Deep learning for radar and communications automatic target recognition:
"This exciting resource identifies technical challenges, benefits, and directions of Deep Learning (DL) based object classification using radar data (i.e., Synthetic Aperture Radar / SAR and High range resolution Radar / HRR data). An overview of machine learning (ML) theory to include a histor...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Norwood, MA
Artech House
[2020]
|
| Schriftenreihe: | Artech House radar library.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "This exciting resource identifies technical challenges, benefits, and directions of Deep Learning (DL) based object classification using radar data (i.e., Synthetic Aperture Radar / SAR and High range resolution Radar / HRR data). An overview of machine learning (ML) theory to include a history, background primer, and example and performance of ML algorithm (i.e., DL method) on video imagery is provided. Radar data with issues of collection, application, and examples for SAR/HRR data and communication signals analysis is also discussed. Practical considerations of deploying such techniques, including performance evaluation, hardware issues, and the future unresolved issues are presented."--Amazon.com |
| Beschreibung: | 1 online resource (xvii, 294 pages) illustrations (black and white) |
| Bibliographie: | Includes bibliographical references and index |
| ISBN: | 9781630816391 1630816396 |
Internformat
MARC
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| 520 | |a "This exciting resource identifies technical challenges, benefits, and directions of Deep Learning (DL) based object classification using radar data (i.e., Synthetic Aperture Radar / SAR and High range resolution Radar / HRR data). An overview of machine learning (ML) theory to include a history, background primer, and example and performance of ML algorithm (i.e., DL method) on video imagery is provided. Radar data with issues of collection, application, and examples for SAR/HRR data and communication signals analysis is also discussed. Practical considerations of deploying such techniques, including performance evaluation, hardware issues, and the future unresolved issues are presented."--Amazon.com | ||
| 588 | 0 | |a Online resource; title from digital title page (viewed on December 01, 2020) | |
| 505 | 0 | |a Deep Learning for Radar and Communications Automatic Target Recognition -- Contents -- Foreword -- Preface -- CHAPTER 1 Machine Learning and Radio Frequency: Past, Present, and Future -- 1.1 Introduction -- 1.1.1 Radio Frequency Signals -- 1.1.2 Radio Frequency Applications -- 1.1.3 Radar Data Collection and Imaging -- 1.2 ATR Analysis -- 1.2.1 ATR History -- 1.2.2 ATR from SAR -- 1.3 Radar Object Classification: Past Approach -- 1.3.1 Template-Based ATR -- 1.3.2 Model-Based ATR -- 1.4 Radar Object Classification: Current Approach -- 1.5 Radar Object Classification: Future Approach | |
| 505 | 8 | |a 1.5.1 Data Science -- 1.5.2 Artificial Intelligence -- 1.6 Book Organization -- 1.7 Summary -- References -- CHAPTER 2 Mathematical Foundations for Machine Learning -- 2.1 Linear Algebra -- 2.1.1 Vector Addition, Multiplication, and Transpose -- 2.1.2 Matrix Multiplication -- 2.1.3 Matrix Inversion -- 2.1.4 Principal Components Analysis -- 2.1.5 Convolution -- 2.2 Multivariate Calculus for Optimization -- 2.2.1 Vector Calculus -- 2.2.2 Gradient Descent Algorithm -- 2.3 Backpropagation -- 2.4 Statistics and Probability Theory -- 2.4.1 Basic Probability -- 2.4.2 Probability Density Functions | |
| 505 | 8 | |a 2.4.3 Maximum Likelihood Estimation -- 2.4.4 Bayes' Theorem -- 2.5 Summary -- References -- CHAPTER 3 Review of Machine Learning Algorithms -- 3.1 Introduction -- 3.1.1 ML Process -- 3.1.2 Machine Learning Methods -- 3.2 Supervised Learning -- 3.2.1 Linear Classifier -- 3.2.2 Nonlinear Classifier -- 3.3 Unsupervised Learning -- 3.3.1 K-Means Clustering -- 3.3.2 K-Medoid Clustering -- 3.3.3 Random Forest -- 3.3.4 Gaussian Mixture Models -- 3.4 Semisupervised Learning -- 3.4.1 Generative Approaches -- 3.4.2 Graph-Based Methods -- 3.5 Summary -- References | |
| 505 | 8 | |a CHAPTER 4 A Review of Deep Learning Algorithms -- 4.1 Introduction -- 4.1.1 Deep Neural Networks -- 4.1.2 Autoencoder -- 4.2 Neural Networks -- 4.2.1 Feed Forward Neural Networks -- 4.2.2 Sequential Neural Networks -- 4.2.3 Stochastic Neural Networks -- 4.3 Reward-Based Learning -- 4.3.1 Reinforcement Learning -- 4.3.2 Active Learning -- 4.3.3 Transfer Learning -- 4.4 Generative Adversarial Networks -- 4.5 Summary -- References -- CHAPTER 5 Radio Frequency Data for ML Research -- 5.1 Introduction -- 5.2 Big Data -- 5.2.1 Data at Rest versus Data in Motion | |
| 505 | 8 | |a 5.2.2 Data in Open versus Data of Importance -- 5.2.3 Data in Collection versus Data from Simulation -- 5.2.4 Data in Use versus Data as Manipulated -- 5.3 Synthetic Aperture Radar Data -- 5.4 Public Release SAR Data for ML Research -- 5.4.1 MSTAR: Moving and Stationary Target Acquisition and Recognition Data Set -- 5.4.2 CVDome -- 5.4.3 SAMPLE -- 5.5 Communication Signals Data -- 5.5.1 RF Signal Data Library -- 5.5.2 Northeastern University Data Set RF Fingerprinting -- 5.6 Challenge Problems with RF Data -- 5.7 Summary -- References | |
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Artificial intelligence. |0 http://id.loc.gov/authorities/subjects/sh85008180 | |
| 650 | 0 | |a Synthetic aperture radar. |0 http://id.loc.gov/authorities/subjects/sh85131656 | |
| 650 | 0 | |a Remote sensing. |0 http://id.loc.gov/authorities/subjects/sh85112798 | |
| 650 | 2 | |a Artificial Intelligence |0 https://id.nlm.nih.gov/mesh/D001185 | |
| 650 | 2 | |a Remote Sensing Technology |0 https://id.nlm.nih.gov/mesh/D058998 | |
| 650 | 2 | |a Machine Learning |0 https://id.nlm.nih.gov/mesh/D000069550 | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 6 | |a Radar à synthèse d'ouverture. | |
| 650 | 6 | |a Télédétection. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a remote sensing. |2 aat | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Remote sensing |2 fast | |
| 650 | 7 | |a Synthetic aperture radar |2 fast | |
| 700 | 1 | |a Garren, David A. |e author | |
| 700 | 1 | |a Blasch, Erik P. |e author | |
| 758 | |i has work: |a Deep learning for radar and communications automatic target recognition (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGQ6vJgTQJCQptDfXQTg83 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Majumder, Uttam K. |t Deep learning algorithms for radar and communications automatic target recognition. |d Norwood, MA : Artech House, [2020] |z 9781630816377 |w (OCoLC)1142512340 |
| 830 | 0 | |a Artech House radar library. |0 http://id.loc.gov/authorities/names/n84729160 | |
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Datensatz im Suchindex
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| _version_ | 1813901692104605696 |
| adam_text | |
| any_adam_object | |
| author | Majumder, Uttam K. Garren, David A. Blasch, Erik P. |
| author_facet | Majumder, Uttam K. Garren, David A. Blasch, Erik P. |
| author_role | aut aut aut |
| author_sort | Majumder, Uttam K. |
| author_variant | u k m uk ukm d a g da dag e p b ep epb |
| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | Q - Science |
| callnumber-label | Q325 |
| callnumber-raw | Q325.5 .M357 2020eb |
| callnumber-search | Q325.5 .M357 2020eb |
| callnumber-sort | Q 3325.5 M357 42020EB |
| callnumber-subject | Q - General Science |
| collection | ZDB-4-EBA |
| contents | Deep Learning for Radar and Communications Automatic Target Recognition -- Contents -- Foreword -- Preface -- CHAPTER 1 Machine Learning and Radio Frequency: Past, Present, and Future -- 1.1 Introduction -- 1.1.1 Radio Frequency Signals -- 1.1.2 Radio Frequency Applications -- 1.1.3 Radar Data Collection and Imaging -- 1.2 ATR Analysis -- 1.2.1 ATR History -- 1.2.2 ATR from SAR -- 1.3 Radar Object Classification: Past Approach -- 1.3.1 Template-Based ATR -- 1.3.2 Model-Based ATR -- 1.4 Radar Object Classification: Current Approach -- 1.5 Radar Object Classification: Future Approach 1.5.1 Data Science -- 1.5.2 Artificial Intelligence -- 1.6 Book Organization -- 1.7 Summary -- References -- CHAPTER 2 Mathematical Foundations for Machine Learning -- 2.1 Linear Algebra -- 2.1.1 Vector Addition, Multiplication, and Transpose -- 2.1.2 Matrix Multiplication -- 2.1.3 Matrix Inversion -- 2.1.4 Principal Components Analysis -- 2.1.5 Convolution -- 2.2 Multivariate Calculus for Optimization -- 2.2.1 Vector Calculus -- 2.2.2 Gradient Descent Algorithm -- 2.3 Backpropagation -- 2.4 Statistics and Probability Theory -- 2.4.1 Basic Probability -- 2.4.2 Probability Density Functions 2.4.3 Maximum Likelihood Estimation -- 2.4.4 Bayes' Theorem -- 2.5 Summary -- References -- CHAPTER 3 Review of Machine Learning Algorithms -- 3.1 Introduction -- 3.1.1 ML Process -- 3.1.2 Machine Learning Methods -- 3.2 Supervised Learning -- 3.2.1 Linear Classifier -- 3.2.2 Nonlinear Classifier -- 3.3 Unsupervised Learning -- 3.3.1 K-Means Clustering -- 3.3.2 K-Medoid Clustering -- 3.3.3 Random Forest -- 3.3.4 Gaussian Mixture Models -- 3.4 Semisupervised Learning -- 3.4.1 Generative Approaches -- 3.4.2 Graph-Based Methods -- 3.5 Summary -- References CHAPTER 4 A Review of Deep Learning Algorithms -- 4.1 Introduction -- 4.1.1 Deep Neural Networks -- 4.1.2 Autoencoder -- 4.2 Neural Networks -- 4.2.1 Feed Forward Neural Networks -- 4.2.2 Sequential Neural Networks -- 4.2.3 Stochastic Neural Networks -- 4.3 Reward-Based Learning -- 4.3.1 Reinforcement Learning -- 4.3.2 Active Learning -- 4.3.3 Transfer Learning -- 4.4 Generative Adversarial Networks -- 4.5 Summary -- References -- CHAPTER 5 Radio Frequency Data for ML Research -- 5.1 Introduction -- 5.2 Big Data -- 5.2.1 Data at Rest versus Data in Motion 5.2.2 Data in Open versus Data of Importance -- 5.2.3 Data in Collection versus Data from Simulation -- 5.2.4 Data in Use versus Data as Manipulated -- 5.3 Synthetic Aperture Radar Data -- 5.4 Public Release SAR Data for ML Research -- 5.4.1 MSTAR: Moving and Stationary Target Acquisition and Recognition Data Set -- 5.4.2 CVDome -- 5.4.3 SAMPLE -- 5.5 Communication Signals Data -- 5.5.1 RF Signal Data Library -- 5.5.2 Northeastern University Data Set RF Fingerprinting -- 5.6 Challenge Problems with RF Data -- 5.7 Summary -- References |
| ctrlnum | (OCoLC)1204225395 |
| dewey-full | 006.31 |
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| dewey-ones | 006 - Special computer methods |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| id | ZDB-4-EBA-on1204225395 |
| illustrated | Illustrated |
| indexdate | 2024-10-25T15:50:56Z |
| institution | BVB |
| isbn | 9781630816391 1630816396 |
| language | English |
| oclc_num | 1204225395 |
| open_access_boolean | |
| owner | MAIN |
| owner_facet | MAIN |
| physical | 1 online resource (xvii, 294 pages) illustrations (black and white) |
| psigel | ZDB-4-EBA |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Artech House |
| record_format | marc |
| series | Artech House radar library. |
| series2 | Artech house radar series |
| spelling | Majumder, Uttam K. author Deep learning for radar and communications automatic target recognition Uttam K. Majumder, Erik P. Blasch, David A. Garren Norwood, MA Artech House [2020] 1 online resource (xvii, 294 pages) illustrations (black and white) text txt rdacontent computer c rdamedia online resource cr rdacarrier Artech house radar series Includes bibliographical references and index "This exciting resource identifies technical challenges, benefits, and directions of Deep Learning (DL) based object classification using radar data (i.e., Synthetic Aperture Radar / SAR and High range resolution Radar / HRR data). An overview of machine learning (ML) theory to include a history, background primer, and example and performance of ML algorithm (i.e., DL method) on video imagery is provided. Radar data with issues of collection, application, and examples for SAR/HRR data and communication signals analysis is also discussed. Practical considerations of deploying such techniques, including performance evaluation, hardware issues, and the future unresolved issues are presented."--Amazon.com Online resource; title from digital title page (viewed on December 01, 2020) Deep Learning for Radar and Communications Automatic Target Recognition -- Contents -- Foreword -- Preface -- CHAPTER 1 Machine Learning and Radio Frequency: Past, Present, and Future -- 1.1 Introduction -- 1.1.1 Radio Frequency Signals -- 1.1.2 Radio Frequency Applications -- 1.1.3 Radar Data Collection and Imaging -- 1.2 ATR Analysis -- 1.2.1 ATR History -- 1.2.2 ATR from SAR -- 1.3 Radar Object Classification: Past Approach -- 1.3.1 Template-Based ATR -- 1.3.2 Model-Based ATR -- 1.4 Radar Object Classification: Current Approach -- 1.5 Radar Object Classification: Future Approach 1.5.1 Data Science -- 1.5.2 Artificial Intelligence -- 1.6 Book Organization -- 1.7 Summary -- References -- CHAPTER 2 Mathematical Foundations for Machine Learning -- 2.1 Linear Algebra -- 2.1.1 Vector Addition, Multiplication, and Transpose -- 2.1.2 Matrix Multiplication -- 2.1.3 Matrix Inversion -- 2.1.4 Principal Components Analysis -- 2.1.5 Convolution -- 2.2 Multivariate Calculus for Optimization -- 2.2.1 Vector Calculus -- 2.2.2 Gradient Descent Algorithm -- 2.3 Backpropagation -- 2.4 Statistics and Probability Theory -- 2.4.1 Basic Probability -- 2.4.2 Probability Density Functions 2.4.3 Maximum Likelihood Estimation -- 2.4.4 Bayes' Theorem -- 2.5 Summary -- References -- CHAPTER 3 Review of Machine Learning Algorithms -- 3.1 Introduction -- 3.1.1 ML Process -- 3.1.2 Machine Learning Methods -- 3.2 Supervised Learning -- 3.2.1 Linear Classifier -- 3.2.2 Nonlinear Classifier -- 3.3 Unsupervised Learning -- 3.3.1 K-Means Clustering -- 3.3.2 K-Medoid Clustering -- 3.3.3 Random Forest -- 3.3.4 Gaussian Mixture Models -- 3.4 Semisupervised Learning -- 3.4.1 Generative Approaches -- 3.4.2 Graph-Based Methods -- 3.5 Summary -- References CHAPTER 4 A Review of Deep Learning Algorithms -- 4.1 Introduction -- 4.1.1 Deep Neural Networks -- 4.1.2 Autoencoder -- 4.2 Neural Networks -- 4.2.1 Feed Forward Neural Networks -- 4.2.2 Sequential Neural Networks -- 4.2.3 Stochastic Neural Networks -- 4.3 Reward-Based Learning -- 4.3.1 Reinforcement Learning -- 4.3.2 Active Learning -- 4.3.3 Transfer Learning -- 4.4 Generative Adversarial Networks -- 4.5 Summary -- References -- CHAPTER 5 Radio Frequency Data for ML Research -- 5.1 Introduction -- 5.2 Big Data -- 5.2.1 Data at Rest versus Data in Motion 5.2.2 Data in Open versus Data of Importance -- 5.2.3 Data in Collection versus Data from Simulation -- 5.2.4 Data in Use versus Data as Manipulated -- 5.3 Synthetic Aperture Radar Data -- 5.4 Public Release SAR Data for ML Research -- 5.4.1 MSTAR: Moving and Stationary Target Acquisition and Recognition Data Set -- 5.4.2 CVDome -- 5.4.3 SAMPLE -- 5.5 Communication Signals Data -- 5.5.1 RF Signal Data Library -- 5.5.2 Northeastern University Data Set RF Fingerprinting -- 5.6 Challenge Problems with RF Data -- 5.7 Summary -- References Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Synthetic aperture radar. http://id.loc.gov/authorities/subjects/sh85131656 Remote sensing. http://id.loc.gov/authorities/subjects/sh85112798 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Remote Sensing Technology https://id.nlm.nih.gov/mesh/D058998 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Radar à synthèse d'ouverture. Télédétection. artificial intelligence. aat remote sensing. aat Artificial intelligence fast Machine learning fast Remote sensing fast Synthetic aperture radar fast Garren, David A. author Blasch, Erik P. author has work: Deep learning for radar and communications automatic target recognition (Text) https://id.oclc.org/worldcat/entity/E39PCGQ6vJgTQJCQptDfXQTg83 https://id.oclc.org/worldcat/ontology/hasWork Print version: Majumder, Uttam K. Deep learning algorithms for radar and communications automatic target recognition. Norwood, MA : Artech House, [2020] 9781630816377 (OCoLC)1142512340 Artech House radar library. http://id.loc.gov/authorities/names/n84729160 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2646702 Volltext CBO01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2646702 Volltext |
| spellingShingle | Majumder, Uttam K. Garren, David A. Blasch, Erik P. Deep learning for radar and communications automatic target recognition Artech House radar library. Deep Learning for Radar and Communications Automatic Target Recognition -- Contents -- Foreword -- Preface -- CHAPTER 1 Machine Learning and Radio Frequency: Past, Present, and Future -- 1.1 Introduction -- 1.1.1 Radio Frequency Signals -- 1.1.2 Radio Frequency Applications -- 1.1.3 Radar Data Collection and Imaging -- 1.2 ATR Analysis -- 1.2.1 ATR History -- 1.2.2 ATR from SAR -- 1.3 Radar Object Classification: Past Approach -- 1.3.1 Template-Based ATR -- 1.3.2 Model-Based ATR -- 1.4 Radar Object Classification: Current Approach -- 1.5 Radar Object Classification: Future Approach 1.5.1 Data Science -- 1.5.2 Artificial Intelligence -- 1.6 Book Organization -- 1.7 Summary -- References -- CHAPTER 2 Mathematical Foundations for Machine Learning -- 2.1 Linear Algebra -- 2.1.1 Vector Addition, Multiplication, and Transpose -- 2.1.2 Matrix Multiplication -- 2.1.3 Matrix Inversion -- 2.1.4 Principal Components Analysis -- 2.1.5 Convolution -- 2.2 Multivariate Calculus for Optimization -- 2.2.1 Vector Calculus -- 2.2.2 Gradient Descent Algorithm -- 2.3 Backpropagation -- 2.4 Statistics and Probability Theory -- 2.4.1 Basic Probability -- 2.4.2 Probability Density Functions 2.4.3 Maximum Likelihood Estimation -- 2.4.4 Bayes' Theorem -- 2.5 Summary -- References -- CHAPTER 3 Review of Machine Learning Algorithms -- 3.1 Introduction -- 3.1.1 ML Process -- 3.1.2 Machine Learning Methods -- 3.2 Supervised Learning -- 3.2.1 Linear Classifier -- 3.2.2 Nonlinear Classifier -- 3.3 Unsupervised Learning -- 3.3.1 K-Means Clustering -- 3.3.2 K-Medoid Clustering -- 3.3.3 Random Forest -- 3.3.4 Gaussian Mixture Models -- 3.4 Semisupervised Learning -- 3.4.1 Generative Approaches -- 3.4.2 Graph-Based Methods -- 3.5 Summary -- References CHAPTER 4 A Review of Deep Learning Algorithms -- 4.1 Introduction -- 4.1.1 Deep Neural Networks -- 4.1.2 Autoencoder -- 4.2 Neural Networks -- 4.2.1 Feed Forward Neural Networks -- 4.2.2 Sequential Neural Networks -- 4.2.3 Stochastic Neural Networks -- 4.3 Reward-Based Learning -- 4.3.1 Reinforcement Learning -- 4.3.2 Active Learning -- 4.3.3 Transfer Learning -- 4.4 Generative Adversarial Networks -- 4.5 Summary -- References -- CHAPTER 5 Radio Frequency Data for ML Research -- 5.1 Introduction -- 5.2 Big Data -- 5.2.1 Data at Rest versus Data in Motion 5.2.2 Data in Open versus Data of Importance -- 5.2.3 Data in Collection versus Data from Simulation -- 5.2.4 Data in Use versus Data as Manipulated -- 5.3 Synthetic Aperture Radar Data -- 5.4 Public Release SAR Data for ML Research -- 5.4.1 MSTAR: Moving and Stationary Target Acquisition and Recognition Data Set -- 5.4.2 CVDome -- 5.4.3 SAMPLE -- 5.5 Communication Signals Data -- 5.5.1 RF Signal Data Library -- 5.5.2 Northeastern University Data Set RF Fingerprinting -- 5.6 Challenge Problems with RF Data -- 5.7 Summary -- References Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Synthetic aperture radar. http://id.loc.gov/authorities/subjects/sh85131656 Remote sensing. http://id.loc.gov/authorities/subjects/sh85112798 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Remote Sensing Technology https://id.nlm.nih.gov/mesh/D058998 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Radar à synthèse d'ouverture. Télédétection. artificial intelligence. aat remote sensing. aat Artificial intelligence fast Machine learning fast Remote sensing fast Synthetic aperture radar fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85008180 http://id.loc.gov/authorities/subjects/sh85131656 http://id.loc.gov/authorities/subjects/sh85112798 https://id.nlm.nih.gov/mesh/D001185 https://id.nlm.nih.gov/mesh/D058998 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Deep learning for radar and communications automatic target recognition |
| title_auth | Deep learning for radar and communications automatic target recognition |
| title_exact_search | Deep learning for radar and communications automatic target recognition |
| title_full | Deep learning for radar and communications automatic target recognition Uttam K. Majumder, Erik P. Blasch, David A. Garren |
| title_fullStr | Deep learning for radar and communications automatic target recognition Uttam K. Majumder, Erik P. Blasch, David A. Garren |
| title_full_unstemmed | Deep learning for radar and communications automatic target recognition Uttam K. Majumder, Erik P. Blasch, David A. Garren |
| title_short | Deep learning for radar and communications automatic target recognition |
| title_sort | deep learning for radar and communications automatic target recognition |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Synthetic aperture radar. http://id.loc.gov/authorities/subjects/sh85131656 Remote sensing. http://id.loc.gov/authorities/subjects/sh85112798 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Remote Sensing Technology https://id.nlm.nih.gov/mesh/D058998 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Radar à synthèse d'ouverture. Télédétection. artificial intelligence. aat remote sensing. aat Artificial intelligence fast Machine learning fast Remote sensing fast Synthetic aperture radar fast |
| topic_facet | Machine learning. Artificial intelligence. Synthetic aperture radar. Remote sensing. Artificial Intelligence Remote Sensing Technology Machine Learning Apprentissage automatique. Intelligence artificielle. Radar à synthèse d'ouverture. Télédétection. artificial intelligence. remote sensing. Artificial intelligence Machine learning Remote sensing Synthetic aperture radar |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2646702 |
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