DFT(Discrete Fourier Transformation)是數(shù)字信號分析與處理如圖形、語音及圖像等領域的重要變換工具，直接計算DFT的計算量與變換區(qū)間長度N的平方成正比。當N較大時，因計算量太大，直接用DFT算法進行譜分析和信號的實時處理是不切實際的。快速傅立葉變換(Fast Fourier Transformation，簡稱FFT)使DFT運算效率提高1～2個數(shù)量級。其原因是當N較大時，對DFT進行了基4和基2分解運算。FFT算法除了必需的數(shù)據(jù)存儲器ram和旋轉(zhuǎn)因子rom外，仍需較復雜的運算和控制電路單元，即使現(xiàn)在,實現(xiàn)長點數(shù)的FFT仍然是很困難。本文提出的FFT實現(xiàn)算法是基于FPGA之上的，算法完成對一個序列的FFT計算，完全由脈沖觸發(fā)，外部只輸入一脈沖頭和輸入數(shù)據(jù)，便可以得到該脈沖頭作為起始標志的N點FFT輸出結(jié)果。由于使用了雙ram，該算法是流型(pipelined)的，可以連續(xù)計算N點復數(shù)輸入FFT，即輸入可以是分段N點連續(xù)復數(shù)數(shù)據(jù)流。采用DIF(Decimation In Frequency)-FFT和DIT(Decimation In Time)-FFT對于算法本身來說是無關緊要的，因為兩種情況下只是存儲器的讀寫地址有所變動而已，不影響算法的結(jié)構和流程，也不會對算法復雜度有何影響。
VHDL implementation of the twofish cipher for 128,192 and 256 bit keys.
The implementation is in library-like form All needed components up to, including the round/key schedule circuits are implemented, giving the flexibility to be combined in different architectures (iterative, rolled out/pipelined etc). Manual in English is included with more details about how to use the components and/or how to optimize some of them. All testbenches are provided (tables, variable key/text, ECB/CBC monte carlo) for 128, 192 and 256 bit key sizes, along with their respective vector files.
In communication systems channel poses an important role. channels can convolve many different kind of distortions to our information. In perticular wireless channels multipath distortion is sevear.
and more sevear is such distortion is random.
To handle this, multipath affected channels require Equalizers at receaver end.
such equalizer uses different learning Algorithms for identifying channels continuously.
This project is VHDL implementation of LMS learning algorithm with pipelined architecture. so this implementation can work with higher data rates with less clock speed requirments and so with less power consumpiton
It uses Fixed point arithmatic blocks for filtering so suitable for coustom asic.
The Hilbert Transform is an important component in communication systems, e.g. for single sideband modulation/demodulation, amplitude and phase detection, etc. It can be formulated as filtering operation which makes it possible to approximate the Hilbert Transform with a digital filter. Due to the non-causal and infinite impulse response of that filter, it is not that easy to get a good approximation with low hardware resource usage. Therefore, different filters with different complexities have been implemented.
The detailed discussion can be found in "Digital Hilbert Transformers or FPGA-based Phase-Locked Loops" (http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4629940).
The design is fully pipelined for maximum throughput.
