OTHR impulsive interference suppression in strong clutter background

External interferences can severely degrade the performance of an Over-the-horizon radar (OTHR), so suppression of external interferences in strong clutter environment is the prerequisite for the target detection. The traditional suppression solutions usually began with clutter suppression in either time or frequency domain, followed by the interference detection and suppression. Based on this traditional solution, this paper proposes a method characterized by joint clutter suppression and interference detection: by analyzing eigenvalues in a short-time moving window centered at different time position, Clutter is suppressed by discarding the maximum three eigenvalues at every time position and meanwhile detection is achieved by analyzing the remained eigenvalues at different position. Then, restoration is achieved by forward-backward linear prediction using interference-free data surrounding the interference position. In the numeric computation, the eigenvalue decomposition (EVD) is replaced by values decomposition (SVD) based on the equivalence of these two processing. Data processing and experimental results show its efficiency of noise floor falling down about 10-20 dB.

Using social networking tools for teaching and learning: a perspective of University lecturers and students

The use of online social networking tools (SNTs) has become commonplace within higher education. In this paper a definition and a typology of educational affordance of social networking service (SNS) are presented. The paper also explores the educational affordances whilst examining how university lecturers and students use SNTs to support their educational activities. The data presented here were obtained through a survey in which 38 participants from three universities took part; two universities in Uganda and one in the United Kingdom. The results show that Facebook is the most popular tool with 75 % of participants having profiles. Whilst most participants perceived the educational significance of these tools, social affordances remain more pronounced compared to pedagogical and technological affordances. The limitations of this study have also been discussed.

Students’ perception of privacy risks in using social networking sites for learning: a study of Uganda Christian University

Although social networking sites (SNSs) present a great deal of opportunities to support learning, the privacy risk is perceived by learners as a friction point that affects their full use for learning. Privacy risks in SNSs can be divided into risks that are posed by the SNS provider itself and risks that result from user’s social interactions. Using an online survey questionnaire, this study explored the students’ perception of the benefits in using social networking sites for learning purposes and their perceived privacy risks. A sample of 214 students from Uganda Christian University in Africa was studied. The results show that although 88 % of participants indicated the usefulness of SNSs for learning, they are also aware of the risks associated with these sites. Most of the participants are concerned with privacy risks such as identity theft, cyber bullying, and impersonation that might influence their online learning participation in SNSs.

Bigger data for Big Data: from Twitter to brain-computer interface

We are sympathetic with Bentley et al’s attempt to encompass the wisdom of crowds in a generative model, but posit that success at using Big Data will include more sensitive measurements, more and more varied sources of information, as well as build from the indirect information available through technology, from ancillary technical features to data from brain-computer interface.