This Source-Navigator, an IDE for C/C++/Fortran/Java/Tcl/PHP/Python
and a host of other languages. Source-Navigator includes powerful
source code comprehension features that help a developer understand
Complex relationships between elements of a program s source.
Abstract—Wireless networks in combination with image
sensors open up a multitude of previously unthinkable sensing
applications. Capable tools and testbeds for these wireless image
sensor networks can greatly accelerate development of Complex,
yet efficient algorithms that meet application requirements. In this
paper, we introduce WiSNAP, a Matlab-based application
development platform intended for wireless image sensor
networks. It allows researchers and developers of such networks
to investigate, design, and evaluate algorithms and applications
using real target hardware. WiSNAP offers standardized and
easy-to-use Application Program Interfaces (APIs) to control
image sensors and wireless motes, which do not require detailed
knowledge of the target hardware. Nonetheless, its open system
architecture enables support of virtually any kind of sensor or
wireless mote. Application examples are presented to illustrate the
usage of WiSNAP as a powerful development tool.
FFTW, a collection of fast C routines to compute the Discrete
Fourier Transform in one or more dimensions.The fftw/ directory contains the source code for the Complex transforms,
and the rfftw/ directory contains the source code for the real transforms.
This book provides a complete intermediate-level discussion of microcontroller programming using
the C programming language. It covers both the adaptations to C necessary for targeting an
embedded environment, and the common components of a successful development project.
C is the language of choice for programming larger microcontrollers (MCU), those based on 32-bit
cores. These parts are often derived
from their general-purpose
counterparts, and are both as
Complex and feature-rich. As a result, C (and C++) compilers are necessary and readily available for
these MCUs.
是初學入們，嵌入式的好教材！@簡單易懂
Add myaa.m to your path and enjoy anti-aliased professionally looking graphics in Matlab at any time. Myaa works with any kind of graphic (3-D, plots, scatterplots, ...) and even adds anti-aliasing to text, ui controls and grids. Myaa is ideal for Complex, cluttered and saturated plots.
一種比較好的抗鋸齒算法
Add myaa.m to your path and enjoy anti-aliased professionally looking graphics in Matlab at any time. Myaa works with any kind of graphic (3-D, plots, scatterplots, ...) and even adds anti-aliasing to text, ui controls and grids. Myaa is ideal for Complex, cluttered and saturated plots.
See attached screenshot for a demonstration. More examples included in the code, just run help myaa .
Curiosa:
For those of you who publish your code often, an undocumented anti-aliasing option is included in the snapnow.m function in Matlab. To publish a file called test.m you can do:
opts.figureSnapMethod = antialiased
publish( test.m ,opts)
However, you will have more control over the process using myaa, which is also the best choice when using Matlab interactively.
16點FFT VHDL源程序，The xFFT16 fast Fourier transform (FFT) Core computes a 16-point Complex FFT. The input data
is a vector of 16 Complex values represented as 16-bit 2’s complement numbers – 16-bits for
each of the real and imaginary component of a datum.
非線性有限元程序，NONSAP is a general finite element program for the nonlinear static and dynamic analysis of Complex structures. The program is very flexible and was designed to be extended and modified by the user. In particular the program can easily be modified to use a different formulation of the equations of motions, different time integration operators and other additional options.
* Lightweight backpropagation neural network.
* This a lightweight library implementating a neural network for use
* in C and C++ programs. It is intended for use in applications that
* just happen to need a simply neural network and do not want to use
* needlessly Complex neural network libraries. It features multilayer
* feedforward perceptron neural networks, sigmoidal activation function
* with bias, backpropagation training with settable learning rate and
* momentum, and backpropagation training in batches.
