Drone Based Method of Connectivity Assurance for Clustered Network

2019 ◽  
Author(s):  
Jaroslaw Michalak ◽  
Leszek Nowosielski
Keyword(s):  
Clustered Network

A Novel Hybrid Cache Coherence with Global Snooping for Many-core Architectures

2022 ◽  
Vol 27 (1) ◽  
pp. 1-31
Author(s):  
Sri Harsha Gade ◽  
Sujay Deb
Keyword(s):  
Lower Energy ◽  
Cache Coherence ◽  
Network On Chip ◽  
Highly Efficient ◽  
Wireless Links ◽  
Coherence Protocols ◽  
High Area ◽  
On Chip ◽  
Many Core ◽  
Clustered Network

Cache coherence ensures correctness of cached data in multi-core processors. Traditional implementations of existing protocols make them unscalable for many core architectures. While snoopy coherence requires unscalable ordered networks, directory coherence is weighed down by high area and energy overheads. In this work, we propose Wireless-enabled Share-aware Hybrid (WiSH) to provide scalable coherence in many core processors. WiSH implements a novel Snoopy over Directory protocol using on-chip wireless links and hierarchical, clustered Network-on-Chip to achieve low-overhead and highly efficient coherence. A local directory protocol maintains coherence within a cluster of cores, while coherence among such clusters is achieved through global snoopy protocol. The ordered network for global snooping is provided through low-latency and low-energy broadcast wireless links. The overheads are further reduced through share-aware cache segmentation to eliminate coherence for private blocks. Evaluations show that WiSH reduces traffic by and runtime by , while requiring smaller storage and lower energy as compared to existing hierarchical and hybrid coherence protocols. Owing to its modularity, WiSH provides highly efficient and scalable coherence for many core processors.

scholarly journals Modeling Major Rural Land-Use Changes Using the GIS-Based Cellular Automata Metronamica Model: The Case of Andalusia (Southern Spain)

2020 ◽  
Vol 9 (7) ◽  
pp. 458 ◽  
Author(s):  
Rafael M. Navarro Cerrillo ◽  
Guillermo Palacios Rodríguez ◽  
Inmaculada Clavero Rumbao ◽  
Miguel Ángel Lara ◽  
Francisco Javier Bonet ◽  
...  
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Cover ◽  
Land Use Changes ◽  
Growth Patterns ◽  
Kappa Index ◽  
Rural Land ◽  
Dry Land ◽  
Rural Land Use ◽  
Clustered Network

The effective and efficient planning of rural land-use changes and their impact on the environment is critical for land-use managers. Many land-use growth models have been proposed for forecasting growth patterns in the last few years. In this work; a cellular automata (CA)-based land-use model (Metronamica) was tested to simulate (1999–2007) and predict (2007–2035) land-use dynamics and land-use changes in Andalucía (Spain). The model was calibrated using temporal changes in land-use covers and was evaluated by the Kappa index. GIS-based maps were generated to study major rural land-use changes (agriculture and forests). The change matrix for 1999–2007 showed an overall area change of 674971 ha. The dominant land uses in 2007 were shrubs (30.7%), woody crops on dry land (17.3%), and herbaceous crops on dry land (12.7%). The comparison between the reference and the simulated land-use maps of 2007 showed a Kappa index of 0.91. The land-cover map for the projected PRELUDE scenarios provided the land-cover characteristics of 2035 in Andalusia; developed within the Metronamica model scenarios (Great Escape; Evolved Society; Clustered Network; Lettuce Surprise U; and Big Crisis). The greatest differences were found between Great Escape and Clustered Network and Lettuce Surprise U. The observed trend (1999–2007–2035) showed the greatest similarity with the Big Crisis scenario. Land-use projections facilitate the understanding of the future dynamics of land-use change in rural areas; and hence the development of more appropriate plans and policies

scholarly journals Cascade dynamics on clustered network

2010 ◽  
Vol 221 ◽  
pp. 012005 ◽  
Author(s):  
Y Ikeda ◽  
T Hasegawa ◽  
K Nemoto
Keyword(s):  
Clustered Network

scholarly journals Unsupervised learning and clustered connectivity enhance reinforcement learning in spiking neural networks

2020 ◽  
Author(s):  
Philipp Weidel ◽  
Renato Duarte ◽  
Abigail Morrison
Keyword(s):  
Reinforcement Learning ◽  
Unsupervised Learning ◽  
Activity Patterns ◽  
Receptive Fields ◽  
Place Cells ◽  
Spiking Neural Networks ◽  
Complex Environments ◽  
Proposed Model ◽  
Clustered Network ◽  
Better Than

ABSTRACTReinforcement learning is a learning paradigm that can account for how organisms learn to adapt their behavior in complex environments with sparse rewards. However, implementations in spiking neuronal networks typically rely on input architectures involving place cells or receptive fields. This is problematic, as such approaches either scale badly as the environment grows in size or complexity, or presuppose knowledge on how the environment should be partitioned. Here, we propose a learning architecture that combines unsupervised learning on the input projections with clustered connectivity within the representation layer. This combination allows input features to be mapped to clusters; thus the network self-organizes to produce task-relevant activity patterns that can serve as the basis for reinforcement learning on the output projections. On the basis of the MNIST and Mountain Car tasks, we show that our proposed model performs better than either a comparable unclustered network or a clustered network with static input projections. We conclude that the combination of unsupervised learning and clustered connectivity provides a generic representational substrate suitable for further computation.

scholarly journals Improving Energy Consumption of a Node in Wireless Sensor Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 11696-11698
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Sink Node ◽  
Single Event ◽  
Intermediate Node ◽  
Event Information ◽  
Clustered Network ◽  
Total Network

A node in a Wireless Sensor Networks (WSN) can spend its energy by sending a packet to next node and receiving a packet from other node. A node is having a more number of neighbours. It can lose its energy very quickly when compared with less number of neighbouring nodes. That is intermediate node will always be a transceiver. Most of the time, nodes in the environment spend its energy for sending a repeated data or information. For ex: If any event occurred, single event information is passed to the sink node multiple times. Due to this repeated message, a node lost its energy by sending and receiving the packet to other node. In this paper we proposed an Energy Consumption (ECON) model that will filter the repeated message and it can save the energy of a node. This model will work efficiently in clustered network. Because of this model, the total network lifetime is also increased

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