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Bistatic SAR/ISAR/FSR : theory algorithms and program implementation / / Andon Dimitrov Lazarov, Todor Pavlov Kostadinov
Bistatic SAR/ISAR/FSR : theory algorithms and program implementation / / Andon Dimitrov Lazarov, Todor Pavlov Kostadinov
Autore Lazarov Andon Dimitrov
Edizione [1st edition]
Pubbl/distr/stampa ISTE Ltd ; ; John Wiley & Sons : , : London, England : , : Hoboken, New Jersey, , 2014
Descrizione fisica 1 online resource (194 p.)
Disciplina 621.3848
Altri autori (Persone) KostadinovTodor Pavlov
Collana Focus Series
Soggetto topico Bistatic radar
Signal processing
Synthetic aperture radar
Algorithms
ISBN 1-118-86347-X
1-118-86344-5
1-118-86352-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title page; Contents; ACKNOWLEDGEMENT; CHAPTER 1. BISTATIC SYNTHETIC APERTURE RADAR (BSAR) SURVEY; 1.1. Introduction and main definitions; 1.2. Passive space-surface bistatic and multistatic SAR; 1.3. Forward scattering radars; 1.4. A moving target problem as an inversion problem in multistatic SAR; 1.5. BSAR models, imaging, methods and algorithms; 1.5.1. Range migration algorithm for invariant and variant flying geometry; 1.5.2. Bistatic point target reference spectrum based on Loffeld's bistatic formula; 1.5.3. Target parameters extraction; CHAPTER 2. BSAR GEOMETRY
2.1. BGISAR geometry and kinematics2.2. Multistatic BSAR geometry and kinematics; 2.3. BFISAR geometry and kinematics; 2.3.1. Kinematic parameter estimation; CHAPTER 3. BSAR WAVEFORMS AND SIGNAL MODELS; 3.1. Short pulse waveform and the BSAR signal model; 3.1.1. Short pulse waveform; 3.1.2. Short pulse BSAR signal model; 3.1.3. Target's parameters estimation in short range BFISAR scenario; 3.2. LFM pulse waveform; 3.2.1. LFM BSAR signal model; 3.3. CW LFM waveform and modeling of deterministic components of BSAR signal; 3.4. Phase code modulated pulse waveforms; 3.4.1. Barker phase code
3.4.2. Complementary code synthesis3.4.3. BSAR-transmitted complementary phase code modulated waveforms; 3.4.4. GPS C/A phase code; 3.4.5. GPS P phase code; 3.4.6. DVB-T waveform; CHAPTER 4. BSAR IMAGE RECONSTRUCTION ALGORITHMS; 4.1. Image reconstruction from a short pulse BSAR signal; 4.2. LFM BSAR image reconstruction algorithm; 4.3. PCM BSAR image reconstruction algorithm; 4.4. Autofocus algorithm with entropy minimization; 4.5. Experiment with the multistatic SAR LFM image reconstruction algorithm; CHAPTER 5. ANALYTICAL GEOMETRICAL DETERMINATION OF BSAR RESOLUTION
5.1. Generalized BSAR range and Doppler resolution5.1.1. BSAR range resolution; 5.1.2. BSAR Doppler resolution; 5.2. Along-track range resolution; 5.3. Range resolution along a target-receiver line of sight; CHAPTER 6. BSAR EXPERIMENTAL RESULTS; 6.1. Example 1: BFISAR with short-pulse waveform; 6.1.1. BFISAR parameters estimation; 6.1.2. BFISAR signal formation algorithm; 6.2. Example 2: BFISAR with pulse LFM waveform; 6.2.1. BFISAR geometry and isorange ellipse parameter estimation; 6.2.2. BFISAR LFM signal formation algorithm; 6.2.3. Image reconstruction algorithm and experimental results
6.3. Example 3: asymmetric geometry of BFISAR with pulse LFM waveform6.3.1. BFISAR LFM signal formation algorithm; 6.3.2. BFISAR image reconstruction algorithm and experimental results; 6.4. Example 4: BGISAR with Barker PCM waveform; 6.4.1. BGISAR Barker PCM signal formation algorithm; 6.4.2. BGISAR image reconstruction algorithm and experimental results; 6.5. Example 5: BGISAR with GPS C/A PCM waveform; 6.5.1. BGISAR GPS C/A PCM signal formation algorithm; 6.5.2. BGISAR image reconstruction algorithm and experimental results; 6.6. Example 6: BGISAR with GPS P PCM waveform
6.6.1. BGISAR GPS P PCM signal formation algorithm
Record Nr. UNINA-9910138978703321
Lazarov Andon Dimitrov  
ISTE Ltd ; ; John Wiley & Sons : , : London, England : , : Hoboken, New Jersey, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Bistatic SAR/ISAR/FSR : theory algorithms and program implementation / / Andon Dimitrov Lazarov, Todor Pavlov Kostadinov
Bistatic SAR/ISAR/FSR : theory algorithms and program implementation / / Andon Dimitrov Lazarov, Todor Pavlov Kostadinov
Autore Lazarov Andon Dimitrov
Edizione [1st edition]
Pubbl/distr/stampa ISTE Ltd ; ; John Wiley & Sons : , : London, England : , : Hoboken, New Jersey, , 2014
Descrizione fisica 1 online resource (194 p.)
Disciplina 621.3848
Altri autori (Persone) KostadinovTodor Pavlov
Collana Focus Series
Soggetto topico Bistatic radar
Signal processing
Synthetic aperture radar
Algorithms
ISBN 1-118-86347-X
1-118-86344-5
1-118-86352-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title page; Contents; ACKNOWLEDGEMENT; CHAPTER 1. BISTATIC SYNTHETIC APERTURE RADAR (BSAR) SURVEY; 1.1. Introduction and main definitions; 1.2. Passive space-surface bistatic and multistatic SAR; 1.3. Forward scattering radars; 1.4. A moving target problem as an inversion problem in multistatic SAR; 1.5. BSAR models, imaging, methods and algorithms; 1.5.1. Range migration algorithm for invariant and variant flying geometry; 1.5.2. Bistatic point target reference spectrum based on Loffeld's bistatic formula; 1.5.3. Target parameters extraction; CHAPTER 2. BSAR GEOMETRY
2.1. BGISAR geometry and kinematics2.2. Multistatic BSAR geometry and kinematics; 2.3. BFISAR geometry and kinematics; 2.3.1. Kinematic parameter estimation; CHAPTER 3. BSAR WAVEFORMS AND SIGNAL MODELS; 3.1. Short pulse waveform and the BSAR signal model; 3.1.1. Short pulse waveform; 3.1.2. Short pulse BSAR signal model; 3.1.3. Target's parameters estimation in short range BFISAR scenario; 3.2. LFM pulse waveform; 3.2.1. LFM BSAR signal model; 3.3. CW LFM waveform and modeling of deterministic components of BSAR signal; 3.4. Phase code modulated pulse waveforms; 3.4.1. Barker phase code
3.4.2. Complementary code synthesis3.4.3. BSAR-transmitted complementary phase code modulated waveforms; 3.4.4. GPS C/A phase code; 3.4.5. GPS P phase code; 3.4.6. DVB-T waveform; CHAPTER 4. BSAR IMAGE RECONSTRUCTION ALGORITHMS; 4.1. Image reconstruction from a short pulse BSAR signal; 4.2. LFM BSAR image reconstruction algorithm; 4.3. PCM BSAR image reconstruction algorithm; 4.4. Autofocus algorithm with entropy minimization; 4.5. Experiment with the multistatic SAR LFM image reconstruction algorithm; CHAPTER 5. ANALYTICAL GEOMETRICAL DETERMINATION OF BSAR RESOLUTION
5.1. Generalized BSAR range and Doppler resolution5.1.1. BSAR range resolution; 5.1.2. BSAR Doppler resolution; 5.2. Along-track range resolution; 5.3. Range resolution along a target-receiver line of sight; CHAPTER 6. BSAR EXPERIMENTAL RESULTS; 6.1. Example 1: BFISAR with short-pulse waveform; 6.1.1. BFISAR parameters estimation; 6.1.2. BFISAR signal formation algorithm; 6.2. Example 2: BFISAR with pulse LFM waveform; 6.2.1. BFISAR geometry and isorange ellipse parameter estimation; 6.2.2. BFISAR LFM signal formation algorithm; 6.2.3. Image reconstruction algorithm and experimental results
6.3. Example 3: asymmetric geometry of BFISAR with pulse LFM waveform6.3.1. BFISAR LFM signal formation algorithm; 6.3.2. BFISAR image reconstruction algorithm and experimental results; 6.4. Example 4: BGISAR with Barker PCM waveform; 6.4.1. BGISAR Barker PCM signal formation algorithm; 6.4.2. BGISAR image reconstruction algorithm and experimental results; 6.5. Example 5: BGISAR with GPS C/A PCM waveform; 6.5.1. BGISAR GPS C/A PCM signal formation algorithm; 6.5.2. BGISAR image reconstruction algorithm and experimental results; 6.6. Example 6: BGISAR with GPS P PCM waveform
6.6.1. BGISAR GPS P PCM signal formation algorithm
Record Nr. UNISA-996208436103316
Lazarov Andon Dimitrov  
ISTE Ltd ; ; John Wiley & Sons : , : London, England : , : Hoboken, New Jersey, , 2014
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Bistatic SAR/ISAR/FSR : theory algorithms and program implementation / / Andon Dimitrov Lazarov, Todor Pavlov Kostadinov
Bistatic SAR/ISAR/FSR : theory algorithms and program implementation / / Andon Dimitrov Lazarov, Todor Pavlov Kostadinov
Autore Lazarov Andon Dimitrov
Edizione [1st edition]
Pubbl/distr/stampa ISTE Ltd ; ; John Wiley & Sons : , : London, England : , : Hoboken, New Jersey, , 2014
Descrizione fisica 1 online resource (194 p.)
Disciplina 621.3848
Altri autori (Persone) KostadinovTodor Pavlov
Collana Focus Series
Soggetto topico Bistatic radar
Signal processing
Synthetic aperture radar
Algorithms
ISBN 1-118-86347-X
1-118-86344-5
1-118-86352-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title page; Contents; ACKNOWLEDGEMENT; CHAPTER 1. BISTATIC SYNTHETIC APERTURE RADAR (BSAR) SURVEY; 1.1. Introduction and main definitions; 1.2. Passive space-surface bistatic and multistatic SAR; 1.3. Forward scattering radars; 1.4. A moving target problem as an inversion problem in multistatic SAR; 1.5. BSAR models, imaging, methods and algorithms; 1.5.1. Range migration algorithm for invariant and variant flying geometry; 1.5.2. Bistatic point target reference spectrum based on Loffeld's bistatic formula; 1.5.3. Target parameters extraction; CHAPTER 2. BSAR GEOMETRY
2.1. BGISAR geometry and kinematics2.2. Multistatic BSAR geometry and kinematics; 2.3. BFISAR geometry and kinematics; 2.3.1. Kinematic parameter estimation; CHAPTER 3. BSAR WAVEFORMS AND SIGNAL MODELS; 3.1. Short pulse waveform and the BSAR signal model; 3.1.1. Short pulse waveform; 3.1.2. Short pulse BSAR signal model; 3.1.3. Target's parameters estimation in short range BFISAR scenario; 3.2. LFM pulse waveform; 3.2.1. LFM BSAR signal model; 3.3. CW LFM waveform and modeling of deterministic components of BSAR signal; 3.4. Phase code modulated pulse waveforms; 3.4.1. Barker phase code
3.4.2. Complementary code synthesis3.4.3. BSAR-transmitted complementary phase code modulated waveforms; 3.4.4. GPS C/A phase code; 3.4.5. GPS P phase code; 3.4.6. DVB-T waveform; CHAPTER 4. BSAR IMAGE RECONSTRUCTION ALGORITHMS; 4.1. Image reconstruction from a short pulse BSAR signal; 4.2. LFM BSAR image reconstruction algorithm; 4.3. PCM BSAR image reconstruction algorithm; 4.4. Autofocus algorithm with entropy minimization; 4.5. Experiment with the multistatic SAR LFM image reconstruction algorithm; CHAPTER 5. ANALYTICAL GEOMETRICAL DETERMINATION OF BSAR RESOLUTION
5.1. Generalized BSAR range and Doppler resolution5.1.1. BSAR range resolution; 5.1.2. BSAR Doppler resolution; 5.2. Along-track range resolution; 5.3. Range resolution along a target-receiver line of sight; CHAPTER 6. BSAR EXPERIMENTAL RESULTS; 6.1. Example 1: BFISAR with short-pulse waveform; 6.1.1. BFISAR parameters estimation; 6.1.2. BFISAR signal formation algorithm; 6.2. Example 2: BFISAR with pulse LFM waveform; 6.2.1. BFISAR geometry and isorange ellipse parameter estimation; 6.2.2. BFISAR LFM signal formation algorithm; 6.2.3. Image reconstruction algorithm and experimental results
6.3. Example 3: asymmetric geometry of BFISAR with pulse LFM waveform6.3.1. BFISAR LFM signal formation algorithm; 6.3.2. BFISAR image reconstruction algorithm and experimental results; 6.4. Example 4: BGISAR with Barker PCM waveform; 6.4.1. BGISAR Barker PCM signal formation algorithm; 6.4.2. BGISAR image reconstruction algorithm and experimental results; 6.5. Example 5: BGISAR with GPS C/A PCM waveform; 6.5.1. BGISAR GPS C/A PCM signal formation algorithm; 6.5.2. BGISAR image reconstruction algorithm and experimental results; 6.6. Example 6: BGISAR with GPS P PCM waveform
6.6.1. BGISAR GPS P PCM signal formation algorithm
Record Nr. UNINA-9910809840303321
Lazarov Andon Dimitrov  
ISTE Ltd ; ; John Wiley & Sons : , : London, England : , : Hoboken, New Jersey, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
COBOL software modernization : from principles to iplementation with the BLU AGE ® method / / Franck Barbier, Jean-Luc Recoussine
COBOL software modernization : from principles to iplementation with the BLU AGE ® method / / Franck Barbier, Jean-Luc Recoussine
Autore Barbier Franck
Pubbl/distr/stampa London, England ; : , : Hoboken, New Jersey : , : ISTE : , : Wiley, , 2015
Descrizione fisica 1 online resource (282 p.)
Disciplina 005.133
Collana Computer Engineering Series
Soggetto topico COBOL (Computer program language)
Software architecture
ISBN 1-119-07314-6
1-119-07308-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright; Contents; Acknowledgments; Acronyms; Introduction; I.1. Behind software modernization is "modernization": the car metaphor; I.2. COBOL; I.3. Why the Cloud?; I.4. Legacy2Cloud; I.5. Human weight on successful modernization; I.6. This book's structure; 1: Software Modernization: a Business Vision; 1.1. Software-based business; 1.2. Information-driven business; 1.2.1. Adaptation to business; 1.3. The case of tourism industry; 1.4. IT progress acceleration; 1.5. Legacy world; 1.5.1. Exiting the legacy world; 1.5.2. Legacy world professionals; 1.6. Conclusions
2: Software Modernization: Technical Environment2.1. Legacy system; 2.2. Modernization; 2.2.1. Replacement; 2.2.2. Migration; 2.2.3. Modernization versus migration; 2.2.4. The superiority of white-box modernization; 2.3. Software engineering principles underpinning modernization; 2.3.1. Re-engineering in action; 2.3.2. Re-engineering challenges; 2.4. Conclusions; 3: Status of COBOL Legacy Applications; 3.1. OLTP versus batch programs; 3.2. Mainframes; 3.3. Data-driven design; 3.4. COBOL degeneration principle; 3.5. COBOL pitfalls; 3.6. Middleware for COBOL
3.7. Moving COBOL OLTP/batch programs to Java3.8. COBOL is not a friend of Java, and vice versa; 3.9. Spaghetti code; 3.9.1. Spaghetti code sample; 3.9.2. Code comprehension; 3.10. No longer COBOL?; 3.11. Conclusions; 4: Service-Oriented Architecture (SOA); 4.1. Software architecture versus information system urbanization; 4.2. Software architecture evolution; 4.3. COBOL own style of software architecture; 4.4. The one-way road to SOA; 4.5. Characterization of SOA; 4.5.1. Preliminary note; 4.5.2. From objects to components and services; 4.5.3. Type versus instance
4.5.4. Distribution concerns4.5.5. Functional grouping; 4.5.6. Granularity; 4.5.7. Technology-centrism; 4.5.8. Composition at design time (... is definitely modeling); 4.5.9. Composition at runtime; 4.6. Conclusions; 5: SOA in Action; 5.1. Service as materialized component; 5.2. Service as Internet resource; 5.2.1. Pay-per-use service; 5.2.2. Free service; 5.2.3. Data feed service; 5.3. High-end SOA; 5.4. SOA challenges; 5.5. The Cloud; 5.5.1. COBOL in the Cloud; 5.5.2. Computing is just resource consumption; 5.5.3. Cloud computing is also resource consumption, but...
5.5.4. Everything as a service5.5.5. SOA in the Cloud; 5.5.6. The cloud counterparts; 5.6. Conclusions; 6: Model-Driven Development (MDD); 6.1. Why MDD?; 6.2. Models, intuitively; 6.3. Models, formally; 6.4. Models as computerized objects; 6.5. Model-based productivity; 6.6. Openness through standards; 6.6.1. Model-Driven Architecture (MDA); 6.7. Models and people; 6.8. Metamodeling; 6.8.1. Metamodeling, put simply; 6.9. Model transformation; 6.10. Model transformation by example; 6.11. From contemplative to executable models; 6.12. Model execution in action
6.13. Toward Domain-Specific Modeling Languages (DSMLs)
Record Nr. UNINA-9910132298003321
Barbier Franck  
London, England ; : , : Hoboken, New Jersey : , : ISTE : , : Wiley, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
COBOL software modernization : from principles to iplementation with the BLU AGE ® method / / Franck Barbier, Jean-Luc Recoussine
COBOL software modernization : from principles to iplementation with the BLU AGE ® method / / Franck Barbier, Jean-Luc Recoussine
Autore Barbier Franck
Pubbl/distr/stampa London, England ; : , : Hoboken, New Jersey : , : ISTE : , : Wiley, , 2015
Descrizione fisica 1 online resource (282 p.)
Disciplina 005.133
Collana Computer Engineering Series
Soggetto topico COBOL (Computer program language)
Software architecture
ISBN 1-119-07314-6
1-119-07308-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright; Contents; Acknowledgments; Acronyms; Introduction; I.1. Behind software modernization is "modernization": the car metaphor; I.2. COBOL; I.3. Why the Cloud?; I.4. Legacy2Cloud; I.5. Human weight on successful modernization; I.6. This book's structure; 1: Software Modernization: a Business Vision; 1.1. Software-based business; 1.2. Information-driven business; 1.2.1. Adaptation to business; 1.3. The case of tourism industry; 1.4. IT progress acceleration; 1.5. Legacy world; 1.5.1. Exiting the legacy world; 1.5.2. Legacy world professionals; 1.6. Conclusions
2: Software Modernization: Technical Environment2.1. Legacy system; 2.2. Modernization; 2.2.1. Replacement; 2.2.2. Migration; 2.2.3. Modernization versus migration; 2.2.4. The superiority of white-box modernization; 2.3. Software engineering principles underpinning modernization; 2.3.1. Re-engineering in action; 2.3.2. Re-engineering challenges; 2.4. Conclusions; 3: Status of COBOL Legacy Applications; 3.1. OLTP versus batch programs; 3.2. Mainframes; 3.3. Data-driven design; 3.4. COBOL degeneration principle; 3.5. COBOL pitfalls; 3.6. Middleware for COBOL
3.7. Moving COBOL OLTP/batch programs to Java3.8. COBOL is not a friend of Java, and vice versa; 3.9. Spaghetti code; 3.9.1. Spaghetti code sample; 3.9.2. Code comprehension; 3.10. No longer COBOL?; 3.11. Conclusions; 4: Service-Oriented Architecture (SOA); 4.1. Software architecture versus information system urbanization; 4.2. Software architecture evolution; 4.3. COBOL own style of software architecture; 4.4. The one-way road to SOA; 4.5. Characterization of SOA; 4.5.1. Preliminary note; 4.5.2. From objects to components and services; 4.5.3. Type versus instance
4.5.4. Distribution concerns4.5.5. Functional grouping; 4.5.6. Granularity; 4.5.7. Technology-centrism; 4.5.8. Composition at design time (... is definitely modeling); 4.5.9. Composition at runtime; 4.6. Conclusions; 5: SOA in Action; 5.1. Service as materialized component; 5.2. Service as Internet resource; 5.2.1. Pay-per-use service; 5.2.2. Free service; 5.2.3. Data feed service; 5.3. High-end SOA; 5.4. SOA challenges; 5.5. The Cloud; 5.5.1. COBOL in the Cloud; 5.5.2. Computing is just resource consumption; 5.5.3. Cloud computing is also resource consumption, but...
5.5.4. Everything as a service5.5.5. SOA in the Cloud; 5.5.6. The cloud counterparts; 5.6. Conclusions; 6: Model-Driven Development (MDD); 6.1. Why MDD?; 6.2. Models, intuitively; 6.3. Models, formally; 6.4. Models as computerized objects; 6.5. Model-based productivity; 6.6. Openness through standards; 6.6.1. Model-Driven Architecture (MDA); 6.7. Models and people; 6.8. Metamodeling; 6.8.1. Metamodeling, put simply; 6.9. Model transformation; 6.10. Model transformation by example; 6.11. From contemplative to executable models; 6.12. Model execution in action
6.13. Toward Domain-Specific Modeling Languages (DSMLs)
Record Nr. UNINA-9910813257703321
Barbier Franck  
London, England ; : , : Hoboken, New Jersey : , : ISTE : , : Wiley, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Comparable corpora and computer-assisted translation / / Estelle Maryline Delpech ; series editor, Narendra Jussien
Comparable corpora and computer-assisted translation / / Estelle Maryline Delpech ; series editor, Narendra Jussien
Autore Delpech Estelle Maryline
Pubbl/distr/stampa London, England ; : , : Hoboken, New Jersey : , : iSTE : , : Wiley, , 2014
Descrizione fisica 1 online resource (xiv, 287 pages)
Disciplina 410.285
Collana Cognitive Science and Knowledge Management Series
Soggetto topico Computational linguistics
Corpora (Linguistics)
Translators (Computer programs)
ISBN 1-119-00265-6
1-119-00252-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright; Contents; Acknowledgments; Introduction; PART 1: Applicative and Scientific Context; Chapter 1: Leveraging Comparable Corpora for Computer-assisted Translation ; 1.1. Introduction; 1.2. From the beginnings of machine translation to comparable corpora processing; 1.2.1. The dawn of machine translation; 1.2.2. The development of computer-assisted translation; 1.2.3. Drawbacks of parallel corpora and advantages of comparable corpora; 1.2.4. Difficulties of technical translation; 1.2.5. Industrial context
1.3. Term alignment from comparable corpora: a state-of-the-art1.3.1. Distributional approach principle; 1.3.2. Term alignment evaluation; 1.3.2.1. Precision at rank N or TopN; 1.3.2.2. MRR; 1.3.2.3. MAP; 1.3.3. Improvement and variants of the distributional approach; 1.3.3.1. Favoring distributional symmetry; 1.3.3.2. Using syntactic contexts; 1.3.3.3. Relying on trusted elements; 1.3.3.4. Improving semantic information representation; 1.3.3.5. Using second-order semantic affinities; 1.3.3.6. Improving the bilingual resource with semantic classes; 1.3.3.7. Translating polylexical units
1.3.4. Influence of data and parameters on alignment quality1.3.4.1. Data; 1.3.4.2. Parameters; 1.3.5. Limits of the distributional approach; 1.4. CAT software prototype for comparable corpora processing; 1.4.1. Implementation of a term alignment method; 1.4.1.1. Implementation and data; 1.4.1.2. Extraction of the terms to be aligned; 1.4.1.3. Collecting context vectors; 1.4.1.3.1. Monolexical term context vectors; 1.4.1.4. Polylexical term context vectors; 1.4.1.5. Translation of the source context vectors; 1.4.1.6. Term alignment; 1.4.2. Terminological records extraction
1.4.3. Lexicon consultation interface1.5. Summary; Chapter 2: User-Centered Evaluation of Lexicons Extracted from Comparable Corpora; 2.1. Introduction; 2.2. Translation quality evaluation methodologies; 2.2.1. Machine translation evaluation; 2.2.1.1. Automatic evaluation measures; 2.2.1.2. Human MT evaluation; 2.2.2. Human translation evaluation; 2.2.2.1. Quantitative models; 2.2.2.2. Non-quantitative models; 2.2.3. Discussion; 2.3. Design and experimentation of a user-centered evaluation; 2.3.1. Methodological aspects; 2.3.1.1. Evaluation criteria and purpose
2.3.1.2. Subject matter expertise2.3.1.3. Basis for comparison; 2.3.2. Experimentation protocol; 2.3.2.1. Data; 2.3.2.1.1. Comparable corpora and extracted lexica; 2.3.2.1.2. Texts to be translated; 2.3.2.1.3. Resources used in the translation situation; 2.3.2.1.4. Translators and judges; 2.3.2.2. Evaluation progress; 2.3.2.2.1. Translation phase; 2.3.2.2.2. Translation quality evaluation phase; 2.3.3. Results; 2.3.3.1. Lexicons usability; 2.3.3.1.1. Translation speed; 2.3.3.1.2. Use of resources; 2.3.3.1.3. Translators' impressions on the lexicons extracted from comparable corpora
2.3.3.2. Quality of the generated translations
Record Nr. UNINA-9910132185103321
Delpech Estelle Maryline  
London, England ; : , : Hoboken, New Jersey : , : iSTE : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Comparable corpora and computer-assisted translation / / Estelle Maryline Delpech ; series editor, Narendra Jussien
Comparable corpora and computer-assisted translation / / Estelle Maryline Delpech ; series editor, Narendra Jussien
Autore Delpech Estelle Maryline
Pubbl/distr/stampa London, England ; : , : Hoboken, New Jersey : , : iSTE : , : Wiley, , 2014
Descrizione fisica 1 online resource (xiv, 287 pages)
Disciplina 410.285
Collana Cognitive Science and Knowledge Management Series
Soggetto topico Computational linguistics
Corpora (Linguistics)
Translators (Computer programs)
ISBN 1-119-00265-6
1-119-00252-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright; Contents; Acknowledgments; Introduction; PART 1: Applicative and Scientific Context; Chapter 1: Leveraging Comparable Corpora for Computer-assisted Translation ; 1.1. Introduction; 1.2. From the beginnings of machine translation to comparable corpora processing; 1.2.1. The dawn of machine translation; 1.2.2. The development of computer-assisted translation; 1.2.3. Drawbacks of parallel corpora and advantages of comparable corpora; 1.2.4. Difficulties of technical translation; 1.2.5. Industrial context
1.3. Term alignment from comparable corpora: a state-of-the-art1.3.1. Distributional approach principle; 1.3.2. Term alignment evaluation; 1.3.2.1. Precision at rank N or TopN; 1.3.2.2. MRR; 1.3.2.3. MAP; 1.3.3. Improvement and variants of the distributional approach; 1.3.3.1. Favoring distributional symmetry; 1.3.3.2. Using syntactic contexts; 1.3.3.3. Relying on trusted elements; 1.3.3.4. Improving semantic information representation; 1.3.3.5. Using second-order semantic affinities; 1.3.3.6. Improving the bilingual resource with semantic classes; 1.3.3.7. Translating polylexical units
1.3.4. Influence of data and parameters on alignment quality1.3.4.1. Data; 1.3.4.2. Parameters; 1.3.5. Limits of the distributional approach; 1.4. CAT software prototype for comparable corpora processing; 1.4.1. Implementation of a term alignment method; 1.4.1.1. Implementation and data; 1.4.1.2. Extraction of the terms to be aligned; 1.4.1.3. Collecting context vectors; 1.4.1.3.1. Monolexical term context vectors; 1.4.1.4. Polylexical term context vectors; 1.4.1.5. Translation of the source context vectors; 1.4.1.6. Term alignment; 1.4.2. Terminological records extraction
1.4.3. Lexicon consultation interface1.5. Summary; Chapter 2: User-Centered Evaluation of Lexicons Extracted from Comparable Corpora; 2.1. Introduction; 2.2. Translation quality evaluation methodologies; 2.2.1. Machine translation evaluation; 2.2.1.1. Automatic evaluation measures; 2.2.1.2. Human MT evaluation; 2.2.2. Human translation evaluation; 2.2.2.1. Quantitative models; 2.2.2.2. Non-quantitative models; 2.2.3. Discussion; 2.3. Design and experimentation of a user-centered evaluation; 2.3.1. Methodological aspects; 2.3.1.1. Evaluation criteria and purpose
2.3.1.2. Subject matter expertise2.3.1.3. Basis for comparison; 2.3.2. Experimentation protocol; 2.3.2.1. Data; 2.3.2.1.1. Comparable corpora and extracted lexica; 2.3.2.1.2. Texts to be translated; 2.3.2.1.3. Resources used in the translation situation; 2.3.2.1.4. Translators and judges; 2.3.2.2. Evaluation progress; 2.3.2.2.1. Translation phase; 2.3.2.2.2. Translation quality evaluation phase; 2.3.3. Results; 2.3.3.1. Lexicons usability; 2.3.3.1.1. Translation speed; 2.3.3.1.2. Use of resources; 2.3.3.1.3. Translators' impressions on the lexicons extracted from comparable corpora
2.3.3.2. Quality of the generated translations
Record Nr. UNINA-9910819095203321
Delpech Estelle Maryline  
London, England ; : , : Hoboken, New Jersey : , : iSTE : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Regularization and Bayesian methods for inverse problems in signal and image processing / / edited by Jean-François Giovannelli, Jérôme Idier
Regularization and Bayesian methods for inverse problems in signal and image processing / / edited by Jean-François Giovannelli, Jérôme Idier
Pubbl/distr/stampa London, [England] ; ; Hoboken, New Jersey : , : ISTE Limited : , : Hoboken, New Jersey, , 2015
Descrizione fisica 1 online resource (323 p.)
Disciplina 515.35
Collana Digital Signal and Image Processing Series
Soggetto topico Inverse problems (Differential equations)
Bayesian statistical decision theory
Signal processing - Mathematics
Image processing - Mathematics
ISBN 1-118-82698-1
1-118-82725-2
1-118-82707-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright; Contents; Introduction; I.1. Bibliography; 1: 3D Reconstruction in X-ray Tomography: Approach Example for Clinical Data Processing; 1.1. Introduction; 1.2. Problem statement; 1.2.1. Data formation models; 1.2.2. Estimators; 1.2.3. Algorithms; 1.3. Method; 1.3.1. Data formation models; 1.3.2. Estimator; 1.3.3. Minimization method; 1.3.3.1. Algorithm selection; 1.3.3.2. Minimization procedure; 1.3.4. Implementation of the reconstruction procedure; 1.4. Results; 1.4.1. Comparison of minimization algorithms; 1.4.2. Using a region of interest in reconstruction
1.4.3. Consideration of the polyenergetic character of the X-ray source1.4.3.1. Simulated data in 2D; 1.4.3.2. Real data in 3D; 1.5. Conclusion; 1.6. Acknowledgments; 1.7. Bibliography; 2: Analysis of Force-Volume Images in Atomic Force Microscopy Using Sparse Approximation; 2.1. Introduction; 2.2. Atomic force microscopy; 2.2.1. Biological cell characterization; 2.2.2. AFM modalities; 2.2.2.1. Isoforce and isodistance images; 2.2.2.2. Force spectroscopy; 2.2.2.3. Force-volume imaging; 2.2.3. Physical piecewise models; 2.2.3.1. Approach phase models; 2.2.3.2. Retraction phase models
2.3. Data processing in AFM spectroscopy2.3.1. Objectives and methodology in signal processing; 2.3.1.1. Detection of the regions of interest; 2.3.1.2. Parametric model fitting; 2.3.2. Segmentation of a force curve by sparse approximation; 2.3.2.1. Detecting jumps in a signal; 2.3.2.2. Joint detection of discontinuities at different orders; 2.3.2.3. Scalar and vector variable selection; 2.4. Sparse approximation algorithms; 2.4.1. Minimization of a mixed l2-l0 criterion; 2.4.2. Dedicated algorithms; 2.4.3. Joint detection of discontinuities; 2.4.3.1. Construction of the dictionary
2.4.3.2. Selection of scalar variables2.4.3.3. Selection of vector variables; 2.5. Real data processing; 2.5.1. Segmentation of a retraction curve: comparison of strategies; 2.5.2. Retraction curve processing; 2.5.3. Force-volume image processing in the approach phase; 2.6. Conclusion; 2.7. Bibliography; 3: Polarimetric Image Restoration by Non-local Means; 3.1. Introduction; 3.2. Light polarization and the Stokes-Mueller formalism; 3.3. Estimation of the Stokes vectors; 3.3.1. Estimation of the Stokes vector in a pixel; 3.3.1.1. Problem formulation
3.3.1.2. Properties of the constrained optimization problem3.3.1.3. Optimization algorithm; 3.3.2. Non-local means filtering; 3.3.3. Adaptive non-local means filtering; 3.3.3.1. The function φ; 3.3.3.2. Patches size and shape; 3.3.4. Application to the estimation of Stokes vectors; 3.4. Results; 3.4.1. Results with synthetic data; 3.4.1.1. Synthetic data and context evaluation presentation; 3.4.1.2. Results; 3.4.1.3. Significance of the proposed method for the estimation of the weights; 3.4.2. Results with real data; 3.5. Conclusion; 3.6. Bibliography
4: Video Processing and Regularized Inversion Methods
Record Nr. UNISA-996212920703316
London, [England] ; ; Hoboken, New Jersey : , : ISTE Limited : , : Hoboken, New Jersey, , 2015
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Regularization and Bayesian methods for inverse problems in signal and image processing / / edited by Jean-François Giovannelli, Jérôme Idier
Regularization and Bayesian methods for inverse problems in signal and image processing / / edited by Jean-François Giovannelli, Jérôme Idier
Pubbl/distr/stampa London, [England] ; ; Hoboken, New Jersey : , : ISTE Limited : , : Hoboken, New Jersey, , 2015
Descrizione fisica 1 online resource (323 p.)
Disciplina 515.35
Collana Digital Signal and Image Processing Series
Soggetto topico Inverse problems (Differential equations)
Bayesian statistical decision theory
Signal processing - Mathematics
Image processing - Mathematics
ISBN 1-118-82698-1
1-118-82725-2
1-118-82707-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright; Contents; Introduction; I.1. Bibliography; 1: 3D Reconstruction in X-ray Tomography: Approach Example for Clinical Data Processing; 1.1. Introduction; 1.2. Problem statement; 1.2.1. Data formation models; 1.2.2. Estimators; 1.2.3. Algorithms; 1.3. Method; 1.3.1. Data formation models; 1.3.2. Estimator; 1.3.3. Minimization method; 1.3.3.1. Algorithm selection; 1.3.3.2. Minimization procedure; 1.3.4. Implementation of the reconstruction procedure; 1.4. Results; 1.4.1. Comparison of minimization algorithms; 1.4.2. Using a region of interest in reconstruction
1.4.3. Consideration of the polyenergetic character of the X-ray source1.4.3.1. Simulated data in 2D; 1.4.3.2. Real data in 3D; 1.5. Conclusion; 1.6. Acknowledgments; 1.7. Bibliography; 2: Analysis of Force-Volume Images in Atomic Force Microscopy Using Sparse Approximation; 2.1. Introduction; 2.2. Atomic force microscopy; 2.2.1. Biological cell characterization; 2.2.2. AFM modalities; 2.2.2.1. Isoforce and isodistance images; 2.2.2.2. Force spectroscopy; 2.2.2.3. Force-volume imaging; 2.2.3. Physical piecewise models; 2.2.3.1. Approach phase models; 2.2.3.2. Retraction phase models
2.3. Data processing in AFM spectroscopy2.3.1. Objectives and methodology in signal processing; 2.3.1.1. Detection of the regions of interest; 2.3.1.2. Parametric model fitting; 2.3.2. Segmentation of a force curve by sparse approximation; 2.3.2.1. Detecting jumps in a signal; 2.3.2.2. Joint detection of discontinuities at different orders; 2.3.2.3. Scalar and vector variable selection; 2.4. Sparse approximation algorithms; 2.4.1. Minimization of a mixed l2-l0 criterion; 2.4.2. Dedicated algorithms; 2.4.3. Joint detection of discontinuities; 2.4.3.1. Construction of the dictionary
2.4.3.2. Selection of scalar variables2.4.3.3. Selection of vector variables; 2.5. Real data processing; 2.5.1. Segmentation of a retraction curve: comparison of strategies; 2.5.2. Retraction curve processing; 2.5.3. Force-volume image processing in the approach phase; 2.6. Conclusion; 2.7. Bibliography; 3: Polarimetric Image Restoration by Non-local Means; 3.1. Introduction; 3.2. Light polarization and the Stokes-Mueller formalism; 3.3. Estimation of the Stokes vectors; 3.3.1. Estimation of the Stokes vector in a pixel; 3.3.1.1. Problem formulation
3.3.1.2. Properties of the constrained optimization problem3.3.1.3. Optimization algorithm; 3.3.2. Non-local means filtering; 3.3.3. Adaptive non-local means filtering; 3.3.3.1. The function φ; 3.3.3.2. Patches size and shape; 3.3.4. Application to the estimation of Stokes vectors; 3.4. Results; 3.4.1. Results with synthetic data; 3.4.1.1. Synthetic data and context evaluation presentation; 3.4.1.2. Results; 3.4.1.3. Significance of the proposed method for the estimation of the weights; 3.4.2. Results with real data; 3.5. Conclusion; 3.6. Bibliography
4: Video Processing and Regularized Inversion Methods
Record Nr. UNINA-9910140479103321
London, [England] ; ; Hoboken, New Jersey : , : ISTE Limited : , : Hoboken, New Jersey, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Regularization and Bayesian methods for inverse problems in signal and image processing / / edited by Jean-François Giovannelli, Jérôme Idier
Regularization and Bayesian methods for inverse problems in signal and image processing / / edited by Jean-François Giovannelli, Jérôme Idier
Pubbl/distr/stampa London, [England] ; ; Hoboken, New Jersey : , : ISTE Limited : , : Hoboken, New Jersey, , 2015
Descrizione fisica 1 online resource (323 p.)
Disciplina 515.35
Collana Digital Signal and Image Processing Series
Soggetto topico Inverse problems (Differential equations)
Bayesian statistical decision theory
Signal processing - Mathematics
Image processing - Mathematics
ISBN 1-118-82698-1
1-118-82725-2
1-118-82707-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright; Contents; Introduction; I.1. Bibliography; 1: 3D Reconstruction in X-ray Tomography: Approach Example for Clinical Data Processing; 1.1. Introduction; 1.2. Problem statement; 1.2.1. Data formation models; 1.2.2. Estimators; 1.2.3. Algorithms; 1.3. Method; 1.3.1. Data formation models; 1.3.2. Estimator; 1.3.3. Minimization method; 1.3.3.1. Algorithm selection; 1.3.3.2. Minimization procedure; 1.3.4. Implementation of the reconstruction procedure; 1.4. Results; 1.4.1. Comparison of minimization algorithms; 1.4.2. Using a region of interest in reconstruction
1.4.3. Consideration of the polyenergetic character of the X-ray source1.4.3.1. Simulated data in 2D; 1.4.3.2. Real data in 3D; 1.5. Conclusion; 1.6. Acknowledgments; 1.7. Bibliography; 2: Analysis of Force-Volume Images in Atomic Force Microscopy Using Sparse Approximation; 2.1. Introduction; 2.2. Atomic force microscopy; 2.2.1. Biological cell characterization; 2.2.2. AFM modalities; 2.2.2.1. Isoforce and isodistance images; 2.2.2.2. Force spectroscopy; 2.2.2.3. Force-volume imaging; 2.2.3. Physical piecewise models; 2.2.3.1. Approach phase models; 2.2.3.2. Retraction phase models
2.3. Data processing in AFM spectroscopy2.3.1. Objectives and methodology in signal processing; 2.3.1.1. Detection of the regions of interest; 2.3.1.2. Parametric model fitting; 2.3.2. Segmentation of a force curve by sparse approximation; 2.3.2.1. Detecting jumps in a signal; 2.3.2.2. Joint detection of discontinuities at different orders; 2.3.2.3. Scalar and vector variable selection; 2.4. Sparse approximation algorithms; 2.4.1. Minimization of a mixed l2-l0 criterion; 2.4.2. Dedicated algorithms; 2.4.3. Joint detection of discontinuities; 2.4.3.1. Construction of the dictionary
2.4.3.2. Selection of scalar variables2.4.3.3. Selection of vector variables; 2.5. Real data processing; 2.5.1. Segmentation of a retraction curve: comparison of strategies; 2.5.2. Retraction curve processing; 2.5.3. Force-volume image processing in the approach phase; 2.6. Conclusion; 2.7. Bibliography; 3: Polarimetric Image Restoration by Non-local Means; 3.1. Introduction; 3.2. Light polarization and the Stokes-Mueller formalism; 3.3. Estimation of the Stokes vectors; 3.3.1. Estimation of the Stokes vector in a pixel; 3.3.1.1. Problem formulation
3.3.1.2. Properties of the constrained optimization problem3.3.1.3. Optimization algorithm; 3.3.2. Non-local means filtering; 3.3.3. Adaptive non-local means filtering; 3.3.3.1. The function φ; 3.3.3.2. Patches size and shape; 3.3.4. Application to the estimation of Stokes vectors; 3.4. Results; 3.4.1. Results with synthetic data; 3.4.1.1. Synthetic data and context evaluation presentation; 3.4.1.2. Results; 3.4.1.3. Significance of the proposed method for the estimation of the weights; 3.4.2. Results with real data; 3.5. Conclusion; 3.6. Bibliography
4: Video Processing and Regularized Inversion Methods
Record Nr. UNINA-9910828770503321
London, [England] ; ; Hoboken, New Jersey : , : ISTE Limited : , : Hoboken, New Jersey, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui