Location | E11-2048 |
Academic Staff in charge | Prof. Feng WAN |
Technician | Pedro Antonio MOU |
Telephone | (853) 8822-4518 |
Open Access Reservation Period | Monday: 10:00 – 12:00 Tuesday: 10:00 – 12:00 Wednesday: 10:00 – 12:00 Thursday: 10:00 – 12:00, 15:00 – 18:00 |
Objective
- To support R&D towards realizing intelligent cabled/wireless/portable/wearable health monitoring & diagnosing paradigm, which includes following technical fields:
- Design of noise-control biomedical signal sampling circuits and communication network
- Biomedical signal pre- and post- processing
- Decision-making support for biomedical signal interpretation
- Mobile computing
- Artificial intelligence
- Encourage students to deploy their genius on following fields:
- Distributed and intelligent e-healthcare system
- Bio-information management
- Uplink, Update, and Synchronize (UUS) scheme
Facilities
Equipments
- 3M 3200 Bluetooth Electronic Stethoscope Black
- Digital Stethoscope MLT208
- HEM-907 BP Monitor for NIBP
- MICROLIFE 3MD1-1 BP meter
- HK2000 Series Pulse Sensor
- Bluetooth network development kit
- HHARM2410-Audio-R1 development kit
- cDAQ-9191 CompactDAQ Chassis
- NI 9792: Programmable Real-Time Controller
- NI 9239 4-Ch ±10 V, 50k S/s/Ch, 24-bit, Ch-to-Ch, isolated AI Module
- NI 9971 Backshell for 2-pos connector block
Instruments
- N6457A Oscilloscope, mixed signal, 4+16-channel, 100 MHz
- Function Generator:Instek GFG8216A
- PowerLab 8/35,8-Channel Recorder with LabChart Pro Software
N6457A Oscilloscope, mixed signal, 4+16-channel, 100 MHz | Function Generator:Instek GFG8216A |
PowerLab 8/35,8-Channel Recorder with LabChart Pro Software |
Software
- QuartusII
- LabChart Pro Software
- LabVIEW Wireless Sensor Network Module
- NI Academic Site License
Experiments
- R&D AI system of CVD diagnosis based on SPG signal
- R&D AI system of CVD diagnosis based on ECG signal
- R&D AI system of CVD diagnosis based on HS signal
- R&D CVD diagnosis system by using complexity and similarity analysis
- R&D biomedical signal de-noising technology
- R&D biomedical signal data compression and di-compression technology
Courses supported
ECEB410 | Design Project I |
ECEB420 | Design Project II |
ELCE709 | Expert System |
ELCE797 | Applied Thesis |
ELCE798 | Academic Thesis |