Kang Yang

• Doctor of Engineering
• PhD Candidate at University of California, Merced

LoRa networks have been deployed in many orchards for environmental monitoring and crop management. An accurate propagation model is essential for efficiently deploying a LoRa network in orchards, e.g. , determining gateway coverage and sensor placement. Although some propagation models have been studied for LoRa networks, they are not suitable for...

We present Generalizable Wireless Radiance Fields (GWRF), a framework for modeling wireless signal propagation at arbitrary 3D transmitter and receiver positions. Unlike previous methods that adapt vanilla Neural Radiance Fields (NeRF) from the optical to the wireless signal domain, requiring extensive per-scene training, GWRF generalizes effective...

This work presents ARD2, a framework that enables real-time through-wall surveillance using two aerial drones and an augmented reality (AR) device. ARD2 consists of two main steps: target direction estimation and contour reconstruction. In the first stage, ARD2 leverages geometric relationships between the drones, the user, and the target to projec...

This paper presents a novel map matching framework that adopts deep learning techniques to map a sequence of cell tower locations to a trajectory on a road network. Map matching is an essential pre-processing step for many applications, such as traffic optimization and human mobility analysis. However, most recent approaches are based on hidden Mar...

Both our experiments and previous studies show that LoRa links vary dynamically, which makes data transmission unreliable and consumes much energy of sensor nodes by retransmissions. This paper presents, a Rateless-enabled link Adaptation system for LoRa networks. Rateless coding approaches the optimal data rate of a link by continuously transmitti...

This paper presents a novel system, LLDPC , which brings Low-Density Parity-Check (LDPC) codes into Long Range (LoRa) networks to improve Forward Error Correction, a task currently managed by less efficient Hamming codes. Three challenges in achieving this are addressed: First, Chirp Spread Spectrum (CSS) modulation used by LoRa produces only hard...

Predicting the next application (app) a user will open is essential for improving user experience, e.g., app pre-loading and app recommendation. Unlike previous solutions that only predict which app the user will open, this paper predicts both the next app and the time to open it. Time prediction is essential to avoid loading the next app too early...

Low-Density Parity-Check (LDPC) codes have been widely used for Forward Error Correction (FEC) in wireless networks, since they can approach the capacity of wireless links with light-weight encoding complexity. Although LoRa networks have been deployed for many applications, they still adopt a simple FEC code, Hamming codes, which provides limited...

This paper presents GeoDMA , which processes the GPS data from multiple vehicles to detect anomalous driving maneuvers, such as rapid acceleration, sudden braking, and rapid swerving. First, an unsupervised deep auto-encoder is designed to learn a set of unique features from the normal historical GPS data of all drivers. We consider the temporal de...

This paper aims to predict a set of apps a user will open on her mobile device in the next time slot. Such an information is essential for many smartphone operations, e.g., app pre-loading and content pre-caching, to improve user experience. However, it is hard to build an explicit model that accurately captures the complex environment context and...

This paper aims to predict the apps a user will open on her mobile device next. Such an information is essential for many smartphone operations, e.g., app pre-loading and content pre-caching, to save mobile energy. However, it is hard to build an explicit model that accurately depicts the affecting factors and their affecting mechanism of time-vary...