Short Video wastage. (In processing)
University of Notre Dame | 2022
The project aims to understand the causes of short video data wastage in terms of viewing time and buffering. Analyzed the reasons for the waste of different short video servers at the pre-load level. Designed an algorithm to reduce the short video wastage from pre-loading and buffer level.
Measurement Study across Short Video Services
University of Notre Dame & AT&T Labs Research | 2021 - 2022
Short videos have emerged recently as a popular form of short-duration User Generated Content (UGC) within modern social media. This work provided a comprehensive comparison of four popular short video services. In particular, the work focuses on exploring content characteristics and evaluating the video quality across resolutions for each service. By conducting an experimental study, the work investigates data consumption and finally evaluates achieved QoE under different network scenarios and application configurations. Read more
Congestion Prediction in WiFi Video Streaming.
University of Notre Dame | 2019 - 2020
This research aims to detect the network changes to deliver a better rate adaptation mechanism for adaptive video streaming. This research proposes an algorithm, Congestion Prediction-DASH (CP- DASH), which is designed to prevent stalling and maximize video quality. The CP-DASH algorithm leverages the aggregate MAC protocol data unit information provided by Block Acknowledgement aggregate control packets to allow a WiFi device to passively predict link congestion. Read more
Jumping Motion Recognition.
Figur8. Inc | Feb - Jul 2018
The purpose of this research is to extract the motion features, such as jumping up and down. The data comes from a specific muscle sensor, which records muscle stretch during jumping. Through Recurrent Neural Network modeling, the movement characteristics of the jump are separated, and extracted the jumping features to recognize the specific activities. Read more
Activities Recognition in Smart home.
Ball State Univeristy | 2017
The research proposed a signals solution, Activities Recognition based on Signal Filter (ARSF), to recognize human activities through commodity off-the-shelf radar sensors. The radar sensors detect abnormal and instantaneous motion situations by high-frequency radio signal. The research purpose is to recognize the specific activities of residents in smart homes. During this project, I designed a motion separation algorithm, which extracts the corresponding motion data from the whole data background. Developed an activity recognition system based on distinct features, which distinguishes the distinct motions. Read more
User Localization based on Ultrasonic Beamforming.
Ball State Univeristy | 2016 - 2017
This research work designs and develops a noninvasive distance-based user localization and tracking solution, DiLT, for smart systems. DiLT consists of a mechanical ultrasonic beam-forming design for omni-space sensing, a contrastive divergence learning to localize a user and a binary back-off algorithm to track the motion of the user. During this project, I designed an ultrasonic scanning platform to track the users' location. Developed a location tracking algorithm to filter the environment and noise to identify the specific users' location. Read more