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[null,"1.","Record Nr.",null,"UNINA9910456239603321"]
[null,null,"Autore",null,"Vuolevi Joel"]
[null,null,"Titolo",null,"Distortion in RF power amplifiers / / Joel Vuolevi, Timo Rahkonen"]
[null,null,"Pubbl/distr/stampa",null,"Boston : , : Artech House, , ©2003"]
[{"$nulls":4},"[Piscataqay, New Jersey] : , : IEEE Xplore, , [2003]"]
[null,null,"ISBN",null,"1-58053-629-8"]
[null,null,"Descrizione fisica",null,"1 online resource (268 p.)"]
[null,null,"Collana",null,"Artech House microwave library"]
[null,null,"Disciplina",null,"621.384/12"]
[null,null,"Soggetti",null,"Power amplifiers"]
[{"$nulls":4},"Amplifiers, Radio frequency"]
[{"$nulls":4},"Electric distortion - Prevention"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Machine generated contents note: Ch. 1 Introduction -- Ch. 2 Some Circuit Theory and Terminology -- Ch. 3 Memory Effects in RF Power Amplifiers -- Ch. 4 Volterra Model -- Ch. 5"]
[null,null,null,"Sommario/riassunto","\"Distortion in RF Power Amplifiers explains that by minimizing memory effects, it is possible to use simple linearizers, such as analog predistortion, and still achieve sufficient performance. Including over 120 equations and more than 110 illustrations, this practical reference provides the assistance professionals need to create amplifiers suitable for linear transmitters, and offers new views of semi-conductor modeling.\"--Jacket."]
[null,"2.","Record Nr.",null,"UNINA9910141327103321"]
[null,null,"Titolo",null,"Jan Walravens en het experiment / / onder redactie van Lars Bernaerts, Hans Vandevoorde en Bart Vervaeck"]
[null,null,"Pubbl/distr/stampa",null,"Gent : , : Academia Press, , 2012"]
[null,null,"ISBN",null,"9789038217079"]
[null,null,"Descrizione fisica",null,"1 online resource (229 pages) : illustrations; digital, PDF file(s)"]
[null,null,"Collana",null,"SEL-reeks  Jan Walravens en het experiment"]
[null,null,"Soggetti",null,"Languages & Literatures"]
[{"$nulls":4},"Germanic Literature"]
[null,null,"Lingua di pubblicazione",null,"Olandese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,null,"Sommario/riassunto","Jan Walravens (1920-1965) played a central role in Flemish literature as he introduced and facilitated literary experiments after the second world war. He was a leading essayist and literary critic, who wrote thousands of reviews showing the younger generation the way to French existentialism and to the international avant-garde. In addition to novels and shorter fiction, he published in-depth philosophical essays on Kierkegaard, Sade and Sartre. His international network of artistic relations turned him into a central figure in the Flemish literary world, a position he held until his untimely death at the age of forty-five. Jan Walravens and the Experiment presents ten essays that chart the various aspects of Walravens’ immense activity: his philosophical thinking on Sade, his relation with the visual arts, his position as an avant-gardist, his defense of poetry old and new, his view on the French nouveau roman, his novels, and his propagation of a new kind of literary diary. The authors have used the Walravens archive and unearthed some material that has never before been brought to the public’s attention, most notably the facsimile of a ‘cadavre exquis’ Walravens created with Albert Bontridder and Florent Welles."]
[null,"3.","Record Nr.",null,"UNINA9910697347103321"]
[null,null,"Titolo",null,"Preliminary results of an experiment to evaluate transfer of low-cost, simulator-based airplane upset-recovery training [[electronic resource] /] / Rodney O. Rodgers ... [and others]"]
[null,null,"Pubbl/distr/stampa",null,"Washington, DC : , : Federal Aviation Administration, Office of Aerospace Medicine, , [2007]"]
[null,null,"Descrizione fisica",null,"iii, 17 pages : digital, PDF file"]
[null,null,"Soggetti",null,"Flight training - United States"]
[{"$nulls":4},"Flight simulators"]
[{"$nulls":4},"Aviation medicine - United States"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Title from title screen (viewed Dec. 9, 2008)."]
[{"$nulls":4},"\"October 2007.\""]
[{"$nulls":4},"\"DOT/FAA/AM-07/27.\""]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,"4.","Record Nr.",null,"UNINA9910483376403321"]
[null,null,"Titolo",null,"Deep Learning Techniques for Biomedical and Health Informatics / / edited by Sujata Dash, Biswa Ranjan Acharya, Mamta Mittal, Ajith Abraham, Arpad Kelemen"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020"]
[null,null,"ISBN",null,"3-030-33966-1"]
[null,null,"Edizione",null,"[1st ed. 2020.]"]
[null,null,"Descrizione fisica",null,"1 online resource (395 pages)"]
[null,null,"Collana",null,"Studies in Big Data, , 2197-6511 ; ; 68"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Engineering - Data processing"]
[{"$nulls":4},"Biomedical engineering"]
[{"$nulls":4},"Big data"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Data Engineering"]
[{"$nulls":4},"Biomedical Engineering and Bioengineering"]
[{"$nulls":4},"Big Data"]
[{"$nulls":4},"Artificial Intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"MedNLU: Natural Language Understander for Medical Texts -- Deep Learning Based Biomedical Named Entity Recognition Systems -- Disambiguation Model for Bio-Medical Named Entity Recognition -- Applications of Deep Learning in Healthcare and Biomedicine -- Deep Learning for Clinical Decision Support Systems: A Review from the Panorama of Smart Healthcare -- Review of Machine Learning and Deep Learning based Recommender Systems for Health Informatics -- Deep Learning and Explainable AI in Healthcare using EHR -- Deep Learning for Analysis of Electronic Heath Records -- Bioinformatics Using Deep Architecture -- Intelligent, Secure Big Health Data Management using Deep Learning and Blockchain Technology: An Overview -- Malaria Disease Detection using CNN Technique with SGD, RMSprop and ADAM Optimizers -- Deep Reinforcement Learning based Personalized Health Recommendations."]
[null,null,null,"Sommario/riassunto","This book presents a collection of state-of-the-art approaches for deep-learning-based biomedical and health-related applications. The aim of healthcare informatics is to ensure high-quality, efficient health care, and better treatment and quality of life by efficiently analyzing abundant biomedical and healthcare data, including patient data and electronic health records (EHRs), as well as lifestyle problems. In the past, it was common to have a domain expert to develop a model for biomedical or health care applications; however, recent advances in the representation of learning algorithms (deep learning techniques) make it possible to automatically recognize the patterns and represent the given data for the development of such model. This book allows new researchers and practitioners working in the field to quickly understand the best-performing methods. It also enables them to compare different approaches and carry forward their research in an important area that has a direct impact on improving the human life and health. It is intended for researchers, academics, industry professionals, and those at technical institutes and R&D organizations, as well as students working in the fields of machine learning, deep learning, biomedical engineering, health informatics, and related fields. ."]