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Wyświetlanie 1-100 z 284
Tytuł:
CHARACTERISATION OF BIOMEDICAL TITANIUM LAYERS DEPOSITED BY A VACUUM PLASMA SPRAY PROCESS.
Autorzy:
Mrdak, Mihailo
Bajić, Darko
Veljič, Darko
Rakin, Marko
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Temat:
BIOMEDICAL engineering
TITANIUM
PLASMA spraying
MICROHARDNESS testing
SCANNING electron microscopes
Alternatywny tytuł:
KARAKTERIZACIJA BIOMEDICINSKIH TITANOVIH PLASTI, NANEŠENIH S POSTOPKOM VAKUUMSKEGA PLAZEMSKEGA NAPRŠEVANJA. (Slovenian)
Źródło:
Materials & Technologies / Materiali in Tehnologije; Mar/Apr2021, Vol. 55 Issue 2, p231-235, 5p
Czasopismo naukowe
Szczegóły
Tytuł:
Shaping the Future of IoT with Edge Intelligence : How Edge Computing Enables the Next Generation of IoT Applications
Autorzy:
Rute C. Sofia
John Soldatos
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Temat:
Edge computing
Internet of things
Typ zasobu:
eBook.
Kategorie:
COMPUTERS / Distributed Systems / Cloud Computing
COMPUTERS / Artificial Intelligence / General
COMPUTERS / Data Science / Machine Learning
COMPUTERS / Internet of Things (IoT)
Książka elektroniczna
Pełny tekst  Link otwiera się w nowym oknieSzczegóły
Tytuł:
MECHANICAL AND STRUCTURAL CHARACTERISTICS OL ATMOSPHERIC PLASMA-SPRAYED MULTIFUNCTIONAL TiO2 COATINGS.
Autorzy:
Mrdak, Mihailo
Bajić, Darko
Veljić, Darko
Rakin, Marko
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Temat:
TITANIUM dioxide
MICROSTRUCTURE
CORROSION & anti-corrosives
MICROSCOPY
MICROHARDNESS
Alternatywny tytuł:
MEHANSKE IN STRUKTURNE LASTNOSTI VEČFUNKCIONALNEGA OKSIDNEGA NANOSA NA OSNOVI TiO2, IZDELANEGA Z ATMOSFERSKIM PLAZEMSKIM NAPRŠEVANJEM. (Slovenian)
Źródło:
Materials & Technologies / Materiali in Tehnologije; 2020, Vol. 54 Issue 6, p807-812, 6p
Czasopismo naukowe
Szczegóły
Tytuł:
Making Muslim Women European : Voluntary Associations, Gender, and Islam in Post-Ottoman Bosnia and Yugoslavia (1878-1941)
Autorzy:
Fabio Giomi
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Temat:
Muslim women--Yugoslavia--Social conditions
Muslim women--Bosnia and Herzegovina--Social conditions
Women--Yugoslavia--Societies and clubs--History
Women--Bosnia and Herzegovina--Societies and clubs--History
Women--Europe--Social conditions
Typ zasobu:
eBook.
Kategorie:
HISTORY / Europe / Eastern
Książka elektroniczna
Pełny tekst  Link otwiera się w nowym oknieSzczegóły
Tytuł:
Estudio comparativo de técnicas de visión artificial y procesamiento de imágenes enfocadas a la detección de cambios en coberturas boscosas ; Comparative study of artificial vision techniques and image processing focused on changes detection in forest covers
Autorzy:
Moreno Revelo, Mónica Yolanda
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Temat:
000 - Ciencias de la computación
información y obras generales
620 - Ingeniería y operaciones afines::621 - Física aplicada
deeep learning
agrupamiento no supervisado
red neuronal convolucional
red neuronal recurrente
imagen satelital
procesamiento de imágenes - técnicas digitales
deep learning
satellite image
unsupervised clustering
convolutional neural network
recurrent neural network
Image processing - digital techniques
Opis pliku:
application/pdf
Relacje:
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In: IEEE Geoscience and Remote Sensing Letters 12 (2014), Nr. 4, S. 741–745.; BASTIDAS, A; BRAVO, L: Análisis de imágenes satelitales para clasificación de biomasa en el departamento de Nariño, Universidad de Nariño, Colombia, Tesis de pregrado, 2017.; BRAGILEVSKY, Lior; BAJIC, Ivan V.: Deep learning for Amazon satellite image analysis. In: 2017 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), Victoria, Canada IEEE, 2017, S. 1–5.; CASTRO, Jose B.; FEITOSA, Raul Q.; HAPP, Patrick N.: An Hybrid Recurrent Convolutional Neural Network for Crop Type Recognition Based on Multitemporal Sar Image Sequences. In: IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Espa˜na IEEE, 2018, S. 3824–3827.; CHAMORRO, Jorge; CASTRO, Jose; LA ROSA, Laura; HAPP, Patrick; QUEIROZ, Raul: A comparison of fully convolutional and recurrent networks for multitemporal crop recognition using SAR images. In: SBSR 2018-2018 simposio brasileiro de sensoriamento remoto, Sao Paulo, Brasil, 2018, S. 3824–3827.; CHEN, Yushi; ZHU, Kaiqiang; ZHU, Lin; HE, Xin; GHAMISI, Pedram; BENEDIKTSSON, Jon Atli: Automatic design of convolutional neural network for hyperspectral image classification. In: IEEE Transactions on Geoscience and Remote Sensing 57 (2019), Nr. 9, S. 7048–7066.; CHENG, Gong; HAN, Junwei; LU, Xiaoqiang: Remote sensing image scene classification: Benchmark and state of the art. In: Proceedings of the IEEE 105 (2017), Nr. 10, S. 1865–1883; CHENG, Yuqi; YU, Le; XU, Yidi; LU, Hui; CRACKNELL, Arthur P.; KANNIAH, Kasturi; GONG, Peng: Mapping oil palm plantation expansion in Malaysia over the past decade (2007–2016) using ALOS-1/2 PALSAR-1/2 data. In: International Journal of Remote Sensing 40 (2019), Nr. 19, S. 7389–7408.; DAMIAN, Junior Melo; PIAS, Osmar Henrique de C.; CHERUBIN, Mauricio R.; FONSECA, Alencar Zachi d.; FORNARI, Ezequiel Z.; SANTI, Antˆonio Luis: Applying the NDVI from satellite images in delimiting management zones for annual crops. In: Scientia Agricola 77 (2020), Nr. 1, S. 1–11.; DING, Jun; CHEN, Bo; LIU, Hongwei; HUANG, Mengyuan: Convolutional neural network with data augmentation for SAR target recognition. In: IEEE Geoscience and remote sensing letters 13 (2016), Nr. 3, S. 364–368.; GARCIA, E; GAREA, E; GIL, JL; MART´I N, G; RIVERO, LB; PONVERT-DELISLES, DR: Enfoque metodol´ogico para el monitoreo de la salinidad del suelo empleando im´agenes de satélite”. In: Trabajo presentado en el Congreso de Geomática, La Habana. S/n p, 2002, S. 1–14.; GÓMEZ-MENDOZA, Juan B.: A contribution to mouth structure segmentation in images towards automatic mouth gesture recognition, Lyon, INSA, Tesis de doctorado, 2012.; GRINIAS, Ilias; PANAGIOTAKIS, Costas; TZIRITAS, Georgios: MRF-based segmentation and unsupervised classification for building and road detection in peri-urban areas of highresolution satellite images. In: ISPRS journal of photogrammetry and remote sensing 122 (2016), S. 145–166.; GU, Jiuxiang; WANG, Zhenhua; KUEN, Jason; MA, Lianyang; SHAHROUDY, Amir; SHUAI, Bing; LIU, Ting; WANG, Xingxing; WANG, Gang; CAI, Jianfei u. a.: Recent advances in convolutional neural networks. In: Pattern Recognition 77 (2018), S. 354–377.; GU NGOR, Emre; O ZMEN, Ahmet: Distance and density based clustering algorithm using Gaussian kernel. In: Expert Systems with Applications 69 (2017), S. 10–20.; HAMSA, Camalia S.; KANNIAH, Kasturi D.; MUHARAM, Farrah M.; IDRIS, Nurul H.; ABDULLAH, Zainuriah; MOHAMED, Luqman: Textural measures for estimating oil palm age. In: International Journal of Remote Sensing 40 (2019), Nr. 19, S. 7516–7537; HARSONO, Tri; BASUKI, Achmad u. a.: Cloud Satellite Image Segmentation using Meng Hee Heng K-Means and DBSCAN Clustering. In: 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) IEEE, 2018, S. 367–371.; HE, Lili; OUYANG, Dantong; WANG, Meng; BAI, Hongtao; YANG, Qianglong; LIU, Yaqing; JIANG, Yu: A method of identifying thunderstorm clouds in satellite cloud image based on clustering. In: Computers, Materials & Continua 57 (2018), Nr. 3, S. 549–570.; HEUPEL, Katharina; SPENGLER, Daniel; ITZEROTT, Sibylle: A progressive crop-type classification using multitemporal remote sensing data and phenological information. In: PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science 86 (2018), Nr. 2, S. 53–69.; HURTADO, Leonardo: Cuantificación de la deforestación de coberturas boscosas a partir del análisis de vegetación fotosintética y modelos automcu. Caso de estudio Orinoquía de Colombia. In: Revista de Topografía AZIMUT 7 (2016), Nr. 1, S. 15–21.; JARAMILLO, LV; ANTUNES, AF: Detección de cambios en la cobertura vegetal mediante interpretación de imágenes Landsat por redes neuronales artificiales (RNA). Caso de estudio: Región Amazónica Ecuatoriana. In: Revista de Teledetecci´on (2018), Nr. 51, S. 33–46.; JEPPESEN, Jacob H.; JACOBSEN, Rune H.; INCEOGLU, Fadil; TOFTEGAARD, Thomas S.: A cloud detection algorithm for satellite imagery based on deep learning. In: Remote Sensing of Environment 229 (2019), S. 247–259.; JI, Shunping; ZHANG, Chi; XU, Anjian; SHI, Yun; DUAN, Yulin: 3D convolutional neural networks for crop classification with multi-temporal remote sensing images. In: Remote Sensing 10 (2018), Nr. 1, S. 75.; JOSHI, Pratik P.; WYNNE, Randolph H.; THOMAS, Valerie A.: Cloud detection algorithm using SVM with SWIR2 and tasseled cap applied to Landsat 8. In: International Journal of Applied Earth Observation and Geoinformation 82 (2019), S. 101898.; KAMILARIS, Andreas; PRENAFETA-BOLD´U, Francesc X.: Deep learning in agriculture: A survey. In: Computers and electronics in agriculture 147 (2018), S. 70–90.; KENDUIYWO, Benson K.; BARGIEL, Damian; SOERGEL, Uwe: Higher order dynamic conditional random fields ensemble for crop type classification in radar images. In: IEEE Transactions on Geoscience and Remote Sensing 55 (2017), Nr. 8, S. 4638–4654.; KHOSRAVI, Iman; ALAVIPANAH, Seyed K.: A random forest-based framework for crop mapping using temporal, spectral, textural and polarimetric observations. In: International Journal of Remote Sensing 40 (2019), Nr. 18, S. 7221–7251.; KIM, Phil: Matlab deep learning. In:With Machine Learning, Neural Networks and Artificial Intelligence 130 (2017).; KUSSUL, Nataliia; LAVRENIUK, Mykola; SKAKUN, Sergii; SHELESTOV, Andrii: Deep learning classification of land cover and crop types using remote sensing data. In: IEEE Geoscience and Remote Sensing Letters 14 (2017), Nr. 5, S. 778–782.; LA ROSA, Laura Elena C.; HAPP, Patrick N.; FEITOSA, Raul Q.: Dense Fully Convolutional Networks for Crop Recognition from Multitemporal SAR Image Sequences. In: IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Espa˜na IEEE, 2018, S. 7460–7463.; LANGKVIST, Martin; KISELEV, Andrey; ALIREZAIE, Marjan; LOUTFI, Amy: Classification and segmentation of satellite orthoimagery using convolutional neural networks. In: Remote Sensing 8 (2016), Nr. 4, S. 329.; LI, Yansheng; TAO, Chao; TAN, Yihua; SHANG, Ke; TIAN, Jinwen: Unsupervised multilayer feature learning for satellite image scene classification. In: IEEE Geoscience and Remote Sensing Letters 13 (2016), Nr. 2, S. 157–161.; LIN, Zhongqi; MU, Shaomin; HUANG, Feng; MATEEN, Khattak A.; WANG, Minjuan; GAO, Wanlin; JIA, Jingdun: A Unified Matrix-Based Convolutional Neural Network for Fine-Grained Image Classification of Wheat Leaf Diseases. In: IEEE Access 7 (2019), S. 11570–11590.; LOTTES, Philipp; BEHLEY, Jens; MILIOTO, Andres; STACHNISS, Cyrill: Fully convolutional networks with sequential information for robust crop and weed detection in precisión farming. In: IEEE Robotics and Automation Letters 3 (2018), Nr. 4, S. 2870–2877; LU, Yan; PEREZ, Daniel; DAO, Minh; KWAN, Chiman; LI, Jiang: Deep learning with synthetic hyperspectral images for improved soil detection in multispectral imagery. In: Proceedings of the IEEE Ubiquitous Computing, Electronics & Mobile Communication Conference, New York, NY, USA, 2018, S. 8–10.; NDIKUMANA, Emile; HO TONG MINH, Dinh; BAGHDADI, Nicolas; COURAULT, Dominique; HOSSARD, Laure: Deep recurrent neural network for agricultural classification using multitemporal SAR Sentinel-1 for Camargue, France. In: Remote Sensing 10 (2018), Nr. 8, S. 1217.; NGO, Long T.; MAI, Dinh S.; PEDRYCZ, Witold: Semi-supervising Interval Type-2 Fuzzy C-Means clustering with spatial information for multi-spectral satellite image classification and change detection. In: Computers & geosciences 83 (2015), S. 1–16; ORYNBAIKYZY, Aiym; GESSNER, Ursula; CONRAD, Christopher: Crop type classification using a combination of optical and radar remote sensing data: a review. In: international journal of remote sensing 40 (2019), Nr. 17, S. 6553–6595.; PAOLETTI, ME; HAUT, JM; PLAZA, J; PLAZA, A: A new deep convolutional neural network for fast hyperspectral image classification. In: ISPRS journal of photogrammetry and remote sensing 145 (2018), S. 120–147.; PERBET, Pauline; FORTIN, Michelle; VILLE, Anouk; B´E LAND, Martin: Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors. In: International Journal of Remote Sensing 40 (2019), Nr. 19, S. 7439–7458.; PERSELLO, C; TOLPEKIN, VA; BERGADO, JR; BY, RA de: Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping. In: Remote Sensing of Environment 231 (2019), S. 111253.; PICOLI, Michelle Cristina A.; CAMARA, Gilberto; SANCHES, Ieda; SIM˜OES, Rolf; CARVALHO, Alexandre; MACIEL, Adeline; COUTINHO, Alexandre; ESQUERDO, Julio; ANTUNES, Joao; BEGOTTI, Rodrigo A. u. a.: Big earth observation time series analysis for monitoring Brazilian agriculture. In: ISPRS journal of photogrammetry and remote sensing 145 (2018), S. 328–339.; ROKNI, Komeil; AHMAD, Anuar; SOLAIMANI, Karim; HAZINI, Sharifeh: A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques. In: International Journal of Applied Earth Observation and Geoinformation 34 (2015), S. 226–234.; SANCHES, Ieda D.; FEITOSA, Raul Q.; DIAZ, Pedro Marco A.; SOARES, Marinalva D.; LUIZ, Alfredo Jos´e B.; SCHULTZ, Bruno; MAURANO, Luis Eduardo P.: Campo Verde Database: Seeking to Improve Agricultural Remote Sensing of Tropical Areas. In: IEEE Geoscience and Remote Sensing Letters 15 (2018), Nr. 3, S. 369–373.; SCARPA, Giuseppe; GARGIULO, Massimiliano; MAZZA, Antonio; GAETANO, Raffaele: A CNN-based fusion method for feature extraction from sentinel data. In: Remote Sensing 10 (2018), Nr. 2, S. 236.; SCOTT, Grant J.; ENGLAND, Matthew R.; STARMS, William A.; MARCUM, Richard A.; DAVIS, Curt H.: Training deep convolutional neural networks for land–cover classification of high-resolution imagery. In: IEEE Geoscience and Remote Sensing Letters 14 (2017), Nr. 4, S. 549–553.; SHAO, Zhenfeng; PAN, Yin; DIAO, Chunyuan; CAI, Jiajun: Cloud Detection in Remote Sensing Images Based on Multiscale Features-Convolutional Neural Network. In: IEEE Transactions on Geoscience and Remote Sensing 57 (2019), Nr. 6, S. 4062–4076.; SIDIKE, Paheding; SAGAN, Vasit; MAIMAITIJIANG, Maitiniyazi; MAIMAITIYIMING, Matthew; SHAKOOR, Nadia; BURKEN, Joel; MOCKLER, Todd; FRITSCHI, Felix B.: dPEN: deep Progressively Expanded Network for mapping heterogeneous agricultural landscape using WorldView-3 satellite imagery. In: Remote sensing of environment 221 (2019), S. 756–772.; SINGH, Rahul D.; MITTAL, Ajay; BHATIA, Rajesh K.: 3D convolutional neural network for object recognition: a review. In: Multimedia Tools and Applications 78 (2019), Nr. 12, S. 15951–15995.; SUMAIYA, MN; KUMARI, R Shantha S.: Satellite Image Change Detection Using Laplacian–Gaussian Distributions. In: Wireless Personal Communications 97 (2017), Nr. 3, S. 4621–4630.; TEIMOURI, Nima; DYRMANN, Mads; JØRGENSEN, Rasmus N.: A Novel Spatio-Temporal FCN-LSTM Network for Recognizing Various Crop Types Using Multi-Temporal Radar Images. In: Remote Sensing 11 (2019), Nr. 8, S. 990.; USEYA, Juliana; CHEN, Shengbo; MUREFU, Mike: Cropland Mapping and Change Detection: Toward Zimbabwean Cropland Inventory. In: IEEE Access 7 (2019), S. 53603–53620.; WAN, Shiuan; CHANG, Shih-Hsun: Crop classification with WorldView-2 imagery using Support Vector Machine comparing texture analysis approaches and grey relational analysis in Jianan Plain, Taiwan. In: International Journal of Remote Sensing 40 (2019), Nr. 21, S. 8076–8092.; WANG, Xuejun; ZHANG, Yuxing; YAN, Enping; HUANG, Guosheng; CAO, Chunxiang; NI, Xiliang: Deforestation area estimation in China based on Landsat data. In: 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec, Canada IEEE, 2014, S. 4254–4256.; XU, Xiaodong; LI, Wei; RAN, Qiong; DU, Qian; GAO, Lianru; ZHANG, Bing: Multisource remote sensing data classification based on convolutional neural network. In: IEEE Transactions on Geoscience and Remote Sensing 56 (2017), Nr. 2, S. 937–949.; YANG, Jingyu; GUO, Jianhua; YUE, Huanjing; LIU, Zhiheng; HU, Haofeng; LI, Kun: CDnet: CNN-Based Cloud Detection for Remote Sensing Imagery. In: IEEE Transactions on Geoscience and Remote Sensing 57 (2019), Nr. 8, S. 6195 – 6211.; YAO, Guangle; LEI, Tao; ZHONG, Jiandan: A review of Convolutional-Neural-Networkbased action recognition. In: Pattern Recognition Letters 118 (2019), S. 14–22.; YU, Shiqi; JIA, Sen; XU, Chunyan: Convolutional neural networks for hyperspectral image classification. In: Neurocomputing 219 (2017), S. 88–98.; ZHANG, Ce; SARGENT, Isabel; PAN, Xin; LI, Huapeng; GARDINER, Andy; HARE, Jonathon; ATKINSON, Peter M.: An object-based convolutional neural network (OCNN) for urban land use classification. In: Remote sensing of environment 216 (2018), S. 57–70.; ZHANG, Peng; NIU, Xin; DOU, Yong; XIA, Fei: Airport detection on optical satellite images using deep convolutional neural networks. In: IEEE Geoscience and Remote Sensing Letters 14 (2017), Nr. 8, S. 1183–1187.; ZHANG, Yongjun; LIU, Xinyi; ZHANG, Yi; LING, Xiao; HUANG, Xu: Automatic and Unsupervised Water Body Extraction Based on Spectral-Spatial Features Using GF-1 Satellite Imagery. In: IEEE Geoscience and Remote Sensing Letters 16 (2018), Nr. 6, S. 927–931.; ZHU, Zhe; WOODCOCK, Curtis E.: Continuous change detection and classification of land cover using all available Landsat data. In: Remote sensing of Environment 144 (2014), S. 152–171.; https://repositorio.unal.edu.co/handle/unal/77681
Dostępność:
https://repositorio.unal.edu.co/handle/unal/77681
Czasopismo naukowe
Szczegóły
Tytuł:
MECHANICAL PROPERTIES AND THE MICROSTRUCTURE OF THE PLASMASPRAYED ZrO2Y2O3/ZrO2Y2O3CoNiCrAlY/ CoNiCrAlY COATING.
Autorzy:
Mrdak, Mihailo R.
Pokaż więcej
Alternatywny tytuł:
МЕХАНИЧЕСКИЕ ХАРАКТЕРИСТИКИ И МИКРОСТРУКТУРА ПОКРЫТИЙ ZrO2Y2O3/ZrO2Y2O3CoNiCrAlY / CoNiCrAlY, НАНЕСЕННЫХ ВОЗДУШНО-ПЛАЗМЕННЫМ НАПЫЛЕНИЕМ
МЕХАНИЧКЕ ОСОБИНЕ И МИКРОСТРУКТУРА ПЛАЗМА НАПРСКАНЕ ПРЕВЛАКЕ ZrO2Y2O3/ ZrO2Y2O3CoNiCrAlY / CoNiCrAlY
Źródło:
Military Technical Courier / Vojnotehnicki Glasnik. jan-mar2017, Vol. 65 Issue 1, p30-44. 15p.
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknieSzczegóły
Tytuł:
CHARACTERIZATION OF Cu10wt.%Al INTERMETALLIC COATINGS APPLIED BY THE ATMOSPHERIC PLASMA SPRAYING PROCESS.
Autorzy:
Mrdak, Mihailo R.
Pokaż więcej
Alternatywny tytuł:
KARAKTERIZACIJA INTERMETALNE PREVLAKE Cu10tež.%Al NAPRSKANE ATMOSFERSKIM PLAZMA SPREJ POSTUPKOM.
ХАРАКТЕРИЗАЦИЯ ИНТЕРМЕТАЛЛИЧЕСКОГО ПОКРЫТИЯ Cu10вес.%Al НАНЕСЕННОГО ПЛАЗМЕННЫМ НАПЫЛЕНИЕМ
Źródło:
Military Technical Courier / Vojnotehnicki Glasnik. 2016, Vol. 64 Issue 4, p949-965. 17p.
Czasopismo naukowe
Pełny tekst  Link otwiera się w nowym oknieSzczegóły
Wyświetlanie 1-100 z 284

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