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[null,"1.","Record Nr.",null,"UNISA996389560003316"]
[null,null,"Autore",null,"Norwood Richard <1590?-1675.>"]
[null,null,"Titolo",null,"Trigonometrie: or, The doctrine of triangles [[electronic resource] ] : divided into two books. The first shewing the mensuration of right lined triangles: the second of spherical, with the grounds and demonstrations thereof. Both performed by that late and excellent invention of logarithms, after a more easie and compendious manner, than hath been formerly taught. Whereunto is annexed (chiefly for the use of seamen) a treatise of the application thereof, in the three principal kinds of sailing. With exact tables of the suns declination, newly calculated: and tables of the right ascension and declination of some eminent fixed stars, with the true times of the coming to the meridian at four of the clock in the morning, fitted for the present season, and may serve for many years without any alteration. Also other necessary tables used in navigation. By Richard Norwood, reader of the mathematicks"]
[null,null,"Pubbl/distr/stampa",null,"London, : printed by R. W. for William Fisher, at the Postern near Tower-Hill, T. Passenger, at the three Bibles on London-Bridge, R. Boulter at the Turks-Head, and R. Smith, at the sign of the Bible in Cornhill, 1678"]
[null,null,"Edizione",null,"[This seventh edition being diligently corrected, in divers difficult places explained; new tables of the stars right ascensions and declinations added, and the whole work very much enlarged, by the author himself.]"]
[null,null,"Descrizione fisica",null,"[6], 170, 163-165, [153] p. : ill., tables"]
[null,null,"Soggetti",null,"Trigonometry"]
[{"$nulls":4},"Logarithms"]
[{"$nulls":4},"Navigation"]
[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","Publication date from Wing."]
[{"$nulls":4},"Imperfect: trimmed at foot, affecting imprint; -2A1."]
[{"$nulls":4},"\"Chilias\" table has separate register."]
[{"$nulls":4},"Contains \"A triangular cannon logarithmical: or, A table of artificial sines, tangents; and the complements arithmetical of sines, supplying the use of secants, the radius 10.0000000. and to every degree and minute of the quadrant.\" (divisional title); pagination and register continuous (2nd series)."]
[{"$nulls":4},"Reproduction of the original in the British Library."]
[null,null,null,"Sommario/riassunto","eebo-0018"]
[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."]