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A number of traffic characterization studies have been carried out on wireless LANs, which indicate that the wireless settings pose major challenges, especially for high bandwidth and delay sensitive applications. This paper aims to evaluate a number of Quality of Service (QoS) parameters related to video conferencing over three major WLAN Standards 802.11a, 802.11b and 802.11g. To study the traffic characterization behaviour of these WLAN standards, we have simulated the environment for each of these standards and performed experiments. Results are verified through the delivery of successful H.261 video traffic import in OPNET-14 Network simulator. We found that a trade-off exists between the selected data rate, physical characteristics and the frequency spectrum (number of channels) for every standard. The traffic of video conferencing is characterized over each standard in terms of delay performance, traffic performance and load and throughput performance. The results show that quality of video traffic is a function of the frequency band, physical characteristic, maximum data rate and buffer sizes of WLAN standards.

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The smart agricultural robotic system can decrease the dependence on various traditional agriculture crop spraying methods such as pesticides, herbicides, and fertilizer. To meet the world population food requirements, conventional schemes are not sufficient for spraying agrochemicals to control the weeds and increase crop production. Therefore, a smart and intelligent farming system is introduced to increase the production of crops and to reach crop production target. In this paper, Deep Learning (DL) based algorithms is applied for the identification and classification of weed plants using combination of Convolutional Neural Networks (CNN) and Long-Short- Term Memory (LSTM). Convolutional Neural Networks (CNN) has a unique structure to get discriminative features for the input images, and LSTM allows to jointly optimize the classification. To validate the proposed scheme, nine kinds of weeds are classified using the proposed method such as vine weeds, three-leaf weeds, spiky weeds, and invasive creeping weeds. We carried out several extensive experiments and 99.36% of average classification accuracy is achieved. The obtained results show that the combination of CCN-LSTM has significantly higher classification capabilities in comparison to other existing prominent approaches.