In this internet and digitalization age, information in any form is very important to perform a digital task. The processing of any information to obtain the desired results requires a specific medium and sometimes to...
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Data stream is an ordered sequence of data objects that can be read only once or a small number of times. The characteristics of data stream are very large, continuous, high dimensional, immeasurable, dynamically high...
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A new framework for time domain voiced phoneme recognition is shown. Each speech frame taken for training and recognition is bounded by consecutive glottal closures. A preprocessing stage is designed and implemented t...
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A new framework for time domain voiced phoneme recognition is shown. Each speech frame taken for training and recognition is bounded by consecutive glottal closures. A preprocessing stage is designed and implemented to model pitch synchronous frames with Gaussian mixture models. Component analysis carried out on the data shows optimal performance with a very small number of components, requiring low computational power. We designed a new clustering technique that, using the pitch period, gives better results than other well known clustering algorithms like k-means.
In stream data mining, stream clustering algorithms provide summaries of the relevant data objects that arrived in the stream. The model size of the clustering, i.e. the granularity, is usually determined by the speed...
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We consider the problem of clustering incomplete data drawn from a union of subspaces. Classical subspace clustering methods are not applicable to this problem because the data are incomplete, while classical low-rank...
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It is common practice in the field of machine learning to make use of a clustering algorithm. The task of breaking up data on patients who have similar characteristics according to those qualities, which is essential ...
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Data Analysts have to deal with an ever-growing amount of data resources. One way to make sense of this data is to extract features and use clustering algorithms to group items according to a similarity measure. Algor...
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With the rapid development of Internet+education and the implementation of the "Three Connections and Two Platforms Project", online education resources are becoming more and more abundant, and society is in...
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In current scenario, wireless sensor networks (WSNs) have lives of many years. One very important application is environmental monitoring of wireless sensor networks (WSNs). The restriction to carrying energy within t...
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ISBN:
(数字)9789811527807
ISBN:
(纸本)9789811527791
In current scenario, wireless sensor networks (WSNs) have lives of many years. One very important application is environmental monitoring of wireless sensor networks (WSNs). The restriction to carrying energy within the charging backup of sensing point creates large problem to achieve a better network lifetime, which becomes a bottleneck in such applications of WSNs. The prime objective of the framework is to decrease the battery requirement and communication budget while confirming the data processing and data transmission. In this framework, data processing and gathering are achieved by using TDMA scheduling protocol and HEED clustering algorithm. For clustering, we use dual Wiener prediction scheme with optimized step size by reducing the mean-square derivation (MSD), in a way that the zonal supervisor can obtain a good approximation of the real data from the sensor nodes. A centralized principal component analysis (PCA) technique is utilized to perform the compression and recovery for the predicted data on the CHs and the sink, separately in order to save the communication cost and to eliminate the spatial redundancy of these used data about environment [1]. For all the nodes in every group, a deterministic weight (w) is extended depends on the instants period, it sense or achieve the message information from different neighboring source nodes. Each node of every group can start to work depending on the group of node’s weight. The weight of the node’s group is defined as a group of timing parts to every sensing point source in that group. That is why in a hierarchal flow diagram, there may be many node groups of individual importance. A group of largest importance (low timing part) can be assigned the preferable timing part. Here weighing importance is enhanced based on the message information collected to the nodding point from different nodding points and surroundings. All generated errors of same operation are finally examined conceptually, which come out to
This paper uses potential clustering approach to perform online fuzzy clustering. This method is an improvement of the subtractive clustering which is a noniterative clustering algorithm and so is suitable for online ...
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