Learning-by-examples techniques as applied to electromagnetics

Abstract There is a wide number of problems in electromagnetic (EM) engineering that require a real-time response or in which the input–output relationship is not a-priori known or cannot be defined due to the complexity of the scenario at hand. These issues have induced researchers toward the development and application of Learning-by-Examples (LBE) techniques thanks to their extremely high computational efficiency and to their capability to emulate the behavior of complex systems on the basis of a set of collected examples. In this framework, this paper aims to present an overview of the state-of-the-art and recently developed LBE-based strategies as applied to the solution of engineering problems in the field of Electromagnetics. Starting from a general and basic introduction to LBE techniques, the most popular LBE methods used in EM engineering will be re-called. Afterwards, a review on how the LBE methodologies have been applied by various researchers in different application contexts, including among others antennas, microwave circuits, inverse scattering and remote sensing, will be given. Finally, current research trends and envisaged developments are discussed.

[1]  Tsung-Nan Lin,et al.  Application of neural networks to analyses of nonlinearly loaded antenna arrays including mutual coupling effects , 2005, IEEE Transactions on Antennas and Propagation.

[2]  John W. Bandler,et al.  Reliable Microwave Modeling by Means of Variable-Fidelity Response Features , 2015, IEEE Transactions on Microwave Theory and Techniques.

[3]  Filiz Güneş,et al.  KNOWLEDGE-BASED SUPPORT VECTOR SYNTHESIS OF THE MICROSTRIP LINES , 2009 .

[4]  P. Rocca,et al.  Complex radome design through the Systems-by-Design approach , 2015, 2015 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting.

[5]  Jose C. Principe,et al.  Handbook of Neural Network Signal Processing , 2018 .

[6]  Zhimin Zhou,et al.  Ultrawideband Synthetic Aperture Radar Landmine Detection , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Jin Au Kong,et al.  Support Vector Machine and Neural Network Classification of Metallic Objects Using Coefficients of the Spheroidal MQS Response Modes , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Marc Lambert,et al.  Kriging-based generation of optimal databases as forward and inverse surrogate models , 2010 .

[9]  Michael Georgiopoulos,et al.  Applications of Neural Networks in Electromagnetics , 2001 .

[10]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.

[11]  Rae. Z.H. Aliyev,et al.  Interpolation of Spatial Data , 2018, Biomedical Journal of Scientific & Technical Research.

[12]  E.K. Murphy,et al.  RBF network optimization of complex microwave systems represented by small FDTD modeling data sets , 2006, IEEE Transactions on Microwave Theory and Techniques.

[13]  Luigi Bianchi,et al.  Movement Detection of Human Body Segments: Passive radio-frequency identification and machine-learning technologies. , 2015, IEEE Antennas and Propagation Magazine.

[14]  W. Tabbara,et al.  Electromagnetic Fields Estimation Using Spatial Statistics , 2006 .

[15]  Chin-Teng Lin,et al.  Direction of arrival estimation based on phase differences using neural fuzzy network , 2000 .

[16]  Paolo Gamba,et al.  Electromagnetic detection of dielectric cylinders by a neural network approach , 1999, IEEE Trans. Geosci. Remote. Sens..

[17]  F. Las-Heras,et al.  Support vector regression for the design of array antennas , 2005, IEEE Antennas and Wireless Propagation Letters.

[18]  Youngwook Kim,et al.  Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks , 2016, IEEE Geoscience and Remote Sensing Letters.

[19]  R. S. Chen,et al.  A combination of FDTD and least-squares support vector machines for analysis of microwave integrated circuits , 2005 .

[20]  Federico Viani,et al.  System-by-design: A new paradigm for handling design complexity , 2014, The 8th European Conference on Antennas and Propagation (EuCAP 2014).

[21]  Yuehang Xu,et al.  PERMEABILITY MEASUREMENT OF FERROMAGNETIC MATERIALS IN MICROWAVE FREQUENCY RANGE USING SUPPORT VECTOR MACHINE REGRESSION , 2007 .

[22]  Andrea Boni,et al.  A classification approach based on SVM for electromagnetic subsurface sensing , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Xin Li,et al.  Regression Kriging-Based Upscaling of Soil Moisture Measurements From a Wireless Sensor Network and Multiresource Remote Sensing Information Over Heterogeneous Cropland , 2015, IEEE Geoscience and Remote Sensing Letters.

[24]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[25]  Hendrik Rogier,et al.  Surrogate-based infill optimization applied to electromagnetic problems , 2010 .

[26]  C.G. Christodoulou,et al.  Beamforming using support vector machines , 2005, IEEE Antennas and Wireless Propagation Letters.

[27]  Mauro Brunato,et al.  Reactive Search and Intelligent Optimization , 2008 .

[28]  P. Rocca,et al.  Electromagnetic tracking of transceiver-free targets in wireless networked environments , 2011, Proceedings of the 5th European Conference on Antennas and Propagation (EUCAP).

[29]  Tah-Hsiung Chu,et al.  Analysis of wire scatterers with nonlinear or time-harmonic loads in the frequency domain , 1993 .

[30]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[31]  Davide Anguita,et al.  A Comparative Study of NN and SVM-Based Electromagnetic Inverse Scattering Approaches to On-Line Detection of Buried Objects , 2003 .

[32]  John W. Bandler,et al.  Rapid Yield Estimation and Optimization of Microwave Structures Exploiting Feature-Based Statistical Analysis , 2015, IEEE Transactions on Microwave Theory and Techniques.

[33]  Filiz Güneş,et al.  Signal‐noise support vector model of a microwave transistor , 2007 .

[34]  S. Costanzo,et al.  SUPPORT VECTOR REGRESSION MACHINES TO EVALUATE RESONANT FREQUENCIES OF ELLIPTIC SUBSTRATE INTEGRATED WAVEGUIDE RESONATORS , 2008 .

[35]  Guy A. E. Vandenbosch,et al.  An Efficient Method for Antenna Design Optimization Based on Evolutionary Computation and Machine Learning Techniques , 2014, IEEE Transactions on Antennas and Propagation.

[36]  Xing Chen,et al.  Application of support vector machines to the antenna design , 2011 .

[37]  Paolo Rocca,et al.  Efficient synthesis of complex antenna devices through System-by-Design , 2014, 2014 IEEE Symposium on Computational Intelligence for Communication Systems and Networks (CIComms).

[38]  Michael Georgiopoulos,et al.  A neural network-based smart antenna for multiple source tracking , 2000 .

[39]  Daniel M. Tartakovsky,et al.  Subsurface characterization with support vector machines , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[40]  S. Koziel,et al.  Two-Stage Framework for Efficient Gaussian Process Modeling of Antenna Input Characteristics , 2014, IEEE Transactions on Antennas and Propagation.

[41]  P. Rocca,et al.  Evolutionary optimization as applied to inverse scattering problems , 2009 .

[42]  Manuel Davy,et al.  An abrupt change detection algorithm for buried landmines localization , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[43]  Emmanuel Nicolas,et al.  Computation of the field radiated by a FM transmitter by means of ordinary kriging , 2011, Ann. des Télécommunications.

[44]  Alejandro Álvarez Melcón,et al.  Fast and Efficient Calculation of the Multilayered Shielded Green's Functions Employing Neural Networks , 2005 .

[45]  Michael Georgiopoulos,et al.  Performance of radial-basis function networks for direction of arrival estimation with antenna arrays , 1997 .

[46]  G. Camps-Valls,et al.  A Support Vector Machine MUSIC Algorithm , 2012, IEEE Transactions on Antennas and Propagation.

[47]  P. Rocca,et al.  An Innovative Multiresolution Approach for DOA Estimation Based on a Support Vector Classification , 2009, IEEE Transactions on Antennas and Propagation.

[48]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[49]  Jing He,et al.  Design of Environmental Sensor Networks Using Evolutionary Algorithms , 2016, IEEE Geoscience and Remote Sensing Letters.

[50]  Tom Dhaene,et al.  Accurate Hotspot Localization by Sampling the Near-Field Pattern of Electronic Devices , 2013, IEEE Transactions on Electromagnetic Compatibility.

[51]  Li Zhang,et al.  Nonlinear optimization for adaptive antenna array receivers with a small data-record size , 2009, Wirel. Commun. Mob. Comput..

[52]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[53]  Mustafa Secmen,et al.  Hierarchical Reconstruction and Structural Waveform Analysis for Target Classification , 2016, IEEE Transactions on Antennas and Propagation.

[54]  Yide Wang,et al.  Time Delay and Permittivity Estimation by Ground-Penetrating Radar With Support Vector Regression , 2014, IEEE Geoscience and Remote Sensing Letters.

[55]  P. Rocca,et al.  Differential Evolution as Applied to Electromagnetics , 2011, IEEE Antennas and Propagation Magazine.

[56]  Qi-Jun Zhang,et al.  Neural Network Inverse Modeling and Applications to Microwave Filter Design , 2008, IEEE Transactions on Microwave Theory and Techniques.

[57]  G. Camps‐Valls,et al.  Kernel Antenna Array Processing , 2007, IEEE Transactions on Antennas and Propagation.

[58]  R. Xu,et al.  LTCC INTERCONNECT MODELING BY SUPPORT VECTOR REGRESSION , 2007 .

[59]  R. S. Chen,et al.  Combination of particle‐swarm optimization with least‐squares support vector machine for FDTD time series forecasting , 2006 .

[60]  Hwanjo Yu,et al.  SVM Tutorial - Classification, Regression and Ranking , 2012, Handbook of Natural Computing.

[61]  Yiqiang Yu,et al.  A Time-Domain Collocation Meshless Method With Local Radial Basis Functions for Electromagnetic Transient Analysis , 2014, IEEE Transactions on Antennas and Propagation.

[62]  Eric L. Miller,et al.  Statistical Classification of Buried Unexploded Ordnance Using Nonparametric Prior Models , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[63]  F. Las-Heras,et al.  Multiple Support Vector Regression for Antenna Array Characterization and Synthesis , 2007, IEEE Transactions on Antennas and Propagation.

[64]  T. Dhaene,et al.  Variable-Fidelity Electromagnetic Simulations and Co-Kriging for Accurate Modeling of Antennas , 2013, IEEE Transactions on Antennas and Propagation.

[65]  Youngwook Kim,et al.  Human Detection Using Doppler Radar Based on Physical Characteristics of Targets , 2015, IEEE Geoscience and Remote Sensing Letters.

[66]  Sushanta K. Mandal,et al.  ANN- and PSO-Based Synthesis of On-Chip Spiral Inductors for RF ICs , 2008, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[67]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[68]  Abdesselam Bouzerdoum,et al.  Automatic Classification of Ground-Penetrating-Radar Signals for Railway-Ballast Assessment , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[69]  Andrea Boni,et al.  A SVM-based approach to microwave breast cancer detection , 2006, Eng. Appl. Artif. Intell..

[70]  Emilio Arnieri,et al.  Support Vector Regression Machines to Evaluate Resonant Frequency of Elliptic Substrate Integrate Waveguide Resonators , 2008 .

[71]  Tom Dhaene,et al.  Cost-efficient electromagnetic-simulation-driven antenna design using co-Kriging , 2012 .

[72]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[73]  J.L.G. Tornero,et al.  A neural-network method for the analysis of multilayered shielded microwave circuits , 2006, IEEE Transactions on Microwave Theory and Techniques.

[74]  Slawomir Koziel,et al.  Multi-Objective Design of Antennas Using Variable-Fidelity Simulations and Surrogate Models , 2013, IEEE Transactions on Antennas and Propagation.

[75]  Antonios Giannopoulos,et al.  Model-Based Evaluation of Signal-to-Clutter Ratio for Landmine Detection Using Ground-Penetrating Radar , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[76]  S. Koziel,et al.  Simulation-Based Design of Microstrip Linear Antenna Arrays Using Fast Radiation Response Surrogates , 2015, IEEE Antennas and Wireless Propagation Letters.

[77]  Youssef Tawk,et al.  Reconfigurable Antennas: Design and Applications , 2015, Proceedings of the IEEE.

[78]  S. Bilicz,et al.  Solution of Inverse Problems in Nondestructive Testing by a Kriging-Based Surrogate Model , 2012, IEEE Transactions on Magnetics.

[79]  Paolo Rocca,et al.  Compressive Sensing in Electromagnetics - A Review , 2015, IEEE Antennas and Propagation Magazine.

[80]  C. Reboud,et al.  A learning-by-examples approach for non-destructive localization and characterization of defects through eddy current measurements , 2015, 2015 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting.

[81]  Luigi Ferrigno,et al.  Crack Shape Reconstruction in Eddy Current Testing Using Machine Learning Systems for Regression , 2008, IEEE Transactions on Instrumentation and Measurement.

[82]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[83]  Amit Agarwal,et al.  A new machine learning paradigm for terrain reconstruction , 2006, IEEE Geoscience and Remote Sensing Letters.

[84]  Mario Bertero,et al.  Introduction to Inverse Problems in Imaging , 1998 .

[85]  Luis S. Rosado,et al.  Defect Characterization With Eddy Current Testing Using Nonlinear-Regression Feature Extraction and Artificial Neural Networks , 2013, IEEE Transactions on Instrumentation and Measurement.

[86]  Cheng Liao,et al.  HYBRID-SURROGATE-MODEL-BASED EFFICIENT GLOBAL OPTIMIZATION FOR HIGH-DIMENSIONAL ANTENNA DESIGN , 2012 .

[87]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[88]  Slawomir Koziel,et al.  Antenna Design by Simulation-Driven Optimization , 2014 .

[89]  Slawomir Koziel,et al.  Feature-based surrogates for low-cost microwave modelling and optimisation , 2015 .

[90]  M. McKee,et al.  SOIL MOISTURE PREDICTION USING SUPPORT VECTOR MACHINES 1 , 2006 .

[91]  D. Lesselier,et al.  Adaptive Metamodels for Crack Characterization in Eddy-Current Testing , 2011, IEEE Transactions on Magnetics.

[92]  P. Rocca,et al.  Passive imaging strategies for real-time wireless localization of non-cooperative targets in security applications , 2015, 2015 9th European Conference on Antennas and Propagation (EuCAP).

[93]  Slawomir Koziel,et al.  Rapid optimisation of omnidirectional antennas using adaptively adjusted design specifications and kriging surrogates , 2013 .

[94]  Andrea Massa,et al.  A Multi-Source Strategy based on a Learning-by-Examples Technique for Buried Object Detection , 2004 .

[95]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[96]  P. Saratchandran,et al.  Direction of Arrival (DoA) Estimation Under Array Sensor Failures Using a Minimal Resource Allocation Neural Network , 2007, IEEE Transactions on Antennas and Propagation.

[97]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[98]  Philippe Richaume,et al.  Soil Moisture Retrieval Using Neural Networks: Application to SMOS , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[99]  Gustavo Avolio,et al.  Hybrid Nonlinear Modeling Using Adaptive Sampling , 2015, IEEE Transactions on Microwave Theory and Techniques.

[100]  Tomoo Ushio,et al.  A Neural-Network-Based Beamformer for Phased Array Weather Radar , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[101]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[102]  Jae-Young Chung,et al.  Characterisation of antenna substrate properties using surrogate-based optimisation , 2015 .

[103]  S. Barro,et al.  Element failure detection in linear antenna arrays using case-based reasoning , 2008, IEEE Antennas and Propagation Magazine.

[104]  Christos G. Christodoulou,et al.  Neural network-based adaptive beamforming for one- and two-dimensional antenna arrays , 1998 .

[105]  Leonardo Lizzi,et al.  Object tracking through RSSI measurements in wireless sensor networks , 2008 .

[106]  Emmanuel Vazquez,et al.  Characterization of a 3D defect using the expected improvement algorithm , 2008 .

[107]  Tom Dhaene,et al.  Data-driven model based design and analysis of antenna structures , 2016 .

[108]  Slawomir Koziel,et al.  Simulation-Driven Design of Microstrip Antenna Subarrays , 2014, IEEE Transactions on Antennas and Propagation.

[109]  Andrea Massa,et al.  KERNELS EVALUATION OF SVM-BASED ESTIMATORS FOR INVERSE SCATTERING PROBLEMS , 2005 .

[110]  Christophe Reboud,et al.  Real-Time NDT-NDE Through an Innovative Adaptive Partial Least Squares SVR Inversion Approach , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[111]  Andrea Massa,et al.  An adaptive Learning-by-Examples strategy for efficient Eddy Current Testing of conductive structures , 2016, 2016 10th European Conference on Antennas and Propagation (EuCAP).

[112]  Andrea Boni,et al.  An innovative real-time technique for buried object detection , 2003, IEEE Trans. Geosci. Remote. Sens..

[113]  Federico Viani,et al.  Electromagnetic passive localization and tracking of moving targets in a WSN-infrastructured environment , 2010 .

[114]  Christos Christodoulou,et al.  Support Vector Machines for Antenna Array Processing and Electromagnetics , 2006, Support Vector Machines for Antenna Array Processing and Electromagnetics.

[115]  Paolo Rocca,et al.  Enabling the optimization-based design of complex EM devices through the System-by-Design approach , 2016, 2016 10th European Conference on Antennas and Propagation (EuCAP).

[116]  Paolo Rocca,et al.  Surrogate-Assisted Optimization of Metamaterial Devices for Advanced Antenna Systems , 2015, 2015 IEEE Symposium Series on Computational Intelligence.

[117]  Leonardo Lizzi,et al.  Estimation of the Directions-of-Arrival of correlated signals by means of a SVM-based multi-resolution approach , 2010, 2010 IEEE Antennas and Propagation Society International Symposium.

[118]  T. Dhaene,et al.  Automated Near-Field Scanning Algorithm for the EMC Analysis of Electronic Devices , 2012, IEEE Transactions on Electromagnetic Compatibility.

[119]  M. Pastorino,et al.  A smart antenna system for direction of arrival estimation based on a support vector regression , 2005, IEEE Transactions on Antennas and Propagation.

[120]  Andy J. Keane,et al.  Engineering Design via Surrogate Modelling - A Practical Guide , 2008 .

[121]  Tom Dhaene,et al.  Efficient Multi-Objective Simulation-Driven Antenna Design Using Co-Kriging , 2014, IEEE Transactions on Antennas and Propagation.

[122]  Slawomir Koziel,et al.  Expedited Geometry Scaling of Compact Microwave Passives by Means of Inverse Surrogate Modeling , 2015, IEEE Transactions on Microwave Theory and Techniques.

[123]  David Baudry,et al.  Postprocessing of Near-Field Measurement Based on Neural Networks , 2011, IEEE Transactions on Instrumentation and Measurement.

[124]  Andrea Massa,et al.  On the training patterns of a neural network for target localization in the spatial domain , 2001 .

[125]  Mohamed S. El-Mahallawy,et al.  Material Classification of Underground Utilities From GPR Images Using DCT-Based SVM Approach , 2013, IEEE Geoscience and Remote Sensing Letters.

[126]  T. H. O'Donnell,et al.  Direction finding in phased arrays with a neural network beamformer , 1995 .