Kooperativer Bibliotheksverbund

UID:

Format: 1 online resource (various pagings) : , illustrations.

ISBN: 9780750339797 , 9780750339780

Series Statement: [IOP release $release]

Content: This book explores the development of electromagnetic theory in the field of scattering. Solutions to applied problems of electromagnetic wave scattering are presented and include resonant electromagnetic wave scattering, solved by the integral equation method, and 2D and 3D scattering problems, solved by the asymptotical (short wave) method. The book provides worked examples throughout for analysis of the electromagnetic wave scattering processes by different objects. The key audience for this book includes researchers in the field of electromagnetic scattering and designers of radar and radio equipment.

Note: "Version: 20221001"--Title page verso. , 1. Using an integral equation method for solving problems of resonant electromagnetic wave scattering / Sergey Nechitaylo, Valery Orlenko, Oleg Sukharevsky, Vitaly Vasilets and Gennady Zalevsky -- 2. Asymptotic methods for solving some applied problems / Sergey Nechitaylo, Valery Orlenko, Oleg Sukharevsky, Vitaly Vasilets and Gennady Zalevsky. , Also available in print. , Mode of access: World Wide Web. , System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

Additional Edition: Print version: ISBN 9780750339773

Additional Edition: ISBN 9780750339803

Language: English

DOI: 10.1088/978-0-7503-3979-7

URL: https://iopscience.iop.org/book/edit/978-0-7503-3979-7

UID:

Format: 1 online resource (various pagings) : , illustrations (some color).

ISBN: 9780750349369 , 9780750349352

Series Statement: [IOP release $release]

Content: This book provides a review of state-of-the-art technological developments in applied ultrasonics with a focus on recent advances in ultrasonic research, covering metrological applications, non-destructive evaluation, sensing, devices, and physics, as well as medical diagnosis and treatment. The first part of this book focuses on the physics of acoustic waves, and their propagation and addresses viscoelasticity, as well as metrological applications including laser ultrasonics. Part two reviews some recent developments of importance to industrial applications, while the final part introduces developments in biomedical applications.

Note: "Version: 20221001"--Title page verso. , Preface from the Institute for Ultrasonic Electronics -- part I. Basic physics and measurements. 1. Ultrasound propagation / Pak-Kon Choi -- 2. Wave propagation in/on liquids and spectroscopy of viscoelasticity and surface tension / Keiji Sakai -- 3. Optical measurements of ultrasonic fields in air/water and ultrasonic vibration in solids / Kentaro Nakamura -- 4. Picosecond laser ultrasonics / Osamu Matsuda and Oliver B. Wright , part II. Industrial applications. 5. Ball surface acoustic wave sensor and its application to trace gas analysis / Kazushi Yamanaka, Takamitsu Iwaya and Shingo Akao -- 6. Phase adjuster in a thermoacoustic system / Shin-ichi Sakamoto and Yoshiaki Watanabe , part III. Biological and medical applications. 7. Ultrasonic characterization of bone / Mami Matsukawa -- 8. Acceleration and control of protein aggregation / Hirotsugu Ogi -- 9. High-frame-rate medical ultrasonic imaging / Hideyuki Hasegawa -- 10. High-intensity focused ultrasound / Shin Yoshizawa and Shin-ichiro Umemura. , Also available in print. , Mode of access: World Wide Web. , System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

Additional Edition: Print version: ISBN 9780750349345

Additional Edition: ISBN 9780750349376

Language: English

DOI: 10.1088/978-0-7503-4936-9

URL: https://iopscience.iop.org/book/edit/978-0-7503-4936-9

UID:

Format: 1 online resource (various pagings) : , illustrations (some color).

ISBN: 9780750340519 , 9780750340502

Series Statement: [IOP release $release]

Content: This book explores the biggest gaps in current research of the universe. The text covers topics such as dark matter, dark energy, the Hubble constant/tension, deaths of massive stars, mysteries associated with black holes, neutron stars, and binary/ multiple systems. Written at a general and accessible level, each chapter also contains separate panel inserts with more technical explanations, as well as references for further details. As a highly useful reference book it provides a summary of where mysteries in the universe lie and exciting new avenues of future research. The text fills an important gap in current scientific literature and appeals to general audiences, astronomy students, and scientists in other disciplines.

Note: "Version: 20221001"--Title page verso. , 1. Introduction -- 1.1. Solving scientific problems -- 1.2. Solving astrophysical mysteries--deep space forensics -- 1.3. Further reading , 2. How we see the universe -- 2.1. Light -- 2.2. Particles -- 2.3. Gravitational waves -- 2.4. Looking back in time -- 2.5. Further reading , 3. Inventory of the universe--something is missing! -- 3.1. A note about distance and scale -- 3.2. Baryonic matter -- 3.3. Dark matter -- 3.4. Dark energy -- 3.5. Further reading , 4. The expansion of the universe and the Hubble tension -- 4.1. Standard cosmological model -- 4.2. Hubble's law -- 4.3. Where to go from here -- 4.4. Further reading , 5. The first stars and galaxies -- 5.1. The first stars -- 5.2. The first galaxies -- 5.3. Further reading , 6. Couples+ -- 6.1. Bound or interacting stars -- 6.2. Binary formation -- 6.3. Bound or interacting galaxies -- 6.4. Further reading , 7. How massive stars die -- 7.1. Supernovae -- 7.2. Gamma-ray bursts -- 7.3. Further reading , 8. Matter at extreme densities--neutron stars -- 8.1. Superdense matter--the neutron star equation of state -- 8.2. Mysteries associated with 'ordinary' pulsars -- 8.3. Fast radio bursts -- 8.4. Neutron star mergers and heavy element production -- 8.5. Further reading , 9. And then what? ... black holes! -- 9.1. Stellar mass black holes -- 9.2. Intermediate mass black holes--where are they? -- 9.3. Supermassive black holes -- 9.4. Black hole-disk phenomena -- 9.5. Primordial black holes -- 9.6. Further reading , 10. Looking forward -- 10.1. Looking to the future -- 10.2. Final thoughts -- 10.3. Further reading. , Also available in print. , Mode of access: World Wide Web. , System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

Additional Edition: Print version: ISBN 9780750340496

Additional Edition: ISBN 9780750340526

Language: English

DOI: 10.1088/2514-3433/ac8975

URL: https://iopscience.iop.org/book/mono/978-0-7503-4051-9

UID:

Format: 1 online resource (various pagings) : , illustrations (some color).

ISBN: 9780750335959 , 9780750335942

Series Statement: [IOP release $release]

Content: Within this first volume dealing with lung and kidney cancer, the editors and authors detail the latest research related to the application of artificial intelligence (AI) to cancer diagnosis and prognosis and summarize its advantages. It is the intention of the editors and authors to explore how AI assists in these activities, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. Ways will also be demonstrated as to how these methods in AI are advancing the field. There have been thousands of papers written between 1995 and 2019 related to AI for cancer diagnosis and prognosis. However, to date (to the best of our knowledge) there has not yet been published a comprehensive overview of the latest findings pertaining to these AI technologies, within a single book project. Therefore, the purpose of this three-volume work, and particularly for this first volume dealing with lung and kidney cancer, is to present a compendium of these findings related to these two pervasive cancers. Within this coverage it is our hope that scientists, researchers and clinicians can successfully incorporate these techniques into other significant cancers such as pancreatic, esophageal leukemia, melanoma, etc. Part of IPEM-IOP Series in Physics and Engineering in Medicine and Biology.

Note: "Version: 20221001"--Title page verso. , 1. American Joint Committee on Cancer staging of lung and renal cancers using a recurrent deep neural network model / Dipanjan Moitra -- 2. Neural-ensemble-based detection : a modern way to diagnose lung cancer / Sharayu Govardhane, Sahil Gandhi and Pravin Shende -- 3. Computed tomography and magnetic resonance imaging machine learning applications for renal cell carcinoma / Elvira Guerriero, Arnaldo Stanzione, Lorenzo Ugga and Renato Cuocolo -- 4. Pulmonary nodule-based feature learning for automated lung tumor grading using convolutional neural networks / Supriya Suresh and Subaji Mohan -- 5. Detection of lung contours using closed principal curves and machine learning / Tao Peng, Yihuai Wang, Thomas Canhao Xu, Lianmin Shi, Jianwu Jiang and Shilang Zhu -- 6. Bytes, pixels, and bases : machine learning in imaging-omics for renal cell carcinoma / Ruchi Chauhan, C.V. Jawahar and P.K. Vinod -- 7. Detection, growth quantification, and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans / Xavier Rafael-Palou, Anton Aubanell, Mario Ceresa, Vicent Ribas, Gemma Piella and Miguel A González Ballester -- 8. Training a deep multiview model using small samples of medical data / Junzhou Huang, Xinliang Zhu and Jiawen Yao -- 9. Overview of deep learning for lung cancer diagnosis / Boran Sekeroglu, Daniel Chwaifo Malann and Kubra Tuncal -- 10. Artificial intelligence for cancer diagnosis / Sura Khalil Abd, Mustafa Musa Jaber, Sarah Yahya Ali and Mohammed Hasan Ali -- 11. Lung cancer diagnosis using 3D-CNN and spherical harmonics expansions / Ahmed Shaffie, Ahmed Soliman, Ali Mahmoud, Fatma Taher, Mohammed Ghazal and Ayman El-Baz. , Also available in print. , Mode of access: World Wide Web. , System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

Additional Edition: Print version: ISBN 9780750335935

Additional Edition: ISBN 9780750335966

Language: English

DOI: 10.1088/978-0-7503-3595-9

URL: https://iopscience.iop.org/book/edit/978-0-7503-3595-9

UID:

Format: 1 online resource (various pagings) : , illustrations (some color).

ISBN: 9780750335997 , 9780750335980

Series Statement: [IOP release $release]

Content: Within this second volume dealing with breast and bladder cancer, the editors and authors detail the latest research related to the application of artificial intelligence (AI) to cancer diagnosis and prognosis and summarize its advantages. It is the intention of the editors and authors to explore how AI assists in these activities, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. Ways will also be demonstrated as to how these methods in AI are advancing the field. There have been thousands of papers written between 1995 and 2019 related to AI for cancer diagnosis and prognosis. However, to date (to the best of our knowledge) there has not yet been published a comprehensive overview of the latest findings pertaining to these AI technologies, with a single book project. Therefore, the purpose of this three-volume work, and particularly for this second volume dealing with breast and bladder cancer, is to present a compendium of these findings related to these two pervasive cancers. Many of the chapter authors are world class researchers in these technologies. Within this coverage it is our hope that scientists, researchers and clinicians can successfully incorporate these techniques into other significant cancers such as pancreatic, esophageal, leukemia, melanoma, etc. Part of IPEM-IOP Series in Physics and Engineering in Medicine and Biology.

Note: "Version: 20221001"--Title page verso. , 1. Development of artificial neural networks for breast histopathological image analysis / Chen Li, Yuchao Zheng, Haiqing Zhang, Xiaomin Zhou, Yin Dai and Xiaoyan Li -- 2. Machine learning in bladder cancer diagnosis / Elliot S. Kim, Valentina L. Kouznetsova and Igor F. Tsigelny -- 3. Deep learning in photoacoustic breast cancer imaging / Changchun Yang and Fei Gao -- 4. Histopathological breast cancer image classification with feature prioritization using a heuristic algorithm / Abdullah-Al Nahid, Johir Raihan, Niloy Sikder and Saifur Rahman Sabuj -- 5. The use of machine learning and biofluid metabolomics in breast cancer diagnosis / Mashiro Sugimoto -- 6. AUTO-BREAST : a fully automated pipeline for breast cancer diagnosis using AI technology / Nagia M. Ghanem, Omneya Attallah, Fatma Anwar and Mohamed A. Ismail -- 7. Diagnosis of breast cancer from histopathological images using artificial intelligence / R. Rashmi, Keerthana Prasad and Chethana Babu K. Udupa -- 8. The role of artificial intelligence in the field of bladder cancer / Agus Rizal A.H. Hamid, Prasandhya A. Yusuf and Anindya Pradipta -- 9. Exploring data science paradigms in breast cancer classification : linking data, learning, and artificial intelligence in medical diagnosis / Shomona Gracia Jacob and Bensujin Bennet -- 10. Automatic detection and classification of invasive ductal carcinoma in histopathology images using convolutional neural networks / R. Karthiga, K. Narasimhan and N. Raju -- 11. Machine learning analysis of breast cancer single-cell omics data / Shenghua Tian and Tao Huang -- 12. Radiomics, deep learning, and breast cancer detection / Y. Jiménez Gaona, M.J. Rodríguez-Álvarez, D. Castillo Malla and V. Lakshminarayanan -- 13. Artificial-intelligence-based techniques for the diagnosis of bladder and breast cancer / Shadab Momin, Yang Lei, Tian Liu and Xiaofeng Yang. , Also available in print. , Mode of access: World Wide Web. , System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

Additional Edition: Print version: ISBN 9780750335973

Additional Edition: ISBN 9780750336000

Language: English

DOI: 10.1088/978-0-7503-3599-7

URL: https://iopscience.iop.org/book/edit/978-0-7503-3599-7

UID:

Format: 1 online resource (various pagings) : , illustrations (some color).

ISBN: 9780750336437 , 9780750336420

Series Statement: [IOP release $release]

Content: The aim of this reference text is to provide a common language and consistent approach to risk assessment across science and engineering disciplines through the collation of detailed, real examples and scenarios. Examples are drawn from a variety of industries and cover key risk topics such as tolerability, data collection, hazard identification and hazard interpretation. The approaches communicate common challenges and are compared and contrasted, with a particular focus on the terminology used. The book is essential reading for scientists and engineers that undertake risk assessment, including industry practitioners, researchers in academia and those with a general interest in improving risk assessment processes. It is also a valuable reference for science and engineering students that regularly undertake risk assessments or study courses on risk assessment.

Note: "Version: 20221001"--Title page verso. , Introduction to challenges in Risk Analysis for science and engineering: development of a common language -- 1. An introduction to risk analysis for scientists and engineers / F. Persico and E. Gutierrez Carazo -- 2. On the tolerability of risk, public and private / M.R. Williams -- 3. Storage of military ammunition and explosives--risk evaluation and hazard management / D. Skelly -- 4. The application of systems thinking to risk assessment : left shifting safety / D. Holley, P. Gill, N. Mai and R. Vrcelj -- 5. Influence of automation on human factor integration in risk assessment systems / B. Thawani, R. Critchley and R. Hazael -- 6. Minimising the risks to decision-making by selecting representative experimental methods for environmental science research / E. Gutierrez Carazo, F. Persico, M. Ladyman, T. Temple and F. Coulon -- 7. The importance of the conceptual site module in the assessment of land contamination / P. Burden, A.-M. Deloughry and J. Morgan -- 8. Land Release : a risk management approach for mine action / R. Evans -- 9. Risk assessment and management at a heritage site / G. Hooper -- 10. Amazonian non-timber forest products and ways forward to ensure their sustainability at low risk : acai case study / K.K.L. Yamaguchi, J. Campos Zeballos, S. Janzen, E.B.F. Galante and V.F. Veiga-Junior -- 11. Development of a risk matrix for low-cost engineering solutions : a systematic approach / L Roberts, R Critchley, D Salberg, S Bloodworth-Race and R Hazael -- 12. Assessing the academic and experiential risk to students from the transition to hybrid and online delivery / M. Ladyman, T. Temple and A. Temple -- 13. Practical application of a decision framework to mitigate environmental risks posed by the treatment of explosive contaminated wastewater / W. Gilroy-Hirst, J. Padfield, M. Ladyman, F. Coulon and T. Temple -- 14. Risk recognition and continuous risk management / Z. Rustom -- 15. Conclusion : challenges in risk analysis / M. Ladyman and T. Temple. , Also available in print. , Mode of access: World Wide Web. , System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

Additional Edition: Print version: ISBN 9780750336413

Additional Edition: ISBN 9780750336444

Language: English

DOI: 10.1088/978-0-7503-3643-7

URL: https://iopscience.iop.org/book/edit/978-0-7503-3643-7

UID:

Format: 1 online resource (various pagings) : , illustrations (some color).

ISBN: 9780750336031 , 9780750336024

Series Statement: [IOP release $release]

Content: Within this third volume dealing with brain and prostate cancer, the editors and authors detail the latest research related to the application of artificial intelligence (AI) to cancer diagnosis and prognosis and summarize its advantages. It is the intention of the editors and authors to explore how AI assists in these activities, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. Ways will also be demonstrated as to how these methods in AI are advancing the field. There have been thousands of papers written between 1995 and 2019 related to AI for cancer diagnosis and prognosis. However, to date (to the best of our knowledge) there has not yet been published a comprehensive overview of the latest findings pertaining to these AI technologies, within a single book project. Therefore, the purpose of this three-volume work, and particularly for this third volume dealing with brain and prostate cancer, is to present a compendium of these findings related to these two pervasive cancers. Within this coverage it is our hope that scientists, researchers and clinicians can successfully incorporate these techniques into other significant cancers such as pancreatic, esophageal, leukemia, melanoma, etc. Part of IPEM-IOP Series in Physics and Engineering in Medicine and Biology.

Note: "Version: 20221001"--Title page verso. , 1. Artificial intelligence in prostate cancer treatment with image-guided radiation therapy / Yading Yuan, Ren-Dih Sheu, Tzu-Chi Tseng, James Tam, Yeh-Chi Lo and Richard Stock -- 2. Artificial-intelligence-based diagnosis of brain tumor diseases / Samir Kumar Bandyopadhyay, Vishal Goyal and Shawni Dutta -- 3. Multisite brain tumor segmentation using a unified generative adversarial network / Jia Wei, Zecheng Liu, Wenguang Yuan and Rui Li -- 4. Role of artificial intelligence in automatic segmentation of brain metastases for radiotherapy / Prabhakar Ramachandran, Venkatakrishnan Seshadri, Ben Perrett, Akash Mehta, Davide Fontanarosa, Mark Pinkham and Matthew Foote -- 5. Applications of artificial intelligence in the fields of brain and prostate cancer / Ayturk Keles and Ali Keles -- 6. AI-based non-deep learning and deep learning techniques used to accurately predict prostate cancer / Lal Hussain and Adeel Ahmed Abbasi -- 7. Intelligent brain tumor classification using deep convolutional neural networks with transfer learning / Chung-Ming Lo and Cheng-Yeh Hsieh -- 8. Big data applications in radiation oncology : challenges and opportunities / William C. Sleeman IV, Sriram Srinivasan, Preetam Ghosh, Jatinder Palta and Rishabh Kapoor -- 9. A hybrid approach to the hyperspectral classification of in vivo brain tissue : linear unmixing with spatial coherence and machine learning / Ines A. Cruz-Guerrero, Daniel U. Campos-Delgado, Aldo R. Mejia-Rodriguez, Himar Fabelo, Samuel Ortega and Gustavo M. Callico -- 10. Application and post-hoc explainability of deep convolutional neural networks for bone cancer metastasis classification in prostate patients / Serafeim Moustakidis, Charis Ntakolia, Dimitrios E. Diamantis, Nikolaos Papandrianos and Elpiniki I. Papageorgiou -- 11. Prostate cancer detection using histopathology image analysis / Sarah M. Ayyad, Mohamed Shehata, Ahmed Shalaby, Mohamed Abou El-Ghar, Mohammed Ghazal, Moumen El-Melegy, Ali Mahmoud, Nahla B. Abdel-Hamid, Labib M. Labib, H. Arafat Ali and Ayman El-Baz -- 12. Machine learning of gliomas in 3D dynamic contrast enhanced MRI : automatic segmentation and classification / Jiwoong Jason Jeong, Yang Lei, Zhen Tian, Hui Mao, Tian Liu and Xiaofeng Yang. , Also available in print. , Mode of access: World Wide Web. , System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

Additional Edition: Print version: ISBN 9780750336017

Additional Edition: ISBN 9780750336048

Language: English

DOI: 10.1088/978-0-7503-3603-1

URL: https://iopscience.iop.org/book/edit/978-0-7503-3603-1

UID:

Format: 1 online resource (various pagings) : , illustrations.

ISBN: 9780750332279 , 9780750332262

Series Statement: [IOP release $release]

Content: This book covers quantum field theory at an introductory level appropriate for graduate students in physics. The first volume aims to allow students to begin their research in fields using quantum field theory, such as particle physics, nuclear physics, cosmology and astrophysics and condensed matter physics. The key areas the book explores include free (noninteracting) fields, field quantization, interacting fields, Feynman diagrams, scattering, cross sections and decay rates; renormalization; symmetry, symmetry breaking and Goldstone bosons. Graduate students studying particle, nuclear, and condensed matter physics are the key audience for this volume. It will also be useful to researchers looking for a modern overview of quantum field theory.

Note: "Version: 20221001"--Title page verso. , 1. Introduction to quantum field theory -- 1.1. Natural units -- 1.2. The simple harmonic oscillator in classical mechanics -- 1.3. The harmonic oscillator in quantum mechanics -- 1.4. Photons -- 1.5. Paths to quantum field theory , 2. Quantum mechanics and path integrals -- 2.1. Classical mechanics and fields -- 2.2. Quantum mechanics -- 2.3. The Feynman path integral for one degree of freedom , 3. Classical fields -- 3.1. Wave equations in classical mechanics and quantum mechanics -- 3.2. Special relativity -- 3.3. The Lagrangian formalism for fields -- 3.4. Continuous symmetries in classical field theory -- 3.5. The Hamiltonian formalism -- 3.6. Causality , 4. Free quantum fields -- 4.1. The Feynman path integral for field theories -- 4.2. Free scalar fields -- 4.3. Another approach to the functional integral -- 4.4. Interpretation of Z[0] for free fields -- 4.5. Vacuum energy examples -- 4.6. Fock space -- 4.7. Relativistic invariance and Fock space -- 4.8. Free quantum fields in Fock space -- 4.9. The canonical commutation relations and causality -- 4.10. Equivalence to the functional integral formalism -- 4.11. Continuous symmetries in quantum field theories , 5. Interacting quantum fields -- 5.1. Perturbation theory and Feynman diagrams -- 5.2. Feynman diagrams in position space -- 5.3. Feynman diagrams in momentum space -- 5.4. Scattering theory -- 5.5. A toy model of nucleons and pions -- 5.6. The CPT theorem -- 5.7. Cross-sections and decay rates , 6. Renormalization -- 6.1. Mass renormalization -- 6.2. Coupling constant renormalization -- 6.3. Field renormalization -- 6.4. Renormalization : a systematic process -- 6.5. Renormalizability -- 6.6. Matrix elements and the LSZ reduction formula , 7. Symmetries and symmetry breaking -- 7.1. Internal symmetries -- 7.2. Spontaneous symmetry breaking and perturbation theory -- 7.3. Broken continuous symmetries and Goldstone bosons -- 7.4. Renormalization of models with spontaneous symmetry breaking , 8. Fermions -- 8.1. Introduction to the Dirac equation -- 8.2. Representations of the Lorentz group -- 8.3. The Dirac equation -- 8.4. Solutions of the Dirac equation -- 8.5. The free Dirac field -- 8.6. Dirac bilinears -- 8.7. Chiral symmetry and helicity -- 8.8. Charge conjugation and coupling to the electromagnetic field -- 8.9. Functional integration for fermions -- 8.10. Feynman rules and scattering for a Yukawa field theory -- 8.11. Interpreting the boson and fermion functional determinants -- 8.12. The linear sigma model of mesons and nucleons. , Also available in print. , Mode of access: World Wide Web. , System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

Additional Edition: Print version: ISBN 9780750332255

Additional Edition: ISBN 9780750332286

Language: English

DOI: 10.1088/978-0-7503-3227-9

URL: https://iopscience.iop.org/book/mono/978-0-7503-3227-9

UID:

Format: 1 online resource (various pagings) : , illustrations (some color).

Edition: Second edition.

ISBN: 9780750338912 , 9780750338905

Series Statement: [IOP release $release]

Content: Electrostatic phenomena, ubiquitous on Earth, also occur on many planetary bodies of the solar system. This book describes what is known about the electrostatic environment on and near the different planetary surfaces in the solar system based on experiments on Earth, as well as what is being learned from instrumentation on space exploration missions of the last few decades. The book presents brief reviews of the basic principles in electrostatics as well as of the fundamentals of space radiation. It then describes the different planetary environments where electrostatic phenomena take place: atmospheres and planetary surfaces. The second edition includes two new chapters on Space Radiation Fundamentals and The Electrostatic Environment of Jupiter. Other updates include updated models of the lunar electrical environment, recent measurements from NASA's Curiosity rovers, recent discoveries of the properties of the Venusian atmosphere and new data on Mercury. The key audience for this research and reference text includes researchers and students in the physical sciences.

Note: "Version: 20221001"--Title page verso. , 1. Introduction -- 2. Electrostatics principles -- 2.1. Coulomb's law and the principle of superposition -- 2.2. The electric field -- 2.3. Gauss's law -- 2.4. Electric potential -- 2.5. Conductors in electrostatic fields -- 2.6. Capacitance -- 2.7. Electrostatic breakdown -- 2.8. Dielectrics in electric fields -- 2.9. Dielectrophoretic forces -- 2.10. Plasmas , 3. Space radiation fundamentals -- 3.1. Sources and types of radiation found in space -- 3.2. Measurement of energetic particles in space -- 3.3. Effects of space radiation -- 3.4. Mitigating radiation effects , 4. Electrical breakdown and charge decay in planetary atmospheres -- 4.1. Electrical breakdown in planetary atmospheres -- 4.2. Glow discharges and ion wind -- 4.3. Charge mobility -- 4.4. Charge decay and conductivity in planetary atmospheres , 5. The terrestrial electrostatic environment -- 5.1. The earth's atmosphere -- 5.2. Electrical breakdown in the terrestrial atmosphere -- 5.3. Radiation from the Sun : the solar wind -- 5.4. Radiation belts -- 5.5. Aurora , 6. Spacecraft and satellites in the electrostatic environment of the Earth -- 6.1. Spacecraft and satellite orbits -- 6.2. Spacecraft charging -- 6.3. Spacecraft charging in LEO -- 6.4. Charging of the ISS -- 6.5. Spacecraft charging in MEO -- 6.6. Spacecraft charging in GEO -- 6.7. Mitigation techniques , 7. The electrostatic environment of the Moon -- 7.1. The lunar surface environment -- 7.2. The lunar electrostatic environment -- 7.3. Electrostatic charging of the lunar regolith -- 7.4. Triboelectric charging on the lunar surface -- 7.5. Planned measurements with lunar landers -- 7.6. Dust removal from surfaces with dielectrophoretic forces , 8. The electrostatic environment of asteroids -- 8.1. The asteroid electrostatic environment -- 8.2. Electrostatic dust transport -- 8.3. Cohesive forces in asteroids , 9. The Martian electrostatic environment -- 9.1. The Martian atmosphere -- 9.2. Electrical breakdown in the Martian atmosphere -- 9.3. Electrostatic charge and size of Martian atmospheric dust particles , 10. The electrostatic environments of Venus and Mercury -- 10.1. Electrical phenomena in the Venusian atmosphere -- 10.2. The electrostatic environment of Mercury , 11. The electrostatic environment of Jupiter -- 11.1. The electrostatic and magnetic environments of Jupiter -- 11.2. Lightning on Jupiter , 12. The electrostatic environments of the outer planets -- 12.1. The electrostatic environment of Saturn -- 12.2. The electrostatic environments of Uranus and Neptune -- 12.3. The electrostatic environment of Saturn's moon Titan. , Also available in print. , Mode of access: World Wide Web. , System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

Additional Edition: Print version: ISBN 9780750338899

Additional Edition: ISBN 9780750338929

Language: English

DOI: 10.1088/2514-3433/ac878e

URL: https://iopscience.iop.org/book/mono/978-0-7503-3891-2

UID:

Format: 1 online resource (various pagings) : , illustrations (some color).

ISBN: 9780750340243 , 9780750340236

Series Statement: [IOP release $release]

Content: Artificial intelligence is gaining traction in areas of social responsibility. From climate change to social polarization to epidemics, humankind has been seeking new solutions to these ever-present problems. Deep learning (DL) techniques have increased in power in recent years, with algorithms already exhibiting tremendous possibilities in domains such as scientific research, agriculture, smart cities, finance, healthcare, conservation, the environment, industry and more. Innovative ideas using appropriate DL frameworks are now actively employed for the development of and delivering a positive impact on smart cities and societies. This book highlights the importance of specific frameworks such as IoT-enabled frameworks or serverless cloud frameworks that are applying DL techniques for solving persistent societal problems. It addresses the challenges of DL implementation, computation time, and the complexity of reasoning and modelling different types of data. In particular, the book explores and emphasises techniques involved in DL such as image classification, image enhancement, word analysis, human-machine emotional interfaces and the applications of these techniques for smart cities and societal problems. To extend the theoretical description, the book is enhanced through case studies, including those implemented using tensorflow2 and relevant IoT-specific sensor/actuator frameworks. The broad coverage will be essential reading not just to advanced students and academic researchers but also to practitioners and engineers looking to deliver an improved society and global health. Part of IOP Series in Next Generation Computing.

Note: "Version: 20221001"--Title page verso. , part I. Introduction. 1. Deep learning for social good--an introduction -- 1.1. Deep learning--a subset of AI -- 1.2. History of deep learning -- 1.3. Trends--deep learning for social good -- 1.4. Motivations -- 1.5. Deep learning for social good--a need -- 1.6. Intended audience -- 1.7. Chapters and descriptions -- 1.8. Reading flow , 2. Applications for social good -- 2.1. Characteristics of social-good applications -- 2.2. Generic architecture--entities -- 2.3. Applications for social good -- 2.4. Technologies and techniques -- 2.5. Technology--blockchain -- 2.6. AI/machine learning/deep learning techniques -- 2.7. The Internet of things/sensor technology -- 2.8. Robotic technology -- 2.9. Computing infrastructures--a needy technology -- 2.10. Security-related techniques , 3. Computing architectures--base technologies -- 3.1. History of computing -- 3.2. Types of computing -- 3.3. Hardware support for deep learning -- 3.4. Microcontrollers, microprocessors, and FPGAs -- 3.5. Cloud computing--an environment for deep learning -- 3.6. Virtualization--a base for cloud computing -- 3.7. Hypervisors--impact on deep learning -- 3.8. Containers and Dockers -- 3.9. Cloud execution models -- 3.10. Programming deep learning tasks--libraries -- 3.11. Sensor-enabled data collection for DLs -- 3.12. Edge-level deep learning systems , part II. Deep learning techniques. 4. CNN techniques -- 4.1. CNNs--introduction -- 4.2. CNNs--nuts and bolts -- 4.3. Social-good applications--a CNN perspective -- 4.4. CNN use case--climate change problem -- 4.5. CNN challenges , 5. Object detection techniques and algorithms -- 5.1. Computer vision--taxonomy -- 5.2. Object detection--objectives -- 5.3. Object detection--challenges -- 5.4. Object detection--major steps or processes -- 5.5. Object detection methods -- 5.6. Applications -- 5.7. Exam proctoring--YOLOv5 -- 5.8. Proctoring system--implementation stages , 6. Sentiment analysis--algorithms and frameworks -- 6.1. Sentiment analysis--an introduction -- 6.2. Levels and approaches -- 6.3. Sentiment analysis--processes -- 6.4. Recommendation system--sentiment analysis -- 6.5. Movie recommendation--a case study -- 6.6. Metrics -- 6.7. Tools and frameworks -- 6.8. Sentiment analysis--sarcasm detection , 7. Autoencoders and variational autoencoders -- 7.1. Introduction--autoencoders -- 7.2. Autoencoder architectures -- 7.3. Types of autoencoder -- 7.4. Applications of autoencoders -- 7.5. Variational autoencoders -- 7.6. Autoencoder implementation--code snippet explanation , 8. GANs and disentangled mechanisms -- 8.1. Introduction to GANs -- 8.2. Concept--generative and descriptive -- 8.3. Major steps involved -- 8.4. GAN architecture -- 8.5. Types of GAN -- 8.6. StyleGAN -- 8.7. A simple implementation of a GAN -- 8.8. Quality of GANs -- 8.9. Applications and challenges , 9. Deep reinforcement learning architectures -- 9.1. Deep reinforcement learning--an introduction -- 9.2. The difference between deep reinforcement learning and machine learning -- 9.3. The difference between deep learning and reinforcement learning -- 9.4. Reinforcement learning applications -- 9.5. Components of RL frameworks -- 9.6. Reinforcement learning techniques -- 9.7. Reinforcement learning algorithms -- 9.8. Integration into real-world systems , 10. Facial recognition and applications -- 10.1. Facial recognition--a historical view -- 10.2. Biometrics using faces -- 10.3. Facial detection versus recognition -- 10.4. Facial recognition--processes -- 10.5. Applications -- 10.6. Emotional intelligence--a facial recognition application -- 10.7. Emotion detection--database creation -- 10.8. Challenges and future work , part III. Security, performance, and future directions. 11. Data security and platforms -- 11.1. Security breaches -- 11.2. Security attacks -- 11.3. Deep-learning-related security attacks -- 11.4. Metrics -- 11.5. Execution environments -- 11.6. Using deep learning to enhance security , 12. Performance monitoring and analysis -- 12.1. Performance monitoring -- 12.2. The need for performance monitoring -- 12.3. Performance analysis methods/approaches -- 12.4. Performance metrics -- 12.5. Evaluation platforms , 13. Deep learning--future perspectives -- 13.1. Data diversity and generalization -- 13.2. Applications. , Also available in print. , Mode of access: World Wide Web. , System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

Additional Edition: Print version: ISBN 9780750340229

Additional Edition: ISBN 9780750340250

Language: English

DOI: 10.1088/978-0-7503-4024-3

URL: https://iopscience.iop.org/book/mono/978-0-7503-4024-3