PSoC. The external parts in this project can be obtained easily in the market.
The BeeStack Application Development Guide describes how to develop an application for
BeeStack, including discussions on major considerations for commercial applications.
This document is intended for software developers who write applications for BeeStack-based
products using Freescale development tools.
It is assumed the reader is a programmer with at least rudimentary skills in the C programming
language and that the reader is already familiar with the edit/compile/debug process.
Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal
％ component subspace U of dimension PPCA_DIM using a centred covariance
matrix X. The variable VAR contains the off-subspace variance (which
is assumed to be spherical), while the vector LAMBDA contains the
variances of each of the principal components. This is computed
using the eigenvalue and eigenvector decomposition of X.
％ DYNMODES calculates ocean dynamic vertical modes
％ taking a column vector of Brunt-Vaisala values (Nsq) at
％ different pressures (p) and calculating some number of
％ dynamic modes (nmodes).
％ Note: The input pressures need not be uniformly spaced,
％ and the deepest pressure is assumed to be the bottom.
