International Journal of Computers and Communications

E-ISSN: 2074-1294
Volume 15, 2021

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of NAUN Journals is adapted to the 'continuously updated' model. What this means is that instead of being seperated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

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Volume 15, 2021 


Title of the Paper: Computational Analysis of Incremental Clustering Approaches for Large Data

 

Authors: Arun Pratap Singh Kushwah, Shailesh Jaloree, Ramjeevan Singh Thakur

Pages: 14-18

DOI: 10.46300/91013.2021.15.3     XML

Abstract: Clustering is an approach of data mining, which helps us to find the underlying hidden structure in the dataset. K-means is a clustering method which usages distance functions to find the similarities or dissimilarities between the instances. DBSCAN is a clustering algorithm, which discovers the arbitrary shapes & sizes of clusters from huge volume of using spatial density method. These two approaches of clustering are the classical methods for efficient clustering but underperform when the data is updated frequently in the databases so, the incremental or gradual clustering approaches are always preferred in this environment. In this paper, an incremental approach for clustering is introduced using K-means and DBSCAN to handle the new datasets dynamically updated in the database in an interval.


Title of the Paper: Performance of Beamforming for Smart Antenna using Traditional LMS Algorithm for Various Parameters

 

Authors: Vishal V. Sawant, Mahesh Chavan

Pages: 8-13

DOI: 10.46300/91013.2021.15.2     XML

Abstract: Adaptive signal processing sensor arrays, known also as smart antennas. The smart antenna adaptive algorithms achieve the best weight vector for beam forming by iterative means. The Least Mean Square (LMS) algorithm, is an adaptive algorithm .LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which eventually leads to the minimum mean square error. Beam forming is directly determined by the two factors. The performance of the traditional LMS algorithm for different parameters is analysed in this paper. This algorithm can be applied to beam forming with the software Matlab. The result obtain can achieve faster convergence and lower steady state error. The algorithms can be simulated in MATLAB 7.10 version.


Title of the Paper: TTS-driven Embodied Conversation Avatar for UMB-SmartTV

 

Authors: Matej Rojc, Zdravko Kačič, Marko Presker, Izidor Mlakar

Pages: 1-7

DOI: 10.46300/91013.2021.15.1     XML

 

Abstract: When human-TV interaction is performed by remote controller and mobile devices only, the interactions tend to be mechanical, dreary and uninformative. To achieve more advanced interaction, and more human-human like, we introduce the virtual agent technology as a feedback interface. Verbal and co-verbal gestures are linked through complex mental processes, and although they represent different sides of the same mental process, the formulations of both are quite different. Namely, verbal information is bound by rules and grammar, whereas gestures are influenced by emotions, personality etc. In this paper a TTS-driven behavior generation system is proposed for more advanced interface used for smart IPTV platforms. The system is implemented as a distributive non-IPTV service and integrated into UMB-SmartTV in a service-oriented fashion. The behavior generation system fuses speech and gesture production models by using FSMs and HRG structures. Features for selecting the shape and alignment of co-verbal movement are based on linguistic features (that can be extracted from arbitrary input text), and prosodic features (as predicted within several processing steps in the TTS engine). At the end, the generated speech and co-verbal behavior are animated by an embodied conversational agent (ECA) engine and represented to the user within the UMBSmarTV user interface.