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About 10,000 papers for “network performance”

Network performance

G. J. Cook · Institution of Engineering and Technology eBooks · 1997

In this Chapter, it has only been possible to give a brief introduction to some aspects of network performance and its relation to the quality of service seen by customers, including the role of the ITU in these areas. Key network performance parameters have been identified, together with recommended objectives for appropriate end-to-end values to meet customer requirements. Finally, the Chapter gives some indication as to how a network may be designed to meet the end-to-end objectives, including the need to identify and specify all the contributing factors.

Cited by 0

Network performance.

Martin Boerlin (355887), Sophie Denève (355889) · Figshare · 2015

<p>(A) Input and output spike trains on a single trial. A stimulus with constant drift and diffusion is presented for 500 ms (gray area). (B) Time evolution of the stimulus posterior for the ideal observer (blue) and the network read-out (red). Thick lines show the mean of the posterior and narrow lines the corresponding width. The stimulus trajectory is shown in black. The dashed black line indicates the predictable (drift) part of the stimulus that the network is tracking during the memory period. (C) Snapshots of the posteriors, from left to right; after 500ms (end of integration period), after 2000 ms and after 5000 ms. (D) Coding performance measured as the standard deviation of the stimulus estimate around its real value . The blue and red curves depict the performance of the ideal observer and the network respectively and the green curve shows the performance of a network without slow currents . (E) Width of the posterior decoded from the ideal observer (blue), the full network model (described in equations 7 and 8) (red), a network in which we approximate the nonlocal term in the slow currents by a linear term (see equation 10) (green) and a network for which we completely remove the nonlocal term (magenta).</p>

Cited by 0Open access

Network Performance

Denita Cepiku · 2013

This chapter is a theory-building endeavor aimed at developing an interpretative model of network performance by systematizing and enhancing the existing literature and by analyzing a longitudinal case study. The case study used in this chapter—a network active in the fight against malaria in the northern Ethiopian region of Tigray—has been chosen as it is particularly appropriate for the aims of this research as both the problem it tackles and the characteristics of the network established to deal with it are very complex. Furthermore, the analysis covers a five-year time frame, permitting the investigation of the network in a dynamic rather than static way. The network includes a diversified pool of actors (public, private and nonprofit, operating globally and locally, located in Europe and Africa). The partners included the Italian Ministry of Health, the World Health Organization (WHO), Novartis-Italia, the Tigray Health Bureau (THB), the community health workers (CHWs), the San Gallicano Dermatological Institute, and the Italian Hospital of Makallè 1 .

Cited by 0

network performance

Amba Axi Ip, Brad Taylor, Ralph Wittig et al. · 1993

28nm process node First wave of 28nm HK/MG devices from fabless Si vendors >2x device capacity over 40nm devices ~50 % total power reduction over 40nm devices Xilinx 28nm High-Performance, Low-Power process High-K Metal Gate (HK/MG) Developed by Xilinx and TSMC- optimized for high performance & low power 65 % lower static power than 28nm variants offering similar performance

Cited by 0

Performance Network Symposium

Arca (British Columbia Electronic Library Network)

The Performance Network Symposium brought together curators from across Canada to develop and explore networking and exchange opportunities in the performance art sector via three days of attending LIVE events and discussions for participants, followed by a full day of public presentations at the Western Front.

Cited by 0

Network stability performance.

Houssem R. E. H. Bouchekara (17743281), Abdulazeez F. Salami (17743284), Yusuf A. Sha’aban (16052906) et al. · Figshare · 2024

<div><p>This paper is a follow-up to a recent work by the authors on recoverable UAV-based energy-efficient reconfigurable routing (RUBER) scheme for addressing sensor node and route failure issues in smart wireless livestock sensor networks. Time complexity and processing cost issues connected to the RUBER scheme are consequently treated in this article by proffering a time-aware UAV-based energy-efficient reconfigurable routing (TUBER) scheme. TUBER scheme employs a synchronized clustering-with-backup strategy, a minimum-hop neighborhood recovery mechanism, and a redundancy minimization technique. Comparative network performance of TUBER was investigated and evaluated with respect to RUBER and UAV-based energy-efficient reconfigurable routing (UBER) schemes. The metrics adopted for this comparative performance analysis are Cluster Survival Ratio (CSR), Network Stability (NST), Energy Dissipation Ratio (EDR), Network Coverage (COV), Packet Delivery Ratio (PDR), Fault Tolerance Index (FTI), Load Balancing Ratio (LBR), Routing Overhead (ROH), Average Routing Delay (ARD), Failure Detection Ratio (FDR), and Failure Recovery Ratio (FRR). With reference to best-obtained values, TUBER demonstrated improvements of 36.25%, 24.81%, 34.53%, 15.65%, 38.32%, 61.07%, 31.66%, 63.20%, 68.96%, 66.19%, and 78.63% over RUBER and UBER in terms of CSR, NST, EDR, COV, PDR, FTI, LBR, ROH, ARD, FDR, and FRR, respectively. These experimental results confirmed the relative effectiveness of TUBER against the compared routing schemes.</p></div>

Cited by 0Open access

Improving network performance.

P.G.H. Engel · Data Archiving and Networked Services (DANS) · 1995

Agricultural innovation emerges from the social interaction between many different stakeholders such as farmers, researchers, traders, extensionists, bankers and policy makers and their respective organisations, institutions or agencies. They all try to improve what they are doing continuously. Of course, each of them looks at agricultural development from a very different angle and as a consequence, has very different purposes in mind. Yet their social interaction determines to a large extent what type of technological development will eventually be achieved. To develop sustainable forms of agriculture, therefore, technology development is not enough. We have to address the interaction between relevant stakeholders as well. Evidence shows that farmers are not only active experimenters, they are active networkers as well (e.g. Alders et al., 1993). But also NGOs, researchers, agro−industries, policy makers and traders actively seek relationships with others to exchange knowledge, information and experiences and to build alliances to develop and implement new ways of doing things. We call such efforts networking. Networks are more or less formal, more or less durable relational patterns that emerge as a result. They form part of what we might call the social organisation of innovation. Traditionally, extension is concerned with interaction between researchers and farmers mostly.

Cited by 3Open access

Network Performance Monitoring

Shriram Raghavendra Ramamurthy · OhioLink ETD Center (Ohio Library and Information Network) · 2012

Cited by 0Open access

Yeast network performance.

Jesse Gillis (174666), Paul Pavlidis (335) · Figshare · 2015

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017258#pone-0017258-t003" target="_blank">Table 3:</a> Seven yeast networks were assessed according to the Dice-Jaccard coefficient of the links they select. All exhibit strongly functionally significant links compared to random data which possesses a Dice-Jaccard coefficient of 0.23. However, a large fraction of this performance is present in the individual property networks (IPN) which also exhibit strongly significant links. The network from aggregating the true networks and IPN networks is shown. Aggregation is accomplished by including all links present in the original networks; thus, the aggregate of the true networks includes all network links present in the true networks.</p>

Cited by 0Open access

Network training performance.

Jonathan M. Matthews (14094267), Brooke Schuster (14094270), Sara Saheb Kashaf (3755179) et al. · Figshare · 2022

<p><b>(a)</b> The network was trained on 66 manually labeled microscopy images of organoids derived from pancreatic ductal adenocarcinoma (PDAC) samples from two patients. Organoids were cultured in a well plate or microfluidic format and imaged through phase-contrast or brightfield microscopy. Images were then split into datasets for network training (80%) and validation (20%). <b>(b)</b> The 52 images in the training dataset were passed through a series of random transformations to produce an augmented dataset of 2,000 images. <b>(c)</b> Network training was stopped after 37 epochs, once a minimum binary cross-entropy loss on the validation dataset was reached. <b>(d)</b> The OrganoID neural network predicts the probability that an organoid is present at each pixel. Shown are network predictions produced by intermediate models at selected epochs through the training process.</p> <p>(TIF)</p>

Cited by 0Open access

MI Network Performance

Erwu Liu, Zhi Sun, Rui Wang et al. · Cambridge University Press eBooks · 2024

In this chapter, the network throughput and capacity are derived and analyzed. The deployment strategies for MI networks are first introduced. Then, we present the typical network topologies for MI networks. After that, we compare the performance of different network topologies regarding congestion, node failure, and power consumption, among others.

Cited by 0

Governance Network Performance

Christopher Koliba · 2013

If we are to assess the performance of networks within a public administration and policy context, we must regard them as tangible, observable structures comprised of nodes (or agents) and ties that formally or informally, tightly or loosely, couple two or more nodes together. The kind of “network logic” that accompanies the study of networks bears a significant impact on our understandings of network performance. Although there is still likely room for debate, a comprehensive view of this network logic for the field of public administration and policy studies is emerging around a number of givens about multi-organizational networks found within the public administration and policy studies literature. These givens, however, are punctuated by a number of compelling methodological and theoretical challenges that may be best served by understanding network performance as an integral feature of a complex adaptive system. To describe and evaluate in such a manner, a comprehensive framework for categorizing network performance is needed.

Cited by 0

Network Survivability Performance

J. Cassano, Robert Doverspike, George R. Koerner et al. · 1993

Abstract : This Technical Report has been developed to address the survivability of telecommunications networks including services. It responds to the need for a common understanding of, and assessment techniques for network survivability, availability, integrity, and reliability. It provides a basis for designing and operating telecommunications networks to user expectations for network survivability and a foundation for continuing industry activities in the subject area. This report focuses on the survivability of both public and private networks and covers a wide range of users. Two frameworks aare established for quantifying and categorizing service outages, and for classifying network survivability techniques and measures. The performance of the network survivability techniques is considered; however, recommended objectives are not established for network survivability performance.

Cited by 6