Network Engineer Intern.

Early-stage startup; owned end-to-end development of the smart-farm prototype across hardware, network, and cloud.

Network

• Architected and implemented a low-cost LoRa mesh network on ESP32 + SX1276 (LoRa PHY) for smart agriculture.
• Integrated and modified the MCCI LoRaWAN LMIC library at the register level to control SX1276 mode transitions, interrupts, Tx/Rx scheduling, and ESP32 OS.Job timing.
• Designed the auto-join/routing protocol between gateway and nodes, proposed robustness strategies, and validated the system through Duke Smart Farm field tests and NS-3 simulation.
• Investigated mesh networking for LoRa PHY networks.

Cloud & ML

• Delivered the sensor-to-cloud data pipeline from sensors to MCU to LoRa to Raspberry Pi gateway to InfluxDB/Azure, enabling data fusion and ML-based monitoring.

Hardware

• Designed and assembled a backscatter-tag PCB in KiCad, including component selection and layout.

Other

• Standardized reusable MCU libraries and KiCad schematic/footprint libraries for team-wide reuse.
• Organized WiLOLoRa.lib, WiLOSensors.lib and WiLOPCB.lib to support LoRa mesh operation, multi-sensor interfaces, integrated circuit drivers, and code reuse.

School-Enterprise Joint Cultivation Program.

• Learned about server hardware structure and testing process.
• Built Log Server using Docker container technology to make client nodes send log information to a server node and render the log information divided by day, stored by client IP and accessed through the browser.
• Outcome: Project. [link]

Error-control coding simulations for file transmission and reconstruction.

• Converted TXT, PNG, and WAV files to bit streams; encoded with (7,4) Hamming and (n,k) systematic cyclic codes.
• Simulated binary symmetric channels; decoded with syndrome/error-trapping decoders and reconstructed files.
• Outcome: Report. Location: LaTeX/report.pdf [link]

Database-backed stream-processing system with web control and visualization.

• Built a multithreaded Python data generator and stream processor with a database-backed web control interface.
• Designed socket/HTTP data paths and WebSocket control channels across components.
• Outcome: Report. Location: Project/Report/E6889 Stream Project Report.pdf [link]

Fast moving-object detection in high-resolution video.

• Built Clear Focus, a system for fast moving-object detection in high-resolution video; tested it on sports video and daily scenes.
• Used robust PCA to initialize the L1 tracker and super-resolution reconstruction to speed processing while preserving high-resolution output.
• Outcome: Report. Location: Project/Report/E6876_Report_cy2540_yl4189.pdf [link]

• Programmed ESP8266 with MicroPython and C for I/O, SPI/I2C bus communication, interrupts, APIs, server mode, MongoDB connectivity, and gesture recognition.
• Built a distributed, scalable system to monitor soil conditions over long time at multiple locations via LoRa wireless communication; data was stored in MongoDB and visualized through a dashboard for spatial/temporal data and system control.
• Outcome: Embedded system and website. An ESP8266 smart watch, a soil condition sensing system, and a website. [link]

• Reproduced the results of uniform samples in "Bandlimited Field Reconstruction for Wireless Sensor Networks".
• Tested Gaussian sampling, analyzed the resulting differences, and discussed insufficient evidence for one conclusion in the paper.
• Outcome: Report. Location: Project/Paper & Presentation/RMT_Report_ChenyeYang_cy2540.pdf [link]

• Reproduced identification/authentication results from "Smartphone and Smartwatch-Based Biometrics Using Activities of Daily Living" with the WISDM dataset using decision tree, random forest, and K-NN.
• Trained SVM on the same dataset and compared the results across methods.
• Outcome: Report. Location: Project/Report/StatL_Report_cy2540_bw2632_zx2269.pdf [link]

• Built peripheral circuit and car structure based on myRIO development board, and programmed on myRIO with LabVIEW software.
• Manually realized a remote motion control of vehicle to enable it to go straight, swerve, back up, etc.
• Identified vehicle’s location by the infrared sensor and realized automatic obstacles avoidance.
• Outcome: Project. A toy car which can be remotely controlled or run spontaneously to bypass obstacles.

• Implemented fixed-increment batch perceptron, fixed-increment single-sample perceptron, and Widrow-Hoff algorithms in MATLAB.
• Realized real time face detection captured by camera based on OpenCV and using the Python programming language.
• Built face recognition model based on CNN, training classifier and identified human face in real-time camera data.
• Outcome: Project. [link]