cv

AI Engineer Intern

AiFi Inc.

Enhanced the customer-item assoication through characterizing item motion through RFID and customer motion through camera in the autonomous store.

• 95.8% RFID-based item motion classification accuracy
• Coherent feature extraction from camera and RFID data

PhD

Universit of Michigan, Ann Arbor

Embedded Systems

Master

University of Michigan, Ann Arbor

Embedded Systems

Qualcomm Innovation Fellowship

Qualcomm

The Qualcomm Innovation Fellowship (QIF) program is focused on recognizing, rewarding, and mentoring PhD and Masters* students across a broad range of technical research, based on Qualcomm's core values of innovation, execution, and teamwork. The program empowers graduate students to take giant steps toward achieving their research goals.

FloHR: Ubiquitous Heart Rate Measurement using Indirect Floor Vibration Sensing

Jesse R. Codling, Jeffrey D. Shulkin, Yen-Cheng Chang, *Jiale Zhang*, Hugo Latapie, Hae Young Noh, Pei Zhang, Yiwen Dong

The paper presents FloHR, an innovative system for contactless heart rate monitoring that utilizes heartbeat-induced floor vibrations. FloHR contributes a sensitive vibration sensing setup that accurately detects minor vibrations from heartbeats at a distance and an algorithmic framework that distinguishes heartbeats from ambient noise, achieving near-medical accuracy even when sensors are up to 2 meters away from the subject

Poster: Drive-by City Wide Trash Sensing for Neighborhood Sanitation Need

Fernandez, Tomas and Chang, Yen Cheng and Codling, Jesse and Dong, Yiwen and *Zhang, Jiale* and Joe-Wong, Carlee and Noh, Hae Young and Zhang, Pei

We propose a framework for labeling and self-training of in-car video to detect trash on the roads, providing a scalable solution for city-wide trash and air-pollution sensing.

Poster Abstract: Vibration-Based Object Classification with Structural Response of Ambient Music

*Zhang, Jiale* and Pati, Shweta and Codling, Jesse and Bannis, Adeola and Ruiz, Carlos and Noh, Hae Young and Zhang, Pei

The paper presents a novel approach to object classification using vibration-based sensing activated by ambient music. By playing music through a shelf with a sound exciter, the vibration responses vary based on the objects placed on the shelf. The system achieves an accuracy of 98.6% in classifying different objects.

Directly Controlling the Perceived Difficulty of a Shooting Game by the Addition of Fake Enemy Bullets

*Zhang, Jiale*

Adjusting the balance between the player's game skill and the difficulty level is one of the most important factors to improve the player's engagement. This work proposes the concept of user-perceived difficulty and investigates its relationship with the actual game difficulty.

Deep-Learning-Enabled Microwave-Induced Thermoacoustic Tomography Based on Sparse Data for Breast Cancer Detection

*J. Zhang*, C. Li, W. Jiang, Z. Wang, L. Zhang and X. Wang

This work proposes a novel deep-learning-enabled microwave-induced thermoacoustic tomography (DL-MITAT) modality to address the sparse data reconstruction problem and applies it in breast cancer detection. The applied deep learning network is a domain transform network called feature projection network (FPNet) + ResU-Net.