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. The MATSNL code and manual can be found at the bottom of this page. A related list of publications describing the models used in MATSNL is posted on the ENALAB part of the 2 project at http://www.eng.yale.edu/enalab/aspire.htm
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.
In this paper, a new method is introduced to implement chaotic generators based on the Henon map and Lorenz chaotic generators given by the state equations using the Field Programmable Gate Array (FPGA). The aim of this method is to increase the frequency of the chaotic generators. The new method is based on the MATLAB® Software, Xilinx System Generator, Xilinx
Alliance tools and Synplicity Synplify.
The Fuzzy Logic Toolbox™ product extends the MATLAB® technical computing environment with tools for designing systems based on fuzzy logic. Graphical user interfaces (GUIs) guide you through the steps of fuzzy inference system design. Functions are provided for many common fuzzy logic methods, including fuzzy clustering and adaptive neurofuzzy learning.
The matlab code implements the ensemble of decision tree classifiers proposed in: "L. Nanni and A. Lumini, Input Decimated Ensemble based on Neighborhood Preserving Embedding for spectrogram classification, Expert Systems With Applications doi:10.1016/j.eswa.2009.02.072 "
