MATLAB implementation of time series prediction Based on the VQTAM method described in the following papers:
G. A. Barreto & A. F. R. Araujo (2004)
"Identification and Control of Dynamical Systems Using the Self-Organizing Map"
IEEE Transactions on Neural Networks, vol. 15, no. 5.
TreeMenu Component
==================
The TreeMenu is a descendant of TTreeView.
It is a "stealth-realese" because the TreeMenu is created only run time
and it changes a design time defined simple TreeView to self.
Using it is very simple. Please see the demo files.
Author:
=======
Pal Sitkei
sitkei@chello.hu
pdf格式的英文文獻(xiàn)，是關(guān)于認(rèn)知無線電網(wǎng)絡(luò)的，編者是加拿大桂爾夫大學(xué)的Qusay H. Mahmoud。ISBN：978-0-470-06196-1
章節(jié)內(nèi)容：
1 Biologically Inspired Networking
2 The Role of Autonomic Networking in Cognitive Networks
3 Adaptive Networks
4 Self-Managing Networks
5 Machine Learning for Cognitive Networks: Technology Assessment
and Research Challenges
6 Cross-Layer Design and Optimization in Wireless Networks
等，共計(jì)13章，全書348頁(yè)，pdf文件383頁(yè)。
Exceptional C++ shows by example how to go about solid software engineering. Along with a lot of other material, this book includes expanded versions of the first 30 issues of the popular Internet C++ feature Guru of the Week (or, in its short form, GotW), a series of self-contained C++ engineering problems and solutions that illustrate specific design and coding techniques.
wxPython In Action，By combining introductions, overviews, and how-to examples, the In Action
books are designed to help learning and remembering. According to research in
cognitive science, the things people remember are things they discover during
self-motivated exploration.
A fractal is generally "a rough or fragmented geometric shape that can be split into parts, each of which is (at least approximately) a reduced-size copy of the whole,"[1] a property called self-similarity. The term was coined by Benoî t Mandelbrot in 1975 and was derived from the Latin fractus meaning "broken" or "fractured." A mathematical fractal is based on an equation that undergoes iteration, a form of feedback based on recursion.[2]
This Symbian C++ code example demonstrates how to easily use the onboard camera with zoom and autofocus, utilising an accompanying CameraWrapper made by Forum Nokia. The Camera Wrapper supports all Nokia s S60 devices based on S60 3rd Edition and newer, providing a unified interface for various Symbian and S60 camera APIs some of which have previously been Feature Pack specific or only available via an SDK plug-in. The example application supports the use of both the keypad and touch UI. The application can be self-signed, but it also provides an option to use the dedicated camera key (Symbian signing required). The example application replaces the separate examples published for S60 3rd Edition, FP1 (S60 Platform: Camera Example with AutoFocus Support v2.2) and FP2 (S60 Camera Example AutoFocus 3rd Ed FP2).
Abstract-In this paper, simple autonomous chaotic circuits
coupled by resistors are investigated. By carrying out computer
calculations and circuit experiments, irregular self-switching phenomenon
of three spatial patterns characterized by the phase
states of quasi-synchronization of chaos can be observed from
only four simple chaotic circuits. This is the same phenomenon
as chaotic wandering of spatial patterns observed very often from
systems with a large number of degrees of freedom. Namely, one
of spatial-temporal chaos observed from systems of large size can
be also generated in the proposed system consisting of only four
chaotic circuits. A six subcircuits case and a coupled chaotic circuits
networks are also studied, and such systems are confirmed
to produce more complicated spatio-temporal phenomena.
Behavioral models are used in games and computer graphics for
realistic simulation of massive crowds. In this paper, we present a
GPU based implementation of Reynolds [1987] algorithm for simulating
flocks of birds and propose an extension to consider environment
self occlusion. We performed several experiments and
the results showed that the proposed approach runs up to three
times faster than the original algorithm when simulating high density
crowds, without compromising significantly the original crowd
behavior.
