Computational Electromagnetics in Scattering Interactions of Earth … · 2019. 10. 8. · 1 REPORT...

AFRL-AFOSR-JP-TR-2019-0033

Computational Electromagnetics in Scattering Interactions of Earth Terrain for Remote Sensing Modeling

Hong Tat EweUNIVERSITI TUNKU ABDUL RAHMANNO.9 JALAN BERSATU 13/4PETALING JAYA, 46200MY

05/06/2019Final Report

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3. DATES COVERED (From - To) 21 Dec 2016 to 20 Dec 2018

4. TITLE AND SUBTITLEComputational Electromagnetics in Scattering Interactions of Earth Terrain for Remote Sensing Modeling

5a. CONTRACT NUMBER

5b. GRANT NUMBERFA2386-17-1-0010

5c. PROGRAM ELEMENT NUMBER61102F

6. AUTHOR(S)Hong Tat Ewe

5d. PROJECT NUMBER

5e. TASK NUMBER

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)UNIVERSITI TUNKU ABDUL RAHMANNO.9 JALAN BERSATU 13/4PETALING JAYA, 46200 MY

8. PERFORMING ORGANIZATION REPORT NUMBER

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)AOARDUNIT 45002APO AP 96338-5002

10. SPONSOR/MONITOR'S ACRONYM(S)AFRL/AFOSR IOA

11. SPONSOR/MONITOR'S REPORT NUMBER(S)AFRL-AFOSR-JP-TR-2019-0033

12. DISTRIBUTION/AVAILABILITY STATEMENTA DISTRIBUTION UNLIMITED: PB Public Release

13. SUPPLEMENTARY NOTES

14. ABSTRACTIn this work, total of 9 published papers/book chapter and 11 conference paper/poster/presentation. In collaboration with AOARD, ONRG and ITC-PAC, the team organized the Technical Interchange Meeting for ASEAN Region Basic Science Research (TIM-ASEAN 2017) at Universiti Tunku Abdul Rahman on March 23-24, 2017 with the attendance over 70 researchers from 10 ASEAN countries, Australia, Japan and USA.

15. SUBJECT TERMSComputational Electromagnetics, microwave scattering, earth terrain, equivalence principle algorithm

16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT

SAR

18. NUMBER OF PAGES

19a. NAME OF RESPONSIBLE PERSONKIM, TONY

19b. TELEPHONE NUMBER (Include area code)315-227-7008

Standard Form 298 (Rev. 8/98)Prescribed by ANSI Std. Z39.18

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REPORT FOR AOARD PROJECT FA2386-17-1-0010

(21 Dec 2016 – 20 Dec 2018)

Computational Electromagnetics in Scattering Interactions of Earth

Terrain for Remote Sensing Modeling

H.T.Ewe1 (PI), LiJun Jiang2 (Co-PI), H.T. Chuah1, W.C. Chew3 and Y.J.Lee1

1Universiti Tunku Abdul Rahman, Malaysia 2University of Hong Kong, Hong Kong

3University of Illinois at Urbana Champaign, U.S.A.

Abstract

This project incorporates computational electromagnetics in the modeling of earth terrain

for better way of reconstructing realistic physical model for the computation of microwave

interactions in earth terrain. The project focuses on the development of such techniques

with the construction of random discrete medium with phase matrices of scatterers of

different shapes calculated through such techniques. The model was compared with

conventional methods and the implementation of such techniques was also carried out in

remote sensing applications that benefit user community. In addition, related technologies

and applications were also developed for further development of such techniques.

1. Introduction:

This report provides the final documentation of the work done under this grant from Dec

2016 - Dec 2019 with the topic of Computational Electromagnetics in Scattering

Interactions of Earth Terrain for Remote Sensing Modeling under FA2386-17-1-0010.

The research objectives of this project are:

(i) To develop theoretical model that will incorporate new equivalence

principle based computational electromagnetics techniques in the study of

microwave scattering and interactions in earth terrain

(ii) To extend the theoretical framework to cover wide variety of earth terrain

types with practical optimization for fast computation of radar returns

(iii) To validate simulated results with measurement data and provide detailed

analysis of scattering mechanisms for real or simulated earth terrain

scenarios

This project is a collaborative research efforts from researchers from three institutions

(Universiti Tunku Abdul Rahman in Malaysia; University of Hong Kong in Hong Kong,

and University of Illinois at Urbana Champaign in USA). The project activities are also

supported through the collaboration of the project team members with other researchers as

listed in item 3 Collaborators below.

DISTRIBUTION A: Approved for public release: distribution unlimited

2

2. Research Outcome and Achievements:

During this project, the project team members focused on this area of development of

computational electromagnetics in remote sensing and designed and developed a number

of new methods that provide improvement to the theoretical modelling of microwave

remote sensing, inversion of physical parameters and its applications. These research

outputs are summarized as follows:

A. Backscattering from Snow with Relaxed Hierachical Equivalent Source Algorithm (RHESA) and Measurement Comparison

Traditionally, Radiative Transfer (RT) formulation has been used to simulate scattering

from snow layer with the incorporation of phase matrix of scatterers in the formulation.

Typically, spherical scatterers were assumed to represent the ice particles embedded inside

the air medium and Mie phase matrix was applied. However, the real scatterers in the actual

snow are generally more irregular than the assumed spherical shape due to the metamorphism and sintering process. In this work, a numerical solution based on the

Relaxed Hierarchical Equivalent Source Algorithm (RHESA) had been developed to

calculate the phase matrix of scatterers of other shapes to be incorporated in the existing

RT formulation and the effects of different shapes, frequency, layer thickness and volume

fraction were also studied as compared with the conventional RT-PACT (Radiative

Transfer – Phase and Amplitude Correction Theory) model with Mie scatterers. Results

from the new model showed good agreement with the ground truth measurement data

collected from NASA Cold Land Processes Field Experiment (CLPX). The comparison

showed that the scatterer shape could have significant contribution on total backscattering

when the frequency is high.


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Fig. A.1 Overview of design flow for the proposed RT-RHESA theoretical model.

Fig A.2. Total backscattering comparison between the RT-PACT and RT-RHESA model

against 6 different incidence angles with 3 different shapes of scatterers for VV and HH

polarizations using frequency 15.50GHz and 3 different layer thicknesses (0.1m, 0.5m,

5.0m).


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Fig A.3. Total backscattering comparison between five ice shelf sites A, B, C, I and P in

Antarctica between 2002 and 2004 using 3 different shapes of scatterers which are sphere,

cylinder and peanut shapes for HH polarization at 30° incidence angle at C-Band.


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Fig A.4. Total backscattering comparison between sphere, cylinder and peanut shapes with

data collected from CLPX during IOP1 (A, B) on 21/2/2002 and IOP4 (C, D) on 30/3/2003

for VV and HH polarizations at incidence angle 40° at frequency of 13.95GHz.

B. Inverse Model for Sea Ice Physical Parameter Retrieval Using Simulated Annealing

An inverse model for applications in sea ice parameter estimation was investigated. The

algorithm utilizes a forward model based on Radiative Transfer theory and Dense Medium

and Amplitude Correction Theory (DMPACT), together with a global optimizer known as

Simulated Annealing. The purpose of the forward model was to calculate the radar

backscatter data from a set of input parameters. Simulated Annealing was then applied to

minimize the difference between the forward model calculation and the measurement data

by changing one or more of the unknown parameters. By deducing the value of the

unknown parameter which gives the best minimum, the model was able to predict the

corresponding sea ice parameter. The data from ground truth measurements at Ross Island,

Antarctica and the radar backscatter data from satellite images of the same area have been

used for the simulation of the inverse model with promising results.


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Fig B.1. Flow chart of the Radiative Transfer Inverse Scattering Model with simulated

annealing.

Fig B.2 Radarsat-1 image of Ross Island, Antarctica in the Year 2006


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Fig B.3. Sensitivity analysis of radar backscatter in HH polarization against sea ice

thickness for site 7 (Cape Evans), 2006. The inversion model predicted a sea ice thickness

of 1.68 m as compared with the measured 2.0 m, which is in the range of saturation as

shown in this figure.

C. Theoretical Modelling of Vegetation with Application in Oil Palm Monitoring

To further analyze and interpret the SAR images of tropical plantation as well as scattering

mechanism involved, a SAR image at L-Band was acquired on May 1st, 2017 through

Japanese Satellite ALOS2- PALSAR2 for the area of study at Lekir Oil Palam Estate, Perak

State, Malaysia. A research team was also sent to perform ground truth measurement at the

study site from May 1st-5th, 2017 to systematically collect physical parameters of oil palm

plantation at different growth stages for better representation of model configuration. The

theoretical model with the input of ground truth measurement was developed with the

consideration of electrically dense medium where PAFCT (Phase, Amplitude and Fresnel

Correction Theory) had been incorporated. The results provide a good correlation between

the total petiole cross section area (related to growth stages of oil palm) and backscattering

data. In addition, theoretical analysis of scattering mechanisms involved was conducted

and the comparison of model prediction (the model developed and model with assumption

of sparse medium) showed good agreement with the satellite SAR measurement data.


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Fig. C.1. Theoretical modeling of oil palm with a random discrete medium. This can be

extended to modeling of other vegetation and crops.

Fig. C.2. Field measurement conducted to collect ground truth data for the construction

of theoretical vegetation model.


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Fig. C.3. L Band SAR Image acquired for Lekir Oil Palam Estate through ALOS-PALSAR2 with Composite Color (HH = Red, HV= Blue, VV = Green).

Fig C.4. The correlation between total petiole cross section of oil palm and satellite

backscattering coefficient (HH).


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Fig C.5. Analysis of scattering mechanisms from various components of oil palm (pinnae,

petiole and trunk).


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Fig C.6. Comparison of model predictions and satellite measurement data.

D. Generalized Debye Source Based EFIE Method on the Subdivision Surfaces

The electric field integral equation is a well-known workhorse for obtaining fields scattered

by a perfect electric conducting object. As a result, the nuances and challenges of solving

this equation have been examined for a while. Unlike traditional work that uses equivalent

currents defined on surfaces, recent research proposes a technique that results in well-

conditioned systems by employing generalized Debye sources (GDS) as unknowns. In a

complementary effort, some of us developed a method that exploits the same representation

for both the geometry (subdivision surface representations) and functions defined on the

geometry, also known as isogeometric analysis (IGA). The challenge in generalizing GDS

method to a discretized geometry is the complexity of the intermediate operators. However,

thanks to earlier work on subdivision surfaces, the additional smoothness of geometric


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representation permits discretizing these intermediate operations. In this work, we employ

both ideas to present a well-conditioned GDS-electric field integral equation. Here, the

intermediate surface Laplacian is well discretized by using subdivision basis. Likewise,

using subdivision basis to represent the sources results in an efficient and accurate IGA

framework. Numerous results have demonstrated the efficacy of the proposed approach.

Figure D. 1 Bistatic RCS solutions at φ = 0 cut for a sphere with radius r = 0.67λ.

Figure D. 2 Convergence history for a sphere with radius r = 0.67λ illuminated.


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Figure D. 3 The real part of the surface current density distribution on a plane model.

Figure D. 4 The imaginary part of the surface current density distribution on a plane

model.

E. Snow Parameter Inversion from Passive Microwave Remote Sensing Measurements by Deep Convolutional Neural Networks

The significant informative indicator for climate change, snowpack presents both the

surface energy but water balance in a certain region. Passive microwave remote sensing

(PMRS) data have been widely utilized to analyze snowpack, because passive microwave


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remote sensing can work in various weather and can penetrate clouds and snow. The

analysis and retrieval of snowpack by passive microwave measurements is based on the

physical scattering model, which can produce both backscatter and brightness temperature

measured from the physical parameters of the snowpack.

This work proposes a novel inverse method based on the deep convolutional neural

network (ConvNet) to extract the layer thickness and temperature of snow from the passive

microwave remote sensing (PMRS). The proposed ConvNet is trained using simulated data

obtained through conventional computational electromagnetic methods. Compared with

the conventional inverse method, the trained ConvNet can predict result with higher

accuracy. Besides, the proposed method has strong tolerance to noises. The proposed

ConvNet composes of three pairs of convolutional and activation layers with one additional

fully connected layer to realize regression, i.e., the inversion of parameters of snow. The

feasibility of the proposed method in realizing the inversion of parameters of snow is

validated by numerical examples. The inversion results indicate that the ratio of the

correlation coefficient (R2) between the proposed ConvNet and conventional methods

reaches 4.8, while that ratio for the root mean square error (RMSE) is only 0.18. Hence,

the proposed method experiments a novel path to improve the inversion of passive

microwave remote sensing through deep learning approaches.

Figure E. 1 (Left) the brightness temperature 𝐵𝑣 in vertical polarization and 𝐵ℎ in horizontal polarization with 𝑡=30cm and 𝑇=260K; (Right) the ‘field-data’ [𝐵𝑣, 𝐵ℎ] as the input of ConvNet.


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Figure E. 2 Inverted result of (Left) the thickness and (Right) the temperature of the

snowpack by the proposed ConvNet method.

Table E.1

F. Machine Learning Based PML for the FDTD Method

In this work, a novel absorbing boundary condition (ABC) computation method for Finite-

Difference Time-Domain (FDTD) is proposed based on the machine learning approach.

The hyperbolic tangent basis function (HTBF) neural network is introduced to replace

traditional perfectly matched layer (PML) ABC during the FDTD solving process. The

field data on the interface of conventional PML are employed to train HTBF based PML

model. Compared to the conventional approach, the novel method greatly decreases the

size of computation domain and the computation complexity of FDTD because the new

model only involves the one-cell boundary layer. Numerical examples are provided to

benchmark the performance of the proposed method. The results demonstrate that the

newly proposed method could replace conventional PML and could be integrated into

FDTD solving process with satisfactory accuracy and compatibility to FDTD. According

to our knowledge, this proposed model combined ANN model is an unreported new

approach based on machine learning based for FDTD.


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Figure F. 1. Configuration of HTBF based PML in a 2D TEz FDTD lattice.

Figure F. 2. The FDTD grid geometry on the 15mm×15mm area with one excited source

and with two probes at points A and B. (a) HTBF based PML. (b) Conventional PML with

the size of 5 cell.


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Figure F. 3. Comparison of relative error between HTBF based PML, 1 cell conventional

PML and 5 cell conventional PML. (a) Relative error at Point A and Point B. (b) Relative

error of the entire 15×15 square.

3. Collaborators:

a) Professor J. Shutt-Aine, University of Illinois at Urbana-Champaign, IL, USA. b) Professor G.H. Chen, Dept. of Chemistry, University of Hong Kong, Hong

Kong.

c) Professor T. Itoh, Dept. of ECE, UCLA, USA. d) Professor A. Ruehli, EMC Lab, Missouri Univ. of Science and Technology,

Rolla, MO, USA.

e) Professor F. Yang, Dept. of EE, Tsinghua University, China. f) Professor H. Bagci, KAUST, Thuwal, Saudi Arabia. g) Dr. Koay J.Y., Institute of Astronomy and Astrophysics, Academia Sinica,

Taiwan

h) Prof. Du Yang, Zhejiang University, China i) Ms. Syabeela Syahali, Multimedia University, Malaysia. j) Mr. Seng-Heng Tey, Applied Agricultural Resources Sdn. Bhd., Malaysia

4. Published papers/book chapter:

1. J.Y. Koay, Y.J. Lee, H.T. Ewe and H.T.Chuah, “Electromagnetic Wave Scattering in Dense Media: Applications in the Remote Sensing of Sea Ice and Vegetation,” Electromagnetic

Scattering - A Remote Sensing Perspective, edited by Du Yang (Ed), World Scientific, pp.

303-339, Jan 2017 (ISBN 978-981-3209-86-2).

2. Chan-Fai Lum, Fu Xin, H.T. Ewe, and Li-Jun Jiang, “A Study of Scattering from Snow Embedded with Non-Spherical Shapes of Scatterers with Relaxed Hierarchical Equivalent

Source Algorithm (RHESA),” Progress in Electromagnetics Research, Vol 61, Oct 2017, pp 51-60.

3. X.Fu, J. Li, L. J. Jiang and B. Shanker, “Generalized Debye Sources Based EFIE Solver on Subdivision Surfaces”, IEEE Trans. Antennas Propagat., vol. 65, no. 10, pp. 5376 ~ 5386, Oct.

2017.


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4. Heming Yao, Wei Sha, and Lijun Jiang, "Applying Convolutional Neural Networks for the Source Reconstruction," PIERS M, vol. 76, pp. 91-99, 2018.

5. L.L. Meng, M. Hidayetoǧlu, T. Xia, W. E. I. Sha, L.J. Jiang, and W.C. Chew, "A Wide-band Two-Dimensional Fast Multipole Algorithm with a Novel Diagonalization Form," IEEE Trans.

on Ant. & Propag., vol. 66, no. 12, pp 7477-7482, Dec. 2018.

6. Heming Yao and L.J. Jiang, "Machine Learning Based PML for the FDTD Method," IEEE AWPL, vol. 18, no. 1, pp. 192-196, Jan. 2019.

7. C.M. Toh, H.T. Ewe, S.H. Tey and Y.H. Tay, “A Study on Oil Palm Remote Sensing at L Band with Dense Medium Microwave Backscattering Model,” IEEE Transactions on Geoscience and

Remote Sensing (accepted for publication)

8. Y. J. Lee, K. C. Yeong and H. T. Ewe, “A Study of an Inversion Model for Sea Ice Thickness Retrieval Using Simulated Annealing,” IEEE Geoscience and Remote Sensing Letters (under

revision after review)

9. Heming Yao, Yanming Zhang, H.T. Ewe, and Lijun Jiang, "Snow Parameters Inversion from Passive Microwave Remote Sensing Measurements by Deep Convolutional Neural Networks,"

submitted to IEEE Trans. on Geoscience and Remote Sensing.

5. Conference paper/poster/presentation:

1. H. M. Yao, L. J. Jiang and Y.W. Qin, “Machine Learning Based Method of Moments (ML-MoM),” IEEE International Symposium on APS/URSI, San Diego, USA, Jul. 2017. (Highly

Interested Paper)

2. X. Fu, J. Li, L. J. Jiang and B. Shanker, “Using Subdivision Surface Technique to Solve Generalized Debye Sources based EFIE”, in Proc. IEEE AP-S Int. Symp. Antennas Propag. and

URSI Radio Sci. Mtg., San Diego, USA, Jul. 2017.

3. Y.J. Lee, K.C. Yeong and H.T.Ewe, “An Inverse Model for Sea Ice Physical Parameter Retrieval Using Simulated Annealing,” Proceedings of IEEE International Geoscience and Remote Sensing

Symposium (IGARSS 2017), Fort Worth, USA, July 23-28 July 2017.

4. Chan Fai Lum, Hong Tat Ewe, Fu Xin, LiJun Jiang, H.T. Chuah, “An Analysis of Scattering from Snow Embedded with Different Shapes of Scatterers with Relaxed Hierachical Equivalent Source

Algorithm,” Proceedings of IEEE International Geoscience and Remote Sensing Symposium

(IGARSS 2017), Fort Worth, USA, July 23-28 July 2017.

5. Y.S. Cao, X. Wang, W. Mai, Y. Wang, L. Jiang, A. Ruehli, S. He, H. Zhao, J. Hu, J. Fan, and J. Drewniak, “Characterizing EMI radiation physics for edge and broad-side coupled connectors,”

IEEE Int. Symposium on Electromagnetic Compatibility, Washington DC, USA, Aug. 2017.

6. C. M. Toh, H. T. Ewe, S. H. Tey, and Y. H. Tay, “A Study on Leaf Area Index and SAR Image of Oil Palm with Entropy Decomposition and Deep Learning Classification,” Proceedings of

Progress in Electromagentics Research Symposium (PIERS 2017), Singapore, 19-22 Nov 2017.

7. C. M. Toh, H.T. Ewe, S.H. Tey, and Y.H. Tay, “A Study on the Influence of Oil Palm Biophysical Parameters on Backscattering Returns with ALOS-PALSAR2 Image,” Proceedings of IEEE

International Geoscience and Remote Sensing Symposium (IGARSS 2018), Valencia, Spain, 23-

27 July 23-27 2018.

8. Luke Lee Chee Chien, H. T. Ewe and S. H. Saw, “Understanding the Correlation in Scattering Mechanisms between H-Alpha Decomposition and Theoretical Modelling,” Proceedings of

Progress in Electromagnetics Research Symposium (PIERS 2018), Toyama, Japan, 1-4 August

2018.

9. P. Li, L.J. Jiang, and H. Bagci, “Numerical modeling of graphene nano-ribbon by DGTD taking into account spatial dispersion effect,” 2018 Progress in Electromagnetics Research Symp., Aug.

1-4, Toyama, Japan 2018. (Young Scientist Award)

10. C.M. Toh, Mohd. Izzuddin Anuar, H.T. Ewe and Idris Abu Seman, “Analysis of Oil Palms with Basal Stem Rot Disease with L Band SAR Data,” IEEE Geoscience and Remote Sensing

Symposium (IGARSS 2019), Yokohama, Japan, 28 July – 2 August, 2019. (accepted for

presentation).

11. Yanming Zhang, LiJun Jiang and H.T. Ewe, “Analysis of Sea Clutter Using Dynamic Mode


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Decomposition,” IEEE Geoscience and Remote Sensing Symposium (IGARSS 2019), Yokohama,

Japan, 28 July – 2 August, 2019. (accepted for presentation).

6. Award of fund received related to the research efforts (funding in 2016-2018):

1. University of Hong Kong GRF 17210815, HKD 462,696.00 (~USD 60k), GRF 17209918, HKD 330,057.00 (~USD 43k), Industrial Contract Project

200008604, HKD 203,448 HKD (~USD 26k)

2. Flagship Research Grant, Ministry of Science, Technology and Innovation (MOSTI), Malaysia for Project “Development of microwave remote sensing

model for monitoring of sea ice changes in global climate system” (2014-2017).

RM 467,603.00 (~USD114k)

3. Funding from Malaysia Oil Palm Board for Project “Design and Development of Radar Technology for Detection of Ganoderma Disease in Oil Palm

Plantations” (2017-2020), RM 198,400.00 (~USD 48k)

4. Funding from Applied Agricultural Research S/B (AARSB) for Project “The study of oil palm growth variation and yield prediction with microwave remote

sensing” (additional funding in 2018), RM 50,000 (~USD 12k)

7. Organizing of TIM-ASEAN 2017

In collaboration with AOARD, ONRG and ITC-PAC, the team organized the

Technical Interchange Meeting for ASEAN Region Basic Science Research (TIM-

ASEAN 2017) at Universiti Tunku Abdul Rahman on March 23-24, 2017 with the

attendance over 70 researchers from 10 ASEAN countries, Australia, Japan and USA.


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8. Others

• Invited Session Organizer for Progress in Electromagnetics Research Symposium (PIERS, Shanghai Aug 2016 and Singapore Nov 2017 and Toyama Aug 2018).


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• Hosting exchange students Steven Okada and Crystal Tsui (Jan 2017), Ashisha Persad (Jan 2018) and Olutimilehin Omotunde (Jan 2019) from Department of

Electrical Engineering and Computer Science of MIT, USA (Jan, 2017) for

attachment to the project.

• Prof. Lijun Jiang (Co-PI) is elevated to IEEE Fellow starting from 2019.

• Dr. Ping Li (Team member) received the Young Scientist Award at 2018 International Applied Computational Electromagnetics Society Symposium in

China (ACES-China 2018), Beijing, and the Young Scientist Award at PIERS

2018, Toyama, Japan.

Acknowledgement

The project team would like to acknowledge and thank AOARD/AFOSR and

ONRG for the grant awarded and strong support given to the project.

Acknowledgement also goes to other related external funding agencies, the

universities and organizations involved for their support and assistance in this

project.


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