Relevant bibliographies by topics / Computer network architectures

Academic literature on the topic 'Computer network architectures'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Computer network architectures"

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Gudhka, Drashti. "Computer Network." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (January 31, 2024): 78–87. http://dx.doi.org/10.22214/ijraset.2024.57862.

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Abstract: This paper presents a comprehensive overview of computer networking, covering fundamental concepts and modern advancements. It explores core networking principles, including models, architectures, and essential layers. Emphasising contemporary trends, it delves into topics like network security (Zero Trust Architecture, AI/ML), Software-Defined Networking (SDN), IoT security challenges, 5G and Mobile Edge Computing (MEC), network performance optimisation, Big Data analytics, and eco-friendly networking strategies. Aimed at students, researchers, and professionals, this overview serves as a valuable resource for understanding both foundational principles and cutting-edge developments in networking.
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Wang, Yan, and Jun Hui Zheng. "A Well Modularized Computer Network Architecture." Applied Mechanics and Materials 631-632 (September 2014): 902–5. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.902.

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By analyzing a variety of computer network architectures, we can find that researchers establish different computer network models from their different starting points and get different computer network architectures by different modularization methods. We establish a well modularized non-layered computer network architecture. This paper compares it with the layered architecture and obtains a conclusion that it is superior to the layered architecture. We have developed two framework prototypes of it. In the one of them we develop some application softwares of TCP/IP, including E-mail, FTP, Web and standard IP telephone, which have been tested by the third-party. It could show the accuracy and easily implemented property of this architecture.
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DELGADO-FRIAS, JOSE G., STAMATIS VASSILIADIS, and JAMSHID GOSHTASBI. "SEMANTIC NETWORK ARCHITECTURES: AN EVALUATION." International Journal on Artificial Intelligence Tools 01, no. 01 (March 1992): 57–83. http://dx.doi.org/10.1142/s0218213092000132.

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Semantic networks as a means for knowledge representation and manipulation are used in many artificial intelligence applications. A number of computer architectures, that have been reported for semantic network processing, are presented in this paper. A novel set of evaluation criteria for such semantic network architectures has been developed. Semantic network processing as well as architectural issues are considered in such evaluation criteria. A study of how the reported architectures meet the requirements of each criterion is presented. This set of evaluation criteria is useful for future designs of machines for semantic networks because of its comprehensive range of issues on semantic networks and architectures.
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Zhang, Xinyu, Vincent C. S. Lee, Jia Rong, Feng Liu, and Haoyu Kong. "Multi-channel convolutional neural network architectures for thyroid cancer detection." PLOS ONE 17, no. 1 (January 21, 2022): e0262128. http://dx.doi.org/10.1371/journal.pone.0262128.

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Early detection of malignant thyroid nodules leading to patient-specific treatments can reduce morbidity and mortality rates. Currently, thyroid specialists use medical images to diagnose then follow the treatment protocols, which have limitations due to unreliable human false-positive diagnostic rates. With the emergence of deep learning, advances in computer-aided diagnosis techniques have yielded promising earlier detection and prediction accuracy; however, clinicians’ adoption is far lacking. The present study adopts Xception neural network as the base structure and designs a practical framework, which comprises three adaptable multi-channel architectures that were positively evaluated using real-world data sets. The proposed architectures outperform existing statistical and machine learning techniques and reached a diagnostic accuracy rate of 0.989 with ultrasound images and 0.975 with computed tomography scans through the single input dual-channel architecture. Moreover, the patient-specific design was implemented for thyroid cancer detection and has obtained an accuracy of 0.95 for double inputs dual-channel architecture and 0.94 for four-channel architecture. Our evaluation suggests that ultrasound images and computed tomography (CT) scans yield comparable diagnostic results through computer-aided diagnosis applications. With ultrasound images obtained slightly higher results, CT, on the other hand, can achieve the patient-specific diagnostic design. Besides, with the proposed framework, clinicians can select the best fitting architecture when making decisions regarding a thyroid cancer diagnosis. The proposed framework also incorporates interpretable results as evidence, which potentially improves clinicians’ trust and hence their adoption of the computer-aided diagnosis techniques proposed with increased efficiency and accuracy.
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Yan, Jiamiao. "Application of CNN in computer vision." Applied and Computational Engineering 30, no. 1 (January 22, 2024): 104–10. http://dx.doi.org/10.54254/2755-2721/30/20230081.

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Today's deep learning continues to be hot, and the application of machine learning can be seen in more and more fields. A neural network model called a Convolutional Neural Network (CNN) was created to imitate the structure of the human brain. It is a convolution operation that maps the relationship between input features and output features to a two-dimensional in the vector space of , the network can effectively process the input data. CNN emerged to solve the computational bottleneck problem faced by traditional networks. This paper discusses the application of the deep learning model CNN in image classification, target detection and face recognition. In these fields, models are continuously proposed, and architectures in each field are constantly emerging. Among them will be the classic architecture of CNN in this field. These classic architectures have their advantages, but there will also be improvements brought about by the shortcomings of the classic architecture. Through the application of these different fields, we can see that CNN-based deep learning can help various fields, and the efficiency will be improved, but it is not perfect and needs continuous improvement.
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Kaiser, Marcus. "Brain architecture: a design for natural computation." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365, no. 1861 (September 13, 2007): 3033–45. http://dx.doi.org/10.1098/rsta.2007.0007.

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Fifty years ago, John von Neumann compared the architecture of the brain with that of the computers he invented and which are still in use today. In those days, the organization of computers was based on concepts of brain organization. Here, we give an update on current results on the global organization of neural systems. For neural systems, we outline how the spatial and topological architecture of neuronal and cortical networks facilitates robustness against failures, fast processing and balanced network activation. Finally, we discuss mechanisms of self-organization for such architectures. After all, the organization of the brain might again inspire computer architecture.
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Duan, Qiang. "Intelligent and Autonomous Management in Cloud-Native Future Networks—A Survey on Related Standards from an Architectural Perspective." Future Internet 13, no. 2 (February 5, 2021): 42. http://dx.doi.org/10.3390/fi13020042.

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Cloud-native network design, which leverages network virtualization and softwarization together with the service-oriented architectural principle, is transforming communication networks to a versatile platform for converged network-cloud/edge service provisioning. Intelligent and autonomous management is one of the most challenging issues in cloud-native future networks, and a wide range of machine learning (ML)-based technologies have been proposed for addressing different aspects of the management challenge. It becomes critical that the various management technologies are applied on the foundation of a consistent architectural framework with a holistic vision. This calls for standardization of new management architecture that supports seamless the integration of diverse ML-based technologies in cloud-native future networks. The goal of this paper is to provide a big picture of the recent developments of architectural frameworks for intelligent and autonomous management for future networks. The paper surveys the latest progress in the standardization of network management architectures including works by 3GPP, ETSI, and ITU-Tand analyzes how cloud-native network design may facilitate the architecture development for addressing management challenges. Open issues related to intelligent and autonomous management in cloud-native future networks are also discussed in this paper to identify some possible directions for future research and development.
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Rowshanrad, Shiva, Mohamad Reza Parsaei, and Manijeh Keshtgari. "IMPLEMENTING NDN USING SDN: A REVIEW ON METHODS AND APPLICATIONS." IIUM Engineering Journal 17, no. 2 (November 30, 2016): 11–20. http://dx.doi.org/10.31436/iiumej.v17i2.590.

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In recent years many claims about the limitations of todays’ network architecture, its lack of flexibility and ability to response to ongoing changes and increasing users demands. In this regard, new network architectures are proposed. Software Defined Networking (SDN) is one of these new architectures which centralizes the control of network by separating control plane from data plane. This separation leads to intelligence, flexibility and easier control in computer networks. One of the advantages of this framework is the ability to implement and test new protocols and architectures in actual networks without any concern of interruption.Named Data Networking (NDN) is another paradigm for future network architecture. With NDN the network becomes aware of the content that is providing, rather than just transferring it among end-points. NDN attracts researchers’ attention and known as the potential future of networking and internet. Providing NDN functionalities over SDN is an important requirement to enable the innovation and optimization of network resources. In this paper first we describe about SDN and NDN, and then we introduce methods for implementing NDN using SDN. We also point out the advantages and applications of implementing NDN over SDN.
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Zkik, Karim, Said EL Hajji, and Ghizlane Orhanou. "A centralized secure plan for detecting and mitigation incidents in hybrid SDN." MATEC Web of Conferences 189 (2018): 10015. http://dx.doi.org/10.1051/matecconf/201818910015.

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The information technology sector has experienced phenomenal growth during recent years. To follow this development many new technologies have emerged to satisfy the expectations of businesses and customers, such as Cloud Computing, mobility, virtualization, Internet of things and big data. Traditional network cannot longer support this growth and suffers more and more in terms of misconfiguration,management and configurations complexity. Software defined network (SDN) architectures can be considered as a big revolution in the field of computer networks, because they offer a centralized control on infrastructure, services and the applications deployed which facilitate configuration and management on the network. The implementation of this type of architecture is not obvious and requires great expertise and good handling and management of network equipment. To remedy this problem the SDN architectures have evolved towards distributed and hybrid architectures. Despites the advantages of using SDN, security issues remain a real obstacle in front of the deployment of this type of architecture. The centralized architecture of this type of networks makes it vulnerable to several types of attacks and intrusions, and the implementation of security equipment generally causes a decrease in performance and increase latency.
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Dinn, Neil F. "Network architectures." Future Generation Computer Systems 7, no. 1 (October 1991): 79–89. http://dx.doi.org/10.1016/0167-739x(91)90018-s.

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Dissertations / Theses on the topic "Computer network architectures"

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Seah, Peng Leong Chung Wai Kong. "Architectures for device aware network /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Mar%5FSeah.pdf.

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Chung, Wai Kong. "Architectures for device aware network." Thesis, Monterey, California. Naval Postgraduate School, 2005. http://hdl.handle.net/10945/2306.

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In today's heterogeneous computing environment, a wide variety of computing devices with varying capabilities need to access information in the network. Existing network is not able to differentiate the different device capabilities, and indiscriminatingly send information to the end-devices, without regard to the ability of the end-devices to use the information. The goal of a device-aware network is to match the capability of the end-devices to the information delivered, thereby optimizing the network resource usage. In the battlefield, all resources - including time, network bandwidth and battery capacity - are very limited. A device-aware network avoids the waste that happens in current, device-ignorant networks. By eliminating unusable traffic, a device-aware network reduces the time the end-devices spend receiving extraneous information, and thus saves time and conserves battery-life. In this thesis, we evaluated two potential DAN architectures, Proxy-based and Router-based approaches, based on the key requirements we identified. To demonstrate the viability of DAN, we built a prototype using a hybrid of the two architectures. The key elements of our prototype include a DAN browser, a DAN Lookup Server and DAN Processing Unit (DPU). We have demonstrated how our architecture can enhance the overall network utility by ensuring that only appropriate content is delivered to the end-devices.
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Crowley, Patrick. "Design and analysis of architectures for programmable network processing systems /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/6991.

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McKenzie, Neil R. "The Cranium network interface architecture : support for message passing on adaptive packet routing networks /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/6874.

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Umeh, Njideka Adaku. "Security architecture methodology for large net-centric systems." Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.mst.edu/thesis/Umeh_09007dcc8049b3f0.pdf.

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Thesis (M.S.)--University of Missouri--Rolla, 2007.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed December 6, 2007) Includes bibliographical references (p. 60-63).
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Diana, Gary M. "Internetworking : an analysis and proposal /." Online version of thesis, 1990. http://hdl.handle.net/1850/10605.

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Becker, Russell W. "A test bed for detection of botnet infections in low data rate tactical networks." Thesis, Monterey, California : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/Sep/09Sep%5FBecker.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2009.
Thesis Advisor(s): JMcEachen, John ; Tummala, Murali. "September 2009." Description based on title screen as viewed on November 04, 2009. Author(s) subject terms: Botnet, Tactical Network, BotHunter, Honeynet, Honeypot, Low Data Rate, Network Security Includes bibliographical references (p. 57-59). Also available in print.
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Nguyen, Thanh Vinh. "Content distribution networks over shared infrastructure a paradigm for future content network deployment /." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060509.094632/index.html.

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Wing, Peter D. "Enhancements to the XNS authentication-by-proxy model /." Online version of thesis, 1990. http://hdl.handle.net/1850/10613.

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Suryanarayanan, Deepak. "A Methodology for Study of Network Processing Architectures." NCSU, 2001. http://www.lib.ncsu.edu/theses/available/etd-20010720-154055.

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A new class of processors has recently emerged that encompasses programmable ASICs and microprocessors that can implement adaptive network services. This class of devices is collectively known as Network Processors (NP). NPs leverage the flexibility of software solutions with the high performance of custom hardware. With the development of such sophisticated hardware, there is a need for a holistic methodology that can facilitate study of Network Processors and their performance with different networking applications and traffic conditions. This thesis describes the development of Component Network Simulator (ComNetSim) that is based on such a tech-nique. The simulator demonstrates the implementation of Diffserv applications on a Network Processor architecture and the performance of the system under different network traffic conditions.

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Books on the topic "Computer network architectures"

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Communications/Information Systems Planning and Critical Path Planning Service., ed. Alternative network architectures. Boston, Mass. (89 Broad Street, Boston 02110): Yankee Group, 1986.

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David, Hutchison. Local area network architectures. Wokingham, England: Addison-Wesley, 1988.

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Sunshine, Carl A., ed. Computer Network Architectures and Protocols. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-0809-6.

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A, Sunshine Carl, ed. Computer network architectures and protocols. 2nd ed. New York: Plenum Press, 1989.

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Malamud, Carl. DEC networks and architectures. New York: Intertext Publications, 1989.

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Halabi, Bassam. Internet routing architectures. Indianapolis, IN: Cisco Press, 1997.

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Bill, Hancock. Network concepts and architectures. Wellesley, Mass: QED Information Sciences, 1989.

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Danny, McPherson, and Cisco Systems Inc, eds. Internet routing architectures. 2nd ed. Indianapolis, Ind: Cisco, 2000.

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), ACM SIGCOMM '88 Symposium (1988 Stanford Calif. Communications, architectures & protocols. New York, N.Y: Association for Computing Machinery, 1988.

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William, Stallings, ed. Tutorial: Computer communications : architectures, protocols, and standards. 2nd ed. Washington, D.C: IEEE Computer Society Press, 1987.

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Book chapters on the topic "Computer network architectures"

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Ciubotaru, Bogdan, and Gabriel-Miro Muntean. "Network Architectures." In Computer Communications and Networks, 3–28. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5292-7_2.

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Freer, John R. "Layered network architectures." In Computer Communications and Networks, 133–70. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-1041-9_5.

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Bianchi, Filippo Maria, Enrico Maiorino, Michael C. Kampffmeyer, Antonello Rizzi, and Robert Jenssen. "Recurrent Neural Network Architectures." In SpringerBriefs in Computer Science, 23–29. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70338-1_3.

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Cai, Han, and Song Han. "Efficient Neural Network Architectures." In Low-Power Computer Vision, 173–90. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003162810-8.

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Fischer, V., and H. Niemann. "Parallelism in a Semantic Network for Image Understanding." In Parallel Computer Architectures, 203–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-21577-7_15.

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Sunshine, Carl A. "A Brief History of Computer Networking." In Computer Network Architectures and Protocols, 3–6. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-0809-6_1.

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Bhushan, Abhay K., and Dennis G. Frahmann. "Xerox Network Systems Architecture." In Computer Network Architectures and Protocols, 417–47. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-0809-6_15.

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Pozefsky, Diane P., Daniel A. Pitt, and James P. Gray. "IBM’s Systems Network Architecture." In Computer Network Architectures and Protocols, 449–509. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-0809-6_16.

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Sunshine, Carl A. "Network Interconnection and Gateways." In Computer Network Architectures and Protocols, 329–46. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-0809-6_11.

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Folts, Harold C. "Circuit-Switched Network Layer." In Computer Network Architectures and Protocols, 193–210. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-0809-6_7.

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Conference papers on the topic "Computer network architectures"

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Kasiviswanathan, Shiva Prasad, Nina Narodytska, and Hongxia Jin. "Network Approximation using Tensor Sketching." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/321.

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Deep neural networks are powerful learning models that achieve state-of-the-art performance on many computer vision, speech, and language processing tasks. In this paper, we study a fundamental question that arises when designing deep network architectures: Given a target network architecture can we design a `smaller' network architecture that 'approximates' the operation of the target network? The question is, in part, motivated by the challenge of parameter reduction (compression) in modern deep neural networks, as the ever increasing storage and memory requirements of these networks pose a problem in resource constrained environments.In this work, we focus on deep convolutional neural network architectures, and propose a novel randomized tensor sketching technique that we utilize to develop a unified framework for approximating the operation of both the convolutional and fully connected layers. By applying the sketching technique along different tensor dimensions, we design changes to the convolutional and fully connected layers that substantially reduce the number of effective parameters in a network. We show that the resulting smaller network can be trained directly, and has a classification accuracy that is comparable to the original network.
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Can, Ferit, and Can Eyupoglu. "Convolutional Neural Network Architectures Used in Computer Vision." In 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE, 2021. http://dx.doi.org/10.1109/ismsit52890.2021.9604557.

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Pimentel, J. R. "Integrated manufacturing network architectures." In IEEE INFOCOM '89, Proceedings of the Eighth Annual Joint Conference of the IEEE Computer and Communications Societies. IEEE, 1989. http://dx.doi.org/10.1109/infcom.1989.101569.

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Zion, Idongesit, Simeon Ozuomba, and Philip Asuquo. "An Overview of Neural Network Architectures for Healthcare." In 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS). IEEE, 2020. http://dx.doi.org/10.1109/icmcecs47690.2020.246980.

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Tang, Yufang, Xueming Li, Yang Liu, Chenyu Liu, and Yan Xu. "Review of content distribution network architectures." In 2013 3rd International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2013. http://dx.doi.org/10.1109/iccsnt.2013.6967223.

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Neifeld, M. A., A. Yamamura, Seiji Kobayashi, and D. Psaltis. "Optical disk based architectures for pattern recognition and neural network implementation." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/oam.1989.mx2.

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Optical disks have emerged as attractive candidates for both memory and SLM elements in optical information processing architectures. The features which make optical disks particularly well suited for these types of application include their large storage capacity (>1010 bits), parallel readout capability, convenient scanning mechanism, and their computer addressability. We have previously described disk based image correlators which can compare a 2-D input with as many as 40,000 computer specified reference images per second. In this paper we present two specific applications of optical disk based architectures. First we describe a disk based correlation architecture for use in a handwritten character recognition system.
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Gratz, Paul, Changkyu Kim, Robert McDonald, Stephen W. Keckler, and Doug Burger. "Implementation and Evaluation of On-Chip Network Architectures." In 2006 International Conference on Computer Design. IEEE, 2006. http://dx.doi.org/10.1109/iccd.2006.4380859.

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Diniz, Jessica Barbosa, Filipe R. Cordeiro, Pericles B. C. Miranda, and Laura A. Tomaz Da Silva. "A Grammar-based Genetic Programming Approach to Optimize Convolutional Neural Network Architectures." In XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4406.

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Deep Learning is a research area under the spotlight in recent years due to its successful application to many domains, such as computer vision and image recognition. The most prominent technique derived from Deep Learning is Convolutional Neural Network, which allows the network to automatically learn representations needed for detection or classification tasks. However, Convolutional Neural Networks have some limitations, as designing these networks are not easy to master and require expertise and insight. In this work, we present the use of Genetic Algorithm associated to Grammar-based Genetic Programming to optimize Convolution Neural Network architectures. To evaluate our proposed approach, we adopted CIFAR-10 dataset to validate the evolution of the generated architectures, using the metric of accuracy to evaluate its classification performance in the test dataset. The results demonstrate that our method using Grammar-based Genetic Programming can easily produce optimized CNN architectures that are competitive and achieve high accuracy results.
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Tyagi, Sapna, Piyush Maheshwari, Amit Agarwal, and Vinay Avasthi. "Exploring 3D Network-on-Chip Architectures and Challenges." In 2017 International Conference on Computer and Applications (ICCA). IEEE, 2017. http://dx.doi.org/10.1109/comapp.2017.8079768.

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Narayanaswamy, G., P. Balaji, and W. Feng. "Impact of Network Sharing in Multi-Core Architectures." In 17th International Conference on Computer Communications and Networks 2008. IEEE, 2008. http://dx.doi.org/10.1109/icccn.2008.ecp.61.

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Reports on the topic "Computer network architectures"

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Johannes, James D., Andrew Fanning, Kyle Hoover, Tim Lewis, and Marsha Robinson. Computer Network Security and Directory Services Architecture. Fort Belvoir, VA: Defense Technical Information Center, March 2000. http://dx.doi.org/10.21236/ada392366.

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Belzer, M. R., and Y. M. Cho. Micro-Computer Network Architecture for Range Instrumentation Applications. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada196971.

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Markova, Oksana, Serhiy Semerikov, and Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, May 2018. http://dx.doi.org/10.31812/0564/2250.

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The role of neural network modeling in the learning сontent of special course “Foundations of Mathematic Informatics” was discussed. The course was developed for the students of technical universities – future IT-specialists and directed to breaking the gap between theoretic computer science and it’s applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic “Neural network and pattern recognition” of the special course “Foundations of Mathematic Informatics” are shown. The program code was presented in a CofeeScript language, which implements the basic components of artificial neural network: neurons, synaptic connections, functions of activations (tangential, sigmoid, stepped) and their derivatives, methods of calculating the network`s weights, etc. The features of the Kolmogorov–Arnold representation theorem application were discussed for determination the architecture of multilayer neural networks. The implementation of the disjunctive logical element and approximation of an arbitrary function using a three-layer neural network were given as an examples. According to the simulation results, a conclusion was made as for the limits of the use of constructed networks, in which they retain their adequacy. The framework topics of individual research of the artificial neural networks is proposed.
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McGarrigle, Malachy. Watchpoints for Consideration When Utilising a VDI Network to Teach Archicad BIM Software Within an Educational Programme. Unitec ePress, October 2023. http://dx.doi.org/10.34074/ocds.099.

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This research identifies factors to be considered in the adoption of a virtual desktop infrastructure (VDI) accommodating the software needs of a tertiary institution. The study discusses the potential advantages and disadvantages of VDI, focusing specifically on the performance of the architectural software Archicad when used virtually. The findings will be relevant to similar programmes, such as Revit, and software used in other disciplines, especially where processing power is important. Aims discussed include reducing high-specification computers rarely used to capacity, assessing user experience, and feasibility of VDI remote access. Primarily a case study, this project centres around delivery of papers in the New Zealand Diploma of Architectural Technology programme at Unitec | Te Pūkenga that employ Archicad. Software efficiency and performance was monitored throughout teaching across numerous semesters. Incidents were logged and VDI operation tracked, especially during complex tasks such as image rendering. Load testing was also carried out to assess the implications of large user numbers simultaneously performing such complex tasks. Project findings indicate that Archicad performance depends on the design and specification of the virtual platform. Factors such as processing power, RAM allocation and ratio of users to virtual machines (VM)s proved crucial. Tasks executed by the software and how software itself uses hardware are other considerations. This research is important, as its findings could influence the information technology strategies of both academic institutions and industry in coming years. Virtual computing provides many benefits, and this project could provide the confidence for stakeholders to adopt new strategies using VDI instead of the traditional approach of computers with locally installed software applications.
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Cheng, Julian. Integrated Wavelength-Space-Time Optical Multiplexing Technologies and Architectures for Large-Scale, Reconfigurable, Multiple-Access Computer Networks. Fort Belvoir, VA: Defense Technical Information Center, March 2000. http://dx.doi.org/10.21236/ada375981.

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Meier, Kristina, Raymond Newell, and Nicholas Dallmann. Space-based quantum networks are an essential component of future architecture for distributed quantum computers and quantum-enhanced secure communication. Office of Scientific and Technical Information (OSTI), May 2023. http://dx.doi.org/10.2172/1972170.

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Kotenko, I. V. Formal Methods for Information Protection Technology. Task 2: Mathematical Foundations, Architecture and Principles of Implementation of Multi-Agent Learning Components for Attack Detection in Computer Networks. Part 2. Fort Belvoir, VA: Defense Technical Information Center, November 2003. http://dx.doi.org/10.21236/ada427492.

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