Informatics Publishing Limited

A. Sudhakar ¹, M. Satyanarayana ², M. Sunil Prakash ², Sudhir Kumar Sharma ³

Affiliations
1 ECE Department, GMR Institute of Technology, Rajam - 532127, Andhra Pradesh, IN
2 ECE Department, MVGR College of Engineering, Vizianagaram - 535005, Andhra Pradesh, IN
3 ECE Department, Jaipur National University, Jaipur - 302017, Rajasthan, IN

Abstract

In this paper, a printed Ultra Wide Band (UWB) antenna of small size with variable frequency band-notched properties is presented. Finite element based HFSS software is used to simulate the proposed antenna and then fabricated. The parameters of fabricated antenna are measured with E5071C vector network analyser. The measured and simulated results are in good agreement. A stepped radiating patch is printed on one side and stepped ground plane on the other side of FR- 4 substrate to increase the bandwidth from 4.3 GHz to 11.28 GHz. Introduction of two protruding strips having a length of λ/2 in rectangular slot results to get frequency notch characteristics in WLAN (5.15–5.85 GHz) band. The effects of length and thickness of individual protruding strips on band notch characteristics are examined. By varying either the length or the thickness of protruding strips incorporated in the rectangular slot, we can control the band notch frequency and its band width. The presented antenna has a small size of 12 x 18 x 1.6 mm3and reveals a steady gain throughout the UWB except at notched band of 4.9 GHz 6.1 GHz. The radiation pattern in the E-plane and H-plane is like a figure of eight.

Keywords

Antenna, Frequency Notch, Protruding Strips, Rectangular Slot, UWB

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Pankaj Kumar Varshney ¹, G. S. Agrawal ¹, Sudhir Kumar Sharma ²

Affiliations
1 Department of Computer Applications, FET, Mangalayatan University, Beswan, Aligarh-U.P, IN
2 School of Engineering, Jaipur National University, Jaipur-Raj, IN

Abstract

A Wireless Ad hoc Networks consists of mobile platforms (e.g., a router with multiple hosts and wireless communications devices) here in simply referred to as "nodes" which are free to move about arbitrarily; thus, the network topology which is typically multi-hop may change randomly and rapidly at unpredictable times, and may consist of both bidirectional and unidirectional links. So that the development of dynamic routing protocols that can efficiently find routes between two communications nodes when nodes are mobile is very challenging task. To accomplish this, a number of ad hoc routing protocols had been proposed and implemented. Performance evolution of the protocols is the key step before selecting a particular protocol. In this paper, the performance of Optimized Link State Routing (OLSR) and Source Tree Adaptive Routing (STAR) protocol has been compared with respect tp varying node density using Qualnet 5.0.2 simulator. The average jitter, end-to-end delay, throughput, First Packet Receive (FPR), Last Packet Receive (LPR), Total Bytes Receive (TBR), and Total Packet Receive (TPR) are the common measures used for the comparison of the performance of above protocols. The experimental results show that overall performance of OLSR routing protocol is better than STAR routing protocol as increase the node density in a particular area.

Keywords

OLSR, STAR, QualNet 5.0, Wireless Ad Hoc Networks.

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Sudhir Kumar Sharma ¹, Ximi Hoque ²

Affiliations
1 Institute of Information Technology & Management, Institutional Area, Janakpuri, New Delhi – 110058, Delhi, IN
2 KIIT College of Engineering, Gurgaon – 122102, Haryana, IN

Abstract

Objectives: This paper analyzes odd-even traffic scheme using tweets posted on Twitter from December 2015 to August 2016. Twitter is a social network where users post their feelings, opinions and sentiments for any event using hashtags and mentions. The tweets posted publicly can be viewed by anyone interested. This paper transforms the unstructured tweets into structured information using open source libraries. Further objective is to build a model using machine learning classification techniques to classify unseen tweets on the same context. Methods/Analysis: This paper collects tweets on this event under hashtags. This study explores Dandelion Application Programming Interface for annotation of tweets for academic research. This paper uses machine learning classifications approaches for sentiment analysis and opinion mining. This paper presents empirical comparison of three supervised classification algorithms namely, Multinomial Naïve Bayes, Support Vector Machines (SVM) and Multiclass Logistic Regression. The performances of these classifiers are evaluated through standard evaluation metrics. Findings: The experimental results reveal that SVM classifier outperforms the other two classification algorithms. This study may help in decision making of this event to some extent. Application: A large number of applications of sentiment and opinion mining can be designed using packages and freely open resources within a time frame now a days.

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