Relevant bibliographies by topics / Computer interface

Academic literature on the topic 'Computer interface'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 January 2023

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Journal articles on the topic "Computer interface"

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Vadza, Kejal Chintan. "Brain Gate & Brain Computer Interface." International Journal of Scientific Research 2, no. 5 (June 1, 2012): 45–49. http://dx.doi.org/10.15373/22778179/may2013/19.

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BABUŠIAK, Branko, and Martin KNOCIK. "BIO-AMPLIFIER FOR BRAIN COMPUTER INTERFACE." Acta Electrotechnica et Informatica 14, no. 3 (September 1, 2014): 11–15. http://dx.doi.org/10.15546/aeei-2014-0022.

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Bartz, Christina. "Der Computer in der Küche." Zeitschrift für Medien- und Kulturforschung 9, no. 2 (2018): 13–26. http://dx.doi.org/10.28937/1000108172.

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Der Honeywell Kitchen Computer von 1969 ist einer der ersten Rechner, der für den Heimgebrauch hergestellt wurde. Schon allein aufgrund seines wenig benutzerfreundlichen Interfaces, das im Widerspruch zur nicht-professionellen Nutzung in der häuslichen Sphäre steht, stellt er eine Kuriosität dar. Zugleich weist er Aspekte auf, die die Idee eines Computers zu Hause plausibilisieren. Dazu gehört u.a. die Gestaltung des Interfaces, aber auch die Küche als Ort der heimischen Arbeit. In 1969, the Honeywell Kitchen Computer was the first data processor that was built explicitly for home use. Resembling something of an oddity, most of all because of its non-user-friendly interface that conflicts with the conditions of non-professional domestic use, the Honeywell Kitchen Computer at the same time shows some aspects which make the use of a computer at home plausible, i. a. the design of the interface and the factor of a kitchen being the place of domestic work
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Williams, Evelyn, and Evelyn Hewlett-Packard. "Panel on Visual Interface Design." Proceedings of the Human Factors Society Annual Meeting 33, no. 5 (October 1989): 323–24. http://dx.doi.org/10.1177/154193128903300519.

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User interface design has many components. Usable computer interfaces should be easy to learn, result in high user productivity and high user satisfaction. There are a number of components in user interface design that affect the usability of the interface. Within the human factors community we tend to emphasize the ergonomic and cognitive components of the computer interface. There is another component that is frequently ignored, the visual interface design. This panel will present information on the visual component in various user-computer interfaces and will discuss the contributions of the visual designer to the interfaces and usability.
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ELLENBOGEN, RICHARD G., and TIMOTHY H. LUCAS. "Brain Computer Interface." Neurosurgery 58, no. 6 (June 2006): N6. http://dx.doi.org/10.1227/01.neu.0000310229.79613.24.

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Dingra Ruchika Khaitan, Spardha Taneja Jyotika. "Brain Computer Interface." IOSR Journal of Computer Engineering 16, no. 2 (2014): 41–47. http://dx.doi.org/10.9790/0661-162124147.

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Arbel, Yael. "Brain-Computer Interface." ASHA Leader 12, no. 12 (September 2007): 14–15. http://dx.doi.org/10.1044/leader.ftr5.12122007.14.

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Berger, Nevin. "Computer humor interface." Interactions 12, no. 5 (September 2005): 72. http://dx.doi.org/10.1145/1082369.1082423.

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ELLENBOGEN, RICHARD G., and TIMOTHY H. LUCAS. "Brain Computer Interface." Neurosurgery 58, no. 6 (June 1, 2006): N6. http://dx.doi.org/10.1227/00006123-200606000-00031.

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Yoon, Joongsun. "A Brain-Computer Interface Based Human-Robot Interaction Platform." Journal of the Korea Academia-Industrial cooperation Society 16, no. 11 (November 30, 2015): 7508–12. http://dx.doi.org/10.5762/kais.2015.16.11.7508.

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Dissertations / Theses on the topic "Computer interface"

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Sirobaba, N. S., and D. O. Marchenko. "Brain-computer interface." Thesis, Вид-во СумДУ, 2011. http://essuir.sumdu.edu.ua/handle/123456789/22533.

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A brain–computer interface (BCI), sometimes called a direct neural interface or a brain–machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often aimed at assisting, augmenting or repairing human cognitive or sensory-motor functions. The field of BCI has advanced mostly toward neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-nineties. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/22533
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Shliahetskiy, A. A. "Brain - computer interface." Thesis, Sumy State University, 2016. http://essuir.sumdu.edu.ua/handle/123456789/46934.

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Brain -computer interface - the interface that implements the connection between the human brain and the computer. The main idea is that when you think about action and do it, the same part of the brain is activated.In the middle of the XIX century, Emil Du Bois- Reymond showed the relationship between electric current and nerve impulses; in 1875. Richard ketone managed to register the electrical activity of the brain of animals. The psychiatrist Hans Berger in 1924 invented a method to record the electrical activity of the human brain. In 1967, psychiatrist Edmond Dewan published a paper in which he described the experiment where a man was trying to send a message to electroencephalogram by means of dot-and-dash,using brain activity.One of the first practically implemented IMC is considered a virtual keyboard made by Farwell and Donchyn which was created in 1988.
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Almeida, Luís Filipe Martinho de. "Brain computer interface." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/21618.

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Mestrado em Engenharia Eletrónica e Telecomunicações
A investigação e desenvolvimento de sistemas BCI, Brain Computer Interface tem crescido de ano para ano, com resultados cada vez melhores. Uma das principais vertentes para a qual estes sistemas têm sido usados é na área da neuroprostética. Desta forma tem-se demonstrado em vários estudos e investiga ções a possibilidade de controlar membros completos ou parciais robóticos por nós seres humanos, dando assim uma liberdade e conquista de movimentos perdidos a pessoas incapacitadas. No entanto uma grande parte dos melhores resultados obtidos envolve a utilização de BCI invasivos, o que necessita de ser implantado diretamente no cérebro humano, através de uma operação cirúrgica. Isto é ainda um dos grandes inconvenientes que esta abordagem implica e também o facto de uma grande parte destes estudos ainda estarem na fase de testes. Este trabalho teve como objetivo tentar comprovar que os BCI não invasivos também conseguem obter bons resultados apesar das suas limitações e pior aquisição de resultados devido à inclusão de ruído por parte do nosso crânio e cabelo, assim como a inclusão dos Parâmetros Hjorth proporciona melhores resultados na identificação das classes desejadas. Dividiu-se o trabalho em duas partes, uma para a identificação das classes de “Piscar de Olho” e outra para identificação das classes de “Ações Pensadas” . Os resultados foram todos obtidos tendo em conta apenas um utilizador. Relativamente à deteção do “Piscar de Olho” comprovou-se que ́e facilmente conseguido com resultados quase perfeitos, com uma precisção de 99 . 98%. Relativamente à deteção de “Ações Pensadas” não foi possível comprovar a sua deteçãao usando sessções de gravação diferentes, no entanto verificou-se que a classificação das classes tendo em conta a mesma sessão de gravação, obtém resultados muito bons com valores acima dos 99% para o melhor m ́etodo preditivo. A inclusão dos Parâmetros Hjorth foi em todos os casos de estudo, a opção em que os resultados foram sempre melhores, demonstrando assim que a inclusão dos mesmos é uma opção aconselhável, pois em alguns casos, a precisão na deteção das classes aumento para duas ou mais vezes. Os resultados são promissores e apesar de não ter conseguido obter os melhores resultados para sessões de gravação independentes na classificação de “Ações Pensadas” , indico nas análises os passos necessáios para a obtenção de melhores resultados e a possibilidade de generalização do processo para diversos utilizadores.
The research and development of BCI systems, Brain Computer Interface has grown from year to year, with better and better results. One of the main areas for which these systems have been used is the neuroprosthetic. Several studies and investigations have shown the possibility of controlling complete or partial robotic members by people, thus giving a freedom and conquest of lost movements to incapacitated persons. However, a great part of the best results obtained involves the use of invasive BCI, which needs to be implanted directly into the human brain through a sirurgical operation. This is still one of the great drawbacks that this approach entails and also the fact that a large part of these studies are still in the testing phase. The aim of this study was to try and prove that non-invasive BCI can also achieve good results despite their limitations and inferior quality on the acquisition of data due to the inclusion of noise from our skull and hair, and also that the inclusion of the Hjorth Parameters on the analysis provides better results in identifying the desired classes. The work was split into two parts, one for the identification of “Eye Blinking” classes and the other for “Thought Actions” classes. The results were all obtained with only one user in mind. Regarding the detection of “Eye Blinking” it has been found that it is easily achieved with near-perfect results, with an accuracy of 99 . 98%. Regarding the detection of “Thought Actions” it was not possible to verify its detection using different recording sessions, however it was verified that the classification of classes taking into account the same recording session, obtains very good results with values above 99% for the best predictive method. The inclusion of Hjorth Parameters was in all study cases, the option in which the results were always better, thus demonstrating that their inclusion is an advisable option, since in some cases, the accuracy in detecting classes doubled or more. The results are promising and although I haven’t been able to obtain the best results for independent recording sessions in the classification of “Thought actions” , I indicate in the analysis some steps necessary to obtain better results and the possibility of generalizing the process for several users.
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Skidmore, Trent A. "The electroencephalographic human-computer interface." Ohio : Ohio University, 1991. http://www.ohiolink.edu/etd/view.cgi?ohiou1173327705.

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Halder, Sebastian [Verfasser]. "Prediction of Brain-Computer Interface Performance: For P300 and Motor Imagery Brain-Computer Interfaces / Sebastian Halder." München : Verlag Dr. Hut, 2011. http://d-nb.info/1015607330/34.

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Lachman, Richard W. 1972. "Animist interface : experiments in mapping character animation to computer interface." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/61831.

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Soukup, Michael. "Brain-Computer Interface In Control Systems." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-25749.

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A Brain-Computer Interface (BCI) is a system that allows for direct communication between the brain and an external device. Originally, the motivation for developing BCIs has been to provide severely disabled individuals with a basic communication system. Recent years, BCIs directed at regular consumers in practical control applications have gained popularity as well, for which the ultimate goal is to provide a more natural way of communicating with machines. However, BCIs intended at use in control systems face several challenges and are still inferior to conventional controllers in terms of usability, reliability and practical value. In this thesis, we explore two novel concepts that can enforce BCIs. The first concept relies on detection of so-called Error-Related Potentials (ErrPs), which are the response in brainwaves to an erroneous event. We argue for that these potentials can serve as reward-based signals that give feedback to the system, which enables the BCI to adapt to the user. The second concept is to use sequence labeling frameworks based on Conditional Random Fields (CRFs) to translate brainwaves into control signals with greater accuracy. We also suggest how these two concepts can be combined.Our experiments to detect ErrPs in BCI control applications using a consumer grade headset to obtain EEG measurements indicate no presence of ErrPs, however, the reliability of the EEG recordings is questionable. Furthermore, we have developed a new implementation of the so-called Sparse Hidden-Dynamic CRF (SHDCRF) and measure its performance on a common BCI classification task. In our experiment, the model outperforms similar classifiers that represent the state-of-the-art, and the results suggest that the proposed model is superior in terms of accuracy and modeling capacity.
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Kaanta, Bradley C. (Bradley Carter) 1980. "PINS : a haptic computer interface system." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28419.

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Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 73-74).
The research goal was to develop a dense array of discreet vertical actuators as an input and output device with haptic feedback for Human Computer Interaction (HCI). This expands upon the current research of table surfaces as medium for HCI by adding a third dimension that both a user and a computer can control. The use of vertical actuation makes possible new kinds of physical interactions with virtual objects and allows a computer to maintain constancy with the physical representation and the digital information. This requires the design and constructions of an elegant, reliable, and economically reasonable actuator array. Each array element requires autonomy to quickly and accurately move to a precise height. As an array, combined elements must provide enough resolution so that the user perceives the array as a continuously morphing, three-dimensional surface. Shape transformations are accomplished either indirectly by digital means or directly by user touch. The proposed research will focus on development of a real-time haptic actuation arrays supporting technology. The process includes working on the design, function, appearance, response, and implementation.
by Bradley C. Kaanta.
M.Eng.and S.B.
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Wiklund, Victor, and Axel Karlsson. "Generalisation in brain computer interface classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 1992. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229999.

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Brain computer interfaces (BCIs) are systems that allow users to interact with devices without relying on the neuromuscular pathways. This interaction is achieved by allowing the system to read the electrical activity of the brain and teaching it to map certain patterns of activation to certain commands. There are many applications for BCIs ranging from controlling prosthetics to gaming, but adapting both the user and the system to one another is a time and resource consuming process. Even more problematic, BCIs tend to only perform well for a single user and only for a limited time. This paper aims to investigate the accuracy of single-subject singlesession BCIs on other subjects and other sessions. To that end three different classifiers, a Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Long Short-Term Memory network (LSTM) are developed and tested on a data set consisting of five subjects, two sessions for a binary classification task. Our results show that training on single-subject single-session data leads to an average cross-subject accuracy of 45-50% and an average cross-session accuracy of 50-55%. We find that there is no statistically significant difference in accuracy depending on the classifier used and discuss factors that affect generalization such as model complexity and good subjects.
Brain computer interfaces (BCIs) är system som gör det möjligt för användare att interagera med apparater utan behov av de neuromuskulära banorna. Den här interaktionen möjliggörs genom att systemet läser den elektriska aktiviteten i hjärnan och lär sig associera vissa mönster av aktivitet till vissa kommandon. Det finns många användningsområden för BCIs, från att kontrollera proteser till spel, men att anpassa både användaren och systemet till varandra är en process som kräver både tid och resurser. Än värre, BCIs tenderar att bara funka bra för en enskild användare och bara under en begränsad tid. Den här rapporten avser undersöka hur bra ett BCI system tränat på data för ett subjekt och en session är på klassificering av data för andra subjekt och andra sessioner. Tre typer av klassificerare, en Support Vector Machine (SVM), Convolutional Neural Network (CNN) och Long Short-Term Memory network (LSTM) byggs och utvärderas på data från fem subjekt över två sessioner på en binär klassificeringuppgift. Våra resultat indikerar att träning på data för ett subjekt, en session leder till en genomsnittlig pricksäkerhet på 45-50% på andra subjekt, 50-55% på andra sessioner. Vi finner även att det inte finns någon statistiskt signifikant skillnad i pricksäkerhet beroende på vilken typ av klassificerare som används och diskuterar faktorer som påverkar generalisering såsom modellkomplexitet och bra subjekt.
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Oates, Shawn P. "CHILD-COMPUTER INTERACTION: EXPLORING INTERFACE DESIGN." Miami University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=miami1133800774.

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Books on the topic "Computer interface"

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Sutcliffe, Alistair. Human-computer interface design. New York, NY, USA: Springer-Verlag New York Inc., 1989.

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Sau-Ying, Chin Janet, and Reed Theodore Niles, eds. The computer graphics interface. Oxford [England]: Butterworth-Heinemann, 1991.

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Sutcliffe, Alistair. Human-computer interface design. Basingstoke: Macmillan Education, 1988.

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Guger, Christoph, Brendan Z. Allison, and Michael Tangermann, eds. Brain-Computer Interface Research. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60460-8.

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Guger, Christoph, Brendan Z. Allison, and Aysegul Gunduz, eds. Brain-Computer Interface Research. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79287-9.

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Guger, Christoph, Brendan Allison, and Mikhail Lebedev, eds. Brain-Computer Interface Research. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64373-1.

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Guger, Christoph, Natalie Mrachacz-Kersting, and Brendan Z. Allison, eds. Brain-Computer Interface Research. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05668-1.

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Guger, Christoph, Theresa Vaughan, and Brendan Allison, eds. Brain-Computer Interface Research. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09979-8.

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Guger, Christoph, Brendan Z. Allison, and Günter Edlinger, eds. Brain-Computer Interface Research. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36083-1.

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Sutcliffe, Alistair. Human-Computer Interface Design. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4899-6749-7.

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Book chapters on the topic "Computer interface"

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Weik, Martin H. "host computer interface." In Computer Science and Communications Dictionary, 735. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_8477.

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Weik, Martin H. "user-computer interface." In Computer Science and Communications Dictionary, 1871. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_20570.

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Nam, Chang S., Inchul Choi, Amy Wadeson, and Mincheol Whang. "Brain–Computer Interface." In Brain–Computer Interfaces Handbook, 11–52. Boca Raton : Taylor & Francis, CRC Press, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351231954-1.

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Majumdar, Kaushik. "Brain–Computer Interface." In A Brief Survey of Quantitative EEG, 153–96. Boca Raton : Taylor & Francis, 2018.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315117256-7.

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Mukherjee, Abhishek, Madhurima Gupta, and Shampa Sen. "Brain–Computer Interface." In Machine Learning and IoT, 265–86. Boca Raton : Taylor & Francis, 2019.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351029940-16.

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Sengar, Prashant, and Shawli Bardhan. "Brain–Computer Interface." In Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications, 23–38. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003226147-2.

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Hubbold, Roger. "Human-Computer Interface." In Scientific Visualization, 113–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-76942-9_5.

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Edlinger, Günter, Cristiano Rizzo, and Christoph Guger. "Brain Computer Interface." In Springer Handbook of Medical Technology, 1003–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-540-74658-4_52.

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Gunkel, David J. "Brain–Computer Interface." In Reimagining Communication: Mediation, 303–20. Abingdon, Oxon ; New York, NY : Routledge, 2020.: Routledge, 2020. http://dx.doi.org/10.4324/9781351015431-19.

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Weik, Martin H. "interface." In Computer Science and Communications Dictionary, 812. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_9326.

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Conference papers on the topic "Computer interface"

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Nawrocka, Agata, and Andrzej Kot. "Brain - computer interface." In 2012 13th International Carpathian Control Conference (ICCC). IEEE, 2012. http://dx.doi.org/10.1109/carpathiancc.2012.6228699.

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"Brain computer interface." In 2016 9th International Conference on Human System Interactions (HSI). IEEE, 2016. http://dx.doi.org/10.1109/hsi.2016.7529655.

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Jantz, Jay, Adam Molnar, and Ramses Alcaide. "A brain-computer interface for extended reality interfaces." In SIGGRAPH '17: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3089269.3089290.

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Fong, Manson Cheuk-Man, James William Minett, Thierry Blu, and William Shi-Yuan Wang. "Brain--computer interface (BCI)." In the 10th asia pacific conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2350046.2350071.

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Girouard, Audrey. "Adaptive brain-computer interface." In the 27th international conference extended abstracts. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1520340.1520436.

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Dollman, Gavin J., Lizette De Wet, and Tanya R. Beelders. "Commercial Brain Computer Interface." In the 2015 Annual Research Conference. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2815782.2815797.

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Bularka, Szilard, and Aurel Gontean. "Brain-Computer Interface review." In 2016 12th IEEE International Symposium on Electronics and Telecommunications (ISETC). IEEE, 2016. http://dx.doi.org/10.1109/isetc.2016.7781096.

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Larrue, Florian, Hélène Sauzéon, Lioubov Aguilova, Fabien Lotte, Martin Hachet, and Bernard NKaoua. "Brain computer interface vs walking interface in VR." In the 18th ACM symposium. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2407336.2407359.

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Gong, Xiaodong. "Webpage Human Computer Interface Design." In 2010 International Conference on Multimedia Technology (ICMT). IEEE, 2010. http://dx.doi.org/10.1109/icmult.2010.5631233.

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"HICOPS: Human Interface Computer Program." In 55th International Astronautical Congress of the International Astronautical Federation, the International Academy of Astronautics, and the International Institute of Space Law. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.iac-04-g.2.04.

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Reports on the topic "Computer interface"

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Shea, Thomas B. Optimization of Neuronal-Computer Interface. Fort Belvoir, VA: Defense Technical Information Center, June 2009. http://dx.doi.org/10.21236/ada515409.

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Satran, J., K. Meth, C. Sapuntzakis, M. Chadalapaka, and E. Zeidner. Internet Small Computer Systems Interface (iSCSI). RFC Editor, April 2004. http://dx.doi.org/10.17487/rfc3720.

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Gorczyca, John, and Eduardo Sanchez Villagran. Small computer system interface (SCSI) command system:. Gaithersburg, MD: National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.ir.89-4023.

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Knee, H. (Man-machine interface of computer controlled reactors). Office of Scientific and Technical Information (OSTI), November 1989. http://dx.doi.org/10.2172/5387979.

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Elliston, B. Encapsulating IP with the Small Computer System Interface. RFC Editor, May 1997. http://dx.doi.org/10.17487/rfc2143.

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6

Campbell, Nancy, Glenn Osga, David Kellmeyer, Daniel Lulue, and Earl Williams. A Human-Computer Interface Vision for Naval Transformation. Fort Belvoir, VA: Defense Technical Information Center, June 2003. http://dx.doi.org/10.21236/ada427415.

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Chadalapaka, M., J. Satran, K. Meth, and D. Black. Internet Small Computer System Interface (iSCSI) Protocol (Consolidated). RFC Editor, April 2014. http://dx.doi.org/10.17487/rfc7143.

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8

Resnick, David Richard, and Mike Ignatowski. Proposing an Abstracted Interface and Protocol for Computer Systems. Office of Scientific and Technical Information (OSTI), July 2014. http://dx.doi.org/10.2172/1171427.

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Resnick, David Richard, and Mike Ignatowski. Proposing an Abstracted Interface and Protocol for Computer Systems. Office of Scientific and Technical Information (OSTI), July 2014. http://dx.doi.org/10.2172/1171432.

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Bakke, M., J. Hafner, J. Hufferd, K. Voruganti, and M. Krueger. Internet Small Computer Systems Interface (iSCSI) Naming and Discovery. RFC Editor, April 2004. http://dx.doi.org/10.17487/rfc3721.

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