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Goodrich版算法設(shè)計與基礎(chǔ)
標(biāo)簽： 算法
上傳時間： 2016-02-09
上傳用戶：半年不見
調(diào)制解調(diào)課程設(shè)計 16QAM調(diào)制解調(diào)代碼。包括星座圖，頻譜分析，誤碼率分析。-16QAM modulation and demodulation curriculum design code modulation and demodulation. Including constellation, spectrum analysis, bit error rate analysis.
上傳時間： 2016-05-02
上傳用戶：ylqylq
一本從數(shù)據(jù)結(jié)構(gòu)的基礎(chǔ)到深入剖釋的書籍，值得收藏。另一方面，為了獲取積分才把珍藏的英文版放上來。
標(biāo)簽： Structures Algorithm Analysis Data and
上傳時間： 2016-07-26
上傳用戶：cee16
LDO 的環(huán)路分析，對涉及、分析LDO的工作原理有幫助。
上傳時間： 2017-09-09
上傳用戶：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.
標(biāo)簽： frequency-domain interpolation performance Design kbit_s speech coder based and of
上傳時間： 2018-04-08
上傳用戶：kilohorse
Test Analysis Software
標(biāo)簽： NDepend
上傳時間： 2018-07-28
上傳用戶：teomondo
統(tǒng)計領(lǐng)域經(jīng)典書籍，貝葉斯數(shù)據(jù)分析（英文原版），第三版
標(biāo)簽： Bayesian Analysis Data
上傳時間： 2018-10-23
上傳用戶：fuchuchaoyue
使用matlab實現(xiàn)gibbs抽樣，MCMC: The Gibbs Sampler 多元高斯分布的邊緣概率和條件概率 Marginal and conditional distributions of multivariate normal distribution
上傳時間： 2019-12-10
上傳用戶：real_
深度學(xué)習(xí)，神經(jīng)網(wǎng)絡(luò)，卷積神經(jīng)網(wǎng)絡(luò) Analysis of Deep Learning Models using CNN Techniques
標(biāo)簽： 卷積 神經(jīng)網(wǎng)絡(luò) 模型分析
上傳時間： 2020-01-02
上傳用戶：wzy2020
壓縮包中有5篇論文，分別為《Data-driven analysis of variables and dependencies in continuous optimization problems and EDAs》這是一篇博士論文，較為詳細(xì)的介紹了各種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》《基于一般二階混合矩的高斯分布估計算法》介紹了一些基于EDA的創(chuàng)新算法。
上傳時間： 2020-05-25
上傳用戶：duwenhao
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