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  1. 13201
    “…For instance, as a use case, we have implemented the L2C2 cache with STT-RAM technology. It has affordable hardware overheads compared to that of a baseline NV-LLC without compression in terms of area, latency and energy consumption, and increases up to 6-37 times the time in which 50% of the effective capacity is degraded, depending on the variability in the manufacturing process. …”
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    Online Artículo Texto
    L2C2: Last-level compressed-contents non-volatile cache and a procedure to forecast performance and lifetime
  2. 13202
    “…The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. …”
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    Online Artículo Texto
    Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis
  3. 13203
    “…Nonunion 0.4%, infections 1.1%, nerve injury 2.2%, avascular necrosis 0%, hallux varus 0.09%, transfer metatarsalgia 0.1% and hardware removal 6.2%. CONCLUSION: Minimally invasive surgery for the treatment of hallux valgus utilizing the Shannon burr appears to be a safe and effective therapy with appropriate correction of the hallux valgus deformity, improvement in functional outcomes, high patient satisfaction, low recurrence, and acceptable complication rates.…”
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    Online Artículo Texto
    The Surgical Treatment of Hallux Valgus Utilization Minimally Invasive and Percutaneous Techniques with a Shannon Burr: A Systematic Review
  4. 13204
    por Adam, W., Bergauer, T., Deldicque, C., Ero, J., Fruehwirth, R., Jeitler, M., Kastner, K., Kostner, S., Neumeister, N., Padrta, M., Porth, P., Rohringer, H., Sakulin, H., Strauss, J., Taurok, A., Walzel, G., Wulz, C.-E., Lowette, S., Van De Vyver, B., De Lentdecker, G., Vanlaer, P., Delaere, C., Lemaitre, V., Ninane, A., Van der Aa, O., Damgov, J., Karimaki, V., Kinnunen, R., Lampen, T., Lassila-Perini, K., Lehti, S., Nysten, J., Tuominiemi, J., Busson, P., Todorov, T., Schwering, G., Gras, P., Daskalakis, G., Sfyrla, A., Barone, M., Geralis, T., Markou, C., Zachariadou, K., Hidas, P., Banerjee, S., Mazumdar, K., Abbrescia, M., Colaleo, A., D'Amato, N., De Filippis, N., Giordano, D., Loddo, F., Maggi, M., Silvestris, L., Zito, G., Arcelli, S., Bonacorsi, D., Capiluppi, P., Dallavalle, G.M., Fanfani, A., Grandi, C., Marcellini, S., Montanari, A., Odorici, F., Travaglini, R., Costa, S., Tricomi, A., Ciulli, V., Magini, N., Ranieri, R., Berti, L., Biasotto, M., Gulmini, M., Maron, G., Toniolo, N., Zangrando, L., Bellato, M., Gasparini, U., Lacaprara, S., Parenti, A., Passaseo, M., Ronchese, P., Vanini, S., Ventura, S., Zotto, P.L., Benedetti, D., Biasini, M., Fano, L., Servoli, L., Bagliesi, Giuseppe, Boccali, T., Dutta, S., Gennai, S., Giassi, A., Palla, F., Segneri, G., Starodumov, A., Tenchini, R., Meridiani, P., Organtini, G., Amapane, N., Bertolino, F., Cirio, R., Kim, J.Y., Lim, I.T., Pac, M.Y., Joo, K.K., Kim, S.B., Choi, Y.I., Yu, I.T., Cho, K., Chung, J., Ham, S.W., Kim, D.H., Kim, G.N., Kim, W., Kim, J.C, Oh, S.K., Park, H., Ro, S.R., Son, D.C., Suh, J.S., Aftab, Z., Hoorani, H., Osman, A., Bunkowski, K., Cwiok, M., Dominik, W., Doroba, K., Kazana, M., Krolikowski, J., Kudla, Ignacy M., Pietrusinski, M., Pozniak, K., Zabolotny, W., Zalipska, J., Zych, P., Goscilo, L., Gorski, M., Wrochna, G., Zalewski, P., Alemany-Fernandez, R., Almeida, C., Almeida, N., Da Silva, J.C., Santos, M., Teixeira, I., Teixeira, J.P., Varela, J., Vaz Cardoso, N., Konoplyanikov, V., Urkinbaev, A., Toropin, A., Gavrilov, V., Kolosov, V., Krokhotin, A., Oulianov, A., Stepanov, N., Kodolova, Olga, Vardanyan, I., Ilic, J., Skoro, G., Albajar, C., de Troconiz, J.F., Calderon, A., Lopez Virto, M.A., Marco, R., Martinez Rivero, C., Matorras, F., Vila, I., Cucciarelli, S., Konecki, M., Ashby, S., Barney, D., Bartalini, P., Benetta, R., Brigljevic, V., Bruno, G., Cano, E., Cittolin, S., Della Negra, M., De Roeck, A., Favre, P., Frey, A., Funk, W., Futyan, D., Gigi, D., Glege, F., Gutleber, J., Hansen, M., Innocente, V., Jacobs, C., Jank, W., Kozlovszky, M., Larsen, H., Lenzi, M., Magrans, I., Mannelli, M., Meijers, F., Meschi, E., Mirabito, L., Murray, S.J., Oh, A., Orsini, L., Palomares Espiga, C., Pollet, L., Racz, A., Reynaud, S., Samyn, D., Schar-Hansen, P., Schwick, C., Sguazzoni, G., Sinanis, N., Sphicas, P., Spiropulu, M., Strandlie, A., Taylor, B.G., Van Vulpen, I., Wellisch, J.P., Winkler, M., Kotlinski, D., Prokofiev, Kirill, Speer, T., Dumanoglu, I., Bailey, D.S., Brooke, J.J., Cussans, D., Heath, G.P., Machin, D., Nash, S.J., Newbold, D.M., Probert, M.G., Coughlan, J.A., Halsall, R., Haynes, W.J., Tomalin, I.R., Marinelli, N., Nikitenko, A., Rutherford, S., Seez, C., Sharif, O., Antchev, G., Hazen, E., Rohlf, J., Wu, S., Breedon, R., Cox, P.T., Murray, P., Tripathi, M., Cousins, R., Erhan, S., Hauser, J., Kreuzer, P., Lindgren, M., Mumford, J., Schlein, P., Shi, Y., Tannenbaum, Benn, Valuev, V., Von Der Mey, M., Andreeva, I., Clare, R., Villa, S., Bhattacharya, S., Branson, J.G., Fisk, I., Letts, J., Mojaver, M., Paar, H.P., Trepagnier, E., Litvine, Vladimir A., Shevchenko, S., Singh, S., Wilkinson, R., Aziz, S., Bowden, M., Elias, J.E., Graham, G., Green, D., Litmaath, M., Los, S., O'Dell, V., Ratnikova, N., Suzuki, I., Wenzel, H., Acosta, D., Bourilkov, D., Korytov, A., Madorsky, A., Mitselmakher, Guenakh, Rodriguez, J.L., Scurlock, B., Abdullin, S., Baden, D., Eno, S.C., Grassi, T., Kunori, S., Pavlon, S., Sumorok, K., Tether, S., Cremaldi, L.M., Sanders, D., Summers, D., Osborne, I., Taylor, L., Tuura, Lassi A., Fisher, W.C., Mans, J., Stickland, D., Tully, C., Wildish, T., Wynhoff, S., Padley, B.Paul, Chumney, P., Dasu, S., Smith, W.H.
    Publicado 2005
    “…At the Compact Muon Solenoid experiment the original collision rate is reduced by a factor of O (1000) using a Level-1 hardware trigger. A subsequent factor of O(1000) data reduction is obtained by a software-implemented High Level Trigger (HLT) selection that is executed on a multi-processor farm. …”
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    The CMS High Level Trigger
  5. 13205
    Enlace del recurso
    Libro
    Practical algorithms for 3D computer graphics /
  6. 13206
    por Stockner, M
    Publicado 2007
    “…Over a period of two years the whole BLM system was tested under real accelerator conditions, which resulted in several updates and upgrades of the hardware and software. The parasitic measurements of the tails of the hadronic showers induced by the impacting protons were compared to Geant4 simulations. …”
    Enlace del recurso
    Beam Loss Calibration Studies for High Energy Proton Accelerators
  7. 13207
    por Bartmann, W
    Publicado 2009
    “…This lattice suffers from complexity in hardware and operation but it has the big advantage of avoiding transition. …”
    Enlace del recurso
    Design of Injection and Extraction Systems with Optimisation of Lattice and Layout for the CERN PS2 Synchrotron
  8. 13208
    por McGlone, Helen Marie
    Publicado 2009
    “…The ATLAS Collaboration, an international group of 2000 analysts, scientists, software developers and hardware experts, seeks to push the boundaries of our current understanding of the Universe, and our ability to undertake such studies. …”
    Enlace del recurso
    Neural Network Analysis in Higgs Search using $t\overline{t}H,H \to b\overline{b}$ and TAG Database Development for ATLAS
  9. 13209
    por Kasprowicz, Grzegorz
    Publicado 2011
    “…The scope of the thesis includes measurement system conception, hardware design and development, algorithm development and tests.…”
    Enlace del recurso
    Determination of Beam Intensity and Position in a Particle Accelerator
  10. 13210
    por Leahu, Lucian
    Publicado 2013
    “…This real-time selection is performed using dedicated hardware in the first level and large farms of computers in the next two levels, interconnected by a dedicated high speed Ethernet network. …”
    Enlace del recurso
    Analysis and predictive modeling of the performance of the ATLAS TDAQ network
  11. 13211
    “…Next, they cover the FreeBSD kernel from the system-call level down-from the interface to the kernel to the hardware. Explaining key design decisions, they detail the concepts, data structures, and algorithms used in implementing each significant system facility, including process management, security, virtual memory, the I/O system, filesystems, socket IPC, and networking. …”
    Enlace del recurso
    The design and implementation of the FreeBSD operating system
  12. 13212
    por Giangiacomi, Nico
    Publicado 2019
    “…As a result, the old readout chain still used for the rest of the Pixel Detector was completely saturated, and it was gradually replaced by the new system (IBL-ROD and IBL-BOC). While the hardware is already in place, the firmware and software utilities of the Pixel Detector readout chain are in constant evolution, in order to be able to provide good quality data even at the harsher en- vironmental conditions of Phase-I LHC. …”
    Enlace del recurso
    ATLAS Pixel Detector and readout upgrades for the improved LHC performance
  13. 13213
    por Strong, Giles Chatham
    Publicado 2021
    “…The combined effect of advances in both hardware and algorithms, and the vast datasets now available (both collected and simulated), has facilitated the paradigm shift towards deep learning (DL). …”
    Enlace del recurso
    Deep Learning Methods Applied to Higgs Physics at the LHC
  14. 13214
    por Berthold, Anne-Sophie
    Publicado 2023
    “…The results of the tests of one of the neural networks on the hardware will be presented along with the test setup. …”
    Enlace del recurso
    Machine Learning for Real-Time Processing of ATLAS Liquid Argon Calorimeter Signals with FPGAs
  15. 13215
    por Ventura Gonzalez, Salvador
    Publicado 2023
    “…A subset of the original data is sent with low latency to the hardware trigger system, while the full data are buffered until the reception of trigger accept signals. …”
    Enlace del recurso
    Development of the ATLAS Liquid Argon Calorimeter Readout Electronics for the HL-LHC
  16. 13216
    por Voigt, Johann Christoph
    Publicado 2023
    “…The results of the tests of one of the NNs on the hardware will be presented along with the test setup. …”
    Enlace del recurso
    Machine Learning for Real-Time Processing of ATLAS Liquid Argon Calorimeter Signals with FPGAs
  17. 13217
    por Andeen, Timothy Robert
    Publicado 2023
    “…A subset of the original data is sent with low latency to the hardware trigger system, while the full data are buffered until the reception of trigger accept signals. …”
    Enlace del recurso
    Development of the ATLAS Liquid Argon Calorimeter Readout Electronics for the HL-LHC
  18. 13218
    por Chiedde, Nemer
    Publicado 2023
    “…The results of the tests of one of the NNs on the hardware will be presented along with the test setup. …”
    Enlace del recurso
    Machine Learning for Real-Time Processing of ATLAS Liquid Argon Calorimeter Signals with FPGAs
  19. 13219
    por Straessner, Arno
    Publicado 2023
    “…A subset of the original data is sent with low latency to the hardware trigger system, while the full data are buffered until the reception of trigger accept signals. …”
    Enlace del recurso
    Development of the ATLAS Liquid Argon Calorimeter Readout Electronics for the HL-LHC
  20. 13220
    por Crosetto, D
    Publicado 1999
    “…Details are also given on timing and synchronization issues, ASIC design verification, real-time performance monitoring and design (software and hardware) development tools. (37 refs).…”
    Enlace del recurso
    Enlace del recurso
    LHCb base-line level-0 trigger 3D-flow implementation
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