DISTRIBUTED MULTI-HOP ROUTING ALGORITHM FOR WIRELESS SENSOR NETWORKS

In a Wireless Sensor Network (WSN), routing is the process of finding a cost-effective route in terms of power consumption. As an evaluation criterion for the WSN performance, network lifetime is directly affected by the routing method. In a wide variety of WSNs, different techniques are used as routing methods, such as shortest distance path. In this paper, we propose a novel algorithm, optimizing power consumption in WSN nodes, based on the shortest path algorithm. In this approach, the energy level of nodes and their geographical distance from each other contribute to the weight of the connecting path. The proposed algorithm is used as a data dissemination method in WSNs with randomly scattered nodes. We also apply Dijkstra’s shortest path algorithm to the same networks. The results showed that the proposed algorithm increases the network lifetime up to 30 % by preventing nodes with low charge levels from early disconnection

Time difference of arrival estimation of sound source using cross correlation and modified maximum likelihood weighting function

The Generalized Cross Correlation (GCC) framework is one of the most widely used methods for Time Difference Of Arrival (TDOA) estimation and Sound Source Localization (SSL). TDOA estimation using cross correlation without any pre-filtering of the received signals has a large number of errors in real environments. Thus, several filters (weighting functions) have been proposed in the literature to improve the performance of TDOA estimation. These functions aim to mitigate TDOA estimation error in noisy and reverberant environments. Most of these methods consider the noise or reverberation, and as one of them increases, TDOA estimation error increases. In this paper, we propose a new weighting function. This function is a combined and modified version of Maximum Likelihood (ML) and PHAT-rho gamma functions. We named our proposed function as Modified Maximum Likelihood with Coherence (MMLC). This function has merits of both ML and PHAT-rho gamma functions and can work properly in both noisy and reverberant environments. We evaluate our proposed weighting function using real and synthesized datasets. Simulation results show that our proposed filter has better performance in terms of TDOA estimation error and anomalous estimations. (c) 2017 Sharif University of Technology. All rights reserved.info:eu-repo/semantics/publishedVersio

A NEW ANOMALOUS TEXT DETECTION APPROACH USING UNSUPERVISED METHODS

Increasing size of text data in databases requires appropriate classification and analysis in order to acquire knowledge and improve the quality of decision-making in organizations. The process of discovering the hidden patterns in the data set, called data mining, requires access to quality data in order to receive a valid response from the system. Detecting and removing anomalous data is one of the pre-processing steps and cleaning data in this process. Methods for anomalous data detection are generally classified into three groups including supervised, semi-supervised, and unsupervised. This research tried to offer an unsupervised approach for spotting the anomalous data in text collections. In the proposed method, a combination of two approaches (i.e., clustering-based and distance-based) is used for detecting anomaly in the text data. In order to evaluate the efficiency of the proposed approach, this method is applied on four labeled data sets. The accuracy of Na¨ıve Bayes classification algorithms and decision tree are compared before and after removal of anomalous data with the proposed method and some other methods such as Density-based spatial clustering of applications with noise (DBSCAN). Our proposed method shows that accuracy of more than 92.39% can be achieved. In general, the results revealed that in most cases the proposed method has a good performance

Pairwise document similarity measure based on present term set

Abstract Measuring pairwise document similarity is an essential operation in various text mining tasks. Most of the similarity measures judge the similarity between two documents based on the term weights and the information content that two documents share in common. However, they are insufficient when there exist several documents with an identical degree of similarity to a particular document. This paper introduces a novel text document similarity measure based on the term weights and the number of terms appeared in at least one of the two documents. The effectiveness of our measure is evaluated on two real-world document collections for a variety of text mining tasks, such as text document classification, clustering, and near-duplicates detection. The performance of our measure is compared with that of some popular measures. The experimental results showed that our proposed similarity measure yields more accurate results

Analytical Modelling of ANCH Clustering Algorithm for WSNs

In this thesis, I am critically assessing contemporary environmental discourse at the governmental level in China. Taking the perspective of the Copenhagen School’s securitization theory, this thesis dwells on the question of how the Chinese government might attempt to use discourse in official policies in order to securitize the question of climate change. To answer this question, I have collected policy documents from three parts of the central government – the Ministry of Ecology and Environment, the State Council, and the National Development and Reform Commission as well as speeches from President Xi Jinping. In this thesis I seek not only to discover how climate change is represented by the Chinese government, but also who or what they believe the referent object ought to be. This thesis also aims at critically describing, interpreting, and explaining the ways in which these discourses might construct, maintain, and legitimize social inequalities. The findings imply that the government has attempted to securitize the issue of climate change for controlling the Chinese society and further consolidating their power. The Communist Party of China (CPC) utilized security speech acts which represented climate change as a threat to natural resources, the environment, and human health which served as proxies for the de facto referent object – which was interpreted to be the development process and the legitimacy of the CPC