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Open accessProceedings ArticleDOI
11 Mar 2019
29 Citations
Our solution, WiWear, has two key innovations: 1) beamforming WiFi transmissions to significantly boost the energy that a receiver can harvest ~2-3 meters away, and 2) smart zero-energy, triggering of inertial sensing, that allows intelligent duty-cycled operation of devices whose transient power consumption far exceeds what can be instantaneously harvested.
For home and work environments, aggressive WiFi scans can significantly improve the speed at which mobile nodes join the WiFi network.
Our methods enable the application of signal to interference and noise (SINR) based scheduling algorithms to WiFi networks resulting in tremendous increase in throughput and QoS/fairness.
We develop a new digital signal processing algorithm called Common Multiple Folding (CMF) that accurately amplifies periodic beacons in WiFi interference signals.
Open accessProceedings ArticleDOI
15 Apr 2019
5 Citations
Indeed, burstiness in modern WiFi standards may improve network performance, and we show that a Bursty QUIC (BQUIC), i. e., a customized version of QUIC that is targeted to increase its burstiness, can achieve better performance in WiFi.
However, the multi-path fading of WiFi signals causes time-varying received signal strengths of WiFi signals, which leads to poor accuracy of WiFi localization.
Our results supported that the WiFi-aided MM algorithm provided more reliable solutions than both WiFi and MM in the areas that have poor WiFi signal distribution or indistinctive magnetic-gradient features.
Proceedings ArticleDOI
Lixing Song, Aaron Striegel 
12 Jun 2017
16 Citations
We show not only how frame aggregation breaks existing lightweight mechanisms for link characterization but also how to carefully construct packet sequences that induce frame aggregation to capture the WiFi available bandwidth.
This method results in an Android-based personal navigation system that works robustly with only few locations of the WiFi access points known in advance, avoiding the need to survey WiFi signal in the whole area.
The proposed algorithm provided more reliable solutions than both PDR/WiFi and PDR/MM, especially in areas with poor WiFi signal distribution or indistinctive magnetic features.

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How does increased parameters in transformer models affect inference latency?
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