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prediction

灰色預測GM（1

灰色預測GM（1，1）模型的matlab源代碼，包括預測模型的建立，以及模型的精度檢驗指標c,p的計算-Gray prediction GM (1,1) model matlab source code, including the prediction model, as well as indicators of model accuracy test c, p calculation

標簽： 灰色預測

上傳時間： 2017-07-25

上傳用戶：refent
基于頻率插值的4.0kbps 語音編碼器的性能和設計（英文）

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
Bi-density twin support vector machines

In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data classification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local scaling heuristic strategy, then optimize a pair of nonparallel hyperplanes through two smaller sized support vector machine (SVM)-typed problems. In the prediction stage, BDTWSVMs assign to the class label depending on the kernel density degree-based distances from each test point to the two hyperplanes. BDTWSVMs not only inherit good properties from twin support vector machines (TWSVMs) but also give good description for data points. The experimental results on toy as well as publicly available datasets indicate that BDTWSVMs compare favorably with classical SVMs and TWSVMs in terms of generalization

標簽： recognition Bi-density machines support pattern vector twin for

上傳時間： 2019-06-09

上傳用戶：lyaiqing
ESD_protection_for_RF_and_AMS_ICs

This paper reviews key factors to practical ESD protection design for RF and analog/mixed-signal (AMS) ICs, including general challenges emerging, ESD-RFIC interactions, RF ESD design optimization and prediction, RF ESD design characterization, ESD-RFIC co-design technique, etc. Practical design examples are discussed. It means to provide a systematic and practical design flow for whole-chip ESD protection design optimization and prediction for RF/AMS ICs to ensure 1 st Si design success.

標簽： ESD_protection_for_RF_and_AMS_ICs

上傳時間： 2020-06-05

上傳用戶：shancjb
淺析卡爾曼濾波器

在信號的產生、傳輸、接收過程當中，必定會遭受外部環境擾動和內部設備噪聲的影響，為獲得需求信號或狀態的最有效估計，要排除無用干擾，這就叫做濾波。“濾波”的術語在無線電領域首先出現。由于隨機信號功率譜的確定性，有用信號和無用信號必定不同，從而可以根據其差異來設計濾波器。1960年，卡爾曼發表了用遞歸方法解決離散數據線性濾波問題的論文（A New Approach to Linear Filtering and prediction Problems）。在這篇文章里，一種克服了維納濾波缺點的新方法被提出來，這就是我們今天稱之為卡爾曼濾波的方法。卡爾曼濾波應用廣泛且功能強大，它可以估計信號的過去和當前狀態，甚至能估計將來的狀態，即使并不知道模型的確切性質。其基本思想是：以最小均方誤差為最佳估計準則，采用信號與噪聲的狀態空間模型，利用前一時刻的估計值和當前時刻的觀測值來更新對狀態變量的估計，求出當前時刻的估計值，算法根據建立的系統方程和觀測方程對需要處理的信號做出滿足最小均方誤差的估計。

標簽： 卡爾曼濾波器

上傳時間： 2022-07-04

上傳用戶：
卡爾曼濾波算法及C語言實現資料分享

近來發現有些問題很多人都很感興趣。所以在這里希望能盡自己能力跟大家討論一些力所能及的算法。現在先討論一下卡爾曼濾波器，如果時間和能力允許，我還希望能夠寫寫其他的算法，例如遺傳算法，傅立葉變換，數字濾波，神經網絡，圖像處理等等。因為這里不能寫復雜的數學公式，所以也只能形象的描述。希望如果哪位是這方面的專家，歡迎討論更正。卡爾曼濾波器 – Kalman Filter 1．什么是卡爾曼濾波器（What is the Kalman Filter?）在學習卡爾曼濾波器之前，首先看看為什么叫“卡爾曼”。跟其他著名的理論（例如傅立葉變換，泰勒級數等等）一樣，卡爾曼也是一個人的名字，而跟他們不同的是，他是個現代人！卡爾曼全名Rudolf Emil Kalman，匈牙利數學家，1930年出生于匈牙利首都布達佩斯。1953，1954年于麻省理工學院分別獲得電機工程學士及碩士學位。1957年于哥倫比亞大學獲得博士學位。我們現在要學習的卡爾曼濾波器，正是源于他的博士論文和1960年發表的論文《A New Approach to Linear Filtering and prediction Problems》（線性濾波與預測問題的新方法）。如果對這編論文有興趣

標簽： 卡爾曼濾波算法 C語言

上傳時間： 2022-07-23

上傳用戶：qdxqdxqdxqdx

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