General

Zedong Nie, Ph.D., Professor, doctoral supervisor of Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (SIAT-CAS). Dr. Nie has long been engaged in wearable health technology. His research interests focused on Wearable-AI systems/devices, including: RF/bio-electronic sensor design, human body communication system , wearable physiological signal processing/ machine learning/deep learning, biometric, that by using hardware-software co-AI design methodology to solve the challenge of chronic diseases management, such as non-invasive blood glucose monitoring. Research achievement won the first prize of Creative award hold by China Creative Studies Institute in 2020, the second prize of Guangdong Technical Invention Award in 2019 and the second prize of Shenzhen Technical Invention Award in 2017. He has applied for more than 80 invention patents and 15 PCT patents. In addition, 47 invention patents have been authorized and more than 20 JCR Q1 and JCR Q2 papers has been published. He is the evaluation expert of National Natural Science Foundation of China and Guangdong provincial science and technology department, and he is some committee members in wearable computing and AI healthcare; He won the honor of innovation and entrepreneurial talent of Shenzhen and excellent employee of SIAT more than five times.


Research Areas

RF/Bioelectrical/ Electrochemical Sensor design

Electromagnetic Fields and Waves/Bioelectromagnetic

Human body communication

Body sensor network / Healthcare internet of things

Wearable AI

Cloud computing in healthcare

Non-invasive glucose monitoring

Deep Learning and Machine Learning

Biometrics

Education

2009.07-2013.01, Doctor degree, University of Chinese Academy of Sciences
2004.09-2007.07, Master degree, Wuhan University of Science and Technology
2000.09-2004.07, Bachelor degree, Wuhan University of Science and Technology


Experience

2007.05-now, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences


Publications

   
Papers

[1]   J. Li, J. Ma, O. M. Omisore, Y. Liu, H. Tang, P. Ao, Y. Yan, L. Wang, and Z. Nie, “Noninvasive Blood Glucose Monitoring Using Spatiotemporal ECG and PPG Feature Fusion and Weight-Based Choquet Integral Multimodel Approach,” IEEE Trans Neural Netw Learn Syst, vol. Pp, Jun 8, 2023.

[2]   A. Kandwal, L. W. Liu, J. Li, Y. Liu, H. Tang, Z. Ju, T. Igbe, R. Jasrotia, and Z. Nie, “Designing Highly Sensitive Microwave Antenna Sensor with Novel Model for Noninvasive Glucose Measurements,” Progress In Electromagnetics Research, vol. 176, pp. 129-141, 2023.

[3]   J. Li, J. Lu, I. Tobore, Y. Liu, A. Kandwal, L. Wang, X. Ma, W. Lu, Y. Bao, J. Zhou, and Z. Nie, “Gradient variability coefficient: a novel method for assessing glycemic variability and risk of hypoglycemia,” Endocrine, 2022/01/23, 2022.

[4]   A. Kandwal, J. Li, T. Igbe, Y. Liu, R. Das, B. K. Kanaujia, and Z. Nie, “Young’s Double Slit Method-Based Higher Order Mode Surface Plasmon Microwave Antenna Sensor: Modeling, Measurements, and Application,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-11, 2022.

[5]   J. Li, I. Tobore, Y. Liu, A. Kandwal, L. Wang, and Z. Nie, “Non-invasive Monitoring of Three Glucose Ranges Based On ECG By Using DBSCAN-CNN,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 9, pp. 3340-3350, 2021.

[6]   J. Li, J. Lu, I. Tobore, Y. Liu, A. Kandwal, L. Wang, J. Zhou, and Z. Nie, “Towards noninvasive and fast detection of Glycated hemoglobin levels based on ECG using convolutional neural networks with multisegments fusion and Varied-weight,” Expert Systems with Applications, vol. 186, pp. 115846, 2021/12/30/, 2021.

[7]   A. Kandwal, Z. Nie, T. Igbe, J. Li, Y. Liu, L. W. Liu, and Y. Hao, “Surface Plasmonic Feature Microwave Sensor With Highly Confined Fields for Aqueous-Glucose and Blood-Glucose Measurements,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-9, 2021.

[8]   A. Kandwal, L. W. Liu, T. Igbe, J. Li, Y. Liu, R. Das, B. K. Kanaujia, L. Wang, and Z. Nie, “A Novel Method of Using Bifilar Spiral Resonator for Designing Thin Robust Flexible Glucose Sensors,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-10, 2021.

[9]   T. Igbe, J. Li, A. Kandwal, O. M. Omisore, E. Yetunde, L. Yuhang, L. Wang, and Z. Nie, “An absolute magnitude deviation of HRV for the prediction of prediabetes with combined artificial neural network and regression tree methods,” Artificial Intelligence Review, 2021/08/24, 2021.

[10] I. Tobore, A. Kandwal, J. Li, Y. Yan, O. M. Omisore, E. Enitan, L. Sinan, L. Yuhang, L. Wang, and Z. Nie, “Towards adequate prediction of prediabetes using spatiotemporal ECG and EEG feature analysis and weight-based multi-model approach,” Knowledge-Based Systems, vol. 209, pp. 106464, 2020/12/17/, 2020.

[11] J. Li, X. Ma, I. Tobore, Y. Liu, A. Kandwal, L. Wang, J. Lu, W. Lu, Y. Bao, J. Zhou, and Z. Nie, “A Novel CGM Metric-Gradient and Combining Mean Sensor Glucose Enable to Improve the Prediction of Nocturnal Hypoglycemic Events in Patients with Diabetes,” Journal of Diabetes Research, vol. 2020, pp. 8830774, 2020.

[12] A. Kandwal, J. Li, T. Igbe, Y. Liu, S. Li, L. Wang, Y. Hao, and Z. Nie, “Broadband Frequency Scanning Spoof Surface Plasmon Polariton Design with Highly Confined Endfire Radiations,” Scientific Reports, vol. 10, no. 1, Jan 10, 2020.

[13] A. Kandwal, T. Igbe, J. Li, Y. Liu, S. Li, L. W. Y. Liu, and Z. Nie, “Highly Sensitive Closed Loop Enclosed Split Ring Biosensor With High Field Confinement for Aqueous and Blood-Glucose Measurements,” Scientific reports, vol. 10, no. 1, pp. 4081-4081, 2020 Mar, 2020.

[14] I. Tobore, J. Li, Y. Liu, Y. Al-Handarish, A. Kandwal, Z. Nie, and L. Wang, “Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations,” Jmir Mhealth and Uhealth, vol. 7, no. 8, Aug 2, 2019.

[15] I. Tobore, J. Li, A. Kandwal, Y. Liu, Z. Nie, and L. Wang, “Statistical and spectral analysis of ECG signal towards achieving non-invasive blood glucose monitoring,” Bmc Medical Informatics and Decision Making, vol. 19, Dec 19, 2019.

[16] L. W. Y. Liu, A. Kandwal, Q. Cheng, H. Shi, I. Tobore, and Z. Nie, “Non-Invasive Blood Glucose Monitoring Using a Curved Goubau Line,” Electronics, vol. 8, no. 6, Jun, 2019.

[17] A. Kandwal, Z. Nie, L. Wang, L. W. Y. Liu, and R. Das, “Realization of Low Profile Leaky Wave Antennas Using the Bending Technique for Frequency Scanning and Sensor Applications,” Sensors, vol. 19, no. 10, May 2, 2019.

[18] A. Kandwal, Z. Nie, J. Li, Y. Liu, L. W. Y. Liu, and R. Das, “Bandwidth and Gain Enhancement of Endfire Radiating Open-Ended Waveguide Using Thin Surface Plasmon Structure,” Electronics, vol. 8, no. 5, May, 2019.

[19] T. Igbe, J. Li, Y. Liu, S. Li, A. Kandwal, Z. Nie, and W. Lei, "Analysis of ECG Segments for Non-Invasive Blood Glucose Monitoring." pp. 1-6.

[20] N. Zeng, J. Li, T. Igbe, Y. Liu, C. Yan, Z. Nie, and Ieee, Investigation on Dielectric Properties of Glucose Aqueous Solutions at 500 KHz-5MHz for Noninvasive Blood Glucose Monitoring, 2018.


Patents

提案名称

申请号

一种低血糖预警方法

PCT/CN2020/129155

一种血糖波动评价方法

PCT/CN2020/129205

基于深度学习的多频段光声无创血糖浓度预测系统

CN201811426266.7

一种穿戴式设备身份识别技术

CN201611103894.2

一种基于心电与脑电信息结合的糖尿病前期检测方法

PCT/CN2020/128559

一种座椅调节控制方法及系统

CN201310590601.8

一种基于可穿戴监测方法的闭环人工胰腺系统

PCT/CN2020/128560

PCB板元件送递系统及方法

CN201310629119.0

辅助阅读设备及辅助阅读系统和辅助阅读方法

CN201310634661.5

心理调节方法和装置

CN201410821853.1

基于人体通信的汽车监控系统和方法

CN201410175982.8

一种骨传导耳机、多媒体发射装置及系统

CN201310624948.X

应用于人体通信信道的纠错编解码方法及其装置

CN201310226302.6

实现FFT/IFFT变换的电路及方法

CN201110430750.9

实现FFT/IFFT变换的电路及方法

CN201110430773.X

用于信息交互的服务器终端、客户终端以及信息交互系统

CN201410137022.2

ECG去除工频干扰的方法及系统

CN201210160663.0

一种医用输液滴速评价方法及系统

CN201510915398.6

SPI控制器及通信方法

CN201210572669.9

一种基于人体生理信息采集的运动提醒系统及方法

CN201310474400.1

一种多媒体播放器的自动关闭系统及方法

CN201310362397.4

音频通信系统

CN201410138505.4

信息处理装置、方法和信息交换系统

CN201410220305.3

一种调节乒乓球台高度的控制方法、装置及系统

CN201310659599.5

具有安全机制的GPIO IP

CN201210579534.5

一种儿童坠床预警系统及方法

CN201310641891.4

一种儿童坠床预警系统及方法

CN201310641576.1

一种基于心电的糖化血红蛋白水平无创检测方法

CN202011429924.5

基于人体通信的跌倒检测系统及方法

CN201611155006.1

一种基于心电与脑电信息结合的糖尿病前期检测方法

CN202010475003.6

一种基于可穿戴监测方法的闭环人工胰腺系统

CN202010424624.1

一种基于数据和生理信息融合驱动的可穿戴低血糖预警方法

CN202010411935.4

一种基于多模融合的低血糖预警方法

CN202010408213.3

一种可穿戴自适应优化的人体通信方法及装置

CN202010111260.1

一种低血糖预警方法

CN201911395132.8

一种血糖波动评价方法

CN201911378693.7

一种制备生物组织模拟材料的方法

PCT/CN2016/110365

一种红外理疗效果的评估方法

CN201710718408.6

一种红外理疗效果的评估方法

PCT/CN2017/098284

基于人体通信的身份识别的方法

PCT/CN2017/074537

基于闭环的脑控功能性电刺激系统

PCT/CN2016/105385

基于人体通信的盲人导航系统

CN201621373229.0

基于人体通信的身份识别的方法

CN201710100699.2

基于生物介电谱的皮肤生化指标检测技术

CN201710047116.4

 

基于人体通信的可穿戴式、轻量级身份认证方法

CN201610672053.7

基于人体通信的可穿戴式、轻量级身份认证方法

PCT/CN2016/102686

一种衡量输液滴速安全和药效的方法

PCT/CN2016/102671

一种基于人体通信的无线充电技术

CN201710167720.0

基于人体通信的跌倒检测系统及方法

PCT/CN2016/109844

一种制备生物组织模拟材料的方法

CN201611123843.6

一种基于场路结合的血脂检测建模方法

PCT/CN2016/103029

一种建立血液电磁仿真模型的方法及装置

PCT/CN2016/103033

一种身份识别装置及方法

PCT/CN2016/103024

基于多模态的身份识别系统及方法

CN201610632580.5

一种血脂浓度可变的血液电磁仿真模型的建立方法

CN201610862038.9

一种基于场路结合的血脂检测建模方法

CN201610857006.X

用于信息交互的服务器终端、客户终端以及信息交互系统

CN201420165182.3

医疗电子芯片设计与验证平台

CN201110071283.5

基于人体的双向通信收发器系统

CN201410833993.0

门禁系统及其控制方法

CN201410088823.4

基于人体通信的体重管理系统

CN201410301229.9

基于人体信道的密码输入系统

CN201210160655.6

基于人体通信系统的健康服务网络

CN201110430748.1

人体通信系统和基于人体通信系统的健康服务网络

PCT/CN2012/079067

多功能仓储管理系统及其管理方法

CN201410284920.0

皮肤水分检测仪

CN201310645552.3

一种人体通信信道建模方法和系统

CN201510489984.9

用于人体通信的收发器结构、通信系统及方法

CN201410683212.4

人体通信监测系统

CN201420504381.2

基于人体通信的智能家居交互系统及方法

CN201310628781.4

医疗电子芯片设计与验证平台

CN201120079568.9

基于人体通信的移动终端、公交刷卡机及公交刷卡系统

CN201410200521.1

基于非均匀介质的人体通信信道建模方法和系统

CN201410281066.2

随身物品的监控装置、应答装置及防盗系统

CN201410136917.4

集成卡、接收器、集成卡系统以及传输数据的方法、装置

CN201310687381.0

闭环的脑控功能性电刺激系统

CN201510953497.3

人体通信装置中的数据发送和接收方法

CN201210544311.5

基于人体通信的公共交通支付系统及方法

CN201310750351.X

无线体域网中的数据传输方法和系统

CN201210546312.3

一种先进先出缓存器及其读写数据的方法

CN201310451428.3

一种身份认证方法及系统

CN201510553204.2

用于人体通信的数据通信系统及方法

CN201410256458.3

优化室内空气质量设备放置位置和室内环境质量的方法

CN201510494013.3

后天性盲人脑海成像感知外界的系统及方法

CN201310424624.1

一种人体近场通信音乐分享耳机

CN201520809260.3

一种多节点人体通信组网方法及装置

CN202011208552.3


Research Interests

Body Area network, Non-invasive glucose monitoring, Human body communication, AI in wearable devices.


Requirements major for student enrollment

1,Electromagnetic Fields and Waves;

2,Biomedical Engineering

3,Electric,Computer,Communication,Control,Mechatronics

4,Mathematics

5,Chemical, Biochemistry, Materials


Postdoctoral Jobs

PIFI/Postdoc in AI healthcare

The Postdoctoral Research Associate will apply his/her technical skills toward development and implementation of machine learning,deep learning, and other algorithms for analysis of physiolical signlas  and prognostication as well as to other tasks related to this research program in order to solve important clinically relevant/werable healthcare problems. 

Required Qualifications: 

The candidate for this position will be a highly motivated individual with a track record of academic achievements and a Ph.D. degree (or will be in the process of completing a Ph.D. program) in computer science, electrical engineering, biomedical engineering, biomedical informatics, biostatisticsmathmatics or a related discipline.

Required qualifications include (1) expertise and experience in machine learning, (2) experience with deep learning, (3) proficiency in computer programming, (4) good verbal and written communication skills, (5) more than two first-author journal papers published or accepted for publication in a good international journal or multiple papers in top conferences in computer science.


 

PIFI/Postdoc in RF sensor


We are seeking a driven and results-oriented Postdoctoral Appointee to join our hardworking and dynamic team to model, simulation, design, testing Radio Frequency /Microwave sensors for physiological signals sensing The core theme of the research would be to develop an wearable ,continuous and non-invasive biomedical applicaiton devices .

   

Required Qualifications: 

Ph.D. in Electrical Engineering with emphasis on antennas, RF systems and/or electromagnetics.

Expertise of electromagnetic simulation software, such as, HFSS, Designer, ADS.

Strong record in journal and conference publications in the areas of antennas, RF systems and/or electromagnetics.

Strong academic record on courses related to electromagnetics, fields & waves, antennas, RF design, microwave engineering or related courses.