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To reduce the huge consumption of traditional sensing, a multi-centers estimation based sensing scheme is proposed in this paper. Firstly, all potential channels are clustered into highly related groups with some channels selected as detecting channels (DCs) using an unsupervised algorithm. In each group, the states of other channels (estimated channels, ECs) are estimated according to their correlations...
This paper introduces EveTrack, an online and distributed method for localization and tracking of global and composite events. Based on hyper-ellipsoid clustering model, we compute the percentage contributions of the individual attributes in multi-attribute and correlated events. In addition, EveTrack utilizes spatio-temporal correlations between multiple events during its event identification phase...
The energy-efficient data aggregation in Wireless Sensor Networks (WSNs) has grown as one of the promising area in many applications. Prior research works have suggested several spatio-temporal models for effectively reducing the data collection costs, but the models are limited to their specificity. This paper presents a pre-filtration method in every sensor node. While using pre-filtration techniques...
In ocean environment, to ensure the safety of navigation for unmanned underwater vehicle (UUV), it is the most important to obtain the position information and moving information of obstacles. In order to distinguish between static and dynamic obstacles, the detection of moving property is used to detect property of obstacles. Then, to meet the requirement of obstacle configurations, k-means clustering...
Telecom Networks produce huge amount of daily alarm logs. These alarms usually arrive from different regions and network equipments of mobile operators at different times. In a typical network operator, Network Operations Centers (NOCs) constantly monitor those alarms in a central location and try to fix issues raised by intelligent warning systems by performing a trouble ticketing based management...
With the rapid development of cellular network systems, the operators need more experience to deal with complicated network management system and wide range of Key Performance Indicators (KPIs). There are many indicators related to each other due to the definition or communication process. But several implicit associations still exist among these KPIs. This paper proposes an approach to figure out...
Privacy preserving techniques have been actively studied on the time-series data in various fields like financial, medical and weather analysis. I focused towards preserving the data through anonymity and generalization, to resist homogeneity attack. First investigate, what's the privacy to be incorporated in the time-series data and after finding the data which needs to be preserved various perturbation...
The flourishing fame and development of big data in recent years made researchers to have a detailed study. Of the all entire emerging big data research topics, classification of data from big data is identified as a great challenge to address as of our analysis. The Classification is the process of categorizing data for its most effective and efficient use. While analyzing large scale patient records,...
In this paper a novel super-resolution wavefront extraction algorithm is introduced that merges the advantages of the efficiency correlation techniques based on [1] and super-resolution ability of DCM techniques [2]. The introduced ADCM algorithm is based on the correlation method DCM, but in contrast this algorithm uses a set of weighted differently shifted reference pulses. Due to the clustering...
One of the prominent clinical manifestations of schizophrenia is flat or altered facial activity, and flattening of emotional expressiveness (Flat Affect). In this study we used a structured-light depth camera and dedicated software to automatically measure the facial activity of schizophrenia patients and healthy individuals during a short structured interview. Based on K-means clustering analysis,...
This paper presents a framework of automatic clustering to determine correctly matched keypoints locations in aerial images for visual-based attitude estimation. In this work, correct and false matches are automatically identified using a clustering technique which utilizes the outlier information to determine the initial number of clusters and cross-correlation. The proposed framework has been tested...
This study proposed automatic detection and segmentation of brain lesion in diffusion-weighted magnetic resonance images (DWI) based on Fuzzy C-Means (FCM). Due to noises and intensity inhomogeneity, FCM technique fails in producing accurate results. Active contour and correlation template are integrated to overcome this problem. The brain lesions are acute stroke and solid tumor foe hyperintense...
Clustering dynamic data is a challenge inidentifying and forming groups. This unsupervised learningusually leads to undirected knowledge discovery. The clusterdetection algorithm searches for clusters of data which aresimilar to one another by using similarity measures.Determining the suitable algorithm which can bring theoptimized groups cluster could be an issue. Depending on theparameters and attributes...
Mining useful knowledge from corpus of data has become an important application in many fields. Data mining algorithms like clustering, classification work on this data and provide crisp information for analysis. As these data are available through various channels into public domain, privacy for the owners of the data is increasing need. Though privacy can be provided by hiding sensitive data, it...
Modern botnets such as Zeus and Conficker com-monly utilize a technique called domain fluxing or a Domain Generation Algorithm (DGA) to generate a large number of pseudo-random domain names dynamically for botnet operators to control their bots. These botnets are becoming one of the most serious threats to the Internet security on a global scale. In this paper, we present a method based on analyzing...
Data aggregation has been an important mechanism for achieving energy efflciency in WSN's. The aggregation reduces the transmission of redundant data which results in improved energy usage. A large number of data aggregation protocols have been developed in the past based on various techniques of optimizing the delay and energy. This paper surveyed the most prominent data aggregation protocols recently...
In this paper, we propose a novel error concealment method based on multiscale patch clustering and low-rank minimization. In order to collect more reliable patches to form a genuine low-rank matrix, an image pyramid is formed utilizing an effective down-sampling process. The classic singular value thresholding (SVT) is modified into a global iteration to solve the low-rank minimization problem. Extensive...
Soft play is a form of cheating where players deliberately play easy against each other. We evaluate different methods for detecting the players engaging in soft play in shooter games using data generated with synthetic players. These methods are used when analysing the hit matrix of the game.
In many real-world networks, interactions between entities are observed at specific moments in continuous time, such as email, SMS messaging, and IP traffic. The majority of methods for analyzing such data first aggregate communication over designated time blocks, resulting in one or more discrete time series, to which existing tools can be applied. However, regardless of how the block lengths are...
Clustering explores meaningful patterns in the non-labeled data sets. Cluster Ensemble Selection (CES) is a new approach, which can combine individual clustering results for increasing the performance of the final results. Although CES can achieve better final results in comparison with individual clustering algorithms and cluster ensemble methods, its performance can be dramatically affected by its...
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