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[null,"1.","Record Nr.",null,"UNINA9910136477803321"]
[null,null,"Autore",null,"R. Hemer Susan"]
[null,null,"Titolo",null,"Emotions, senses, spaces : ethnographic engagements and intersections / / edited by Susan R. Hemer and Alison Dundon [[electronic resource]]"]
[null,null,"Pubbl/distr/stampa",null,"University of Adelaide Press, 2016"]
[{"$nulls":4},"Adelaide : , : The University of Adelaide Press, , 2016"]
[null,null,"Descrizione fisica",null,"1 online resource (ix, 191 pages) : digital, PDF file(s)"]
[null,null,"Disciplina",null,"152.4"]
[null,null,"Soggetti",null,"Emotions"]
[{"$nulls":4},"Space perception"]
[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 publisher's bibliographic system (viewed on 11 Aug 2017)."]
[null,null,null,"Sommario/riassunto","This volume draws together three core concerns for the social sciences: the senses and embodiment, emotions, and space and place. In so doing, these collected essays consider the ways in which these core concerns are mutually constitutive. This includes how spaces evoke, constrain or are composed by the senses and emotions; the ways in which emotions are generated or transformed in certain spaces and through sensual engagement; and the processes by which embodied senses create spaces and emotions."]
[null,"2.","Record Nr.",null,"UNINA9910557148403321"]
[null,null,"Autore",null,"Kavzoglu Taskin"]
[null,null,"Titolo",null,"Artificial Neural Networks and Evolutionary Computation in Remote Sensing"]
[null,null,"Pubbl/distr/stampa",null,"Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021"]
[null,null,"Descrizione fisica",null,"1 online resource (256 p.)"]
[null,null,"Soggetti",null,"Research and information: general"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images. ANNs are effective in finding underlying relationships and structures within multidimensional datasets. Thanks to new sensors, we have images with more spectral bands at higher spatial resolutions, which clearly recall big data problems. For this purpose, evolutionary algorithms become the best solution for analysis. This book includes eleven high-quality papers, selected after a careful reviewing process, addressing current remote sensing problems. In the chapters of the book, superstructural optimization was suggested for the optimal design of feedforward neural networks, CNN networks were deployed for a nanosatellite payload to select images eligible for transmission to ground, a new weight feature value convolutional neural network (WFCNN) was applied for fine remote sensing image segmentation and extracting improved land-use information, mask regional-convolutional neural networks (Mask R-CNN) was employed for extracting valley fill faces, state-of-the-art convolutional neural network (CNN)-based object detection models were applied to automatically detect airplanes and ships in VHR satellite images, a coarse-to-fine detection strategy was employed to detect ships at different sizes, and a deep quadruplet network (DQN) was proposed for hyperspectral image classification."]