Results 11 to 20 of about 1,213,728 (294)
Near Real-Time Detection of EVI Time-Series Breakpoints Using Bayesian Inference for Deforestation Monitoring in the Chaco Forest [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information SciencesDeforestation poses a significant threat to natural ecosystems, particularly in Argentina’s Chaco region—one of the world’s most rapidly changing forest areas.F. Grings, F. Grings, F. González Bianco, F. González Bianco, E. Roitberg, E. Roitberg, E. Roitberg, N. Morandeira, N. Morandeira, J. Arellana, J. Arellana, J. Arellana, M. Gayol, M. Gayol, M. Gayol +14 moredoaj +1 more sourceMeasurement of the energy resolution and calibration of hybrid pixel
detectors with GaAs:Cr sensor and Timepix readout chip [PDF]
, 2015 This paper describes an iterative method of per-pixel energy calibration of
hybrid pixel detectors with GaAs:Cr sensor and Timepix readout chip. A
convolution of precisely measured spectra of characteristic X-rays of different
metals with the resolution ...Bell, S. T., Butler, A. P., Butler, P. H., Chelkov, G. A., Dedovich, D. V., Demichev, M. A., Elkin, V. G., Gostkin, M. I., Kotov, S. A., Kozhevnikov, D. A., Kruchonak, U. G., Nozdrin, A. A., Porokhovoy, S. Yu., Potrap, I. N., Smolyanskiy, P. I., Zakhvatkin, M. M., Zhemchugov, A. S. +16 morecore +3 more sourcesRESEARCH AND PROTOTYPING METHODS OF STEGANOGRAPHY USING MOSAIC
Сучасні інформаційні системи, 2020 The paper considers the possibility of using a mosaic composed of many miniature images to hide the fact of text information transmission. The results of the study of optimal mosaic construction when using steganographic methods of hiding information are Volodymyr Pevnev, Yurii Voikovdoaj +1 more sourceFully Depleted, Trench-Pinned Photo Gate for CMOS Image Sensor Applications
Sensors, 2020 Tackling issues of implantation-caused defects and contamination, this paper presents a new complementary metal−oxide−semiconductor (CMOS) image sensor (CIS) pixel design concept based on a native epitaxial layer for photon detection, charge ...Francois Roy, Andrej Suler, Thomas Dalleau, Romain Duru, Daniel Benoit, Jihane Arnaud, Yvon Cazaux, Catherine Chaton, Laurent Montes, Panagiota Morfouli, Guo-Neng Lu +10 moredoaj +1 more sourcePrototype ATLAS IBL Modules using the FE-I4A Front-End Readout Chip [PDF]
, 2012 The ATLAS Collaboration will upgrade its semiconductor pixel tracking
detector with a new Insertable B-layer (IBL) between the existing pixel
detector and the vacuum pipe of the Large Hadron Collider.Albert, J., Alex, M., Alimonti, G., Allport, P., Altenheiner, S., Ancu, L.S., Andreazza, A., Arguin, J., Arutinov, D., Backhaus, M., Bagolini, A., Ballansat, J., Barbero, M., Barbier, G., Bates, R., Battistin, M., Baudin, P., Beau, T., Beccherle, R., Beck, H., Benoit, M., Bensinger, J., Bomben, M., Borri, M., Boscardin, M., Botelho-Direito, J., Bousson, N., Boyd, R.G., Breugnon, P., Bruni, G., Bruschi, M., Buchholz, P., Buttar, C., Cadoux, F., Calderini, G., Caminada, L., Capeans, M., Casse, G., Catinaccio, A., Cavalli-Sforza, M., Chauveau, J., Chu, M., Ciapetti, M., Cindro, V., Citterio, M., Clark, A., Cobal, M., Coelli, S., Colijn, A., Colin, D., Collot, J., Crespo-Lopez, O., Dalla Betta, G., Darbo, G., DaVia, C., David, P., Debieux, S., Delebecque, P., Devetak, E., DeWilde, B., Di Girolamo, G., Dinu, N., Dittus, F., Diyakov, D., Djama, F., Dobos, D., Doonan, K., Dopke, J., Dorholt, O., Dube, S., Dushkin, A., Dzahini, D., Egorov, K., Ehrmann, O., Elldge, D., Elles, S., Elsing, M., Eraud, L., Ereditato, A., Eyring, A., Falchieri, D., Falou, A., Fang, X., Fausten, C., Favre, Y., Ferrere, D., Fleta, C., Fleury, J., Flick, T., Forshaw, D., Fougeron, D., Fritzsch, T., Gabrielli, A., Gaglione, R., Gallrapp, C., Gan, K., Garcia-Sciveres, M., Gariano, G., Gastaldi, T., Gemme, C., Gensolen, F., George, M., Ghislain, P., Giacomini, G., Gibson, S., Giordani, M., Giugni, D., Gjersdal, H., Glitza, K., Gnani, D., Godlewski, J., Gonella, L., Gorelov, I., Gorisek, A., Gossling, C., Grancagnolo, S., Gray, H., Gregor, I., Grenier, P., Grinstein, S., Gromov, V., Grondin, D., Grosse-Knetter, J., Hansen, T., Hansson, P., Harb, A., Hartman, N., Hasi, J., Hegner, F., Heim, T., Heinemann, B., Hemperek, T., Hessey, N., Hetmanek, M., Hoeferkamp, M., Hostachy, J., Hugging, F., Husi, C., Iacobucci, G., Idarraga, J., Ikegami, Y., Janoska, Z., Jansen, J., Jansen, L., Jensen, F., Jentzsch, J., Joseph, J., Kagan, H., Karagounis, M., Kass, R., Kenney, C., Kersten, S., Kind, P., Klingenberg, R., Kluit, R., Kocian, M., Koffeman, E., Kok, A., Korchak, O., Korolkov, I., Kostyukhin, V., Krieger, N., Kruger, H., Kruth, A., Kugel, A., Kuykendall, W., La Rosa, A., Lai, C., Lantzsch, K., Laporte, D., Lapsien, T., Lounis, A., Lozano, M., Lu, Y., Lubatti, H., Macchiolo, A., Mallik, U., Mandic, I., Marchand, D., Marchiori, G., Masso, N., Matthias, W., Mattig, P., Mekkaoui, A., Menouni, M., Menu, J., Meroni, C., Mesa, J., Micelli, A., Micha, S., Miglioranzi, S., Mikuz, M., Mitsui, S., Monti, M., Moore, J., Morettini, P., Muenstermann, D., Murray, P., Nellist, C., Nelson, D., Nessi, M., Neumann, M., Nisius, R., Nordberg, M., Nuiry, F., Oppermann, H., Oriunno, M., Padilla, C., Parker, S., Pellegrini, G., Pelleriti, G., Pernegger, H., Piacquadio, N., Picazio, A., Pohl, D., Polini, A., Popule, J., Portell Bueso, X., Povoli, M., Puldon, D., Pylypchenko, Y., Quadt, A., Quirion, D., Ragusa, F., Rambure, T., Richards, E., Ristic, B., Rothermund, M., Rovani, A., Rozanov, A., Rubinskiy, I., Rudolph, M., Rummler, A., Ruscino, E., Røhne, O., Salek, D., Salzburger, A., Sandaker, H., Schipper, J., Schneider, B., Schorlemmer, A., Schroer, N., Schwemling, P., Seidel, S., Seiden, A., Sicho, P., Skubic, P., Sloboda, M., Smith, D., Sood, A., Spencer, E., Strang, M., Stugu, B., Stupak, J., Su, D., Takubo, Y., Tassan, J., Teng, P., Terada, S., Todorov, T., Tomasek, M., Toms, K., Travaglini, R., Trischuk, W., Troncon, C., Troska, G., Tsiskaridze, S., Tsurin, I., Tsybychev, D., Unno, Y., Vacavant, L., Verlaat, B., Vianello, E., Vigeolas, E., von Kleist, S., Vrba, V., Vuillermet, R., Wang, R., Watts, S., Weber, M., Weber, M., Weigell, P., Weingarten, J., Welch, S., Wenig, S., Wermes, N., Wiese, A., Wittig, T., Yildizkaya, T., Zeitnitz, C., Ziolkowski, M., Zivkovic, V., Zoccoli, A., Zorzi, N., Zwalinsk, L. +294 morecore +4 more sourcesUsing a Neural Network Method to Solve Image Segmentation Problems
Современные информационные технологии и IT-образование, 2022 Image segmentation plays an important role in detecting various diseases and pathologies through medical image processing. Over the years, a number of traditional approaches such as the binary threshold method (Otsu method), watershed method, and K-means Moutouama N’dah Bienvenu Moualedoaj +1 more source