06269 am 22007093u 450 991033822920332120230125185215.03-030-21293-910.1007/978-3-030-21293-3(CKB)4100000009160271(DE-He213)978-3-030-21293-3(MiAaPQ)EBC5924755(Au-PeEL)EBL5924755(OCoLC)1120206294(oapen)https://directory.doabooks.org/handle/20.500.12854/32968(PPN)248604163(EXLCZ)99410000000916027120190827d2019 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierBrain and Human Body Modeling[electronic resource] Computational Human Modeling at EMBC 2018 /edited by Sergey Makarov, Marc Horner, Gregory Noetscher1st ed. 2019.ChamSpringer Nature2019Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (XI, 402 p. 175 illus., 148 illus. in color.)3-030-21292-0 Chapter 1. SimNIBS 2.1: A Comprehensive Pipeline for Individualized Electric Field Modelling for Transcranial Brain Stimulation -- Chapter 2. Electric Field Modeling for Transcranial Magnetic Stimulation and Electroconvulsive Therapy -- Chapter 3. Estimates of Peak Electric Fields Induced by Transcranial Magnetic Stimulation in Pregnant Women as Patients or Operators Using an FEM Full-Body Model -- Chapter 4. Finite element modelling framework for electroconvulsive therapy and transcranial stimulation -- Chapter 5. Design and Analysis of a Whole Body Non-Contact Electromagnetic Subthreshold Stimulation Device with Field Modulation Targeting Nonspecific Neuropathic Pain -- Chapter 6. Insights from Computer Modeling: Analysis of Physical Characteristics of Glioblastoma in Patients Treated with Tumor Treating Fields -- Chapter 7. Simulating the Effect of 200 kHz AC Electric Fields on Tumor Cell Structures to Uncover the Mechanism of a Cancer -- Chapter 8. Investigating the connection between Tumor Treating Fields distribution in the brain and Glioblastoma patient outcomes. A simulation-based study utilizing a novel model creation technique -- Chapter 9. Advanced Multiparametric Imaging for Response Assessment to TTFields in Patients with Glioblastoma -- Chapter 10: Estimation of TTFields Intensity and Anisotropy with Singular Value Decomposition. A New and Comprehensive Method for Dosimetry of TTFields -- Chapter 11. The Bioelectric Circuitry of the Cell -- Chapter 12. Dose Coefficients for Use in Rapid Dose Estimation in Industrial Radiography Accidents -- Chapter 13. Brain Haemorrhage Detection Through SVM Classification of Electrical Impedance Tomography Measurements -- Chapter 14. Patient-specific RF safety assessment in MRI: progress in creating surface-based human head and shoulder models -- Chapter 15. Calculation of MRI RF-Induced Voltages for Implanted Medical Devices Using Computational Human Models -- Chapter 16. Effect of non-parallel applicator insertion on 2.45 GHz microwave ablation zone size and shape -- Chapter 17. A Robust Algorithm for Voxel-to-Polygon Mesh Phantom Conversion -- Chapter 18. FEM Human Body Model with Embedded Respiratory Cycles for Antenna and E&M Simulations -- Chapter 19. Radio Frequency Propagation Close to the Human Ear and Accurate Ear Canal Models -- Chapter 20. Water-content Electrical Property Tomography (wEPT) for mapping brain tissues' conductivity in the 200-1000 kHz range: Results of an animal study.This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields.Biomedical engineeringElectronic circuitsMicrowavesOptical engineeringBiomedical Engineering and Bioengineeringhttps://scigraph.springernature.com/ontologies/product-market-codes/T2700XCircuits and Systemshttps://scigraph.springernature.com/ontologies/product-market-codes/T24068Microwaves, RF and Optical Engineeringhttps://scigraph.springernature.com/ontologies/product-market-codes/T24019EngineeringBiomedical engineeringElectronic circuitsMicrowavesOptical engineeringBiomedical engineering.Electronic circuits.Microwaves.Optical engineering.Biomedical Engineering and Bioengineering.Circuits and Systems.Microwaves, RF and Optical Engineering.610.28Makarov Sergeyedt1325425Makarov Sergeyedthttp://id.loc.gov/vocabulary/relators/edtHorner Marcedthttp://id.loc.gov/vocabulary/relators/edtNoetscher Gregoryedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910338229203321Brain and Human Body Modeling3362202UNINA04543nam 2201129z- 450 991058593560332120220812(CKB)5600000000483129(oapen)https://directory.doabooks.org/handle/20.500.12854/91127(oapen)doab91127(EXLCZ)99560000000048312920202208d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierUnmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and NetworkingBaselMDPI - Multidisciplinary Digital Publishing Institute20221 online resource (264 p.)3-0365-4663-4 3-0365-4664-2 The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network's infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out.Unmanned Aerial Vehicle History of engineering and technologybicsscTechnology: general issuesbicssc5Gaerial communicationblind beamformingcellular communicationsclustered two-stage-fusion cooperative spectrum sensingcognitive UAV networkscommunicationcontinuous hidden Markov modelD2Ddata deliveryDeep Q-learning (DQL)detection techniquesDouble Deep Q-learning (DDQL)drone-based mobile secure zonedronesDTNdynamic selectiondynamic spectrum accessdynamic spectrum sharingFANETfriendly jammingglobal positioning systemGPS spoofing attacksHigh Altitude Platform Station (HAPS)hyperparameter tuninginterference managementInternet of dronesinternet of thingsIoTmachine learningmillimeter-wave bandMIMOmobilitymobility schedulemulti-armed banditn/anetworknon-orthogonal multiple accessnot-spotspower controlprivacyquality of servicereinforcement learningresource allocationresource managementRF radio communicationrouting algorithmssecuritysignal recoverySNR estimationstratospheric communication platformtransmit time allocationUAVUAV base stationUAV positioningUAV relay networksUAV-assisted networkultra reliable low latency communicationunmanned aerial vehicleunmanned aerial vehiclesuplink transmissionWi-Fi directwireless communicationsHistory of engineering and technologyTechnology: general issuesDeruyck Margotedt1278410Deruyck MargotothBOOK9910585935603321Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking3013234UNINA