用Fourier變換求取信號的功率譜---周期圖法
用Fourier變換求取信號的功率譜---分段周期圖法
用Fourier變換求取信號的功率譜---welch方法
功率譜估計----多窗口法（multitaper method ,MTM法）
功率譜估計----最大熵法（maxmum entmpy method,MEM法）
功率譜估計----多信號分類法（multiple signal classification,music法）Fourier transform to strike a signal to the power spectrum - the cycle of plans
Fourier transform to strike a signal to the power spectrum - Sub-cycle Method
Fourier transform to strike a signal to the power spectrum --- welch method
Power spectrum estimated more than window ---- Law (multitaper method, MTM)
---- Power spectrum estimate of maximum entropy (maxmum entmpy method, MEM)
---- More than the estimated power spectrum signal classification (multiple signal classification, music)
內存管理程序，功能與FASTMM相似，PLEASE NOTE: There are two ways to install BigBrain. You may use the
memory manager code natively compiled into your EXE or you can use
an included external DLL which will allow you to share memory across
multiple DLLs with one central place for memory management. Using the DLL
allows your application to share strings, and serves the same purpose
as the ShareMem unit included with Delphi. BigBrainShareMem.dll should
be 100％ compatible with the DelphiMM.dll and could even simply be renamed
to DelphiMM.dll to simplify deployment.
A Module-based Wireless Node (MW-Node) is a Node with wireless and mobile capabilities added by means of modules. It is not a new node object derived from Node. Rather it is a new layout of mostly existing components. Rationale for this new design has been presented in [1]. The MW-Node provides a flexible support for wireless and mobile networking and in particular:
support for multiple interfaces/multiple channels, and
a common basis for the implementation of wireless routing protocols.
BPMLL is a package for training multi-label BP neural networks. The package includes the MATLAB code of the algorithm BP-MLL, which is designed to deal with multi-label learning. It is in particular useful when a real-world object is associated with multiple labels simultaneously
This demo shows the BER performance of linear, decision feedback (DFE), and maximum likelihood sequence estimation (MLSE) equalizers when operating in a static channel with a deep null. The MLSE equalizer is invoked first with perfect channel knowledge, then with an imperfect, although straightforward, channel estimation algorithm. The BER results are determined through Monte Carlo simulation. The demo shows how to use these equalizers seamlessly across multiple blocks of data, where equalizer state must be maintained between data blocks.
This a simple genetic algorithm implementation where the evaluation function takes positive values only and the fitness of an individual is the same as the value of the objective function
We describe and demonstrate an algorithm that takes as input an
unorganized set of points fx1 xng IR3 on or near an unknown
manifold M, and produces as output a simplicial surface that
approximates M. Neither the topology, the presence of boundaries,
nor the geometry of M are assumed to be known in advance — all
are inferred automatically from the data. This problem naturally
arises in a variety of practical situations such as range scanning
an object from multiple view points, recovery of biological shapes
from two-dimensional slices, and interactive surface sketching.
