We study the interface exciton at lateral type II heterojunctions of monolayer transition metal dichalcogenides (TMDs), where the electron and hole prefer to stay at complementary sides of the junction. We find that the 1D interface exciton has giant binding energy in the same order as 2D excitons in pristine monolayer TMDs although the effective radius (electron-hole seperation) of interface exciton is much larger than that of 2D excitons. The binding energy, exciton radius and optical dipole strongly depends on the band offset at the junction. The inter-valley coupling induced by the electron-hole Coulomb exchange interaction and the quantum confinement effect at interface of a closed triangular shape are also investigated. Small triangles realize 0D quantum dot confinement of excitons, and we find a transition from non-degenerate ground state to degenerate ones when the size of the triangle varies. Our findings may facilitate the implementation of the optoelectronic devices based on the lateral heterojunction structures in monolayer semiconductors.
Security becomes one of the major necessities in our lives nowadays however criminal activities are still at large with criminals unable to be persecuted without eligible proofs of their misdeeds. Surveillance Camera is one of the better solutions to these problems in which they can be positioned at every corner of a building even streets and alleys. Their functions can be enhanced by adding algorithms that can identify objects. Frame Differences method is an algorithm to identify an object’s motion. Using this algorithm, we could differentiate an object moving in the environment. Background subtraction is one of the methods suitable to further improve frame differences thus increasing its effectiveness and precision. After implementing the method on a camera, the luminosity was founded to influence the threshold value significantly, the threshold value of 35 is the optimal value.
Abstract-Competition between telephone providers to attract new customers can be seen through advertisment war on TVs, posters and radios nearly every moment. Question is arise on how do we measure the quality of these providers in order choose the best one for oneself. This paper is written to solve the question by measuring customers satisfaction by using text mining. Sample model is extracted from social media Twitter and the sentiment polarity is measured using Naï ve Bayes classifier method. The model shows a promising result on defining the popularity based on customer's satisfaction and therefore defining the best provider to be used Index Terms-Naï ve bayesian, sentiment analysis, telephone provider, text mining.
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