One of the most important issues affecting the implementation of microcontroller software deals with the data-decision algorithm. Data-decision refers to decoding the DIO-pin from the CC400/CC900. Two main principles exist for decoding Manchester-coded data: Data decision based on timing the period between transitions, and data decision based on oversampling.
標簽： microcontroller implementation important affecting
上傳時間： 2013-12-18
上傳用戶：671145514
LVQ學習矢量化算法源程序 This directory contains code implementing the Learning vector quantization network. Source code may be found in LVQ.CPP. Sample training data is found in LVQ1.PAT. Sample test data is found in LVQTEST1.TST and LVQTEST2.TST. The LVQ program accepts input consisting of vectors and calculates the LVQ network weights. If a test set is specified, the winning neuron (class) for each neuron is identified and the Euclidean distance between the pattern and each neuron is reported. Output is directed to the screen.
標簽： implementing quantization directory Learning
上傳時間： 2015-05-02
上傳用戶：hewenzhi
bayeserr - Computes the Bayesian risk for optimal classifier. ％ bayescln - Classifier based on Bayes decision rule for Gaussians. ％ bayesnd - Discrim. function, dichotomy, max aposteriori probability. ％ bhattach - Bhattacharya s upper limit of mean class. error. ％ pbayescln - Plots discriminat function of Bayes classifier.
標簽： Classifier classifier bayeserr Computes
上傳時間： 2015-06-14
上傳用戶：sunjet
matlab數據挖掘算法。實用cart決策樹進行分類，可識別多類。decision tree algorithm, classification.
上傳時間： 2014-12-06
上傳用戶：xsnjzljj
The source code for this package is located in src/gov/nist/sip/proxy. The proxy is a pure JAIN-SIP application: it does not need proprietary nist-sip classes in addition of those defined in JAIN-SIP 1.1, you can substitute the NIST-SIP stack by another JAIN-SIP-1.1 compliant stack and it should interoperate. he proxy can act as presence server and be able to process NOTIFY and SUBSCRIBE requests. If this parameter is disabled, the proxy will simply forward those kind of requests following the appropriate routing decision.
標簽： proxy The JAIN-SIP package
上傳時間： 2015-11-30
上傳用戶：ippler8
ITU-T G.729語音壓縮算法。 description: Fixed-point description of commendation G.729 with ANNEX B Coding of Speech at 8 kbit/s using Conjugate-Structure Algebraic-Code-Excited Linear-Prediction (CS-ACELP) with Voice Activity Decision(VAD), Discontinuous Transmission(DTX), and Comfort Noise Generation(CNG).
標簽： description commendation Fixed-point 729
上傳時間： 2014-11-23
上傳用戶：thesk123
Hidden_Markov_model_for_automatic_speech_recognition This code implements in C++ a basic left-right hidden Markov model and corresponding Baum-Welch (ML) training algorithm. It is meant as an example of the HMM algorithms described by L.Rabiner (1) and others. Serious students are directed to the sources listed below for a theoretical description of the algorithm. KF Lee (2) offers an especially good tutorial of how to build a speech recognition system using hidden Markov models.
標簽： Hidden_Markov_model_for_automatic speech_recognition implements left-right
上傳時間： 2016-01-23
上傳用戶：569342831
This directory contains code implementing the K-means algorithm. Source code may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS program accepts input consisting of vectors and calculates the given number of cluster centers using the K-means algorithm. Output is directed to the screen.
標簽： code implementing directory algorithm
上傳時間： 2014-01-15
上傳用戶：woshini123456
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
標簽： meta-learning classifiers combining Boosting
上傳時間： 2016-01-30
上傳用戶：songnanhua
Adaptive Filter. This script shows the BER performance of several types of equalizers in a static channel with a null in the passband. The script constructs and implements a linear equalizer object and a decision feedback equalizer (DFE) object. It also initializes and invokes a maximum likelihood sequence estimation (MLSE) equalizer. The MLSE equalizer is first invoked with perfect channel knowledge, then with a straightforward but imperfect channel estimation technique.
標簽： performance equalizers Adaptive several
上傳時間： 2016-02-16
上傳用戶：yan2267246
