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Articoli di riviste sul tema "Computer network architectures"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 29 luglio 2024

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1

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 serve

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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

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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

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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 fra

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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 i

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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.

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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 foun

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8

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 todaysâ€™ 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 actu

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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 contro

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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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Dovrolis, Constantine, and J. Todd Streelman. "Evolvable network architectures." ACM SIGCOMM Computer Communication Review 40, no. 2 (April 9, 2010): 72–77. http://dx.doi.org/10.1145/1764873.1764886.

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12

Kryukov, Ya V., D. A. Pokamestov, E. V. Rogozhnikov, S. A. Novichkov, and D. V. Lakontsev. "Analysis of Computational Complexity and Processing Time Evaluation of the Protocol Stack in 5G New Radio." Proceedings of Tomsk State University of Control Systems and Radioelectronics 23, no. 3 (September 25, 2020): 31–37. http://dx.doi.org/10.21293/1818-0442-2020-23-3-31-37.

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Currently, an active deployment of radio access networks for mobile communication systems 5G New Radio is being observed. The architecture of networks is developing rapidly, where significant part of the functions is performed in a virtual cloud space of a personal computer. The computing power of a personal computer must be sufficient to execute network protocols in real time. To reduce the cost of deploying 5G NR networks, the configuration of each remote computer must be optimally matched to the scale of a particular network. Therefore, an urgent direction of research is the assessment of t

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Hác, Anna. "Wireless ATM network architectures." International Journal of Network Management 11, no. 3 (May 2001): 161–67. http://dx.doi.org/10.1002/nem.399.

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Altukhov, V. G. "Plant disease severity estimation by computer vision methods." Siberian Herald of Agricultural Science 51, no. 2 (June 7, 2021): 107–12. http://dx.doi.org/10.26898/0370-8799-2021-2-13.

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The first stage results within the framework of the thesis “Investigation of computer vision methods and algorithms in the field of plant diseases detection” are presented. The analysis of the work related to the automatic assessment of plant disease severity was carried out. It was established that for solving problems in this field, convolution neural networks are promising methods, which are currently superior to classical methods of computer vision in terms of accuracy. To assess the severity degree, classification and segmentation architectures of convolutional neural networks are used. C

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State, Radu. "Review: Network Security Architectures." Queue 3, no. 1 (February 2005): 61. http://dx.doi.org/10.1145/1046931.1046951.

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Yuan, Peisen, Yi Sun, and Hengliang Wang. "Heterogeneous Information Network-Based Recommendation with Metapath Search and Memory Network Architecture Search." Mathematics 10, no. 16 (August 12, 2022): 2895. http://dx.doi.org/10.3390/math10162895.

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Recommendation systems are now widely used on the Internet. In recommendation systems, user preferences are predicted by the interaction of users with products, such as clicks or purchases. Usually, the heterogeneous information network is used to capture heterogeneous semantic information in data, which can be used to solve the sparsity problem and the cold-start problem. In a more complex heterogeneous information network, the types of nodes and edges are very large, so there are lots of types of metagraphs in a complex heterogeneous information network. At the same time, machine learning ta

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Khasambiev, I. V., and E. A. Guseva. "Network architectures and protocols of M2M communications." Journal of Physics: Conference Series 2176, no. 1 (June 1, 2022): 012019. http://dx.doi.org/10.1088/1742-6596/2176/1/012019.

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Abstract The issues of building the architecture of M2M networks and managing M2M services using the capabilities of the IMS platform are considered Introduction. M2M (Machine to Machine) is a complex of technologies that provide automatic interaction between devices (things) without human intervention. The Internet of Things is not a single technology, but a whole system of technological solutions. It is a global network infrastructure, which consists of computer networks of physical objects, the traditional IP Internet, and various devices connecting these networks.

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Cîrneanu, Andrada-Livia, Dan Popescu, and Dragoș Iordache. "New Trends in Emotion Recognition Using Image Analysis by Neural Networks, a Systematic Review." Sensors 23, no. 16 (August 10, 2023): 7092. http://dx.doi.org/10.3390/s23167092.

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Facial emotion recognition (FER) is a computer vision process aimed at detecting and classifying human emotional expressions. FER systems are currently used in a vast range of applications from areas such as education, healthcare, or public safety; therefore, detection and recognition accuracies are very important. Similar to any computer vision task based on image analyses, FER solutions are also suitable for integration with artificial intelligence solutions represented by different neural network varieties, especially deep neural networks that have shown great potential in the last years du

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Marsden, Brian W. "Local Area Network Architectures." Computer Communications 12, no. 2 (April 1989): 107. http://dx.doi.org/10.1016/0140-3664(89)90066-2.

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Nafiiyah, Nur. "Identifikasi Tumor Otak Citra MRI dengan Convolutional Neural Network." Jurnal Informatika: Jurnal Pengembangan IT 8, no. 3 (September 17, 2023): 213–19. http://dx.doi.org/10.30591/jpit.v8i3.4985.

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The science of artificial intelligence and computer vision is beneficial in facilitating the detection of diseases in the medical field. Computer-based disease detection can save time. However, identifying and detecting tumors on MRI images require seriousness and is time-consuming. Due to the diversity of structures in size, shape, and intensity of the image, accuracy is needed in identifying the original organ structure and the diseased one. Previous studies have proposed a method for identifying brain tumors to produce the correct precision. In previous studies, neural network-based methods

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P, Shanmugavadivu, Mary Shanthi Rani M, Chitra P, Lakshmanan S, Nagaraja P, and Vignesh U. "Bio-Optimization of Deep Learning Network Architectures." Security and Communication Networks 2022 (September 20, 2022): 1–11. http://dx.doi.org/10.1155/2022/3718340.

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Deep learning is reaching new heights as a result of its cutting-edge performance in a variety of fields, including computer vision, natural language processing, time series analysis, and healthcare. Deep learning is implemented using batch and stochastic gradient descent methods, as well as a few optimizers; however, this led to subpar model performance. However, there is now a lot of effort being done to improve deep learning’s performance using gradient optimization methods. The suggested work analyses convolutional neural networks (CNN) and deep neural networks (DNN) using several cutting-

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Alekhina, Anna E., Mikhail G. Dorrer, and Alexander G. Ovchinnikov. "Smart eco-friendly refrigerator based on implementation of architectures of convolutional neural networks." E3S Web of Conferences 390 (2023): 03010. http://dx.doi.org/10.1051/e3sconf/202339003010.

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The article discusses the solution to the problem of choosing the architecture of a convolutional neural network for use in the computer vision of a smart vending refrigerator. Comparative tests decided the architectures of convolutional neural networks YOLOv2, YOLOv3, YOLOv4, Mask R-CNN, and YOLACT ++ on a standard MS COCO dataset, and then on datasets formed from images of typical smart refrigerator products. As a result of comparative tests, the best performance was demonstrated by the YOLOv3 architecture, trained based on a normalized dataset, supplemented with examples with complex inters

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Andriyanov, Nikita. "Application of Graph Structures in Computer Vision Tasks." Mathematics 10, no. 21 (October 29, 2022): 4021. http://dx.doi.org/10.3390/math10214021.

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On the one hand, the solution of computer vision tasks is associated with the development of various kinds of images or random fields mathematical models, i.e., algorithms, that are called traditional image processing. On the other hand, nowadays, deep learning methods play an important role in image recognition tasks. Such methods are based on convolutional neural networks that perform many matrix multiplication operations with model parameters and local convolutions and pooling operations. However, the modern artificial neural network architectures, such as transformers, came to the field of

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Summers, Kenneth L., Thomas Preston Caudell, Kathryn Berkbigler, Brian Bush, Kei Davis, and Steve Smith. "Graph Visualization for the Analysis of the Structure and Dynamics of Extreme-Scale Supercomputers." Information Visualization 3, no. 3 (July 8, 2004): 209–22. http://dx.doi.org/10.1057/palgrave.ivs.9500079.

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We are exploring the development and application of information visualization techniques for the analysis of new massively parallel supercomputer architectures. Modern supercomputers typically comprise very large clusters of commodity SMPs interconnected by possibly dense and often non-standard networks. The scale, complexity, and inherent non-locality of the structure and dynamics of this hardware, and the operating systems and applications distributed over them, challenge traditional analysis methods. As part of the á la carte (A Los Alamos Computer Architecture Toolkit for Extreme-Scale Arc

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Bezirganyan, Grigor, and Hayk Akarmazyan. "Improving Differentiable Neural Architecture Search with Sparse Connections and Model Pruning." “Katchar” Collection of Scientific Articles International Scientific-Educational Center NAS RA, no. 1 (July 26, 2022): 203–19. http://dx.doi.org/10.54503/2579-2903-2022.1-203.

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Neural networks have contributed to many breakthroughs across several disciplines. Their ease of use and scalability have motivated the development of many techniques in computer vision, natural language processing, audio analysis, etc. The neural network architecture plays a dominant role in its performance, and there have been many advances on designs and strategies for defining efficient neural networks. However, manually tuning neural architectures requires a significant amount of time and expert knowledge. To overcome the difficulty of manually setting up the architecture for a neural net

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Broustis, Ioannis, and Michalis Faloutsos. "Routing in Vehicular Networks: Feasibility, Modeling, and Security." International Journal of Vehicular Technology 2008 (April 21, 2008): 1–8. http://dx.doi.org/10.1155/2008/267513.

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Vehicular networks are sets of surface transportation systems that have the ability to communicate with each other. There are several possible network architectures to organize their in-vehicle computing systems. Potential schemes may include vehicle-to-vehicle ad hoc networks, wired backbone with wireless last hops, or hybrid architectures using vehicle-to-vehicle communications to augment roadside communication infrastructures. Some special properties of these networks, such as high mobility, network partitioning, and constrained topology, differentiate them from other types of wireless netw

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Al Bataineh, Ali, Devinder Kaur, Mahmood Al-khassaweneh, and Esraa Al-sharoa. "Automated CNN Architectural Design: A Simple and Efficient Methodology for Computer Vision Tasks." Mathematics 11, no. 5 (February 24, 2023): 1141. http://dx.doi.org/10.3390/math11051141.

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Convolutional neural networks (CNN) have transformed the field of computer vision by enabling the automatic extraction of features, obviating the need for manual feature engineering. Despite their success, identifying an optimal architecture for a particular task can be a time-consuming and challenging process due to the vast space of possible network designs. To address this, we propose a novel neural architecture search (NAS) framework that utilizes the clonal selection algorithm (CSA) to automatically design high-quality CNN architectures for image classification problems. Our approach uses

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Fethellah, Nour El Houda, Hafida Bouziane, and Abdallah Chouarfia. "NECS-based Cache Management in the Information Centric Networking." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 21 (November 9, 2021): 172. http://dx.doi.org/10.3991/ijim.v15i21.20011.

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The Information Centric Networking ICN architectures are proposed to overcome the problems of the actual internet architecture. One of the main straight points of the ICN architectures is the in-network caching. The ICN performance is influenced by efficiency of the adopted caching strategy which manages the contents in the network and decides where caching them. However, the major issue which faces the caching strategies in the ICN architectures is the strategic election of the cache routers to store the data through its delivery path. This will reduce congestion, optimize the distance betwee

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Xia, Chengpeng, Yawen Chen, Haibo Zhang, Hao Zhang, Fei Dai, and Jigang Wu. "Efficient neural network accelerators with optical computing and communication." Computer Science and Information Systems, no. 00 (2022): 66. http://dx.doi.org/10.2298/csis220131066x.

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Conventional electronic Artificial Neural Networks (ANNs) accelerators focus on architecture design and numerical computation optimization to improve the training efficiency. However, these approaches have recently encountered bottlenecks in terms of energy efficiency and computing performance, which leads to an increase interest in photonic accelerator. Photonic architectures with low energy consumption, high transmission speed and high bandwidth have been considered as an important role for generation of computing architectures. In this paper, to provide a better understanding of optical tec

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NAKANO, KOJI. "A BIBLIOGRAPHY OF PUBLISHED PAPERS ON DYNAMICALLY RECONFIGURABLE ARCHITECTURES." Parallel Processing Letters 05, no. 01 (March 1995): 111–24. http://dx.doi.org/10.1142/s0129626495000102.

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A dynamically reconfigurable architecture is a parallel computer architecture that supports a physical switching of communication patterns during a computation. Basically, the dynamically reconfigurable architecture consists of locally controllable switches, which enables flexible-connection patterns of the network. The bibliography attempts to classify published papers on dynamically reconfigurable architectures according to the problems that are dealt with.

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Shin, Jiyong, Kyongseok Park, and Dae-Ki Kang. "TA-DARTS: Temperature Annealing of Discrete Operator Distribution for Effective Differential Architecture Search." Applied Sciences 13, no. 18 (September 8, 2023): 10138. http://dx.doi.org/10.3390/app131810138.

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In the realm of machine learning, the optimization of hyperparameters and the design of neural architectures entail laborious and time-intensive endeavors. To address these challenges, considerable research effort has been directed towards Automated Machine Learning (AutoML), with a focus on enhancing these inherent inefficiencies. A pivotal facet of this pursuit is Neural Architecture Search (NAS), a domain dedicated to the automated formulation of neural network architectures. Given the pronounced impact of network architecture on neural network performance, NAS techniques strive to identify

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Akbar, F., A. Ghosh, S. Young, S. Akhter, Z. Ahmad Dar, V. Ansari, M. V. Ascencio, et al. "Vertex finding in neutrino-nucleus interaction: a model architecture comparison." Journal of Instrumentation 17, no. 08 (August 1, 2022): T08013. http://dx.doi.org/10.1088/1748-0221/17/08/t08013.

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Abstract We compare different neural network architectures for machine learning algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package “Multi-node Evolutionary Neural Networks for Deep Learning” (MENNDL), developed at Oak Ridge National Laboratory. While the domain-expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed as well. There is always a trade-

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Bhatt, Dulari, Chirag Patel, Hardik Talsania, Jigar Patel, Rasmika Vaghela, Sharnil Pandya, Kirit Modi, and Hemant Ghayvat. "CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope." Electronics 10, no. 20 (October 11, 2021): 2470. http://dx.doi.org/10.3390/electronics10202470.

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Computer vision is becoming an increasingly trendy word in the area of image processing. With the emergence of computer vision applications, there is a significant demand to recognize objects automatically. Deep CNN (convolution neural network) has benefited the computer vision community by producing excellent results in video processing, object recognition, picture classification and segmentation, natural language processing, speech recognition, and many other fields. Furthermore, the introduction of large amounts of data and readily available hardware has opened new avenues for CNN study. Se

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Korchagin, Valeriy Dmitrievich. "Analysis of modern SOTA-architectures of artificial neural networks for solving problems of image classification and object detection." Программные системы и вычислительные методы, no. 4 (April 2023): 73–87. http://dx.doi.org/10.7256/2454-0714.2023.4.69306.

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The scientific research is focused on conducting a study of current artificial neural network architectures in order to highlight the advantages and disadvantages of current approaches. The relevance of the research relies on the growing interest in machine learning technologies and regular improvement of computer vision algorithms.Within the scope of this paper, an analytical study of the advantages and disadvantages of existing solutions has been conducted and advanced SOTA architectures have been reviewed. The most effective approaches to improve the accuracy of basic models have been studi

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Sanjar, Karshiev, Olimov Bekhzod, Jaeil Kim, Jaesoo Kim, Anand Paul, and Jeonghong Kim. "Improved U-Net: Fully Convolutional Network Model for Skin-Lesion Segmentation." Applied Sciences 10, no. 10 (May 25, 2020): 3658. http://dx.doi.org/10.3390/app10103658.

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The early and accurate diagnosis of skin cancer is crucial for providing patients with advanced treatment by focusing medical personnel on specific parts of the skin. Networks based on encoder–decoder architectures have been effectively implemented for numerous computer-vision applications. U-Net, one of CNN architectures based on the encoder–decoder network, has achieved successful performance for skin-lesion segmentation. However, this network has several drawbacks caused by its upsampling method and activation function. In this paper, a fully convolutional network and its architecture are p

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Xie, Xiaoying, and Qitong Wang. "Parameterization of Chinese Ancient Architecture on the Basis of Modulo Relationships." SHS Web of Conferences 171 (2023): 03031. http://dx.doi.org/10.1051/shsconf/202317103031.

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Contemporary systems are trending toward 3D computer-aided design systems that integrate, network, and exhibit intelligence. The integration of parametric technology with ancient Chinese architecture can enhance the efficiency and quality of managing information on ancient buildings, thereby expanding the application scenarios of ancient architectural information models. By analyzing the construction characteristics of ancient Chinese carpentry work and modular systems, this research outlines the logic and methods for generating Chinese ancient architecture. The program’s parametric technology

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Freeman, Donald T. "Computer Applications in Otolaryngology: Computer Recognition of Brain Stem Auditory Evoked Potential Wave V by a Neural Network." Annals of Otology, Rhinology & Laryngology 101, no. 9 (September 1992): 782–90. http://dx.doi.org/10.1177/000348949210100913.

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A neural network simulator was used for the recognition of the presence and location of the peak of wave V of the brain stem auditory evoked potential (BAEP) test. Waveforms selected from BAEPs performed in the last 4 years at the University of Pittsburgh Presbyterian University Hospital were digitized and sampled, and the resulting amplitudes were normalized. A training set was composed of the waveforms resulting from the stimulation of 50 ears. The normalized amplitudes were used as the initial activation values for the networks. The desired outputs (the target locations for wave V) were rep

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Guesmi, Tawfik, Anwar Kalghoum, Badr M. Alshammari, Haitham Alsaif, and Ahmed Alzamil. "Leveraging Software-Defined Networking Approach for Future Information-Centric Networking Enhancement." Symmetry 13, no. 3 (March 9, 2021): 441. http://dx.doi.org/10.3390/sym13030441.

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Information-centric networking (ICN) has been developed as a potential candidate for future networks. In this model, users are provided with content rather than communication channels between the different hosts. The ICN network has several problems such as scalability issues and bandwidth consumption. However, software-defined networking (SDN) has been proposed to improve the networking architectures. The goal of our paper is to propose a new approach to named-data networking (NDN) based on the paradigm of SDN. Our work introduces various research studies carried out in the SDN and ICN contex

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Sharp, Duane E. "Network Architectures and Performance." Information Systems Management 15, no. 2 (March 1998): 7–12. http://dx.doi.org/10.1201/1078/43184.15.2.19980301/31113.2.

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Gebizlioglu, Osman S., Vijay Jain, and John Spencer. "Optical network architectures [Series Editorial]." IEEE Communications Magazine 51, no. 5 (May 2013): 116–17. http://dx.doi.org/10.1109/mcom.2013.6515055.

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41

Zemrane, Hamza, Youssef Baddi, and Abderrahim Hasbi. "Routing Communication Inside Ad Hoc Drones Network." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 17 (September 6, 2021): 192. http://dx.doi.org/10.3991/ijim.v15i17.19179.

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Abstract (sommario):

The world knows a constant development of technology applied in different sectors of activities: health, factories, homes, transportation, and others, one of the big axes that take a lot of attention today is the drone’s field. To communicate information a fleet of drones can use different communication architectures: centralized communication architecture, satellite communication architecture, cellular network communication architecture and a specific AdHoc communication architecture called the UAANET drones architecture. In our work we focused specifically on the routing of information insid

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42

Suganuma, Masanori, Masayuki Kobayashi, Shinichi Shirakawa, and Tomoharu Nagao. "Evolution of Deep Convolutional Neural Networks Using Cartesian Genetic Programming." Evolutionary Computation 28, no. 1 (March 2020): 141–63. http://dx.doi.org/10.1162/evco_a_00253.

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The convolutional neural network (CNN), one of the deep learning models, has demonstrated outstanding performance in a variety of computer vision tasks. However, as the network architectures become deeper and more complex, designing CNN architectures requires more expert knowledge and trial and error. In this article, we attempt to automatically construct high-performing CNN architectures for a given task. Our method uses Cartesian genetic programming (CGP) to encode the CNN architectures, adopting highly functional modules such as a convolutional block and tensor concatenation, as the node fu

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Le, Nam Tuan, Mohammad Arif Hossain, Amirul Islam, Do-yun Kim, Young-June Choi, and Yeong Min Jang. "Survey of Promising Technologies for 5G Networks." Mobile Information Systems 2016 (2016): 1–25. http://dx.doi.org/10.1155/2016/2676589.

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Abstract (sommario):

As an enhancement of cellular networks, the future-generation 5G network can be considered an ultra-high-speed technology. The proposed 5G network might include all types of advanced dominant technologies to provide remarkable services. Consequently, new architectures and service management schemes for different applications of the emerging technologies need to be recommended to solve issues related to data traffic capacity, high data rate, and reliability for ensuring QoS. Cloud computing, Internet of things (IoT), and software-defined networking (SDN) have become some of the core technologie

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Bashar, Dr Abul. "SURVEY ON EVOLVING DEEP LEARNING NEURAL NETWORK ARCHITECTURES." December 2019 2019, no. 2 (December 14, 2019): 73–82. http://dx.doi.org/10.36548/jaicn.2019.2.003.

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The deep learning being a subcategory of the machine learning follows the human instincts of learning by example to produce accurate results. The deep learning performs training to the computer frame work to directly classify the tasks from the documents available either in the form of the text, image, or the sound. Most often the deep learning utilizes the neural network to perform the accurate classification and is referred as the deep neural networks; one of the most common deep neural networks used in a broader range of applications is the convolution neural network that provides an automa

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Thompson, Lionel R. "Local area network architectures." Microprocessors and Microsystems 13, no. 1 (January 1989): 64. http://dx.doi.org/10.1016/0141-9331(89)90040-9.

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Thodberg, Hans Henrik. "IMPROVING GENERALIZATION OF NEURAL NETWORKS THROUGH PRUNING." International Journal of Neural Systems 01, no. 04 (January 1991): 317–26. http://dx.doi.org/10.1142/s0129065791000352.

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Abstract (sommario):

A technique for constructing neural network architectures with better ability to generalize is presented under the name Ockham's Razor: several networks are trained and then pruned by removing connections one by one and retraining. The networks which achieve fewest connections generalize best. The method is tested on a classification of bit strings (the contiguity problem): the optimal architecture emerges, resulting in perfect generalization. The internal representation of the network changes substantially during the retraining, and this distinguishes the method from previous pruning studies.

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47

Sesha Saiteja, Maddula N. V., K. Sai Sumanth Reddy, D. Radha, and Minal Moharir. "Multi-Core Architecture and Network on Chip: Applications and Challenges." Journal of Computational and Theoretical Nanoscience 17, no. 1 (January 1, 2020): 239–45. http://dx.doi.org/10.1166/jctn.2020.8657.

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Technology improves performance and reduces in size day by day. Reduction in size can increase the density and which in turn can improve the performance. These statements suit very well for the computer architecture improvement. The whole System on Chip (SoC) brought the concept of multiple cores on a single chip. The multi-core or many-core architectures are the future of computing. Technology has improved in reducing the size and increasing the density, but improving the performance to an expectation of including more cores is a challenge of many-core technology. Utilization of all cores and

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Chen, Chang Wen, and Yu Wang. "Chain-Type Wireless Sensor Network for Monitoring Long Range Infrastructures: Architecture and Protocols." International Journal of Distributed Sensor Networks 4, no. 4 (October 1, 2008): 287–314. http://dx.doi.org/10.1080/15501320701260261.

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We present in this paper an investigation of a special class of wireless sensor networks for monitoring critical infrastructures that may extend for hundreds of miles in distances. Such networks are fundamentally different from traditional sensor networks in that the sensor nodes in this class of networks are deployed along narrowly elongated geographical areas and form a chain-type topology. Based on careful analysis of existing sensor network architectures, we first demonstrate the need to develop new architecture and networking protocols to match the unique topology of chain-type sensor net

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Dautel, Alexander Jakob, Wolfgang Karl Härdle, Stefan Lessmann, and Hsin-Vonn Seow. "Forex exchange rate forecasting using deep recurrent neural networks." Digital Finance 2, no. 1-2 (March 27, 2020): 69–96. http://dx.doi.org/10.1007/s42521-020-00019-x.

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Abstract Deep learning has substantially advanced the state of the art in computer vision, natural language processing, and other fields. The paper examines the potential of deep learning for exchange rate forecasting. We systematically compare long short-term memory networks and gated recurrent units to traditional recurrent network architectures as well as feedforward networks in terms of their directional forecasting accuracy and the profitability of trading model predictions. Empirical results indicate the suitability of deep networks for exchange rate forecasting in general but also evide

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Vitevitch, Michael S., Leo Niehorster-Cook, and Sasha Niehorster-Cook. "Exploring How Phonotactic Knowledge Can Be Represented in Cognitive Networks." Big Data and Cognitive Computing 5, no. 4 (September 23, 2021): 47. http://dx.doi.org/10.3390/bdcc5040047.

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Abstract (sommario):

In Linguistics and Psycholinguistics, phonotactics refers to the constraints on individual sounds in a given language that restrict how those sounds can be ordered to form words in that language. Previous empirical work in Psycholinguistics demonstrated that phonotactic knowledge influenced how quickly and accurately listeners retrieved words from that part of memory known as the mental lexicon. In the present study, we used three computer simulations to explore how three different cognitive network architectures could account for the previously observed effects of phonotactics on processing.

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