This paper presents a simple approach to characterize the effects of scattering objects around indoor links at 28 GHz while the link is fully blocked by a human body. The effects of scattering objects nearby the link were studied by conducting measurements with a metallic re ector and the human body.

In recent years, many studies target anti-social behaviour such as offensive language and cyberbullying in online communication. Typically, these studies collect data from various reachable sources, the majority of the datasets being in English. However, to the best of our knowledge, there is no dataset collected from the YouTube platform targeting Arabic text and overall there are only a few datasets of Arabic text, collected from other social platforms for the purpose of offensive language detection. Therefore, in this paper we contribute to this field by presenting a dataset of YouTube comments in Arabic, specifically designed to be used for the detection of offensive language in a machine learning scenario. Our dataset contains a range of offensive language and flaming in the form of YouTube comments. We document the labelling process we have conducted, taking into account the difference in the Arab dialects and the diversity of perception of offensive language throughout the Arab world. Furthermore, statistical analysis of the dataset is presented, in order to make it ready for use as a training dataset for predictive modelling.

We present the results of predictive modelling for the detection of anti-social behaviour in online communication in Arabic, such as comments which contain obscene or offensive words and phrases. We collected and labelled a large dataset of YouTube comments in Arabic which contains a broad range of both offensive and inoffensive comments. We used this dataset to train a Support Vector Machine classifier and experimented with combinations of word-level features, N-gram features and a variety of pre-processing techniques. We summarise the pre-processing steps and features that allow training a classifier which is more precise, with 90.05% accuracy, than classifiers reported by previous studies on Arabic text.

This paper presents some initial studies for characterizing effects of human body movements on short range indoor links at 18-22 GHz. Firstly, measurement system is described, and then, calibration measurements along with initial results of the impact of human body movement on the channel are presented for some scenarios. 

Mobile wireless technologies have followed different evolutionary (generation) paths aimed at unified target related to the

performance and efficiency in high mobile environment, which provides access to wide range of telecommunication services

including advanced mobile services supported by mobile and fixed networks. This paper illustrate the developments of the

mobile wireless communication, focus on the specification and capability for each technology to make an idea about the future

technology what will offer.