UWB 功率控制 容量
Main Matlab script is in runsim.m. It generates random topologies,
optimizes, and display results.
IMPORTANT: you may need to add manually the lib path in Matlab in order to
get all the necessary functions.
Reference: Radunovic, Le Boudec, "Joint Power Control, Scheduling and Routing in UWB NETWORKS"
there are some newly released Neural Network Example Programs for Character Recognition, which based on Image Processing Toolbox,Neural Network Toolbox in MATLAB, which is quite informative for the beginners in Neural NETWORKS applicators
The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural NETWORKS featuring different adaptation algorithms.
Abstract—Mobile devices performing video coding and
streaming over wireless and pervasive communication NETWORKS
are limited in energy supply. To prolong the operational lifetime of
these devices, an embedded video encoding system should be able
to adjust its computational complexity and energy consumption
as demanded by the situation and its environment.
Sector is a system infrastructure software that provides functionality for distributed data storage, access, and analysis/processing. It automatically manages large volumetric data across servers or clusters, even those over distributed wide area high speed NETWORKS. Sector provides simple tools and APIs to access and/or process the data. Data and server locations are transparent to users, as the whole Sector network is a single networked super computer to the users.
n this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian NETWORKS. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
On-Line MCMC Bayesian Model Selection
This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural NETWORKS. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural NETWORKS . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
模式識別學習綜述.該論文的英文參考文獻為303篇.很有可讀價值.Abstract— Classical and recent results in statistical pattern
recognition and learning theory are reviewed in a two-class
pattern classification setting. This basic model best illustrates
intuition and analysis techniques while still containing the essential
features and serving as a prototype for many applications.
Topics discussed include nearest neighbor, kernel, and histogram
methods, Vapnik–Chervonenkis theory, and neural NETWORKS. The
presentation and the large (thogh nonexhaustive) list of references
is geared to provide a useful overview of this field for both
specialists and nonspecialists.
