Beamforming network using switch line phase shifter

The rapid progresses in radio technology is creating new and improve service at lower cost, which results in increases in air-time usage the number of subscribers. Wireless revenues are currently growing between 20% and 30% per year, and these broad trends are likely to continue for several years. Wireless system designer are faced with a number of challenges one of them is interference. In indoor wireless communication environments, however, reflections from walls, the floor, or the ceiling cause many signal propagation paths and delays, consequently degrading the received signal quality and receiver performance. One of possible solutions is a beamforming technique to direct antenna’s main beam towards a transmitter and to direct nulls towards interference or multipath signal directions, such that incoming signals from reflection paths are suppressed while increasing the antenna gain for a desired signal direction. This project shows the steps of designing beamforming network by implement an active component. It was designed to operate at 2.4GHz for WLAN application. The switch line phase shifter is chosen as way to build a beamforming network. This was designed to provide four different progressive phase shifts -45o, +135o, -135o, +45o that coupled to an antenna array. It is made up from two 900 hybrid coupler, eight phase shifters and eights PIN diode each component is designed and simulated using Agilent ADS software and fabricated on FR4 board. This network is then combined with linear antenna arrays with the aim to produce four independent beams at four different directions. The obtained results shows that 4 beams are generated by rectangular patch antenna array, it is produced half power beam width for each beams about 30o and manage to cover 120o area. Finally, it can be concluded that the objective of this project to design beamforming was achieved

Term-class-max-support (TCMS): A simple text document categorization approach using term-class relevance measure

In this paper, a simple text categorization method using term-class relevance measures is proposed. Initially, text documents are processed to extract significant terms present in them. For every term extracted from a document, we compute its importance in preserving the content of a class through a novel term-weighting scheme known as Term_Class Relevance (TCR) measure proposed by Guru and Suhil (2015) [1]. In this way, for every term, its relevance for all the classes present in the corpus is computed and stored in the knowledgebase. During testing, the terms present in the test document are extracted and the term-class relevance of each term is obtained from the stored knowledgebase. To achieve quick search of term weights, Btree indexing data structure has been adapted. Finally, the class which receives maximum support in terms of term-class relevance is decided to be the class of the given test document. The proposed method works in logarithmic complexity in testing time and simple to implement when compared to any other text categorization techniques available in literature. The experiments conducted on various benchmarking datasets have revealed that the performance of the proposed method is satisfactory and encouraging.Comment: 4 Pages, 4 Figures; 2016 Intl. Conference on Advances in Computing, Communications and Informatics (ICACCI), Sept. 21-24, 2016, Jaipur, Indi

Feature selection of interval valued data through interval k-means clustering

This paper introduces a novel feature selection model for supervised interval valued data based on interval K-Means clustering. The proposed model explores two kinds of feature selection through feature clustering viz., class independent feature selection and class dependent feature selection. The former one clusters the features spread across all the samples belonging to all the classes, whereas the latter one clusters the features spread across only the samples belonging to the respective classes. Both feature selection models are demonstrated to explore the generosity of clustering in selecting the interval valued features. For clustering, the kernel of the K-means clustering has been altered to operate on interval valued data. For experimentation purpose four standard benchmarking datasets and three symbolic classifiers have been used. To corroborate the effectiveness of the proposed model, a comparative analysis against the state-of-the-art models is given and results show the superiority of the proposed model

A new Laplacian method for arbitrarily-oriented word segmentation in video

Word segmentation from video text line is challenging because video poses several challenges, such as complex background, low resolution, arbitrary orientation, etc. Besides, word segmentation is essential for improving text recognition accuracy. Therefore, we propose a novel method for segmenting words by exploring zero crossing points for each sliding window over text line. The candidate zero crossing pointes are defined based on characteristics of positive and negative Laplacian values at text region and non-text region. The percentage of candidate zero crossing points is calculated for each sliding window and is used for identifying the seed window that represents space between words. For the seed window, we propose a novel idea of horizontal and vertical sampling based on the percentage values to estimate the width and the height of the word spacing. Then the width and the height of the word spacing are used to validate the actual word spacing. Experimental results comparing with an existing method show that the proposed method is better than the existing method in terms of recall, precision and f-measure on curved, horizontal, non-horizontal, Hua's video data, as well as ICDAR data. We also test it on our own data containing multiscript text lines to show the robustness of the proposed method