Goodrich版算法设计与基础
标签： 算法
上传时间： 2016-02-09
上传用户：半年不见
调制解调课程设计 16QAM调制解调代码。包括星座图，频谱分析，误码率分析。-16QAM modulation and demodulation curriculum design code modulation and demodulation. Including constellation, spectrum analysis, bit error rate analysis.
上传时间： 2016-05-02
上传用户：ylqylq
一本从数据结构的基础到深入剖释的书籍，值得收藏。另一方面，为了获取积分才把珍藏的英文版放上来。
标签： Structures Algorithm Analysis Data and
上传时间： 2016-07-26
上传用户：cee16
LDO 的环路分析，对涉及、分析LDO的工作原理有帮助。
上传时间： 2017-09-09
上传用户：banjieshubi
The 4.0 kbit/s speech codec described in this paper is based on a Frequency Domain Interpolative (FDI) coding technique, which belongs to the class of prototype waveform Interpolation (PWI) coding techniques. The codec also has an integrated voice activity detector (VAD) and a noise reduction capability. The input signal is subjected to LPC analysis and the prediction residual is separated into a slowly evolving waveform (SEW) and a rapidly evolving waveform (REW) components. The SEW magnitude component is quantized using a hierarchical predictive vector quantization approach. The REW magnitude is quantized using a gain and a sub-band based shape. SEW and REW phases are derived at the decoder using a phase model, based on a transmitted measure of voice periodicity. The spectral (LSP) parameters are quantized using a combination of scalar and vector quantizers. The 4.0 kbits/s coder has an algorithmic delay of 60 ms and an estimated floating point complexity of 21.5 MIPS. The performance of this coder has been evaluated using in-house MOS tests under various conditions such as background noise. channel errors, self-tandem. and DTX mode of operation, and has been shown to be statistically equivalent to ITU-T (3.729 8 kbps codec across all conditions tested.
标签： frequency-domain interpolation performance Design kbit_s speech coder based and of
上传时间： 2018-04-08
上传用户：kilohorse
Test Analysis Software
标签： NDepend
上传时间： 2018-07-28
上传用户：teomondo
统计领域经典书籍，贝叶斯数据分析（英文原版），第三版
上传时间： 2018-10-23
上传用户：fuchuchaoyue
使用matlab实现gibbs抽样，MCMC: The Gibbs Sampler 多元高斯分布的边缘概率和条件概率 Marginal and conditional distributions of multivariate normal distribution
上传时间： 2019-12-10
上传用户：real_
深度学习，神经网络，卷积神经网络 Analysis of Deep Learning Models using CNN Techniques
上传时间： 2020-01-02
上传用户：wzy2020
压缩包中有5篇论文，分别为《Data-driven analysis of variables and dependencies in continuous optimization problems and EDAs》这是一篇博士论文，较为详细的介绍了各种EDA算法；《Anisotropic adaptive variance scaling for Gaussian estimation of distribution algorithm》《Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive》《Niching an Archive-based Gaussian Estimation of Distribution Algorithm via Adaptive Clustering》《Supplementary material for Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive》《基于一般二阶混合矩的高斯分布估计算法》介绍了一些基于EDA的创新算法。
上传时间： 2020-05-25
上传用户：duwenhao
