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Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Network module"

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Hwang, Soyoun, Taekeon Lee, and Youngmi Yoon. "Exploring disease comorbidity in a module–module interaction network." Journal of Bioinformatics and Computational Biology 18, no. 02 (April 2020): 2050010. http://dx.doi.org/10.1142/s0219720020500109.

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Understanding disease comorbidity contributes to improved quality of life in patients who are suffering from multiple diseases. Therefore, to better explore comorbid diseases, the clarification of associations between diseases based on biological functions is essential. In our study, we propose a method for identifying disease comorbidity in a module-based network, named the module–module interaction (MMI) network, which represents how biological functions influence each other. To construct the MMI network, we detected gene modules — sets of genes that have a higher probability of taking part in specific functions — and established a link between these modules. Subsequently, we constructed disease-related networks in the MMI network to understand inherent disease mechanisms and calculated comorbidity scores of disease pairs using Gene Ontology (GO) terms. Our results show that we can obtain further information on disease mechanisms by considering interactions between functional modules instead of between genes. In addition, we verified that predicted comorbid relationships of disease pairs based on the MMI network are more significant than those based on the protein–protein interaction (PPI) network. This study can be useful to elucidate the mechanisms underlying comorbidities for further study, which will provide a broader insight into the pathogenesis of diseases.
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2

He, Li, Xian-Xu Song, Mei Wang, and Ben-Zhuo Zhang. "Screening feature modules and pathways in glioma using EgoNet." Open Life Sciences 12, no. 1 (October 23, 2017): 277–84. http://dx.doi.org/10.1515/biol-2017-0032.

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AbstractBackgroundTo investigate differential egonetwork modules and pathways in glioma using EgoNet algorithm.MethodologyBased on microarray data, EgoNet algorithm mainly comprised three stages: construction of differential co-expression network (DCN); EgoNet algorithm used to identify candidate ego-network modules based on the increased classification accuracy; statistical significance for candidate modules using random permutation testing. After that, pathway enrichment analysis for differential ego-network modules was implemented to illuminate the biological processes.ResultsWe obtained 109 ego genes. From every ego gene, we progressively grew the ego-networks by levels; we extracted 109 ego-networks and the mean node size in an ego-network was 6. By setting the classification accuracy threshold at 0.90 and the count of nodes in an ego-network module at 10, we extracted 8 candidate ego-network modules. After random permutation test with 1000 times, 5 modules including module 59, 72, 78, 86, and 90 were identified to be significant. Of note, the genes of module 90 and 86 were enriched in the pathway of resolution of sister chromatid cohesion and mitotic prometaphase, respectively.ConclusionThe identified modules and their corresponding ego genes might be beneficial in revealing the pathology underlying glioma and give insight for future research of glioma.
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Qiao, Hu, Zhaohui Xu, Jiang He, and Ying Xiang. "Product Module Network Modeling and Evolution Analysis." Computational Intelligence and Neuroscience 2019 (March 6, 2019): 1–8. http://dx.doi.org/10.1155/2019/2186916.

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Modular technology for product design and manufacturing is an effective way to solve mass customization problems. One difficulty in the application of modular technology is that the characteristics of mass customization, such as multi batch and small batch, easily increase the complexity of the module structure of the enterprise products. To address this problem, based on complex network theory, the enterprise products module is mapped as the vertex of the network, the number of modules used is mapped as the node weight, the dependency between the modules is mapped to the edge, and the product module network is established. The brittleness risk entropy of the product module network is put forward by considering the internal and external factors that influence the application of the enterprise module to determine the rationality of the required modules' organizational structures. Then, the stability uncertainty of the product module network can be determined by calculating the brittleness risk entropy, in which the subsystem that is the most brittle risk entropy can be identified. And the evolution of the product module network can be promoted by changing factors of the entropy maximum subsystem. To analyze the change in the product module network caused by module evolution, a BBV (Barrat–Barthelemy–Vespignani) model of the product module network is established to dynamically determine the brittle risk of the product module network. Finally, the modularity structure of a series of special vehicles is used as an example to verify the presented method, and the results confirm the rationality and effectiveness of the method.
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Wu, Wei, Guangmin Hu, and Fucai Yu. "An Unsupervised Learning Method for Attributed Network Based on Non-Euclidean Geometry." Symmetry 13, no. 5 (May 19, 2021): 905. http://dx.doi.org/10.3390/sym13050905.

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Many real-world networks can be modeled as attributed networks, where nodes are affiliated with attributes. When we implement attributed network embedding, we need to face two types of heterogeneous information, namely, structural information and attribute information. The structural information of undirected networks is usually expressed as a symmetric adjacency matrix. Network embedding learning is to utilize the above information to learn the vector representations of nodes in the network. How to integrate these two types of heterogeneous information to improve the performance of network embedding is a challenge. Most of the current approaches embed the networks in Euclidean spaces, but the networks themselves are non-Euclidean. As a consequence, the geometric differences between the embedded space and the underlying space of the network will affect the performance of the network embedding. According to the non-Euclidean geometry of networks, this paper proposes an attributed network embedding framework based on hyperbolic geometry and the Ricci curvature, namely, RHAE. Our method consists of two modules: (1) the first module is an autoencoder module in which each layer is provided with a network information aggregation layer based on the Ricci curvature and an embedding layer based on hyperbolic geometry; (2) the second module is a skip-gram module in which the random walk is based on the Ricci curvature. These two modules are based on non-Euclidean geometry, but they fuse the topology information and attribute information in the network from different angles. Experimental results on some benchmark datasets show that our approach outperforms the baselines.
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Zhou, Hao, Jun Ping Wang, and Suo Ju He. "Self-Adapted Admission Control Model for Parlay on Heterogeneous Network." Advanced Materials Research 546-547 (July 2012): 1164–70. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1164.

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Parlay Gateway has played an important role in application development on heterogeneous networks. For many third part’s applications accessing Telecommunication Network through it, it is likely to become the bottleneck of whole system. In this paper, the author proposed a self-adapted control model which is effective in admission control of Parlay Gateway. This method is made up of five modules, which are admission control module, waiting queue module, token generating module, scheduling module, and the overload detecting module. According to some simulation results, the author found it is useful and easy to be implemented.
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6

Zhang, Shuqin. "Hierarchical Modular Structure Identification with Its Applications in Gene Coexpression Networks." Scientific World Journal 2012 (2012): 1–8. http://dx.doi.org/10.1100/2012/523706.

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Network module (community) structure has been a hot research topic in recent years. Many methods have been proposed for module detection and identification. Hierarchical structure of modules is shown to exist in many networks such as biological networks and social networks. Compared to the partitional module identification methods, less research is done on the inference of hierarchical modular structure. In this paper, we propose a method for constructing the hierarchical modular structure based on the stochastic block model. Statistical tests are applied to test the hierarchical relations between different modules. We give both artificial networks and real data examples to illustrate the performance of our approach. Application of the proposed method to yeast gene coexpression network shows that it does have a hierarchical modular structure with the modules on different levels corresponding to different gene functions.
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7

TSIOUTSIAS, DIMITRIS I., and ERIC MJOLSNESS. "OPTIMIZATION DYNAMICS FOR PARTITIONED NEURAL NETWORKS." International Journal of Neural Systems 05, no. 04 (December 1994): 275–86. http://dx.doi.org/10.1142/s0129065794000281.

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Given a relaxation-based neural network and a desired partition of the neurons in the network into modules with relatively slow communication between modules, we investigate relaxation dynamics for the resulting partitioned neural network. In particular, we show how the slow inter-module communication channels can be modeled by means of certain transformations of the original objective function which introduce new state variables for the inter-module communication links. We report on a parallel implementation of the resulting relaxation dynamics, for a two-dimensional image segmentation network, using a network of workstations. Experiments demonstrate a functional and efficient parallelization of this neural network algorithm. We also discuss implications for analog hardware implementations of relaxation networks.
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8

Liang, Gen, Xiaoxue Guo, Guoxi Sun, and Jingcheng Fang. "A User-Oriented Intelligent Access Selection Algorithm in Heterogeneous Wireless Networks." Computational Intelligence and Neuroscience 2020 (November 24, 2020): 1–20. http://dx.doi.org/10.1155/2020/8828355.

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A heterogeneous wireless network (HWN) contains many kinds of wireless networks with overlapping areas of signal coverage. One of the research topics on HWNs is how to make users choose the most suitable network. This paper designs a user-oriented intelligent access selection algorithm in HWNs with five modules (input, user preference calculation, candidate network score calculation, output, and learning). Essentially, the input module uses a utility function to calculate the utility value of the judgment parameter; the user preference calculation module calculates the weight of the judgment parameter using the fuzzy analysis hierarchy process (FAHP) approach; the candidate network score calculation module calculates the network score through a fuzzy neural network; the output module calculates the error between the actual output value and the expected output value; and the learning module corrects the parameter of the membership function in the fuzzy neural network structure according to the error. Simulation results show that the algorithm proposed in this paper can enable users to select the most suitable network according to service characteristics and can enable users to obtain higher gains.
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9

Perrin, Dimitri, and Guido Zuccon. "Recursive module extraction using Louvain and PageRank." F1000Research 7 (August 14, 2018): 1286. http://dx.doi.org/10.12688/f1000research.15845.1.

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Biological networks are highly modular and contain a large number of clusters, which are often associated with a specific biological function or disease. Identifying these clusters, or modules, is therefore valuable, but it is not trivial. In this article we propose a recursive method based on the Louvain algorithm for community detection and the PageRank algorithm for authoritativeness weighting in networks. PageRank is used to initialise the weights of nodes in the biological network; the Louvain algorithm with the Newman-Girvan criterion for modularity is then applied to the network to identify modules. Any identified module with more than k nodes is further processed by recursively applying PageRank and Louvain, until no module contains more than k nodes (where k is a parameter of the method, no greater than 100). This method is evaluated on a heterogeneous set of six biological networks from the Disease Module Identification DREAM Challenge. Empirical findings suggest that the method is effective in identifying a large number of significant modules, although with substantial variability across restarts of the method.
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Fu, Zeyuan. "Computer Network Intrusion Anomaly Detection with Recurrent Neural Network." Mobile Information Systems 2022 (March 7, 2022): 1–11. http://dx.doi.org/10.1155/2022/6576023.

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Network intrusion anomaly detection technique has been widely employed in computer network environments as a highly effective security prevention method. As network technology and network applications have advanced at a rapid pace, so too has network data traffic, resulting in an increase in virus and attack kinds. In the face of large-scale traffic and characteristic information, traditional intrusion detection will have problems such as low detection accuracy, high false negatives, and reliance on dimensionality reduction algorithms. Therefore, it is particularly important to establish a fast and efficient network intrusion anomaly detection method to deal with the current complex network environment. This work designs a computer network intrusion detection model with a recurrent neural network in order to explore a new intrusion detection method. The main purpose of this article include the following: (1) design a network security emergency response system architecture with the recurrent neural network model. This system consists of a management center module, a knowledge database module, a data acquisition module, a risk detection tool module, a risk analysis and processing module, a data protection module, and a remote connection auxiliary module. The modules cooperate with each other to complete system functions. (2) Aiming at the risk analysis and processing module, a network intrusion detection model combining bidirectional long short-term memory (BiLSTM) and deep neural network (DNN) is designed. In view of the lack of consideration of the before-and-after relevance of intrusion data features and the multifeature problem in existing models, the use of BiLSTM to extract the relevance between features and the use of DNN to extract deeper features are proposed. Aiming at the problem that the model lacks consideration of the importance of features, it is proposed to embed an attention mechanism into the network to increase consideration for the importance of features. (3) Massive experiments have verified the reliability and effectiveness of the method proposed in this work.
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Дисертації з теми "Network module"

1

Naidoo, Vaughn. "Policy Based Network management of legacy network elements in next generation networks for Voice Services." Thesis, University of the Western Cape, 2002. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_5830_1370595582.

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Gen-Kuong, Fernando, and Alex Karolys. "Smart Sensor Network System." International Foundation for Telemetering, 1997. http://hdl.handle.net/10150/607534.

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International Telemetering Conference Proceedings / October 27-30, 1997 / Riviera Hotel and Convention Center, Las Vegas, Nevada
This paper describes a Smart Sensor Network System for applications requiring sensors connected in a multidrop configuration in order to minimize interconnecting cables. The communication protocol was optimized for high speed data collection. The Smart Sensor Network System was developed with the following goals in mind: cost reduction, reliability and performance increase.
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3

Kobo, Hlabishi. "Situation-aware routing for wireless mesh networks with mobile nodes." Thesis, University of the Western Cape, 2012. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_6647_1370594682.

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4

Persson, Anders. "A TDMA Module for Waterborne Communication with Focus on Clock Synchronization." Thesis, Linköpings universitet, Programvara och system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-103028.

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This bachelor thesis has been carried out at the company Didamus which is located in Mjärdevi, Linköping. The company is currently developing a dive-console which aims to take the scuba diving experience to a whole new level and also to make scuba diving more secure. An assembly of scuba divers that can communicate with each other during a dive session might be the difference between life and death. Many seas around the world have muddy water which means poor visibility. In each situation a computer providing a scuba diver with information about others connected to the network, hazardous accidents can possibly be avoided.  The network itself consist of 10 nodes that need a network protocol which provides stability and reliability for every participant. The nodes themselves have a distributed responsibility to make the network reliable. The type of network implemented was a regular Time Division Multiple Access (TDMA) network where different nodes were given permission to access the medium in different instances of time. A global reference of time is always needed in a TDMA network to make it function properly. In a typical TDMA network a GPS-service gives each and every node information about the global time.  Unfortunately, GPS-services do not work well in water so a Master-Slave method was used instead. The master provides the rest of the nodes in the network with a global time reference. After a successful reception of a global time reference, the slave will be granted access to the network. The communication between the nodes is based on ultrasonic waves propagating in the water. The velocity of ultrasonic waves in water is only 1500 meters per second, explained in Discovery of Sound in the Sea by University of Rhode Island, which is a relatively slow signal speed. With the slow velocity taken into account an efficient TDMA protocol was developed, to perform communication under water.
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5

Tan, Hailun Computer Science &amp Engineering Faculty of Engineering UNSW. "Secure network programming in wireless sensor networks." Awarded By:University of New South Wales. Computer Science & Engineering, 2010. http://handle.unsw.edu.au/1959.4/44835.

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Network programming is one of the most important applications in Wireless Sensor Networks as It provides an efficient way to update program Images running on sensor nodes without physical access to them. Securing these updates, however, remains a challenging and important issue, given the open deployment environment of sensor nodes. Though several security schemes have been proposed to impose the authenticity and Integrity protection on network programming applications, they are either energy Inefficient as they tend to use digital signature or lacks the data confidentiality. In addition, due to the absence of secure memory management in the current sensor hardware, the attacker could inject malicious code into the program flash by exploiting buffer overflow In the memory despite the secure code dissemination. The contribution of this thesis Is to provide two software-based security protocols and one hardware-based remote attestation protocol for network programming application. Our first protocol deploys multiple one-way key chains for a multi-hop sensor network. The scheme Is shown to be lower In computational, power consumption and communication costs yet still able to secure multi??hop propagation of program images. Our second protocol utilizes an Iterative hash structure to the data packets in network programming application, ensuring the data confidentiality and authenticity. In addition, we Integrated confidentiality and DoS-attack-resistance in a multi??hop code dissemination protocol. Our final solution is a hardware-based remote attestation protocol for verification of running codes on sensor nodes. An additional piece of tamper-proof hardware, Trusted Platform Module (TPM), is imposed into the sensor nodes. It secures the sensitive information (e.g., the session key) from attackers and monitors any platform environment changes with the Internal registers. With these features of TPM, the code Injection attack could be detected and removed when the contaminated nodes are challenged in our remote attestation protocol. We implement the first two software-based protocols with Deluge as the reference network programming protocol in TinyOS, evaluate them with the extensive simulation using TOSSIM and validate the simulation results with experiments using Tmote. We implement the remote attestation protocol on Fleck, a sensor platform developed by CSIRO that Integrates an Atmel TPM chip.
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6

Zhang, Yuji. "Module-based Analysis of Biological Data for Network Inference and Biomarker Discovery." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28482.

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Systems biology comprises the global, integrated analysis of large-scale data encoding different levels of biological information with the aim to obtain global insight into the cellular networks. Several studies have unveiled the modular and hierarchical organization inherent in these networks. In this dissertation, we propose and develop innovative systems approaches to integrate multi-source biological data in a modular manner for network inference and biomarker discovery in complex diseases such as breast cancer. The first part of the dissertation is focused on gene module identification in gene expression data. As the most popular way to identify gene modules, many cluster algorithms have been applied to the gene expression data analysis. For the purpose of evaluating clustering algorithms from a biological point of view, we propose a figure of merit based on Kullback-Leibler divergence between cluster membership and known gene ontology attributes. Several benchmark expression-based gene clustering algorithms are compared using the proposed method with different parameter settings. Applications to diverse public time course gene expression data demonstrated that fuzzy c-means clustering is superior to other clustering methods with regard to the enrichment of clusters for biological functions. These results contribute to the evaluation of clustering outcomes and the estimations of optimal clustering partitions. The second part of the dissertation presents a hybrid computational intelligence method to infer gene regulatory modules. We explore the combined advantages of the nonlinear and dynamic properties of neural networks, and the global search capabilities of the hybrid genetic algorithm and particle swarm optimization method to infer network interactions at modular level. The proposed computational framework is tested in two biological processes: yeast cell cycle, and human Hela cancer cell cycle. The identified gene regulatory modules were evaluated using several validation strategies: 1) gene set enrichment analysis to evaluate the gene modules derived from clustering results; (2) binding site enrichment analysis to determine enrichment of the gene modules for the cognate binding sites of their predicted transcription factors; (3) comparison with previously reported results in the literatures to confirm the inferred regulations. The proposed framework could be beneficial to biologists for predicting the components of gene regulatory modules in which any candidate gene is involved. Such predictions can then be used to design a more streamlined experimental approach for biological validation. Understanding the dynamics of these gene regulatory modules will shed light on the related regulatory processes. Driven by the fact that complex diseases such as cancer are “diseases of pathways”, we extended the module concept to biomarker discovery in cancer research. In the third part of the dissertation, we explore the combined advantages of molecular interaction network and gene expression profiles to identify biomarkers in cancer research. The reliability of conventional gene biomarkers has been challenged because of the biological heterogeneity and noise within and across patients. In this dissertation, we present a module-based biomarker discovery approach that integrates interaction network topology and high-throughput gene expression data to identify markers not as individual genes but as modules. To select reliable biomarker sets across different studies, a hybrid method combining group feature selection with ensemble feature selection is proposed. First, a group feature selection method is used to extract the modules (subnetworks) with discriminative power between disease groups. Then, an ensemble feature selection method is used to select the optimal biomarker sets, in which a double-validation strategy is applied. The ensemble method allows combining features selected from multiple classifications with various data subsampling to increase the reliability and classification accuracy of the final selected biomarker set. The results from four breast cancer studies demonstrated the superiority of the module biomarkers identified by the proposed approach: they can achieve higher accuracies, and are more reliable in datasets with same clinical design. Based on the experimental results above, we believe that the proposed systems approaches provide meaningful solutions to discover the cellular regulatory processes and improve the understanding about disease mechanisms. These computational approaches are primarily developed for analysis of high-throughput genomic data. Nevertheless, the proposed methods can also be extended to analyze high-throughput data in proteomics and metablomics areas.
Ph. D.
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7

Anderson, Mary L. "Design and implementation of a token-ring optic local area network interface module." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/26967.

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Roa, Christian Raphael. "Smart Power Module for Distributed Sensor Power Network of an Unmanned Ground Vehicle." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/64467.

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Energy efficiency is a driving factor in modern electronic design particularly in power conversion where conversion losses directly set the upper limit of system efficiency. A wide variety of commercially available DC-DC conversion elements have inefficiencies in the 90-97% range. The efficiency range of most common commercial-off-the-shelf (COTS) power supplies is 75-85%, highlighting the fact that COTS power supplies have not kept pace with efficiency improvements of modern conversion elements. Unmanned ground vehicles (UGVs) is an application where efficiency can be crucial in extending tight power budgets. In autonomous ground vehicles, geographic diversity with regard to sensor location is inherent because sensor orientation and placement are crucial to performance. Sensor power, therefore, is also distributed by nature of the devices being supplied. This thesis presents the design and evaluation of a smart power module used to implement a distributed power network in an autonomous ground vehicle. The module conversion element demonstrated an average efficiency of 96.7% for loads from 1-4A. Current monitoring and an adjustable output current limit were provided through a second circuit board within the same module enclosure. The module processing element sends periodic updates and receives commands over a CAN bus. The smart power modules successfully supply critical sensing and communication components in an operational autonomous ground vehicle.
Master of Science
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9

Liang, Xiaoyu. "Computational Methods for Cis-Regulatory Module Discovery." Ohio University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1288578177.

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Heins, Robert J. "COMMAND CENTER FOR THE SDI DELTA 181 SENSOR MODULE." International Foundation for Telemetering, 1992. http://hdl.handle.net/10150/608917.

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International Telemetering Conference Proceedings / October 26-29, 1992 / Town and Country Hotel and Convention Center, San Diego, California
An orbiting sensor module, designed by The Johns Hopkins University Applied Physics Laboratory (JHU/APL), performed a number of significant Strategic Defense Initiative (SDI) Delta 181 program experiments. These experiments required on-orbit command and monitor operations involving a worldwide network of ground facilities. A major component was the sensor module command center (SMCC), which was designed and integrated by JHU/APL. The SMCC, located at Cape Canaveral Air Force Station (CCAFS), connected to a network of Eastern Test Range, Air Force Satellite Control Network (AFSCN), Kennedy Space Center, and Western Test Range assets. The complex nature of the mission presented numerous challenges to the design, integration, and operation of the SMCC. This paper presents a functional overview of SMCC design as well as unique aspects of supporting ground network telemetry and command operation.
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Книги з теми "Network module"

1

Group, Automotive Training. Network diagnostics & module programming. San Diego, C.A: ATG, Inc., 2010.

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2

1959-, Moran Diane, ed. ECDL module 7. London: Springer, 2000.

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3

Anderson, Mary L. Design and implementation of a token-ring optic local area network interface module. Monterey, Calif: Naval Postgraduate School, 1989.

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4

University of Kent at Canterbury. Department of Information Technology. The learning at work project: Work-based module : network systems management : student's journal. Canterbury: University of Kent at Canterbury, 1994.

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5

Green, Samantha J. Cost effectiveness analysis of converting a classroom course to a network based instruction module. Monterey, Calif: Naval Postgraduate School, 1997.

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6

Odvina, Cesar A. An analysis of the initial decision process of organizing the Navy Medical Department's Executive Management Education module conversion to network-based instruction. Monterey, Calif: Naval Postgraduate School, 1998.

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7

Arvind, Durai, ed. Cisco secure firewall services module (FWSM). Indianapolis, IN: Cisco Press, 2009.

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8

Advances in transport network technologies: Photonic networks, ATM, and SDH. Boston: Artech House, 1996.

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9

Healey, Steven Thomas. Abstract partitioning and routing of logic networks for custom module generation. Urbana, Ill. (1304 W. Springfield Ave., Urbana 61801-2987): Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1987.

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10

Topór-Kamiński, Lesław. Bezinercyjne elementy osobliwe jako modele elektrycznych układów aktywnych. Gliwice: Wydawn. Politechniki Śląskiej, 1996.

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Частини книг з теми "Network module"

1

Zhang, Junhua. "Module Network." In Encyclopedia of Systems Biology, 1446–47. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_479.

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Horvath, Steve. "Clustering Procedures and Module Detection." In Weighted Network Analysis, 179–206. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8819-5_8.

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Vogl, Sebastian, Fatih Kilic, Christian Schneider, and Claudia Eckert. "X-TIER: Kernel Module Injection." In Network and System Security, 192–205. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38631-2_15.

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Yannakoudakis, E. J., and C. P. Cheng. "Module Language in SQL." In Standard Relational and Network Database Languages, 43–50. London: Springer London, 1988. http://dx.doi.org/10.1007/978-1-4471-3287-5_4.

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Yannakoudakis, E. J., and C. P. Cheng. "Module Language in NDL." In Standard Relational and Network Database Languages, 91–99. London: Springer London, 1988. http://dx.doi.org/10.1007/978-1-4471-3287-5_9.

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de Bollivier, M., P. Gallinari, and S. Thiria. "Multi-Module Neural Networks for Classification." In International Neural Network Conference, 777–80. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_73.

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Horvath, Steve. "Evaluating Whether a Module is Preserved in Another Network." In Weighted Network Analysis, 207–47. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8819-5_9.

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Kaushika, N. D., Anuradha Mishra, and Anil K. Rai. "Solar PV Module and Array Network." In Solar Photovoltaics, 81–92. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72404-1_7.

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Moustafa, Nour, Gideon Creech, and Jill Slay. "Flow Aggregator Module for Analysing Network Traffic." In Advances in Intelligent Systems and Computing, 19–29. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7871-2_3.

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Śmieja, F. J. "Multiple Network Systems (Minos) Modules: Task Division and Module Discrimination." In AISB91, 13–25. London: Springer London, 1991. http://dx.doi.org/10.1007/978-1-4471-1852-7_2.

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Тези доповідей конференцій з теми "Network module"

1

Guzzi, Pietro H., Pierangelo Veltri, Swarup Roy, and Jugal K. Kalita. "MODULA: A network module based local protein interaction network alignment method." In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2015. http://dx.doi.org/10.1109/bibm.2015.7359918.

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Chang, Yu-Teng, and Dimitrios Pantazis. "Multi-view network module detection." In 2013 Asilomar Conference on Signals, Systems and Computers. IEEE, 2013. http://dx.doi.org/10.1109/acssc.2013.6810435.

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Mocella, Michael T., James A. Bondur, and Terry R. Turner. "Etch process characterization using neural network methodology: a case study." In Process Module Metrology, Control and Clustering, edited by Cecil J. Davis, Irving P. Herman, and Terry R. Turner. SPIE, 1992. http://dx.doi.org/10.1117/12.56637.

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Wang, Pingn. "Design of Embedded Network Communication Module." In 8th International Conference on Social Network, Communication and Education (SNCE 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/snce-18.2018.136.

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Prajeesha, Kushagra Sinha, Aditya Tripathi, and Naman Agarwal. "Security Detection Module of IPv6 Network." In 2021 6th International Conference for Convergence in Technology (I2CT). IEEE, 2021. http://dx.doi.org/10.1109/i2ct51068.2021.9417923.

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Zhang, Xiao, Chunsheng Liu, and Faliang Chang. "Guidance Module Network for Video Captioning." In 2021 40th Chinese Control Conference (CCC). IEEE, 2021. http://dx.doi.org/10.23919/ccc52363.2021.9550288.

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Ren, Xiaoming, Hui Liu, Zuhui Yue, and Yimeng Li. "Research on secure communication module based on subscriber identity module as root of trust." In International Conference on Network Communication and Information Security (ICNIS 2021), edited by Fengjie Cen. SPIE, 2022. http://dx.doi.org/10.1117/12.2628455.

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Grofu, Florin. "EXPERIMENTAL�MODULE�FOR�TRAINING�WITH�NETWORK�ANALYZER." In SGEM2012 12th International Multidisciplinary Scientific GeoConference and EXPO. Stef92 Technology, 2012. http://dx.doi.org/10.5593/sgem2012/s10.v3015.

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Zeng, Tao, Ruozhao Wang, and Luonan Chen. "Module network rewiring in response to therapy." In 2012 IEEE 6th International Conference on Systems Biology (ISB). IEEE, 2012. http://dx.doi.org/10.1109/isb.2012.6314136.

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Grondahl, Mika, Risto Hookana, and Jyri Rajamaki. "Emergency management module UArctic network online course." In 2014 International Conference on Interactive Collaborative Learning (ICL). IEEE, 2014. http://dx.doi.org/10.1109/icl.2014.7017892.

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Звіти організацій з теми "Network module"

1

Sivakumar, S., C. Jacquenet, S. Vinapamula, and Q. Wu. A YANG Module for Network Address Translation (NAT) and Network Prefix Translation (NPT). Edited by M. Boucadair. RFC Editor, January 2019. http://dx.doi.org/10.17487/rfc8512.

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2

Chassin, David P. GridLAB-D Technical Support Document: Network Module Version 1.0. Office of Scientific and Technical Information (OSTI), May 2008. http://dx.doi.org/10.2172/939870.

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Perreault, S., T. Tsou, S. Sivakumar, and T. Taylor. Deprecation of MIB Module NAT-MIB: Managed Objects for Network Address Translators (NATs). RFC Editor, October 2015. http://dx.doi.org/10.17487/rfc7658.

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4

Carley, Denise. Apparel Research Network (ARN); Apparel Order Processing Module (AOPM): Field User Manual, Version 1. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada347181.

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Carley, Denise. Apparel Research Network (ARN). Apparel Order Processing Module (AOPM) Interfaced with The Electronic Order Form (EOF) (AOPM/EOF). Fort Belvoir, VA: Defense Technical Information Center, April 1999. http://dx.doi.org/10.21236/ada364073.

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Brekhus, Dennis A. Apparel Research Network (ARN) Apparel Order Processing Module (AOPM). Application Program for Management of Special Measurement Clothing Orders. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada347142.

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7

Semerikov, Serhiy O., Illia O. Teplytskyi, Yuliia V. Yechkalo, and Arnold E. Kiv. Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2648.

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Анотація:
The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of third-party add-ins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet add-ins for non-linear optimization, creation of neural networks in the spreadsheet environment without add-ins and macros. After analyzing a collection of writings of 1890-1950, the research determines the role of the scientific journal “Bulletin of Mathematical Biophysics”, its founder Nicolas Rashevsky and the scientific community around the journal in creating and developing models and methods of computational neuroscience. There are identified psychophysical basics of creating neural networks, mathematical foundations of neural computing and methods of neuroengineering (image recognition, in particular). The role of Walter Pitts in combining the descriptive and quantitative theories of training is discussed. It is shown that to acquire neural simulation competences in the spreadsheet environment, one should master the models based on the historical and genetic approach. It is indicated that there are three groups of models, which are promising in terms of developing corresponding methods – the continuous two-factor model of Rashevsky, the discrete model of McCulloch and Pitts, and the discrete-continuous models of Householder and Landahl.
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Thubert, P., R. Wakikawa, and V. Devarapalli. Network Mobility Home Network Models. RFC Editor, July 2007. http://dx.doi.org/10.17487/rfc4887.

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9

Moreaux, Alexandre, Samantha Gamboa, David Griffith, and Richard Rouil. UE-to-Network Relay Model B Discovery in ProSe-Enabled LTE Networks. National Institute of Standards and Technology, April 2021. http://dx.doi.org/10.6028/nist.tn.2149.

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Nolan, Parker Stephen. Network Theory: How Can Its Application Cultivate the Conditions to Support Young Creatives? Creative Generation, October 2021. http://dx.doi.org/10.51163/creative-gen004.

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Анотація:
As observers to the intersectional fields of culture, education, and social change, Creative Generation witnessed the chosen organizational structure of “networks” come into vogue – particularly as smaller, community-based organizations have begun to participate in larger-scale, collaborative initiatives. In almost all examples, the individuals and organizations involved do their collaborative work through a “network,” using any number of connections and patterns. This qualitative inquiry sought to understand how applying Network Theory to organizational structures can cultivate the conditions to support young creatives. Through literature and conducting interviews with leaders of diverse networks in the arts and cultural education fields, this project provides an overview of Network Theory and examines examples of various models. This report proposes the following set of provocations for the field to interrogate the use of Network Theory in their projects’ implementation: strong connections between the network and its participants, shared power among network leadership and participants, clear expectations about funding, and specific role for young creatives in decision-making.
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