MATSNL is a package of MATLAB M-Files for computing wireless sensor node
lifetime/power budget and solving optimal node architecture choice problems. It is intended
as an analysis and simulation tool for researchers and educators that are easy to use and
modify. MATSNL is designed to give the rough power/ lifetime predictions based on node
and application specifications while giving useful insight on platform design for the large
node lifetime by providing side-by-side comparison across various platforms.
list of matlab M-Files on matlab 7.0. learning , support vector machine and some utility routines : autocorrelation, linearly scale randomize the row order of a matrix
his folder contains the following files:
1. 02490rxP802-15_SG3a-Channel-Modeling-Subcommittee-Report-Final.doc: This is the final
report of the channel modeling sub-committee.
2. cmx_imr.csv (x=1, 2, 3, and 4) represent the files containing the actual 100 channel
realizations for CM1, CM2, CM3, and CM4. The columns are organized as (time, amp, time, amp,...)
3. cmx_imr_np.csv (x=1, 2, 3, and 4) represent the files containing the number of paths in
each of the 100 multipath realizations.
4. cmx_imr.mat (x=1, 2, 3, and 4) are the .mat files that can be loaded directly into
Matlab (TM).
5. *.m files are the Matlab (TM) files used to generate the various channel realizations.
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
基于simulink的uwb仿真
uwb.mdl: UWB model - open this to begin.
uwb_lib.mdl: Library blocks for UWB model.
uwb_init.m: Initialization called before model is loaded.
uwb_settings: Sets up structure containing system parameters ( uwb in workspace).
uwb_imr.m: Initializes UWB channel impulse response.
uwb_sv_*.m: Four M-Files used to generate channel impulse responses (MAT files).
