Image orientation detection requires high-level scene understanding. Humans use object recognition and contextual scene information to correctly orient images. In literature, the problem of image orientation detection is mostly confronted by using low-level vision features, while some approaches incorporate few easily detectable semantic cues to gain minor improvements. The vast amount of semantic content in images makes orientation detection challenging, and therefore there is a large semantic gap between existing methods and human behavior. Also, existing methods in literature report highly discrepant detection rates, which is mainly due to large differences in datasets and limited variety of test images used for evaluation. In this work, for the first time, we leverage the power of deep learning and adapt pre-trained convolutional neural networks using largest training dataset to-date for the image orientation detection task. An extensive evaluation of our model on different public datasets shows that it remarkably generalizes to correctly orient a large set of unconstrained images; it also significantly outperforms the state-of-the-art and achieves accuracy very close to that of humans. 1
Coal miners encounter many problems in terms of safety during coal production and development of coal industry. During mining operations at deeper and remote areas of the mine, it is hazardous to work without any communication facilities. Miners are constantly exposed to drastic change in environment with fluctuating temperature and pressure. Often it goes unnoticed as accidents do occur in coal mines and hence disaster.Ad-hoc networks are characterized by random and quickly changing topologies which demand for an efficient routing protocol to accommodate the particular environment. In this paper, the mining environment and its constrains are considered to find the suitable and effective protocol. The two most popular and distinct feature protocols: proactive(DSDV) and reactive(AODV) are considered. NS-2 simulator is used to create the mining scenario having access points and miners(mobile nodes). Various characteristics of the network such as packet delivery fraction (PDF), end to end delay using AWK files, varying the speed of mobile nodes and the simulation time. We have achieved the design of a prototype, by considering a base station and mobile node (miner). The prototype senses the changes in environmental conditions and warns the miner of forth coming disaster.
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