Maseeh College of Engineering & Computer Science

Tracking the movement of people in indoor environments is useful for a variety of applications including elderly care, study of shopper behavior in shopping centers, security etc. There have been several previous efforts at solving this problem but with limited success. Our approach uses inexpensive pressure sensors, placed in a specific manner, that allows us to identify multiple people. Given this information, our algorithm can track multiple people across the floor even in the presence of large sensor error. The algorithm we develop is evaluated for a variety of different movement patterns that include turning and path crossing. The error in correct path detection is shown to be very small even in the most complex movement scenario. We note that our algorithm does not use any a priori information such as weight, rfid tags, knowledge of number of people, etc.

Written Chinese has tens of thousands of characters. But most available fonts contain only around 6 to 12 thousand common characters that can meet the needs of everyday users. However, in publications and information exchange in many professional fields, a number of rare characters that are not in common fonts are needed in each document. This paper describes a method of typesetting such rare characters in LTEX. The document author describes a rare character in HanGlyph when such need arises. A Chinese character synthesis system renders the glyph according to this description and collects the newly created glyphs into a font so they are available in the body of the LTEX document.

Social media popularity has increased over the
years. Millions of images are uploaded and shared on social
media daily. Image popularity prediction is extensively studied
over past years. Several research works have shown that image
content and social context play an important role in predicting
image popularity. However, the impact of image aesthetics and
quality on popularity has not been studied in detail until now.
In this paper, we report a detailed study and analysis for
finding the correlation between image quality/aesthetics and
image popularity. To solidify the understanding of the impact,
we present experimental results that are conducted on SMP 2020
and ICIP image popularity 2020 datasets to investigate if a high
quality image or an aesthetically pleasing image is likely to be
popular. We found that image aesthetics is moderately correlated
with image popularity while quality is less so.