Mobile Broadband and the Core Network Evolution

2013 ◽  
pp. 3-13
Author(s):  
Magnus Olsson ◽  
Shabnam Sultana ◽  
Stefan Rommer ◽  
Lars Frid ◽  
Catherine Mulligan
Keyword(s):  
Network Evolution ◽  
Core Network ◽  
Mobile Broadband ◽  
The Core

Mobile broadband and the core network evolution

2010 ◽  
pp. 1-13
Author(s):  
Magnus Olsson ◽  
Shabnam Sultana ◽  
Stefan Rommer ◽  
Lars Frid ◽  
Catherine Mulligan
Keyword(s):  
Network Evolution ◽  
Core Network ◽  
Mobile Broadband ◽  
The Core

A controlled deflection routing and wavelength assignment based scheme in Optical Burst Switched (OBS) networks

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Bakhe Nleya ◽  
Philani Khumalo ◽  
Andrew Mutsvangwa
Keyword(s):  
Wavelength Conversion ◽  
Wavelength Assignment ◽  
Wavelength Converters ◽  
Core Network ◽  
Least Cost Path ◽  
Deflection Routing ◽  
Backbone Networks ◽  
The Core ◽  
Optical Burst

AbstractHeterogeneous IoT-enabled networks generally accommodate both jitter tolerant and intolerant traffic. Optical Burst Switched (OBS) backbone networks handle the resultant volumes of such traffic by transmitting it in huge size chunks called bursts. Because of the lack of or limited buffering capabilities within the core network, burst contentions may frequently occur and thus affect overall supportable quality of service (QoS). Burst contention(s) in the core network is generally characterized by frequent burst losses as well as differential delays especially when traffic levels surge. Burst contention can be resolved in the core network by way of partial buffering using fiber delay lines (FDLs), wavelength conversion using wavelength converters (WCs) or deflection routing. In this paper, we assume that burst contention is resolved by way of deflecting contending bursts to other less congested paths even though this may lead to differential delays incurred by bursts as they traverse the network. This will contribute to undesirable jitter that may ultimately compromise overall QoS. Noting that jitter is mostly caused by deflection routing which itself is a result of poor wavelength and routing assigning, the paper proposes a controlled deflection routing (CDR) and wavelength assignment based scheme that allows the deflection of bursts to alternate paths only after controller buffer preset thresholds are surpassed. In this way, bursts (or burst fragments) intended for a common destination are always most likely to be routed on the same or least cost path end-to-end. We describe the scheme as well as compare its performance to other existing approaches. Overall, both analytical and simulation results show that the proposed scheme does lower both congestion (on deflection routes) as well as jitter, thus also improving throughput as well as avoiding congestion on deflection paths.

scholarly journals Periinfarct rewiring supports recovery after primary motor cortex stroke

2021 ◽  
pp. 0271678X2110029
Author(s):  
Mitsouko van Assche ◽  
Elisabeth Dirren ◽  
Alexia Bourgeois ◽  
Andreas Kleinschmidt ◽  
Jonas Richiardi ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Spatial Scales ◽  
Premotor Cortex ◽  
Core Network ◽  
Small Spatial Scale ◽  
The Core ◽  
Hand Dexterity ◽  
Motor Network ◽  
Motor Improvement

After stroke restricted to the primary motor cortex (M1), it is uncertain whether network reorganization associated with recovery involves the periinfarct or more remote regions. We studied 16 patients with focal M1 stroke and hand paresis. Motor function and resting-state MRI functional connectivity (FC) were assessed at three time points: acute (<10 days), early subacute (3 weeks), and late subacute (3 months). FC correlates of recovery were investigated at three spatial scales, (i) ipsilesional non-infarcted M1, (ii) core motor network (M1, premotor cortex (PMC), supplementary motor area (SMA), and primary somatosensory cortex), and (iii) extended motor network including all regions structurally connected to the upper limb representation of M1. Hand dexterity was impaired only in the acute phase ( P = 0.036). At a small spatial scale, clinical recovery was more frequently associated with connections involving ipsilesional non-infarcted M1 (Odds Ratio = 6.29; P = 0.036). At a larger scale, recovery correlated with increased FC strength in the core network compared to the extended motor network (rho = 0.71; P = 0.006). These results suggest that FC changes associated with motor improvement involve the perilesional M1 and do not extend beyond the core motor network. Core motor regions, and more specifically ipsilesional non-infarcted M1, could hence become primary targets for restorative therapies.

Object Detection in Fog Computing Using Machine Learning Algorithms

2020 ◽  
pp. 90-107
Author(s):  
Peyakunta Bhargavi ◽  
Singaraju Jyothi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Object Detection ◽  
Fog Computing ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Core Network ◽  
The Core ◽  
The Moment ◽  
Algorithmic Approaches

The moment we live in today demands the convergence of the cloud computing, fog computing, machine learning, and IoT to explore new technological solutions. Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation, communication, storage, and analytics closer to the end users. Machine learning is a subfield of computer science and is a type of artificial intelligence (AI) that provides machines with the ability to learn without explicit programming. IoT has the ability to make decisions and take actions autonomously based on algorithmic sensing to acquire sensor data. These embedded capabilities will range across the entire spectrum of algorithmic approaches that is associated with machine learning. Here the authors explore how machine learning methods have been used to deploy the object detection, text detection in an image, and incorporated for better fulfillment of requirements in fog computing.

scholarly journals Development of effective connectivity in the core network for face perception

2015 ◽  
Vol 36 (6) ◽  
pp. 2161-2173 ◽  
Author(s):  
Wei He ◽  
Marta I. Garrido ◽  
Paul F. Sowman ◽  
Jon Brock ◽  
Blake W. Johnson
Keyword(s):  
Face Perception ◽  
Effective Connectivity ◽  
Core Network ◽  
The Core

scholarly journals A meta-analysis of neuroimaging studies on pain empathy: investigating the role of visual information and observers’ perspective

2019 ◽  
Vol 14 (8) ◽  
pp. 789-813 ◽  
Author(s):  
Josiane Jauniaux ◽  
Ali Khatibi ◽  
Pierre Rainville ◽  
Philip L Jackson
Keyword(s):  
Mental State ◽  
Visual Cues ◽  
Meta Analysis ◽  
Inferior Frontal Gyrus ◽  
Anterior Cingulate ◽  
Precentral Gyrus ◽  
Body Parts ◽  
Core Network ◽  
The Core ◽  
Secondary Networks

Abstract Empathy relies on brain systems that support the interaction between an observer’s mental state and cues about the others’ experience. Beyond the core brain areas typically activated in pain empathy studies (insular and anterior cingulate cortices), the diversity of paradigms used may reveal secondary networks that subserve other more specific processes. A coordinate-based meta-analysis of fMRI experiments on pain empathy was conducted to obtain activation likelihood estimates along three factors and seven conditions: visual cues (body parts, facial expressions), visuospatial (first-person, thirdperson), and cognitive (self-, stimuli-, other-oriented tasks) perspectives. The core network was found across cues and perspectives, and common activation was observed in higher-order visual areas. Body-parts distinctly activated areas related with sensorimotor processing (superior and inferior parietal lobules, anterior insula) while facial expression distinctly involved the inferior frontal gyrus. Self- compared to other-perspective produced distinct activations in the left insula while stimulus- versus other-perspective produced distinctive responses in the inferior frontal and parietal lobules, precentral gyrus, and cerebellum. Pain empathy relies on a core network which is modulated by several secondary networks. The involvement of the latter seems to depend on the visual cues available and the observer's mental state that can be influenced by specific instructions.

Knowledge Exchange and Social Capital for Freshwater Ecosystem Assessments

BioScience ◽  
2020 ◽  
Vol 70 (2) ◽  
pp. 174-183 ◽  
Author(s):  
Lauren M Kuehne ◽  
Angela L Strecker ◽  
Julian D Olden
Keyword(s):  
Knowledge Exchange ◽  
Freshwater Ecosystem ◽  
Core Network ◽  
Research Activity ◽  
Freshwater Conservation ◽  
The Core ◽  
History Of ◽  
Bonding And Bridging ◽  
Bridging Capital ◽  
Freshwater Assessment

Abstract The 1972 Clean Water Act (CWA) provided crucial environmental protections, spurring research and corresponding development of a network of expertise that represents critical human capital in freshwater conservation. We used social network analysis to evaluate collaboration across organizational types and ecosystem focus by examining connections between authors of freshwater assessments published since the CWA. We found that the freshwater assessment network is highly fragmented, with no trend toward centralization. Persistent cohesion around organizational subgroups and minimal bridging ties suggest the network is better positioned for diversification and innovation than for learning and building a strong history of linked expertise. Despite an abundance of research activity from university-affiliated authors, federal agency authors provide a majority of the bonding and bridging capital, and diverse agencies constitute the core network. Together, our results suggest that government agencies currently play a central role in sustaining the network of expertise in freshwater assessment, protection, and conservation.

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