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Subspace clustering for online data streams requires a large amount of memory resources as all subsets of data dimensions must be examined. In order to track the continuous change of clusters for a data stream in a finite memory space, in this paper, we propose a grid-based subspace clustering algorithm that effectively uses memory resources. Given an n-dimensional data stream, the distribution information of data items in data space is monitored by a grid-cell list. When the frequency of data items in the grid-cell list of the first level is high and it becomes a unit grid-cell, the grid-cell list of the next level is created as a child node in order to find clusters of all possible subspaces from the grid-cell. In this way, a maximum n-level grid-cell subspace tree is constructed, and a k-dimensional subspace cluster can be found at the k th level of the subspace grid-cell tree. Through experiments, it was confirmed that the proposed method uses computing resources more efficiently by expanding only the dense space while maintaining the same accuracy as the existing method. [ABSTRACT FROM AUTHOR]

Bluetooth is a common wireless technology that is widely used as a connection medium between various consumer electronic devices. The Bluetooth receiver usually adopts a Viterbi algorithm to improve signal-to-noise ratio performance, but requires complex hardware and calculations for continuous search and estimation for the irrational modulation indexes at the transmission. This paper proposes a non-coherent maximum estimation based 8-State Viterbi FSM to solve these complexity problems. The proposed optimal Viterbi FSM can detect Gaussian frequency-shfit keying symbol without any prior information and estimation for the modulation indexes. The HV1/HV2 packets are used for the estimation of the proposed algorithm and the simulation results have shown performance improvements with about 2dB for 10 -3 BER compared to other ideal approaches such as decision direct method. [ABSTRACT FROM AUTHOR]

The adaptation of smartfarm technology that converges ICT is increasing productivity and competitiveness in the agriculture. Technologies have been developed that enable environmental monitoring through various sensors and automatic control of the cultivation environment, and researches are underway to advance smartfarm technology using data generated from smartfarms. In this paper, an environmental control method to reduce the energy consumption of a smartfarm by using the environment and control data of the smartfarm is proposed. It was confirmed that energy consumption could be reduced compared to an independent environmental control method by creating an environmental prediction model using accumulated environmental data and selecting a control method to minimize energy consumption in a given situation by considering multiple environmental factors. In the future, research is needed to obtain higher energy efficiency through the advancement of the predictive model and the improvement of the complex control algorithms. [ABSTRACT FROM AUTHOR]

Given an undirected simple graph, k- club is one of the proposed structures to model social groups that exist in various types in Social Network Analysis (SNA). Maximum k- Club Problem (MkCP) is to find a k- club of maximum cardinality in a graph. This paper introduces a Genetic Algorithm called HGA+DROP which can be used to approximate maximum k- club in graphs. Our algorithm modifies the existing k- CLIQUE & DROP algorithm and utilizes Heuristic Genetic Algorithms (HGA) to obtain multiple k- clubs. We experiment on DIMACS graphs for k = 2, 3, 4 and 5 to compare the performance of the proposed algorithm with existing algorithms. [ABSTRACT FROM AUTHOR]