Mouse Anti Human endoglin Hybridoma [Q4E2]

In this article, an eight-element ultra-wideband (UWB) Multiple-Input-Multiple Output (MIMO) antenna system is proposed for 3D non-planar applications. The proposed UWB-MIMO antenna is installed around a polystyrene block in the 3D-octagonal arrangement. The eight radiating elements are placed on the sides of the octagonal polystyrene block with top and bottom surfaces left open. The single antenna element consists of a modified Y-shaped radiating patch, epsilon-negative (ENG) metamaterial, and a partial ground plane. A modified pie-shaped decoupling structure is deployed at the back-side of the radiating patch to improve the isolation among array elements. Each antenna element is printed on a low-cost FR-4 substrate with dimensions of 28 mm x 23 mm with coverage of the whole UWB spectrum from 3.1 to 10.6 GHz frequency band. The eight-port UWB-MIMO antenna system consists of symmetric and non-symmetric array configurations. Simulated and measured MIMO performance parameters i.e. Channel Capacity Loss (CCL) < 0.35, Envelope Correlation Coefficient (ECC) < 0.0025 and Total Active Reflection Coefficient (TARC) < -11 dB are in acceptable limits for both symmetric and nonsymmetric configurations. The proposed MIMO antenna system is suitable for 3D system-in-package, indoor localization systems, and wireless personal area network applications in industries where multiple machines are connected to a central server wirelessly through such kinds of antennas in a rich scattering environment.

Sentiment analysis uses supervised and machine learning algorithms. The analysis can be done on movie reviews, twitter reviews, online product reviews, blogs, discussion forums, Myspace comments, and social networks. The twitter data set is analyzed using a support vector machine (SVM) classifier with various parameters. The content of the tweet is classified to find whether it contains fact data or opinion data. The deep analysis is required to find the opinion of the tweets posted by the individuals. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve productivity. The performance of SVM radial kernel, SVM linear grid and SVM Radial Grid was compared and found that SVM linear grid performs better than other SVM models. Disciplinary: Computer and Information Technology. (C) 2020 INT TRANS J ENG MANAG SCI TECH.