Biblio
This paper presents a proximity coupled wideband wearable antenna operating between 4.71 GHz and 5.81 GHz with 5.2 GHz as centre frequency for biomedical telemetry applications in ISM band (IEEE 802.11 Standard). Two layers of different flexible substrate materials, ethylene-vinyl acetate and felt make the design mechanically stable. Bandwidth improvement is achieved by introducing two slots on elliptical ground plane. Highest gain of 3.72 dB and front to back ratio (FBR) of 6.55 is obtained in the given frequency band. The dimensions of antenna have been optimized to have desired bandwidth of 1100 MHz (\$\textbackslashtextbackslashsimeq\$21%). The specific absorption rate (SAR) value is 1.12 \$W/Kg\$ for 1 g of human body tissue. Both simulated and measured results are presented for the structure.
Close physical proximity among wireless devices that have never shared a secret key is sometimes used as a basis of trust. In these cases, devices in close proximity are deemed trustworthy while more distant devices are viewed as potential adversaries. Because radio waves are invisible, however, a user may believe a wireless device is communicating with a nearby device when in fact the user's device is communicating with a distant adversary. Researchers have previously proposed methods for multi-antenna devices to ascertain physical proximity with other devices, but devices with a single antenna, such as those commonly used in the Internet of Things, cannot take advantage of these techniques. We investigate a method for a single-antenna Wi-Fi device to quickly determine proximity with another Wi-Fi device. Our approach leverages the repeating nature Wi-Fi's preamble and the characteristics of a transmitting antenna's near field to detect proximity with high probability. Our method never falsely declares proximity at ranges longer than 14 cm.
The notion of style is pivotal to literature. The choice of a certain writing style moulds and enhances the overall character of a book. Stylometry uses statistical methods to analyze literary style. This work aims to build a recommendation system based on the similarity in stylometric cues of various authors. The problem at hand is in close proximity to the author attribution problem. It follows a supervised approach with an initial corpus of books labelled with their respective authors as training set and generate recommendations based on the misclassified books. Results in book similarity are substantiated by domain experts.